AU2015101665A4 - Methods and systems for identifying, tailoring, providing and displaying data, and verification thereof - Google Patents

Abstract

There are proposed methods and systems for identifying, tailoring providing and displaying data and verification of said data. In particular the methods and systems include the display of product or products suitable for a user to sell which 5 relates to a user's personal profile or a user's pervious commercial activities. In another embodiment the methods and systems include the step of verify the accuracy of recommended product information previously supplied to the user. oc cV-U CD C) (D(CD mL o N C)

Description

2 METHODS AND SYSTEMS FOR IDENTIFYING, TAILORING, PROVIDING AND DISPLAYING DATA, AND VERIFICATION THEREOF FIELD OF THE INVENTION The present invention relates to methods and systems for identifying, 5 tailoring, providing and displaying data and verification of said data. BACKGROUND OF THE INVENTION Identifying, providing and displaying data from multiple sources to a user in a usable and time sensitive manner is a difficult task. One reason is the volume of available data is often too voluminous to review with efficiency. Another reason is 10 compatibility, especially when connecting to multiple data sources. For example, data sources may have proprietary interfaces, and so connecting to any particular source for data retrieval may be difficult, a difficulty compounded by multiple proprietary data sources as often seen in the art. Thus interfacing, identifying and displaying data from multiple data sources, because of data compatibility, interface compatibility, 15 platform compatibility, etc., issues may simply be impossible. Finding relevant data and/or relationships among and between data also may be difficult, simply because display interfaces, whether software based or hardware based often are relatively fixed. For example, what might be relevant data or relationships to one user may well be meaningless to another. 20 These difficulties are complicated by changing data in a time sensitive environment. So for example providing timely data to a user may be impeded by the complications of translating data into useable formats, as well as other difficulties, such as those noted above with regard to data from multiple sources. Prior to the technology afforded by the Internet, such time sensitive data would have been 25 impossible to access and/or utilize. For example, a vendor may well desire to automate a supply chain yet prior to the interconnected network of computers, such a vendor would have been locked into a single vendor's system. Similarly, a vendor would not have been able to obtain time sensitive data such as differentials in pricing for highly desired goods on a time sensitive basis, for example, where a new 30 electronic product or other good might be less expensive in one country or other area, yet more expensive in another, thus making impossible any ability to make a profit from the difference. Indeed, even shipping costs time sensitive changes would 3 have been impossible to track, as well as many other variables, such as available inventory, etc. Displaying desired data provides yet another difficulty. A typical display provided through a web browser or other application has limited "real estate", and 5 efficiently and effectively utilizing that real estate so any such data may displayed in a useable format is a constant difficulty in the on-line environment. SUMMARY OF THE INVENTION The present invention provides methods and systems for identifying, tailoring, providing and displaying data from multiple sources to a user in a usable and time 10 sensitive manner and verification of the relevance of said data at a time thereafter. Embodiments provide for the provision of desired raw data from multiple sources to a server, on a continuing basis for at least some of said data, using said raw data and/or translating and/or processing said raw data into a desired format, and providing at least said some of said data to a user. 15 So for example, an embodiment may provide for receiving raw data (e.g. sales-price per unit) from at least one data source and receiving raw data (e.g. cost to-acquire-goods) from at least one product supplier; translating the raw data (e.g. truncating irrelevant information from the received raw data); processing the raw data into desired seller and sales outputs (e.g. average sales-price) and product cost 20 information outputs (e.g. average cost-to-acquire-goods); further processing of the seller and sales outputs and the product cost information outputs into profitability characteristics (e.g. profit margin) and/or ease of trading metrics (e.g. minimum order quantity); access to yet other raw data for refining time sensitive characteristics (e.g., isolating market information from sources to provide for goods in high current 25 demand, generally or in particular geographic or other markets); providing an interface to a user for selection of display of desired characteristics of said data (e.g. the profitability characteristics, the ease of trading metrics, the product cost information outputs, etc. Moreover, said interface may provide for user customization of said data 30 and/or access to other data as desired (e.g., a series of questions to refine the type of product, i.e. interrogate the potential seller to find out an appropriate product for them to sell,) subsequent user input regarding said data or related data, such as 4 providing for a feedback loop to provide user raw data, (e.g., if a product has been provided by a supplier to a user, either a supplier or user may provide data such as any sale data, cost of transport and sales channels) subsequent provision of further data (e.g., information to users regarding ways to improve their profit, i.e. what trade 5 channels to use, quantities to purchase, etc.) A computer-implemented method for dynamically reloading pricing information within a window displayed in a graphical user interface, the method comprising: - providing or accessing a first data source, 10 - receiving data from said first data source at intervals, wherein said data is comprised of Product Pricing Information types comprised of one or more of the following; Product-Category Information, Product-Description Information, Sales Price per Unit Information, Average Sales-Price Information, Number of Sellers Information, Number of Products Sold Information, Cost to Acquire Goods 15 Information, - translating said data into said Product Pricing Information types comprised of one or more of the following; Product-Category Information, Product-Description Information, Sales-Price per Unit Information, Average Sales-Price Information, Number of Sellers Information, Number of Products Sold Information, 20 wherein said Product Pricing Information is provided within a first window in a graphic user interface; - a display format further providing an Average Cost-to-Acquire-Goods Information type, a Profitability Characteristic Information type, further comprising Profit Margin Information; and/or Ease of Trading Metrics Information type comprising Minimum 25 Order Quantity Information, - said user interface being used for selection of Information types; wherein said method further provides for a Dynamic User Filter, comprising said user interface further providing, through access to a second data source, filtering capabilities for said display format dependent upon user information as disclosed by 30 said second data source and/or user preferences input via said interface. In one form the data from said first data source relates to a product or products that could be offered for sale by said user, wherein said product or products are sorted and displayed to provide recommended product information, the sorting of the data from said first data source being done by profitability and/or the ease with 35 which the product or products can be traded using known trade channels.

5 The user provides permission to said interface processor for access to the second data source being a third party website or a database containing personal information pertaining to said user, or the user provides a web address corresponding to an online store operated by said user containing information 5 pertaining to items currently offered for sale by said user, or the user provides permission to said interface processor to access a database containing information pertaining to previous sales records and/or current inventory. A user's profile or a user's commercial activities may then be interrogated by said interface processor to identify, provide and display data relating to a product or 10 products suitable for the user to sell which relates to the user's profile or the user's pervious commercial activities thereby tailoring the product data displayed. Once the sale has been completed or at a time thereafter the interface processor processes sales records of said user to verify the accuracy of the recommended product information previously supplied to said user. 15 The interface processor obtains, either manually or automatically from said user, sales records including actual sale details, cost of transport and sales channels information. The previously supplied recommended product information is then compared to actual sales records information to quantify the accuracy of said recommended product information. This comparison can then be used to improve an 20 algorithm used to produce said recommended product information supplied to said user and/or provide advice to said user regarding ways to improve profit, including alternate trade channels to use, quantities to purchase, recommended retail price or advertising options. BRIEF DESCRIPTION OF THE DRAWINGS 25 The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an implementation of the invention and, together with the description and claims, serve to explain the advantages and principles of the invention. In the drawings, FIG. 1 depicts one embodiment of the steps of the invention; 30 FIG. 1(a) depicts an embodiment as a flow diagram showing overall steps involved in a method for identifying a profitable product; 6 FIG. 1(b) depicts an embodiment of a portion of step for determining seller and sales outputs; FIG. 1(c) depicts an embodiment of a portion of step for determining product cost information outputs; 5 FIG. 2 depicts an embodiment of step of primary processing to yield profitability characteristics and/or ease of trading metrics; FIG. 3 depicts an embodiment of step of displaying at least one of the profitability characteristics, the ease of trading metrics, the product cost information outputs and the seller and sales outputs on at least one 10 graphical user interface (GUI); FIG. 3(a) depicts an embodiment showing a research table displayed on the GUI; FIG. 3(b) depicts an embodiment showing a feasibility table displayed on the GUI; FIG. 3(c) depicts an embodiment showing a purchased data table displayed on the GUI; 15 FIG. 3(d) depicts an embodiment showing a product details page displayed on the GUI; FIG. 4 depicts an embodiment of components of a system for identifying a profitable product; FIG. 5 depicts one embodiment of the tailoring steps displayed as a flow chart; 20 FIG. 6 depicts another embodiment of the tailoring steps displayed as a flow chart; and FIG. 7 depicts an exemplary embodiment of the verification steps displayed as a flow chart. DETAILED DESCRIPTION OF THE ILLUSTRATED AND EXEMPLIFIED 25 EMBODIMENTS Similar reference characters indicate corresponding parts throughout the drawings. Reference is made herein to the accompanying drawings that form a part thereof, where depictions are made, by way of illustration, of specific embodiments in 7 which the invention may be practiced. It is to be understood that other embodiments may be utilized and changes may be made without departing from the scope of the invention. Preferred embodiments provide methods and systems, for identifying, 5 tailoring, providing and displaying data from multiple sources to a user in a usable and time sensitive manner. One or more user interfaces are provided. Embodiments may be local, networked, available through a distributed architecture, server side, client side, web services, applications, as otherwise known in the art, etc. Turning to FIG. 1, a preferred embodiment is shown. Data sources, e.g., data 10 warehouses, data marts, operational datastores, local databases, content aggregators, web services, raw data, real time data, line data feed from sensors and the like, raw data generators, and other mechanisms, manipulated (which term includes analyzed and collected) data, data from manual or other input, and as otherwise known in the art, etc., are shown generally at 110. Any or all of the data 15 sources are local, networked, available through a distributed architecture, server side, client side, as otherwise known in the art, etc. One or more of the data sources provides data 'a' to data storage component 120. The one or more data sources could be generally understood to make up the first data source or the first data source could be a compilation of all the . Data 20 storage component 120 is for providing data 'a' to processor 200 as further described below, and may be a database, spreadsheet, cache, or other storage component, as otherwise known in the art, etc. Data 'a' is used here for illustrative purposes, in various embodiments data 'a' may be one or more datums from any and/or all of data sources 110. Also, in 25 embodiments, data storage component 120 may not be present or used for less than all data sources. An alternative data path 120X is shown for such embodiments in FIG. 1. Data 'a' may require translation, and so data translation component 125 is shown, which is for translating into a preferred format, as is further described below. 30 Data translation component 125 may be manual, automatic, a combination of manual and automatic, as otherwise known in the art, etc.

8 Processor 200 is for processing the data into desired outputs and to interface with interface processor 225. Processing may occur according to data 'a', its characteristics, relationships between and among data, user selections or other characteristics, and as otherwise as may be desired, as is further described below. 5 Rules may be used in processing, (for example, a rule may be used to provide a modification of data depending upon a range: if a datum has a value within an acceptable range, a rule may provide an output of said datum) and may be created and/or implemented according to desired output and/or provided input. Rules may use a single datum or data and be single (uni-variant,) multivariant, combine data as 10 desired, nested (one rule's output or outputs can be another rule or rule's input or inputs) etc. Rules may have more than one input and more than one output. Rules may also be a constant or result in a constant (e.g., a rule with no input, etc.) and may be present where desired. Interface processor 225 builds and/or formats data provided by processor 200 15 into a display format and provides said data to interface 250. Input and/or output may be controlled by rules here as well, so that, for example, a rule may be implemented by user selection, so that a user may desirably see only desired data in a customized display format via interface 250. Interface 250 may be of various types dependent upon user characteristics or other desired variables. So for example, a 20 user may have certain levels of access for certain types of data, as when an authorized user at a certain Level A may have a different presentation than an authorized Level B user; a user may select certain information of interest in order for interface processor 225 to modify said display, and/or processor 200 modify data provided, etc. 25 An interface (not shown) is a visual display upon a screen, accessible by a user with a World Wide Web browser for display of the information provided by interface 250. Interfaces, in this and other embodiments, may be programmable. So for example here the interface is programmed using HTML code so as to be available using a compatible browser. Here information received by interface processor 22 30 may be displayed using appropriate code. Of course, coding may be as desired, and may be interface dependant to some degree. For example, JavaScript or other code may be used in a WWW embodiment, as otherwise known in the art, etc. FIG. 1(a) is a flow chart of a preferred embodiment. In this and other embodiments, the data sources may be provided in various manners. So for 9 example Data Source 102 may be searched for by the user from a preselected list, which may be a customized preselected list as well, so that for example, a user may evidence interest in one or more areas, and be provided with data sources responsive to those interests, as is further described below. 5 Data sources may also be provided by being searched for by the user using a general search function for the Internet generally. So that, for example, a user may decide to examine a retail area as is further described below, utilizing a search function such as a link to a general search engine such as Google, and found data may be automatically incorporated through various techniques such as data mining, 10 screen scraping, downloading and the like, so for example, data mining may be through API's and/or third parties utilizing spiders, web-crawlers, scripts, and the like, which may result in data which would then be offered to the user as data sources in a manner further described below. Data sources may also be provided to the user, automatically and/or 15 manually, etc. through user selection and/or in a manner determined by predetermined parameters. For example, a Data Source 102 may be provided to a user depending on a user selection for type of data, so that, for example, a user may desire to search for product Data Sources, in a wholesale environment and limited to electronics, and may be shown preselected Data Sources within that environment. 20 Such Data Sources may be updated by a search mechanism (not shown) in order to constantly provide the user with time sensitive Data Sources, as for example, when numerous retail and/or wholesale sellers may come on line, change their products, change their product availability, change overhead costs such as shipping, etc., e.g., Yahoo@, Apple@, Amazon@, eBay@, Dell@, Staples@, Walmart@, Kohls@, Office 25 Depot@, Sears@, Macy's@, Overstock@, Home Depot@, Costco@, BestBuy@, Target@, etc. and constantly changing in any number of ways. Data sources may be meta sources, such as price comparison sites like www.pricegrabber.com, www.pricewatch.com, etc. (Of course an entity like Google may be a search engine, seller, price comparison site, etc. and the nature of the entity should not be 30 understood to affect its definition as a data source for purposes of various embodiments.) Data sources may also be provided by third parties on a permanent basis and/or a temporary basis. So for example, an embodiment may have predetermined third party suppliers as well as bringing "on line" on an as used basis, such as when 10 a third party may be notified of a user's search and/or predetermined parameters for a user, and so be able to come "on line" as a Data Source for such user, thus providing time sensitive access to the third party data. Any such data and found data may be automatically incorporated through various techniques such as data 5 mining, screen scraping, downloading and the like, so for example, data mining may be through API's and/or third parties utilizing spiders, web-crawlers, scripts, and the like. [For example, such data mining may involve tracking and monitoring traffic characteristics of a given Data-Source 102, in an aggregate manner.] Data sources are for providing data of course, and returning briefly to Figure 1 10 as noted above, data provided via data sources may require translation, which word is meant here to describe translation of the data provided by data sources (generally "raw data") in order to provide a consistent data format for the user. For example, a type of raw data received from a third party may be a for a product such as a telephone or computer headset, and the received raw data from a third party data 15 spruce is "super cheap 24 Hz Bluetooth headset with extras *** Local Express Deliver" then this raw data may be translated to "24 Hz Bluetooth headset" thus enabling consistent data formatting among this and other raw data from various data sources in this product category. So for example, in various embodiments different types of the raw data may 20 be sourced from (i.e. received from) different Data Sources 102. In some embodiments, as illustrated in Figure 1(b) the raw data received from at least one Data Source 102 may be with respect to at least one specific product, a number of products, and/or groups. So for example the raw data from at least one Data Source 102 may be a Product-Name 112, a Product-Category 113, a Product-Description 25 114, a Sales-Price per Unit 108, an Avg (average) Sales-Price 109, a Number of Sellers 110, a Number of Products Sold 111, and the like. In some embodiments, the step of receiving the raw data 105 may comprise a step of Searching 101 for at least one Data Source 102. In some embodiments, the step of Searching 101 for the at least one data source may comprise manual entry of 30 at least one Data Source 102 to be targeted for receiving the raw data 105. Thus, in some embodiments, when Staff 950 learns of and/or may be made aware of a particular Data Source 102, Staff 950 may manually enter various details associated with the particular Data Source 102, into Database 404. Staff 950 may be an agent (e.g. an employee) the Operational-Entity. The various details may be comprise 11 contact information of the particular Data Source 102. The contact information may comprise for the particular Data Source 102: entity name, physical address, mailing address, phone number(s), email address(es), website URL(s) (universal resource locator), instructions for contacting the particular Data Source 102, and the like. 5 In some embodiments, the step of Searching 101 for at least one Data Source 102 may comprise the step of receiving the raw data 105 from Third Parties 103. In some embodiments, Third Parties 103 may be selected from the group comprising of one or more of at least one Data Source 102 and/or from a third party application program interface (API). That is, in some embodiments, Data Source 10 102 may be Third Party 103. Various Third Parties 103 may have access to the raw data. Various Third Parties 103 may provide access to the raw data for a fee and/or under various contractual provisions with the Operational-Entity. In some embodiments, at least some of the raw data may be provided by API' s and/or received from the API's. 15 In some embodiments, the step of step of Searching 101 for at least one Data Source 102 may comprise a step of data mining publicly available information 104 to locate Data Source 102. In some embodiments, the step of step of receiving the raw data 105 from at least one Data Source 102 may comprise a step of data mining publicly available information associated with at lease one Data Source 102. 20 In some embodiments, the step of receiving the raw data 105 from at least one Data Source 102 may comprise direct receiving and/or indirect receiving of the raw data from at least one Data Source 102. In some embodiments, the step of direct receiving the raw data from at least one Data Source 102 may occur when at least one Data Source 102 may actively transmit the raw data received (e.g. transmitting to 25 at least one Server 401 associated with the Operational-Entity). In some embodiments, active transmission of the raw data by the at least one data source may occur when at least one Data Source 102 may be Third Party 103, a third party API, and/or when at least one Data Source 102 may be under contract with an entity implementing the method, e.g. the Operational-Entity. 30 In some embodiments, the step of indirect receiving the raw data from at least one Data Source 102 may occur when at least one Data Source 102, may be data mined of public information available from at least one Data Source 102.

12 Processing may occur according to data 'a', its characteristics, relationships between and among data, user selections or other characteristics, and as otherwise as may be desired, as is further described above. In embodiments where processing occurs according to rules, raw data may be organized in any number of ways. For 5 example raw data providing a product, may also provide Product-Category 113, Product- Description 114, Sales-Price per Unit 108, Avg. (average) Sales-Price 109, Number of Sellers 110, Number of Products Sold 111, and the like. Rules may also cross reference data, such as embodiments providing Number of Products Sold 111 which correspond to a measure of demand, with 10 demand determined by data sources data, such as sales frequency, etc. It should be noted in this and other embodiments, data and/or rules may be provided manually and/or automatically. Rules may also have predetermined and/or dynamic outputs as well. So for example, outputs of the processed 106 raw data 105 may be Seller & Sales Outputs 15 107. Rules may also provide other desired information. So for example, at FIG. 2 a Rule is used to yield Profitability Characteristics 210 and/or Ease of Trading Metrics 250 for a given product. It should be noted that rule input may include preselected and/or dynamic information, so for example in some embodiments, a rule for 20 providing Profitability Characteristics 210 may be selected from the group comprising one or more of a Profit per Unit 211, Profit Margin 212, a Hotness Level 213, a Sellability Score 214, a Demand-Supply-Ratio 215 (D/S215), a Competitive-Quantity 216, and the like. As another example, a rule for providing Profit per Unit 211 may be calculated 25 by subtracting Avg. Cost-to-Acquire-Goods 124 from Avg. Sales-Price 109 per a given product. Avg. Cost-to-Acquire-Goods 124 may be a component of Product Cost Information Outputs 120. Avg Sales-Price 109 may be a component of Seller & Sales Outputs 107. That is, in some embodiments, Profit per Unit 211 may be calculated per the following formula: (Avg Sales Price 109) - (Avg. Cost-to-Acquire 30 Goods 124) = Profit per Unit 211. As another example, a rule for providing Profit per Unit 211 may be calculated by subtracting Avg. Cost-to-Acquire-Goods 124 and an average Cost of Sales 109 from Avg Sales-Price 109 per a given product. Avg. Cost-to-Acquire-Goods 124 may 13 be a component of Product Cost Information Outputs 120. Avg Sales-Price 109 may be a component of Seller & Sales Outputs 107. That is, in some embodiments, Profit per Unit 211 may be calculated per the following formula: (Avg Sales Price 109) - ([average Cost of Sales] + [Avg. Cost-to 5 Acquire-Goods 124]) = Profit per Unit 211. As another example, a rule for providing Profit margin 212 may be a percentage of Profit per Unit 211 with respect to Avg Sales Price 109 for a given product. Profit margin 212 may be calculated by taking Profit per Unit 211 and dividing Profit per Unit 211 by Avg Sales Price 109, and then multiplying that result 10 by 100. That is, in some embodiments, Profit Margin 212 may be calculated per the following formula: [(Profit per Unit 211) 1 (Avg. Sales-Price 109)] * 100 = Profit Margin 212. As another example, a rule for providing hotness level 213 may be a normalized metric varying in positive whole numbers. This normalized metric may 15 be an indicator of product demand for a given product sold over a certain frame within a particular market, wherein the hotness level may be calculated from Number of Products Sold 111. Number of Products Sold 111 may be a component of the Seller & Sales Outputs 107. Number of Products Sold 111 may be a measure or indicator of product demand. 20 For example, in some embodiments, hotness level 213 may be a normalized metric varying in positive whole numbers from one to five, or in other embodiments from one to ten. In some embodiments, a smaller hotness level 213 may indicate greater demand. In some embodiments, hotness level 213 of one may indicate more than 1,000 particular products were sold in a certain time frame. In some 25 embodiments, hotness level 213 of two may indicate that 500 to 1,000 particular products were sold in the certain time frame. In some embodiments, hotness level 213 of three may indicate that 250 to 499 particular products were sold in the certain time frame. In some embodiments, hotness level 213 of four may indicate that 100 to 249 particular products were sold in the certain time frame. In some embodiments, 30 hotness level 213 of five may indicate that less than 100 particular products were sold in the certain time frame. A higher hotness level may indicate greater demand. As another example, rules may also be utilized to provide data by geographic, industrial and/or other markets, so that for example a market may be a national 14 market, such as the United States, Australia, and the like; an industrial market, such as hospitals and or other health care entities, etc. Rules may also be used to provide data by time frames and/or intervals, such as days, weeks, etc. 5 As another example, a rule for providing Sellability Score 214 may be measure of demand (product demand). Sellability Score 214 may be derived a number of ways, such as, for example, in part from average inventory turnover for a given product. As another example, a rule for providing inventory turnover and/or average 10 inventory turnover may be calculated per traditional inventory turnover calculations in the field of economics. In some embodiments, inventory turnover and/or average inventory turnover output may be a number and a low inventory turnover number may indicate slow inventory turnover, a high inventory turnover number may indicate fast inventory turnover, etc. Data used as rule input to generate such inventor 15 turnover calculations may derive for how long (e.g. months) it may take a given User or Seller to sell all of inventory of a given purchased product. Thus, in some embodiments, inventory turnover, average inventory turnover, and Sellability Score 214 may depend upon receiving inventory data from users and/or sellers [and/or other sources, such as a governments productions figures in certain areas]. In some 20 embodiments, the average inventory turnover may a component of Seller & Sales Outputs 107. Sellability Score 214 may also predict, indicate, or suggest inventory turnover trends and it should be noted that Sellability Score 214 may be similar but different than inventory turnover, in that Sellability Score 214 may include products for which there may currently be no or limited sales inventory data on, e.g. new 25 products to a market. As another example, a rule for providing Demand-Supply-Ratio 215 (D/S 215) may be calculated by dividing a demand measure (demand indicator) by a supply measure (supply indicator) at a particular moment in time. A rule may be provided to calculate demand measure and/or supply measure (supply indicator) as well [nested 30 input output] such as for example when a supply indicator rule is provided using the by the active number of sellers selling a given product within a given market, i.e. by Number of Sellers 110. In some embodiments, a demand measure (demand indicator) may be provided by the Number of Products Sold 111, across all Sellers, 15 for a given product. As another example, a measure of demand may be provided by the inventory-turnover for a given product and a particular moment in time may be provided as being the moment when the demand and the supply may be measured. In some embodiments, this measurement may be done on preselected and/or 5 desired time intervals, for example daily, weekly, monthly, etc. for a given product. For example, and without limiting the scope of the present invention, D/S 215 may be calculated as follows in the below example: Product-Name Particular Supply: Number Demand: Number of 112 Moment in Time of Sellers 110 Products Sold 111 Kids motocross Goggles 01/09/2014 4 20 Kids motocross Goggles 02/09/2014 4 25 Kids motocross Goggles 03/09/2014 4 30 Kids motocross Goggles 04/09/2014 5 30 Kids motocross Goggles 05/09/2014 1 30 Then DIS 215 may be calculated as follows: 10 01/09/2014: DIS 215 = 2014 = 5 02/09/2014: DIS 215 = 2514 = 6.25 03/09/2014: DIS 215 = 3014 = 7.5 04/09/2014: DIS 215 = 3015 = 6 15 05/09/2014: DIS 215 = 30/1 = 30 The higher the number for a given DIS 215 calculation the better for User 850, as this may indicate higher demand in conjunction with less supply, i.e. less competitors currently serving that demand, which may signal opportunities for User 16 850. In some embodiments, a raw calculated DIS 215 may be normalized on a scale, such as of positive whole number from one to ten. Additionally, in some embodiments, a trending of how DIS 215 may be changing over time may be displayed. Such display may be done graphically by plotting D/S 215 against time 5 and/or first derivatives may be reported to demonstrate changes in trending of DIS 215. In some embodiments, Competitive-Quantity 216 may be a point where supply equals demand. In some embodiments, Competitive-Quantity 216 may be determined by plotting supply and demand on a same chart, and wherein the point of 10 intersection between the supply curve and the demand curve may yield Competitive Quantity 216. Competitive-Quantity 216 may be important, because the value may provide User 850 with an optimal amount of inventory to carry for that particular moment in time that the supply and demand values were obtained from. For example, in the above table example, the demand and the supply were measured on 15 a monthly basis, however, that measurement basis may also be daily, weekly, and the like. In some embodiments, Ease of Trading Metrics 250 may be selected from the group comprising one or more of: Number of Sellers 110, MOQ 122, Size of Product 126, Weight of Product 127, a Time of Production 251, a Transit Delivery Days to 20 Destination 252, an Importation Risk 253, a Return Merchandise Authorization 254, an After Sales Support Severity 255, and the like. In some embodiments, Number of Sellers 110 may be a component of Seller & Sales Outputs 107. In some embodiments, Number of Sellers 110 may be a supply measure (supply indicator). 25 In some embodiments, such as illustrated in Figure 1(c), MOQ 122 may be a component of Product Cost Information Outputs 120. MOQ 122 may be the minimum number of a given product that a Seller must order from Product-Supplier 116. The larger MOQ 122, the greater that such a factor may act as a barrier to entry to other Sellers. 30 In some embodiments, the step of receiving the raw data from at least one Product-Supplier 116 may be preceded by first generating a Request-for-Quote 115 (RFQ 115) with respect to a given product and secondly by transmitting RFQ 115 to at least one Product- Supplier 116.

17 In some embodiments, the generated RFQ 115 may be submitted (transmitted) electronically to at least one Product-Supplier 116. In some embodiments, electronic means of such submission (transmission) may comprise one or more of email, fax (facsimile) message, web form submission, and the like. 5 For example, and without limiting the scope of the present invention, generated RFQs 115 may be submitted by automatically generated email to at least one email address of a given Product-Supplier 116. For example, and without limiting the scope of the present invention, generated RFQs 115 may be submitted by automatically generated fax message to at least one email address of a given 10 Product-Supplier 116. Other submission (transmission) means may also be used, such as phone calls and/or physical mail. In some embodiments, RFQs 115 may be both generated and submitted automatically by Method 1. In some embodiments, RFQs 115 may be generated manually by Staff 950. In some embodiments, generated RFQs 115 may be 15 manually submitted by Staff 950. In some embodiments, Product-Suppliers 116 who may receive the generated RFQ 115 may be selected from a plurality of Product-suppliers 116. The plurality of Product- suppliers 116 may be non-transitorily stored within a memory as a plurality of unique product- supplier-details. Each product-supplier-details may 20 comprise information (e.g. contact information) of that particular Product-supplier 116. The plurality of unique product-supplier-details may be non-transitorily stored within Database 404. In some embodiments, Size of Product 126 may be a component of Product Cost Information Outputs 120. In some embodiments, Size of Product 126, may be 25 the per unit packaging dimensions of a single packaged product. In some embodiments, size of product 126 may be displayed as net length, height, width in cm (centimeters), without packing material external to the single product package. In some embodiments, such as illustrated in Figure 2, Weight of Product 127 may be a component of Product Cost Information Outputs 120. In some 30 embodiments, Weight of Product 127, may be the per unit packaging weight of a single packaged product as shipped by Product-Supplier 116. In some embodiments, Weight of Product 127 may be displayed as net kilograms, grams, pounds, and the like.

18 In some embodiments, Time of Production 251 may be the time it may take to manufacture a given product in a factory. In some embodiments, Transit Delivery Days to Destination 252 may be a number of days it takes to deliver an order of given products from Product-Supplier's 5 116 location to the Seller's location (or end Buyer's location in the case of drop shipping arrangements). In some embodiments, Importation Risk 253 may be a normalized metric presented in positive whole numbers, with a higher Importation Risk 253 indicating greater risks associated with importing a given product. Importation Risk 253 may be 10 a measure noting that some products may have a higher risk of various importation controls, that may increase the Seller's costs to acquire the given product and/or increase the time it may take to obtain the given product. For example, some products may require inspections and/or other product may require quarantines. Food, plants, and agricultural products often have such importation risks. In some 15 embodiments, Importation Risk 253 may be presented (displayed) as a normalized positive whole number. For example, a range from one to ten may be assigned for Importation Risk 253, with ten being products with the greatest such risks. In some embodiments, Return Merchandise Authorization 254 (RMA 254) may be a normalized positive whole number indicating a risk of a given product not 20 functioning as intended. For example, and without limiting the scope of the present invention, electronic devices may have a larger RMA 254 than clothing. Products with higher RMA 254 may be returned more frequently and thus lower a Seller's profitability. In some embodiments, After Sales Support Severity 255 may be a normalized 25 positive whole number indicating a risk of a given product requiring after sales support which may be an added cost that detracts from profitability. For example, After Sales Support Severity 255 (i.e. post-sales support), may be a customer service representative being available to explain products and/or to assist in trouble shooting problems with products. Generally, the more expensive and/or the more 30 complicated a product, the higher After Sales Support Severity 255. Such customer service may be provided email and/or phone call. In some embodiments, Profitability Characteristics 210 and/or Ease of Trading Metrics 250 may also include a breakeven analysis. In some embodiments, 19 a breakeven analysis may require knowledge of fixed and/or variable costs of User 850 and of revenue of User 850. A breakeven point may be a point of revenue per a number of sold units, at which received revenue from the sale of such units equals the total costs associated with obtaining that revenue, wherein revenue received is 5 plotted against units sold for each product. In some embodiments, breakeven analysis may determine the point at which revenue received equals the costs associated with receiving the revenue. Breakeven analysis may determine what is known as a margin of safety, i.e., which may an amount of revenue received exceeding the breakeven point. This may be an 10 amount that revenue may fall while still staying above the breakeven point. Thus if User 850 may sell less units than the breakeven point User 850 may not be making a profit from selling that given product. Conversely, if User 850 may sell more units than the breakeven point, then User 850 may be making a profit from selling that given product. Thus, in order to make breakeven point calculations and perform 15 breakeven analysis Method 1 and/or System 400 may receive revenue and cost data (fixed and/or variable costs) from User 850. For example and without limiting the scope of the present invention, if it costs $50 to produce a widget, and there are fixed costs of $1,000, the breakeven point for selling the widgets may be, depending upon the sale price point: If selling the widgets 20 for $100, then the breakeven point is 20 widgets. This breakeven point may be calculated as follows: ($1,000)/($100 - $50). That is, the formula may be: (total fixed costs)/(sales price - cost of goods). If selling the widgets for $200, then the breakeven point is 7 widgets. This breakeven point may be calculated as follows: ($1,000)/($200 - $50), which actually yields 6.7, but the value is rounded up the 25 nearest whole widget. In this example, if someone sells the widget for a higher price, the breakeven point will come faster. However, breakeven analysis does not show is that it may be easier to sell 20 widgets at $100 each than to sell 7 widgets at $200 each, i.e. as the sales price is increased the demand decreases. A demand-side analysis may tend to give User 850 such information. The above example only 30 included fixed costs, in reality, variable costs may be also be a factor and may be accounted for in the formula by adding on to the fixed costs. In some embodiments, all the breakeven point formula inputs may be received by Method 1 and/or system 400 from User 850.

20 In some embodiments, Profitability Characteristics 210 and/or Ease of Trading Metrics 250 may also include a return on investment ratio (ROI) ratio analysis. In some embodiments, the ROI ratio may be Profit per Unit 211 divided by Sales-Price per Unit 108. Generally, Sales-Price per Unit 108 may be greater than 5 Profit per Unit 211 and so the ROI ratio may generally be a value less than one. A high ROI ratio may be good for User 850. In some embodiments, a ROI ratio of 0.25 may be acceptable, with ROI ratios higher than 0.25 desirable for User 850 and with ROI ratios less than 0.25 less desirable for User 850. ROI ratio may help User 850 to allocate investment towards products that may exhibit a better return on the 10 investment. Three examples: Example 1): Given a Sales-Price per Unit 108 of $10 and a Profit per Unit 211 of $5, the ROI ratio is: 5/10 = 0.5. Such a ROI ratio may be good for User 850. Example 2): Given a Sales-Price per Unit 108 of $20 and a Profit per Unit 211 15 of $5, the ROI ratio is: 5/10 = 0.25. Such a ROI ratio may be acceptable for User 850. Example 3): Given a Sales-Price per Unit 108 of $40 and a Profit per Unit 211 of $5, the ROI ratio is: 5/10 = 0.13. Such a ROI ratio may be too low for User 850. 20 GUI - stop here FIG. 3 may depict an exemplary embodiment of step 300, of displaying and/or presenting at least one of Profitability Characteristics 210, Ease of Trading Metrics 250, Product Cost Information Outputs 120, and Seller & Sales Outputs 107 on at 25 least one GUL. In some embodiments, the at least one GUI 310, including a Display Output 305, may present and/or display the at least one of Profitability Characteristics 210, Ease of Trading Metrics 250, Product Cost Information Outputs 120, and Seller & Sales Outputs 107 in one or more interactive tables and/or one or more webpages 30 from Input 301. In some embodiments, the one or more interactive tables may be interacted with by receiving a command to sort a table column from high to low or low to high; and/or by receiving a command to search for a keyword. In some 21 embodiments, the one or more interactive tables may be selected from the group comprising one or more of: a Research Table 320, a Feasibility Table 330, a Purchased Data Table 350, and the like. The one or more webpages may display the one or more interactive tables. In some embodiments, the one or more webpages 5 may display at least one Product Details Page 350. FIG. 3(a) depicts an embodiment of a screenshot showing Research Table 320 as Research Table 320 may be displayed on/in the GUI. In some embodiments, upon Method 1 receiving login credentials of User 850, the GUI may display Research Table 320. 10 In some embodiments, Research Table 320 may display for a given product one or more of: Hotness Levels 213, Product-Names 111, Product-Categories 113, Number of Products Sold 111, and the like. In some embodiments, Research Table 320 may display for the given product one or more of: Hotness Levels 213, Product Names 111, Product-Categories 113, Number of Products Sold 111, and the like; 15 wherein each category may display under complimentary column headers of: Hotness Level 213, Product-Name 111, Product-Category 113, Number of Products Sold 111, and the like. Such information may be displayed in a table format. Each display column header may be sortable. Each display column header may be sortable by clicking on a given column header. 20 In some embodiments, Research Table 320 may display a field for entering keywords, i.e. Keyword Search 302. Upon receiving a keyword search query Research Table 320 may re-display with updated data fields pursuant to the keyword search query or wherein a no results found message may be displayed. In some embodiments, Number of Products Sold 111 (i.e. the "Items Sold" in 25 FIG.3A) may refer to the number of products sold over a period of time, e.g. 30 days, and by one or more Sellers. In some embodiments, Number of Products Sold 111 may be a component of Seller & Sales Outputs 107. In some embodiments, User 850 searching by Keyword Search 302 and/or sorting by Hotness Level 213, Product-Name 112, Product-Category 113, and/or 30 Number of Products Sold 111, may allow User 850 to perform some initial product research into potential products of interests. Results of such searching and/or sorting may be displayed on the GUI in a tabular form, i.e. with rows and columns, with the data fields of Hotness Level 213, Product-Name 112, Product-Category 113, and 22 Number of Products Sold 111 corresponding to column headers. In some embodiments, such a table may be styled (labelled) as "Product Research" i.e. as Research Table 320. In other embodiments, a different name other than "Product Research" may be used for Research Table 320. 5 In some embodiments, clicking on any product listed in Research Table 320 (e.g. by clicking any displayed Product-Name 112) may then take User 850 to Feasibility Table 330 for that particular selected product, if User 850 may have an appropriate paid subscription account permitting access to Feasibility Table 330. In some embodiments, clicking on any product listed in Research Table 320 10 may then take User 850 to Product Details Page 350, a webpage, for that particular selected product, if User 850 may have purchased data on that particular selected product. FIG. 3(b) may depict an exemplary embodiment of a screenshot showing Feasibility Table 330 as the Feasibility Table 330 may be displayed on the GUI (e.g. 15 User-GUI 802). In some embodiments, Feasibility Table 330 may display for a given product one or more of: Product-Name 112 column header without displaying actual Product-Names 112, Product-categories 113, Profit Margin 212, profit per Unit 211, an aggregate profit over a time period (e.g. "Profit Last 30 Days"), Number of Products Sold 111 (e.g. "Items Sold"), and Purchase Data Means 331. 20 In some embodiments, Feasibility Table 330 may only be assessable by User 850 with an appropriate paid subscription account. In some embodiments, User 850 searching by Keyword Search 302 and/or sorting by Product-Category 113, Profit per Unit 211, Profit Margin 213, Number of Products Sold 111 (i.e. "Items Sold" in FIG. 3(b)), and the aggregate profit over last 25 30 days, may allow User 850 to perform some product profitability research into potential profitable products of interest. Results of such searching and/or sorting may be displayed on the GUI in a tabular form, i.e. with rows and columns, with the data fields of Product-Name 112, Product-Category 113, Profit per Unit 211, Profit Margin 212, Number of Products Sold 111, and the aggregate profit over last 30 days 30 corresponding to column headers. In some embodiments, such a table may be styled (labelled/titled) as "Feasibility Product" i.e. as Feasibility Table 330 may reflect that at least some data populating of the table may be derived from feasibility calculations. In other embodiments, a different name other than "Feasibility Product" may be used 23 for Feasibility Table 330. For example, and without limiting the scope of the present invention, Feasibility Table 330 may be styled (labelled/titled) as "MerchMiner Profitable Products Database." In some embodiments, some Feasibility calculations, the results of which may 5 be displayed in Feasibility Table 330 may provide values for Profit Margin 212, Profit per Unit 211, aggregate profit over last 30 days, and the like. In some embodiments, in Feasibility Table 330, the actual Product-Names 112 below Product-Name 112 column header may not be displayed, i.e. the actual Product-Names 112 may be hidden from User 850, until User 850 may purchase the data for that product. 10 In some embodiments, such Feasibility Table 330 may comprise an additional column of Purchase Data Means 331, wherein a given product may be selected for purchasing the full set of Profitability-Characteristics 210, Ease of Trading Metrics 250, and/or Products-Details-Information 125 associated with that selected product. Completing such a purchase transaction may permit User 850 to access to the given 15 Product Details Page 350. Such a data purchase option in Feasibility Table 330 may be depicted by as a single buy button, a Purchase Data Means 331, for each row with product data displayed in Feasibility Table 330. See e.g., FIG. 3(b). In some embodiments, keeping the actual Product-Names 112 (as well as Product-Supplier 116 contact information) hidden may be an incentive for User 850 to engage 20 Purchase Data Means 331 and acquire access to this hidden information. In some embodiments, clicking on any product listed in Feasibility Table 330 may then take User 850 to Product Details Page 350, a webpage, for that particular selected product, if User 850 may have purchased data on that particular selected product. 25 In some embodiments, the Profit (over) Last 30 Days data (see e.g., FIG. 3(b) and/or FIG. 3(c)), that may be presented beneath the column header of the name "Profit Last 30 Days", and may present aggregate profit data for a 30 day time window for a given product. In some embodiments, the profit over last 30 days for a given product may be calculated by Method 1 in Step 200 and/or by System 400 by 30 multiplying the items sold (Number of Products Sold 111) by Profit per Unit 211 for the given product. Aggregate profit may be calculated over other time periods as well, such as hourly, weekly, quarterly, and the like. Aggregate profit calculated over a time period may be a component of Profitability Characteristics 210.

24 FIG. 3(c) may depict an exemplary embodiment of a screenshot showing Purchased Data Table 340 as Purchased Data Table 340 may be displayed on the GUI. In some embodiments, Purchased Data Table 340 may display for a given product one or more of: Hotness Levels 213, Product-Names 112, Product 5 categories 113, Profit Margin 212, Profit per Unit 211, an aggregate profit over a time period, and Number of Products sold 111 (e.g. "Items Sold"), and the like. In some embodiments, User 850 may also have access to a "Your Purchased Data" table, i.e. Purchased Data Table 340. See e.g., FIG. 3(c). Purchased Data Table 340 table may display on the GUI column headers of Hotness Level 213, 10 Product-Name 112, Product-Category 113, Profit per Unit 211, Profit Margin 212, aggregate profit over last 30 days, Number of Products Sold 111 (e.g. "Items Sold" in FIG. 3(c)), and the like. In some embodiments, clicking on any product listed in Purchased Data Table 340 may then take User 850 to Product Details Page 350 for that particular selected product. 15 FIG. 3(d) may depict an exemplary embodiment of a screenshot showing Product Details Page 350 as Product Details Page 350 may be displayed on the GUI. In some embodiments, the one or more webpages may be a given Product Details Page 350 for each specific product. In some embodiments, Product Details Page 350 may display for a given product one or more of: Product-Details-Information 125, 20 Size of Product 126, Weight of Product 127, Product Cost Information Outputs 120, Cost-Information 121, and Seller & Sales Outputs 107. For example, and without limiting the scope of the present invention, Product Details Page 350 as depicted in FIG. 3(d), may display: Avg. Sales-Price 109 (e.g. "Average Sales Price"), Number of Products Sold 111 (e.g. "Items Sold"), Product 25 Name 112, Product-Category 113, Minimum Order Quantity 122 (e.g. "MOQ-Unit"), Cost-to-Acquire-Goods 123 (e.g.: FOB Price, CIF, FOB + Shipment, Shipment Air, Shipment Sea, Custom Duty, GST, and Final Cost), Avg. Cost-to-Acquire-Goods 124 (e.g.: FOB Price, CIF, FOB + Shipment, Shipment Air, Shipment Sea, Custom Duty, GST, and Final Cost), Size of Product 126, Weight of Product 127, Profit per 30 Unit 211, Profit Margin 212, and Cost of Sales. In some embodiments, the FOB Price, CIF, FOB + Shipment, Shipment Air, Shipment Sea, Custom Duty, GST, and/or Final Cost displayed may be a best (i.e. lowest) Cost-to-Acquire-Goods 123. In some embodiments, the FOB Price, CIF, FOB + Shipment, Shipment Air, 25 Shipment Sea, Custom Duty, GST, and/or Final Cost displayed may be a best Avg. Cost-to-Acquire-Goods 124. In some embodiments, Product Details Page 350 may display at least one Product-Supplier 116 contact information details. 5 FIG. 4 may depict an exemplary embodiment of components of a System 400 for identifying a profitable product, shown as a block diagram. In some embodiments, System 400 may comprise: at least one Server 401, at least one Data-Source 102, at least one Product-Supplier 116, GUI, a Communication Network 501, and the like. In some embodiments, at least one Server 401 may comprise Memory 402, 10 Processor 405, Network Adapter 406, and the like. Memory 402 may be computer readable media. Memory 404 may non-transitorily store Software 403 and a Database 404. In some embodiments, Software 403 may code for various instructions to perform the various steps of Method 1. In some embodiments, Database 404 may be a SQL database or equivalent 15 database. In some embodiments, Processor 405 may execute Software 403. Processor 405 and Software 403 may be in electronic communication with each other. In some embodiments, Network Adapter 406 may be controlled by Processor 405. Network Adapter 406 may be configured for electronic communications across Communication Network 501. In some embodiments, Communication Network 501 20 may be a wide area network (WAN), such as the internet, and/or a local area network (LAN). Network Adapter 406 and Processor 405 may be in electronic communication with each other. Network Adapter 406 may facilitate external communications with at least one Server 401. In some embodiments, as noted above in the discussion of Method 1, raw 25 data may be received from at least one Data-Source 102 by at least one Server 401 across Communication Network 501. Such received raw data may be non-transitorily stored within Database 404. In some embodiments, as noted above in the discussion of Method 1, additional raw data may be received from at least one Product-Supplier 116 by at 30 least one Server 401 across Communication Network 501. Such received additional raw data may be non-transitorily stored within Database 404. Note, "additional raw data" as used herein may be to note that the raw data received from at least one 26 Product-Supplier 116 may comprise different information than the raw data received from at least one Data-Source 102. In some embodiments, Software 403 may comprise instructions for Pre Processing 106 of the raw data into Seller & Sale Outputs 107. In some 5 embodiments, Seller & Sale Outputs 107 may be non-transitorily stored within Database 404. In some embodiments, Software 403 may comprise instructions for More-Pre-Processing 118 of the additional raw data into Product Cost Information Outputs 120. In some embodiments, Product Cost Information Outputs 120 may be non-transitorily stored within Database 404. In some embodiments, Software 403 10 may comprise instructions for primary processing of Seller & Sale Outputs 107 and Product Cost Information Outputs 120 into Profitability Characteristics 210 and/or Ease of Trading Metrics 250. In some embodiments, Profitability Characteristics 210 and Ease of Trading Metrics 250 may be non-transitorily stored within Database 404. In some embodiments, Software 403 may comprise instructions for 15 generating graphical user interfaces (GUls). Such GUIs may display at least one of Profitability Characteristics 210, Ease of Trading Metrics 250, Product Cost Information Outputs 120, Seller & Sale Outputs 107, and the like. In some embodiments, System 400 may further comprise: at least one User Computing-Device 801 and at least one Staff-Computing-Device 901. In some 20 embodiments, User-Computing-Device 801 may be a desktop computer or a server. In some embodiments, User-Computing-Device 801 may be selected from various mobile computing devices, such as, but not necessarily limited to, smart phones, laptops, tablet computing devices, smart watches, and the like. In some embodiments, Staff-Computing-Device 901 may be a desktop computer or a server. 25 In some embodiments, Staff-Computing-Device 901 may be selected from various mobile computing devices, such as, but not necessarily limited to, smart phones, laptops, tablet computing devices, smart watches, and the like. In some embodiments, at least one User-Computing-Device 801 may comprise a User-Graphical-User-Interface 802 (User-GUI 802). In some 30 embodiments, at least one Staff- Computing-Device 901 may comprise a Staff Graphical-User-Interface 902 (Staff-GUI 902). In some embodiments, the GUI may comprise User-GUI 802 and Staff-GUI 902. In some embodiments, Software 403 may comprise instructions for displaying on User-GUI 802 and/or Staff-GUI 902.

27 Note, the interactive tables and webpages displayed on User-GUI 802 and Staff-GUI 902 may not be the same. Staff-GUI 902 may have access to a greater diversity of interactive tables and webpages than may be available to User-GUI 802. For example, and without limiting the scope of the present invention, Staff-GUI 902 may 5 have access to various administrative interactive tables, webpages, an ability to edit various data fields stored within Database 404. In some embodiments, the various GUIs may be accessible by User 850 and/or Staff 950 navigating to a domain name or URL (universal resource locator) associated with the Operational-Entity. For example, and without limiting the scope of 10 the present invention, such GUIs may be accessible by User 850 and/or Staff 950 navigating to a domain name or URL of MerchMiner.com. With respect to Database 404, for example, and without limiting the scope of the present invention, there may be about 75,000 different products included within Database 404 with respect to an Australian market. Each such different product may 15 include data as displayed Product Details Page 350, see e.g., FIG. 3(d). For example, and without limiting the scope of the present invention, there may be about 75,000 different Product-Names 112 within Database 404 with respect to the Australian market. In some embodiments, data displayed in User-GUI 802 may be only be that of profitable products, whereas, Database 404 may also include data 20 with respect to non-profitable products. For example, and without limiting the scope of the present invention, there may be over 300,000 raw data entries received (extracted) daily from a given market, e.g. the Australian market. But not all such raw data entries may be for profitable products. Turning to FIG 5 there is illustrated an example of an embodiment of the 25 present invention. In the present example the Seller 850 signs up to the Proprietary System 400. The signing up process includes the process of permitting the Proprietary System 400 to access the Seller's Website or Online Profile 970 being a second data source. For instance the Seller 850 may permit the Proprietary System 400 to access and interrogate their Facebook@ profile to identify products that they 30 would likely be interested in selling or have knowledge about. This will assist the Proprietary System 400 in providing product details to the Seller 850 that are relevant to the age, sex, interests or demographics of the Seller 850. The Seller 850 will therefore be less likely to be provided with details of products for which they have no interest or knowledge thereof.

28 In another example the Seller 850 will permit access to their existing online store or website that they are currently selling products through. This will permit the Proprietary System 400 to interrogate the products currently sold by the Seller 850 to identify the types of products that are currently sold by the Seller 850 or to identify 5 complementary products that could be sold by the Seller 850. The Proprietary System 400 can also identify gaps in the Seller's product range. For instance if the Seller 850 is selling children's shoe the Proprietary System 400 could identify that a gap in their range of products includes children's socks. The Proprietary System 400 therefore sorts the Product Data 972, which 10 comprises the information on the Internet regarding potential products to produce Ranked Data 974 comprising a ranked list of products that is tailored to the Seller's profile and interests or previous successful sales. The Seller 850 can then use the Ranked Data 974 for the Purchase of Product/s 976 based upon the tailored suggestions. This will improve the Seller's 15 chances of making a successful sale because information regarding product/s is based upon their interests or existing products they sell. The Proprietary System 400 may also be given permission to access the Seller's actual Sales Details 978, as illustrated in FIG 6. This information regarding actual sales 978 may be sales that have occurred as a direct result of the purchases 20 976 made resulting from the information that was provided by the Proprietary System 400 in the Ranked Data 974. Alternatively, the information regarding actual sales 978 may be sales that the Seller 850 has made previously based upon their own decisions or research. The Proprietary System 400 therefore interrogates the Sales Details 978, to 25 identify product lines that the seller has been/is successful in selling. This information 978 can then be used to further tailor the product list 974, based upon the successful sales record 978. The Sales Details 978 may also include information regarding all the inventory purchased, stock that has been written off, returned stock or any other information regarding the Seller's purchases that provides a full picture 30 of the Seller's previous/current transactions. All this information can then be used to present the Seller 850 with a ranked list of potential products that they could sell which is based upon their profile/interests and successful/unsuccessful sales. This 29 will accordingly improve the chances of the Seller 850 making a future successful sale based upon the products suggested by the Proprietary System 400. As a first step the Proprietary System 400 identifies profitable products based on market analysis. Products that are in high demand are identified and filtered by 5 categories (product title + average sales price). The Proprietary System 400 acquires purchase offers from suppliers and manufacturers, as well as listing qualified products by profit ranking. In one embodiment the User/Seller 850 accesses the Proprietary System 400 by way of a webpage, although the reader should appreciate that the User/Seller 850 10 could also access the Proprietary System 400 by way of a designated App (application software) for a mobile device or downloadable software for a desktop/laptop computer. The User/Seller 850 accesses the Proprietary System 400 database through a member login, the data is then sorted by needs and then the data is exported as, 15 for instance a CSV or PDF file for further usage. To permit access to the Seller's website or profile 970 the User/Seller 850 enters his/her website URL into the Proprietary System 400 and clicks "go". The Proprietary System 400 crawler will then access the user/seller's website and identifies listed products and extract the data. The Proprietary System 400 crawler 20 scans the Seller's website and identifies currently listed products (Product titles, Categories and prices). For instance based on the User/Seller 850 existing categories in their online store the Proprietary System 400 will create user portfolio and generate a characteristic pattern. As indicated by the arrow in FIG 5 after interrogation of the Seller's website or 25 profile 970 the data will be retrieved by the Proprietary System 400 for comparison. The Proprietary System's 400 algorithm will identify profitable products and provide a list of suggested items 974 to add to Seller's website. If the User/Seller 850 does not have a web-store or website, than then Proprietary System 400 can conduct the product suggestion based upon the Seller 30 850 Online Profile.

30 In such a case the Proprietary System 400 will be given permission to access User/Seller's 850 social media accounts after an authentication process. Facebook@, Linkedln@, Twitter@, g+@, ebay@ or any other online profile can be used for this purpose. The Proprietary System 400 will identify patterns to analyse users 5 lifestyle, likes, dislikes, hobbies, preferences or other user relevant information. Information will be returned to the Proprietary System 400 and an algorithm will be used to match the best-fit category and products for the Seller 850. Data will be transferred to the Proprietary System 400 for crosschecking to find matching and unmatched profitable products. 10 The Proprietary System 400 may upload the new products directly to the Seller's 850 online store. The reader should appreciate that crawling of the Seller's 850 existing online store will be undertaken to cross check with Proprietary System 400 database before new product/s is/are suggested. The Proprietary System 400 may identify 15 industry/categories and focus on same/similar products that don't currently exist in Seller's 850 online store. The Proprietary System 400 may also identify Seller's 850 existing products price patterns and make suggestions to adjust them to market price. For example if Seller's 850 pricing strategy is wrong and therefore they cannot sell their products the 20 Proprietary System 400 will provide up to date pricing suggestions to them. At the end of crosschecking steps the process data will be saved in a Seller's 850 account where they can login to Proprietary System's 400 website to view their user account and download the file to be uploaded to their online store, or the data can be upload directly to their online store, either manually or automatically. 25 Where the Seller 850 does not have an online store or is new to ecommerce, the Seller 850 can signup to an account using his social media account. As described above the Seller 850 then allows the Proprietary System 400 to run its search in their social media account to establish the pattern about them and the rest of the process will be the same as described immediately above. 30 A profitability calculator of the Proprietary System 400 estimates the Seller's 850 potential market penetration and number of units they can potentially sell. For example, an inexperienced seller who decides to sell "Bluetooth motorbike helmet" 31 on ebay@ and on his own website. The Proprietary System 400 will assess Seller's 850 capability/opportunity of selling the given product. The Proprietary System 400 will give a score from 1 to 100, 1 being lowest ability to sell and 100 is the highest. In the present example, assuming the Seller 850 gets a score of 5 and assume the 5 number of sold Bluetooth motorbike helmets in the last 30 days was 100 units. The Proprietary System 400 will assign 5 units of Bluetooth motorbike helmet to the Seller and estimate that they will sell 5 units in 30 days taken their skills and past trading and ecommerce experience. The present invention may include validation or verification step/s, wherein 10 once the product has been sold the details of the sales are uploaded to the Proprietary System 400, for comparison with the earlier suggestion made by the Proprietary System 400. The details of the sales may be uploaded automatically to the Proprietary System 400 or they may be entered or uploaded by the Seller 850. Other information such as, but not limited to, cost of transport, postage, written off 15 stock or returns may also be uploaded to the Proprietary System 400 to provide a full picture of the Seller's activities. The validation or verification step/s can be used to, quantify the original information provided, improve the algorithm used to produce the original recommendations and/or provide advice to Seller 850 regarding ways to improve 20 their profit i.e. what trade channels to use, quantities to purchase, etc. As illustrated in FIG 7, the Proprietary System 400 ranks potential product information retrieved from the Internet 980. The ranking may be as simple as ranking based upon profit margins or may be ranked based upon the profit margins in conjunction with the profile/interests of the Seller 850 or previous sales/activity data. 25 The Seller 850 then makes a purchase from a Third Party Supplier 990 based upon the suggestion contained within the Ranked product list 980. Sale of Product by Seller 982 then occurs 982 and the details of the Sale 984 are supplied, either automatically or manually, to the Proprietary System 400. The Proprietary System 984 then undertakes a Comparison 986 of the actual 30 sales data with the previously supplied ranked product information. This comparison is then used to validate or amend the algorithms used by the Proprietary System 984.

32 The Proprietary System 984 can then provide Supplementary Ranked Data 988 to the Seller 850 than has been further refined to the Seller's 850 capacities and actual sales results. Accordingly over time the Proprietary System 984 is able to continue to refine the suggested products to the Seller 850 based upon ongoing 5 sales activates. This is important as the Proprietary System 984 provides suggested products that have the best profit margin, but also products that the Seller 850 is actually able to sell. According, the Seller 850 over time is provided with up-to-date suggestions based upon their successful sales and business activity. As the reader will now appreciate the Proprietary System 984 will compare 10 actual sales with forecast sales, and provide feedback for improvements to achieve more sales, such as lowering transport costs of suggesting new Suppliers 990. A method and system for identifying products to sell which may be profitable has been described. The foregoing description of the various exemplary embodiments of the invention has been presented for the purposes of illustration and 15 disclosure. It is not intended to be exhaustive or to limit the invention to the precise form disclosed. Many modifications and variations are possible in light of the above teaching without departing from the spirit of the invention. While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiments, it is to be 20 understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. These and other advantages and features of the present invention are described herein with specificity so as to make the present invention understandable 25 to one of ordinary skill in the art, both with respect to how to practice the present invention and how to make the present invention. It should further be appreciated that any discussion of the prior art throughout the specification is included solely for the purpose of providing a context for the present invention and should in no way be considered as an admission that such 30 prior art was widely known or formed part of the common general knowledge in the field as it existed before the priority date of the application.

Claims (5)

1. A computer-implemented method for dynamically reloading pricing information within a window displayed in a graphical user interface, the method comprising: - providing or accessing a first data source, 5 - receiving data from said first data source at intervals, wherein said data is comprised of Product Pricing Information types comprised of one or more of the following; Product-Category Information, Product-Description Information, Sales Price per Unit Information, Average Sales-Price Information, Number of Sellers Information, Number of Products Sold Information, Cost to Acquire Goods 10 Information, - translating said data into said Product Pricing Information types comprised of one or more of the following; Product-Category Information, Product-Description Information, Sales-Price per Unit Information, Average Sales-Price Information, Number of Sellers Information, Number of Products Sold Information, 15 wherein said Product Pricing Information is provided within a first window in a graphic user interface; - a display format further providing an Average Cost-to-Acquire-Goods Information type, a Profitability Characteristic Information type, further comprising Profit Margin Information; and/or Ease of Trading Metrics Information 20 type comprising Minimum Order Quantity Information, - said user interface being used for selection of Information types; wherein said method further provides for a Dynamic User Filter, comprising said user interface further providing, through access to a second data source, filtering capabilities for said display format dependent upon user information as disclosed 25 by said second data source and/or user preferences input via said interface.

2. The method in accordance with claim 1, wherein the data from said first data source relates to a product or products that could be offered for sale by said user, wherein said product or products are sorted and displayed to provide recommended product information, the sorting of the data from said first data 30 source being done by profitability and/or the ease with which the product or products can be traded using known trade channels.

3. The method in accordance with claim 2, wherein said user provides permission to said interface processor for access to said second data source being a third 34 party website or a database containing personal information pertaining to said user, or the user provides a web address corresponding to an online store operated by said user containing information pertaining to items currently offered for sale by said user, or the user provides permission to said interface processor 5 to access a database containing information pertaining to previous sales records and/or current inventory, wherein a user's profile or a user's commercial activities are interrogated by said interface processor to identify, provide and display data relating to a product or products suitable for the user to sell which relates to the user's profile or the user's pervious commercial activities. 10

4. The method in accordance with claim 2, wherein after sale of a product or products by said user, the interface processor processes sales records of said user to verify the accuracy of the recommended product information previously supplied to said user.

5. The method in accordance with claim 4, wherein the interface processor obtains, 15 either manually or automatically from said user, sales records including sales details, cost of transport and sales channels information, wherein said previously supplied recommended product information is compared to actual sales records information to quantify the accuracy of said recommended product information, wherein the comparison can be used to improve an algorithm used to produce 20 said recommended product information supplied to said user and/or provide advice to said user regarding ways to improve profit, including alternate trade channels to use, quantities to purchase, recommended retail price or advertising options.