DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of the Claims
The pending claims in the present application are original claims 1-20 of the “AMENDMENT AND RESPONSE UNDER 37 CFR § 1.111” of 02 March 2026 (hereinafter referred to as the “Amendment/Response”).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The paragraphs below provide rationales for the rejection. The rationales are based on the multi-step subject matter eligibility test outlined in MPEP 2106.
Step 1 of the eligibility analysis involves determining whether a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101. (See MPEP 2106.03(I).) That is, Step 1 asks whether a claim is to a process, machine, manufacture, or composition of matter. (See MPEP 2106.03(II).) Referring to the pending claims, the “method” of claims 1-10 constitutes a machine under 35 USC 101, the “method” of claims 11-20 constitutes a process under the statute. Accordingly, claims 1-20 meet the criteria of Step 1 of the eligibility analysis. The claims, however, fail to meet the criteria of subsequent steps of the eligibility analysis, as explained in the paragraphs below.
The next step of the eligibility analysis, Step 2A, involves determining whether a claim is directed to a judicial exception. (See MPEP 2106.04(II).) This step asks whether a claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. (See id.) Step 2A is a two-prong inquiry. (See MPEP 2106.04(II)(A).) Prong One and Prong Two are addressed below.
In the context of Step 2A of the eligibility analysis, Prong One asks whether a claim recites an abstract idea, law of nature, or natural phenomenon. (See MPEP 2106.04(II)(A)(1).) Using claim 1 as an example, the claim recites the following abstract idea limitations:
“... retail forecasting and task management, ... comprising: ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... storing sales histories associated with a plurality of store locations; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... cluster a plurality of store locations based on shared characteristics, the plurality of store locations including the store location; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... for each category of a plurality of categories of items sold at the store location: determine a local sales forecast value on a future date for the store location based on a sales history of the store location using at least a first forecast model; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... determine a group sales forecast value on the future date based on sales histories of other store locations of the plurality of store locations using at least the first forecast model; and ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... determine an adjusted sales forecast for the store location based on the local sales forecast value and the group sales forecast value; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... rank the plurality of categories based on the adjusted sales forecast determined for each of the plurality of categories; and ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... selectively display ... a subset of the plurality of categories based on the ranking and the adjusted sales forecast for each category of the subset of the plurality of categories, where each category of the subset of the plurality of categories is selectable to display additional information related to a key item within the category.” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
The above-listed limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, fall under enumerated groupings of abstract ideas outlined in MPEP 2106.04(a). For example, limitations of the claim can be characterized as: fundamental economic principles or practices, including mitigating risk by forecasting sales; and commercial interactions, including marketing or sales activities or behaviors via retail sales forecasting; which fall under the certain methods of organizing human activity grouping of abstract ideas (see MPEP 2106.04(a)). Limitations of the claim also can be characterized as: concepts performed in the human mind, including observation (e.g., the recited “storing” and “display” step), and evaluation, judgment, and/or opinion (e.g., the recited “cluster,” “determine,” “rank,” and “selectively” steps), which fall under the mental processes grouping of abstract ideas (see MPEP 2106.04(a)). Accordingly, for at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong One of the eligibility analysis.
In the context of Step 2A of the eligibility analysis, Prong Two asks if the claim recites additional elements that integrate the judicial exception into a practical application. (See MPEP 2106.04(II)(A)(2).) Continuing to use claim 1 as an example, the claim recites the following additional element limitations:
The claimed “retail forecasting and task management” is performed by a “system” - See below regarding MPEP 2106.05(a)-(c), (f), and (h)
The claimed “storing” is by “a sales history database” - See below regarding MPEP 2106.05(a)-(c), (f), and (h)
“... a network adapter; and a control circuit coupled to the sales history database and the network adapter and configured to: provide, via the network adapter, a retail task user interface on a user device at a store location” - See below regarding MPEP 2106.05(a)-(c), (f), and (h)
The claimed “display” is “on the retail task user interface” - See below regarding MPEP 2106.05(a)-(c), (f), and (h)
The above-listed additional element limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, are analogous to: accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, mere automation of manual processes, instructions to display two sets of information on a computer display in a non-interfering manner, without any limitations specifying how to achieve the desired result, and arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, which courts have indicated may not be sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)); a commonplace business method being applied on a general purpose computer, gathering and analyzing information using conventional techniques and displaying the result, and selecting a particular generic function for computer hardware to perform (e.g., buffering content) from within a range of fundamental or commonplace functions performed by the hardware, which courts have indicated may not be sufficient to show an improvement to technology (see MPEP 2106.05(a)(II)); a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions, and merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, which do not qualify as a particular machine or use thereof (see MPEP 2106.05(b)(I)); a machine that is merely an object on which the method operates, which does not integrate the exception into a practical application (see MPEP 2106.05(b)(II)); use of a machine that contributes only nominally or insignificantly to the execution of the claimed method, which does not integrate a judicial exception (see MPEP 2106.05(b)(III)); transformation of an intangible concept such as a contractual obligation or mental judgment, which is not likely to provide significantly more (see MPEP 2106.05(c)); remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, which courts have found to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome (see MPEP 2106.05(f)); use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea, a commonplace business method or mathematical algorithm being applied on a general purpose computer, and requiring the use of software to tailor information and provide it to the user on a generic computer, which courts have found to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process (see MPEP 2106.05(f)); and specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, which courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). For at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong Two of the eligibility analysis.
The next step of the eligibility analysis, Step 2B, asks whether a claim recites additional elements that amount to significantly more than the judicial exception. (See MPEP 2106.05(II).) The step involves identifying whether there are any additional elements in the claim beyond the judicial exceptions, and evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept. (See id.) The ineligibility rationales applied at Step 2A, Prong Two, also apply to Step 2B. (See id.) For all of the reasons covered in the analysis performed at Step 2A, Prong Two, independent claim 1 fails to meet the criteria of Step 2B. Further, claim 1 also fails to meet the criteria of Step 2B because at least some of the additional elements are analogous to: receiving or transmitting data over a network, e.g., electronic recordkeeping, and storing and retrieving information in memory, which courts have recognized as well-understood, routine, conventional activity, and as insignificant extra-solution activity (see MPEP 2106.05(d)(II)). As a result, claim 1 is rejected under 35 USC 101 as ineligible for patenting.
Regarding claims 2-10, the claims depend from independent claim 1, and expand upon limitations introduced by claim 1. The dependent claims are rejected at least for the same reasons as claim 1. For example, the dependent claims recite abstract idea elements similar to the abstract idea elements of claim 1, that fall under the same abstract idea groupings as the abstract idea elements of claim 1 (e.g., the “wherein the shared characteristics comprise geographical location, past store sales, customer demographic, and/or store size” of claim 2, the “wherein the adjusted sales forecast is determined based on applying a first weighting factor to the local sales forecast value and a second weighting factor to the group sales forecast value” of claim 3, the “retrieve the first weighting factor and the second weighting factor from a plurality of weighting factors based on: a holiday associated with the future date, a category of the plurality of categories associated with the local sales forecast value, a sub-category associated with the local sales forecast value, and/or an identifier associated with the store location” of claim 4, the “determine the first weighting factor and the second weighting factor based on comparing a prior local sales forecast and a prior group sales forecast with actual sales of a prior date” of claim 5, the “determine a retail task based on the adjusted sales forecast; and cause the retail task to be instructed” of claim 6, the “wherein the adjusted sales forecast comprises a percentage sales lift compared to a baseline sales volume” of claim 7, the “wherein the adjusted sales forecast is further associated with a particular item of the category of items” of claim 8, the “wherein the first forecast model comprises a forecast model for time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, and holiday effects” of claim 9, and the “wherein the local sales forecast value is determined based on generating a first forecast value using the first forecast model, generating a second forecast value using a second forecast model, and combining the first forecast value and the second forecast value” of claim 10). The dependent claims recite further additional elements that are similar to the additional elements of claim 1, that fail to warrant eligibility for the same reasons as the additional elements of claim 1 (e.g., the “system” of claims 2-10, the “wherein the control circuit is configured to” of claims 4-6, and the “first forecast model and the second forecast model are machine learning models trained using sales data and event information” of claim 10). Accordingly, claims 2-10 also are rejected as ineligible under 35 USC 101.
Regarding claims 11-20, while the claims are of different scope relative to claims 1-10, the claims recite limitations similar to the limitations of claims 11-20. As such, the rejection rationales applied to reject claims 1-10 also apply for purposes of rejecting claims 11-20. Claims 11-20 are, therefore, also rejected as ineligible under 35 USC 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-9 and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. No. 10,140,623 B1 to Lloyd et al. (hereinafter referred to as “Lloyd”), in view of U.S. Pat. App. Pub. No. 2008/0294996 A1 to Hunt et al. (hereinafter referred to as “Hunt”).
Regarding claim 1, Lloyd discloses the following limitations:
“A system for retail forecasting and task management, the system comprising: ...” - Lloyd discloses, “FIG. 1 illustrates an example environment 100 for a payment and information service according to some implementations. For instance, the environment 100 may enable a service provider to receive merchant analytics information for merchant locations, and associate the merchant analytics information with related merchants. Based at least in part on analysis of the merchant analytics information, the service provider may provide various personalized explanations, recommendations and predictions to the merchants, such as to assist the merchants in optimizing and improving their businesses” (col. 3, l. 60 to col. 4, l. 3). The environment for providing predictions and recommendations to merchants, in Lloyd, reads on the recited limitation.
“... a sales history database storing sales histories associated with a plurality of store locations; ...” - Lloyd discloses, “A service provider may receive merchant analytics information from a plurality of merchant devices, and may associate the merchant analytics information with particular merchant profiles” (col. 2, ll. 5-9), “The service provider may further aggregate and segment merchant profiles into merchant categories, e.g., groups of merchant profiles that share certain characteristics. For example, the service provider can create subsets of merchant profiles based on various merchant information including, for example, geographic region” (col. 2, ll. 30-35), “individual models may incorporate a set of core features for predicting the metric represented by the model. In some examples, the core features may include the day of the week, historic sales data, seasonality, local weather, economic health, local events, etc. Further, the core features incorporated into the model may be based in part on one or more merchant categories associated with the merchant profile. For instance, the cores features incorporated into the model may be based upon the geographic region of the merchant” (col. 2, ll. 50-59), and various types of “memory ... data structures ... storage” (col. 17, ll. 41-53). The memory storing the historical sales data of merchants in geographic regions, in Lloyd, reads on the recited limitation.
“... a network adapter; and ...” - Lloyd discloses, “The communication interface(s) 806 may include one or more interfaces and hardware components for enabling communication with various other devices, such as over the network(s) 106” (col. 18, ll. 40-43). The communication interfaces, in Lloyd, read on the recited limitation.
“... a control circuit coupled to the sales history database and the network adapter and configured to: ...” - See the aspects of Lloyd that have been cited above. Lloyd also discloses, “logical circuitries ... logical circuits” (col. 17, ll. 31-34). The logical circuits operatively coupled to the memory via the network and the communication interfaces, in Lloyd, read on the recited limitation.
“... provide, via the network adapter, a retail task user interface on a user device at a store location; ...” - See the aspects of Lloyd that have been cited above. Lloyd also discloses, “one or more service computing devices 102 of the service provider are able to communicate with one or more merchant devices 104 over one or more networks 106” (col. 4, ll. 4-7), and “FIG. 4 illustrates an example graphical user interface 400 for presenting forecast data to a merchant according to some implementations. For example, a forecast 402 may be presented on a display 404 associated with a merchant device or may be presented to the merchant using any other suitable communication technology” (col. 10, ll. 15-20). The service computing device providing, via the network and the communication interfaces, the graphical user interface on the merchant device of the merchant in its geographic region, in Lloyd, reads on the recited limitation.
“... cluster a plurality of store locations based on shared characteristics, the plurality of store locations including the store location; ...” - Lloyd discloses, “The service provider may further aggregate and segment merchant profiles into merchant categories, e.g., groups of merchant profiles that share certain characteristics. For example, the service provider can create subsets of merchant profiles based on various merchant information including, for example, geographic region” (col. 2, ll. 30-35). The aggregating of merchants based on the merchants having profiles that share certain characteristics, including geographic region information of the merchants, in Lloyd, reads on the recited limitation.
“... determine a local sales forecast value on a future date for the store location based on a sales history of the store location using at least a first forecast model; ...” - See the aspects of Lloyd that have been cited above. Lloyd also discloses, “a model may represent sales predictions for a particular item, sales predictions for a particular merchant location, sales predictions for a group of merchant locations in a geographic area” (col. 2, ll. 46-49). The service computing device determining sales predictions for the particular merchant location, based on the historic sales data of the merchant, using the model, in Lloyd, reads on the recited limitation.
“... determine a group sales forecast value on the future date based on sales histories of other store locations of the plurality of store locations using at least the first forecast model; and ...” -See the aspects of Lloyd that have been cited above. Lloyd also discloses, “the service provider may generate a category specific time series model for a subset of merchant profiles based at least in part on aggregated merchant analytics information associated with a plurality of merchant profiles associated with a merchant category” (col. 2, ll. 60-64). The service computing device determining sales predictions for the group of merchant locations in the geographic area, based on the historic sales data, using the model, in Lloyd, reads on the recited limitation.
“... determine an adjusted sales forecast for the store location based on the local sales forecast value and the group sales forecast value; and ...” - See the aspects of Lloyd that have been cited above. Lloyd also discloses, “the service provider may generate a category specific time series model for a subset of merchant profiles based at least in part on aggregated merchant analytics information associated with a plurality of merchant profiles associated with a merchant category. In addition, the service provider may generate a weighted combination of a merchant specific model and a category specific model for use as a forecast model” (col. 2, ll. 60-67), and “the service provider may compare sales predictions of a model for a merchant to actual sales by the merchant, and detect a residual of the actual sales with respect to the sales predictions that is larger than a predetermined threshold” (col. 3, ll. 9-14). The service computing system determining the sales forecast for the merchant location based on the weighted combination of the merchant specific model and the category specific model, in Lloyd, reads on the recited limitation.
The combination of Lloyd and Hunt (hereinafter referred to as “Lloyd/Hunt”) teaches limitations below of claim 1 that do not appear to be disclosed in their entirety by Lloyd:
“... for each category of a plurality of categories of items sold at the store location: ...” - Hunt discloses, “the granting matrix 120 may be associated with rules that relate to statistical releasability, private label masking, venue group scoping, category scoping, measure restrictions, category weights, and so on. Statistical releasability may be associated with an application of statistical releasability rules to measures or classes of measures. Private label masking may be associated with the masking of private label attributes. Venue group scoping may be associated determining which venue groups can be used by which customers for which purposes, and the like. Category scoping may be associated with limiting access to categories of data, or specific items within categories, to particular customers, by venue groups, and so on. Measure restrictions may be associated with restricting access to measures according to a set of business rules” (para. [0378]), “Measures that may be applied to the sales performance facility include standard measures such as Base Unit Sales, Base Volume Sales, Base Dollar Sales, Incremental Unit Sales, Incremental Volume Sales, Incremental Dollar Sales, Weighted Average Base Price per Unit, Price per Unit, Price per Volume, ACV Weighted Distribution, % Increase in Units, % Increase in Dollars, % Increase in Volume, Category Dollar Share, Category Unit Share, and Category Volume Share. Additional measures may include Total Category Dollar Sales, Total Category Unit Sales, Total Category Volume Sales, Account Sales Rate (Units) Index, Account Sales Rate (Dollars) Index, Account Sales Rate (Volume) Index, Product Sales Rate (Units) Index, Product Sales Rate (Dollars) Index, Product Sales Rate (Volume) Index, Product Price Index, Dollar Sales Category Rank, Unit Sales Category Rank, Volume Sales Category Rank” (para. [0506]), and “The analytical data platform may provide the assortment analysis by using multiple dimensions received from the user. The multiple dimensions for the assortment analysis may include customer, product, geography, time and measures. The customer dimension may include behavioral segment and the spending segment. For example, a user may choose between the consumer segment and the spending segment for the assortment analysis of the particular product. The product dimension may include category and item selection. For example, the user may choose different items for the assortment analysis. The geography dimension may include selection in a particular geography or store cluster hierarchy. For example, the user may choose a particular geography or a particular store hierarchy for the assortment analysis for the particular geography. The time dimension may include a definite period. The definite period may be a week, a quarter or a year. For example, the user may choose a year or a time period for the assortment analysis. The measure dimension may include the net money of sales, advertisement, operation, profit and the like. For example, the user may choose the total amount of money required for the advertisement of the particular product for the assortment analysis of that particular product” (para. [1250]). The users setting scopes, establishing rules, and making choices for processing to provide measures about categories of items, and about items (particular products), in Hunt, reads on the recited limitation.
“... rank the plurality of categories based on the adjusted sales forecast determined for each of the plurality of categories; and ...” - See the aspects of Lloyd and Hunt that have been cited above. Hunt also discloses, “the content and solution platform 188 possesses features that enable data access and reporting. Content platform features may include on-demand and scheduled reports, automated scheduled report delivery, multi-page and multi-pane reports for guided analysis, interactive drill down/up, swap, and pivot, dynamic filter/sort/rank and attribute filtering, conditional formatting and highlighting, on-the-fly custom hierarchies and aggregates, calculated measures and members, built-in chart types, interactive drillable charts in 100% thin client UI, data export to spreadsheet and presentation software or files with single click refresh capability, integrated alerts with optional email delivery, folders for organizing links and documents, multi-user collaboration and report sharing, printing and export to HTML, PDF, spreadsheet files, and presentation files with configurable print templates, dashboards with summary views and graphical dial indicators, publication and subscription of reports and dashboards, and the like” (para. [0312]), “The sales performance facility may enable sales performance evaluation and detailed analysis for each stakeholder, such as Performance Ranking, Leader Report, Laggard Report, Performance Analysis (Sales Decomposition), Category Review, Account Review, and the like. The sales performance facility may enable sales plan projections based on current sales rates and trends. Sales plan projections may include Projected Sales by Product, Projected Sales by Account, Projected Sales by Geography, Projected Sales Performance Ranking, and the like” (para. [0318]), “stakeholder reports may provide detailed evaluation and sales performance insights for each stakeholder (e.g., sales representatives, managers and executives) including plan tracking, account, product and geography snapshots, sales report cards, performance rankings” (para. [0367]), and “Product Sales Rate (Units) Index, Product Sales Rate (Dollars) Index, Product Sales Rate (Volume) Index, Product Price Index, Dollar Sales Category Rank, Unit Sales Category Rank, Volume Sales Category Rank” (para. [0506]). Ranking the categories based on sales, including projected sales, in Hunt, when applied in the context of the forecasts, in Lloyd, reads on the recited limitation.
“... selectively display, on the retail task user interface, a subset of the plurality of categories based on the ranking and the adjusted sales forecast for each category of the subset of the plurality of categories, where each category of the subset of the plurality of categories is selectable to display additional information related to a key item within the category.” - See the aspects of Lloyd and Hunt that have been cited above. The service computing system providing, on the graphical user interface of the merchant device, the sales predictions of the weighted model, in Lloyd, reads on the recited “selectively display, on the retail task user interface” limitation. Hunt also discloses, “The methods and systems disclosed herein include an analytic platform, with a customized retailer portal application, that may be used to perform data projection and release methodologies in order to create an integrated, flexible, actionable view of data such as data relating to consumers, consumer behavior, commodity sales, and other commercial activities like those pertaining to relationships between consumers and stores” (Abstract), “the content and solution platform 188 possesses features that enable data access and reporting. Content platform features may include on-demand and scheduled reports, automated scheduled report delivery, multi-page and multi-pane reports for guided analysis, interactive drill down/up, swap, and pivot, dynamic filter/sort/rank and attribute filtering, conditional formatting and highlighting, on-the-fly custom hierarchies and aggregates, calculated measures and members, built-in chart types, interactive drillable charts in 100% thin client UI, data export to spreadsheet and presentation software or files with single click refresh capability, integrated alerts with optional email delivery, folders for organizing links and documents, multi-user collaboration and report sharing, printing and export to HTML, PDF, spreadsheet files, and presentation files with configurable print templates, dashboards with summary views and graphical dial indicators, publication and subscription of reports and dashboards, and the like” (para. [0312]), and “Referring to FIG. 65, in embodiments, the unified reporting and solution framework may include on-demand and scheduled reports, automated scheduled report, multi-page and multi-pane reports for guided analysis, interactive drill down, dynamic filter/sort/rank, multi-user collaboration, dashboards with summary views and graphical dial indicators, flexible formatting options--dynamic titles, sorting, filtering, exceptions, data and conditional formatting tightly integrated with Excel and PowerPoint” (para. [1198]). The platform portal for displaying reports and dashboards, for analytics of any categories of items or products with filtering and sorting by rank, wherein all aspects are user-definable, in Hunt, reads on the recited “a subset of the plurality of categories based on the ranking and the adjusted sales forecast for each category of the subset of the plurality of categories, where each category of the subset of the plurality of categories is selectable to display additional information related to a key item within the category” limitation. See also, e.g., all depicted functionalities and features in FIGS. 50-59 of Hunt.
Hunt discloses “an analytic platform” and “to perform data projection” (Abstract), similar to the claimed invention and to Lloyd. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the merchant analytics method and system, of Lloyd, to include category level data in addition to item level data, and to provide associated visualization aspects, of Hunt, to address a need for a flexible, extendable analytic platform, the architecture for which is designed to support a broad array of evolving market analysis needs, and to provide better business intelligence, per Hunt (para. [0009]).
Regarding claim 2, Lloyd/Hunt teaches the following limitations:
“The system of claim 1, wherein the shared characteristics comprise geographical location, past store sales, customer demographic, and/or store size.” - See the aspects of Lloyd that have been cited above. the merchant profiles being placed into subsets based on geographic location, in Lloyd, reads on the recited limitation.
Regarding claim 3, Lloyd/Hunt teaches the following limitations:
“The system of claim 1, wherein the adjusted sales forecast is determined based on applying a first weighting factor to the local sales forecast value and a second weighting factor to the group sales forecast value.” - See the aspects of Lloyd that have been cited above. The sales forecasts being determined based on the merchant specific model, the category specific model, and the weighted combination of the merchant specific model and the category specific model, in Lloyd, reads on the recited limitation.
Regarding claim 4, Lloyd/Hunt teaches the following limitations:
“The system of claim 3, wherein the control circuit is configured to retrieve the first weighting factor and the second weighting factor from a plurality of weighting factors based on: a holiday associated with the future date, a category of the plurality of categories associated with the local sales forecast value, a sub-category associated with the local sales forecast value, and/or an identifier associated with the store location.” - See the aspects of Lloyd and Hunt that have been cited above. Lloyd also discloses, “the model generator 134 may build a weighted model, including merchant information 122 (not shown in FIG. 5) associated with the merchant 108-1 and other merchants 108(N) in the same geographic region as the merchant 108-1” (col. 11, ll. 55-58). The logical circuits applying weights for the weighted model based on the geographic region of the merchant, in Lloyd, when applied in combination with the analytics about categories, in Hunt, reads on the recited limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 1, also apply to this rejection of claim 4.
Regarding claim 5, Lloyd/Hunt teaches the following limitations:
“The system of claim 3, wherein the control circuit is further configured to: determine the first weighting factor and the second weighting factor based on comparing a prior local sales forecast and a prior group sales forecast with actual sales of a prior date.” - See the aspects of Lloyd that have been cited above. Lloyd also discloses, “The service provider may compare a forecast model (e.g., a merchant specific model predicting values of a metric, a weighted model predicting values of a metric, etc) to the observed values of the metric, and detect lifts between the forecast model and the observed values. In some examples, detecting lifts may include identifying statistically significant deviations between a data point of the prediction model and a corresponding data point of the observed values at the same time interval” (col. 3, ll. 1-10). The logical circuit determining the weights for the weighted combination of the merchant specific model and the category specific model based on detecting deviations between data points of the models and data points of observed values, in Lloyd, reads on the recited limitation.
Regarding claim 6, Lloyd/Hunt teaches the following limitations:
“The system of claim 1, wherein the control circuit is further configured to: determine a retail task based on the adjusted sales forecast; and cause the retail task to be instructed via the retail task user interface.” - See the aspects of Lloyd that have been cited above. The logical circuit determining recommendations based on the sales forecast of the weighted combination of the merchant specific model and the category specific model, and causing presenting the recommendations on the graphical user interface of the merchant device, in Lloyd, reads on the recited limitation.
Regarding claim 7, Lloyd/Hunt teaches the following limitations:
“The system of claim 1, wherein the adjusted sales forecast comprises a percentage sales lift compared to a baseline sales volume.” - See the aspects of Lloyd that have been cited above. Lloyd also discloses, “the service provider may compare sales predictions of a model for a merchant to actual sales by the merchant, and detect a residual of the actual sales with respect to the sales predictions that is larger than a predetermined threshold” (col. 3, ll. 9-14). The sales prediction of the weighted combination of the models being compared to actual sales to detect actual sales larger than the sales predictions by the predetermined threshold, in Lloyd, reads on the recited limitation. Additionally or alternatively, any time that a sales forecast is greater than a sales volume would read on the limitation.
Regarding claim 8, Lloyd/Hunt teaches the following limitations:
“The system of claim 1, wherein the adjusted sales forecast is further associated with a particular item of the category of items.” - See the aspects of Lloyd and Hunt that have been cited above. Lloyd also discloses “the model may represent expected sales of an item” (col. 2, ll. 42 and 43). The sales forecasts of the weighted combination of the models being associated with the sales of the item, in Lloyd, when combined with the analytics for categories of items, in Hunt, reads on the recited limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 1, also apply to this rejection of claim 8.
Regarding claim 9, Lloyd/Hunt teaches the following limitations:
“The system of claim 1, wherein the first forecast model comprises a forecast model for time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, and holiday effects.” - See the aspects of Lloyd that have been cited above. Lloyd also discloses, “The feature detection module 138 may detect prospective features based in part on event data 142 received from the event store 140, and store information associated with the prospective features as features 136. The event store(s) 140 may contain information including weather data, informational sources (e.g., news articles, blog entries, web content, press releases), economic data, financial data, calendars, and so forth. As an example, the feature detection module 138 may receive a calendar of religious holidays from the event store 140, and generate a feature representing the calendar. As another example, the feature detection module 138 may implement machine learning techniques to detect a business trend from a plurality of press releases received from the event store. Additionally, the feature detection module 138 may generate a feature representing the business trend, and store the generated feature as a feature 136” (col. 7, ll. 43-58). The forecast model involving use of calendar data and trends data, wherein calendars have yearly, weekly, daily, and holiday indicators, and determining effects of each on sales forecasts, in Lloyd, reads on the recited limitation. New forecasts being generated after prior forecasts have been made, on updated inputs, makes the methodology in Lloyd “additive.”
Regarding claims 11-19, while the claims are of different scope relative to claims 1-9, the claims recite limitations similar to those of claims 1-9. As such, the rationales applied to reject claims 1-9 also apply for purposes of rejecting claims 11-19. Claims 11-19 are, therefore, also rejected under 35 USC 103 as obvious in view of Lloyd/Hunt.
Regarding claim 20, Lloyd/Hunt teaches the following limitations:
“The method of claim 11, wherein the local sales forecast value is determined based on generating a first forecast value using the first forecast model, generating a second forecast value using a second forecast model, and combining the first forecast value and the second forecast value.” - See the aspects of Lloyd that have been cited above. The service provider generating the merchant specific model, the category specific model, and the weighted combination of the merchant specific model and the category specific model, in Lloyd, reads on the recited limitation.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Lloyd, in view of Hunt, and further in view of U.S. Pat. no. 12,182,680 B1 to Wolf et al. (hereinafter referred to as “Wolf”).
Regarding claim 10, Lloyd/Hunt teaches the following limitations:
“The system of claim 1, wherein the local sales forecast value is determined based on generating a first forecast value using the first forecast model, generating a second forecast value using a second forecast model, and combining the first forecast value and the second forecast value; and ...” - See the aspects of Lloyd that have been cited above. The service provider generating the merchant specific model, the category specific model, and the weighted combination of the merchant specific model and the category specific model, in Lloyd, reads on the recited limitation.
The combination of Lloyd, Hunt, and Wolf (hereinafter referred to as “Lloyd/Hunt/Wolf”) teaches limitations below of claim 10 that do not appear to be taught in their entirety by Lloyd/Hunt:
“... the first forecast model and the second forecast model are machine learning models trained using sales data and event information.” - See the aspects of Lloyd that have been cited above. Lloyd also discloses, “As illustrated in FIG. 1, the service computing device 102 includes a model generator 134 and features 136. The model generator 134 may train time series models that predict merchant metrics based on information included in the merchant analytics information 122. In addition, the model generator 134 may periodically update and re-train the model based on new training data to keep the model up to date. As used herein, the term metrics includes business measurements such as business numbers and/or measurements of merchant activity or merchant operations. Illustratively, examples of metrics may include gross revenue, gross profit, item sales, inventory turnover, foot traffic, buyer profitability, return on capital invested, sales per square foot, visit to buy ratio, wage cost, cost of goods sold, inventory value, inventory turnover, taxes owed, customer retention, customer satisfaction, incremental sales, average purchase value, point of purchase, etc.” (col. 7, ll. 1-18), and “As an example, the feature detection module 138 may receive a calendar of religious holidays from the event store 140, and generate a feature representing the calendar. As another example, the feature detection module 138 may implement machine learning techniques to detect a business trend from a plurality of press releases received from the event store” (col. 7, ll. 50-56). While Lloyd discloses models and machine learning, Lloyd does not appear to provide sufficient specifics. The examiner turns to Wolf. Wolf discloses, “A system and method are disclosed to generate, modify, and deploy machine learning models. Embodiments include a database comprising historical sales data and a server comprising a processor and memory. Embodiments receive historical sales data comprising aggregated sales data for one or more items sold in one or more stores over one or more past time periods. Embodiments train a first machine learning model to learn model parameters and generate sales predictions by identifying one or more causal factors that influence the sale of one or more items. Embodiments train a second machine learning model, based on the first machine learning model, to generate second predictions” (Abstract), and “Training data 220 of model training system 110 database 114 comprises a selection of one or more periods of historical supply chain data aggregated or disaggregated at various levels of granularity and presented to machine learning model 204 to generate trained models. According to one embodiment, training data 220 comprises historic sales patterns, prices, promotions, weather conditions, and other factors influencing future demand of a particular item sold in a given store on a specific day. Training data 220 may also comprise time series data, such as, for example, a list of products sold at various locations or retailers at recorded dates and times. As described in more detail below, model training system 110 may receive training data 220 from archiving system 120, one or more supply chain planning and execution systems 130, one or more supply chain entities 140, computer 150, or one or more data storage locations local to, or remote from, supply chain network 100 and model training system 110” (col. 9, ll. 14-31). The machine learning and models, in Lloyd, including the first and second machine learning models, trained on historical sales data for items sold in past time periods, as in Wolf, reads on the recited limitation.
Wolf discloses, “data processing for retail and demand forecasting” (col. 1, ll. 24 and 25), similar to the claimed invention and to Lloyd/Hunt. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the machine learning and models, of Lloyd/Hunt, to include the trained machine learning models, of Wolf, for accuracy of forecasts, per Wolf (col. 2, ll. 49 and 50).
Response to Arguments
On pp. 7-12 of the Amendment/Response, the applicant requests reconsideration and withdrawal of the claim rejection under 35 USC 101. With regarding to Step 2A, Prong One of the eligibility analysis, the applicant argues that the claims do not recite an abstract idea, but instead recite features necessarily rooted in computer technology to overcome a problem specifically arising in graphical user interfaces. (Amendment/Response, p. 8.) According to the applicant, the claim recite features related to improved graphical user interfaces. (Amendment/Response, p. 8.) The applicant also argues that the claimed invention is analogous to claim 2 of Example 23. (Amendment/Response, pp. 8 and 9.) The applicant also argues that the claimed invention does not recite mental processes. (Amendment/Response, p. 9.) The examiner disagrees. The claims do indeed recite an abstract idea. Retail forecasting and task management is the abstract idea. Neither concept is rooted in computer technology. Both concepts have been performed long before use of computer technology, using the mind and pen and paper (e.g., business records). The claims in no way address a problem with graphical user interfaces. The claims merely recite use of generic, conventional graphical user interfaces for displaying retail forecasting and task management information. Improvements in the displayed information are improvements to the abstract idea, not to technology or a technical field. Example 23 involves improving operation of graphical user interfaces. The applicant’s claims have no analogous steps. Use of graphical user interfaces for displaying retail forecasting and task management information is not an improvement to operation of graphical user interfaces. If Example 23 is eligible, it is because it lacks any mention of retail forecasting and task management, or anything similar.
With respect to Step 2A, Prong Two of the eligibility analysis, the applicant argues that the claims provide integration into the particular technological environment of graphical user interface technologies for generating and selectively automatically displaying forecast values for relevant item categories. (Amendment/Response, p. 10.) The applicant also argues that claim 10 is eligible per Ex Parte Carmody. (Amendment/Response, pp. 10 and 11.) The examiner disagrees. For all of the reasons specified in the 35 USC 101 section above, with respect to Step 2A, Prong Two, using generic, conventional graphical user interfaces to display retail forecasting and task management information does not provide the required integration. Many examples and rationales are listed in the rejection section above. All conventional, generic graphical user interfaces generate and selectively automatically display all manner of values and other information. The values being forecast values does not involve any improvement to the conventional, generic graphical user interfaces. Rather, the forecast values are concerned with improving retail forecasting and task management, not any technology or technical field. Further, the applicant’s claims lack anything similar or analogous to the eligibility-warranting improvements in the claims at issue in Ex Parte Carmody, and thus, the rationales from that decision are inapplicable to the applicant’s claims.
With respect to Step 2B of the eligibility analysis, the applicant argues that the claims recite features that are not well-understood, routine, or conventional activity, or add unconventional features that confine the claim to a particular useful application. (Amendment/Response, p. 11.) The examiner disagrees. With respect to MPEP 2106.05(d), the question that is asked is “whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry.” Using graphical user interfaces to display characters, values, images, and the like is well-understood, routine, conventional activity. The displayed items being related to sales forecasting is not inherent to the additional element. Those are abstract idea elements. Looking at the additional elements, it is clear that there is nothing unconventional being claimed.
On pp. 12-14 of the Amendment/Response, the applicant requests reconsideration and withdrawal of the claim rejection under 35 USC 102. The applicants arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Such prior art includes the following:
U.S. Pat. App. Pub. No. 2020/0250688 A1 to Ohana et al. discloses, “Methods of the present disclosure focus on a class or category of items at the store level as the unit of measure. Classes or categories of items can include, for example, women's clothing, produce, or electronics. The store level refers to examining individual retail stores' performance.” “[0162] The methods and systems described herein further provide the advantage of producing reports that can be manipulated to view larger trends within a retail enterprise. Item categories can be grouped together to identify larger trends for different types of items. Similar stores can be grouped together to identify greater trends that are specific to a geographic area or type of store. These reports can be manipulated within a planner dashboard to produce visualizations of the data that inform various strategic decisions for managing the retail enterprise.” (para. [0030]).
Grewal, Dhruv, et al. "Planning merchandising decisions to account for regional and product assortment differences." Journal of Retailing 75.3 (1999): 405-424.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS Y. HO, whose telephone number is (571)270-7918. The examiner can normally be reached Monday through Friday, 9:30 AM to 5:30 PM Eastern.
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/THOMAS YIH HO/Primary Examiner, Art Unit 3624
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