Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office action to Application Serial Number 18/595,160, filed on March 4, 2024. In response to Examiner’s Non-Final Office Action of August 8, 2025, Applicant, on October 30, 2025, amended claims 1, 3, 6, 9-11, 14, 16, 19, and 20; and cancelled claims 4-5 and 7. Claims 1-3, 6, and 8-20 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are acknowledged.
Regarding 35 U.S.C. § 101 rejection, the amended claims have been considered
and are insufficient to overcome the rejection. Please refer to the 35 U.S.C. § 101 rejection for further explanation and rationale.
The 35 U.S.C. § 103 rejections are hereby amended pursuant to applicants amendments. Updated 35 U.S.C. § 103 rejections have been applied to amended claims. Please refer to the § 103 rejection for further explanation and rationale.
Response to Arguments
Applicant’s arguments filed October 30, 2025 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed October 30, 2025.
On Pgs. 8-10, regarding the 35 U.S.C. § 101 rejection, Applicant states amended claims provides an improvement that addresses the technical problem of training data scarcity for computer models- an improvement that is rooted in computer technology. See MPEP 2106.05(a). In response, The claims primarily recite the additional element of using computer components to perform each step. The “system”, “orchestrator”; “memory”, and “processor” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). The machine learning is a tool to perform the judicial exception.
On Pgs. 11-12, regarding the 35 U.S.C. § 101 rejection, Applicant states given guidance as set forth in Desjardins and Enfish, Applicant submits that claim 1 is patent eligible under 35 U.S.C. § 101. In response. Examiner finds the present claim improves an existing business process of model analysis and there are currently no functional advancement to any technology or technological field, in order for the claim elements to be considered significantly more than the abstract idea itself. Utilizing computer structure and technology to analyze legal claim data are all, both individually and in combination, generic computer functions such as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network) and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). The general use of a machine learning/ artificial intelligence analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the GAM modeling is solely used a tool to perform the instructions of the abstract idea.
On Pgs. 12-13, regarding the 35 U.S.C. § 101 rejection, Applicant states (ii)Claim 1 is patent eligible because any reasonable interpretation of claim 1 demonstrates that claim 1 is not an attempted monopolization of a mental process. See MPEP 2106.05(e) and (iii) Amended claim 1 does not merely recite the words "apply it" on a computer. See MPEP 2106.05(f). In response, Examiner asserts when performing the § 101 analysis, Examiner did consider each claim and every limitation, both individually and in combination as according to the PTO's guidelines for § 101 eligibility.
On Pgs. 13-14, regarding the 35 U.S.C. § 103 rejection, the rejection has withdrawn.
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 1-3, 6, and 8-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-3, 6, and 8-20 are directed to similar item forecasting.
Claim 1 recites a method for similar item forecasting, Claim 16 recites a system for similar item forecasting and Claim 20 recites an apparatus for similar item forecasting, which include determining that an item-specific generalized additive mixed (GAM) forecasting model is unable to generate a demand forecast for a selected item based at least in part on a lack of training data associated with the selected item, wherein determining that the item-specific GAM forecasting model is unable to generate the demand forecast for the selected item based at least in part on a lack of training data associated with the selected item comprises determining that a number of training instances for the item- specific GAM forecasting model is below a threshold amount of data required to sufficiently train the item-specific GAM forecasting model; in response to determining that the item-specific GAM forecasting model is unable to generate a demand forecast for the selected item, determining a set of similar items for the selected item, wherein determining the set of similar items for the selected item comprises determining a respective similarity score between the selected item and each similar item in the set of similar items based on a similarity of embeddings generated for the selected item and embeddings generated for each similar item in the set of similar items; for each similar item in the set of similar items, applying a respective item-specific GAM forecasting model sufficiently trained to generate forecasts for the similar item the forecasting model to determine a respective demand forecast for the similar item; determining the demand forecast for the selected item by aggregating respective demand forecasts of similar items of the set of similar items, wherein aggregating the respective demand forecasts of the similar items of the set of similar items comprises weighing the respective demand forecasts by respective similarity scores between the selected item and the similar items in the set of similar items; displaying, a visualization that displays the demand forecast for the selected item over time and a table that displays the set of similar items and the respective similarity scores between the selected item and the similar items in the set of similar items providing, the demand forecast to a forecast consumer application and in response to receiving the demand forecast, automatically adjusting, by the forecast consumer application, a number of items of the selected item at a physical location according to the demand forecast. (Claim 1). A similarity scoring system; a forecasting model; determine, for a selected item, whether the forecasting model is configured to generate a demand forecast for the selected item, wherein determining whether the forecasting model is configured to generate the demand forecast for the selected item comprises determining that a number of training instances for the forecasting model is below a threshold amount of data required to sufficiently train the forecasting model; in response to determining that the forecasting model is not configured to generate the demand forecast for the selected item based on a lack of training data associated with the selected item, determine a set of similar items for the selected item by using the similarity scoring system, wherein determining the set of similar items for the selected item comprises determining a similarity score between the selected item and each similar item in the set of similar items based on a similarity of embeddings generated for the selected item and embeddings generated for each similar item in the set of similar items; for each similar item in the set of similar items, apply a respective forecasting model sufficiently trained to generate forecasts for the similar item to determine a respective demand forecast for the similar item; determine the demand forecast for the selected item by aggregating respective demand forecasts of similar items of the set of similar items, wherein aggregating the respective demand forecasts of the similar items of the set of similar items comprises weighing the respective demand forecasts by respective similarity scores between the selected item and the similar items in the set of similar items; provide for display, a visualization that displays the demand forecast for the selected item over time and a table that displays the set of similar items and the respective similarity scores between the selected item and the similar items in the set of similar items; providing, the demand forecast to a forecast consumer application, wherein the forecast consumer application is configured to automatically adjust a number of items of the selected item at a physical location according to the demand forecast. (Claim 16) Determine that a forecasting model is unable to generate a demand forecast for a selected item based at least in part on a lack of training data associated with the selected item, wherein determining that the forecasting model is unable to generate the demand forecast for the selected item based at least in part on a lack of training data is based on a determination that a number of training instances for the forecasting model is insufficient to train the forecasting model; determine a set of similar items for the selected item, wherein determining the set of similar items for the selected item comprises determining a similarity score between the selected item and each similar item in the set of similar items based on a similarity of embeddings generated for the selected item and embeddings generated for each similar item in the set of similar items; for each similar item in the set of similar items, apply a respective forecasting model to determine a respective demand forecast for the similar item; and determine the demand forecast for the selected item by aggregating respective demand forecasts of similar items of the sets of similar items, wherein aggregating the respective demand forecasts of the similar items of the set of similar items comprises weighing the respective demand forecasts by a respective similarity between the selected item and the similar items in the set of similar items; and provide for display, a visualization that displays the demand forecast for the selected item over time and a table that displays the set of similar items and the respective similarity scores between the selected item and the similar items in the set of similar items. (Claim 20).
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Mental Processes” – evaluation. The recitation of “system”, “orchestrator”; “memory”, and “processor”; “graphical user interface”; “application programming interface (API)”, provide nothing in the claim elements to preclude the step from being “Mental Processes”- evaluation. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “system”, “orchestrator”; “memory”, and “processor”; “graphical user interface”; “application programming interface (API)” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Furthermore, claim 1 recites using one or more (GAM) analysis techniques. The specification discloses the analysis at a high-level of generality, providing examples of different techniques that may be applied. The general use of a GAM analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the natural language processing is solely used a tool to perform the instructions of the abstract idea.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in similarity analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “system”, “orchestrator”; “memory”, and “processor”; “graphical user interface”; “application programming interface (API)” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-3, 6, 8-15, and 17-19 recite wherein determining that the forecasting model is unable to generate the demand forecast for the selected item based at least in part on the lack of training data associated with the selected item comprises determining that an amount of historical demand data associated with the selected item is below a threshold amount; the forecasting model includes a plurality of models; and wherein determining that the forecasting model is unable to generate the demand forecast for the selected item comprises determining that the plurality of models do not include a model configured to generate the demand forecast for the selected item; determining the set of similar items for the selected item comprises: determining a plurality of items to evaluate; for each item to evaluate, determine a similarity score between the item and the selected item; and for each item to evaluate, add the item to the set of similar items in response to determining that the similarity score is greater than a threshold; determining the similarity score comprises determining the similarity score based on a demand patterns; determining a second demand forecast for a second selected item by applying the forecasting model, wherein the forecasting model has sufficient training data associated with the second item; and outputting the demand forecast and the second demand forecast to a forecast consumer; determining that the forecasting model is unable to generate a third demand forecast for a third selected item; determining a second set of similar items for the third selected item; determining that a size of the second set of similar items is lower than a threshold size; determining the third demand forecast for the third selected item by applying a category forecast; and outputting the third demand forecast to the forecast consumer; outputting the demand forecast to a forecast consumer in a common format as an output of the forecasting model; the selected item is a new item; the demand forecast for the selected item corresponds to one or more locations or an overall demand forecast: receiving a selection of the selected item via a graphical user interface; receiving a selection of the forecasting model via the graphical user interface; and outputting a visualization of the demand forecast to the graphical user interface; the selected item is offered for sale by a retailer; wherein each similar item of the set of similar items is offered for sale by the retailer; and wherein each similar item of the set of similar items is associated with historical demand data; a validation tool configured to identify a difference between the demand forecast and an actual demand; a catalog of items; wherein each of the selected item and the similar items belong to the catalog of items; determining that the forecasting model is not configured to generate the demand forecast for the selected item comprises identifying a lack of historical demand data associated with the selected item; and wherein aggregating the respective demand forecasts of the similar items of the set of similar items comprises weighing the respective demand forecasts based on one or more of a similarity score or a price adjustment; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 16 and 20. Regarding Claim, 14, and the additional elements of “graphical user interface” it is M2106.05(h)- field of use. Regarding claim 4 and claim 7 and the additional element of “machine learning” and “embedding” - the specification discloses the machine learning at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea..
Reasons Claims are Patentably Distinguishable from the Prior Art
Examiner analyzed Claims 1-3, 6, and 8-20 in view of the prior art on record and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine these references with a reasonable expectation of success as discussed below.
In regards to Claim 1 (similarly Claim 16 and Claim 20), the prior art does not teach or fairly suggest:
“… determining that an item-specific generalized additive mixed (GAM) forecasting model is unable to generate a demand forecast for a selected item based at least in part on a lack of training data associated with the selected item, wherein determining that the item-specific GAM forecasting model is unable to generate the demand forecast for the selected item based at least in part on a lack of training data associated with the selected item comprises determining that a number of training instances for the item- specific GAM forecasting model is below a threshold amount of data required to sufficiently train the item-specific GAM forecasting model; in response to determining that the item-specific GAM forecasting model is unable to generate a demand forecast for the selected item, determining a set of similar items for the selected item, wherein determining the set of similar items for the selected item comprises determining a respective similarity score between the selected item and each similar item in the set of similar items based on a similarity of embeddings generated for the selected item and embeddings generated for each similar item in the set of similar items; for each similar item in the set of similar items, applying a respective item-specific GAM forecasting model sufficiently trained to generate forecasts for the similar item to determine a respective demand forecast for the similar item;”.
Examiner finds that Flunkert et al., (US Patent No. 10936947B1) teaches artificial intelligence service for time series predictions, a recurrent neural network model is trained using a plurality of time series of demand observations to generate demand forecasts for various items. A probabilistic demand forecast is generated for a target item using multiple executions of the trained model. Within the training set used for the model, the count of demand observations of the target item may differ from the count of demand observations of other items. A representation of the probabilistic demand forecast may be provided via a programmatic interface. (see Abstract). In particular, Flunkert discloses if a sufficiently large training data set is used, the model may be general enough to be able to make predictions regarding demands for an item for which no (or very few) demand observations were available for training. For example, if information regarding the similarity of a new item I.sub.new to some set of other items {I.sub.old} along one or more dimensions such as item/product category, price, etc. is provided, where demand observations for {I.sub.old} items were used to train the model while demand observations for I.sub.new was not used to train the model, the model may still be able to provide useful predictions for I.sub.new demand in such an embodiment. The accuracy of the forecasts may increase with the amount of information available about the items being considered in at least some embodiments—e.g., if several weeks of actual demand observations for an item I.sub.j are used to train the model, and several months or years of demand observations for another item I.sub.k are used to train the model, the forecasts for I.sub.k may tend to be more accurate than the forecasts for I.sub.j. The number of actual demand observations for different items which are used as part of the training set of the model may differ in at least some embodiments (for example, the missing data points may be replaced by zero demand data points in some implementations). In at least one embodiment in which information about the similarity between a particular target item I.sub.a (for which demand is to be forecast) and another item I.sub.b (whose demand observations were used for training) is provided as input to the RNN model, the number of actual demand observations available for I.sub.b may exceed the number of demand observations available for I.sub.a. That is, accurate forecasts for I.sub.a may be generated using the RNN model in such an embodiment based on the larger count of demand observations of I.sub.b and the similarity of I.sub.b and I.sub.a, despite the smaller count of available observations of I.sub.a demand..) (see col. 0003-0004).
Bose et al., (US Publication No. 20180341898A1) teaches techniques for demand forecast use a SIM-TO engine to identify similar-to items for a new item, thereby enabling to provide demand forecast for the new item. The SIM-TO engine determines a classification of a new item, identifies a set of attributes of the new item, and searches existing items within the determined classification using the set of attributes. One or more existing items are identified as similar-to items in response to a determination that their respective matching scores are equal to or greater than a predetermined threshold value. (see Abstract).
Ouellet et al., (US Publication No. 20230085704A1 teaches systems and methods for dynamic demand sensing in a supply chain in which constantly-updated data is used to select a machine learning model or retrain a pre-selected machine learning model, for forecasting sales of a product at a specific location. The updated data includes product information and geographic information. (see Abstract). In particular, Ouellet discloses In some embodiments, the request for the forecast is not a first request, and the method further comprises: evaluating, by the monitoring module, a forecast accuracy of the forecast against incoming processed historical product data; and instructing the machine learning module, by the monitoring module, to select the machine learning model if the forecast accuracy falls below a threshold, selecting the machine learning model comprising: training a plurality of machine learning models on a first portion of an expanded data set, the expanded data set comprising the incoming processed historical product data, the processed historical product data and the processed historical location data; (see par. 0012).
Although Flunkert, Bose and Ouellet teach the modelling elements of the claim, none of the cited prior art, singularly or in combination, teach or fairly suggest, the combination of, the forecasting steps and .
Additionally, Examiner finds Iyer et al. (US 20230196391 A1) teaches methods, systems and computer program products associated with t a platform is described for creation, maintenance, and execution of machine learning models within a retail organization having a plurality of locations including retail locations and warehouse locations. The platform includes one or more computing systems each including one or more processors and a memory, the memory storing instructions that cause the one or more computing system to perform: hosting a plurality of interoperable machine learning demand forecasting model components including at least a base forecasting model component, an item lifecycle forecasting model component, an item similarity service, a store similarity service, and a demand transfer component, the plurality of interoperable machine learning demand forecasting model components being trained using a normalized dataset derived from a plurality of disparate data sources (see par. 0008). In particular, Iyer discloses , in the context of a demand forecasting model, a generalized additive model (GAM) may be selected and trained using internal and external data sources, as well as selected model parameters. A model scoring process (at step 408) assesses the accuracy and therefore value of the particular model, with iteration between training and scoring occurring to improve model accuracy. Upon arriving at a generally acceptable level of accuracy, the model may be exposed to an external audience, for example via an API (step 410). (see par. 0060).
Pande et al. (U.S. PG Publication 20210166179 A1) teaches methods and systems for optimizing a product assortment, and managing product fulfillment, (Abstract ). In particular, Pande discloses obtaining transactional data regarding an overall item assortment of a retailer, and, for items within an item category, training a model comprising a graph convolutional network suitable for weighted graphs to learn embeddings for nodes representing potentially substitutable items (see para. [0009];). However Iyer and Pande, individually and in combination, fail to teach the specific case of GAM modelling, Claim 1 (similarly Claim 16 and Claim 20) is eligible over the prior art.
The dependent claims 2-3, 6,8-15, and 17-19 are eligible under 35 U.S.C. 102 and 35 U.S.C. 103 because they depend on claim 1 (claim 16 and claim 20) that is determined to be eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. 20220318711A1 to Recasens et al.- Abstract-“ In an embodiment, a method includes receiving training data representing historic consumer demand for products, detecting changepoints in that data that may be associated with disruptive events, identifying relevant data for modeling, performing clustering, processing configuration information, training one or more machine learning models that are capable of evaluating other received data more accurately, and outputting results to a user display device.”
9THIS ACTION IS MADE FINAL. 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 extension fee 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 Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Applicants are invited to contact the Office to schedule an in-person interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/CHESIREE A WALTON/Examiner, Art Unit 3624