DETAILED ACTION
Status of Claims
This Final Office Action is responsive to Applicant's reply filed 2/18/2026.
Claims 1, 15, and 20 have been amended.
Claims 1-20 are currently pending and have been examined.
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 .
Priority
This application claims priority as a continuation in part of Application 17/529075 filed on 11/17/2021. Applicant's claim for the benefit of this prior-filed application is acknowledged.
Response to Amendments
Applicant’s amendments have been fully considered, but do not overcome the previously pending 35 USC 101 rejections.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
To provide more information on the retraining of models the Examiner points to Figure 8 of Applicant’s specification showing how a model for a frequently sold item will have more available new data for retraining than an infrequently sold item, therefore the model for the frequently sold item will be retrained more frequently than the model for the infrequently sold item. The specification and claims show generic retraining of models based on when/frequency of new data available for retraining these models, which is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106.05).
With regard to the limitations of claims 1-20, Applicant argues that the claims are patent eligible under 35 USC 101 in light of the Desjardins memo. The Examiner respectfully disagrees. The Examiner has already set forth a prima facie case under 35 USC 101. The Examiner has clearly pointed out the limitations directed towards the abstract idea, what the additional elements are and why they do not integrate the abstract idea into a practical application, and why the additional elements and remaining limitations do not amount to significantly more than the abstract idea. The Examiner has examined each limitation individually and as an ordered combination. The Applicant does not point out what in Applicant’s specification recites an improvement. The Examiner asserts that Applicant’s claimed use of machine learning and retraining of models, as more data becomes available, is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106.05). Applicant’s arguments are not persuasive.
Applicant argues the claims do not recite an abstract idea. The Examiner respectfully disagrees. The Examiner agrees AI cannot be done in the human mind, but there is still an abstract idea recited with the demand forecast analysis. Applicant’s arguments are not persuasive.
The Examiner again asserts that Applicant’s claimed use of machine learning and retraining of models, as more data becomes available, is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106.05). Applicant’s arguments are not persuasive.
Applicant further argues the claims recite an improvement to the technology. The Examiner respectfully disagrees. The Examiner asserts that retraining or further training models when new data arrives is generic to machine learning (as shown by the cited prior art). The claims generically recite use of generalized additive models being used/trained with input data from a user, which is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106). The frequency of when the models are retrained is based solely off when there is enough data to retrain the model. For example, Applicant’s claims recite “retraining the third model at a more frequent rate than the second model due to a higher rate of change to demand data for the second item than to demand data for the first item”, what is a more frequent rate? The more frequent rate can just be as new data becomes available on a yearly item for the third model, while the second model is for a seasonal item with no new data available off season and therefore requiring no retraining because there is no additional data available and the second model is already trained appropriately. The Applicant’s claims recite generic training/retraining of model when data for the model becomes available. Applicant’s arguments are not persuasive.
Applicant states the claims cannot merely be applied on a computer. The Examiner respectfully disagrees. The claims recite “A system for forecasting demand, the system comprising: at least one processor and at least one memory device, the memory device storing instructions that, when executed by the at least one processor, cause the system to”, which specifically states how a general purpose computer is being used to implement the models and abstract idea. The Examiner also notes how receiving input data from a human user narrows the abstract idea. The Examiner refers to the response above for details regarding how generic the training/retraining of models is recited. Applicant’s arguments are not persuasive.
Applicant further cites Flook as making the claims eligible, but does not tie Flook with the claimed limitations at hand. Applicant does not point out what limitations go beyond generic recitation of implementation on a computer. The Examiner refers to the rejection below. Applicant’s arguments are not persuasive.
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 non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 15-19 are directed toward a process, claims 20 are directed toward a product, and claims 1-14 are directed toward a system; which are statutory categories of invention.
Additionally (Step 2A Prong One), the independent claims are directed toward a system for forecasting demand, the system comprising: at least one processor and at least one memory device, the memory device storing instructions that, when executed by the at least one processor, cause the system to: train a system of generalized additive models using training data comprising historical demand data for a plurality of items, wherein training the system of generalized additive models comprises: determining that there is insufficient data for a first model to generate a location-specific demand forecast for a first item at a location; training a second model to generate an overall demand forecast for the first item; and training a third model to generate a demand forecast for a second item; responsive to determining that the first model cannot generate the location-specific demand forecast for the first item at the location, generate an overall demand forecast for the first item using the second model; disaggregate the overall demand forecast to attribute a sub-portion of the overall demand forecast to the location; display a user interface, the user interface comprising a demand forecast display including a graph displaying, over time and overlaid on the graph, demand data from the training data and the overall demand forecast; display in the user interface one or more input fields for overriding the overall demand forecast; receive, via the one or more input fields for overriding the overall demand forecast, a user override demand forecast including the first item and one or more override values to override the overall demand forecast for one or more time periods; disaggregate the one or more override values to attribute sub-portions of the one or more override values to the location; update the graph to display, over time and overlaid in the graph, the demand data from the training data, the overall demand forecast, and the one or more override values; and optimize the system of generalized additive models, wherein optimizing the system of generalized additive models comprises selectively retraining a plurality of models by retraining the third model at a first time and retraining the second model at a second time that is delayed relative to the first time, wherein selectively retraining the plurality of models further comprises: retraining the third model at a more frequent rate than the second model due to a higher rate of change to demand data for the second item than to demand data for the first item: and incrementally retraining the third model using only newly available demand data for the second item: eliminate, from the system of generalized additive models, a model that requires greater than one hour of training time wherein the first model, the second model, and the third model are generalized additive models (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106.05). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing forecasting demand for an item using generalized additive models, where a human user can change or input values to change what the forecast results are based on generalized additive models that are updated based on the amount of data received for the items, which is managing how humans interact for commercial purposes (e.g. demand forecasting).
Dependent claims 2-14 and 16-19 further narrow the abstract idea identified in the independent claims, where any additional elements introduced are discussed below.
Step 2A Prong Two: In this application, even if not directed toward the abstract idea, the independent claims additionally recite “a system for forecasting demand, the system comprising: at least one processor and at least one memory device, the memory device storing instructions that, when executed by the at least one processor, cause the system to: train a system of generalized additive models using training data; a user interface; a system (claims 1 and 15)”; “non-transitory computer-readable medium, having stored instructions thereon, which when executed by a processor, cause the processor to forecast demand by performing a method (claim 20)”, which are additional elements that would not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05) and are recited at such a high level of generality. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computer or other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology.
The Examiner asserts that the claimed training and retraining of generalized additive models is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106.05) because the claims generically recite retraining or further training the models as more data becomes available (e.g. update the model based on more data).
In addition, dependent claims 2-14 and 16-19 further narrow the abstract idea and dependent claims 3 and 18 additionally recite “a system for managing inventory or a system for facilitating item transportation; an analytics system”, which are additional elements that do not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05). Further, method; System; and Product claims 1-20 recite “a system for forecasting demand, the system comprising: at least one processor and at least one memory device, the memory device storing instructions that, when executed by the at least one processor, cause the system to: train a system of generalized additive models using training data; a user interface; a system (claims 1 and 15)”; “non-transitory computer-readable medium, having stored instructions thereon, which when executed by a processor, cause the processor to forecast demand by performing a method (claim 20)”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0110-0114 and Figures 9. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. When viewed 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.
In addition, claims 2-14 and 16-19 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 3 and 18 additionally recite “a system for managing inventory or a system for facilitating item transportation; an analytics system”, which are additional elements that do not amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Allowable over 35 USC 103
Claims 1-20 are allowable over the prior art, but remain rejected under §101 for the reasons set forth above. Independent claims 1-20 disclose a system, product, and method for analyzing forecasting demand for an item using generalized additive models, where a human user can change or input values to change what the forecast results are based on generalized additive models that are updated based on the amount of data received for the items, where overrides can be used based on determinations if enough data is available for a model to run properly and disaggregating override values to sub portions when training the models and only training models if they take under a certain amount of time.
Regarding a possible 103 rejection: The closest prior art of record is:
Morgan et al. (US 2020/0134640 A1) – which discloses generating ensemble demand forecasts.
Ohana et al. (US 2020/0250688 A1) – which discloses attribute based forecasting for sales.
Wick (US 2021/0312488 A1) – which discloses price demand elasticity as feature in machine learning model for demand forecasting.
The prior art of record neither teaches nor suggests all particulars of the limitations as recited in claims 1-20, such as analyzing forecasting demand for an item using generalized additive models, where a human user can change or input values to change what the forecast results are based on generalized additive models that are updated based on the amount of data received for the items, where overrides can be used based on determinations if enough data is available for a model to run properly and disaggregating override values to sub portions when training the models. While individual features may be known per se, there is no teaching or suggestion absent applicants’ own disclosure to combine these features other than with impermissible hindsight and the combination/arrangement of features are not found in analogous art. Specifically the claimed “a system for forecasting demand, the system comprising: at least one processor and at least one memory device, the memory device storing instructions that, when executed by the at least one processor, cause the system to: train a system of generalized additive models using training data comprising historical demand data for a plurality of items, wherein training the system of generalized additive models comprises: determining that there is insufficient data for a first model to generate a location-specific demand forecast for a first item at a location; training a second model to generate an overall demand forecast for the first item; and training a third model to generate a demand forecast for a second item; responsive to determining that the first model cannot generate the location-specific demand forecast for the first item at the location, generate an overall demand forecast for the first item using the second model; disaggregate the overall demand forecast to attribute a sub-portion of the overall demand forecast to the location; display a user interface, the user interface comprising a demand forecast display including a graph displaying, over time and overlaid on the graph, demand data from the training data and the overall demand forecast; display in the user interface one or more input fields for overriding the overall demand forecast; receive, via the one or more input fields for overriding the overall demand forecast, a user override demand forecast including the first item and one or more override values to override the overall demand forecast for one or more time periods; disaggregate the one or more override values to attribute sub-portions of the one or more override values to the location; update the graph to display, over time and overlaid in the graph, the demand data from the training data, the overall demand forecast, and the one or more override values; and optimize the system of generalized additive models, wherein optimizing the system of generalized additive models comprises selectively retraining a plurality of models by retraining the third model at a first time and retraining the second model at a second time that is delayed relative to the first time, wherein selectively retraining the plurality of models further comprises: retraining the third model at a more frequent rate than the second model due to a higher rate of change to demand data for the second item than to demand data for the first item: and incrementally retraining the third model using only newly available demand data for the second item: eliminate, from the system of generalized additive models, a model that requires greater than one hour of training time wherein the first model, the second model, and the third model are generalized additive models (as required by independent claims 1-20)”, thus rendering claims 1-20 as allowable over the prior art.
Conclusion
THIS 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 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.
The prior art made of record, but not relied upon is considered pertinent to applicant's disclosure is listed on the attached PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached on Monday-Thursday 9AM-5PM.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625