DETAILED ACTION
The following FINAL office action is in response to Applicant’s amendment filed March 19, 2026. Applicant’s March 19th amendment amended claims 1, 7, 8, 11, 17, 18, and 20. Currently Claims 1-20 are pending. Claims 1, 11 and 20 are the independent claims.
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 .
Response to Amendment
The 35 U.S.C. 101 rejection of claims 1-20 in the previous office action are maintained.
The 35 U.S.C. 103(a) rejections of claims 1-4, 11-14 and 20 in the previous office action are withdrawn in response to Applicant’s amendments to the claims.
Response to Arguments
Applicant’s arguments, see Pages 13, 14, filed March 19, 2026, with respect to Palmer, Guhal, Zenor have been fully considered and are persuasive. The 35 U.S.C. 103(a) rejections of claims 1-4, 11-14 and 20 have been withdrawn.
Applicant's arguments filed March 19, 2026 have been fully considered but they are not persuasive. Specifically, Applicant argues that the claims are patent eligible under 35 U.S.C. 101 as the claims recite technical improvements/integrate the abstract idea into a practical application (e.g. generating a trained machine learning model, utilizing a specific training data set, trained machine learning model to generate forecasted sales data, improves computer functionality; Specification: Paragraphs 3, 21-23; Remarks: Last Paragraph, Page 16; Pages 17, 18); claims are similar to Ex Parte Carmody (Appeal 2025-002843; e.g. relying on Dejardinis et al. decision; Remarks: Page 20).
In response to Applicant’s argument that the claims are patent eligible under 35 U.S.C. 101 as the claims integrate the abstract idea into a practical application, the examiner respectfully disagrees.
The claims are directed to a well-known business practice – sales forecasting – in this case inputting at least one relevant feature (of a store/item) into a trained a machine learning model to predict physical store item sales in a future period. While the claims may represent an improvement to the business process of sales forecasting, they in no way either claimed or disclosed represent a practical application. More specifically, the claims do not improve any of the underlying technology (e.g. processor, memory, etc.; Claims 11-19 fail to positively recite in the body of the claims who or what performs the various method steps). The claims do not improve another technology (e.g. machine learning) or technical field (sales forecasting is not a technical field). The claims do not provide a technical solution to a technical problem.
Under the see MPEP § 2106.05, the claims are evaluated to determine if additional elements that integrate the judicial exception into a practical application (see Manual of Patent Examining Procedure ("MPEP") §§ 2106.05(a)-(c), (e)- (h)). A claim that integrates a judicial exception into a practical application applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.
For example, limitations that are indicative of "integration into a practical application" include:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP § 2106.05(a);
Applying the judicial exception with, or by use of, a particular machine - see MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP § 2106.05(c); and
Applying or using 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 MPEP § 2106.05(e).
In contrast, limitations that are not indicative of "integration into a practical application" include:
Adding the words "apply it" (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP § 2106.05(±);
Adding insignificant extra-solution activity to the judicial exception- see MPEP § 2106.05(g); and
Generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h).
In view of the MPEP § 2106.05, one must consider whether there are additional elements set forth in the claims that integrate the judicial exception into a practical application. The identified additional non-abstract elements recited in the independent claims are the generic memory, processor, database, computing device, computer readable medium having instructions. These generic computer hardware merely performs generic computer functions of receiving, processing and providing data and represent a purely conventional implementation of applicant’s sales forecasting in the general field of business analytics and do not represent significantly more than the abstract idea. See at least MPEP § 2106.05(a) ("Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field").
These recited additional elements are merely generic computer components. The claims do present any other issues as set forth in the MPEP § 2106.05 regarding a determination of whether the additional generic elements integrate the judicial exception into a practical application. Rather, the claims merely use instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea.
The claims do not recite improvements to the functioning of a computer or any other technology field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, the claims to do apply the abstract idea with a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (e.g. data remains data even after processing; MPEP 2106.05(c)), the claims no not apply or use the abstract idea in some other meaningful way beyond generally linking the user of the abstract idea to a particular technological environment (i.e. a generic computer) such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea (MPEP 2106.05(e)). The recited generic computing elements are no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Regarding the recited machine-learning model is recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic trained machine-learning model on a generic computer, also recited at a high level of generality. The trained machine-learning model is used to generally apply the abstract idea without limiting how the trained machine-learning model functions. The trained machine-learning model is described at a high level such that it amounts to using a generic computer with a generic trained machine-learning model to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished.
Thus, under Step 2A, Prong Two (MPEP §§ 2106.05(a)-(c) and (e)- (h)), the claims do not integrate the judicial exception into a practical application.
There is a fundamental difference between computer functionality improvements, on the one hand, and uses of existing computers as tools to perform a particular task, on the other — a distinction that the Federal Circuit applied in Enfish, in rejecting a § 101 challenge at the first stage of the Mayo/Alice framework because the claims at issue focused on a specific type of data structure, i.e., a self-referential table, designed to improve the way a computer stores and retrieves data in memory, and not merely on asserted advances in uses to which existing computer capabilities could be put. See Enfish, 822 F.3d at 1335-36. Here the claims simply use a computer as a tool and nothing more.
For the reasons outlined above, that the claims recite a method of organizing human activity, i.e., an abstract idea, and that the additional element recited in the claim beyond the abstract idea (i.e., memory, processor, database, computing device, computer readable medium having instructions) is no more than a generic computer component used as a tool to perform the recited abstract idea. As such, it does not integrate the abstract idea into a practical application. See Alice Corp., 573 U.S. at 223-24 (“[Wholly generic computer implementation is not generally the sort of ‘additional featur[e]’ that provides any ‘practical assurance that the process is more than a drafting effort designed to monopolize the [abstract idea] itself.’” (quoting Mayo, 566 U.S. at 77)).
Accordingly, the claims are directed to an abstract idea.
Step Two of the Mayo/Alice Framework (Step 2B)
Having determined under step one of the Mayo/Alice framework that the claims are directed to an abstract idea, we next consider under Step 2B of the Guidance, the second step of the Mayo/Alice framework, whether the claims include additional elements or a combination of elements that provides an “inventive concept,” i.e., whether an additional element or combination of elements adds specific limitations beyond the judicial exception that are not “well-understood, routine, conventional activity” in the field (which is indicative that an inventive concept is present) or simply appends well-understood, routine, conventional activities previously known to the industry to the judicial exception. See MPEP § 2106.05.
Under step two of the Mayo/Alice framework, the elements of each claim are considered both individually and “as an ordered combination” to determine whether the additional elements, i.e., the elements other than the abstract idea itself, “transform the nature of the claim” into a patent-eligible application. Alice Corp., 573 U.S. at 217 (citation omitted); see Mayo, 566 U.S. at 72-73 (requiring that “a process that focuses upon the use of a natural law also contain other elements or a combination of elements, sometimes referred to as an ‘inventive concept,’ sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the natural law itself’ (emphasis added) (citation omitted)).
Here the only additional element recited in the claims beyond the abstract idea is a memory, processor, database, computing device, computer readable medium having instructions” i.e., generic computer component. See Alice, 573 U.S. at 223 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). Applicant has not identified any additional elements recited in the claim that, individually or in combination, provides significantly more than the abstract idea.
With regards to argued Specification Paragraph 3 this paragraph discloses at best that the wished for benefit of the sales forecasting system is to improve upon existing methods for tackling store cold start forecasting problems which are prone to predicting all zero values dues to data sparsity (i.e. an improvement in an mathematical operation – times series forecasting and/or improvement in store sales forecasting). This paragraph does not disclose an improvement to any of the underlying technologies (e.g. processor, memory, machine learning). This paragraph does not disclose an improvement in another technology of technical field (sales forecasting is not a technical field). This paragraph does not disclose providing a technical solution to a technical problem inherent in computers or computer networks.
With regards to argued specification Paragraphs 21-23, these paragraphs discuss the well-known business and mathematical problem of forecasting associated with the lack of historical sales data as well as utilizing open-source and customized deep neural network libraries to forecast sales at a target store for a future period levering well-known mathematical techniques such as feed forward neural networks and/or hierarchical deep neural networks. These paragraphs do not disclose an improvement to any of the underlying technologies (e.g. processor, memory, machine learning). These paragraphs do not disclose an improvement in another technology of technical field (sales forecasting is not a technical field). These paragraphs do not disclose providing a technical solution to a technical problem inherent in computers or computer networks.
Similar to the discussion in Uniloc USA, Inc. v. LG Electronics USA, Appeal No. 19-1835 (Fed. Cir. Apr. 30, 2020), where the Federal Circuit reaffirmed that software inventions are patentable in the U.S. with a bright-line statement: “Our precedent is clear that software can make patent-eligible improvements to computer technology, and related claims are eligible as long as they are directed to non-abstract improvements to the functionality of a computer or network platform itself.” the instant application merely applies the abstract idea using a generic computer as a conduit/tool for the abstract idea and does not improve the functioning of a computer or computer networks, does not improve another technical field and does not provide a technical solution to a technical problem.
At best the argued ‘improvements’ are business improvements in the business problem of sales forecasting and in no way represent an improvement in the functioning of a computer or computer network, do not represent an technical solution to a technical problem inherent in computers or another technical field.
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
In response to Applicant’s argument that the claims are patent eligible under 35 U.S.C. 101 as the claims are similar to Ex Parte Carmody and Ex Parte Dejardinis et al., the examiner respectfully disagrees.
While the Desjardins decision cautions against overbroad application of 35 U.S.C. 101 to artificial intelligence inventions, such inventions not categorically excluded from patentability, the thrust of the decision made clear that improvements to an AI model itself can be sufficient for the purpose of patent eligibility, even when the claims recite, on their face, an ostensibly “abstract idea.” Specifically, the Appeals Review Panel found that the claims under review provided a technical improvement in the functioning of machine learning models by enabling continual learning, reducing storage requirements, and preserving performance across tasks. In particular, the decision emphasized that the claimed invention addresses a technical problem ("catastrophic forgetting") and improves the operation of AI systems, not just through generic computer implementation but by a specific training strategy. To support this determination, the Appeals Review Panel looked to the specification which, on its own, disclosed how the invention would improve functioning of an AI model--in particular, the specification explained how the proposed invention would use less “storage capacity” and lead to “reduced system complexity." These improvements, which the Appeals Review Panel found were incorporated into the claims as a whole, constituted an “improvement to how the machine learning model itself operates”.
Similarly in the Carmody PTAB decision, the board found that Applicant’s disclosure explains an improvement in the field of machine learning, specifically that the recited methods represented an improvement in training models for use by the recommendation engine to generate useful orchestrations (see Pages 7, 8).
In sharp constrast, none of Applicant’s arguments, disclosure or claims discusses at any level that the generically applied/utilization of machine learning represents or provides an improvement in machine learning itself or improve in training models as was the case in Carmody.
While the claims recite a series of detailed training steps (e.g. fist dense layer, first embedding layer, etc.) the claimed training as well as the machine learning model are recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic machine learning on a generic computer, also recited at a high level of generality. The machine learning is used to generally apply the abstract idea without limiting how the machine learning functions. The machine learning is described at a high level such that it amounts to using a generic computer with generic machine learning to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished. Further nowhere in Applicant’s disclosure is there any discussion at any level that the utilization of a generic machine learning model to forecast sales or that the specific training steps (e.g. fist dense layer, first embedding layer, etc.) applied to a generic machine learning model improve the general field of machine learning or addresses a technical problem in the field of machine learning or provides an improvement to a specific machine learning model, algorithm, technique or the like.
Applicant's invention is more akin to the recent Recentive Analytics, Inc. V. Fox Corp., No. 2023- 2437 (Fed. Cir. Apr. 18, 2025) wherein the instant application fails to be patent eligible under 35 U.S.C. 101 for very similar reasons the court found Recentive's patents ineligible, namely the claims do no more than apply established methods of machine learning to a new data environment (workflow/process automation). Recentive sued Fox in November 2022 for infringement of four patents - Network Map patents and Machine learning training patents. Recentive asserted that its patents claim eligible subject matter because they involve "the unique application of machine learning to generate customized algorithms, based on training the machine learning model, that can then be used to automatically create event schedules that are updated in real-time." Recentive characterized its patents as introducing "the application of machine learning models to the unsophisticated, and equally niche, prior art field of generating network maps for broadcasting live events and live event schedules." The court did not find Recentive's arguments persuasive and found the patents ineligible under 35 U.S.C. 101. Similar to the discussion on Page 12 of the Recentive decision, the Applicant has failed to provide support or substantative arguments that the disclosed invention or the claimed invention improves the recited first/machine learning processes or the unsupervised machine learning process now claimed ("But Recentive also admits that the patents do not claim a specific method for "improving the mathematical algorithm or making machine learning better." Oral Arg. at 4:40-4:44.). As such the recite machine learning processes are merely tools/conduits for the abstract idea - recited at a high level and applied using a generic computer/computing device which is likewise not improved by the recited or disclosed invention (i.e. claims lack a specific technological improvement). More specifically not only does Applicant's specification fail to disclose a improvement which the newly claimed machine learning processes (first/second machine learning, unsupervised machine learning), Applicant's disclosure and subsequent arguments fila to delineate steps through which the machine learning, now claimed, achieve an improvement. See, e.g., IBM V. Zillow Grp., Inc., 50 F.4th 1371, 1381 (Fed. Cir. 2022) (holding abstract a claim that "d[id] not sufficiently describe how to achieve [its stated] results in a non-abstract way," because "[s]uch functional claim language, without more, is insufficient for patentability under our law." (quoting Two-Way Media Ltd V. Comcast Cable Commc'ns, LLC, 874 F.3d 1329, 1337 (Fed. Cir. 2017))); see also Intell. Ventures I LLC V. Capital One Fin. Corp., 850 F.3d 1332, 1342 (Fed. Cir. 2017) (similar); Elec. Power Grp., LLC V. Alstom S.A., 830 F.3d 1350, 1356 (Fed. Cir. 2016) (similar). "[T]he patent system represents a carefully crafted bargain that encourages both the creation and the public disclosure of new and useful advances in technology, in return for an exclusive monopoly for a limited period of time." Pfaff V. Wells Elecs., 525 U.S. 55, 63 (1998); Sanho Corp. V. Kaijet Tech. Int'ILtd., 108 F.4th 1376, 1382 (Fed. Cir. 2024). Allowing a claim that functionally describes a mere concept without disclosing how to implement that concept risks defeating the very purpose of the patent system. In this respect, the patents' claims are materially different from those in McRO, Inc. V. Bandai Namco Games America Inc., 837 F.3d 1299 (Fed. Cir. 2016), and Koninklijke, the cases on which Recentive relies. Instead of disclosing "a specific implementation of a solution to a problem in the software arts," Enfish, LLC V. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016), or "a specific means or method that solves a problem in an existing technological process," Koninklijke, 942 F.3d at 1150, the only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment. This new environment is identifying and performing process (worklow) variants. Similar to the court's conclusion that simply applying machine learning to a new field of use does not result in patent eligibility, Applicant's disclosure makes clear that the recited machine learning processes are not improved in any way and do not result in an improvement in an underlying technology or another technical field) ("We see no merit to Recentive's argument that its patents are eligible because they apply machine learning to this new field of use. We have long recognized that "[a]n abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment." Intell. Ventures I LLC V. Capital One Bank (USA), 792 F.3d 1363, 1366 (Fed. Cir. 2015); see also Alice, 573 U.S. at 222; Parker V. Flook, 437 U.S. 584, 593 (1978); Stanford, 989 F.3d at 1373 (rejecting argument that a claim was not abstract where patentee contended "the specific application of the steps [was] novel and enable[d] scientists to ascertain more haplotype information than was previously possible").
Examiner suggests Applicant review MPEP § 2106.04(d)(1) as well as the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (2024 AI SME Update) in the Federal Register on July 17, 2024 (https://www.federalregister.gov/public-inspection/2024-15377/guidance-2024-update-on-patent-subject-matter-eligibility-including-on-artificial-intelligence ) and specifically review the three new examples 47-49 announced by the 2024 AI SME Update which provide exemplary SME analyses under 35 U.S.C. 101 of hypothetical claims related to AI inventions (https://www.uspto.gov/sites/default/files/documents/2024-AI-SMEUpdateExamples47-49.pdf).
Accordingly, the claims are more similar to those the court found ineligible under 35 U.S.C. 101.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding independent Claims 1, 11 and 20, the claims are directed to the abstract idea of sales forecasting. This is a process (i.e. a series of steps) which (Statutory Category – Yes –process).
The claims recite a judicial exception, a method for organizing human activity, sales forecasting (Judicial Exception – Yes – organizing human activity). Specifically, the claims are directed to transmit, for display, forecasted sales data of an item at a physical store, wherein sales forecasting is a fundamental economic practice that falls into the abstract idea subcategories of sales activities and/or commercial interactions. See 2106.04(a). Further all of the steps of “receive”, “train”, “receive”, “determine”, “input”, “generate” and “transmit” recite functions of the sales forecasting are also directed to an abstract idea that falls into the abstract idea subcategories of sales activities and/or commercial interactions. The intended purpose of independent claims 1, 11 and 20 appears to be to transmit, for display, forecasted sales data for an in-store item when historical sales data of the item at the store is unavailable.
Accordingly, the claims recite an abstract idea – fundamental economic practice, specifically in the abstract idea subcategories of sales activities and/or commercial interactions. The exceptions are the limitations of generic computer elements: memory, processor, database, computing device, computer readable medium having instructions. Examiner notes claims 11-19 fail to positively recite in the body of the claims, who or what entity performs each of the method steps. See 2106.04(a).
Accordingly, the claims recite an abstract idea under Step 2A, Prong One, we proceed to Step 2A, Prong Two. Considering whether the additional elements set forth in the claim integrate the abstract idea into a practical application (See 2106.04(a)), the previously identified non-abstract elements directed to generic computing components include: memory, processor, database, computing device, computer readable medium having instructions. These generic computing components are merely used to receive/access, process or display data as described extensively in Applicant’s specification (Specification: Figure 2). Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea. Moreover, when viewed as a whole with such additional elements considered as an ordered combination, the claim modified by adding a generic computer would be nothing more than a purely conventional computerized implementation of applicant's sales forecasting in the general field of business management and would not provide significantly more than the judicial exception itself. Note McRo, Inc. v. Bandai Namco Games America Inc. (837 F.3d 1299 (Fed. Cir. 2016)), guides: "[t]he abstract idea exception prevents patenting a result where 'it matters not by what process or machinery the result is accomplished."' 837 F.3d at 1312 (quoting O'Reilly v. Morse, 56 U.S. 62, 113 (1854)) (emphasis added). The claims are not directed to a particular machine nor do they recite a particular transformation (MPEP § 2106.05(b)).
Additionally, the claims do not recite any specific claim limitations that would provide a meaningful limitation beyond generally linking the use of the judicial exception to a particular technological environment. Nor do the claims present any other issues as set forth in the MPEP 2106.04(a) regarding a determination of whether the additional generic elements integrate the judicial exception into a practical application. Rather, the claims on merely use instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea. Thus, under Step 2A, Prong Two (MPEP §§ 2106.05(a)-(c) and (e)- (h)), claims 1-20 do not integrate the judicial exception into a practical application.
Regarding the use of the generic (known, conventional) recited memory, processor, database, computing device, computer readable medium having instructions," the Supreme Court has held "the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 573 U.S. 208, 223. Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea. The claims as a whole do not recite more than what was well-known, routine and conventional in the field (see MPEP § 2106.05(d)). In light of the foregoing and under the MPEP 2106.04(a), that each of the claims, considered as a whole, is directed to a patent-ineligible abstract idea that is not integrated into a practical application and does not include an inventive concept.
Regarding the machine learning model train to generate forecasted sales data of an item at a physical store in a future time period as well as the specific training steps based on a (received) training data set, the recited machine learning model is recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic trained machine learning model on a generic computer, also recited at a high level of generality. The machine learning model is used to generally apply the abstract idea without limiting how the machine learning model functions. The machine learning model is described at a high level such that it amounts to using a generic computer with a generic machine learning model to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished.
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
Additionally, the claims recite a judicial exception, a mental processes, which can be performed in the human mind or via pen and paper (Judicial Exception – Yes – mental process).
The claimed steps of train a machine learning model based on a training dataset, determine at least one relevant feature related to the item and generate forecasted sales data all describe the abstract idea. These limitations as drafted are directed to a process that under its reasonable interpretation covers performance of the steps in the mind but for the recitation of the generic computer components. Other than the recitation of a memory, processor, database, computing device, computer readable medium having instructions nothing in the claimed steps precludes the step from practically being performed in the mind. The claims do not recite additional elements that are sufficient to amount to significantly more than the abstract idea because the step of receive a training data set, receive a forecast request and input the at least one relevant feature are directed to insignificant pre-solution activity (i.e. data gathering). The step of transmit, for display, the forecasted sales data is directed to insignificant post-solution activity (i.e. data output). The mere nominal recitation of a generic processor/computer does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process. (Judicial Exception recited – Yes – mental process).
The claims do not integrate the abstract idea into a practical application. The generic memory, processor, database, computing device, computer readable medium having instructions are each recited at a high level of generality merely performs generic computer functions of receive, process or transmit data. The generic processor/computer merely applies the abstract idea using generic computer components. The elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not recite improvements to the functioning of a computer or any other technology field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, the claims to do apply the abstract idea with a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (e.g. data remains data even after processing; MPEP 2106.05(c)), the claims no not apply or use the abstract idea in some other meaningful way beyond generally linking the user of the abstract idea to a particular technological environment (i.e. a generic computer) such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea (MPEP 2106.05(e)). The recited generic computing elements are no more than mere instructions to apply the exception using a generic computer component.
Regarding the machine learning model train to generate forecasted sales data of an item at a physical store in a future time period the machine learning model is recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic trained neural network on a generic computer, also recited at a high level of generality. The machine learning model is used to generally apply the abstract idea without limiting how the machine learning model functions. The machine learning model is described at a high level such that it amounts to using a generic computer with a generic machine learning model to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished. The recitation of a machine learning model in this claim does not negate the mental nature of these limitations because the machine learning model is merely used at a tool to perform an otherwise mental process.
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Integrated into a Practical Application – No).
As discussed above the additional elements in the claims amount to no more than a mere instruction to apply the abstract idea using generic computing components, wherein mere instructions to apply an judicial exception using generic computer components cannot integrate a judicial exception into a practical application or provide an inventive concept. For the receive a forecast request and transmit the forecasted sales data steps that were considered extra-solution activity, this has been re-evaluated and determined to be well-understood, routine, conventional activity in the field. Applicant’s specification does not provide any indication that the computer/processor is anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. court decisions (MPEP 2106.05(d)(II)) indicate that mere collection or receipt of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is ineligible (Provide Inventive Concept – No).
The claims are ineligible under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Regarding dependent claims 2-10 and 12-19, the claims are directed to the abstract idea of sales forecasting and merely further limit the abstract idea claimed in independent claims 1, 11 and 20.
Claims 2 and 12 further limits the abstract idea by limiting the reasons historical sales data is not available to at least ONE of never offered for sale or not offered for sale during a predetermined time period or missing or inaccessible or confidential (a more detailed abstract idea remains an abstract idea). Claims 3 and 13 further limit the abstract idea wherein the relevant feature is ONE or more of historical sales data/historical availability data at a plurality of similar stores or item related features or store related features or demographic features of item or demographic feature of store or seasonality (a more detailed abstract idea remains an abstract idea). Claims 4 and 14 further limit the abstract wherein the item features is at least ONE of product name or brand name or item description or product hierarchy or catalog ID or merchandise department or merchandise category (a more detailed abstract idea remains an abstract idea). Claims 5 and 15 further limit the abstract idea wherein the plurality of similar stores are based on obtaining store/candidate store features, computer a feature match score, computing a weighted match score, ranking the plurality of stores and determining a top ranked candidate store (a more detailed abstract idea remains an abstract idea). Claims 6 and 16 further limit the abstract idea wherein the store features comprise store format description, state, city, distance between stores, and shelf space ration between two stores and all feature match scores are normalized (a more detailed abstract idea remains an abstract idea). Claims 7 and 17 further limit the abstract idea by limiting the machine learning model to hierarchal feed forward deep neural network trained based on obtaining labeled data set, passing the sales features and availability through embedding layers, passing the items/store features through embedding layers, merging first/second interaction data and training the DD based on minimization of a mean squared error and the labeled sales data (a more detailed abstract idea remains an abstract idea). Claims 8, 9 and 18 further limit the abstract idea herein the labeled sales data is determined based on historical sales, training the DNN comprising updating weights/hyperparameters, item features comprise demand transfer coefficients and availability features comprise availability of substitute items (a more detailed abstract idea remains an abstract idea). Claims 10 and 19 further limit the abstract idea by transmitting a generated recommended assortment data for the store (a more detailed abstract idea remains an abstract idea).
None of the limitations considered as an ordered combination provide eligibility because taken as a whole the claims simply instruct the practitioner to apply the abstract idea to a generic computer.
Further regarding claims 1-20, Applicant’s specification discloses that the claimed elements directed to a memory, processor, database, computing device, computer readable medium having instructions at best merely comprise generic computer hardware which is commercially available (Specification: Figure 2). More specifically Applicant’s claimed features directed to a system do not represent custom or specific computer hardware circuits, instead the terms merely refers to commercially available software and/or hardware. Thus, as to the system recited, "the system claims are no different from the method claims in substance. The method claims recite the abstract idea implemented on a generic computer; the system claims recite a handful of generic computer components configured to implement the same idea." See Alice Corp. Pry. Ltd., 134 S.Ct. at 2360.
Accordingly, the claims merely recite manipulating data utilizing generic computer hardware (e.g. memory, processor, etc.). Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea. Further the lack of detail of the claimed embodiment in Applicant’s disclosure is an indication that the claims are directed to an abstract idea and not a specific improvement to a machine.
Accordingly given the broadest reasonable interpretation and in light of the specification the claims are interpreted to include the process steps being performed by a human mind or via pen and paper. The claim limitations which recite a computer implemented method is at best recite generic, well-known hardware. However, the recited generic hardware simply performs generic computer function of displaying or processing data. Generic computers performing generic, well known computer functions, alone, do not amount to significantly more than the abstract idea. Further the recited memories are part of every conventional general-purpose computer.
Applicant has not demonstrated that a special purpose machine/computer is required to carry out the claimed invention. A special purpose machine is now evaluated as part of the significantly more analysis established by the Alice decision and current 35 U.S.C. 101 guidelines. It involves/requires more than a machine only broadly applying the abstract idea and/or performing conventional functions.
Applicant’s specification discloses that the claimed elements directed to a memory, processor, database, computing device, computer readable medium having instructionss merely comprise generic computer hardware which is commercially available (Specification: Figure 2, Paragraphs 42, 53, Paragraphs 42, 53). More specifically Applicant’s claimed features directed to a system and components do not represent custom or specific computer hardware circuits, instead the term system merely refers to commercially available software and/or hardware. Thus, as to the system recited, "the system claims are no different from the method claims in substance. The method claims recite the abstract idea implemented on a generic computer; the system claims recite a handful of generic computer components configured to implement the same idea." See Alice Corp. Pry. Ltd., 134 S.Ct. at 2360.
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
Allowable Subject Matter
Claims 1-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if re-written to overcome the pending rejection under 35 U.S.C. 101.
The closest prior art Palmer, Guhal, Zenor and Lundell, Predicting Sales with Deep Learning in a Retail Setting (2021) fail to teach or suggest either singularly or in combination a system and method comprising: receive, from a database, a training dataset comprising sales features, availability features, item features, store features, and labelled data: train a machine learning model based on the training dataset wherein: the sales features are input to a first dense layer of the machine learning model and the availability features are input to a second dense layer of the machine learning model to generate a first concatenated output; the item features are input to a first embedding layer of the machine learning model and the store features are input to a second embedding layer of the machine learning model to generate a second concatenated output; the first concatenated output is input to a third dense layer to generate first interaction data characterizing learned interactions between the sales features and the availability features; the second concatenated output is input to a fourth dense layer to generate second interaction data characterizing learned interactions between the item features and the store features: prediction data is generated based on the first interaction data and the second interaction data; and the machine learning model is determined to be trained based on the prediction data and the labelled data; receive, from a computing device, a forecast request seeking sales data of an item if the item is offered for sale at a physical store in a future time period, wherein historical sales data of the item at the physical store is not available, determine, based on the forecast request, at least one relevant feature related to the item or the physical store, input the at least one relevant feature- to the trained machine learning model to generate forecasted sales data characterizing forecasted sales of the item at the physical store in the future time period, and transmit the forecasted sales data to the computing device for display as recited in independent Claims 1, 110 and 20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chienet al., U.S. Patent No. 8065203 discloses a system and method for estimating product purchases (sales) for groups of similar stores, including stores with insufficient historical data
Iyer et al., U.S. Patent No. 12579553 discloses a system and method for forecasting sales/demand for retail stores items, including forecasting/predicting sales/demand at a new store location by utilizing machine learning, item similarities, store similarities as well as the ability to backfill missing historical/past data.
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 SCOTT L JARRETT whose telephone number is (571)272-7033. The examiner can normally be reached M-TH 6am-4:30PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
SCOTT L. JARRETT
Primary Examiner
Art Unit 3625
/SCOTT L JARRETT/Primary Examiner, Art Unit 3625