DETAILED ACTION
This action is in reply to the Amendments filed on 05/27/2026.
Claims 4 and 13 are cancelled.
Claims 21-22 are newly added.
Claims 1-3, 5-12, and 14-22 are rejected.
Claims 1-3, 5-12, and 14-22 are currently pending and have been examined.
Response to Amendment
Applicant’s amendment, filed 05/27/2026, has been entered. Claims 1, 5-6, 9, 11, 14-15, 18, and 20 have been amended.
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 .
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, 5-12, and 14-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories (see MPEP 2106.03). All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the Subject Matter Eligibility Test, it is determined whether the claims are directed to a judicially recognized exception (see MPEP 2106.04). Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 1 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
-a non-transitory memory having instructions stored thereon; and
-at least one processor operatively coupled to the non-transitory memory, and configured to read the instructions to:
-determine a set of product types of a retailer;
-determine a set of item identities (IDs) of the retailer, wherein each item ID belongs to one of the set of product types;
-generate, using a large language model, a virtual catalog of virtual item types based on the set of product types and the set of item IDs;
-determine, using the large language model, a first mapping between the set of product types and the virtual catalog of virtual item types; and
-generate a second mapping between the set of item IDs and the virtual catalog of virtual item types;
-receive, from a computing device, a recommendation request for recommending items to a customer;
-determine, based on the recommendation request, at least one anchor item to be displayed to the customer,
-obtain a first machine learning model trained using [that utilizes] first training data that is based on a first product data granularity, wherein the first training data comprises features related to product types in historical user sessions and transactions of a plurality of customers;
-obtain a second machine learning model trained using [that utilizes] second training data that is based on a second product data granularity different from the first product data granularity, wherein the second training data comprises features related to virtual item types in historical user sessions and the transactions of the plurality of customers;
-determine an anchor identifier of the at least one anchor item, wherein the anchor identifier is one of the set of item IDs;
-determine, based on the anchor identifier, a product type of the set of product types for the at least one anchor item;
-generate, using the first machine learning model, a ranked item type list based on the at least one anchor item and the product type;
-determine, based on the anchor identifier and the second mapping, a virtual item type of the plurality of virtual item types for the at least one anchor item;
-generate, using the second machine learning model, a ranked virtual item type list based on the virtual item type;
-generate a ranked list of recommended items based on the ranked item type list and the ranked virtual item type list; and
-transmit to the computing device the ranked list of recommended items to be displayed to the customer with the at least one anchor item
Examiner notes: the virtual items types are item categories that are generated and the virtual catalog is a catalog of those items (see at least [0066] and Fig. 5 of spec)
The above limitations recite the concept of recommending items to a customer. The above limitations fall within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas, enumerated in MPEP 2106.04(a).
Certain methods of organizing human activity include:
fundamental economic principles or practices (including hedging, insurance, and mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The limitations of determine a set of product types of a retailer; determine a set of item identities (IDs) of the retailer, wherein each item ID belongs to one of the set of product types; generate a second mapping between the set of item IDs and the virtual catalog of virtual item types; determine, based on the recommendation request, at least one anchor item to be displayed to the customer; determine an anchor identifier of the at least one anchor item, wherein the anchor identifier is one of the set of item IDs; determine, based on the anchor identifier, a product type of the set of product types for the at least one anchor item; determine, based on the anchor identifier and the second mapping, a virtual item type of the plurality of virtual item types for the at least one anchor item; and generate a ranked list of recommended items based on the ranked item type list and the ranked virtual item type list are processes that, under their broadest reasonable interpretation, cover a commercial interaction. For example, “determine,” “determine,” “generate,” “determine,” “determine,” “determine,” “determine,” and “generate” in the context of this claim encompass advertising, and marketing or sales activities.
Similarly, the limitations of generate, using a large language model, a virtual catalog of virtual item types based on the set of product types and the set of item IDs; determine, using the large language model, a first mapping between the set of product types and the virtual catalog of virtual item types; and receive, from a computing device, a recommendation request for recommending items to a customer; obtain a first machine learning model trained using [that utilizes] first training data that is based on a first product data granularity, wherein the first training data comprises features related to product types in historical user sessions and transactions of a plurality of customers; obtain a second machine learning model trained using [that utilizes] second training data that is based on a second product data granularity different from the first product data granularity, wherein the second training data comprises features related to virtual item types in historical user sessions and the transactions of the plurality of customers; generate, using the first machine learning model, a ranked item type list based on the at least one anchor item and the product type; generate, using the second machine learning model, a ranked virtual item type list based on the virtual item type; and transmit to the computing device the ranked list of recommended items to be displayed to the customer with the at least one anchor item are processes that, under their broadest reasonable interpretation, cover a commercial interaction. That is, other than reciting that the generating is using a large language model, that the determining is using a large language model, that the receiving is from a computing device, that the first model is a first machine learning model that’s trained, that the first data is first training data, that the second model is a second machine learning model that’s trained, that the second data is second training data, and that the transmitting is to the computing device, nothing in the claim element precludes the step from practically being performed by people. For example, but for the “large language model,” “computing device,” “first machine learning model,” “trained,” “first training data,” “second machine learning model,” and “second training data” language, “generate,” “determine,” “receive,” “obtain,” “obtain,” “generate,” “generate,” and “transmit” in the context of this claim encompasses advertising, and marketing or sales activities.
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO).
-a non-transitory memory having instructions stored thereon; and
-at least one processor operatively coupled to the non-transitory memory, and configured to read the instructions to:
-determine a set of product types of a retailer;
-determine a set of item identities (IDs) of the retailer, wherein each item ID belongs to one of the set of product types;
-generate, using a large language model, a virtual catalog of virtual item types based on the set of product types and the set of item IDs;
-determine, using the large language model, a first mapping between the set of product types and the virtual catalog of virtual item types; and
-generate a second mapping between the set of item IDs and the virtual catalog of virtual item types;
-receive, from a computing device, a recommendation request for recommending items to a customer;
-determine, based on the recommendation request, at least one anchor item to be displayed to the customer,
-obtain a first machine learning model trained using first training data that is based on a first product data granularity, wherein the first training data comprises features related to product types in historical user sessions and transactions of a plurality of customers;
-obtain a second machine learning model trained using second training data that is based on a second product data granularity different from the first product data granularity, wherein the second training data comprises features related to virtual item types in historical user sessions and the transactions of the plurality of customers;
-determine an anchor identifier of the at least one anchor item, wherein the anchor identifier is one of the set of item IDs;
-determine, based on the anchor identifier, a product type of the set of product types for the at least one anchor item;
-generate, using the first machine learning model, a ranked item type list based on the at least one anchor item and the product type;
-determine, based on the anchor identifier and the second mapping, a virtual item type of the plurality of virtual item types for the at least one anchor item;
-generate, using the second machine learning model, a ranked virtual item type list based on the virtual item type;
-generate a ranked list of recommended items based on the ranked item type list and the ranked virtual item type list; and
-transmit to the computing device the ranked list of recommended items to be displayed to the customer with the at least one anchor item
The additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea) as supported by paragraph [0113] of Applicant’s specification – “Each functional component described herein can be implemented in computer hardware, in program code, and/or in one or more computing systems executing such program code as is known in the art.” Specifically, the additional elements of a non-transitory memory having instructions stored thereon, at least one processor operatively coupled to the non-transitory memory, and configured to read the instructions, a large language model, a computing device, a first machine learning model that’s trained, first training data, a second machine learning model that’s trained, and second training data are recited at a high-level of generality (i.e. as a generic processor performing the generic computer functions of determining data, generating data, receiving data, obtaining data, and transmitting data) such that they amount do no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional 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 claim is directed to an abstract idea. Further, the additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application.
Additionally, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO).
In the case of claim 1, taken individually or as a whole, the additional elements of claim 9 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Claim 11 is a method reciting similar functions as claim 1. Examiner notes that claim 11 recites the additional elements of a computer-implemented method, a large language model, a computing device, a first machine learning model that’s trained, first training data, a second machine learning model that’s trained, and second training data, however, claim 11 does not qualify as eligible subject matter for similar reasons as claim 1 indicated above.
Claim 20 is a non-transitory computer readable medium reciting similar functions as claim 1. Examiner notes that claim 20 recites the additional elements of a non-transitory computer readable medium, at least one processor, at least one device, a large language model, a computing device, a first machine learning model that’s trained, first training data, a second machine learning model that’s trained, and second training data, however, claim 20 does not qualify as eligible subject matter for similar reasons as claim 1 indicated above.
Therefore, claims 1, 11, and 20 do not provide an inventive concept and do not qualify as eligible subject matter.
Dependent claims 2-3, 5-10, 12, 14-19, and 21-22, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. § 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-3, 5-10, 12, 14-19, and 21-22 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas in that they recite commercial interactions. Dependent claims 2-3, 8, 10, 12, 17, and 19 do not recite any farther additional elements, and as such are not indicative of integration into a practical application for at least similar reasons discussed above. Dependent claims 5-7, 9, 14-16, 18, and 21-22 recite the additional elements of the at least one processor, virtual item embeddings, an embedding space, item embeddings, the first machine learning model, the second machine learning model, the first training data, the second training data, a same transformer architecture, the first machine learning model being trained, an item embedding, top virtual item embedding, and the second machine learning model being trained, but similar to the analysis under prong two of Step 2A these additional elements are used as a tool to perform the abstract idea. As such, under prong two of Step 2A, claims 2-3, 5-10, 12, 14-19, and 21-22 are not indicative of integration into a practical application for at least similar reasons as discussed above. Thus, dependent claims 2-3, 5-10, 12, 14-19, and 21-22 are “directed to” an abstract idea. Next, under Step 2B, similar to the analysis of claims 1, 11, and 20, dependent claims 2-3, 5-10, 12, 14-19, and 21-22 when analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e. recommending items to a customer) being applied on a general-purpose computer and, therefore, do not amount to significantly more than the abstract idea itself. Accordingly, the Examiner concludes that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
Subject Matter Allowable Over the Prior Art
In the present application, claims 1-3, 5-12, and 14-22 would be allowable if rewritten or amended to overcome the rejections under 35 USC § 101 set forth in this Office action. The following is the Examiner's statement of reasons of allowance:
Regarding 35 U.S.C. §103, upon review of the evidence at hand, it is hereby concluded that the totality of the evidence, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the applicant’s invention. Claims 1-3, 5-12, and 14-22 are allowable over the prior art as follows:
Claims 1-3, 5-12, and 14-22 are allowable over 35 U.S.C. §103 as follows:
The most relevant prior art made of record includes Korpeoglu et al. (US 2021/0233149 A1), Zhang et al. (US 2022/0027562 A1), and Lin et al. (US 2025/0005279 A1).
Claims 1-3, 5-12, and 14-22 are allowable for the reasons detailed in the “Subject Matter Allowable Over the Prior Art” section of the Non-Final Office Action dated 03/17/2026 as “determine a set of product types of a retailer; determine a set of item identities (IDs) of the retailer, wherein each item ID belongs to one of the set of product types; generate, using a large language model, a virtual catalog of virtual item types based on the set of product types and the set of item IDs; determine, using the large language model, a first mapping between the set of product types and the virtual catalog of virtual item types; and generate a second mapping between the set of item IDs and the virtual catalog of virtual item types” has been rolled up into the independent claims.
The most relevant NPL is:
Cited NPL Gatzioura (reference U cited 03/07/2026 and 07/10/2026 in PTO-892) teaches a recommender that uses a hierarchical model for the items and searches for similar sets of items, in order to recommend those that are most likely to satisfy a user, but does not teach or suggest the recited claims.
Response to Arguments
Rejections under 35 U.S.C. §101
Applicant argues that claim 1 is direct to a "system" that includes a "non-transitory memory having instructions stored thereon" and a "at least one processor operatively coupled to the non-transitory memory" and "is configured to read the instructions to:." When considered under the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 4 (January 7, 2019) (hereinafter the "2019 Guidance"), at least as amended, claim 1 recites patentable subject matter. Moreover, claims 11 and 20 recite similar features and thus also recite patentable subject matter. As an initial point, the December Memorandum provides updates to the Manual of Patent Examining Procedure (M.P.E.P.) that took effect immediately. Id., pp. 1, 5. Among the updates, the December Memorandum updates M.P.E.P. 2106.05(a), subsection I. Here, among other features, Applicant's independent claim 1 recites "a first machine learning model trained using first training data that is based on a first product data granularity, wherein the first training data comprises features related to product types in historical user sessions and transactions of a plurality of customers," "a second machine learning model trained using second training data that is based on a second product data granularity different from the first product data granularity, wherein the second training data comprises features related to virtual item types in historical user sessions and the transactions of the plurality of customers," "generate, using the first machine learning model, a ranked item type list based on the at least one anchor item and the product type," and "generate, using the second machine learning model, a ranked virtual item type list based on the virtual item type." When considered in light of Desjardins, claim 1 recites patentable subject matter. Indeed, rather than merely reciting "the concept of recommending items to a customer," the above quoted features recite specific training data that is used to train each of two machine learning models, as well as the use of the each of the trained machine learning models to generate a ranked item type list and a ranked virtual item type list, respectively. To be clear, each of the machine learning models, without the claimed training, could not, and would not, have the ability to generate the claimed outputs (i.e., the ranked item type list and the ranked virtual item type list). The training improves each of the machine learning models to allow them to generate these claimed outputs (Remarks, pages 15-17).
Examiner respectfully disagrees. The additional elements recited in the claims are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea). In Ex Parte Desjardins the claims were not found eligible because they merely trained machine learning models, rather, the claims recited in Ex Parte Desjardins train the machine learning model in such a way that it “allows the model to preserve performance on earlier tasks even as it learns new ones, directly addressing the technical problem of 'catastrophic forgetting' in continual learning systems" (see Ex Parte Desjardins). The machine learning training technique was improved. Unlike Desjardins, the current claims do not improve the training technique (which would improve the machine learning technology itself), rather, the current claims merely improve the data input. Improving data does not improve the machine learning technology itself. Accordingly, the claims are directed to an abstract idea and are not integrated into a practical application.
Applicant further argues that on December 31, 2025, the PTAB issued an opinion in Ex Parle Carmody, Appeal 2025-002843. Relying on Ex Parle Desjardins, the PTAB stated that because "the claims recite an improvement in training of models for use by [a] recommendation engine to generate useful orchestrations," the claims are patent eligible. See Ex Parle Carmody, p. 7. Similarly, here, the claims recite an improvement in training each of the two machine learning models for use to generate the claimed ranked item type list and ranked virtual item type list. As such, at least because claim 1 improves machine learning models to allow them to generate the claimed ranked item type list and ranked virtual item type list, at least under Desjardins and Carmody, claim 1 recites patentable subject matter and is not directed to a Certain Method of Organizing Human Activity (Remarks, pages 17-18).
Examiner respectfully disagrees. In Carmody, like Desjardins, the machine learning itself is improved (i.e. “modular approach to tactic recommendation (i.e., with a separate model for each tactic) enables the model for each tactic to be updated and improved separately and independently from other tactic-specific models, and also enables models for new tactics to be easily incorporated into tactic recommendation model 475 (e.g., as a plug-and-play module)”). Unlike Desjardins and Carmody, the current claims do not improve the machine learning technology itself, rather, the current claims merely improve the data input. Improving data does not improve the machine learning technology itself. Accordingly, the claims are directed to an abstract idea and are not integrated into a practical application.
Applicant further argues that Applicant's specification demonstrates to one of ordinary skill in the art that, at least as amended, claim 1 provides technical several improvements, including efficiently training machine learning models, to allow for the generation of more relevant and diversified recommendations, thereby integrating any alleged abstract idea into a practical application. See Applicant's Specification [0002], [0019]. Indeed, claim 1 requires the claimed dual machine learning model architecture to increase recommendation accuracy and diversification, and allows for the more effective processing of sequential data. Id., ,I [0019]. As such, and as persons of ordinary skill in the art would recognize, the claimed subject matter provides several technical advantages, and is not merely directed to "the concept of recommending items to a customer" (Remarks, page 18).
Examiner respectfully disagrees. Merely improving data used to train a machine learning model does not improve the machine learning technology itself. Additionally, the generation of more relevant and diversified recommendations and increasing recommendation accuracy and diversification are not improvements in the functioning of a computer or an improvement to another technology or technical field. Accordingly, the claims are directed to an abstract idea and are not integrated into a practical application.
Applicant further argues that the claims recite significantly more than "the concept of recommending items to a customer." Indeed, the claims recite "specific limitation[s] other than what is well-understood, routine, conventional activity in the field," and "add[] unconventional steps that confine the claim to a particular useful application," as the prior art fails to teach or suggest the claimed subject matter. See M. P. E. P. § 2106.05; see also supra. (indicating that the claims recite novel and non-obvious subject matter). Moreover, the claimed subject matter is confined to a "particular useful application" that includes the generation of ranked lists of recommended items using two trained machine learning models, each machine learning model trained with specific training data to generate ranked item type lists and ranked virtual item type lists, respectively, thereby allowing for various technical advantages such as those noted above, and further providing for the transmission of the ranked list of recommended items for display to a specific customer. As such, Applicant respectfully submits that even if claim 1 can properly be considered to recite the alleged abstract idea of "the concept of recommending items to a customer," nonetheless claim 1 integrates the alleged abstract idea into a practical application (Remarks, pages 18-19).
Examiner respectfully disagrees. Initially, Examiner points out that novelty is not the test for eligibility (see MPEP 2106.03-2106.06). Additionally, as detailed in response to the arguments above, the recited claims fail to reflect an improvement in the functioning of a computer or an improvement to another technology or technical field.
Furthermore, even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually. Additionally, as is described in the MPEP 2106.05(II) (i.e. “Thus, in Step 2B, examiners should: … Re-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant”), step 2B considers whether additional elements concluded to be insignificant extra-solution activity in Step 2A are more than well-understood, routine, conventional activity in the field. Examiner did not identify any of the additional elements as insignificant extra-solution activity in Step 2A so there weren’t elements to be evaluated in terms of whether they are more than well-understood, routine, conventional activity in the field. Accordingly, the claims do not amount to significantly more than the abstract idea and are ineligible.
Applicant further argues that as indicated in the Aug. Memorandum, "a rejection of a claim should not be made simply because an examiner is uncertain as to the claim's eligibility [and, instead,] must be established by a preponderance of the evidence." See Aug. Memorandum, p. 5. Here, rather than merely claiming the alleged Certain Method of Organizing Human Activity of "the concept of recommending items to a customer," Applicant's claims recite and are directed to statutory subject matter at least for at least the reasons provided herein. Indeed, Applicant respectfully submits that, when properly considered, the preponderance of the evidence does not establish that the claims are merely directed to the alleged abstract idea of "the concept of recommending items to a customer" and instead recite significantly more. As such, Applicant respectfully submits that the claims, at least in amended form, are directed to statutory subject matter (Remarks, page 19).
Examiner respectfully disagrees. The claims were not rejected “simply because an examiner is uncertain as to the claim's eligibility,” rather, the claims have been determined to be ineligible, in accordance with the MPEP, for the reasons and evidence clearly detailed in the 101 rejection above. Accordingly, the claims do not amount to significantly more than the abstract idea and are ineligible.
Applicant further argues that independent claims 1, 11, and 20 are directed to patent-eligible subject matter, and respectfully request the reconsideration and withdrawal of the rejection of these claims under 35 U.S.C. § 101. Further, as claims 2-10 and 12-19 depend from independent claims 1 and 11, these claims are directed to statutory subject matter for at least those reasons set forth above for these independent claims, and for further reasons recited therein (Remarks, page 19).
Examiner respectfully disagrees. As detailed in response to the arguments above, the independent claims are ineligible, and similarly, the dependent claims are ineligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-Rao et al. (US 2024/0249333 A1) teaches ranking categories of products.
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.
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/ARIELLE E WEINER/ Primary Examiner, Art Unit 3689
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