DETAILED ACTION
This action is in reply to the Amendments filed on 09/09/2026.
Claims 1-20 are rejected.
Claims 1-20 are currently pending and have been examined.
Response to Amendment
Applicant’s amendment, filed 09/09/2026, has been entered. Claims 1, 12, 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/09/2026 has been entered.
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., 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 20 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
-A computer system comprising:
-a processor; and
-a non-transitory computer-readable storage medium having instructions that, when executed by the processor, cause the computer system to perform steps comprising:
-receiving, via a first interface of the computer system associated with a request reception module of the computer system, a request signal from an online platform, the request signal including a list of components associated with the online platform and an identity of a user of the computer system;
-responsive to receiving the request signal, identifying, from an item database of the computer system and using information in the request signal, a set of one or more candidate items that match each component from the list of components;
-comparing each component from the list of components with one or more embeddings of the set of one or more candidate items to generate a matching score indicating how much each component from the list of components matches the set of one or more candidate items;
-identifying a number of matches for each component from the list of components indicating a number of candidate items in the set of one or more candidate items;
-accessing a scoring model of the computer system, wherein the scoring model is a machine-learning model trained using [utilizes] information about past engagements of a collection of users of the computer system with a plurality of lists of components to predict a likelihood that the list of components are located at a source;
-applying the scoring model to the matching score for each component from the list of components, the number of matches for each component from the list of components, and past conversion data for the user to generate a confidence score for the list of components that is indicative of the likelihood that the list of components are located at the source, wherein applying the scoring model comprises:
-determining, by the computer system, whether context data for the user is available in a user catalog database of the computer system;
-responsive to determining that the context data is available, operating the scoring model in a personalized mode by applying the scoring model to the context data to generate a personalized version of the confidence score; and
-responsive to determining that the context data is not available, operating the scoring model in a generic mode by applying the scoring model to a generic pantry list retrieved from a data store of the computer system to generate a generic version of the confidence score;
-comparing the confidence score to a threshold score;
-selecting, based on identifying that the confidence score meets or exceeds the threshold score, the list of components for the source;
-responsive to selecting the list of components for the source, generating a user interface signal; and
-sending, via a network and using a second interface of the computer system associated with a content presentation module of the computer system, the user interface signal to a device associated with the user, wherein sending the user interface signal causes the device to display a user interface with [of] the list of components and an identification of the source where components from the list of components are located
The above limitations recite the concept of determining and providing a list of items and their availabilities to a user. 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 limitation of identifying a number of matches for each component from the list of components indicating a number of candidate items in the set of one or more candidate items; applying the scoring model to the matching score for each component from the list of components, the number of matches for each component from the list of components, and past conversion data for the user to generate a confidence score for the list of components that is indicative of the likelihood that the list of components are located at the source, wherein applying the scoring model comprises: responsive to determining that the context data is available, operating the scoring model in a personalized mode by applying the scoring model to the context data to generate a personalized version of the confidence score; comparing the confidence score to a threshold score; and selecting, based on identifying that the confidence score meets or exceeds the threshold score, the list of components for the source are processes that, under their broadest reasonable interpretation, cover a commercial interaction. For example, “identifying,” “applying,” “operating,” “comparing,” and “selecting” in the context of this claim encompass advertising, and marketing or sales activities.
Similarly, the limitations of a computer system comprising: a non-transitory computer-readable storage medium having instructions that, when executed by the processor, cause the computer system to perform steps comprising: receiving, via a first interface of the computer system associated with a request reception module of the computer system, a request signal from an online platform, the request signal including a list of components associated with the online platform and an identity of a user of the computer system; responsive to receiving the request signal, identifying, from an item database of the computer system and using information in the request signal, a set of one or more candidate items that match each component from the list of components; comparing each component from the list of components with one or more embeddings of the set of one or more candidate items to generate a matching score indicating how much each component from the list of components matches the set of one or more candidate items; accessing a scoring model of the computer system, wherein the scoring model is a machine-learning model trained using [utilizes] information about past engagements of a collection of users of the computer system with a plurality of lists of components to predict a likelihood that the list of components are located at a source; determining, by the computer system, whether context data for the user is available in a user catalog database of the computer system; responsive to determining that the context data is not available, operating the scoring model in a generic mode by applying the scoring model to a generic pantry list retrieved from a data store of the computer system to generate a generic version of the confidence score; responsive to selecting the list of components for the source, generating a user interface signal; and sending, via a network and using a second interface of the computer system associated with a content presentation module of the computer system, the user interface signal to a device associated with the user, wherein sending the user interface signal causes the device to display a user interface with [of] the list of components and an identification of the source where components from the list of components are located are processes that, under their broadest reasonable interpretation, cover a commercial interaction. That is, other than reciting that the system is a computer system, that the steps are performed by the computer system comprising a non-transitory computer-readable storage medium having instructions that, when executed by the processor, that the receiving is from an online platform and via a first interface of the computer system associated with a request reception module of the computer system, that the platform is an online platform, that the system is computer system, that the identifying is from an item database of the computer system, that the comparing is with one or more embeddings, that the scoring model is a machine-learning model, that the scoring machine-learning model is trained, that the determining is by the computer system, that the context data is in a user catalog database of the computer system, that the generic pantry list is retrieved from a data store of the computer system, that the signal is a user interface signal, that the sending is via a network and using a second interface of the computer system associated with a content presentation module of the computer system and to a device associated with the user, and that the displaying is of a user interface by a device, nothing in the claim element precludes the step from practically being performed by people. For example, but for the “computer system,” “a non-transitory computer-readable storage medium having instructions that, when executed by the processor,” “a computer system,” “a first interface,” “a request reception module,” “an online platform,” “an item database,” “one or more embeddings,” “a machine-learning model,” “trained,” “a user catalog database of the computer system,” “a data store of the computer system,” “a user interface signal,” “a network,” “a second interface,” “a content presentation module,” “a device associated with the user,” and “a user interface” language, “receiving,” “identifying,” “comparing,” “accessing,” “generating,” and “sending” 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 computer system comprising:
-a processor; and
-a non-transitory computer-readable storage medium having instructions that, when executed by the processor, cause the computer system to perform steps comprising:
-receiving, via a first interface of the computer system associated with a request reception module of the computer system, a request signal from an online platform, the request signal including a list of components associated with the online platform and an identity of a user of the computer system;
-responsive to receiving the request signal, identifying, from an item database of the computer system and using information in the request signal, a set of one or more candidate items that match each component from the list of components;
-comparing each component from the list of components with one or more embeddings of the set of one or more candidate items to generate a matching score indicating how much each component from the list of components matches the set of one or more candidate items;
-identifying a number of matches for each component from the list of components indicating a number of candidate items in the set of one or more candidate items;
-accessing a scoring model of the computer system, wherein the scoring model is a machine-learning model trained using information about past engagements of a collection of users of the computer system with a plurality of lists of components to predict a likelihood that the list of components are located at a source;
-applying the scoring model to the matching score for each component from the list of components, the number of matches for each component from the list of components, and past conversion data for the user to generate a confidence score for the list of components that is indicative of the likelihood that the list of components are located at the source, wherein applying the scoring model comprises:
-determining, by the computer system, whether context data for the user is available in a user catalog database of the computer system;
-responsive to determining that the context data is available, operating the scoring model in a personalized mode by applying the scoring model to the context data to generate a personalized version of the confidence score; and
-responsive to determining that the context data is not available, operating the scoring model in a generic mode by applying the scoring model to a generic pantry list retrieved from a data store of the computer system to generate a generic version of the confidence score;
-comparing the confidence score to a threshold score;
-selecting, based on identifying that the confidence score meets or exceeds the threshold score, the list of components for the source;
-responsive to selecting the list of components for the source, generating a user interface signal; and
-sending, via a network and using a second interface of the computer system associated with a content presentation module of the computer system, the user interface signal to a device associated with the user, wherein sending the user interface signal causes the device to display a user interface with the list of components and an identification of the source where components from the list of components are located
These limitations are not indicative of integration into a practical application because:
The additional elements of claim 20 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 [0098] of Applicant’s specification – “a software module is implemented with a computer program product comprising one or more computer-readable media storing computer program code or instructions, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described. In some embodiments, a computer-readable medium comprises one or more computer-readable media that, individually or together, comprise instructions that, when executed by one or more processors, cause the one or more processors to perform, individually or together, the steps of the instructions stored on the one or more computer-readable media. Similarly, a processor comprises one or more processors or processing units that, individually or together, perform the steps of instructions stored on a computer-readable medium.” Specifically, the additional elements of computer system, a processor, a non-transitory computer-readable storage medium having instructions that, when executed by the processor, a computer system, a first interface, a request reception module, an online platform, an item database, one or more embeddings, a machine-learning model, trained, a user catalog database of the computer system, a data store of the computer system, a user interface signal, a network, a second interface, a content presentation module, a device associated with the user, and a user interface are recited at a high-level of generality (i.e. as a generic processor performing the generic computer functions of receiving data, identifying data, comparing data, accessing data, applying data, determining data, operating models, selecting data, generating data, and sending 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). For example, stating that the system is a computer system only generally links the commercial interactions to a computer environment. 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 20, taken individually or as a whole, the additional elements of claim 20 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 1 is a method reciting similar functions as claim 20. Examiner notes that claim 1 recites the additional elements of computer system, a processor, computer-readable medium, a first interface, a request reception module, an online platform, an item database, one or more embeddings, a machine-learning model, trained, a user catalog database of the computer system, a data store of the computer system, a user interface signal, a network, a second interface, a content presentation module, a device associated with the user, and a user interface, however, claim 1 does not qualify as eligible subject matter for similar reasons as claim 20 indicated above.
Claim 12 is a computer program product reciting similar functions as claim 20. Examiner notes that claim 12 recites the additional elements of a computer program product, a non-transitory computer readable storage medium having instructions encoded thereon, a processor, computer system, a first interface, a request reception module, an online platform, an item database, one or more embeddings, a machine-learning model, trained, a user catalog database of the computer system, a data store of the computer system, a user interface signal, a network, a second interface, a content presentation module, a device associated with the user, and a user interface, however, claim 12 does not qualify as eligible subject matter for similar reasons as claim 20 indicated above.
Therefore, claims 1, 12, and 20 do not provide an inventive concept and do not qualify as eligible subject matter.
Dependent claims 2-11 and 13-19, 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 2-11 and 13-19 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas in that they recite commercial interactions. Dependent claims 5-6 and 15 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 2-4, 7-11, 13-14, and 16-19 recite the additional elements of the request signal, the online platform, the first interface, the item database, an availability machine-learning model, the online system, training the scoring model, the device associated with the user, a network, re-training the scoring model, and the computer system, 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 2-11 and 13-19 are not indicative of integration into a practical application for at least similar reasons as discussed above. Thus, dependent claims 2-11 and 13-19 are “directed to” an abstract idea. Next, under Step 2B, similar to the analysis of claims 1, 12, and 20, dependent claims 2-11 and 13-19 when analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e. determining and providing a list of items and their availabilities to a user) 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 the Prior Art
In the present application, claims 1-20 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 1-20 are allowable over the prior art as follows:
Claims 1-20 are allowable over 35 U.S.C. §103 as follows:
Claims 1-20 are allowable for the reasons detailed in the “Allowable Subject Matter” section of the Non-Final Office Action dated 02/12/2026.
The most relevant prior art made of record includes Faurot et al. (US 2023/0260007 A1), Balasubramanian et al. (US 2023/0186363 A1), Ruan et al. (US 2023/0078450 A1), and Singh et al. (US 2023/0080205 A1).
The most relevant NPL are:
Cited NPL reference U (cited 02/07/2026, 06/18/2026, and 09/12/2026 on PTO-892) teaches utilizing an algorithm to recommend ingredients, but does not teach or suggest the recited limitations.
Response to Arguments
Rejections under 35 U.S.C. §101
Applicant argues that the amended claim 1 integrates the judicial exception into a practical application and is therefore not directed to an abstract idea under Step 2A, Prong Two. The amendment in claim 1 directly solves the technical problem identified in the Declaration under 37 C.F.R. § 1.132 filed herewith. As the Declaration explains, the technical problem is how the host computer system can allow an external system to obtain information stored in databases of the host computer system without exposing, transferring, or otherwise sharing the underlying, large-scale, and confidential item database, user catalog database, and model data maintained by the host computer system. The amended claim 1 solves this problem in a technically specific way: by reciting that the computer system itself- not the online platform - determines whether context data for the user is available in the user catalog database, and routes the scoring model's inference path accordingly. The amended claim 1 confines all access to the confidential user catalog database and the item catalog database to within the computer system. As the Declaration further explains, the conditional mode-switching improves the efficiency and reliability of the scoring model's operation at inference time: by determining, before invoking the scoring model, whether context data for the user is available in the user catalog database, the computer system avoids invoking the personalized inference path against missing or incomplete user data, and instead deterministically routes the request to a generic pantry list already resident in the computer system's data store, allowing a single scoring model to handle both personalized and generic inference paths rather than requiring two separately trained and separately maintained models or duplicate serving infrastructure. The amended claim 1 thus reflects a specific technical solution - internal, conditional branching within the scoring model's inference mechanism - to the longstanding computer-systems problem of enabling cross-system functionality without exposing confidential database contents, and satisfies both requirements of MPEP § 2106.05(a): the Declaration supplies the technical explanation of the improvement, and the amended claim 1 reflects it (Remarks, pages 16-17).
Examiner respectively disagrees. While enabling cross-system functionality without exposing confidential database contents may be a technical problem, neither the claims nor the specification of the current application describe this technical problem nor do they disclose a technical improvement. For instance, Applicant’s disclosure merely describes that, when context data is available, said context data is provided and used to generate a personalized confidence score and, when the context data is not available, a generic pantry list is utilized to generate a generic confidence score; there is no mention of the context data being confidential data, nor is there any mention of the context data being stored in the computer system rather than the online platform for the purpose of data security. The December 2025 Subject Matter Eligibility Declarations Memorandum describes that “in the context of subject matter eligibility, a claimed invention may be an eligible improvement in technology when the specification provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing a technological improvement and the claims reflect the disclosed improvement” (emphasis added). While the declaration submitted by Applicant described various features such as “confining all database access and machine-learning inference to the host computer system itself, and instead of returning raw database contents to the online platform, exchanging only a compact request signal and a resulting confidence score ( or user interface signal) that conveys the outcome of that internal processing without revealing the underlying data,” these features are not supported in Applicant’s disclosure. Additionally, the submitted declaration further describes that the “user catalog database exist in only a single, authoritative location on the computer system, rather than being copied or cached at the external online platform, and no synchronization process is required to keep a remote copy consistent with the source data as it changes. Network bandwidth and transmission latency are reduced because the computer system returns only a small-footprint confidence score or user interface signal to the online platform (e.g., on the order of a constant number of bytes), rather than the underlying candidate item records, embeddings, or user context data that were used to compute the score. Therefore, the size of the data transmitted between systems does not scale with the size of the item database or the user catalog database being queried. The claimed conditional mode-switching also improves the efficiency and reliability of the scoring model's operation at inference time. By determining, before invoking the scoring model, whether context data for the user is available in the user catalog database, the computer system avoids invoking the personalized inference path against missing or incomplete user data (which could otherwise produce an error, a null result, or a degraded score), and instead deterministically routes the request to a generic pantry list already resident in the computer system's data store.” None of this is described in Applicant’s originally filed disclosure in sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing a technological improvement and the claims reflect the disclosed improvement.
Furthermore, utilizing data (i.e. context data) only when it’s available is not a technical improvement as it does not required the additional element to provide the improvement, it merely consists of utilizing context data when it’s available or otherwise utilizing alternate data (i.e. a generic pantry list). MPEP 2106.05(a) states that “the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.” Furthermore, the “conditional mode-switching” merely provides differing types of data to the model rather than improving the machine learning model itself (e.g. neither the claims nor the specification indicate the way in which the scoring model processes the data is any different).
Accordingly, the claims are not integrated into a practical application.
Applicant further argues that the amendment in claim 1 specifies how the scoring model itself operates during inference. The amended claim 1 recites not merely what data the scoring model receives, but the specific conditional branching logic by which the scoring model dynamically switches between a personalized execution mode (using the user's context data retrieved from a user catalog database of the computer system) and a generic execution mode (using a generic pantry list retrieved from the computer system's data store), depending on the availability of user specific data. This is a concrete technical mechanism governing the model's internal operation, not a high-level recitation of data routing or a generic instruction to apply an abstract idea on a computer. The requirements identified in Memorandum: Change to the MPEP in light of Ex Parte Desjardins are satisfied here: (i) the specification provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement in the functioning of a computer, or an improvement to other technology or a technical field; (ii) the claim itself reflects the disclosed improvement, i.e., the claim includes the components or steps of the invention that provide the improvement described in the specification. The amendment in claim 1 further specifies the particular conditional inference mechanism - the computer system's real-time determination of user context availability and the model's corresponding mode-switching behavior, which gives the scoring model its technically distinctive operation. Per the updated guidance following Ex Parte Desjardins, "when evaluating a claim as a whole, examiners should not dismiss additional elements as mere 'generic computer components' without considering whether such elements confer a technological improvement to a technical problem, especially as to improvements to computer components or the computer system. (Memorandum: Change to the MPEP in light of Ex Parte Desjardins.) The amended claim 1 now recites a specific, non-standard mode of model operation: conditional, context-sensitive inference with dynamic mode-switching, which goes well beyond merely linking a commercial interaction to a computer environment and constitutes a technological improvement to the machine-learning system itself (Remarks, pages 17-18).
Examiner respectively disagrees. Initially, Examiner points out that the claims don’t recite “dynamic mode-switching.” Neither the claims nor the specification specifies how the scoring model itself operates during inference, rather, the claims merely recite that, when context data is available, said context data is provided and used to generate a personalized confidence score and, when the context data is not available, a generic pantry list is utilized to generate a generic confidence score; this does not specify how the scoring model operates, rather, it merely specifies which type of data is provided to the scoring model.
Additionally, in Ex Parte Desjardins the specification discussed the technical problem of 'catastrophic forgetting' and further recited an improvement in the technique that the machine learning model was trained which improved the machine learning technology itself. Unlike Ex Parte Desjardins, Applicant’s specification makes no mention of enabling cross-system functionality without exposing confidential database contents and does not describe a technical solution to solve this problem as merely providing differing types of data to the model, based on availability of the data, fails to reflect an improvement in the functioning of a computer or an improvement to another technology or technical field (i.e. the machine learning technology).
Accordingly, the claims are not integrated into a practical application and are ineligible.
Applicant further argues that independent claims 12 and 20 are amended to recite similar limitations in claim 1, and, thus, independent claims 12 and 20, as amended herein, integrate the abstract idea into a practical application for at least the same reasons as amended claim 1. Each of the remaining pending claims depends on claim 1 or claim 12; thus, these claims also integrate the abstract idea into a practical application (Remarks, pages 18-19).
Examiner respectfully disagrees. As detailed in response to the arguments above, claim 1 is not eligible. Accordingly, independent claims 12 and 20 and the dependent claims are ineligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-Naidu et al. (US 2021/0241342 A1) teaches identify each ingredient in an ingredient list of a recipe and generating a list of catalog products for purchase.
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/ARIELLE E WEINER/ Primary Examiner, Art Unit 3689
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