DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 3/2/2026 has been entered.
Claims 1, 11, and 20 are amended, claims 21-26 are added, claims 4-5 and 13-16 are cancelled, and claims 1-3, 6-12, and 17-26 are pending.
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.
Step 1: The claims 1-3, 6-10, 21-26 are a method, claims 11, 17-19 are a computer product, and claim 20 is a system. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claims 1-3, 6-12, and 17-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: The independent claims (1, 11 and 20 taking claim 1 as a representative claim) recites:
A method, performed at a computer system comprising a processor and a computer-readable medium, comprising:
retrieving, at an online system, a set of historical interaction data for a user of the online system describing a set of objects with which the user previously interacted;
receiving a set of health data associated with the user;
accessing a multiclass classification model trained to classify whether the user has each health condition of a set of health conditions, wherein the multiclass classification model is trained by:
receiving historical interaction data describing a plurality of objects with which a plurality of users of the online system previously interacted,
receiving health data for the plurality of users, receiving a set of labels for each user of the plurality of users, wherein each label of the set of labels indicates an existence of a health condition associated with a corresponding user, and training the multiclass classification model based at least in part on the historical interaction data, the health data, and the label for each user of the plurality of users;
generating, using a large language model, a recommendation for the user based on the set of historical interaction data for a user, comprising: transforming the set of historical interaction data to a frequency distribution of a set of dietary attributes of items included in one or more previous orders placed by the user,
applying the multiclass classification model to the frequency distribution of the set of dietary attributes extracted from the set of historical interaction data to classify whether the user has each health condition of a set of health conditions based at least in part on the set of historical interaction data for the user and the set of health data associated with the user,
generating a prompt comprising a set of classes associated with the user and a request for a set of objects appropriate for the user, wherein the set of classes indicates whether the user has each health condition of the set of health conditions and an appropriateness of an object for the user is based at least in part on whether the user has each health condition of the set of health conditions,
providing the prompt to the large language model to obtain a textual output, and
extracting, from the textual output of the large language model, one or more objects for recommendation to the user, wherein the one or more objects comprise one or more of an item or a recipe; and
sending the recommendation for the one or more objects for display to a client device associated with the user.
These limitations, except for the italicized portions, under their broadest reasonable interpretations, recite certain methods of organizing human activity for managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as well as commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). The claimed invention recites steps for receiving historical data related to the user, health data related to the user, applying a model to make health determinations about the user, and providing a recommendation of an item based on the determination. The steps under its broadest reasonable interpretation specifically fall under sales activities. The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
Prong 2: This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of
A method, performed at a computer system comprising a processor and a computer-readable medium, comprising: (claim 1)
A computer program product comprising a non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps comprising: (claim 11)
A computer system comprising: a processor; and
a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, perform actions comprising: (claim 20)
at an online system
accessing a multiclass classification model trained to classify
wherein the multiclass classification model is trained by:
and training the multiclass classification model based at least in part on the historical interaction data, the health data, and the label for each user of the plurality of users;
generating, using a large language model,
providing the prompt to a large language model to obtain a textual output;
extracting, from the textual output of the large language model,
display to a client device
The additional elements of A method, performed at a computer system comprising a processor and a computer-readable medium, comprising: (claim 1); A computer program product comprising a non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps comprising: (claim 11) A computer system comprising: a processor; and a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, perform actions comprising: (claim 20) at an online system; display to a client device are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application – MPEP 2106.05(f).
The recitation of accessing a multiclass classification model trained to classify wherein the multiclass classification model is trained by; and training the multiclass classification model based at least in part on the historical interaction data, the health data, and the label for each user of the plurality of users; generating, using a large language model, providing the prompt to a large language model to obtain a textual output; extracting, from the textual output of the large language model, merely indicates a field of use or technological environment in which the judicial exception is performed. Although these additional elements limit the identified judicial exception, which involves accessing a trained for classifying health conditions, training the model based on a set of data, extracting and providing using a large language model a recommendation for a user this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
Accordingly, these additional elements when considered individually or as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The independent claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong two, the additional elements in the claims amount to no more than mere instructions to apply the judicial exception using a generic computer component and generally linking the judicial exception to a particular technological environment.
Dependent claims 2, 3, 6-10 and 12, 17-19, and 21-26 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. §101 because the additional recited limitations fail to establish that the claims are not directed to the same abstract idea of Independent Claims 1, 11 and 20 without significantly more.
Claim 2 recites wherein extracting the one or more objects for recommendation to the user comprises: predicting an availability of each item included among the one or more objects at a retailer location; and extracting the one or more objects for recommendation to the user based at least in part on the predicted availability of each item included among the one or more objects at the retailer location. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 3 recites wherein the prompt further comprises a set of user data for the user, and wherein the set of user data describes one or more of: a set of preferences associated with the user or the set of historical interaction data for the user. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 6 recites wherein determining, the frequency distribution of the set of dietary attributes of each item included in the one or more previous orders placed by the user comprises determining a frequency distribution of one or more of: an amount of an ingredient in each item included in the one or more previous orders placed by the user or nutritional information associated with each item included in the one or more previous orders placed by the user. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 7 recites further comprising: determining, based at least in part on the set of health data associated with the user, a set of health-related temporal features associated with the user. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 8 recites wherein applying the multiclass classification model to classify whether the user has each health condition of the set of health conditions comprises applying the multiclass classification model to the set of health-related temporal features associated with the user. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 9 recites further comprising: generating a health trend score for the user based at least in part on the set of health data associated with the user, wherein the set of health data describes a health goal associated with the user and a progress of the user towards achieving the health goal; and sending the health trend score for the user for display to the client device associated with the user. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 10 recites further comprising: receiving an additional set of health data associated with the user; updating the health trend score for the user based at least in part on the additional set of health data associated with the user; and sending the updated health trend score for the user for display to the client device associated with the user. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 21 recites wherein the multiclass classification model comprises a plurality of binary classifiers, each binary classifier trained to classify the user into one of two classes indicating whether the user has a particular health condition. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 22 recites wherein training the multiclass classification model comprises training each binary classifier of the plurality of binary classifiers based at least in part on the historical interaction data, the health data, and the label for each user of the plurality of users. While the limitation recites training the multiclass classification model, the training is recited at a high level of generality and the data in the training itself merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 23 recites wherein applying the multiclass classification model comprises: applying each binary classifier of the plurality of binary classifiers to generate a respective output corresponding to a class indicating whether the user is likely to have the particular health condition; and combining the outputs of the plurality of binary classifiers to generate a multiclass output indicating whether the user is likely to have each of the set of health conditions. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 24 recites wherein the large language model is constrained by the set of classes to generate recommendations that are appropriate based on the classified health conditions. While the limitation recites a large language model, the limitation is merely recited at a high level of generality and the generating step and the data that is used is merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 25 recites training the multiclass classification model, comprising: receiving historical interaction data describing a plurality of objects with which a plurality of users of the online system previously interacted; receiving a set of labels for each user of the plurality of users, wherein each label of the set of labels indicates an existence of a health condition associated with a corresponding user; and training the multiclass classification model based at least in part on the historical interaction data, the health data, and the label for each user of the plurality of users. While the limitation recites training the multiclass classification model, the limitation is merely recited at a high level of generality and the receiving step and the data that is used in the training itself merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 26 recites wherein the multiclass classification model is a large language model, wherein applying the multiclass classification model comprises: generating a prompt comprising the set of historical interaction data and the set of health data associated with the user in the prompt and requesting the set of health conditions the user is likely to have based on the set of historical interaction data and the set of health data; and extracting the set of health conditions from a textual output received by executing the multiclass classification model. While the limitation recites a large language model, the limitation is merely recited at a high level of generality and the generating and extraction steps and the data that is used is merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 12, 17-19 recite parallel limitations and are rejected for the reasons set forth above.
For these reasons claim 1-3, 6-12, and 17-26 are rejected under 35 USC 101.
Subject Matter Free of Prior Art
Claims 1, 11 and 20 are determined to have overcome the prior art of rejection and are free of prior art, however the claims remain rejected under 35 USC 101, as set forth above.
Taking amended claim 1 as a representative claim, the claims as amended are found to overcome the prior art rejection for the reasons set forth below.
Claim 1 now recites A method, performed at a computer system comprising a processor and a computer-readable medium, comprising: retrieving, at an online system, a set of historical interaction data for a user of the online system describing a set of objects with which the user previously interacted, extracting, from the set of historical interaction data, a frequency distribution of a set of dietary attributes of items included in one or more previous orders placed by the user; receiving a set of health data associated with the user; accessing a multiclass classification model trained to classify whether the user has each health condition of a set of health conditions, wherein the multiclass classification model is trained by: receiving historical interaction data describing a plurality of objects with which a plurality of users of the online system previously interacted, receiving health data for the plurality of users, receiving a set of labels for each user of the plurality of users, wherein each label of the set of labels indicates an existence of a health condition associated with a corresponding user, and training the multiclass classification model based at least in part on the historical interaction data, the health data, and the label for each user of the plurality of users; applying the multiclass classification model to the frequency distribution of the set of dietary attributes extracted from the set of historical interaction data, to classify whether the user has each health condition of a set of health conditions based at least in part on the set of historical interaction data for the user and the set of health data associated with the user; generating a prompt comprising a set of classes associated with the user and a request for a set of objects appropriate for the user, wherein the set of classes indicates whether the user has each health condition of the set of health conditions and an appropriateness of an object for the user is based at least in part on whether the user has each health condition of the set of health conditions; providing the prompt to a large language model to obtain a textual output; extracting, from the textual output of the large language model, one or more objects for recommendation to the user, wherein the one or more objects comprise one or more of an item or a recipe; and sending a recommendation for the one or more objects for display to a client device associated with the user.
The closest prior art was found to be as follows:
Zhu US 20180113986 discloses [0007] In some embodiments, a real time health monitor deployed on a mobile device is disclosed. From one or more sensors sensing health information of a user, a wearable device automatically obtains at least one health related measurement. The real time health monitor computes at least one of a vitality index and a health index based on at least one health related measurement and classifies, based on the vitality and health indices, the health of the user into some predetermined health condition classes. The real time health monitor then transmits the classified health condition class(es), via network connection, to a health service engine and receives health assistance information that is adaptively determined in accordance with the health condition class(es). [0036] FIG. 10 depicts an exemplary high level system diagram involving an online health condition determiner performing model based health condition classification based on continuously monitored user health data. [0081] Specifically, a real time health monitor is disclosed herein that is capable of continuously classifying a person's health condition into different classes based on trained models, by measuring/gathering various vital signs as well as health data of a user. While Zhou discloses the classification of health data of a user to identify possible medical conditions, the classification is not done based on data from past user purchase history.
Wolf US20210065873 discloses [0084] To predict these responses, the techniques described herein may be utilized. In some examples, this means that recommendations will differ by time of day and by what you previously ate. A UI may display different recommendations and different scoring of foods based on this. For example, it may recommend a higher fat meal at breakfast than at lunch due to the impact of breakfast on fat levels at lunch, or may recommend a higher fat lunch due to the higher glucose responses it predicts at lunch from the mashed potato eaten at breakfast.[0085] The results of this personalized diet recommendation can be improved by getting individuals to carry out standardized tests both as part of the training set data and for the individual wanting recommendations. However. the reference does not disclose providing the prompt to a LLM and extracting textual output of the LLM.
Kartoun US 10971269 discloses the analysis of purchase data of a user to identify their habits and nutritional consumption patterns([Col. 16 lines 24-45]). The disclosure also discloses natural language processing at a high level ([Col. 24 lines 9-10]). However the reference does not disclose determination of the probably medical conditions of the user by “accessing a multiclass classification model trained to classify whether the user has each health condition of a set of health conditions, wherein the multiclass classification model is trained by: receiving historical interaction data describing a plurality of objects with which a plurality of users of the online system previously interacted, receiving health data for the plurality of users, receiving a set of labels for each user of the plurality of users, wherein each label of the set of labels indicates an existence of a health condition associated with a corresponding user, and training the multiclass classification model based at least in part on the historical interaction data, the health data, and the label for each user of the plurality of users; applying the multiclass classification model to the frequency distribution of the set of dietary attributes extracted from the set of historical interaction data, to classify whether the user has each health condition of a set of health conditions based at least in part on the set of historical interaction data for the user and the set of health data associated with the user”.
Groake US 20190018932 discloses [0028] The CDA tracker is configured to analyze the user's historical transactions to identify the foods and medicines purchased by the user. The purchases of specific products can be associated with a purchase frequency. In some embodiments, the CDA tracker may be able to determine probable medical conditions based on the user's historical food and medicine purchases. For example, the CDA tracker may identify occasional purchases of antacid. Based on food, medicine, and/or condition contraindication data received from the external or third party services/open databases described above, the CDA tracker associates antacid with acid reflux. The CDA tracker may cross-reference the antacid with known contra-indicated foods. As such, the CDA tracker may identify a purchase of contra-indicated foods and correlate the purchase of antacid with a purchase of avocados, for example, which are a known risk factor for acid reflux. Thus the CDA tracker may provide an alert to the user to advise a user about the correlation of avocados to acid reflux. However, the reference does not disclose the determination of the probably medical conditions of the user by “accessing a multiclass classification model trained to classify whether the user has each health condition of a set of health conditions, wherein the multiclass classification model is trained by: receiving historical interaction data describing a plurality of objects with which a plurality of users of the online system previously interacted, receiving health data for the plurality of users, receiving a set of labels for each user of the plurality of users, wherein each label of the set of labels indicates an existence of a health condition associated with a corresponding user, and training the multiclass classification model based at least in part on the historical interaction data, the health data, and the label for each user of the plurality of users; applying the multiclass classification model to the frequency distribution of the set of dietary attributes extracted from the set of historical interaction data, to classify whether the user has each health condition of a set of health conditions based at least in part on the set of historical interaction data for the user and the set of health data associated with the user”.
Saunders US 20190108473 [0032] In another embodiment, in operation 105 purchase data from the point of sale direct download may be used to analyze nutritional information, medical/health condition of the user physical activity and related information to determine the user's optimal diet, physical activity to improve the health of the user, however the information is cross-referenced based on inputs from the user and not inferring based applying a two part classification model.
MASCHMEYER US 20240256792 discloses (Abstract) In examples, a prompt to a large language model (LLM) for generating a user-specific textual description is generated, the prompt including the one or more user attributes to include in the generated user-specific textual description and a source text. The prompt is provided to the LLM to receive a generated user-specific textual description. The generated user-specific textual description is provided for display via a user device. However, does not disclose wherein the set of classes indicates whether the user has each health condition of the set of health conditions and an appropriateness of an object for the user is based at least in part on whether the user has each health condition of the set of health conditions; providing the prompt to a large language model to obtain a textual output; extracting, from the textual output of the large language model, one or more objects for recommendation to the user, wherein the one or more objects comprise one or more of an item or a recipe.
NPL: “Sequences of purchases in credit card data reveal lifestyles in urban populations” discloses (Abstract) In human activities, Zipf's law describes, for example, the frequency of appearance of words in a text or the purchase types in shopping patterns. In the latter, the uneven distribution of transaction types is bound with the temporal sequences of purchases of individual choices. In this work, we define a framework using a text compression technique on the sequences of credit card purchases to detect ubiquitous patterns of collective behavior.
It was found that no references alone or in combination, neither anticipates, reasonable teaches, nor renders obvious the below noted features of Applicant’s invention. The features of claim 1 (and parallel claims 1 and 20) in combination that overcome the prior art are:
accessing a multiclass classification model trained to classify whether the user has each health condition of a set of health conditions, wherein the multiclass classification model is trained by:
receiving historical interaction data describing a plurality of objects with which a plurality of users of the online system previously interacted,
receiving health data for the plurality of users,
receiving a set of labels for each user of the plurality of users, wherein each label of the set of labels indicates an existence of a health condition associated with a corresponding user, and training the multiclass classification model based at least in part on the historical interaction data, the health data, and the label for each user of the plurality of users;
applying the multiclass classification model to the frequency distribution of the set of dietary attributes extracted from the set of historical interaction data, to classify whether the user has each health condition of a set of health conditions based at least in part on the set of historical interaction data for the user and the set of health data associated with the user;
generating a prompt comprising a set of classes associated with the user and a request for a set of objects appropriate for the user, wherein the set of classes indicates whether the user has each health condition of the set of health conditions and an appropriateness of an object for the user is based at least in part on whether the user has each health condition of the set of health conditions;
providing the prompt to a large language model to obtain a textual output;
Therefore, none of the cited references disclose or render obvious each and every feature of the claimed invention and the claimed invention is determined to be free of the prior art. Although individually the claimed features could be taught, any combination of references would teach the claimed limitations using a piecemeal analysis, since references would only be combined and deemed obvious based on knowledge gleaned from the applicant's disclosure. Such a reconstruction is improper (i.e., hindsight reasoning). See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). The examiner emphasizes that it is the interrelationship of the limitations that renders these claims free of the prior art/additional art.
Therefore, it is hereby asserted by the Examiner that, in light of the above, that the claims are free of prior art as the references do not anticipate the claims and do not render obvious any further modification of the references to a person of ordinary skill in art.
Relevant Art Not Cited
Chakraborty (US 20210035691) discloses a system for rating a user’s health based on their transaction data
Response to Arguments
Applicant's arguments filed 3/2/2026 have been fully considered but they are not persuasive.
With respect to the remarks directed to 35 USC 101, the examiner does not find the remarks to be persuasive. The examiner asserts that the alleged technical solution is not to a technical problem, but rather the asserted technical solution to the problem of generating recommendations is merely using technology, such as machine learning, to improve a business process (i.e. recommendations). Therefore, at most, the improvement lies in the abstract idea itself and the additional elements are merely carrying out the improved abstract idea.
Limitations noted in the remarks such as “extracts a frequency distribution of dietary attributes from the set of historical interaction data and applies the multiclass classification model to this frequency distribution to generate a set of classes” is considered to be part of the abstract idea as it is merely data processing of the input data to generate an output of the data. The asserted transformation reducing the volume of data that must be provided to the large language model in the prompt is merely consequential to the selected input, model, and then in turn resulting output. If the data is pre-processed and less data is then fed into the subsequent model, then consequentially, the data fed into the LLM will be reduced.
As to transforming sparse historical interaction data, again here the recommendation itself, part of the abstract idea, is improved. Further, as stated in the remarks, the compressed information improved the computational efficiency, however this again is merely consequential in that less data is processed by the computer and thereby the efficiency of the system will then be improved. There is not a technical solution to a technical problem.
With respect to the assertion from the specification, the examiner asserts the remarks are found not to be persuasive for the same reasons set forth above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VICTORIA E. FRUNZI whose telephone number is (571)270-1031. The examiner can normally be reached Monday- Friday 7-4 (EST).
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, Marissa Thein can be reached at (571) 272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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VICTORIA E. FRUNZI
Primary Examiner
Art Unit TC 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 4/13/2026
Prosecution Insights