DETAILED ACTION
Remarks
The instant application having Application Number 19/338,035 filed on September 24, 2025 has a total of 20 claims pending in the application; there are 3 independent claims and 17 dependent claims, all of which are presented for examination by the examiner.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
The examiner requests, in response to this Office action, supports are shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Information Disclosure Statement
As required by M.P.E.P. 609(C), the applicant’s submissions of the Information Disclosure Statements dated 9/24/2025 and 10/15/2025 are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C (2), a copy of the PTOL-1449 initialed and dated by the examiner is attached to the instant office action.
Drawings
The applicant’s drawings submitted are acceptable for examination purposes.
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 an abstract idea without significantly more.
Regarding independent Claims 1, 10, and 19:
Step 1 Analysis:
Claim 1 recites “A method…”, the claim recites a series of steps and therefore is process.
Claim 10 recites “A device …”; therefore, the claim is a machine.
Claim 19 recites “A computer-readable storage medium”, therefore the claim is a manufacture.
Step 2A Prong One Analysis: The claim, under the broadest reasonable interpretation, recites limitations directed to an abstract idea, including mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion), but for the recitation of mere instructions to apply an exception language. In particular, the following limitations are directed to an abstract idea:
constructing resource prompt information based on positive behavior information of a target object for a resource, the positive behavior information being configured for representing a positive behavior of the target object for a resource preference, and the resource prompt information being configured for representing a resource preferred by the target object;
processing the resource prompt information by using a large language model, to obtain a resource text, the resource text being configured for describing the resource preference of the target object in a form of a natural language;
determining, for any candidate resource in a resource library for recommendations, a correlation between the candidate resource and the resource text, the correlation being configured for representing a correlation between the resource preference of the target object and the candidate resource; and
recommending a resource to the target object based on correlations corresponding to multiple candidate resources in the resource library.
This limitation is a process that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “processor”, “memory” and “medium”, nothing in the claim element precludes the steps from practically being performed in a human mind or with the aid of pen and paper. For example, the “constructing”, “processing”, “determining” and “recommending” in the context of this claim encompasses a user mentally, and with the aid of pen and paper writing the changes down on a sheet of paper and examine the list to determine the relevant ones. For example, a human being can ask/receive a question and receive an answer after reading plurality of documents. A human being can analyze document and search for relevant information to produce and send an answer/recommendation based on the relevant portion of the document.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A - Prong Two: Integrated into a Practical Application
The judicial exception is not integrated into a practical application. In particular, the additional steps: the “constructing”, “processing”, “determining” and “recommending” steps mount to data gathering which are considered to be insignificant extra-solution activity (see MPEP 2106.05(g)), and the “determining” and “recommending” steps are considered as a mere instruction to apply an exception to perform an existing process on a generic computer and/or no more than an idea of a solution or outcome on a generic computer (see MPEP 2106.05(f)). Accordingly, the 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, thus fail to integrate the abstract idea into a practical application. See MPEP 2106.05(g).
Step 2B: Claim provides an Inventive Concept
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The insignificant extra-solution activities identified above, which include the data-gathering and the step of “constructing”, “processing”, “determining” and “recommending” are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d)(II)). For these reasons, there is no inventive concept in the claim, and thus it is ineligible.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application.
Independent claims 10 and 19 have the similar limitations as claim 1 and are rejected for at least the same reasons as claim 1.
Regarding claim 2. The method according to claim 1, wherein constructing the resource prompt information based on the positive behavior information of the target object for the resource comprises: determining at least one reference resource based on the positive behavior information of the target object for the resource, the at least one reference resource being a resource for which the target object triggers a positive behavior; and constructing the resource prompt information based on the at least one reference resource and a recommendation requirement, the recommendation requirement being a requirement to be met for recommending a resource in a current recommendation scenario.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 3. The method according to claim 1, wherein processing the resource prompt information by using the large language model, to obtain the resource text comprises:
analyzing the resource prompt information by using the large language model, to determine a target resource type preferred by the target object; obtaining at least one resource type related to the target resource type; and generating the resource text based on the target resource type and the at least one resource type.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 4. The method according to claim 3, wherein obtaining the at least one resource type related to the target resource type comprises at least one of following: obtaining, from the current recommendation scenario based on a correlation relationship between resource types, the at least one resource type related to the target resource type; or determining, based on another recommendation scenario related to the current recommendation scenario, a resource preference of the target object in the another recommendation scenario; and obtaining, from the current recommendation scenario based on the resource preference of the target object in the another recommendation scenario, the at least one resource type related to the target resource type.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 5. The method according to claim 1, wherein determining, for any candidate resource in the resource library for recommendations, the correlation between the candidate resource and the resource text comprises: performing, for the any candidate resource in the resource library for recommendations, feature extraction on the candidate resource based on the large language model, to obtain a resource feature of the candidate resource, the resource feature being configured for representing detailed information of the candidate resource; performing feature extraction on the resource text based on the large language model, to obtain a resource text feature; and determining a similarity between the resource feature of the candidate resource and the resource text feature, the similarity being the correlation between the resource preference of the target object and the candidate resource.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 6. The method according to claim 5, wherein performing, for the any candidate resource in the resource library, the feature extraction on the candidate resource based on the large language model, to obtain the resource feature of the candidate resource comprises: obtaining, for the any candidate resource in the resource library, text information of the candidate resource, the text information being the detailed information of the candidate resource; and performing feature extraction on the text information based on the large language model, to obtain the resource feature of the candidate resource.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 7. The method according to claim 1, wherein recommending the resource to the target object based on the correlations corresponding to the multiple candidate resources in the resource library comprises: sorting the multiple candidate resources in the resource library in descending order of the correlations; and recommending a preset quantity of top-ranked candidate resources to the target object.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 8. The method according to claim 1, wherein a training process of the large language model comprises: constructing sample prompt information based on positive behavior information of a sample object for a resource, the positive behavior information being configured for representing a positive behavior of the sample object for a resource preference, and the sample prompt information being configured for representing a resource preferred by the sample object; processing the sample prompt information by using the large language model, to obtain a sample resource text, the sample resource text being configured for describing the resource preference of the sample object in a form of a natural language; determining a predictive recommendation result based on the sample resource text, the predictive recommendation result being configured for representing a resource predicted by the large language model for recommendation to the sample object; and training the large language model based on the predictive recommendation result and a reference recommendation result, the reference recommendation result being configured for representing a resource recommended to the sample object under a real circumstance.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 9. The method according to claim 8, further comprising: obtaining the large language model obtained through training based on a language text; and keeping a parameter of the large language model unchanged, and adding an adjustable parameter to the large language model; and training the large language model based on the predictive recommendation result and the reference recommendation result comprises: adjusting the adjustable parameter of the large language model, to minimize a difference between the predictive recommendation result and the reference recommendation result.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
With respect to claims 11-18 and 20, these claims have similar limitations of claims 2-9 and do not provide any additional elements that when considered individually or as an ordered combination, amount to significantly more than the abstract idea identified.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Fabian et al. (US Patent Publication No. 2024/0303440 A1, ‘Fabian’, hereafter).
Regarding claim 1. Fabian teaches a resource recommendation method, performed by a computer device, comprising:
constructing resource prompt information based on positive behavior information of a target object for a resource, the positive behavior information being configured for representing a positive behavior of the target object for a resource preference, and the resource prompt information being configured for representing a resource preferred by the target object (Fabian [0027-0028], [0066-0074], [0077-0083]);
processing the resource prompt information by using a large language model, to obtain a resource text, the resource text being configured for describing the resource preference of the target object in a form of a natural language (Fabian [0074], [0077-0083]);
determining, for any candidate resource in a resource library for recommendations, a correlation between the candidate resource and the resource text, the correlation being configured for representing a correlation between the resource preference of the target object and the candidate resource (Fabian [0074], [0077-0083]; and
recommending a resource to the target object based on correlations corresponding to multiple candidate resources in the resource library (Fabian [0086-0087]).
Regarding claim 2. Fabian teaches, wherein constructing the resource prompt information based on the positive behavior information of the target object for the resource comprises:
determining at least one reference resource based on the positive behavior information of the target object for the resource, the at least one reference resource being a resource for which the target object triggers a positive behavior (Fabian [0027-0029], [0072]); and
constructing the resource prompt information based on the at least one reference resource and a recommendation requirement, the recommendation requirement being a requirement to be met for recommending a resource in a current recommendation scenario (Fabian [0027-0029], [0072], [0099]).
Regarding claim 3. Fabian teaches , wherein processing the resource prompt information by using the large language model, to obtain the resource text comprises:
analyzing the resource prompt information by using the large language model, to determine a target resource type preferred by the target object (Fabian [0027-0029]);
obtaining at least one resource type related to the target resource type (Fabian [0027-0029]); and
generating the resource text based on the target resource type and the at least one resource type (Fabian [0027-0029]).
Regarding claim 4. Fabian teaches , wherein obtaining the at least one resource type related to the target resource type comprises at least one of following:
obtaining, from the current recommendation scenario based on a correlation relationship between resource types, the at least one resource type related to the target resource type (Fabian [0029], [0083], [0092], [0098]); or
determining, based on another recommendation scenario related to the current recommendation scenario, a resource preference of the target object in the another recommendation scenario; and obtaining, from the current recommendation scenario based on the resource preference of the target object in the another recommendation scenario, the at least one resource type related to the target resource type (Fabian [0027-0029], [0072-0074], [0099]).
Regarding claim 5. Fabian teaches , wherein determining, for any candidate resource in the resource library for recommendations, the correlation between the candidate resource and the resource text comprises:
performing, for the any candidate resource in the resource library for recommendations, feature extraction on the candidate resource based on the large language model, to obtain a resource feature of the candidate resource, the resource feature being configured for representing detailed information of the candidate resource (Fabian [0028], [0030], [0032], [0084], [0087]);
performing feature extraction on the resource text based on the large language model, to obtain a resource text feature (Fabian [0028], [0030], [0032], [0084], [0087]); and
determining a similarity between the resource feature of the candidate resource and the resource text feature, the similarity being the correlation between the resource preference of the target object and the candidate resource (Fabian [0121]).
Regarding claim 6. Fabian teaches , wherein performing, for the any candidate resource in the resource library, the feature extraction on the candidate resource based on the large language model, to obtain the resource feature of the candidate resource comprises:
obtaining, for the any candidate resource in the resource library, text information of the candidate resource, the text information being the detailed information of the candidate resource (Fabian [0028], [0030], [0032], [0084], [0087]); and
performing feature extraction on the text information based on the large language model, to obtain the resource feature of the candidate resource (Fabian [0028], [0030], [0032], [0084], [0087]).
Regarding claim 7. Fabian teaches , wherein recommending the resource to the target object based on the correlations corresponding to the multiple candidate resources in the resource library comprises:
sorting the multiple candidate resources in the resource library in descending order of the correlations; and recommending a preset quantity of top-ranked candidate resources to the target object (Fabian [0049]).
Regarding claim 8. Fabian teaches , wherein a training process of the large language model comprises:
constructing sample prompt information based on positive behavior information of a sample object for a resource, the positive behavior information being configured for representing a positive behavior of the sample object for a resource preference, and the sample prompt information being configured for representing a resource preferred by the sample object (Fabian [0005], [0027-0029], [0062]);
processing the sample prompt information by using the large language model, to obtain a sample resource text, the sample resource text being configured for describing the resource preference of the sample object in a form of a natural language (Fabian [0005], [0027-0029], [0062]);
determining a predictive recommendation result based on the sample resource text, the predictive recommendation result being configured for representing a resource predicted by the large language model for recommendation to the sample object (Fabian [0005], [0027-0029], [0062]); and
training the large language model based on the predictive recommendation result and a reference recommendation result, the reference recommendation result being configured for representing a resource recommended to the sample object under a real circumstance (Fabian [0005], [0027-0029], [0062]).
Regarding claim 9. Fabian teaches , further comprising:
obtaining the large language model obtained through training based on a language text (Fabian [0029], [0062]); and
keeping a parameter of the large language model unchanged, and adding an adjustable parameter to the large language model (Fabian [0067]); and
training the large language model based on the predictive recommendation result and the reference recommendation result comprises: adjusting the adjustable parameter of the large language model, to minimize a difference between the predictive recommendation result and the reference recommendation result (Fabian [0067]).
Regarding claim 10. Fabian teaches a computer device, comprising one or more processors and a memory containing at least one computer program that, when being executed (computing device 1301 that is representative of any system or collection of systems … Computing device 1301 includes, but is not limited to, processing system 1302, storage system … Processing system 1302 loads and executes software 1305 from storage system … Storage system 1303 may comprise any computer readable storage media readable by processing system 1302 and capable of storing software, Fabian [0122] and Fig. 13), causes the one or more processors to perform:
although claim 10 directed to a device, it is similar in scope to claim 1. The method steps of claim 1 substantially encompass the device recited in claim 10. Therefore; claim 10 is rejected for at least the same reason as claim 1 above.
Regarding claims 11-18, the method steps of claims 2-9 substantially encompass the device recited in claims 11-18. Therefore, claims 11-18 are rejected for at least the same reason as claims 2-9 above.
Regarding claim 19. Fabian teaches a non-transitory computer-readable storage medium containing at least one computer program that, when being executed (computing device 1301 that is representative of any system or collection of systems … Computing device 1301 includes, but is not limited to, processing system 1302, storage system … Processing system 1302 loads and executes software 1305 from storage system … Storage system 1303 may comprise any computer readable storage media readable by processing system 1302 and capable of storing software, Fabian [0122] and Fig. 13), causes at least one processor to perform:
although claim 19 directed to a device, it is similar in scope to claim 1. The method steps of claim 1 substantially encompass the media recited in claim 19. Therefore; claim 19 is rejected for at least the same reason as claim 1 above.
Regarding claim 20, the method steps of claim 2 substantially encompass the medium recited in claim 20. Therefore, claim 20 is rejected for at least the same reason as claim 2 above.
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant’s disclosure.
Yan et al. (Chinese Patent Publication No. CN 116955817 A) discloses a content recommendation method, device, electronic device and storage medium, relating to the technical field of artificial intelligence, especially relating to the field of large model, LLM (Large Language Model), intelligent search, information flow, computer vision and so on. The specific implementation scheme is as follows: generating prompt information according to at least one of the recommended scene information aiming at the object, the recommended candidate set information and the object related information of the object; inputting the prompt information into the large language model to obtain the output information corresponding to the prompt information; and generating a recommended content for the object presentation according to the output information.
Gurgu et al. (US Patent Publication No. 2023/0297887A1) discloses systems and methods for generating training questions are disclosed. The method includes identifying a structure for generating an input; formulating the input according to the structure; providing the input to a first machine learning model; receiving an output from the first machine learning model based on the input; and training a second machine learning model based on the output. The first machine learning model may be a pre-trained generative language model.
Cossler et al. (US Patent Publication No. 2018/0182475 A1) discloses techniques are provided that involve employing artificial intelligence (AI) to facilitate reducing adverse outcomes associated with healthcare delivery. In one embodiment, a computer implemented method comprises monitoring live feedback received over a course of care of a patient, wherein the live feedback comprises physiological information regarding a physiological state of the patient. The method further comprises employing AI to identify, based on the live feedback information, an event or condition associated with the course of care of the patient that warrants clinical attention or a clinical response. The method further comprises generating a response, based on the identification of the event or condition, that facilitates reducing an adverse outcome of the course of care, wherein the response varies based on a type of the event or condition, and providing the response to a device associated with an entity involved with treating the patient in association with the course of care.
Prakash et al. (US Patent Publication No. 2016/0012194 A1) discloses a “behavioral support agent” (BSA) system that facilitates the collection of relevant health-related data on a continuous basis, integrates such data with pertinent personal and aggregate information, enables users to purchase (directly and indirectly) health-related goods and services, and provides credit, discounts and other economic benefits in connection with such purchases that are determined dynamically based upon the nature and extent of users' interaction with the system. The BSA system facilitates a dynamic feedback process by continually monitoring user interaction and medical and financial behavior, which results in dynamic adjustments to their credit levels and offers of discounts and other promotions, which in turn incentivizes users to continue participating in the process (thereby modifying their system interactions and behavior, and thus perpetuating this feedback loop). As a result, users are incentivized to actively participate in the process and thereby enhance their wellness while reducing healthcare costs.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HASANUL MOBIN whose telephone number is (571)270-1289. The examiner can normally be reached on 9AM to 6:00PM EST M-F.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached at 571-272-4085. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HASANUL MOBIN/
Primary Examiner, Art Unit 2168
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