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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 3/24/26 has been entered.
Priority
This application claims the following priority:
This application is a CON of PCT/KR2024/010097 07/15/2024
IDS
The following IDS(s) have been considered by the Examiner: 7/23/2024, 2/13/2025
Examiner Note
Claims 2-3, 6, 11-12, 15, and 20 have been cancelled.
Claims 1, 4-5, 7-10, 13-14, and 16-19 are currently pending in the instant application.
Claims 1, 4-5, 7-10, 13-14, and 16-19 are rejected as set forth below.
Response to Arguments / Amendments
The arguments presented by the applicant in the response filed on 1/30/2026 are addressed below in the body of the rejections due to the fact that they are largely directed to the newly added subject matter.
The rejection of claims 1, 3-10, and 12-19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement has been withdrawn in view of applicant’s amendments.
The rejection of claims 1, 3-10, and 12-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention, has been withdrawn in view of applicant’s amendments.
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, 4-5, 7-10, 13-14, and 16-19 are rejected under 35 U.S.C. 101 because the claims are directed to an abstract idea without significantly more.
Step 1 (Statutory Categories)
Claim(s) 1, 4-5, 7-9: method/process.
Claim(s) 10, 13-14, and 16-18: system/machine.
Claim(s) 19: article of manufacturer
Accordingly, the pending claims pass step 1.
Step 2A, Prong 1 (Does the claim recite an abstract idea, law of nature, or natural phenomena?)
As seen in MPEP 2106.04(a) Abstract Ideas [R-07.2022], the enumerated groupings of abstract ideas are as follows:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Regarding independent claim(s) 1 and 10, the claims are directed to an abstract idea. Representative claim 1 recites:
A method performed by an electronic device, the method comprising:
receiving,
processing,
providing,
wherein the feedback is at least one of explicit or implicit,
wherein the recommendation is generated
wherein the recommendation system is
wherein the
The claim as a whole is directed to creating a recommendation system based on user preference, which is an abstract idea because it is a method of organizing human activity consistent with MPEP 2106.04(a) Abstract Ideas [R-07.2022].
**Strikethrough(s) above are used to designate aspects of the claim(s) that are being interpreted as additional elements and not part of the claimed abstraction.
Step 2A, Prong 2 (Does the claim recite additional elements that integrates the recited judicial exception into a practical application?)
The Supreme Court and Federal Circuit have identified a number of considerations as relevant to the evaluation of whether the claimed additional elements demonstrate that a claim is directed to patent-eligible subject matter.
This judicial exception is not integrated into a practical application. The additional elements designated above are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components or merely uses a computer as a tool to perform an abstract idea. 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 noted use of machine learning and reinforcement learning can be construed as a mathematical prediction, which falls within the mathematical concepts grouping of abstract ideas. As explained in the MPEP, when a claim recites multiple abstract ideas that fall in the same or different groupings, examiners should consider the limitations together as a single abstract idea, rather than as a plurality of separate abstract ideas to be analyzed individually. See MPEP 2106.04, subsection II.B.
The Examiner notes, the applicant argues that the present claims are directed to a recommendation system that is adaptively trained based on received feedback while explicitly maintaining stability of the system by limiting fluctuations in the feedback.
Response to applicants 101 arguments:
The attorney argues, that the limitations: receiving user feedback, processing the received feedback…, and using the numerical reward value as an input to reinforcement learning to update a recommendation system, recite to a specific technical configuration.
The Examiner disagrees, the claims fail short of reciting how the alleged technical improvement is achieved. The claim as written is results oriented towards providing a recommendation, receiving feedback, and converting the feedback into a reward. The claims appear to improve the quality of recommendations, not improving the functioning of the computer or another technology. Improving an abstraction, is not enough for eligibility, because the claims do not recite a specific technical mechanism that includes the method itself.
The claims are directed to generating and updating recommendations for a user based on profile and feedback information, which constitutes an abstract idea, including certain methods of organizing human activity and/or mathematical concepts. Accordingly, the claim does not integrate the judicial exception into a practical application and does not recite significantly more.
Accordingly, the § 101 rejection is maintained on the present record. The Office would reconsider if the claims are amended to recite a concrete technical mechanism, supported by the specification as originally filed, that effects the asserted stability, and such mechanisms yield an improvement to computer functionality or another technology consistent with the 2019 PEG.
Step 2B (Do the claim recite additional elements that amount to an inventive concept (aka “significantly more”) then the recited judicial exception?)
Regarding independent claim(s) 1, 10, and 19, the claims are directed to an abstract idea without significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) (individually and in combination) are merely being used to apply the abstract idea using generic computer components or merely uses a computer as a tool to perform an abstract idea. In conclusion, merely “applying” the exception using generic computer components cannot provide an inventive concept. Therefor the independent claims are not patent eligible under 35 U.S.C. 101.
Dependent claims
Subsequent dependent claims recite, inter alia, the abstract idea of:
wherein the recommendation system is trained by: adapting to a dynamic of user behavior or interest; integrating cross-domain recommendations between services or categories of items in a same service using a deep learning model; and optimizing a cross-domain model.
wherein the receiving of the feedback comprises receiving at least one of a transaction history, a satisfaction survey, a textual user review, or a numeral rating.
wherein the providing of the updated recommendation comprises: processing the received feedback; and generating an evaluation of the feedback from the user.
wherein the training of the recommendation system comprises: applying the received feedback as an input to a training process; and combining a result of the training process with a global reward for machine learning.
wherein the training process comprises: applying feedback-reward processing with the machine learning; and updating a reward function to reflect a current business environment.
wherein the reward function is updated based on at least one of retention, frequency, and monetary.
The abstractions found in these dependent claims merely further limit the abstract idea noted in the independent claims.
Accordingly claims 1, 4-5, 7-10, 13-14, and 16-19 are thereby considered to be ineligible.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 4-5, 7-10, 13-14, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta US2021/0142387 A1 in view of Kumar US2020/0184537 in further view of Krishnan US2020/0151615.
Referring to claim 1. Gupta discloses a method performed by an electronic device, the method comprising:
providing, by the electronic device, a recommendation to a user based on a profile of the user (Gupta paragraph 0022: “In one implementation, the user 202 can choose to provide user information 206 regarding the user 202 to the content personalization system 200. The feature generator 204 can use the user information 206 to generate a user vector. Alternatively or additionally, the user information 206 may be extracted or obtained from a user database. In some implementations, the user information 206 can exclude personally identifiable information or any other privacy information. In some implementations, the user information 206 can be maintained in a privacy preserving manner.”);
receiving, by the electronic device, feedback based on the recommendation (Gupta paragraph 0034: “The content personalization system 200 may include the reward calculator 216 that can calculate a reward value for the user action 214. The reward value may be modeled by the reward calculator 216 and then provided as feedback to the content recommender 210 regarding its selection of the personalized contents 102.”);
processing, by the electronic device, the received feedback to generate a numerical reward value (Gupta paragraph 0035: “In some implementations, the reinforcement learning model 212 can learn to better identify the personalized contents 102 that have the highest optimal matching for the user 202 through the reward calculator 216. When the user 202, for example, clicks on a content, the reward calculator 216 may assign a reward value to the click, and thus the reinforcement learning model 212 can learn that the selection of the content is well received or not well received by the user 202. The reinforcement learning model 212 can then adjust the reinforcement learning algorithm to improve future selections of the personalized contents 102 for the user 202 or for other users based on machine learning. The signal that informs the reinforcement learning model 212 whether a particular content was a good recommendation or not may be the reward value calculated by the reward calculator 216. The reward calculator 216 may use one or more reward formulas, which are described in detail below, to calculate reward values associated with user actions 214.”); and
providing, by the electronic device, an updated recommendation based on the numerical reward value (Gupta paragraph 0035: “The signal that informs the reinforcement learning model 212 whether a particular content was a good recommendation or not may be the reward value calculated by the reward calculator 216.”)
received feedback, wherein the feedback is at least one of explicit or implicit (Gupta 0045: “This data can together be used to build heuristics and/or machine learning (ML) models to derive how much value is given to each click in the gaming ecosystem. Hence, when the user 202 clicks on a content, the reward value may be an indicator on a multivalued scale (rather than a binary indicator of good or bad recommendation) of how well the predefined business goals are being promoted.”);
wherein the recommendation is generated by a recommendation system (Gupta paragraph 0076),
wherein the recommendation system is trained by (Gupta paragraph 0076):
applying machine learning over a pre-trained model (Gupta paragraph 0076);
wherein the numerical reward value is used by the machine learning to update the recommendation system (Gupta paragraph 0076), and
wherein the machine learning comprises reinforcement learning including a proximal policy optimization.
Gupta does not disclose combining the machine learning with an application of at least one cross-domain recommendation.
Kumar in a similar field of endeavor discloses this feature ([0002] The present disclosure generally relates to communication devices for generating recommendations, and more specifically, to communication devices that provide user specific recommendations using cross-domain collaborative filtering.).
This noted feature in Kumar is applicable to the method and system of Gupta as both prior art references share characteristics and capabilities, namely, they are directed to recommendations that are tailored to a user’s interest. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kimble as described above because it is beneficial to have a system which can consider various aspects of a user before making a recommendation (Kumar [0018]).
Gupta does not disclose combining wherein the machine learning comprises reinforcement learning including a proximal policy optimization (PPO).
Krishnan in a similar field of endeavor discloses this feature ([0040]).
This noted feature in Krishnan is applicable to the method and system of Gupta as both prior art references share characteristics and capabilities, namely, they are directed to recommendations that are tailored to a user’s interest. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Gupta as described above because it is beneficial to have a system which can use machine-learning models to generate recommendations for forming a process flow (Kristnan [0003]).
Referring to claim 4. Kumar further discloses a method, wherein the recommendation system is trained by: adapting to a dynamic of user behavior or interest; integrating cross-domain recommendations between services or categories of items in a same service using a deep learning model; and optimizing a cross-domain model (Kumar 0021: “As an example of characterization information which may be used for making a recommendation can include donation history, which could be used and consider for users who have made a previous donation. As another example, characterization information that may be used and considered for a user without donation history, can include user profile information, contacts, merchants, entities, etc. with whom the user may have transacted with, and purchase history and charities associated with the locations where purchases were made. Still as another example, other general characterization information which may be used for making a recommendation may include charitable cause popularity, trends, and other relational information.”).
This noted feature in Kumar is applicable to the method and system of Gupta as both prior art references share characteristics and capabilities, namely, they are directed to recommendations that are tailored to a user’s interest. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kimble as described above because it is beneficial to have a system which can consider various aspects of a user before making a recommendation (Kumar [0018]).
Referring to claim 5. Gupta discloses a method of claim 1, wherein the receiving of the feedback comprises receiving at least one of a transaction history, a satisfaction survey, a textual user review, or a numeral rating ([0021]).
Referring to claim 7. Gupta discloses a method of claim 1, wherein the recommendation system is trained by: applying the received feedback as an input to a training process; and combining a result of the training process with a global reward for machine learning ([0037]).
Referring to claim 8. Gupta discloses a method of claim 7, wherein the training process comprises: applying feedback-reward processing with the machine learning; and updating a reward function to reflect a current business environment ([0037]).
Referring to claim 9. Gupta discloses a method of claim 8, wherein the reward function is updated based on at least one of retention, frequency, and monetary ([Abstract]).
Claims 10 and 12-19 are rejected under the same rationale as set forth above.
Conclusion
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW S GART whose telephone number is (571)272-3955. The examiner can normally be reached M-F 8:30AM-5:00PM.
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/MATTHEW S GART/ Supervisory Patent Examiner, Art Unit 3696
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