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 .
Response to Amendment
The amendment filed October 7, 2025 has been entered. Claims 32, 36-39, 43-46, and 50-51 remain pending in the application. Applicant’s amendments to the Claims have overcome each and every 101 rejections previously set forth in the Non-Final Office Action mailed July 7, 2025.
Claim Objections
Claims 32-51 are objected to because of the following informalities:
Claims 32, 39, and 46 are objected because of capital letters within body of claims, as in “Network Repository Function”, etc. Capital letter should only be used for first letter of claim or abbreviation.
In claim 32, line 12, “from, an analytics function” should read --from an analytics function--.
In claim 32, line 49; and claims 39 and 46, corresponding lines, “the subscription; and” should read --the subscription;--.
In claim 32, line 52; and claims 39 and 46, corresponding lines, “the ratings format” should read --a ratings format--.
Claims 36-38, 43-45, and 50-51 are further objected due to their dependency.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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) 32-51 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (KR 102820764 B1; refer to the English Translation) in view of Saket (US 20200382301 A1).
Regarding Claims 32, 39, and 46, Lee teaches An apparatus for providing a trusted [rating] function in a communication network system, the apparatus comprising: at least one processor; and at least one memory including one or more instructions, the at least one memory and the one or more instructions being configured to, with the at least one processor, cause the apparatus at least to (Lee: Page 34, lines 18-24; Page 36, lines 21-34): A system comprising: an apparatus for providing a trusted [rating] function in a communication network system, the apparatus comprising: at least one processor; and at least one non-transitory computer-readable medium comprising computer-executable instructions that, when executed by the at least one processor, cause the at least one processor to perform the following operations (Lee: Page 34, lines 18-24; Page 36, lines 21-34): A method performed by an apparatus for providing a trusted [rating] function in a communication network system, the method comprising (Lee: Page 34, lines 18-24):
operate as a trusted [rating] network function (TRF) implemented as a network function (NF) of the communication network system, the TRF being integrated with at least one of a Network Repository Function (NRF), a Network Data Analytics Function (NWDAF), or a Unified Data Management (UDM) node (Lee: Page 6, lines 1-13; Page 20, lines 15-23; Page 23, lines 14-22; Page 34, line 18 ~ Page 35, line 16; Page 36, lines 21-34); receive, via a service-based interface (SBI) using an NtrfAnalyticsServiceConsumed service from, an analytics function, a first verification information comprising: an analytics function identifier for the analytics function, a service identifier for an analytics service provided by the analytics function, a service consumer identifier of an analytics consumer, a first identification of a service producer, and … (Lee: Page 6, lines 1-13; Page 20, lines 15-23; Page 23, lines 14-22; Page 13, lines 14-27 teach(es) each instance of an NWDAF device can provide the following information: List of supported Analytic IDs (per supported service) when registering with NRF along with other NRF registration elements in the NF profile. An NF device that requires discovery of an NWDAF instance that provides support for some specific service operation for a specific type of analysis can query the NRF for an NWDAF device that supports the required service operation and the required analysis ID. The NWDAF device performing MTLF can register machine learning model provisioning service and training service (i.e., Nnwdaf_MLModelProvision, Nnwdaf_MLModelInfo, Nnwdaf_MLModelUpdate, Nnwdaf_MLModelTraining, Nnwdaf_MLModelTrainingInfo) when machine learning model provisioning and training are possible for the Analytic ID; When the call of the service operation of the NWDAF device is to modify or cancel a subscription, the NWDAF device (501) may include an identifier to be modified (subscription correlation ID) in the call of the subscription service operation (Nnwdaf_MLModelProvision_Subscribe) for provisioning a machine learning model);
receive, via an NtrfAnalyticsRating service from the analytics consumer, [rating] information and a second verification information related to the analytics consumer, wherein the [rating] information comprises information related to a performance of the analytics service and the AI and ML model, wherein the second verification information comprises a second identification of the analytics consumer and the [token] (Lee: Page 6, lines 1-13; Page 20, lines 15-23; Page 23, lines 14-22; Page 34, lines 18-24; Page 36, lines 21-34, as stated above; Lee: Page 13, lines 14-27 further teach(es) When the call of the service operation of the NWDAF device is to modify or cancel a subscription, the NWDAF device (501) may include an identifier to be modified (subscription correlation ID) in the call of the subscription service operation (Nnwdaf_MLModelProvision_Subscribe) for provisioning a machine learning model); …; …; and
store in response to accepting the [rating] information, in a structured [ratings] database having a schema indexed by service identifier, analytics identifier, and consumer identifier, the [rating] information for the analytics service and the AI and ML model (Lee: Page 34, line 35 ~Page 35, line 6; Page 13, lines 14-27 teach(es) if a feedback indication is enabled in a response service task for an analysis request of network data including the analysis information on the first network data or a notification service task for an analysis subscription of the network data, the consumer network function device can provide feedback on the analysis information on the first network data to the network analysis function device; When the call of the service operation of the NWDAF device is to modify or cancel a subscription, the NWDAF device (501) may include an identifier to be modified (subscription correlation ID) in the call of the subscription service operation (Nnwdaf_MLModelProvision_Subscribe) for provisioning a machine learning model), the [ratings] format comprising: a time when the analytics consumer [rated] the analytics service, the service identifier identifying the analytics service provided by the analytics function, a version of the analytics service, an analytics identifier identifying analytics associated with the analytics service, a [rating] of the analytics service, the service consumer identifier identifying the service consumer that is [rating] the analytics service, an analytics function identifier identifying the analytics function, a version of the analytics function, issue information related to potential problems encountered when relying on the analytics produced by the analytics service, geographical area information related to one or more areas of interest for which the analytics function provided the analytics service, environment condition information related to conditions of the network communication system when the analytics function provided the analytics service, user condition information related to a state of a user involved in the analytics, and service condition information related to an adopted service (Lee: Page 10, line 23 ~ Page 11, line 2; Page 20, lines 3-36; Page 23, lines 3-22; Page 34, line 18 ~ Page 35, line 16; Page 13, lines 14-27).
However, Lee does not explicitly teach rating function, rating, rating information, ratings format, a cryptographically signed token generated by the analytics function for the analytics consumer to rate the analytics service and an artificial intelligence (AD and machine learning (ML) model, validate the cryptographically signed token to authenticate consumption of the analytics service and to prevent unauthorized or fabricated rating submissions, wherein the token is valid for a duration of a subscription to the rated service, and wherein the apparatus automatically invalidates the token upon termination of the subscription, accept the rating information based on: determining that the token within the first verification information matches the token within the second verification information, and determining that the first identification within the first verification information is different from the second identification within the second verification information, wherein the determining prevents a producer network function from rating its own analytics service, thereby improving fairness and reliability of ratings across multiple vendors, and receive a rating discovery request; discover, from the stored rating information and based on the rating discovery request, ratings of at least one service identified by a service identifier and provided by one or more analytics functions; and generate a rating discovery response including the discovered ratings to enable automated benchmarking and model re-training when a stored rating falls below a defined threshold.
Saket from same or similar field of endeavor teaches rating function, rating, rating information, ratings format, a cryptographically signed token generated by the analytics function for the analytics consumer to rate the analytics service and an artificial intelligence (AD and machine learning (ML) model (Saket: Paragraph(s) 0040-0041, 0045-0046, 0160-0165), accept the rating information based on: determining that the token within the first verification information matches the token within the second verification information, and determining that the first identification within the first verification information is different from the second identification within the second verification information, wherein the determining prevents a producer network function from rating its own analytics service, thereby improving fairness and reliability of ratings across multiple vendors (Saket: Paragraph(s) 0086, 0040-0041, 0045, 0048, 0061, 0082, 0097 teach(es) an executing client receives a one-time token or certificate from an authorizing client. The one-time token or certificate allows the executing client to submit a rating anonymously one time, where the anonymous rating is related to the authorizing client. The token or coin could be some standard cryptographically verifiable one-time usable certificate. In one embodiment, the one-time token or certificate is received by the executing client after a good or service has been received), validate the cryptographically signed token to authenticate consumption of the analytics service and to prevent unauthorized or fabricated rating submissions, wherein the token is valid for a duration of a subscription to the rated service, and wherein the apparatus automatically invalidates the token upon termination of the subscription (Saket: Paragraph(s) 0040-0041, 0045, 0048, 0061, 0082 teach(es) A buyer should be able to submit only one rating per business transaction (not to be confused with a smart contract blockchain transaction) he has made with the supplier. This is guaranteed by the supplier providing a one-time use token for every business transaction (e.g. successful delivery of goods). The buyer should be able to give the rating in anonymous manner (i.e. when a buyer submits the rating as part of a smart contract transaction, so that the rating is added to a shared ledger)), and receive a rating discovery request; discover, from the stored rating information and based on the rating discovery request, ratings of at least one service identified by a service identifier and provided by one or more analytics functions; and generate a rating discovery response including the discovered ratings to enable automated benchmarking and model re-training when a stored rating falls below a defined threshold (Saket: Paragraph(s) 0040-0041, 0044, 0160-0165 teach(es) to provide authorized ratings, maintain buyer anonymity, and be verifiable; Machine learning relies on vast quantities of historical data (or training data) to build predictive models for accurate prediction on new data).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Lee to incorporate the teachings of Saket for rating function, rating, rating information, ratings format, a cryptographically signed token generated by the analytics function for the analytics consumer to rate the analytics service and an artificial intelligence (AD and machine learning (ML) model, validate the cryptographically signed token to authenticate consumption of the analytics service and to prevent unauthorized or fabricated rating submissions, wherein the token is valid for a duration of a subscription to the rated service, and wherein the apparatus automatically invalidates the token upon termination of the subscription, accept the rating information based on: determining that the token within the first verification information matches the token within the second verification information, and determining that the first identification within the first verification information is different from the second identification within the second verification information, wherein the determining prevents a producer network function from rating its own analytics service, thereby improving fairness and reliability of ratings across multiple vendors, and receive a rating discovery request; discover, from the stored rating information and based on the rating discovery request, ratings of at least one service identified by a service identifier and provided by one or more analytics functions; and generate a rating discovery response including the discovered ratings to enable automated benchmarking and model re-training when a stored rating falls below a defined threshold.
There is motivation to combine Saket into Lee because Saket’s teachings of one-time token or certificate allowing the executing client to submit a rating anonymously one time would facilitate to make the ratings anonymous and verifiable (Saket: Paragraph(s) 0086).
Regarding Claims 36 and 43, the combination of Lee and Saket teaches all the limitations of claims 32 and 41 above; and Lee further teaches wherein the rating discovery response further includes at least one of an identifier list identifying the one or more analytics functions, and an identifier list identifying models that produce an analytics associated with the service (Lee: Page 6, lines 1-13; Page 20, lines 15-23; Page 23, lines 14-22, as stated above with respect to claim 32).
Regarding Claims 37, 44, and 50, the combination of Lee and Saket teaches all the limitations of claims 36, 43, and 46 above; and Lee further teaches wherein the analytics function identifier identifies, out of a plurality of analytics functions, a certain analytics function using a certain model or providing the service for producing analytics (Lee: Page 4, line 29 ~ Page 5, line 2; Page 6, lines 26-32).
Regarding Claims 38 and 45, the combination of Lee and Saket teaches all the limitations of claims 37 and 44 above; and Lee further teaches wherein the analytics service comprises the analytics service and the AI and ML model, and the service identifier identifies the analytics service and the AI and ML model (Lee: Page 12, lines 26-36; Page 13, lines 24-27; Page 20, lines 2-8).
Regarding Claim 51, the combination of Lee and Saket teaches all the limitations of claim 50 above; and the combination further teaches wherein the analytics function identifier identifies, out of a plurality of analytics functions, a certain analytics function using a certain model or providing the service for producing analytics, and wherein the analytics service comprises the analytics service and the AI and ML model, and the service identifier identifies the analytics service and the AI and ML model, as stated above with respect to claims 37-38.
Response to Arguments
Applicant's arguments filed October 7, 2025 have been fully considered but they are not persuasive.
Regarding applicant’s argument under Claim Rejections - 35 USC § 103 that “Lee does not disclose that rating information is subject to verification, nor that such rating information is accepted only when (i) token in first verification information matches corresponding token in second verification information, and (ii) the analytics function identifier (identifying the service producer) is different from the service consumer identifier (identifying the consumer),” examiner respectfully argues that Saket reference teaches the features as stated above with respect to the 103 rejections (Saket: Paragraph(s) 0086, 0040-0041, 0045, 0048, 0061, 0082, 0097).
Regarding applicant’s argument that “Lee does not disclose or suggest that the NWDAF, or any other network function, performs operations discover from the stored rating information on the rating discovery request, ratings of at least one service identified by a service identifier and provided by one or more analytics functions,” examiner respectfully argues that Saket reference teaches the features as stated above with respect to the 103 rejections (Saket: Paragraph(s) 0040-0041, 0044, 0160-0165).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Vieri (US 20230319060 A1) teaches Portable Trust Rating Method And System, including rating, token, and verification.
Groarke (US 20180285944 A1) teaches Methods And Systems For Use In Providing Spend Profiles For Reviewers, In Response To Requests For Validation Of Reviews Submitted By The Reviewers, including rating and token.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CLAY LEE whose telephone number is (571)272-3309. The examiner can normally be reached Monday-Friday 8-5pm 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, Neha Patel can be reached at (571)270-1492. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CLAY C LEE/Primary Examiner, Art Unit 3699