Detail 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 .
Claims 1-17, 22-24 are pending and presented for examination.
Claims 18-21 were cancelled in preliminary amendments filed 07/02/2025.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/2/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-17, 22-24 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.
Claims 1, 16, 22, 9 and 10 recites the limitation “the prediction” in last line of the last limitation.
Claim 12 recites the limitation “the importance level is not satisfied”.
Claim 14 recites the limitation “the importance level is satisfied”.
There is insufficient antecedent basis for these limitations in the claims, thus rendering the scope of the claim unascertainable.
Dependent claims are rejected due to their dependency on the independent claims.
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 1-17, 22-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims 1, 16 and 22 recites:
“…the method comprising: identifying a relationship between…and determining whether a second input…”
The claim recites identifying a relationship between at least the first NWDAF consumer from a plurality of NWDAF consumers that respectively subscribe to the one or more NWDAFs that have respective machine learning, ML, models that have an overlap between a first input feature [Input] to the respective ML models and respectively have different outputs, wherein at least the first NWDAF consumer uses a first output from the one or more NWDAFs and determining whether a second input feature [second input] to (i) a respective ML model of the one or more NWDAFs or (ii) a new ML model of an unsubscribed NWDAF can make a positive contribution (i.e. improve or enhance) to the prediction of the output of at least the first NWDAF consumer.
The limitations of identifying a relationship between NWDAF consumers and one or more NWDAFs that provide ML models for analytics and determining whether a second input feature or a new ML model can make a positive contribution to the prediction of the ML model, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or using pen and paper) but for the recitation of generic computer components. That is, other than reciting “by a computing device comprising NWDAF that have respective ML models”, nothing in the claimed elements precludes the steps from practically being performed in mind. For example, but for the “by a computing device”, “identifying” in the context of the claim encompasses a user manually identifying relationships such as status of subscriptions of various consumers with various NDWAFs from the available data records and “determining” encompasses the user making a decision whether an input feature was important or not by analyzing or evaluating performance metrics of the ML models.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitations in the mind, but for the recitation of generic computer components, then it falls within the “Mental Processes” groupings of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a COMPUTING DEVICE to perform both the identifying and determining steps. The computing device in both steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of ranking information based on a determined amount of use) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. See MPEP 2106.05(f) and 2106.05(h).
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, the additional element of using a computing device to perform both steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claim 2 recites NWDAF consumer comprises a network function NF.
Claim 3 recites “performing at least one of requesting …”
Claim 4 recites “identifying at least first NWDAF…”
Claim 5 recites “wherein the identifying a relationship is based on a relationship graph …”
Claim 6 recites “performing an analysis …and measure an important level…”
Claim 7 recites “…constructing a graph of relationship…iterating over…”
Claim 8 recites “…building a second ML model…”
Claim 9 recites “…wherein the determining comprises performing an analysis that evaluates model performance…”
Claim 10-11 recites “…importance level is determined based on comparison of a threshold, wherein importance level is based on SHAP or LIME”.
Claim 12-15 recites depending on importance level, performing actions such as requesting or recommending or simply adding model to the NWDAFs and updating subscription data.
Based on broadest reasonable interpretation, nothing in dependent claims 2-15, 17 and 23-24 provides a practical application or significantly more and does not provide an improvement to the functioning of the computer or the computer technology. All the steps in the dependent claims are directed towards analyzing data, performing evaluations using metrics and recommending subscriptions to the consumers in the field of wireless communications. As such, these steps are directed towards mental processes and do not constitute significantly more than the recited judicial exception. Therefore, for the same reasons as set forth above in claim 1, dependent claims are deemed ineligible.
Claim Rejections - 35 USC § 103
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.
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.
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.
Claim(s) 1-5, 15-17, 22-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (hereinafter Lee, US 2022/0108214 A1) in view of LEE et al. (hereinafter LEE II, US 20230244995 A1).
As per claim 1, Lee discloses a computer-implemented method performed by a computing device comprising a network data analytics function, NWDAF, having a function to identify and modify dependencies between one or more NWDAFs and at least a first NWDAF consumer, the method comprising:
identifying a relationship (i.e. status, state, dependencies, etc.: fig. 7: ML model subscription is done and managed by ML Model provision service, fig. 17: ML model provider manages the subscription status of NWDAF model consumer) between at least the first NWDAF consumer from a plurality of NWDAF consumers that respectively subscribe to the one or more NWDAFs that have respective machine learning, ML, models (fig. 5: NWDAF device (consumer) subscribes to the NWDAF device (MTLF) and its ML models, fig. 7: ML model subscription is done and managed by ML Model provision service, [0116-0120, 0129]: search, select and subscribe to NWDAFs MTLFs that provisions ML models for analytics based on analytics filter information> here the relationship is subscription relationships between consumers and one or more discovered instances of NWDAFs, [0166-0169]: relationship can also be tracked and managed based “subscription status” – here ‘already subscribed ML model’ or ‘existing ML model available for subscription’, [0120]: when the NWDAF device is not capable of providing the requested data analytics, the NWDAF device may query an NRF device with a service area of the NF device to determine another target NWDAF device, [0129]) that have an overlap between a first input feature [Input] to the respective ML models and respectively have different outputs ([0212-0216]: ML models uses input from NF devices, DCCF device or OAM device required for ML model training. The collected data from these devices is used to train one or more ML models),
wherein at least the first NWDAF consumer uses a first output from the one or more NWDAFs ([0081]: the NWDAF provides analytics for the 5GC NF and the OAM, [0096]: the 5GC NF and the OAM, which are consumers, may determine how to use data analytics provided by the NWDAF device); and
determining whether a second input feature [second input] to (i) a respective ML model of the one or more NWDAFs or (ii) a new ML model of an unsubscribed NWDAF can make a positive contribution (i.e. improve or enhance) to the prediction of the output of at least the first NWDAF consumer ([0090]: In order to ensure accuracy of the analytics output, the NWDAF may detect and delete input data from abnormal UEs, then generate a new model without abnormal input data and then transmit new model > Here, the self-learning and enhancements and improvement nature of the ML model determines abnormal result, deletes abnormal data from the input and generates new ML model with new input without abnormal input data which improves accuracy of the model or makes a positive contribution, [0216], [0173]).
However, Lee does not teach wherein at least the first NWDAF consumer uses a first output from the one or more NWDAFs to output an action from at least the first NWDAF consumer.
Lee II, from the same field of endeavor, teaches wherein at least the first NWDAF consumer uses a first output from the one or more NWDAFs to output an action from at least the first NWDAF consumer (fig. 1, [0057-0058]: system operator receives analytics and determines appropriate action to perform to achieve optimal operation of entire system).
Therefore, it would have been obvious to a person of ordinary skilled in the art to modify before the effective filing date of the claimed invention to modify Lee in view of Lee II in order to use the inferred analytics (output) from the one or more NWDAFs to output an action from the first NWDAF consumer.
One of ordinary skilled in the art would have been motivated in order to achieve optimal operation of the entire system (Lee II [0058]).
As per claim 2, Lee-Lee II discloses the method of claim 1, wherein the at least a first NWDAF consumer comprises a network function, NF, or an NWDAF (Lee: fig. 3-5: NWDAF AnLF).
As per claim 3, Lee-Lee II discloses the method of claim 1, further comprising: performing (606) at least one of (i) requesting that at least the first NWDAF consumer subscribes to the one or more NWDAFs determined to have a second input feature that can make a positive contribution to the prediction of the output of at least the first NWDAF consumer, and (ii) requesting that at least the first NWDAF consumer subscribes to the new ML model of the unsubscribed NWDAF (Lee: [0120], [0129]: After determining capable NWDAFs during discovery, returning instances of one or more candidate NWDAF devices to the consumer for subscription, [0090]: transmitting new or updated ML model data to the consumer, [0165], [0172-0173]).
As per claim 4, Lee-Lee II discloses the method of claim 1, further comprising: identifying (600) at least the first NWDAF consumer that subscribes to the one or more NWDAFs that have respective ML models that have an overlap between the first input feature and respectively have different outputs (Lee: [0091]: the NWDAF device may notify the NF device which is a consumer of a decrease in accuracy of previous analytics due to noise data> Different ML models may share input data or data sources but may result in different outputs, some more accurate than others).
As per claim 5, Lee-Lee II discloses the method of claim 4 wherein the identifying (602) a relationship is based on a relationship graph of at least the first NWDAF consumer that subscribes to the one or more of the NWDAFs that respectively have the first input feature and respectively have different outputs (Lee: [0098]: subscription data, [0169]: subscription correlation ID is used to track subscription data or graph, i.e. relationships, [0173], [0196: correlation id is used to manage the subscription of the consumers with the NWDAFs).
As per claim 15, Lee-Lee II discloses the method of claims 3,further comprising: updating (610) the subscription of at least the first NWDAF consumer to add at least one of (i) the one or more NWDAFs and (ii) the new ML model of the unsubscribed NWDAF (Lee II: [0176-0183], [0185-0187]: subscription is modified based on updated ML model or new model).
As per claims 16-17 and 22-24, they do not teach or further define over the limitations in claims 1-5 and 15. Therefore, claims 16-17 and 22-24, they are rejected for the same reasons as set forth in claims 1-5 and 15.
Claim(s) 6, 10-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (hereinafter Lee, US 2022/0108214 A1) in view of LEE et al. (hereinafter LEE II, US 20230244995 A1) and further in view of SHARPE et al. (hereinafter SHARPE, US 2024/0037427 A1).
As per claim 6, Lee-LEE II discloses the method of claim 1, as set forth above.
However, Lee-LEE II does not teach performing an analysis that evaluates a second ML model that uses the first and second input features to associate whether the second input feature of a candidate NWDAF results in the prediction of the output of the action by at least the first NWDAF consumer and measuring an importance level of a contribution that the second input feature can make to the prediction of the output of at least the first NWDAF consumer.
NOTE: Lee teaches: [0099-0112]: ML model is evaluated for accuracy by monitoring errors between the analytics generated by the ML model and the actual event and send the monitoring results to the NWDAF including MTLF, then retrained using differences data and updated data [0155], [0151-0155]: Evaluate the impact of changes on the state of the cellular system on the performance of the ML model by receiving and considering updated data comprising differences between actual data/events and the training data sets, the impact of changes or updates, policy changes, and various unexpected events to improve ML model, [0272-0287]).
SHARPE, from the same field of endeavor (AI/ML), teaches performing an analysis that evaluates a second ML model that uses the first and second input features to associate whether the second input feature of a candidate NWDAF results in the prediction of the output of the action by at least the first NWDAF consumer and measuring an importance level of a contribution that the second input feature can make to the prediction of the output of at least the first NWDAF consumer (Abstract, fig. 2, fig. 4, [0014-0025], [0038-0049]).
Therefore, it would have been obvious to a person of ordinary skilled in the art to modify before the effective filing date of the claimed invention to modify Lee-Lee II in view of SHARPE in order to perform an analysis that evaluates importance of different input features and measures an importance level of a contribution that the different feature can make to the prediction.
One of ordinary skilled in the art would have been motivated in order to determine and indicate how influential a corresponding feature is for a decision made by a model (SHARPE: Abstract).
As per claim 10, Lee-LEE II-SHARPE discloses the method of claim 6, wherein the importance level is determined based on a comparison of a threshold value to the contribution that the second input feature can make to the prediction of the output of at least the first NWDAF consumer (SHARPE: [0019], [0043]). Same rationale as in claim 6 applies.
As per claim 11, Lee-Lee II-SHARPE discloses the method of claim 6,wherein the importance level is based on one of a shapley additive explanations, SHAP, and a local interpretable model-agnostic explanation, LIME, analysis that measures and ranks an importance of the second input feature (SHARPE: [0015], [0024]: For example, the importance metrics may be Shapley Additive exPlanations (SHAP) values, local interpretable machine learning (LIME) values, or may be generated using layer-wise relevance propagation techniques, generalized additive model techniques, or a variety of other XAI techniques).
As per claim 12, Lee-Lee II discloses the method of claims 3, as set forth above, further comprising requesting that at least the first NWDAF consumer subscribe to the new ML model of the unsubscribed NWDAF (Lee: [0120], [0129]: After determining capable NWDAFs during discovery, returning instances of one or more candidate NWDAF devices to the consumer for subscription, [0090]: transmitting new or updated ML model data to the consumer, [0165], [0172-0173]).
However, Lee-LEE II does not teach determining when the importance level is not satisfied.
SHARPE teaches determining when the importance level is not satisfied, not using the ML model (SHARPE: [0019], [0043]).
Therefore, it would have been obvious to a person of ordinary skilled in the art to modify before the effective filing date of the claimed invention to modify Lee-Lee II in view of SHARPE in order to perform an analysis that evaluates importance of different input features and measures an importance level of a contribution that the different feature can make to the prediction and in an event when the importance metric is not satisfied, recommend subscriber to subscribe to new ML model with better performance.
One of ordinary skilled in the art would have been motivated in order to recommend new or updated ML models to the consumer or subscriber of analytics.
As per claim 13, Lee-Lee II-SHARPE discloses the method of Claim 12, further comprising: adding the second machine learning model to the candidate NWDAF (LEE II: [0176-0177], [185-187], [0289]).
As per claim 14, Lee-Lee II discloses the method of claims 3, as set forth above, further comprising the requesting comprises that at least the first NWDAF consumer subscribes to the one or more NWDAFs (Lee: [0120], [0129]: After determining capable NWDAFs during discovery, returning instances of one or more candidate NWDAF devices to the consumer for subscription, [0090]: transmitting new or updated ML model data to the consumer, [0165], [0172-0173]).
However, Lee-LEE II does not teach determining when the importance level is satisfied.
SHARPE teaches determining when the importance level is satisfied, using the ML model with the important feature set (SHARPE: [0019], [0043]).
Therefore, it would have been obvious to a person of ordinary skilled in the art to modify before the effective filing date of the claimed invention to modify Lee-Lee II in view of SHARPE in order to perform an analysis that evaluates importance of different input features and measures an importance level of a contribution that the different feature can make to the prediction and in an event when the importance metric is satisfied, recommend subscriber to subscribe to one of the existing ML model with better performance.
One of ordinary skilled in the art would have been motivated in order to recommend new or updated ML models to the consumer or subscriber of analytics.
Allowable Subject Matter
Claims 7-9 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims AND if 35 USC 101 rejection is overcome.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2023/0060071 A1: Inference-Aware ML Model Provisioning.
US 12/349,000 B2: Mechanism for Enabling Custom Analytics (fig. 2).
US 2022/0180209 A1: Fig. 6: Analyzing impact of using one or more types of data for AI model training and calculating total benefit coefficient of adding one type of data for prediction accuracy.
US 2025/0056297 A1: Data Reporting Method.
US 12,346,781 B2: Automated Concept Drift detection in Live Machine Learning Systems for Machine Learning Model Updating (SHAP values may be used to measure impact, importance, and/or relevance of each feature to the corresponding ML model when classifying input feature data. For example, each feature may have a corresponding contribution to a final decision or classification of the input feature data, and therefore SHAP values estimate the strength of each contribution relative to the contributions of other features).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAMAL B DIVECHA whose telephone number is (571)272-5863. The examiner can normally be reached IFP Normal Hours M-F: 8am-4.30pm 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, COLLEEN FAUZ can be reached at 5712721667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
KAMAL B. DIVECHA
Primary Patent Examiner
Art Unit 2453
/KAMAL B DIVECHA/Supervisory Patent Examiner, Art Unit 2453
Prosecution Insights