DETAILED ACTION
Claims 1, 8 and 15 are amended. Claims 1, 6-8, 13-15 and 20 are pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/07/2025 has been entered.
Response to Arguments
Applicant’s arguments filed on 10/07/2025 have been fully considered.
In reference to Applicant’s arguments:
- Claim rejections under 35 USC 101.
Examiner’s response:
Applicant’s arguments have been fully considered. Rejections are withdrawn in view of amendments and applicant’s arguments.
In reference to Applicant’s arguments:
- Claim rejections under 35 USC 103.
Examiner’s response:
Applicant’s arguments are fully considered, but are moot in view of new grounds of rejection.
Claim Rejection - 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.
Claims 1, 8 and 15 are rejected under 35 U.S.C. 103 as being unpatentable in view of Onvlee, et al. (US 20220197264 A1) (hereinafter referred to as “Onvlee”) in view of Talagala, et al., (U.S. Publication No. 2019/0108417) (hereinafter referred to as “Talagala”).
Regarding Claim 1, Onvlee recites “A computer-implemented method for federated learning model lineage, the method comprising:” (Onvlee at 0065: With the present systems and methods, the service provider may repeat the described process(es) until a model has converged, for example (e.g., a ‘federated learning’ approach).)
“receiving, by a model lineage system, an initial model, from an aggregator in a federated learning system, wherein the aggregator starts a round of training the initial model;” (Onvlee at [0011]: “The method comprises providing an initial prediction model comprising a plurality of model parameters to one or more remote locations”. Further at [0018]: “the generating and training of the initial prediction model is performed by a service provider; providing the initial prediction model to the one or more remote locations is performed by the service provider”. Further at [0086]: “Resulting locally updated model parameters (e.g., weights, biases, and/or other model parameters) are returned to the central (e.g. service provider) system, where some form of aggregation (e.g., averaging and/or other aggregation) takes place”. Therefore, this service provider, which initially trains and sends the initial trained model to the remote location and then recovers back from the remote locations any updates to aggregate them, is interpreted as the claimed aggregator);
“dispatching, by the model lineage system, the initial model to workers in the federated learning system;” (Onvlee at 0011: “The method comprises providing an initial prediction model comprising a plurality of model parameters to one or more remote locations”. Further at [0018]: “the generating and training of the initial prediction model is performed by a service provider; providing the initial prediction model to the one or more remote locations is performed by the service provider”. Therefore, the remote locations are interpreted as the workers);
“recording, by the model lineage system, the initial model in a lineage database;” (Onvlee at [0089]: Providing the initial prediction model comprises transmitting and/or otherwise distributing the initial prediction model to the one or more remote locations. Transmitting and/or otherwise distributing the initial predication model may include emailing, texting, and/or other electronic messaging of the model, providing the model via a website, storing and/or providing access to the model via cloud based storage media, storing the model on non-transitory storage media and physically transferring the non-transitory storage media, and/or other transmission or distribution. See also Onvlee at 0092, 0097, 0105. Therefore, this non-transitory storage media is interpreted as a lineage database)
“receiving, by the model lineage system, updates from the workers which train the initial model locally; and” (Onvlee at 0086: “Resulting locally updated model parameters (e.g., weights, biases, and/or other model parameters) are returned to the central (e.g. service provider) system, where some form of aggregation (e.g., averaging and/or other aggregation) takes place”);
“forwarding, by the model lineage system, the updates to the aggregator” (Onvlee at 0086: “Resulting locally updated model parameters (e.g., weights, biases, and/or other model parameters) are returned to the central (e.g. service provider) system, where some form of aggregation (e.g., averaging and/or other aggregation) takes place”);
“training, by the workers, the initial model locally, in response to receiving from the model lineage system the initial model” (Onvlee at 0086: “Resulting locally updated model parameters (e.g., weights, biases, and/or other model parameters) are returned to the central (e.g. service provider) system, where some form of aggregation (e.g., averaging and/or other aggregation) takes place”);
“dispatching, by the workers, the updates to the model lineage system, in response to completion of local training of the initial model;” (Onvlee at 0018: the remote locations and the local data are associated with customers of the service provider; the updated model parameters are received from each of the one or more remote locations by the service provider”. Further at 0086: “Resulting locally updated model parameters (e.g., weights, biases, and/or other model parameters) are returned to the central (e.g. service provider) system, where some form of aggregation (e.g., averaging and/or other aggregation) takes place”);
“receiving, by the aggregator, the updates from the model lineage system; and” (Onvlee at 0018: …the updated model parameters are received from each of the one or more remote locations by the service provider”. Further at 0086: “Resulting locally updated model parameters (e.g., weights, biases, and/or other model parameters) are returned to the central (e.g. service provider) system, where some form of aggregation (e.g., averaging and/or other aggregation) takes place”);
“producing a new model, by the aggregator, using the updates,” (Onvlee at 0018: … the aggregated updated model parameters received from each of the one or more remote locations are determined by the service provider; and the adjusted prediction model is determined by the service provider based on the aggregated updated model parameters. See also Onvlee at 0092, 0097, 0105. Therefore the adjusted precision model is a new model produced using updates from remote locations, i.e., workers, by the service provider, i.e., the aggregator)
However, Onvlee does not explicitly recite:
“dispatching, by the model lineage system, the initial model to workers in the federated learning system, wherein the model lineage system provides routing for multi-party communications between the workers and the aggregator”
“recording, by the model lineage system, the initial model in a lineage database;”
“recording, by the model lineage system, the updates in the lineage database”
“and wherein the model lineage system is integrated into the federated learning system”.
Talagala teaches, in an analogous system,
“dispatching, by the model lineage system, the initial model to workers in the federated learning system, wherein the model lineage system provides routing for multi-party communications between the workers and the aggregator” (see Talagala at [0084]: “In certain embodiments, the history module 402 tracks the lineage, e.g., the interdependencies between each machine learning pipeline 202, 204, 206a-c, including the data input into and output from the machine learning pipelines 202, 204, 206a-c, events that a machine learning pipeline 202, 204, 206a-c generates, messages sent to/from machine learning pipelines 202, 204, 206a-c, and/or user input provided to the machine learning pipelines 202, 204, 206-c. In this manner, the overall behavior or operation of a logical machine learning layer 200, 225, 250, can be determined”. Therefore, this history module is interpreted as the model lineage system, as it provides all the communications in the machine learning pipeline);
“recording, by the model lineage system, the initial model in a lineage database” (see Talagala at [0083]: “The history module 402, in one embodiment, is configured to track a version history for each of the machine learning pipelines 202, 204, 206a-c such that changes to the machine learning pipelines can be rolled-back to a previous version”. Further at [0084]: “In certain embodiments, the history module 402 tracks the lineage, e.g., the interdependencies between each machine learning pipeline 202, 204, 206a-c, including the data input into and output from the machine learning pipelines 202, 204, 206a-c”. Therefore, this tracking of all the history in the learning pipeline and checkpoints for previous versions (including the initial model) is interpreted as the recording in a lineage database);
“recording, by the model lineage system, the updates in the lineage database” (see Talagala at [0084]: “In certain embodiments, the history module 402 tracks the lineage, e.g., the interdependencies between each machine learning pipeline 202, 204, 206a-c, including the data input into and output from the machine learning pipelines 202, 204, 206a-c”. Further at [0086]: “In one embodiment, the history module 402 tracks when updates to different machine learning pipelines 202, 204, 206a-c are made, and may generate checkpoints, on a per-machine learning pipeline 202, 204, 206a-c or per-logical machine learning layer 200, 225, 250”. Therefore, this tracking of updates in the learning pipeline and checkpoints is interpreted as the recording in a lineage database);
“and wherein the model lineage system is integrated into the federated learning system” (see Talagala at [0069]: “In a federated machine learning system, in one embodiment, the training pipelines 204a-c are located on the same physical or virtual devices as the corresponding inference pipelines 206a-c. In such an embodiment, the training pipelines 204a-c generate different machine learning models and send the machine learning models to the model selection module 212, which determines which machine learning model is the best fist for the logical machine learning layer 250, as described above, or combines/merges the different machine learning models, and/or the like”. Further at [0086]: “In one embodiment, the history module 402 tracks when updates to different machine learning pipelines 202, 204, 206a-c are made, and may generate checkpoints, on a per-machine learning pipeline 202, 204, 206a-c or per-logical machine learning layer 200, 225, 250”. Therefore, this tracking of updates in the learning pipeline by this history module is interpreted as being integrated in the federated machine learning system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Onvlee with the above teachings of Talagala by having a federated learning system with aggregator and workers together for communicating updates from the local workers to the aggregator to merge them, as taught by Onvlee, and integrating a model lineage system to provide intercommunication and recording of all the updates being transmitted back and forth, as taught by Talagala. The modification would have been obvious because one of ordinary skill in the art would be motivated to track all the lineage, communication and version history of the updates in the federated machine learning pipeline (as suggested by Talagala at [0083]: “The history module 402, in one embodiment, is configured to track a version history for each of the machine learning pipelines 202, 204, 206a-c such that changes to the machine learning pipelines can be rolled-back to a previous version”. Further at [0084]: “In certain embodiments, the history module 402 tracks the lineage, e.g., the interdependencies between each machine learning pipeline 202, 204, 206a-c, including the data input into and output from the machine learning pipelines 202, 204, 206a-c”).
Regarding Claim 8, claim 8 has substantially similar limitations as claim 1 and claim 8 is rejected for the same rationale as claim 1. Additionally, claim 8 recites “the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by one or more processors, the program instructions executable to:” (Onvlee at 0147: …portions of one or more methods described herein may be performed by computer system 100 in response to processor 104 executing one or more sequences of one or more instructions contained in main memory 106. Such instructions may be read into main memory 106 from another computer-readable medium, such as storage device 110. Execution of the sequences of instructions contained in main memory 106 causes processor 104 to perform the process steps described herein. See also Onvlee at 0092, 0097, 0105.)
Regarding Claim 15, claim 15 has substantially similar limitations as claims 1 and 8, therefore, claim 15 is rejected for the same rationale as claims 1 and 8. Additionally, claim 15 recites “A computer system for federated learning model lineage, the computer system comprising:” (Onvlee at 0145: FIG. 10 is a block diagram that illustrates a computer system 100 that can assist in implementing the methods, flows, or the systems disclosed herein. See also Onvlee at 0092, 0097, 0105.)
“one or more processors,” (Onvlee at 0145: … and a processor 104 (or multiple processors 104 and 105)…)
“one or more computer readable tangible storage devices, and program instructions stored on at least one of the one or more computer readable tangible storage devices for execution by at least one of the one or more processors, the program instructions executable to:” (Onvlee at 0145: Computer system 100 also includes a main memory 106, such as a random access memory (RAM) or other dynamic storage device, coupled to bus 102 for storing information and instructions to be executed by processor 104. Main memory 106 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 104. Computer system 100 further includes a read only memory (ROM) 108 or other static storage device coupled to bus 102 for storing static information and instructions for processor 104. A storage device 110, such as a magnetic disk or optical disk, is provided and coupled to bus 102 for storing information and instructions.)
Claims 6-7, 13-14, 20 are rejected under 35 U.S.C. 103 as being unpatentable in view of Onvlee, et al. (US 20220197264- hereinafter referred to as “Onvlee”) in further view of Talagala, et al., (U.S. Publication No. 2019/0108417- hereinafter referred to as “Talagala”) and further in view of Albero et al (US Pub. 2023/0032597- hereinafter “Albero”).
Regarding Claim 6, the combination of Onvlee and Talagala teaches “The computer-implemented method of claim 1,” however, it fails to teach “wherein model lineage system is invoked on demand by the federated learning system
Albero teaches, in an analogous system, wherein model lineage system is invoked on demand by the federated learning system (see Albero at [0060]: “At step 215, the administrator device 104 may send a data lineage request to the distributed ledger host platform 102 (e.g., requesting information indicate how particular data has been accessed, updated, and/or otherwise modified throughout time). For example, the data lineage request may be part of an audit and/or other compliance review”. Further at [0067]: “At step 335, the computing platform may receive a data lineage request. At step 340, the computing platform may identify the requested data lineage. At step 345, the computing platform may send one or more commands directing the administrative user device to display the data lineage”. Therefore, this request for data lineage for requesting how data has been accessed, modified or updated is analogous to invoking on demand model lineage system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Onvlee and Talagala with the above teachings of Albero by having a federated learning system with aggregator and workers together for communicating updates from the local workers to the aggregator to merge them and integrating a model lineage system to provide intercommunication and recording of all the updates being transmitted back and forth, as taught by Onvlee and Talagala, and invoking the lineage on demand, as taught by Albero. The modification would have been obvious because one of ordinary skill in the art would be motivated to track all the lineage as part of an audit or compliance review upon request (as suggested by Albero at [0060]: “For example, the data lineage request may be part of an audit and/or other compliance review”).
Regarding Claim 7, the combination of Onvlee, Talagala and Albero teaches the computer-implemented method of claim 6, wherein the model lineage system presents an application programming interface (API) to the federated learning system to manually invoke recording model lineage.“ (Talagala teaches at [0050]: “Custom programs may also be included that are developed for each analytic engine using the application programming interface for the analytic engine (e.g., DataSet/DataStream for Flink)”. This is interpreted as the API used for on invoking on demand model lineage. In addition, Albero further teaches the manual invoking of the model lineage, as it can be seen at [0060]: “At step 215, the administrator device 104 may send a data lineage request to the distributed ledger host platform 102 (e.g., requesting information indicate how particular data has been accessed, updated, and/or otherwise modified throughout time). For example, the data lineage request may be part of an audit and/or other compliance review”. Further at [0067]: “At step 335, the computing platform may receive a data lineage request. At step 340, the computing platform may identify the requested data lineage. At step 345, the computing platform may send one or more commands directing the administrative user device to display the data lineage”. Therefore, this request for data lineage for requesting to display how data has been accessed, modified or updated is analogous to invoking on demand model lineage system. It would have been obvious to combine Talagala’s API for handling such on demand lineage as a user interface).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Onvlee and Talagala with the above teachings of Albero by having a federated learning system with aggregator and workers together for communicating updates from the local workers to the aggregator to merge them and integrating a model lineage system to provide intercommunication and recording of all the updates being transmitted back and forth, as taught by Onvlee and Talagala, and invoking the lineage on demand via an API, as taught by Albero. The modification would have been obvious because one of ordinary skill in the art would be motivated to track and visually display all the lineage as part of an audit or compliance review upon request (as suggested by Albero at [0060]: “For example, the data lineage request may be part of an audit and/or other compliance review”).
Regarding Claim 13, claim 13 has substantially similar limitation(s) as claim 6 and claim 13 is rejected for the same rationale as claim 6.
Regarding Claim 14, claim 14 has substantially similar limitation(s) as claim 7 and claim 14 is rejected for the same rationale as claim 7.
Regarding Claim 20, claim 20 has substantially similar limitation(s) as claims 7 and 14, therefore, claim 20 is rejected for the same rationale as claim 7 and claim 14.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUIS A SITIRICHE whose telephone number is (571)270-1316. The examiner can normally be reached M-F 9am-6pm.
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/LUIS A SITIRICHE/Primary Examiner, Art Unit 2126
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