Detailed Action
This office action is in response to applicant’s submission filed on July 9, 2026. Claims 18 and 19 are canceled. Claims 21-22 are new. Claims 1-17 and 20-22 are pending and rejected.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 25, 2026 has been considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, an initialed and dated copy of Applicant’s IDS form 1449 is attached to the instant Office action.
Response to Amendment
This communication is in response to the amendment filed on July 9, 2026. The Examiner has acknowledged the amended claims. Claims 18 and 19 are canceled. Claims 21-22 are new. Claims 1-17 and 20-22 are pending and rejected.
Response to Arguments
Applicant’s Arguments (Remarks) filed July 9, 2026 have been fully considered, but are moot. Note that this action is made FINAL. See MPEP § 706.07(a).
Applicant’s arguments with respect to claim(s) 1-10 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
The rest of the amendments are adding details/rewritten and do not change the rejection rationale.
Claim Rejections - 35 USC § 102
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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 5-6, 8-9, 12-14, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2021/0390447 A1 to Cheruvu et al. (hereinafter, “Cheruvu”).
Regarding claim 1, Cheruvu discloses: A computer-implemented method comprising:
generating a unique agent identifier for each artificial intelligent (AI) agent of a plurality of AI agents utilized in a given networked system (“Implementations of the disclosure address the above-noted data origin traceability problem by providing a system or process for immutable watermarking for authenticating and verifying AI-generated output utilizing a globally unique identifier (GUID). The GUID of implementations of the disclosure is used to authenticate and validate the output of content with respect to the identity of the author or owner of the outputted content (e.g., the ML model or human author). Implementations of the disclosure fuse the content data (e.g., ML-model generated data) with the GUID to enabled provenance identification and verification of the content data. Implementations of the disclosure utilize a watermarking approach based on three components to enable traceability of origin of content” [0021] [Examiner notes that the AI ML model here is being interpreted as the AI agent as the model performs all of the functions attributed to the claimed AI agent]);
detecting each execution of a plurality of executions of each of the plurality of AI agents utilized in the given networked system (“The reporting component 324 may communicate with monitoring system 350 to cause the output of the ML model 326 to be recorded. For example, monitoring system 250 may be a computing device hosting a monitoring consumption application 354 that causes logging of monitored events for analysis and review (e.g., consumption) by an end user” [0058]);
for each detected execution, performing, in real time or near real time, operations comprising: generating a data unique tag comprising the unique agent identifier associated with an AI agent that was executed, a time stamp associated with execution, and contextual metadata; and assigning the data unique tag to AI-generated data generated from the execution of the AI agent (“With respect to the sender component, the hash generator 113 and/or content deployer 114 may be implemented in CPU 111 of content generation platform 110 as hardware, software, and/or firmware of the content generation platform 110. Once content is generated by content generator 112, the hash generator 113 may utilize the ML model's 116 GUID, a ML model ID, the content (e.g., plain text, image, video, etc.), and/or a timestamp for a digital signature. The content deployer 114 may then transmit the digital signature to the verifier component, along with the content (e.g., plain text, image, video, etc.), timestamp, and model ID. In one implementation, the digital signature and content, timestamp, and model ID may be sent over an insecure communication channel 140 to the verifier (e.g., verification component 132)” [0034] [Examiner notes that because the text shows it generates, immediately following content generation by the ML model (the AI agent), a digital-signature package containing the model's GUID/model ID, a timestamp, and information associated with the generated content, that package constitutes the claimed data unique tag. Examiner also notes that this text shows that the digital signature is created using the content and the model-identifying information, and the digital signature is transmitted together with the content. Thus, the signature package and the generated content are associated with one another]);
detecting AI-generated data accessed by a downstream application, the AI-generated data a subset of In one implementation, content generation platform 110 provides for processing of ML data to generate content for consumption by content consumer platform 130. The generated content may be multi-modal content, including, but not limited to, one or more of plaintext, image(s), video(s), audio, and/or any other form of content. Content consumer platform 130 includes one or more processors, such as a CPU 131, where the content generation platform 110 provides for consumption of content generated by a content generation platform, such as content generated by processing of the ML data via ML model 116 at content generation platform 110. Consumption of content may include publishing content to a web page, adding the content into a monitoring system, or other processing of the content, for example. Content consumer platform 130 may include a content consumer component 134 (e.g., provided via CPU 131) that facilitates consumption of the content. For example, content consumer component 134 may include, but is not limited to, a web browser application, a mobile device application, a media player, a monitoring system, and so on” [0028] [Examiner notes that this text shows that content generated by ML model 116 is provided to and consumed by content consumer platform 130 and content consumer component 134, which may be implemented as a web browser, mobile application, media player, or monitoring system. Thus, the generated content (AI-generated data) is accessed by a separate consumer application operating downstream from the content generation platform, corresponding to the claimed downstream application]);
generating a lineage tag for new data created that is created by the downstream application using the AI-generated data, the lineage tag comprising each data unique tag associated with each of the AI-generated data used to generate the new data; and storing the lineage tag with the new data to enable tracing of AI influence across the given networked system (“Implementations of the disclosure implement a watermarking approach (i.e., watermarking pipeline) based on three components to facility traceability of data origin of content. Watermarking as discussed herein refers to the utilization of digital information hidden in a signal that can be used to verify the authenticity or integrity of the owner. The three components of implementations of the disclosure used in the watermarking approach include a sender, a verifier, and a shared AI registry. The three components may each be provided by a trusted entity and, as such, are considered individual trusted entities themselves. In one implementation, the sender may be one or more of the hash generator 113 and/or content deployer 114 of content generation platform 110. In one implementation, the verifier may be a verification component 132 of the content consumer platform 130. In one implementation, the shared AI registry may be the shared AI registry 120 of system 100. With respect to the sender component, the hash generator 113 and/or content deployer 114 may be implemented in CPU 111 of content generation platform 110 as hardware, software, and/or firmware of the content generation platform 110. Once content is generated by content generator 112, the hash generator 113 may utilize the ML model's 116 GUID, a ML model ID, the content (e.g., plain text, image, video, etc.), and/or a timestamp for a digital signature. The content deployer 114 may then transmit the digital signature to the verifier component, along with the content (e.g., plain text, image, video, etc.), timestamp, and model ID. In one implementation, the digital signature and content, timestamp, and model ID may be sent over an insecure communication channel 140 to the verifier (e.g., verification component 132)” [0033-0034] [Examiner notes that this text shows a watermarking pipeline in which a watermark is generated using GUID/model ID, timestamps, and AI generated content produced by an ML model and this watermark is associated with content that is later processed by a downstream verifier/content consumer platform. The watermark is directly derived from the data-origin identifiers of the accesses AI-generated data. Examiner also notes that the watermarking artifacts derived from the ML model identifiers and timestamps are transmitted together with AI-generated content to the downstream verifier where they are maintained and used within a watermarking pipeline to enable authentication and traceability of AI-generated content across the system]; “In one implementation, the digital signature generated by hash generator 228 may be a hash of one or more the generated content (e.g., plaintext), the GUID 270, a ML model ID, and/or a timestamp” [0051]; “Implementations of the disclosure fuse the content data (e.g., ML-model generated data) with the GUID to enabled provenance identification and verification of the content data” [0021]).
Claim 14 recites substantially the same limitation as claim 1, in the form of a system for implementing the corresponding method, therefore it is rejected under the same rationale. Examiner notes that the limitation of “a memory that stores instructions; and one or more processors configured by the instructions to perform operations comprising:” is taught by paragraph 0077 of Cheruvu.
Claim 20 recites substantially the same limitation as claim 1, in the form of a non-transitory
computer readable medium comprising computer readable program code for implementing the
corresponding method, therefore it is rejected under the same rationale.
Regarding claim 5, Cheruvu discloses: wherein the unique agent identifier for each Al agent is generated upon detecting In one implementation, a model developer or model deployer could be responsible for generating the GUIDs and establishing the shared AI registry 120” [0036]).
Regarding claim 6, Cheruvu discloses: storing, in a datastore, each unique agent identifier for each Al agent of the plurality of Al agents utilized in the given networked system (“maintains a data store 365 of ML models' GUIDs” [0064]).
Regarding claim 8, Cheruvu discloses: generating a cryptographic hash of the AI-generated data generated from the execution of the Al agent; and storing the cryptographic hash with the data unique tag associated with the AI- generated data generated from the execution of the Al agent (“In one implementation, the digital signature generated by hash generator 328 may be a hash of one or more the generated content (e.g., image, video, audio, etc.), the GUID 370, a ML model ID, and/or a timestamp. In some implementations, the hash generator 328 can use a HMAC to generate the digital signature. Once the digital signature is generated by hash generator 328, the reporting component 324 may then transmit the digital signature, along with the content (e.g., image, video, etc.), timestamp, and model ID to the monitoring system 350. In one implementation, the digital signature and content, timestamp, and model ID may be sent over an insecure communication channel to the monitoring system 350” [0065] [Examiner notes that this text shows a hash-based digital signature from AI-generated content and associated metadata. The generated content corresponds to the AI-generated data while the GUID, model ID, and timestamp package collectively correspond to the data unique tag that is associated with the AI-generated data. The digital signature is transmitted and maintained with the identifying metadata at the monitoring system]).
Regarding claim 9, Cheruvu discloses: the lineage tag is further based on transformation details indicating how the AI-generated data was used (“System 200 implements a watermarking approach (i.e., watermarking pipeline) to facilitate traceability of data origin with respect to autonomous generation and publication of AI-generated text. For example, system 200 may reflect a news consumption use case. System 200 includes a model developer system 202, a model deployer system 210, an AI/model user system 220, a manual content generator 230, an adversary system 240, a content consumer system 250, and a shared AI registry 260” [0040] [Examiner notes that since publication represents a manner in which the accessed AI-generated text is used, the watermarking approach reflects transformation details indicating that the publication activity can be viewed as a transformation detail indicating how the accessed data was used]).
Regarding claim 12, Cheruvu discloses: wherein storing the lineage tag with the new data comprises embedding the lineage tag directly in the new data or applying a cryptographic lineage tag to the new data (“Another previous solution to the above-noted data origin traceability problem is a watermarking approach for deep neural networks (DNN), where certain content is embedded in original training data as watermarks that are exhibited during inference when providing pre-specified predictions to the model. These prior watermarking approaches apply to protecting the DNN model using the input data (training data), and as such, can introduce distortion to the model. These distortions may, for example, change the meaning of the input data and modify the output of the model (e.g., lead to false negative or false positives)” [0019]).
Regarding claim 13, Cheruvu discloses: detecting that the downstream application links to or imports data generated from execution of one or more Al agent of the plurality of Al agents (“Content consumer platform 130 includes one or more processors, such as a CPU 131, where the content generation platform 110 provides for consumption of content generated by a content generation platform, such as content generated by processing of the ML data via ML model 116 at content generation platform 110. Consumption of content may include publishing content to a web page, adding the content into a monitoring system, or other processing of the content, for example. Content consumer platform 130 may include a content consumer component 134 (e.g., provided via CPU 131) that facilitates consumption of the content”) [0028] [Examiner notes that this text directly shows a downstream application receiving and incorporating AI-generated data as it is receiving external data, integrating it into another system, and storing/using it for processing]).
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.
Claims 2-3 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0390447 A1 to Cheruvu et al. (hereinafter, “Cheruvu”) in view of US 2025/0048085 A1 to Wang et al. (hereinafter, “Wang”).
Regarding claims 2 and 15, Cheruvu discloses the system of claims 1/14.
Cheruvu does not disclose: wherein the unique agent identifier for each AI agent is based on an application name and version, a model name and version of the AI agent, parameters used to execute the AI agent, a knowledge-base version used by the AI agent, and a prompt version.
However, Wang discloses: wherein the unique agent identifier for each AI agent is based on an application name and version, a model name and version of the AI agent, parameters used to execute the AI agent, a knowledge-base version used by the AI agent, and a prompt version (“In some embodiments, an AI/ML model may be run respectively for different signal processing functions. For example, for an AI/ML model for CSI reporting, there may have different model group identifications, model identifications, and version identifications. For an AI/ML model for beam management, there may have other model group identifications, model identifications, and version identifications” [0080] [Examiner notes that since this text already teaches identifying AI/ML models using model identifiers and version identifiers, it would have been obvious to a POSITA to further base the identifier on other known configuration information including parameters and prompt versions, in order to uniquely distinguish AI agents and facilitate compatibility and traceability]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cheruvu with the added structure of Wang in order for the system to be able to efficiently distinguish AI agents and facilitate compatibility and traceability.
Regarding claims 3 and 16, Cheruvu discloses the system of claims 1/14.
Cheruvu further discloses: detecting a release of a new AI agent in the given networked system; and generating a unique agent identifier for the new Al agent (“In one implementation, a model developer or model deployer could be responsible for generating the GUIDs and establishing the shared AI registry 120” [0036])
Cheruvu does not disclose: based on an application name and version, a model name and version of the new Al agent, parameters used to execute the new Al agent, a knowledge-base version used by the new Al agent, and a prompt version.
However, Wang discloses: based on an application name and version, a model name and version of the new Al agent, parameters used to execute the new Al agent, a knowledge-base version used by the new Al agent, and a prompt version (“In some embodiments, an AI/ML model may be run respectively for different signal processing functions. For example, for an AI/ML model for CSI reporting, there may have different model group identifications, model identifications, and version identifications. For an AI/ML model for beam management, there may have other model group identifications, model identifications, and version identifications” [0080] [Examiner notes that since this text already teaches identifying AI/ML models using model identifiers and version identifiers, it would have been obvious to a POSITA to further base the identifier on other known configuration information including parameters and prompt versions, in order to uniquely distinguish AI agents and facilitate compatibility and traceability]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cheruvu with the added structure of Wang in order for the system to be able to efficiently distinguish AI agents and facilitate compatibility and traceability.
Claims 4 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0390447 A1 to Cheruvu et al. (hereinafter, “Cheruvu”) in view of US 2025/0048085 A1 to Wang et al. (hereinafter, “Wang”) and in further view of US 2025/0068893 A1 to Bradea et al. (hereinafter, “Bradea”) and in further view of US 2024/0419493 A1 to Ramanujan et al. (hereinafter, “Ramanujan”).
Regarding claims 4 and 17, a combination of Cheruvu-Wang disclose the system of claims 3/16.
Cheruvu-Wang do not disclose: wherein the parameters include a temperature setting and
However, Bradea discloses: wherein the parameters include a temperature setting (“The personalization module then determines a tuning parameter for a generative AI model, wherein the tuning parameter controls the randomness of the output of the generative AI model. For example, some generative AI models accept a temperature parameter for modulating the randomness of the output. A higher temperature corresponds to more randomness in the output given a prompt, where a lower temperature corresponds to less randomness given the same prompt. The personalization module may use information about the user interactions, including the events associated with those interactions, to determine the tuning parameter. For instance, the personalization module may use counts of events associated with the user or the product to determine the tuning parameter” [0022]) and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cheruvu-Wang with the added structure of Bradea in order for the system to be able to efficiently distinguish AI agents and facilitate compatibility and traceability.
Cheruvu-Wang-Bradea do not disclose: a maximum token setting.
However, Ramanujan discloses: a maximum token setting (“Users can specify the maximum token parameter (e.g., max tokens) to determine the largest number of generated tokens desired from the AI model. If stop logic criterion for the AI model is applied before this maximum token limit, then the AI model stops” [0022]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cheruvu-Wang-Bradea with the added structure of Ramanujan in order for the system to be able to efficiently distinguish AI agents and facilitate compatibility and traceability.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0390447 A1 to Cheruvu et al. (hereinafter, “Cheruvu”) in view of US 2024/0104158 A1 to Chigurupalli et al. (hereinafter, “Chigurupalli”).
Regarding claim 7, Cheruvu discloses the system of claims 1/14.
Cheruvu does not disclose: wherein the data unique tag further comprises a sequence number.
However, Chigurupalli discloses: wherein the data unique tag further comprises a sequence number (“As an example of building a product category tree which includes a sequencing of a product based on a sequence number predicted for the product, suppose that the existing category tree represents the sequencing of products A, B, C, D, E, F, G, H, I, and J, which have the following sequence numbers: product A having sequence number 1, product B having sequence number 2, product C having sequence number 3, product D having sequence number 4, product E having sequence number 5, product F having sequence number 6, product G having sequence number 7, product H having sequence number 8, product I having sequence number 9, and product J having sequence number 10. In this example, suppose that a lower sequence number indicates a higher rank or sequencing within the product category tree. Also suppose that products A, B, C, D, E, F, G, H, I, and J, with their corresponding sequence numbers as the dependent variables (e.g., informative labels) in the modeling dataset, were used to build the ML model configured to predict a sequence number. In this instance, note that a predicted sequence number output by the ML model will be either 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 since the ML model was built from a modeling dataset generated using products A, B, C, D, E, F, G, H, I, and J” [0073]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cheruvu with the added structure of Chigurupalli since sequence numbers are commonly used as metadata associated with data records to preserve ordering and facilitate processing, it would have been obvious to a POSITA to include such a sequence number as part of the data unique tag associated with the AI-generated data because doing so would enable ordered tracking, identification, and traceability of the generated outputs and their relationships within the networked system in order for the system to be able to efficiently distinguish AI agents and facilitate compatibility and traceability.
Claims 10, 11, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0390447 A1 to Cheruvu et al. (hereinafter, “Cheruvu”) in view of US 2017/0278212 A1 to Fry.
Regarding claim 10, Cheruvu discloses the system of claim 9.
Cheruvu does not disclose: wherein the transformation details include one or more of summarization, text-to-text transformation, augmentation, data generation, or data blending.
However, Fry discloses: wherein the transformation details include one or more of summarization, text-to-text transformation, augmentation, data generation, or data blending (“The video ingest system 404 may, upon successful authentication, perform additional processing, such as compositing a watermark onto the video. The watermark may be composited using alpha blending to render a translucent pattern on a portion of the video. The video ingest system may further superimpose metadata onto the video, or alternatively, prepend or append metadata in text form to the video. The watermark serves as a recognizable feature for a user to identify the video as authenticated. The modified video file, which may include the watermark and/or prepended/appended metadata may then be uploaded to a video hosting system 416” [0040]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cheruvu with the added structure of Fry in order to efficiently track and mark digital content as it is processed through a system.
Regarding claim 11, Cheruvu discloses the system of claim 1.
Cheruvu does not disclose: wherein the lineage tag is further based on metadata describing modifications made to the AI-generated data.
However, Fry discloses: wherein the lineage tag is further based on metadata describing modifications made to the AI-generated data (“The video ingest system 404 may, upon successful authentication, perform additional processing, such as compositing a watermark onto the video. The watermark may be composited using alpha blending to render a translucent pattern on a portion of the video. The video ingest system may further superimpose metadata onto the video, or alternatively, prepend or append metadata in text form to the video. The watermark serves as a recognizable feature for a user to identify the video as authenticated. The modified video file, which may include the watermark and/or prepended/appended metadata may then be uploaded to a video hosting system 416” [0040]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cheruvu with the added structure of Fry in order to efficiently track and mark digital content as it is processed through a system.
Regarding claim 21, Cheruvu discloses the system of claim 1.
Cheruvu does not disclose: causing display on a computing device of a lineage for the new data, the lineage based on each lineage tag associated with each of the AI-generated data used to generate the new data.
However, Fry discloses: causing display on a computing device of a lineage for the new data, the lineage based on each lineage tag associated with each of the AI-generated data used to generate the new data (“The video ingest system 404 may, upon successful authentication, perform additional processing, such as compositing a watermark onto the video. The watermark may be composited using alpha blending to render a translucent pattern on a portion of the video. The video ingest system may further superimpose metadata onto the video, or alternatively, prepend or append metadata in text form to the video. The watermark serves as a recognizable feature for a user to identify the video as authenticated. The modified video file, which may include the watermark and/or prepended/appended metadata may then be uploaded to a video hosting system 416” [0040] [Examiner notes that this reference teaches superimposing metadata onto the video, thereby making the metadata available for display to the user. Where the claimed lineage information is metadata identifying the origin of the content including such lineage information in the disclosed metadata would have been an obvious implementation of the disclosed metadata]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cheruvu with the added structure of Fry in order to efficiently track and display digital content trace origin/history as it is processed through a system.
Regarding claim 22, Cheruvu discloses the system of claim 1.
Cheruvu does not disclose: wherein the contextual metadata comprises a business application version or environmental variables of a business application version.
However, Fry discloses: wherein the contextual metadata comprises a business application version or environmental variables of a business application version (“In embodiments, upon stopping the video recording process, the application executing on processor 502 may acquire metadata such as time and date” [0051]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cheruvu with the added structure of Fry in order to efficiently have the metadata processed through a system.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
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/SARON MATTHEWOS WORKU/Examiner, Art Unit 2408
/LINGLAN EDWARDS/Supervisory Patent Examiner, Art Unit 2408
