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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/16/2026 has been entered.
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.
Claim(s) 1 – 9, 11-13 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Buck, publication number: US 2020/0389491 in view of Stapleton, publication number: 2022/0019663.
As per claim 1, Buck teaches an apparatus comprising:
A computing device comprising a memory and processing circuitry (Device 104, [0030]), the processing circuitry is to:
Generate a statement comprising a control plane indicator and information associated with a machine learning model, the control plane indicator reflecting a state of a control plane of a computing device during execution of the machine learning model by the computing device (hardware verification, [0062][0095], metadata related to Machine learning execution environment, [0067]);
Generate, using an attestation key associated with the apparatus, a signature for the statement;
Sign, using the signature, the statement to produce a signed statement (signing using a key associated with the device, [0061]) and
Transmit, via a network connection to a service provider, the signed statement and the signature (Forwarding signed result to a remote service, [0067]),
Buck does not teach receive, from a service provider, a machine learning model
Store, in the memory, the machine learning model
The control pane indicator reflecting a state of a control plane of the computing device during execution of the machine learning model by the computing device, wherein the control plane indicator reflects, at the time the computing device executes the machine learning model, a state of a control plane of the computing device.
In an analogous art, Stapleton teaches receive, from a service provider, a machine learning model
Store, in the memory, the machine learning model (receiving and storing a machine learning model from a provider, [0035])
The control pane indicator reflecting a state of a control plane of the computing device during execution of the machine learning model by the computing device, wherein the control plane indicator reflects, at the time the computing device executes the machine learning model, a state of a control plane of the computing device (receiving indications of compromise from a remote device running a machine learning model, [0039][0093]).
Therefore, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to modify Buck’ secure execution environment to include data about the local configuration as described in Stapleton’s validation system for the advantage of explainable validation.
As per claim 2, the combination teaches where the control plane indicator comprises information about at least part of a data pipeline set-up of the computing device for executing the machine learning model (Buck: Hardware verification, [0095]).
As per claim 3, the combination teaches where the control plane indicator is to indicate that a data pipeline set-up of the computing device for executing the machine learning model complies with a model execution specification associated with the machine learning model (Buck: Hardware verification, [0095]).
As per claim 4, the combination teaches where the information regarding the machine learning model comprises an identity indicator of a signer controlling a first version of the machine learning model (Buck: Identity, [0054]).
As per claim 5, the combination teaches where the statement further comprises an execution indicator associated with using the computing device to execute the machine learning model (Buck: Identity, [0054]).
As per claim 6, the combination teaches where the execution indicator comprises an outcome due to a machine learning module of the computing device executing the machine learning model (Buck: result, [0067]).
As per claim 7, the combination teaches where the outcome comprises a result of executing the machine learning model on input data received by the computing device (Buck: signed input data, [0066]).
As per claim 8, the combination teaches where the outcome comprises a chain of hashed decisions made by the computing device when executing the machine learning model (Buck: signed result, [0067]).
As per claim 9, the combination teaches where the execution indicator comprises an input to a machine learning module of the computing device (Buck: input, [0067]).
As per claim 11, the combination teaches where the execution indicator comprises a result of testing input data monitored by a testing module of the computing device, where the testing module is to test whether or not the input data is anomalous as specified by a model execution specification associated with the machine learning model (check for compromise, [0053-0054]).
As per claim 12, Buck teaches a non-transitory machine readable medium comprising instructions which, when executed by at least one processor, cause the at least one processor to:
Receive, from a control module communicatively coupled to a machine learning module of a computing device, information regarding a data pipeline state of the computing device (hardware verification, [0062][0095]);
Generate a statement comprising a control plane indicator and information associated with the machine learning model, the control plane indicator reflecting the data pipeline state during execution of the machine learning model by the computing device (hardware verification, [0062][0095], metadata related to Machine learning execution environment, [0067]) and
Sign the statement using an attestation key associated with the at least one processor to prove the at least one processor generated the statement (signing using a key associated with the device, [0061])
Buck does not teach the data pipeline state indicating a lineage of how data is processed including by the machine learning model.
In an analogous art, Stapleton teaches the data pipeline state indicating a lineage of how data is processed including by the machine learning model (data indicating unauthorized changes to model parameters, [0039]).
Therefore, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to modify Buck’ secure execution environment to include data about the local configuration as described in Stapleton’s validation system for the advantage of explainable validation.
As per claim 13, Buck teaches a method comprising:
Receiving, from an attestation module forming part of a data pipeline of a computing device configured to execute an artificial intelligence model, a statement signed by the attestation module (Forwarding signed result to a remote service, [0067]);
Determining, based on the statement, whether the computing device is compliant with a model execution specification associated with the artificial intelligence model (checking for compromise, [0053-0055]); and
Facilitating, in response to determining compliance with the model execution specification, access to secured data generated by execution of the artificial intelligent model (blocking access based on a determining step, [0055]),
Wherein the statement comprises:
a set-up indicator indicating a control plane set-up of the computing device and information regarding the artificial intelligence model (hardware verification, [0062][0095], metadata related to Machine learning execution environment, [0067])
Buck does not teach receiving from a service provider, an artificial intelligence model and model execution specification, the model execution specification indicating how the artificial intelligence model is to be executed.
In an analogous art, Stapleton teaches receiving from a service provider, an artificial intelligence model and model execution specification, the model execution specification indicating how the artificial intelligence model is to be executed (receiving and storing a machine learning model from a provider, [0035], information about actions locally allowed to be performed to the model, [0039]).
Therefore, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to modify Buck’ secure execution environment to include data about the local configuration as described in Stapleton’s validation system for the advantage of easily and quickly identifying compromise.
As per claim 16, the combination teaches wherein the computing device is to execute the artificial intelligence model (Buck: secure context, [0055])
As per claim 17, the combination teaches wherein the processing circuitry is further to
receive, from the service provider, instructions to ensure that the machine learning model is correctly handled (Stapleton: information about actions locally allowed to be performed to the model, [0039]).
As per claim 18, the combination teaches wherein the information includes compliance
information of the executed machine learning model relative to a model execution specification (Stapleton: information about actions locally allowed to be performed to the model, [0039]).
As per claim 19, the combination teaches wherein the processing circuitry is further to:
transmit, to the service provider, a request to use the machine learning model; and
in response to the request, receive, from the service provider, the machine learning
model (Stapleton: Downloading model, [0035][0038]).
As per claim 20, the combination teaches wherein the computing device is an endpoint
device or an edge computing device (Stapleton: System 102, [0035]).
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.
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Buck, publication number: US 2020/0389491 in view of Stapleton, publication number: 2022/0019663 in further view of Coenders, publication number: US 2021/0150411.
As per claim 10, Buck and Stapleton teach verifying machine learning model execution context and metadata related to machine learning execution contexts Buck [0067].
The combination does not teach where the execution indicator comprises information regarding a second version of the machine learning model developed in response to the computing device training a first version of the machine learning model.
In an analogous art, Coenders teaches where the execution indicator comprises information regarding a second version of the machine learning model developed in response to the computing device training a first version of the machine learning model (tracking updates related to update machine learning models, [0006]).
Therefore, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to modify Buck and Stapleton’s execution context tracking model version information as described in Coender’s model registration system for the advantage of further ensuring model authentication and validation.
Claim(s) 14-15 are rejected under 35 U.S.C. 103 as being unpatentable Buck, publication number: US 2020/0389491 in view of Stapleton, publication number: 2022/0019663 in further view of Ye, publication number: US 2017/0060559.
As per claim 14, Buck and Stapleton teach verifying machine learning model execution context.
The combination does not teach comprising causing an operating system module forming part of the data pipeline to send an attestation request to the attestation module to cause the attestation module to generate and sign the statement;
prior to causing the operating system module to send the attestation request to the attestation module, sending a nonce to the operating system module such that the nonce is sent with the attestation request to the attestation module,
in response to receiving the nonce with the statement from the attestation module via the operating system module, determining that the statement is trusted.
In an analogous art, Ye teaches teach comprising causing an operating system module forming part of the data pipeline to send an attestation request to the attestation module to cause the attestation module to generate and sign the statement;
prior to causing the operating system module to send the attestation request to the attestation module, sending a nonce to the operating system module such that the nonce is sent with the attestation request to the attestation module,
in response to receiving the nonce with the statement from the attestation module via the operating system module, determining that the statement is trusted (validating received data by receiving a sent nonce, [0046][0056][0066)).
Therefore, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to modify Buck and Stapleton to include validation using a nonce as described in Ye’s data transfer system for the advantage of further validating received data.
As per claim 15, Buck and Stapleton teaches verifying machine learning model execution context.
The combination does not teach where the statement further comprises a data indicator obtained from the data pipeline, the method further comprising:
prior to causing the operating system module to send the attestation request to the attestation module, sending a public key of a public-private key pair to the operating system module such that the public key is sent with the attestation request to the attestation module, and
using a private key of the public-private key pair to decrypt the data indicator encrypted under the public key.
In an analogous art, Ye teaches where the statement further comprises a data indicator obtained from the data pipeline, the method further comprising:
prior to causing the operating system module to send the attestation request to the attestation module, sending a public key of a public-private key pair to the operating system module such that the public key is sent with the attestation request to the attestation module, and
using a private key of the public-private key pair to decrypt the data indicator encrypted under the public key. (validating received data by using keys, [0046][0056][0066)).
Therefore, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to modify Buck and Stapleton to include validation using keys as described in Ye’s data transfer system for the advantage of further validating received data.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLUGBENGA O IDOWU whose telephone number is (571)270-1450. The examiner can normally be reached Monday-Friday 8am - 5pm.
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, Jung Kim can be reached at 5712723804. 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.
/OLUGBENGA O IDOWU/Primary Examiner, Art Unit 2494