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 .
DETAILED ACTION
This action is in response to Applicant’s response, filed on March16, 2026. Applicant elects Group I (claims 1-11 and added new claims 21-29) without traverse. Group II (claims 12-20) has been cancelled without prejudice unelected Group II (claims 12-20).
Currently pending are claims 1-11 and 21-29.
Claims 1-11 and 21-29 (new claims 21-29) are pending for examination in this office action.
This action is response to the application filed on March16, 2026.
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 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)(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 1-8 and 21-28 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jain et al (US 20180359084 A1).
With respect to claims 1 and 21, Jain et al teaches
generating, by a first apparatus, a first intermediate result based on a first data subset and a first model ([0016] machine learning during the training, the server system 120 may transmit intermediate results 132 to the client system 110. intermediate results be a partially trained model);
receiving, by the first apparatus, an encrypted second intermediate result sent by a second apparatus, wherein the second intermediate result is generated based on a second data subset and a second model that correspond to the second apparatus ([0026] machine learning by the remote system, intermediate results may be provided back to the client system. the intermediate result be an encrypted); and
obtaining, by the first apparatus, a first gradient of the first model, wherein the first gradient is generated based on the first intermediate result and the encrypted second intermediate result ([0019] After the encrypted data to train a machine learning model. a standard stochastic gradient descent used over the encrypted data using the provided computation key),
wherein after being decrypted by using a second private key, the first gradient is for updating the first model ([0018] for the encryption scheme, and accuracy and scalability of machine learning algorithms impact. encryption scheme and machine learning (ML) training algorithms. encryption key, a decryption key, encryption and decryption key may be private, [0050] updated model may be encrypted with the encryption key) and the second private key is a decryption key generated by the second apparatus for homomorphic encryption ([0018] The encryption and decryption key be private to the client while the computation key is shared with the server system. [0046] encryption key and the decryption key may be kept private on the client system).
With respect to claims 2 and 22, Jain et al teaches generating, by the first apparatus, a first public key and a first private key for homomorphic encryption; and encrypting, by the first apparatus, the first intermediate result by using the first public key ([0050] updated model may be encrypted with the encryption key) and the second private key is a decryption key generated by the second apparatus for homomorphic encryption ([0018] The encryption and decryption key be private to the client while the computation key is shared with the server system).
With respect to claims 3 and 23, Jain et al teaches first apparatus sends the encrypted first intermediate result to the second apparatus ([0046] encryption key and the decryption key kept private to send on the client system).
With respect to claims 4 and 24, Jain et al teaches decrypting, by the first apparatus, the first gradient of the first model by using the first private key ([0046] encryption key and the decryption key kept private to send on the client system).
With respect to claims 5 and 25, Jain et al teaches first noise to the second apparatus; and receiving, by the first apparatus, the first gradient decrypted by using the second private key, wherein the decrypted gradient comprises the first noise ([0018] for the encryption scheme, and accuracy and scalability of machine learning algorithms impact. encryption scheme and machine learning (ML) training algorithms. encryption key, a decryption key, encryption and decryption key may be private).
With respect to claims 6 and 26, Jain et al teaches receiving, by the first apparatus, a second parameter that is of the second model and that is sent by the second apparatus; determining, by the first apparatus, a second gradient of the second model based on the first intermediate result, the encrypted second intermediate result, and a second parameter set of the second model; and sending, by the first apparatus, the second gradient of the second model to the second apparatus ([0018] for the encryption scheme, and accuracy and scalability of machine learning algorithms impact. encryption scheme and machine learning (ML) training algorithms. encryption key, a decryption key, encryption and decryption key may be private).
With respect to claims 7 and 27, Jain et al teaches determining, by the first apparatus, second noise of the second gradient, wherein the second gradient sent to the second apparatus comprises the second noise ([0018] for the encryption scheme, and accuracy and scalability of machine learning algorithms impact. encryption scheme and machine learning (ML) training algorithms. encryption key, a decryption key, encryption and decryption key may be private).
With respect to claims 8and 28, Jain et al teaches receiving, by the first apparatus, an updated second parameter comprising the second noise, wherein the second parameter set is a parameter set for updating the second model by using the second gradient; and removing, by the first apparatus, the second noise comprised in the updated second parameter ([0018] for the encryption scheme, and accuracy and scalability of machine learning algorithms impact. encryption scheme and machine learning (ML) training algorithms. encryption key, a decryption key, encryption and decryption key may be private).
Allowable Subject Matter
Claims 9-11 and 29 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISAAC M WOO whose telephone number is (571)272-4043. The examiner can normally be reached 9:00 to 5:00.
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, Tony Mahmoudi can be reached at 571-272-4078. 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.
/ISAAC M WOO/Primary Examiner, Art Unit 2163
Prosecution Insights