DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-20 are pending. Claims 1, 8 and 15 are independent.
3. The IDS submitted on 6/6/2025 has been entered.
Claim Rejections - 35 USC § 103
4. 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.
5. 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.
6. Claims 5 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Vahdat (US PG Pub. 2022/0398697) and further in view of Ratis (WO 2022216378).
As regarding claim 1, Vahdat discloses A method, comprising:
training, by a device, one or more machine learning models, with a dataset and one or more obfuscation features, to generate model weights, a latent space, and noising and denoising models [para. 4-5, 26, 69 and 117-119; generating score weights, latent space, and noising and denoising processes using dataset adding noise];
generating, by the device, a decryption model based on the model weights, the latent space, and the noising and denoising models [para. 4-5, 26,30, 40 and 117-119];
Vahdat does not explicitly disclose processing, by the device, an encrypted dataset associated with a target environment, with the decryption model, to determine whether the target environment is valid; and
selectively:
preventing, by the device, decryption of the encrypted dataset, or
processing, by the device, the encrypted dataset, with the decryption model, to generate a decrypted dataset. However, Ratis discloses it [para. 77-79; verifying a node to be in an authorized environment before whether to provide access to the encrypted data (in unencrypted form)].
It would have been obvious to one of ordinary skill in the art at the time the effective filing of the invention to modify Vahdat’s method to further comprise the missing claim features, as disclosed by Ratis, in order to prevent an unauthorized party from accessing the secret key and the encrypted data in an unauthorized environment [para. 79].
As regarding claim 2, Vahdat and Ratis further disclose The method of claim 1, wherein the one or more machine learning models are one or more neural network encryption models [Vahdat para. 30-32 and 39].
As regarding claim 3, Vahdat and Ratis further disclose The method of claim 1, further comprising: generating the one or more machine learning models [Vahdat para. 23, 26 and 29-30].
As regarding claim 4, Vahdat and Ratis further disclose The method of claim 3, wherein generating the one or more machine learning models is based on a dataset descriptor, a dataset geometry, or selected neural network types [Vahdat para. 23, 30 and 43].
As regarding claim 5, Vahdat and Ratis further disclose The method of claim 2, wherein determining whether the target environment is valid is based on one or more rules associated with the target environment [Ratis para. 77-79].
As regarding claim 6, Vahdat and Ratis further disclose The method of claim 1, further comprising: generating the one or more obfuscation features [Vahdat para. 25 and 31-32].
As regarding claim 7, Vahdat and Ratis further disclose The method of claim 1, wherein the one or more obfuscation features include a noise pattern, a synthetic data element, or a generated data transformation [Vahdat para. 25, 31-32 and 119].
As regarding claim 8, Vahdat discloses A device, comprising:
one or more memories [para. 34, 38 and 159]; and
one or more processors, coupled to the one or more memories [para. 34, 38 and 160], configured to:
train one or more machine learning models, with a dataset and one or more obfuscation features, to generate model weights, a latent space, and noising and denoising models [para. 4-5, 26, 69 and 117-119; generating score weights, latent space, and noising and denoising processes using dataset adding noise];
generate a decryption model based on the model weights, the latent space, and the noising and denoising models [para. 4-5, 26,30, 40 and 117-119];
Vahdat does not explicitly disclose process an encrypted dataset associated with a target environment, with the decryption model, to determine whether the target environment is valid; and
selectively:
prevent decryption of the encrypted dataset, or
process the encrypted dataset, with the decryption model, to generate a decrypted dataset. However, Ratis discloses it [para. 77-79; verifying a node to be in an authorized environment before whether to provide access to the encrypted data (in unencrypted form)].
It would have been obvious to one of ordinary skill in the art at the time the effective filing of the invention to modify Vahdat’s device to further comprise the missing claim features, as disclosed by Ratis, in order to prevent an unauthorized party from accessing the secret key and the encrypted data in an unauthorized environment [para. 79].
As regarding claim 9, Vahdat and Ratis further disclose The device of claim 8, wherein the one or more machine learning models are one or more neural network encryption models [Vahdat para. 30-32 and 39].
As regarding claim 10, Vahdat and Ratis further disclose The device of claim 8, wherein the one or more processors are further configured to: generate the one or more machine learning models [Vahdat para. 23, 26 and 29-30].
As regarding claim 11, Vahdat and Ratis further disclose The device of claim 10, wherein generating the one or more machine learning models is based on a dataset descriptor, a dataset geometry, or selected neural network types [Vahdat para. 23, 30 and 43].
As regarding claim 12, Vahdat and Ratis further disclose The device of claim 8, wherein determining whether the target environment is valid is based on one or more rules associated with the target environment [Ratis para. 77-79].
As regarding claim 13, Vahdat and Ratis further disclose The device of claim 8, wherein the one or more processors are further configured to: generate the one or more obfuscation features [Vahdat para. 25 and 31-32].
As regarding claim 14, Vahdat and Ratis further disclose The device of claim 8, wherein the one or more obfuscation features include a noise pattern, a synthetic data element, or a generated data transformation [Vahdat para. 25, 31-32 and 119].
As regarding claim 15, Vahdat discloses A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:
one or more instructions that, when executed by one or more processors of a device, cause the device [para. 149] to:
train one or more machine learning models, with a dataset and one or more obfuscation features, to generate model weights, a latent space, and noising and denoising models [para. 4-5, 26, 69 and 117-119; generating score weights, latent space, and noising and denoising processes using dataset adding noise];
generate a decryption model based on the model weights, the latent space, and the noising and denoising models [para. 4-5, 26,30, 40 and 117-119];
Vahdat does not explicitly disclose process an encrypted dataset associated with a target environment, with the decryption model, to determine whether the target environment is valid; and
selectively:
prevent decryption of the encrypted dataset, or
process the encrypted dataset, with the decryption model, to generate a decrypted dataset. However, Ratis discloses it [para. 77-79; verifying a node to be in an authorized environment before whether to provide access to the encrypted data (in unencrypted form)].
It would have been obvious to one of ordinary skill in the art at the time the effective filing of the invention to modify Vahdat’s medium to further comprise the missing claim features, as disclosed by Ratis, in order to prevent an unauthorized party from accessing the secret key and the encrypted data in an unauthorized environment [para. 79].
As regarding claim 16, Vahdat and Ratis further disclose The non-transitory computer-readable medium of claim 15, wherein the one or more machine learning models are one or more neural network encryption models [Vahdat para. 30-32 and 39].
As regarding claim 17, Vahdat and Ratis further disclose The non-transitory computer-readable medium of claim 15, wherein the one or more instructions further cause the device to: generate the one or more machine learning models [Vahdat para. 23, 26 and 29-30].
As regarding claim 18, Vahdat and Ratis further disclose The non-transitory computer-readable medium of claim 15, wherein determining whether the target environment is valid is based on one or more rules associated with the target environment [Ratis para. 77-79].
As regarding claim 19, Vahdat and Ratis further disclose The non-transitory computer-readable medium of claim 15, wherein the one or more instructions further cause the device to: generate the one or more obfuscation features [Vahdat para. 25 and 31-32].
As regarding claim 20, Vahdat and Ratis further disclose The non-transitory computer-readable medium of claim 15, wherein the one or more obfuscation features include a noise pattern, a synthetic data element, or a generated data transformation [Vahdat para. 25, 31-32 and 119].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THONG P TRUONG whose telephone number is (571)270-7905. The examiner can normally be reached on M-F 8:30AM - 5:30PM.
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/THONG TRUONG/
Examiner, Art Unit 2433
/JEFFREY C PWU/Supervisory Patent Examiner, Art Unit 2433
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