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 .
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 7, 8, 14, 15 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Belton et al. (US 2025/0165850) hereafter Belton
1. Belton discloses a method, comprising:
modifying raw data comprising one or more sensitive entity relationships to generate training data providing privacy for the one or more sensitive entity relationships (para 69-72, data sanitzation); and
fine-tuning a large language model (LLM) according to the generated training data, wherein the fine-tuned LLM excludes the one or more sensitive entity relationships (para 73, adjustment of ML model’s parameters; see further para 69-72).
7. The method of claim 1, further comprising: applying the fine-tuned LLM to generated one or more inferences, the one or more inferences providing privacy for the one or more sensitive entity relationships (para 67).
8. One or more non-transitory, computer-readable storage media, storing program instructions that when executed on or across a plurality of computing devices, cause the plurality of computing devices to implement a machine learning system performing: modifying raw data comprising one or more sensitive entity relationships to generate training data providing privacy for the one or more sensitive entity relationships (para 69-72, data sanitzation); and fine-tuning a large language model (LLM) according to the generated training data, wherein the fine-tuned LLM excludes the one or more sensitive entity relationships (para 73, adjustment of ML model’s parameters; see further para 69-72).
Claim 14 is similar in scope to claim 7 and is rejected under similar rationale.
15. A machine learning system, comprising: at least one processor; and a memory storing program instructions that when executed cause the at least one processor to implement a training system configured to: modify raw data comprising one or more sensitive entity relationships to generate training data providing privacy for the one or more sensitive entity relationships (para 69-72, data sanitzation); and fine-tune a large language model (LLM) according to the generated training data, wherein the fine-tuned LLM excludes the one or more sensitive entity relationships (para 73, adjustment of ML model’s parameters; see further para 69-72).
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.
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.
Claim(s) 2-3, 9-10, 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Belton as applied to claim 1, 8, 15 above, and further in view of Sumedrea et al. (US 2025/0005175) hereafter Sumedrea.
2. The method of claim 1, further comprising: training the LLM prior to fine-tuning the LLM using non-private data (para 73, training an ML model); and
Belton does not explicitly disclose deriving a reference model, subsequent to the training, based at least in part on the trained LLM, the derived reference model excluding the one or more sensitive entity relationships. However, in an analogous art, Sumedrea discloses hybrid sensitive data scrubbing including deriving a reference model, subsequent to the training, based at least in part on the trained LLM, the derived reference model excluding the one or more sensitive entity relationships (fig 3, 306-308, and corresponding text, LLM (reference model), scrubbing candidates; see further para 17, 21, 26, 37-39). It would have been obvious to a person of ordinary skill in the art before the effective filing date to modify the implementation of Belton with the implementation of Sumedrea in order to combine the benefits of pattern matching with the benefits of LLMs (para 15).
3. Belton discloses the method of claim 1, but does not explicitly disclose wherein the modifying comprises: analyzing the raw data to identify the one or more sensitive entity relationships, the one or more sensitive entity relationships individually comprising two or more entities including a first entity and a second entity; and replacing at least one of the two or more entities of individual ones of the one or more sensitive entity relationships with respective entities generated by a reference model to generate the training data. However, in an analogous art, Sumedrea discloses hybrid sensitive data scrubbing including wherein the modifying comprises: analyzing the raw data to identify the one or more sensitive entity relationships, the one or more sensitive entity relationships individually comprising two or more entities including a first entity and a second entity (Sumedrea, fig 3 and corresponding text, para 17, 21-22, 26, 37-39); and replacing at least one of the two or more entities of individual ones of the one or more sensitive entity relationships with respective entities generated by a reference model to generate the training data (Sumedrea, para 17, 22). It would have been obvious to a person of ordinary skill in the art before the effective filing date to modify the implementation of Belton with the implementation of Sumedrea in order to combine the benefits of pattern matching with the benefits of LLMs (para 15).
9. Belton disclose the one or more non-transitory, computer-readable storage media of claim 8, but does not disclose wherein the modifying is performed according to a reference model pretrained to perform next word prediction, and wherein the machine learning system further performs: training the LLM prior to fine-tuning the LLM using non-private data. However, in an analogous art, Sumedrea discloses wherein the modifying is performed according to a reference model pretrained to perform next word prediction, and wherein the machine learning system further performs: training the LLM prior to fine-tuning the LLM using non-private data (fig 3, 306-308, and corresponding text, LLM (reference model), scrubbing candidates; see further para 17, 21, 26, 37-39). It would have been obvious to a person of ordinary skill in the art before the effective filing date to modify the implementation of Belton with the implementation of Sumedrea in order to combine the benefits of pattern matching with the benefits of LLMs (para 15).
Claims 10 is similar in scope to claim 3 and is rejected under similar rationale.
Claim 16 is similar in scope to claim 2 and is rejected under similar rationale.
Claim 17 is similar in scope to claim 3 and is rejected under similar rationale.
Claim(s) 6, 13, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Belton as applied to claim 1, 8, 15 above, and further in view of Peterson et al. (US 11,443,240) hereafter Peterson.
6. Belton discloses the method of claim 1, but does not explicitly disclose wherein an entity relationship of the one or more sensitive entity relationships is determined according to one or more domain-specific or application-specific databases of sensitive entity relationships. However, in an analogous art, Peterson discloses privacy preserving learning with domain adaptation including wherein an entity relationship of the one or more sensitive entity relationships is determined according to one or more domain-specific or application-specific databases of sensitive entity relationships (fig 1, 145 and corresponding text). It would have been obvious to a person of ordinary skill in the art before the effective filing date to modify the implementation of Belton with the implementation of Peterson in order to allow for private, per-user domain adaptation and increase accuracy for all users (col 3, 55-62).
Claim 13 is similar in scope to claim 6 and is rejected under similar rationale.
Claim 20 is similar in scope to claim 6 and is rejected under similar rationale.
Allowable Subject Matter
Claims 4-5, 11-12, 18-19 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 JAMES R TURCHEN whose telephone number is (571)270-1378. The examiner can normally be reached Monday-Friday: 7-3.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Luu Pham can be reached at 571-270-5002. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES R TURCHEN/Primary Examiner, Art Unit 2439
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