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 .
This is in response to Amendments/REMARKS, filed on 06/15/2026.
Claims 3, 10 and 17 are cancelled;
Claim 21 is new.
Claims 1-2, 4-9, 11-16, and 18-21 are pending.
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, 2, 8, 9, 15 & 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Huang” et al. (US 2025/0315555 A1) in view of “Gkoulalas-Divanis” (US 11093646 B2).
Regarding Claim 1/8/15.
Huang discloses One or more non-transitory computer-readable media comprising instructions which, when executed by one or more hardware processors, cause performance of operations; A method and A system, comprising:
obtaining sensitive entity de-identification data comprising a set of entities identified in a first text by a sensitive entity de-identification system as sensitive entities [Huang discloses, ”At 1602, the sensitive data identification engine 150 may identify a plurality of text portions associated with one or more data subjects” (par.0175 with FIG.16)];
sending a prompt to a large language model (LLM) [“In some embodiments, the ML models may, for example, include at least one of the following: a large language model,…” (par.0026)], the prompt comprising the set of entities identified by the sensitive entity de-identification system as sensitive entities and comprising at least a portion of the first text [Huang discloses, “At 1604, the engine 150 may apply a machine learning model (e.g., ML model(s) 210, as shown in FIG. 2) to the identified plurality of portions” (par.0176 with FIG.16)];
obtaining an output of the LLM based on sending the prompt to the LLM, the output identifying an entity that is not included in the set of entities identified by the sensitive entity de-identification system as sensitive entities, wherein the output indicates that the entity is a sensitive entity [Huang discloses, “For example, entity extraction engine 204 may be used to extract one or more entities (e.g., entity(s) 1110) that may be representative of one or more data subjects (e.g., sensitive data subjects 214)” with FIG.16 (also FIGS.2, 11)]; and
storing the second text in a non-transitory computer-readable medium [Huang discloses, “The extracted and labeled entities and/or grouped may be stored in the identified sensitive data 212 and may be used for analysis for presence of sensitive data subjects 214, training of one or more ML model(s) 210, etc.” (par. 0090 with FIG.2; par.0105, 0227-0230)].
Huang may not expressly disclose; but, Gkoulalas-Divanis, analogues art, discloses a second set of entities as sensitive entities, the second set of entities including an entity that is not included in the first set of entities [Gkoulalas-Divanis discloses, “Data records from the second dataset that fail to satisfy de-identification requirements are removed” (Abstract)]; and based on the output identifying the second set of entities as sensitive entities and the sensitive entity de-identification data identifying the first set of entities as sensitive entities: modifying the first text by removing the first set of entities and the second set of entities to generate a second text [Gkoulalas-Divanis discloses, “A resulting dataset is generated that including the first dataset records within a selected region of interestingness and selected records of the second dataset within the same region” (Abstract)].
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the system of Huang by incorporating the teachings of Gkoulalas-Divanis for controlling data access by creating datasets that contain data provided by entities with their consent, and more specifically, to augmenting such datasets with de-identified data of other entities.
Huang in view of Gkoulalas-Divanis further discloses claim 2/9/16. The one or more non-transitory computer-readable media/method/system of claim 1/1/15, wherein the prompt instructs the LLM to determine if all entities of a predetermined sensitive entity type in at least a portion of the first text are included in the first set of entities [Huang disclose, “In particular, the sensitive information/data identification processes executed by the current subject matter enable more accurate identification of all sensitive subjects, including subjects that may be semantically linked to or connected with specific sensitive subjects” (par.0031); “For instance, in a master services agreement document, the grouped entity “name-person” may be used to identify locations within the document and/or other documents that include all data identifying individuals, where the data may include names, signatures (text or image), etc. as being related to a particular sensitive data subject 214, i.e., personal information” (par.0091)].
Claim(s) 4-5. 11-12 & 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Huang” et al. (US 2025/0315555 A1) in view of “Gkoulalas-Divanis” (US 11093646 B2), and further in view of “Luitjens” (US 12259984 B2).
Huang in view of Gkoulalas-Divanis discloses claim 4/11/18. The one or more non-transitory computer-readable media/method/system of claim 1/8/15, wherein: the prompt is a first prompt; the output is a first output; the entity is a first entity [see FIGS., where Huang discloses first prompt, output, entity (Abstract; FIGS.2-4, 16)]; the LLM is a first LLM [“In some embodiments, the ML models may, for example, include at least one of the following: a large language model,…” (par.0026)];
Huang/Gkoulalas-Divanis may not expressly disclose, but, Luitjens, analogous art, discloses the operations further comprise: sending a second prompt to a second large language model (LLM) that is the first LLM or a different LLM, the second prompt comprising a second entity of the first set of entities and at least a portion of the first text [see FIGs.1-3, where Luitjens discloses second ML Model and prompt]; obtaining a second output of the second LLM based on sending the second prompt to the second LLM, the second output indicating that the second entity is not a sensitive entity; and based on the second output indicating that the second entity is not a sensitive entity, generating the second text to include the second entity [Luitjens discloses, “The computing system may generate a first output from the first input by replacing the at least one first data element with the second data element” (Abstract); data replacement process in FIG.7].
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the system of Huang by incorporating the teachings of Luitjens for obfuscating text data elements exchanged with deep learning architectures.
Huang/Gkoulalas-Divanis in view of Luitjens further disclose claim 5/12/19. The one or more non-transitory computer-readable media/method/system of claim 1/8/15, wherein: the prompt is a first prompt; the output is a first output; the entity is a first entity; the LLM is a first LLM [see FIGS., where Huang discloses first prompt, output, entity (Abstract; FIGS.2-4, 16); “In some embodiments, the ML models may, for example, include at least one of the following: a large language model,…” (par.0026)]; the sensitive entity de-identification data is first sensitive entity de-identification data; the first sensitive entity de-identification data indicates that a second entity of the set of entities is a first predetermined sensitive entity type and; the operations further comprise: sending a second prompt to a second large language model (LLM) that is the first LLM or a different LLM, the second prompt comprising the second entity of the first set of entities and at least a portion of the first text [see FIGs.1-3, where Luitjens discloses second ML Model and prompt];
obtaining a second output of the second LLM based on sending the second prompt to the second LLM, the second output indicating that the second entity is a second predetermined sensitive entity type that is not the first predetermined sensitive entity type; based on the second output indicating that the second entity is the second predetermined sensitive entity type that is not the first predetermined sensitive entity type, storing second sensitive entity de-identification data that indicates that second entity is the second predetermined sensitive entity type; and generating the second text based at least in part on the second sensitive entity de-identification data [Luitjens discloses, “The computing system may generate a first output from the first input by replacing the at least one first data element with the second data element” (Abstract); data replacement process in FIG.7].
The motivation to combine is the same as that of claim 4 above.
Allowable Subject Matter
Claim 6-7, 13-14 and 20-21 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
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMARE F TABOR whose telephone number is (571) 270-3155. The examiner can normally be reached Mon.—Fri.: 8:00 AM to 5:00 PM.
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/AMARE F TABOR/Primary Examiner, Art Unit 2434
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