DETAILED ACTION
This office action is in response to the application filed on 7/15/2024. Claim(s) 1-20 is/are pending and are examined.
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 .
Examiner’s Note – Allowable Subject Matter
Claims 2, 4-5, 8, 10-11, 15, and 17-18 overcome the prior art and would otherwise be allowable if incorporated into the base claim along with any intervening claims.
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 of this title, 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, 3, 6-7, 9, 12-14, 16, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fisher (US 2025/0307465 A1), in view of Hachey (US 2021/0256160 A1).
Regarding claims 1, 7, and 14, Fisher teaches:
“A system comprising: one or more processors (Fisher, ¶ 22 and 27 teaches processor, memory and medium to execute method steps); and one or more non-transitory computer-readable media storing computer-executable instructions that, when executed, cause the one or more processors to perform operations (Fisher, ¶ 22 and 27 teaches processor, memory and medium to execute method steps) comprising: receiving a request for a generative machine learned model to perform an action (Fisher, Figs. 2A-2B, ¶ 58 user requests LLM to upload document into LLM); generating, based at least in part on the request, input data to be input to the generative machine learned model (Fisher, Figs. 2A-2B, ¶ 62-63 protection engine generates a document with confidential information obfuscated for input into the LLM), wherein generating the input data is based at least in part on:
receiving, based at least in part on the slot and from a database, data to be input into the slot (Fisher, ¶ 39 and 70-71 teaches retrieving the redacted items from the secure database for use in the LLM inputs); receiving a classification of the data (Fisher, ¶ 3-37 and 59-60 teaches classifying the data); determining, based at least in part on the classification, a mask that anonymizes the data (Fisher, ¶ 41, 61-62, and 72 teaches determining the CI, its location and the masking information); causing the mask to be input into the slot (Fisher, ¶ 41, 61-62, and 72 teaches determining the CI, its location, the masking information and then inserting it into the location); and generating, based at least in part on the mask, the input data (Fisher, ¶ 41, 61-63, and 72 teaches determining the CI, its location, the masking information and then inserting it into the location to create the LLM input); inputting the input data into the generative machine learned model (Fisher, Figs. 2A-2B, ¶ 62-63 protection engine generates a document with confidential information obfuscated for input into the LLM); receiving, from the generative machine learned model, output data (Fisher, Figs. 2A-2B, ¶ 64-68 at step 218 the LLM responds to the fetch document request with the document included obfuscated and non-obfuscated data); and causing the output data to be output to a virtual space (Fisher, Figs. 2A-2B, ¶ 64-68 at step 224 the answer is presented to the user in a graphical user interface including the de-obfuscated and non-obfuscated information)”.
Fisher does not, but in related art, Hachey teaches:
“identifying, based at least in part on the request, a template to organize the input data (Hachey, ¶ 31-33 and 43-44 teaches uses format and zoning interpreter to understand the importance of the information located various positions); identifying a slot associated with the template (Hachey, ¶ 31-33 and 43-44 teaches uses format and zoning interpreter to understand the importance of the information located various positions where confidential information needs to be replaced); receiving a policy (Hachey, ¶ 31-33 teaches rules used for masking data);
determining, based at least in part on the classification and the policy, a mask that anonymizes the data (Hachey, ¶ 31-33 and 37-39 teaches rules used for masking data as well as locations for masking and then creating the masked file)
generating, based at least in part on the mask and the template, the input data (Hachey, ¶ 31-33 and 37-39 teaches rules used for masking data as well as locations for masking and then creating the masked file)”.
Before applicant’s earliest effective filing it would have been obvious to one of ordinary skill in the art, having the teachings of Fisher and Hachey, to modify the LLM anonymizer of Fisher to include the template and policy method for masking as taught in Hachey. The motivation to do so constitutes applying a known technique to known devices and/or methods ready for improvement to yield predictable results.
Regarding claims 3, 9 and 16, Fisher and Hachey teaches:
“The system of claim 1 (Fisher and Hachey teach the limitations of the parent claims as discussed above), wherein receiving the data is further based at least in part on: identifying a reference associated with the slot (Hachey, ¶ 31-33 and 37-39 teaches rules used for masking data as well as locations for masking and then creating the masked file); determining, based at least in part on the reference, a location in the database (Fisher, ¶ 41, 61-62, and 72 teaches determining the CI, its location, the masking information and then inserting it into the location); and retrieving the data from the location in the database (Fisher, ¶ 41, 61-62, and 72 teaches determining the CI, its location, the masking information and then inserting it into the location)”.
Regarding claims 6, 12, and 19, Fisher and Hachey teaches:
“The system of claim 1 (Fisher and Hachey teach the limitations of the parent claims as discussed above), wherein the template includes static content and one or more slots (Hachey, Figs. 5-7 both depict static content including various headings and clits for descriptive patient information)”.
Regarding claim 13 and 20, Fisher and Hachey teaches:
“The one or more non-transitory computer-readable media of claim 7 (Fisher and Hachey teach the limitations of the parent claims as discussed above), wherein generating the input data is further based at least in part on: receiving a request for the generative machine learned model to perform an action (Fisher, Figs. 2A-2B, ¶ 58 user requests LLM to upload document into LLM)”.
Conclusion
In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure: See PTO-892.
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/STEPHEN T GUNDRY/Primary Examiner, Art Unit 2435