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 .
Response to Arguments
Applicant's arguments filed 2/5/26 have been fully considered but they are not persuasive.
– Applicant argues that the feature "to transmit subset of obfuscated data elements 140 to one or more requesting entities 152 within a computing environment 156" described in ARA V AMUDAN does not disclose "producing, via the at least one processor, a response to the query based on the results for the obfuscated queries," as recited in claim 1 (REMARKS, page 10).
Claim 1 recites producing a response “based on the results for the obfuscated queries” and “the results” are derived from the previous limitation which was mapped to Aravamudan (Col 16, Lines 39-57), wherein processor 104 selects subsets of obfuscated data elements 140 from the set of obfuscated data elements 124. The subset transmitted in Col 26, lines 10-45 is the same subset 140 selected at Col 16, lines 39-57.
Further, the Applicant’s specification (see ¶¶ 16 and 48) describes what it means to produce a response to the query. The Applicant’s disclosure states the content provider returns a set of records for the obfuscated queries, selects a subset of those records as the result of the original query, and the selected records are then provided to the user as a result of the original query. The same functionality as described by Aravamudan in the previously stated cited limitations. The Applicant appears to read claim 1 as requiring the results be combined or reconstructed into a response different from the results themselves which is not taught by the claim. It recited only that the response is “based on” the results.
Further, Examiner cited the disclosure of FIG. 1. Aravamudan further provides a non-limiting example with continued reference to FIG. 1, “In another embodiment, processor may temporarily mask one or more private data elements of plurality of private data elements in real-time during access or query operations. As a nonlimiting example, when a request to view or process plurality of private data elements 116 is made, sensitive information may be automatically masked to the user based on the user's access level, wherein plurality of private data elements may remain intact and unaltered within database 112.” (Aravamudan, Column 8, lines 37-45). This explicitly teaches “the results for the obfuscated queries”.
For other examples see Exemplary queries 1-3 (Aravamudan, Column 37, lines 50-67- Column 38, lines 1-39),
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.
Claims 1-25 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Aravamudan et al., US Patent No. 12182179 B1 (hereinafter Aravamudan).
Regarding claim 1, Aravamudan discloses a method of processing queries for secure searches (Col. 9, lines 5-36, e.g., user, such as a healthcare professional, may query database) comprising:
determining for a query, via a first machine learning model of at least one processor (Col. 7, lines 16-38, e.g., processor 104 may perform determinations, classification, and/or analysis steps, methods, processes, or the like as described in this disclosure using machine-learning processes), a region of an embedding space (Col. 6, lines 8-41, e.g., at least a private record 120 may be represented as a vector. In these cases, database 112 containing plurality of private data elements 116, such as, without limitation, a corpus, may be represented as a vector space) corresponding to search results (Col. 4, lines 54-67, e.g., processor 104 is configured to access a database 112 containing a plurality of private data elements 116 belonging to at least a private record);
distorting, via the at least processor (Col.7, lines 39-67, e.g., processor 104 is configured to generate a set of obfuscated data elements 124, representative of at least a private record 120, as a function of plurality of private data elements 116), the region of the embedding space along one or more dimensions to produce distorted regions in the embedding space corresponding to different search results (Col. 12, lines 12-29, e.g., generate a set of obfuscated data elements 124 by sample from a gaussian noise distribution and add the sampled noise to private data elements describing the ages and treatment outcome values);
determining, via a second machine learning model of the at least one processor, modified queries (Col. 12, lines 30-58, e.g., generate, using a generative model 128, set of obfuscated data elements 124 as a function of plurality of private data elements 116) corresponding to the different search results of the distorted regions to produce obfuscated queries (Col. 35, lines 4-17, e.g., obfuscated data elements may be sufficiently distanced from the centroid to be distinguishable from private data elements while still being representative (or related) enough for analytical utility);
obtaining, via the at least processor, results from processing the obfuscated queries (Col. 16, lines 39-57, e.g., Processor 104 may be configured to select a subset of obfuscated data elements 140 from set of obfuscated data elements 124); and
producing, via the at least one processor, a response to the query based on the results for the obfuscated queries (Col. 26, lines, 10-45, e.g., processor 104 may be configured to transmit subset of obfuscated data elements 140 to one or more requesting entities 152 within a computing environment 156).
Regarding claim 2, Aravamudan discloses the method of claim 1. Aravamudan further discloses the first machine learning model and the second machine learning model (Col. 12, lines 59-67, e.g., generative machine learning models may include one or more generative models) each include a large language model (Col. 14, lines 55-67, e.g., exemplary embodiment of generative model 128 may include, without limitation, a large language model (LLM) for textural data generation).
Regarding claim 3, Aravamudan discloses the method of claim 1. Aravamudan further discloses the obfuscated queries include one or more decoy queries (Col. 36, lines 64-67, e.g., generative model 128 as described herein may generate one or more synthetic narrative).
Regarding claim 4, Aravamudan discloses the method of claim 3. Aravamudan further discloses generating, via the at least one processor, the one or more decoy queries (Col. 25, lines 3-25, e.g., k-means clustering algorithm may be configured to calculate degree of similarity index values between cluster centroid and a selected obfuscated data element) based on randomly selected regions in the embedding space (Col. 24, lines 44-67, e.g. generating a k-means clustering algorithm may include generating initial estimates for cluster centroid or k centroids which may be randomly generated or randomly selected from plurality of private data elements).
Regarding claim 5, Aravamudan discloses the method of claim 1. Aravamudan further discloses producing the response to the query comprises: determining regions of the embedding space corresponding to the results for the obfuscated queries (Col. 34, lines 35-50, e.g., obfuscated data elements 204 (represented by black dots) may be positioned relative to at least a private data element 208 (represented by white dot) within a distance range indicated by a minimum threshold 212a (i.e., the innermost dashed circle) and a pre-determined maximum threshold 212b (i.e., the outermost dashed circle) in database 216); and producing the response to the query based on an intersection of the regions corresponding to the results for the modified queries (Col. 35, lines 4-17, e.g., data elements are neither too close nor too far from the centroid and exactly M=5 data elements lie within distance range (i.e., D.sub.min≤ d≤ D.sub.max)).
Regarding claim 6, Aravamudan discloses the method of claim 1. Aravamudan further discloses producing the response to the query comprises: processing the query against the results for the obfuscated queries to produce the response (Col. 38, lines 40-67, e.g., processor 102 may be configured to cluster sets of obfuscated data elements 124 or subsets of obfuscated data elements 140 in addition to plurality of private data elements 116 within database 112).
Regarding claim 7, Aravamudan discloses the method of claim 1. Aravamudan further discloses the region includes one of a rectangular shaped region and a circular shaped region (Col. 34, lines 35-50, e.g., within a distance range indicated by a minimum threshold 212a (i.e., the innermost dashed circle) and a pre-determined maximum threshold 212b (i.e., the outermost dashed circle) in database 216).
Regarding claim 8, Aravamudan discloses the method of claim 1. Aravamudan further discloses the one or more dimensions (Col. 24, lines 44-67, e.g., cluster centroid or k centroids which may be randomly generated or randomly selected from plurality of private data elements) and an amount of distortion (Col. 14, lines 45-54, e.g., produce obfuscated data elements based on random noise) are randomly selected.
System and computer program product claims (9-16 and 17-23) are drawn to the system and computer program product corresponding to the method of using same as claimed in claims (1-8). Therefore, system and computer program product claims (9-16 and 17-23) correspond to method claims (1-8), and are rejected for the same reasons of anticipation as used above.
Regarding claim 24, Aravamudan discloses a method of processing queries for secure searches comprising:
distorting, via at least processor (Col.7, lines 39-67, e.g., processor 104 is configured to generate a set of obfuscated data elements 124, representative of at least a private record 120, as a function of plurality of private data elements 116), a region of an embedding space corresponding to search results for a query along one or more dimensions to produce distorted regions in the embedding space corresponding to different search results (Col. 12, lines 12-29, e.g., generate a set of obfuscated data elements 124 by sample from a gaussian noise distribution and add the sampled noise to private data elements describing the ages and treatment outcome values);
determining, via the at least one processor, modified queries (Col. 12, lines 30-58, e.g., generate, using a generative model 128, set of obfuscated data elements 124 as a function of plurality of private data elements 116) corresponding to the different search results of the distorted regions to produce obfuscated queries (Col. 35, lines 4-17, e.g., obfuscated data elements may be sufficiently distanced from the centroid to be distinguishable from private data elements while still being representative (or related) enough for analytical utility); and
producing, via the at least one processor, a response to the query based on results for the obfuscated queries (Col. 26, lines, 10-45, e.g., processor 104 may be configured to transmit subset of obfuscated data elements 140 to one or more requesting entities 152 within a computing environment 156).
Computer program product claim (25) is drawn to the computer program product corresponding to the method of using same as claimed in claim (24). Therefore, computer program product claim (25) corresponds to method claim (24), and are rejected for the same reasons of anticipation as used above.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/RUPAL DHARIA/Supervisory Patent Examiner, Art Unit 2492
