DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to the RCE filed on 07/20/2026.
Claims 1-9 and 11-21 are currently pending in this application.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after allowance. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, prosecution in this application has been reopened pursuant to 37 CFR 1.114. Applicant's submission filed on 02/27/2026 and 07/20/2026 have been entered/considered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/20/2026 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Examiner’s Note
Applicants are suggested to include information from figures 8 and 10 with related text into the claims to provide a better condition for an allowance.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-9, 11 and 13-21 are rejected under 35 U.S.C. 103 as being unpatentable over Salomon et al. (US 11,328,082 B2) in view of Nerurkar et al. (US 2018/0349384 A1).
As per claim 1, Salomon teaches a system comprising: at least one hardware processor; and at least one memory storing instructions that cause the at least one hardware processor to perform operations [see fig. 5 and associated text for the system components] comprising:
receiving a select query to be executed on a set of entities of a database system, the set of entities comprising a specified entity [figs. 1-4; col. 9, lines 58-66; col. 11, lines 56-67; col. 12, lines 1-4 of Salomon teaches receiving a select query (e.g., the statistical query) to be executed on a set of entities (e.g., the set of data/value stored in the database system) of a database system, the set of entities comprising a specified entity (e.g., the requested data)]; and
during execution of the select query: determining whether the specified entity is differential privacy-protected; and in response to determining that the specified entity is differential privacy-protected: generating a differentially private query result by causing execution of at least a portion of the select query on the specified entity as a differentially private query [figs. 3, 4; col. 12, lines 1-33 of Salomon teaches during execution of the select query (e.g., the statistical query): determining whether the specified entity (e.g., the requested data) is differential privacy-protected (e.g., determining/identifying the differential privacy mechanism applicable to the queried data); and in response to determining that the specified entity is differential privacy-protected: generating a differentially private query result (e.g., the noisified query result) by causing execution of at least a portion of the select query on the specified entity as a differentially private query (e.g., transforming the statistical query on the requested data as the transformed query)],
the execution of at least the portion of the select query on the specified entity as the differentially private query comprises: determining whether execution of at least the portion of the select query on the specified entity involves at least one privacy domain; and in response to determining that execution of at least the portion of the select query on the specified entity involves at least one differential privacy domain, executing at least the portion of the select query on the specified entity as the differentially private query using a set of differential privacy domains for a set of columns of the specified entity [figs. 3, 4; col. 10, lines 33-66; col. 12, lines 1-33 of Salomon teaches the execution of at least the portion of the select query on the specified entity as the differentially private query comprises: determining whether execution of at least the portion of the select query on the specified entity involves at least one privacy domain (e.g., the noisification function based on the differential privacy mechanism or the sensitivity based method); and in response to determining that execution of at least the portion of the select query on the specified entity involves at least one differential privacy domain, executing at least the portion of the select query on the specified entity as the differentially private query using a set of differential privacy domains (e.g., the number of different noise mechanisms, such as the sensitivity based method, the row-level Laplace noise method, the weighted privacy integrated query (PINQ) method, etc.) for a set of columns of the specified entity (e.g., the set of columns of fig. 3)]; and
responding to the select query with a select query result that is based on the differentially private query result [figs. 3, 4; col. 12, lines 3-33 of Salomon teaches responding to the select query with a select query result (e.g., into an aggregate value or at a field-level) that is based on the differentially private query result (e.g., the noisified query result)].
Although Salomon teaches using noisification function to inject the query result – see col. 12, lines 3-15, Salomon does not explicitly disclose, but Nerurkar teaches a system for an individual differential privacy domain of the set of differential privacy domains defining an enumeration of permissible values that bounds values used from a corresponding column of the set of columns to generate the differentially private query result [figs. 2, 4, 6; par. 0134, lines 1-15; par. 0135, lines 1-21; par. 0141, lines 1-17; par. 0144, lines 1-23 of Nerurkar teaches an individual differential privacy domain (e.g., the domain of X) of the set of differential privacy domains (e.g., see fig.2) defining an enumeration of permissible values that bounds values (e.g., the bounding values or percentage) used from a corresponding column of the set of columns (e.g., the set of values of a column of the set of records in the database) to generate the differentially private query result (e.g., satisfying result of the query)].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Salomon with the teaching of Nerurkar to include bounding values for a differentially private query result because it provides fine-grained control of the privacy parameters and privacy budget - see par. 0038 of Nerurkar.
As per claim 2, Salomon in view of Nerurkar teaches the system of claim 1.
Salomon further teaches wherein the determining of whether execution of at least the portion of the select query on the specified entity involves at least one differential privacy domain comprises: obtaining a description of the specified entity; determining whether the description comprises differential privacy domain information for one or more columns of the specified entity; and in response to determining that the description comprises differential privacy domain information, determining that execution of at least the portion of the select query on the specified entity involves at least one differential privacy domain [col. 10, lines 33-62; col. 12, lines 1-15 of Salomon teaches obtaining a description (e.g., including the noisification function information for the requested data based on the identified differential privacy mechanism) of the specified entity (e.g., the requested data); determining whether the description comprises differential privacy domain information for one or more columns of the specified entity (e.g., the noisification function information is a process defined within the transformed query statement specifying data modifications); and in response to determining that the description comprises differential privacy domain information, determining that execution of at least the portion of the select query on the specified entity involves at least one differential privacy domain (e.g., the data modifications inject noise into a query result by injecting into an aggregate value or at a field-level) – see also rejections to the claim 1].
As per claim 3, Salomon in view of Nerurkar teaches the system of claim 2.
Salomon further teaches wherein the executing of at least the portion of the select query on the specified entity using the set of differential privacy domains for the set of columns of the specified entity: parsing the differential privacy domain information for the set of differential privacy domains for the set of columns [fig. 3; col. 10, lines 33-67; col. 12, lines 1-15 of Salomon teaches parsing the differential privacy domain information (e.g., the noisification function information) for the set of differential privacy domains (e.g., the number of different noise mechanisms, such as the sensitivity based method of adding Laplace noise on aggregate data, the row-level Laplace noise method by adding noise at the field level, etc.) for the set of columns (e.g., the set of columns of fig. 3) – see also rejections to the claim 1].
As per claim 4, Salomon in view of Nerurkar teaches the system of claim 1.
Salomon further teaches wherein the determining of whether execution of at least the portion of the select query on the specified entity involves at least one differential privacy domain comprises:
requesting differential privacy domain information for a set of columns of the specified entity being used by at least the portion of the select query; receiving a differential privacy domain object in response to the requesting of the differential privacy domain information for the set of columns, the differential privacy domain object representing the differential privacy domain information [figs. 1, 3, 5, 6; col. 4, lines 31-45; col. 14, lines 9-24, 54-56 of Salomon teaches requesting differential privacy domain information (e.g., requesting/calling the user defined functions, UDFs based on the query) for a set of columns of the specified entity being used by at least the portion of the select query (e.g., the statistical query); receiving a differential privacy domain object (e.g., the object of the UDF) in response to the requesting of the differential privacy domain information for the set of columns, the differential privacy domain object (e.g., the object of the UDF) representing the differential privacy domain information; (e.g., the query transformation component receives the UDFs or the differential privacy domain information to transform the query) – see also rejections to the claim 1];
based on the differential privacy domain object, determining whether at least one column of the set of columns is associated with at least one differential privacy domain; and in response to determining that the at least one column of the set of columns is associated with at least one differential privacy domain, determining that execution of at least the portion of the select query on the specified entity involves at least one differential privacy domain [figs. 1, 3; col. 4, lines 31-61; col. 5, lines 4-29; col. 10, lines 33-45 of Salomon teaches based on the differential privacy domain object (e.g., the object of the UDF), determining (e.g., identifying a differential privacy mechanism applicable to the query and/or the requested data) whether at least one column of the set of columns (e.g., determining the data value of the columns/data set) is associated with at least one differential privacy domain (e.g., one of the number of different noise mechanisms, such as the sensitivity based method, the row-level Laplace noise method, the weighted privacy integrated query (PINQ) method, etc.); and in response to determining that the at least one column of the set of columns is associated with at least one differential privacy domain, determining that execution of at least the portion of the select query on the specified entity involves at least one differential privacy domain (e.g., execution of transformed query on the requested data involves/based on the noise mechanism of database configuration) – see also rejections to the claim 1].
As per claim 5, Salomon in view of Nerurkar teaches the system of claim 4.
Salomon further teaches wherein the executing of at least the portion of the select query on the specified entity using the set of differential privacy domains for the set of columns of the specified entity: parsing the differential privacy domain object for the set of differential privacy domains for the set of columns [fig. 3; col. 10, lines 33-67; col. 12, lines 1-15 of Salomon teaches parsing the differential privacy domain object (e.g., the object of the UDF or the noisification function information) for the set of differential privacy domains (e.g., the number of different noise mechanisms, such as the sensitivity based method of adding Laplace noise on aggregate data, the row-level Laplace noise method by adding noise at the field level, etc.) for the set of columns (e.g., the set of columns of fig. 3) – see also rejections to the claim 1].
As per claim 6, Salomon in view of Nerurkar teaches the system of claim 1.
Salomon further teaches wherein a select differential privacy domain of the set of differential privacy domains for a select column of the set of columns is stored in a data privacy object associated with the select column [fig. 3; col. 4, lines 31-61; col. 5, lines 4-29; col. 12, lines 1-15 of Salomon teaches wherein a select differential privacy domain of the set of differential privacy domains for a select column of the set of columns (e.g., the transformed query including the noisification function operates specific columns relevant to the query) is stored in a data privacy object (e.g., the noisification function information) associated with the select column].
As per claim 7, Salomon in view of Nerurkar teaches the system of claim 1.
Salomon further teaches wherein the select query is associated with a first user (e.g., the owner of the data) – see col. 12, lines 16-33, and wherein the operations comprise:
receiving, from a second user, a command to create the specified entity with the set of columns and with the set of differential privacy domains for the set of columns [figs. 2-4; col. 10, lines 33-37; col. 11, lines 65-67; col. 12, lines 1-12; col. 14, lines 64-67 of Salomon teaches receiving, from a second user (e.g., the user device), a command to create the specified entity (e.g., the request for data stored at the database) with the set of columns (e.g., the columns with values for aggregation) and with the set of differential privacy domains (e.g., the number of different noise mechanisms, such as the sensitivity based method of adding Laplace noise on aggregate data, the row-level Laplace noise method by adding noise at the field level, etc.) for the set of columns]; and
in response to the command: determining whether the second user has a privilege to create the specified entity with the set of columns and with the set of differential privacy domains for the set of columns; and in response to determining that the second user has the privilege, creating the specified entity with the set of columns and with the set of differential privacy domains for the set of columns [fig. 2; col. 8, lines 4-67; col. 10, lines 33-59 of Salomon teaches in response to the command: determining whether the second user has a privilege to create the specified entity (e.g., checking the permission associated with the requested statistical analysis) with the set of columns and with the set of differential privacy domains for the set of columns; and in response to determining that the second user has the privilege, creating the specified entity (e.g., the requested data or the query result) with the set of columns and with the set of differential privacy domains for the set of columns – see also rejections to the claim 1].
As per claim 8, Salomon in view of Nerurkar teaches the system of claim 1.
Salomon further teaches wherein the select query is associated with a first user (e.g., the owner of the data) – see col. 12, lines 16-33, and wherein the operations comprise:
receiving, from a second user, a command to alter the set of columns of the specified entity to have the set of differential privacy domains [figs. 2-4; col. 10, lines 33-37; col. 11, lines 65-67; col. 12, lines 1-12; col. 14, lines 64-67 of Salomon teaches receiving, from a second user (e.g., the user device), a command to alter the set of columns (e.g., adding noise value to the columns or the columns with values for aggregation) of the specified entity (e.g., the requested data) to have the set of differential privacy domains (e.g., applying the number of different noise mechanisms, such as the sensitivity based method of adding Laplace noise on aggregate data, the row-level Laplace noise method by adding noise at the field level, etc.)]; and
in response to the command: determining whether the second user has a privilege to alter the set of columns of the specified entity to have the set of differential privacy domains; and in response to determining that the second user has the privilege, altering the set of columns of the specified entity to have the set of differential privacy domains [fig. 2; col. 8, lines 4-67; col. 10, lines 33-59 of Salomon teaches in response to the command: determining whether the second user has a privilege to alter the set of columns of the specified entity (e.g., checking the permission associated with the requested statistical analysis) to have the set of differential privacy domains; and in response to determining that the second user has the privilege, altering the set of columns of the specified entity the set of differential privacy domains (e.g., adding noise for the requested data or the query result) – see also rejections to the claim 1].
As per claim 9, Salomon in view of Nerurkar teaches the system of claim 1.
Salomon further teaches wherein a select differential privacy domain of the set of differential privacy domains defines a range of values that bounds values used from a corresponding column of the set of columns to generate the differentially private query result [figs. 3, 4; col. 10, lines 33-39; col. 11, lines 39-43 of Salomon teaches wherein a select differential privacy domain of the set of differential privacy domains defines a range of values (e.g., aggregating data according to a particular range) that bounds values used from a corresponding column of the set of columns to generate the differentially private query result (e.g., the noisified query result)].
As per claim 11, Salomon in view of Nerurkar teaches the system of claim 1.
Salomon further teaches:
wherein the select query is associated with a first user, wherein the select query is a first select query, wherein the set of entities is a first set of entities, wherein the specified entity is a first entity, wherein the set of differential privacy domains is a first set of differential privacy domains, wherein the set of columns is a first set of columns, wherein the differentially private query result is a first differentially private query result, wherein the differentially private query is a first differentially private query, wherein the select query result is a first select query result [figs. 2, 3; col. 1, lines 64-67; col. 2, lines 1-35; col. 4, lines 6-21; col. 9, lines 64-67; col. 11, lines 44-47 of Salomon teaches processing a plurality of queries (e.g., a first statistical query, a second statistical query, etc.) for a set of users (e.g., a first user, a second user, etc.) with executing transformed queries to generate query results (e.g., a first/second differentially private query results) according to the supported differential privacy mechanisms (e.g., a first set of differential privacy domains, a second set of differential privacy domains, etc.) – see also the rejections to the claim 1], and
wherein the operations comprise: receiving, from a second user, a second select query to be executed on a second set of entities of the database system, the second set of entities comprising a second entity that is differential privacy-protected, the second select query specifying a second set of differential privacy domains for a second set of columns of the second entity used by the second select query; and during execution of the second select query: determining that the second entity is differential privacy protected; and in response to determining that the specified entity that is differential privacy-protected: generating a second differentially private query result by causing execution of at least the portion of the second select query on the second entity as a second differentially private query using the second set of differential privacy domains; and responding to the second select query with a second select query result that is based on the second differentially private query result [figs. 2, 3; col. 1, lines 64-67; col. 2, lines 1-35; col. 4, lines 6-21; col. 9, lines 64-67; col. 11, lines 44-47 of Salomon teaches processing a plurality of queries (e.g., a first statistical query, a second statistical query, etc.) for a set of users (e.g., a first user, a second user, etc.) with executing transformed queries to generate query results (e.g., a first/second differentially private query results) according to the supported differential privacy mechanisms (e.g., a first set of differential privacy domains, a second set of differential privacy domains, etc.) – see also the rejections to the claim 1].
As per claim 13, Salomon in view of Nerurkar teaches the system of claim 1.
Salomon further teaches wherein the specified entity is differential privacy-protected by a differential privacy policy that is associated with the specified entity [fig. 4; col. 11, lines 65-67; col. 12, lines 1-7 of Salomon teaches wherein the specified entity (e.g., the requested data of the statistical query) is differential privacy-protected by a differential privacy policy (e.g., the applicable differential privacy mechanism) that is associated with the specified entity (e.g., the requested data)].
As per claim 14, Salomon in view of Nerurkar teaches the system of claim 1.
Salomon further teaches wherein the specified entity comprises one of a table or a view [fig. 3; col. 4, lines 1-11 of Salomon teaches wherein the specified entity (e.g., the requested data) comprises one of a table (e.g., rows or table) or a view].
Claims 15-19 and 21 are method claims that correspond to the system claims 1-6, and are analyzed and rejected accordingly.
Claim 20 is a medium claim that corresponds to the system claim 1, and is analyzed and rejected accordingly.
Allowable Subject Matter
Claim 12 is 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
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/MAUNG T LWIN/Primary Examiner, Art Unit 2495
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