Detailed Action
This is a Final Office action in response to communications received on 7/30/2025. Claims 1, 10 14-15 and 19 were amended. Claims 1-21 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 .
Response to Arguments
Applicant’s amendments, filed 7/30/2025, to claim(s) 14 correcting the claim to omit “Kubernetes” are sufficient to overcome the rejection to the aforementioned claim(s). Accordingly, the rejection of claim(s) 14 under 112, second paragraph, as filed in (4) of the Non-Final Office action filed 4/10/2025 is withdrawn.
Applicant’s arguments regarding the rejection under 35 U.S.C. 103 of the claims under Scuderi and Dimmick have been considered, and are found unpersuasive.
Applicant argues on page(s) 9-11 of the Remarks, filed 7/30/2025, the cited prior art fail to teach or suggest “in response to the determination that the query requests the data item that has been classified as a sensitive data item, cause, by the sensitive data classifier, the transaction identifier and classification information that indicates the query requested the data item that has been classified as a sensitive data item to be sent to a collector service” because “Scuderi merely describes locally logging access events and maintaining audit records within the security engine itself, without disclosing any mechanism for transmitting or reporting the transaction identifier along with explicit classification information about the requested data to a collector service.” However, Examiner respectfully disagrees. Scuderi teaches a security engine that both identifies when requested data includes sensitive information and causes associated metadata (including information which identifies the attempted access) to be logged and made available for subsequent auditing (Scuderi; [0021]-[0022], [0039], Fig. 1). The term “collector service,” as broadly claimed, reasonably encompasses a logging system that receives transaction information, such as Scuderi’s access log. Although applicant asserts that Scuderi merely performs “local logging,” the claims do not recite any requirement that the collector service be physically remote or distinct from the classifier. Under BRI, the access log constitutes a “collector service,” as it collects, stores and enables retrieval of transaction data.
Applicant further argues that “Assuming arguendo that Scuderi and Dimmick were to be combined, a person of ordinary skill in the art would not arrive at the claimed features. Scuderi is focused on securing and auditing internal database access to sensitive information, while Dimmick addresses secure transaction reversals in payment systems. These systems operate in different technical domains, solve unrelated problems, and use identifiers for fundamentally different purposes - Dimmick for transaction routing and reversal.” However, Examiner respectfully disagrees. While Dimmick relates to financial processing, one of ordinary skill in the art would have recognized that its teachings of associating and transmitting transaction identifiers between distinct entities are applicable to any secure, auditable system where transaction verification is important. Thus, it would have been obvious to modify Scuderi’s security engine to transmit the logged transaction identifier and classification metadata to an external collector or audit component, as taught by Dimmick’s distributed transaction tracking, to enhance auditability. This represents a predictable use of prior art elements according to known methods.
Applicant argues on page(s) 13-14 of the Remarks, filed 7/30/2025, the cited prior art fail to teach or suggest “determining a quantity of data items, among the plurality of data items, stored in the first probabilistic data structure that are being provided to the entity, by performing an intersection between the first probabilistic data structure and the second probabilistic data structure” because “Wang does not disclose any mechanism for determining the quantity or approximate quantity of specific data items, such as sensitive data elements, that have been returned to a transaction originator or other recipient.” However, Examiner respectfully disagrees. Wang explicitly describes determining whether “there is an intersection,” this operation necessarily involves evaluating the number of matching bits or elements to determine whether it exceeds zero. Thus, the claimed determination of a “quantity of data items” is an obvious variation of Wang’s disclosed intersection operation. Additionally, [0010] of Wang teaches determining “an intersection between at least one element of the first Bloom filter and at least one element of the second Bloom filter,” which implies iterating over a plurality of elements. Applicant’s reliance on [0055] of the specification merely provides a specific example or intersection-based quantity determination, which is an obvious extension of Wang’s disclosure.
Consequently, the rejection of the claims under 35 U.S.C. 103 is sustained.
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 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.
Claims 1-4, 12-13, 15-17 and 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Scuderi (US 20210150057 A1), in view of Dimmick (US 20160148212 A1).
Regarding claim 1, Scuderi teaches the limitations of claim 1 substantially as follows:
A method comprising:
receiving, by a datastore layer service of a plurality of services that compose an application from an upstream service of the plurality of services, a request, (Scuderi; Para(s). [0026]: Each client device is configured to request and access data in the personnel database via the network (i.e. receiving a request))
the request being associated with a transaction submitted to the application, (Scuderi; Para(s). [0017]: data may include financial transaction data, personally identifiable information (“PII”), healthcare records, user data, etc. (i.e. the request being associated with a transaction submitted to the application))
initiating, by the datastore layer service in response to the request, a query against a datastore to obtain a data item based on information included in the request; (Scuderi; Para(s). [0021]: In response to a requesting entity, e.g. one of the client devices, requesting a set of data from the personnel database, the security engine determines if any of the requested data is sensitive data, e.g. PII. Data that is determined to be non-sensitive is retrieved from the personnel database and provided to the requesting entity (i.e. query against a datastore to obtain a data item based on information included in the request))
analyzing, by a sensitive data classifier, query information associated with the query; (Scuderi; Para(s). [0021]: In response to a requesting entity, e.g. one of the client devices, requesting a set of data from the personnel database, the security engine determines if any of the requested data is sensitive data, e.g. PII. Data that is determined to be non-sensitive is retrieved from the personnel database and provided to the requesting entity (i.e. analyzing query information))
determining, by the sensitive data classifier, that the query requests a data item that has been classified as a sensitive data item; and (Scuderi; Para(s). [0021]: In response to a requesting entity, e.g. one of the client devices, requesting a set of data from the personnel database, the security engine determines if any of the requested data is sensitive data, e.g. PII. Data that is determined to be non-sensitive is retrieved from the personnel database and provided to the requesting entity (i.e. determining that the query requests a data item that has been classified as a sensitive data item))
in response to the determination that the query requests the data item that has been classified as a sensitive data item, cause, by the sensitive data classifier, the transaction identifier and classification information that indicates the query requested the data item that has been classified as a sensitive data item to be sent to a collector service. (Scuderi; Para(s). [0021]-[0022]: For each attempt to access the modified sensitive data by the requesting entity, a different entity, and/or a client device, the security engine modifies the access log to identify the attempted access (i.e. the transaction identifier and classification information that indicates the query requested the data item that has been classified as a sensitive data item to be sent to a collector service))
Scuderi does not teach the limitations of claim 1 as follows:
the request including a transaction identifier that uniquely identifies the transaction;
However, in the same field of endeavor, Dimmick discloses the limitations of claim 1 as follows:
the request including a transaction identifier that uniquely identifies the transaction; (Dimmick; Para(s). [0068]: use the transaction information (e.g., the transaction identifier and the resource provider) to identify a transaction (i.e. a transaction identifier that uniquely identifies the transaction))
Dimmick is combinable with Scuderi because all are from the same field of endeavor of identification of sensitive information. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Scuderi to incorporate transaction identifiers as in Dimmick in order to improve the system by providing a means by which specific data entries may be specified in a query.
Regarding claim 2, Scuderi and Dimmick teach the limitations of claim 1.
Scuderi and Dimmick teach the limitations of claim 2 as follows:
The method of claim 1 wherein the query information associated with the query comprises the query, and further comprising: determining, by the sensitive data classifier, that a data catalog that classifies data fields of the datastore exists; and (Scuderi; Para(s). [0018]: The schema may identify the data tables, rows, and columns included in the personnel database. In some embodiments, the schema further identifies types of data, categories of data, or data sensitivity levels in columns of the personnel database (i.e. a data catalog that classifies data fields of the datastore exists))
determining, by the sensitive data classifier, based on the query and the data catalog, that the data item requested by the query is maintained in a data field that has been identified as a sensitive data field. (Scuderi; Para(s). [0031]: the sensitive information tagging module determines that the requested data is sensitive data based on an associated column and/or an associated row of a data table of the personnel database (i.e. the data item requested by the query is maintained in a data field that has been identified as a sensitive data field))
Regarding claim 3, Scuderi and Dimmick teach the limitations of claim 1.
Scuderi and Dimmick teach the limitations of claim 3 as follows:
The method of claim 1 wherein the query information associated with the query comprises the data item obtained from the datastore, and further comprising: processing the data item with at least one regular expression; and (Scuderi; Para(s). [0031]: a requested set of data is determined to be sensitive data based on metadata associated with the requested set of data. In some embodiments, the metadata includes at least one of: a category of data, a type of data, a format of data (i.e. processing the data item with at least one regular expression), a sensitivity of data, and a required authorization level)
determining, based on processing the data item with the at least one regular expression, to classify the data item as a sensitive data item. (Scuderi; Para(s). [0031]: a requested set of data is determined to be sensitive data based on metadata associated with the requested set of data. In some embodiments, the metadata includes at least one of: a category of data, a type of data, a format of data (i.e. classify the data item as a sensitive data item)
Regarding claim 4, Scuderi and Dimmick teach the limitations of claim 1.
Scuderi and Dimmick teach the limitations of claim 4 as follows:
The method of claim 1 wherein sending, by the sensitive data classifier to the collector service, the transaction identifier and the classification information that indicates the query requested the data item that has been identified as a sensitive data item further comprises sending, by the sensitive data classifier to the collector service, the transaction identifier and the classification information that indicates the query requested the data item that has been identified as a sensitive data item and refraining from sending the data item to the collector service. (Scuderi; Para(s). [0021]-[0022]: For each attempt to access the modified sensitive data by the requesting entity, a different entity, and/or a client device, the security engine modifies the access log to identify the attempted access (i.e. transaction identifier and the classification information that indicates the query requested the data item that has been identified as a sensitive data item))
Regarding claim 12, Scuderi and Dimmick teach the limitations of claim 1.
Scuderi and Dimmick teach the limitations of claim 12 as follows:
The method of claim 1 wherein the classification information indicates that the data item is one of a driver’s license identifier, a social security number, and a credit card number. (Scuderi; Para(s). [0017]: Some or all of the data stored in the personnel database may be sensitive data, such as social security numbers, phone numbers, full names of individuals, and/or addresses of individuals)
Regarding claim 13, Scuderi and Dimmick teach the limitations of claim 1.
Scuderi and Dimmick teach the limitations of claim 13 as follows:
The method of claim 1 further comprising: returning, by the datastore layer service to the upstream service, information that identifies the data item that has been classified as a sensitive data item. (Scuderi; Para(s). [0021]-[0022]: For each attempt to access the modified sensitive data by the requesting entity, a different entity, and/or a client device, the security engine modifies the access log to identify the attempted access (i.e. information that identifies the data item that has been classified as a sensitive data item))
Regarding claim 15, Scuderi teaches the limitations of claim 15 substantially as follows:
A computing system comprising: one or more processor devices of one or more computing devices, wherein the one or more processor devices are configured to: (Scuderi; Para(s). [0076]: a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described)
receive, by a datastore layer service of a plurality of services that compose an application from an upstream service of the plurality of services, a request, (Scuderi; Para(s). [0026]: Each client device is configured to request and access data in the personnel database via the network (i.e. receiving a request))
the request being associated with a transaction submitted to the application, (Scuderi; Para(s). [0017]: data may include financial transaction data, personally identifiable information (“PII”), healthcare records, user data, etc. (i.e. the request being associated with a transaction submitted to the application))
initiate, by the datastore layer service in response to the request, a query against a datastore to obtain a data item based on information included in the request; (Scuderi; Para(s). [0021]: In response to a requesting entity, e.g. one of the client devices, requesting a set of data from the personnel database, the security engine determines if any of the requested data is sensitive data, e.g. PII. Data that is determined to be non-sensitive is retrieved from the personnel database and provided to the requesting entity (i.e. query against a datastore to obtain a data item based on information included in the request))
analyze, by a sensitive data classifier, query information associated with the query; (Scuderi; Para(s). [0021]: In response to a requesting entity, e.g. one of the client devices, requesting a set of data from the personnel database, the security engine determines if any of the requested data is sensitive data, e.g. PII. Data that is determined to be non-sensitive is retrieved from the personnel database and provided to the requesting entity (i.e. analyzing query information))
determine, by the sensitive data classifier, that the query requests a data item that has been classified as a sensitive data item; and (Scuderi; Para(s). [0021]: In response to a requesting entity, e.g. one of the client devices, requesting a set of data from the personnel database, the security engine determines if any of the requested data is sensitive data, e.g. PII. Data that is determined to be non-sensitive is retrieved from the personnel database and provided to the requesting entity (i.e. determining that the query requests a data item that has been classified as a sensitive data item))
in response to the determination that the query requests the data item that has been classified as a sensitive data item, cause, by the sensitive data classifier, the transaction identifier and classification information that indicates the query requested the data item that has been classified as a sensitive data item to be sent to a collector service. (Scuderi; Para(s). [0021]-[0022]: For each attempt to access the modified sensitive data by the requesting entity, a different entity, and/or a client device, the security engine modifies the access log to identify the attempted access (i.e. the transaction identifier and classification information that indicates the query requested the data item that has been classified as a sensitive data item to be sent to a collector service))
Scuderi does not teach the limitations of claim 15 as follows:
the request including a transaction identifier that uniquely identifies the transaction;
However, in the same field of endeavor, Dimmick discloses the limitations of claim 15 as follows:
the request including a transaction identifier that uniquely identifies the transaction; (Dimmick; Para(s). [0068]: use the transaction information (e.g., the transaction identifier and the resource provider) to identify a transaction (i.e. a transaction identifier that uniquely identifies the transaction))
Dimmick is combinable with Scuderi because all are from the same field of endeavor of identification of sensitive information. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Scuderi to incorporate transaction identifiers as in Dimmick in order to improve the system by providing a means by which specific data entries may be specified in a query.
Regarding claim 16, Scuderi and Dimmick teach the limitations of claim 15.
Scuderi and Dimmick teach the limitations of claim 16 as follows:
The computing system of claim 15 wherein the query information associated with the query comprises the query, and wherein the one or more processor devices are further configured to: determine, by the sensitive data classifier, that a data catalog that classifies data fields of the datastore exists; and (Scuderi; Para(s). [0018]: The schema may identify the data tables, rows, and columns included in the personnel database. In some embodiments, the schema further identifies types of data, categories of data, or data sensitivity levels in columns of the personnel database (i.e. a data catalog that classifies data fields of the datastore exists))
determine, by the sensitive data classifier, based on the query and the data catalog, that the data item requested by the query is maintained in a data field that has been identified as a sensitive data field. (Scuderi; Para(s). [0031]: the sensitive information tagging module determines that the requested data is sensitive data based on an associated column and/or an associated row of a data table of the personnel database (i.e. the data item requested by the query is maintained in a data field that has been identified as a sensitive data field))
Regarding claim 17, Scuderi and Dimmick teach the limitations of claim 15.
Scuderi and Dimmick teach the limitations of claim 17 as follows:
The computing system of claim 15 wherein the query information associated with the query comprises the data item obtained from the datastore, and wherein the one or more processor devices are further configured to: process the data item with at least one regular expression; and (Scuderi; Para(s). [0031]: a requested set of data is determined to be sensitive data based on metadata associated with the requested set of data. In some embodiments, the metadata includes at least one of: a category of data, a type of data, a format of data (i.e. processing the data item with at least one regular expression), a sensitivity of data, and a required authorization level)
determine, based on processing the data item with the at least one regular expression, to classify the data item as a sensitive data item. (Scuderi; Para(s). [0031]: a requested set of data is determined to be sensitive data based on metadata associated with the requested set of data. In some embodiments, the metadata includes at least one of: a category of data, a type of data, a format of data (i.e. classify the data item as a sensitive data item)
Regarding claim 19, Scuderi teaches the limitations of claim 19 substantially as follows:
A non-transitory computer-readable storage medium that includes executable instructions configured to cause one or more processor devices to: (Scuderi; Para(s). [0076]: a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described)
receive, by a datastore layer service of a plurality of services that compose an application from an upstream service of the plurality of services, a request, (Scuderi; Para(s). [0026]: Each client device is configured to request and access data in the personnel database via the network (i.e. receiving a request))
the request being associated with a transaction submitted to the application, (Scuderi; Para(s). [0017]: data may include financial transaction data, personally identifiable information (“PII”), healthcare records, user data, etc. (i.e. the request being associated with a transaction submitted to the application))
initiate, by the datastore layer service in response to the request, a query against a datastore to obtain a data item based on information included in the request; (Scuderi; Para(s). [0021]: In response to a requesting entity, e.g. one of the client devices, requesting a set of data from the personnel database, the security engine determines if any of the requested data is sensitive data, e.g. PII. Data that is determined to be non-sensitive is retrieved from the personnel database and provided to the requesting entity (i.e. query against a datastore to obtain a data item based on information included in the request))
analyze, by a sensitive data classifier, query information associated with the query; (Scuderi; Para(s). [0021]: In response to a requesting entity, e.g. one of the client devices, requesting a set of data from the personnel database, the security engine determines if any of the requested data is sensitive data, e.g. PII. Data that is determined to be non-sensitive is retrieved from the personnel database and provided to the requesting entity (i.e. analyzing query information))
determine, by the sensitive data classifier, that the query requests a data item that has been classified as a sensitive data item; and (Scuderi; Para(s). [0021]: In response to a requesting entity, e.g. one of the client devices, requesting a set of data from the personnel database, the security engine determines if any of the requested data is sensitive data, e.g. PII. Data that is determined to be non-sensitive is retrieved from the personnel database and provided to the requesting entity (i.e. determining that the query requests a data item that has been classified as a sensitive data item))
in response to the determination that the query requests the data item that has been classified as a sensitive data item, cause, by the sensitive data classifier, the transaction identifier and classification information that indicates the query requested the data item that has been classified as a sensitive data item to be sent to a collector service. (Scuderi; Para(s). [0021]-[0022]: For each attempt to access the modified sensitive data by the requesting entity, a different entity, and/or a client device, the security engine modifies the access log to identify the attempted access (i.e. the transaction identifier and classification information that indicates the query requested the data item that has been classified as a sensitive data item to be sent to a collector service))
Scuderi does not teach the limitations of claim 19 as follows:
the request including a transaction identifier that uniquely identifies the transaction;
However, in the same field of endeavor, Dimmick discloses the limitations of claim 19 as follows:
the request including a transaction identifier that uniquely identifies the transaction; (Dimmick; Para(s). [0068]: use the transaction information (e.g., the transaction identifier and the resource provider) to identify a transaction (i.e. a transaction identifier that uniquely identifies the transaction))
Dimmick is combinable with Scuderi because all are from the same field of endeavor of identification of sensitive information. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Scuderi to incorporate transaction identifiers as in Dimmick in order to improve the system by providing a means by which specific data entries may be specified in a query.
Regarding claim 20, Scuderi and Dimmick teach the limitations of claim 19.
Scuderi and Dimmick teach the limitations of claim 20 as follows:
The non-transitory computer-readable storage medium of claim 19 wherein the query information associated with the query comprises the query, and wherein the instructions are further configured to cause the one or more processor devices to: determine, by the sensitive data classifier, that a data catalog that classifies data fields of the datastore exists; and (Scuderi; Para(s). [0018]: The schema may identify the data tables, rows, and columns included in the personnel database. In some embodiments, the schema further identifies types of data, categories of data, or data sensitivity levels in columns of the personnel database (i.e. a data catalog that classifies data fields of the datastore exists))
determine, by the sensitive data classifier, based on the query and the data catalog, that the data item requested by the query is maintained in a data field that has been identified as a sensitive data field. (Scuderi; Para(s). [0031]: the sensitive information tagging module determines that the requested data is sensitive data based on an associated column and/or an associated row of a data table of the personnel database (i.e. the data item requested by the query is maintained in a data field that has been identified as a sensitive data field))
Regarding claim 21, Scuderi and Dimmick teach the limitations of claim 19.
Scuderi and Dimmick teach the limitations of claim 21 as follows:
The non-transitory computer-readable storage medium of claim 19 wherein the query information associated with the query comprises the data item obtained from the datastore, and wherein the instructions are further configured to cause the one or more processor devices to: process the data item with at least one regular expression; and (Scuderi; Para(s). [0031]: a requested set of data is determined to be sensitive data based on metadata associated with the requested set of data. In some embodiments, the metadata includes at least one of: a category of data, a type of data, a format of data (i.e. processing the data item with at least one regular expression), a sensitivity of data, and a required authorization level)
determine, based on processing the data item with the at least one regular expression, to classify the data item as a sensitive data item. (Scuderi; Para(s). [0031]: a requested set of data is determined to be sensitive data based on metadata associated with the requested set of data. In some embodiments, the metadata includes at least one of: a category of data, a type of data, a format of data (i.e. classify the data item as a sensitive data item)
Claims 5-11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Scuderi (US 20210150057 A1), in view of Dimmick (US 20160148212 A1), as applied to independent claims, further in view of Wang (US 20240195603 A1).
Regarding claim 5, Scuderi and Dimmick teach the limitations of claim 1.
Scuderi and Dimmick teach the limitations of claim 5 as follows:
The method of claim 1 wherein the query information associated with the query comprises a plurality of data items obtained from the datastore, and further comprising: (Scuderi; Para(s). [0012]: The security engine manages access to data in the personnel database (i.e. a plurality of data items obtained from the datastore))
determining, by the sensitive data classifier, that the plurality of data items has been classified as a plurality of sensitive data items; (Scuderi; Para(s). [0031]: The sensitive information tagging module determines whether data requested by a requesting entity is sensitive data (i.e. classified as a plurality of sensitive data items))
sending, by the sensitive data classifier to the collector service, the transaction identifier, classification information that indicates the query requested the plurality of data items that has been identified as a plurality of sensitive data items, and the quantity of the plurality of data items. (Scuderi; Para(s). [0021]-[0022]: For each attempt to access the modified sensitive data by the requesting entity, a different entity, and/or a client device, the security engine modifies the access log to identify the attempted access (i.e. sending, by the sensitive data classifier to the collector service, the transaction identifier, classification information that indicates the query requested the plurality of data items that has been identified as a plurality of sensitive data items))
Scuderi and Dimmick do not teach the limitations of claim 5 as follows:
determining a quantity of the plurality of data items; and
However, in the same field of endeavor, Wang discloses the limitations of claim 5 as follows:
determining a quantity of the plurality of data items; and (Wang; Para(s). [0116]: if the process variable is an impression rate, the MPC cluster can use the impression notification to update a count of the impressions of the digital component (i.e. quantity of the plurality of data items))
Wang is combinable with Scuderi and Dimmick because all are from the same field of endeavor of identification of sensitive information. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified system of Scuderi and Dimmick to incorporate determining a quantity of data as in Wang in order to improve the functionality of the system by providing a means by which a quantity of the data being analyzed may be obtained in the case where more than a single data item is being processed.
Regarding claim 6, Scuderi and Dimmick teach the limitations of claim 1.
Scuderi and Dimmick do not teach the limitations of claim 6 as follows:
The method of claim 1 wherein the query information associated with the query comprises a plurality of data items obtained from the datastore, and further comprising: determining, by the sensitive data classifier, that the plurality of data items has been classified as a plurality of sensitive data items; storing the plurality of data items in a first probabilistic data structure; and
sending, by the sensitive data classifier to the collector service, the first probabilistic data structure.
However, in the same field of endeavor, Wang discloses the limitations of claim 6 as follows:
The method of claim 1 wherein the query information associated with the query comprises a plurality of data items obtained from the datastore, and further comprising: determining, by the sensitive data classifier, that the plurality of data items has been classified as a plurality of sensitive data items; storing the plurality of data items in a first probabilistic data structure; and (Wang; Para(s). [0091]: To securely and efficiently generate a digital component request based on sensitive information, the application can use probabilistic data structures, such as a cuckoo filter or a Bloom filter (i.e. storing the plurality of data items in a first probabilistic data structure))
sending, by the sensitive data classifier to the collector service, the first probabilistic data structure. (Wang; Para(s). [0054] & [0091]: To securely and efficiently generate a digital component request based on sensitive information, the application can use probabilistic data structures, such as a cuckoo filter or a Bloom filter. The computing systems MPC1 and MPC2 can receive the probabilistic data structures from the client devices (i.e. sending, by the sensitive data classifier to the collector service, the first probabilistic data structure))
Wang is combinable with Scuderi and Dimmick because all are from the same field of endeavor of identification of sensitive information. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified system of Scuderi and Dimmick to incorporate a probabilistic Bloom Filter structure for identify sensitive information as in Wang in order to improve the security of the system by providing a secure means of reliably identifying sensitive information.
Regarding claim 7, Scuderi, Dimmick and Wang teach the limitations of claim 6.
Scuderi, Dimmick and Wang teach the limitations of claim 7 as follows:
The method of claim 6 wherein the first probabilistic data structure comprises a Bloom filter. (Wang; Para(s). [0091]: To securely and efficiently generate a digital component request based on sensitive information, the application can use probabilistic data structures, such as a cuckoo filter or a Bloom filter)
The same motivation to combine as in claim 6 is applicable to the instant claim.
Regarding claim 8, Scuderi, Dimmick and Wang teach the limitations of claim 6.
Scuderi, Dimmick and Wang teach the limitations of claim 8 as follows:
The method of claim 6, further comprising: determining, by the sensitive data classifier, that the plurality of data items comprises a first plurality of data items that is associated with a first data field that is classified as a sensitive data field and a second plurality of data items that is associated with a second data field that is classified as a sensitive data field; (Wang; Para(s). [0091]: To securely and efficiently generate a digital component request based on sensitive information, the application can use probabilistic data structures, such as a cuckoo filter or a Bloom filter)
storing the first plurality of data items in the first probabilistic data structure; (Wang; Para(s). [0091]: To securely and efficiently generate a digital component request based on sensitive information, the application can use probabilistic data structures, such as a cuckoo filter or a Bloom filter (i.e. storing the first plurality of data items in the first probabilistic data structure))
storing the second plurality of data items in a second probabilistic data structure; and (Wang; Para(s). [0091]: To securely and efficiently generate a digital component request based on sensitive information, the application can use probabilistic data structures, such as a cuckoo filter or a Bloom filter (i.e. storing the second plurality of data items in a second probabilistic data structure))
sending, by the sensitive data classifier to the collector service, the first probabilistic data structure and the second probabilistic data structure. (Wang; Para(s). [0054] & [0091]: To securely and efficiently generate a digital component request based on sensitive information, the application can use probabilistic data structures, such as a cuckoo filter or a Bloom filter. The computing systems MPC1 and MPC2 can receive the probabilistic data structures from the client devices (i.e. sending, by the sensitive data classifier to the collector service, the first probabilistic data structure and the second probabilistic data structure))
The same motivation to combine as in claim 6 is applicable to the instant claim.
Regarding claim 9, Scuderi, Dimmick and Wang teach the limitations of claim 6.
Scuderi, Dimmick and Wang teach the limitations of claim 9 as follows:
The method of claim 6 wherein the transaction is submitted to the application via a validating service by an entity, and further comprising: (Scuderi; Para(s). [0021]: The security engine manages access to data in the personnel database)
receiving, at the validating service, a response to be provided to the entity, the response comprising the plurality of data items; (Scuderi; Para(s). [0031]: The sensitive information tagging module determines whether data requested by a requesting entity is sensitive data (i.e. response comprising the plurality of data items))
storing, by the validating service, the data items in a second probabilistic data structure; and (Wang; Para(s). [0091]: To securely and efficiently generate a digital component request based on sensitive information, the application can use probabilistic data structures, such as a cuckoo filter or a Bloom filter (i.e. storing the second plurality of data items in a second probabilistic data structure))
sending, by the validating service to the collector service, the second probabilistic data structure. (Wang; Para(s). [0054] & [0091]: To securely and efficiently generate a digital component request based on sensitive information, the application can use probabilistic data structures, such as a cuckoo filter or a Bloom filter. The computing systems MPC1 and MPC2 can receive the probabilistic data structures from the client devices (i.e. sending, by the sensitive data classifier to the collector service, the first probabilistic data structure and the second probabilistic data structure))
The same motivation to combine as in claim 6 is applicable to the instant claim.
Regarding claim 10, Scuderi, Dimmick and Wang teach the limitations of claim 9.
Scuderi, Dimmick and Wang teach the limitations of claim 10 as follows:
The method of claim 9 further comprising:
receiving, by the collector service, the second probabilistic data structure; and (Wang; Para(s). [0054] & [0091]: To securely and efficiently generate a digital component request based on sensitive information, the application can use probabilistic data structures, such as a cuckoo filter or a Bloom filter. The computing systems MPC1 and MPC2 can receive the probabilistic data structures from the client devices (i.e. receiving, by the collector service, the second probabilistic data structure))
determining a quantity of data items, among the plurality of data items, stored in the first probabilistic data structure that are being provided to the entity, by performing an intersection between the first probabilistic data structure and the second probabilistic data structure. (Wang; Para(s). [0010]: determining, using a second secure MPC process in collaboration with the one or more second computers of the secure MPC system, that there is an intersection between at least one element of the first Bloom filter and at least one element of the second Bloom filter (i.e. performing an intersection between the first probabilistic data structure and the second probabilistic data structure))
The same motivation to combine as in claim 6 is applicable to the instant claim.
Regarding claim 11, Scuderi, Dimmick and Wang teach the limitations of claim 6.
Scuderi, Dimmick and Wang teach the limitations of claim 11 as follows:
The method of claim 6 wherein the first probabilistic data structure comprises one or more characteristics selected from the group of a bit size of the first probabilistic data structure and one or more hash functions utilized, and further comprising: (Wang; Para(s). [0091]: The fingerprint of an item is a bit string derived from the hash of that item (i.e. selected from the group of a bit size of the first probabilistic data structure and one or more hash functions utilized))
returning, by the datastore layer service to the upstream service, information that identifies the one or more characteristics of the first probabilistic data structure. (Wang; Para(s). [0054] & [0091]: To securely and efficiently generate a digital component request based on sensitive information, the application can use probabilistic data structures, such as a cuckoo filter or a Bloom filter. The computing systems MPC1 and MPC2 can receive the probabilistic data structures from the client devices (i.e. returning, by the datastore layer service to the upstream service, information that identifies the one or more characteristics of the first probabilistic data structure))
The same motivation to combine as in claim 6 is applicable to the instant claim.
Regarding claim 18, Scuderi and Dimmick teach the limitations of claim 15.
Scuderi and Dimmick do not teach the limitations of claim 18 as follows:
The computing system of claim 15 wherein the query information associated with the query comprises a plurality of data items obtained from the datastore, and wherein the one or more processor devices are further configured to: determine, by the sensitive data classifier, that the plurality of data items has been classified as a plurality of sensitive data items; store the plurality of data items in a first probabilistic data structure; and
send, by the sensitive data classifier to the collector service, the first probabilistic data structure.
However, in the same field of endeavor, Wang discloses the limitations of claim 18 as follows:
The computing system of claim 15 wherein the query information associated with the query comprises a plurality of data items obtained from the datastore, and wherein the one or more processor devices are further configured to: determine, by the sensitive data classifier, that the plurality of data items has been classified as a plurality of sensitive data items; store the plurality of data items in a first probabilistic data structure; and (Wang; Para(s). [0091]: To securely and efficiently generate a digital component request based on sensitive information, the application can use probabilistic data structures, such as a cuckoo filter or a Bloom filter (i.e. storing the plurality of data items in a first probabilistic data structure))
send, by the sensitive data classifier to the collector service, the first probabilistic data structure. (Wang; Para(s). [0054] & [0091]: To securely and efficiently generate a digital component request based on sensitive information, the application can use probabilistic data structures, such as a cuckoo filter or a Bloom filter. The computing systems MPC1 and MPC2 can receive the probabilistic data structures from the client devices (i.e. sending, by the sensitive data classifier to the collector service, the first probabilistic data structure))
Wang is combinable with Scuderi and Dimmick because all are from the same field of endeavor of identification of sensitive information. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified system of Scuderi and Dimmick to incorporate a probabilistic Bloom Filter structure for identify sensitive information as in Wang in order to improve the security of the system by providing a secure means of reliably identifying sensitive information.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Scuderi (US 20210150057 A1), in view of Dimmick (US 20160148212 A1), as applied to independent claims, further in view of Nguyen (US 20240126916 A1).
Regarding claim 14, Scuderi and Dimmick teach the limitations of claim 1.
Scuderi and Dimmick do not teach the limitations of claim 14 as follows:
The method of claim 1 wherein the datastore layer service comprises a first container, the sensitive data classifier comprises a second container, and the first container and the second container execute in a same pod.
However, in the same field of endeavor, Nguyen discloses the limitations of claim 14 as follows:
The method of claim 1 wherein the datastore layer service comprises a first container, the sensitive data classifier comprises a second container, and the first container and the second container execute in a same pod. (Nguyen; Para(s). [0103]: the Kubernetes master node can employ sensitive data detection rules to orchestrate worker nodes and pods of the Kubernetes cluster, wherein the worker nodes and pods are associated with a network provider)
Nguyen is combinable with Scuderi and Dimmick because all are from the same field of endeavor of identification of sensitive information. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified system of Scuderi and Dimmick to incorporate Kubernetes clusters as in Nguyen in order to improve the system by providing a secure means by which information may be shared or compared between systems.
Prior Art Considered But Not Relied Upon
Lally (US 20230315872 A1) which teaches access by decentralized client computers to private information pertaining to a subject stored in a decentralized database is selectively controlled to facilitate an access-control transaction.
Scott (US 20230237180 A1) which teaches a system which may mask the one or more articles of sensitive information within the graphical user interface, generate a second data object indicative of the graphical user interface having the masked articles of sensitive information, and store the second data object in a data repository
Conclusion
For the above-stated reasons, claims 1-21 are rejected.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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