DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This initial written action is responding to the communication dated on 02/01/2025.
Claims 1-23 are submitted for examination.
Claims 1-23 are pending.
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.
Priority
This application filed on February 01, 2025 claims priority of Parent application 17/832,422 filed on June 03, 2022 which claims priority of provisional application filed on March 03, 2022.
Information Disclosure Statement
The following Information Disclosure Statements in the instant application submitted in compliance with the provisions of 37 CFR 1.97, and thus, have been fully considered:
IDS filed on 02 May 2025.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-6, 8-12, 14-17 and 19-23 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-6, 8-16, 18 and 20-22 of U.S. Patent No.12,216,798. Although the claims at issue are not identical, they are not patentably distinct from each other. Please refer to comparison table below.
Instant Application 19/043,451
Patent Application 17/832,422, Patent # 12,216,798
CENTRALIZED DATA TRANSFORMATION IN A MULTI-TENANT COMPUTING ENVIRONMENT
Centralized Data Transformation In A Multi-tenant Computing Environment
1
A method of scrubbing tenant data in a multi-tenant system, the method comprising: accessing tenant data exported from a source data system, the tenant data being in a normalized data format, the tenant data including data objects; identifying at least a subset of the data objects of the tenant data associated with a particular data object type; identifying a set of scrubber rules associated with the particular data object type, each scrubber rule configured to apply a respective predefined transformation to an input value of a data object of the at least a subset of the data objects of the particular data object type to obtain a corresponding scrubbed value, a particular scrubber rule configured to perform a service to ensure sensitive information is removed from the at least a subset of the data objects of the tenant data; applying the set of scrubber rules to the at least a subset of the data objects to obtain scrubbed tenant data; and facilitating importing of the scrubbed tenant data into a target data system.
1
A method of scrubbing tenant data in a multi-tenant system, the method comprising: accessing tenant data exported from a source data system, the tenant data being in a normalized data format, the tenant data including data objects; identifying at least a subset of the data objects of the tenant data associated with a particular data object type; identifying a set of scrubber rules associated with the particular data object type, each scrubber rule configured to apply a respective predefined transformation to an input value of a data object of the at least a subset of the data objects of the particular data object type to obtain a corresponding scrubbed value, a particular scrubber rule configured to perform a service to ensure referential integrity of the at least a subset of the data objects of the tenant data; applying the set of scrubber rules to the at least a subset of the data objects to obtain scrubbed tenant data; and facilitating importing of the scrubbed tenant data into a target data system.
2
The method of claim 1, wherein accessing the tenant data comprises accessing a data repository storing the tenant data in the normalized data format, and wherein facilitating importing of the scrubbed tenant data comprises making the scrubbed tenant data available to the target data system by storing the scrubbed tenant data in the data repository in the normalized data format.
2
The method of claim 1, wherein accessing the tenant data comprises accessing a data repository storing the tenant data in the normalized data format, and wherein facilitating importing of the scrubbed tenant data comprises making the scrubbed tenant data available to the target data system by storing the scrubbed tenant data in the data repository in the normalized data format.
3
The method of claim 2, wherein the tenant data is converted from a first native data format associated with the source data system to the normalized data format prior to being exported to the data repository.
3
The method of claim 2, wherein the tenant data is converted from a first native data format associated with the source data system to the normalized data format prior to being exported to the data repository.
4
The method of claim 3, wherein the scrubbed tenant data is converted from the normalized data format to a second native data format associated with the target data system prior to importing the scrubbed tenant data into the target data system.
4
The method of claim 3, wherein the scrubbed tenant data is converted from the normalized data format to a second native data format associated with the target data system prior to importing the scrubbed tenant data into the target data system.
5
The method of claim 1, further comprising: applying a respective additional set of scrubber rules to each of one or more additional subsets of the data objects of the tenant data to obtain additional scrubbed tenant data, wherein each additional subset of the data objects is associated with a respective corresponding additional data object type; and facilitating importing of the additional scrubbed tenant data into the target data system.
5
The method of claim 1, further comprising: applying a respective additional set of scrubber rules to each of one or more additional subsets of the data objects of the tenant data to obtain additional scrubbed tenant data, wherein each additional subset of the data objects is associated with a respective corresponding additional data object type; and facilitating importing of the additional scrubbed tenant data into the target data system.
6
The method of claim 1, wherein the tenant data is associated with a particular tenant of a plurality of tenants in the multi-tenant system, wherein an existing copy of the tenant data is maintained in the target data system, and wherein the scrubbed tenant data is an additional copy of the tenant data that is imported into the target data system.
6
The method of claim 1, wherein the tenant data is associated with a particular tenant of a plurality of tenants in the multi-tenant system, wherein an existing copy of the tenant data is maintained in the target data system, and wherein the scrubbed tenant data is an additional copy of the tenant data that is imported into the target data system.
8
The method of claim 7, further comprising maintaining, at least in part, referential integrity of the cleaned tenant data.
8
The method of claim 7, wherein the rewritten object identifier maintains, at least in part, a referential integrity of the scrubbed tenant data after it is imported as the second copy of the tenant data into the target data system.
9
The method of claim 1, wherein each scrubber rule in the set of scrubber rules comprises a file path for an object field to be scrubbed, an identifier of a scrubber, and a scrubber parameter comprising an input value to be transformed by the scrubber.
9
The method of claim 1, wherein each scrubber rule in the set of scrubber rules comprises a file path for an object field to be scrubbed, an identifier of a scrubber, and a scrubber parameter comprising an input value to be transformed by the scrubber.
10
The method of claim 9, wherein a first scrubber rule in the set of scrubber rules further comprises a filter, and wherein applying the set of scrubber rules to the at least a subset of the data objects of the tenant data comprises conditionally applying the first scrubber rule to the at least a subset of data objects of the tenant data.
10
The method of claim 9, wherein a first scrubber rule in the set of scrubber rules further comprises a filter, and wherein applying the set of scrubber rules to the at least a subset of the data objects of the tenant data comprises conditionally applying the first scrubber rule to the at least a subset of data objects of the tenant data.
11
The method of claim 10, wherein the conditionally applying the first scrubber rule to the at least a subset of data objects comprises, for each data object: determining, based on the filter, whether the first scrubber rule is applicable to the data object; and when it is determined that the first scrubber rule is applicable to the data object, applying the respective predefined transformation of the first scrubber rule to an input value contained in the object field to be scrubbed in the data object to obtain a corresponding scrubbed data object having a scrubbed value in the object field.
11
The method of claim 10, wherein the conditionally applying the first scrubber rule to the at least a subset of data objects comprises, for each data object: determining, based on the filter, whether the first scrubber rule is applicable to the data object; and when it is determined that the first scrubber rule is applicable to the data object, applying the respective predefined transformation of the first scrubber rule to an input value contained in the object field to be scrubbed in the data object to obtain a corresponding scrubbed data object having a scrubbed value in the object field.
12
The method of claim 1, wherein the set of scrubber rules is applied to the at least a subset of the data objects of the tenant data as the tenant data is being replicated from the source data system to the target data system.
12
The method of claim 1, wherein the set of scrubber rules is applied to the at least a subset of the data objects of the tenant data as the tenant data is being replicated from the source data system to the target data system.
14
A system for scrubbing tenant data in a multi-tenant system, the system comprising: at least one memory storing computer-executable instructions; and at least one processor configured to access the at least one memory and execute the computer-executable instructions to: access tenant data exported from a source data system, the tenant data being in a normalized data format, the tenant data including data objects; identify at least a subset of the data objects of the tenant data associated with a particular data object type; identify a set of scrubber rules associated with the particular data object type, each scrubber rule configured to apply a respective predefined transformation to an input value of a data object of the at least a subset of the data objects of the particular data object type to obtain a corresponding scrubbed value, a particular scrubber rule configured to perform a service to ensure sensitive information is removed from the at least a subset of the data objects of the tenant data; apply the set of scrubber rules to the at least a subset of the data objects to obtain scrubbed tenant data; and facilitate importing of the scrubbed tenant data into a target data system.
13
A system for scrubbing tenant data in a multi-tenant system, the system comprising: at least one memory storing computer-executable instructions; and at least one processor configured to access the at least one memory and execute the computer-executable instructions to: access tenant data exported from a source data system, the tenant data being in a normalized data format, the tenant data including data objects; identify at least a subset of the data objects of the tenant data associated with a particular data object type; identify a set of scrubber rules associated with the particular data object type, each scrubber rule configured to apply a respective predefined transformation to an input value of a data object of the at least a subset of the data objects of the particular data object type to obtain a corresponding scrubbed value, a particular scrubber rule configured to perform a service to ensure referential integrity of the at least a subset of the data objects of the tenant data; apply the set of scrubber rules to the at least a subset of the data objects to obtain scrubbed tenant data; and facilitate importing of the scrubbed tenant data into a target data system.
15
The system of claim 14, wherein the at least one processor is configured to access the tenant data by executing the computer-executable instructions to access a data repository storing the tenant data in the normalized data format, and wherein the at least one processor is configured to facilitate importing of the scrubbed tenant data by executing the computer-executable instructions to make the scrubbed tenant data available to the target data system by storing the scrubbed tenant data in the data repository in the normalized data format.
14
The system of claim 13, wherein the at least one processor is configured to access the tenant data by executing the computer-executable instructions to access a data repository storing the tenant data in the normalized data format, and wherein the at least one processor is configured to facilitate importing of the scrubbed tenant data by executing the computer-executable instructions to make the scrubbed tenant data available to the target data system by storing the scrubbed tenant data in the data repository in the normalized data format.
16
The system of claim 15, wherein the tenant data is converted from a first native data format associated with the source data environment to the normalized data format prior to being exported to the data repository, and wherein the scrubbed tenant data is converted from the normalized data format to a second native data format associated with the target data system prior to importing the scrubbed tenant data into the target data system.
15
The system of claim 14, wherein the tenant data is converted from a first native data format associated with the source data environment to the normalized data format prior to being exported to the data repository, and wherein the scrubbed tenant data is converted from the normalized data format to a second native data format associated with the target data system prior to importing the scrubbed tenant data into the target data system.
17
The system of claim 14, wherein the tenant data is associated with a particular tenant of a plurality of tenants in the multi-tenant system, wherein an existing copy of the tenant data is maintained in the target data system, and wherein the scrubbed tenant data is an additional copy of the tenant data that is imported into the target data system.
16
The system of claim 13, wherein the tenant data is associated with a particular tenant of a plurality of tenants in the multi-tenant system, wherein an existing copy of the tenant data is maintained in the target data system, and wherein the scrubbed tenant data is an additional copy of the tenant data that is imported into the target data system.
19
The system of claim 14, wherein each scrubber rule in the set of scrubber rules comprises a file path for an object field to be scrubbed, an identifier of a scrubber, and a scrubber parameter comprising an input value to be transformed by the scrubber.
18
The system of claim 13, wherein each scrubber rule in the set of scrubber rules comprises a file path for an object field to be scrubbed, an identifier of a scrubber, and a scrubber parameter comprising an input value to be transformed by the scrubber.
20
The system of claim 19, wherein a first scrubber rule in the set of scrubber rules further comprises a filter, and wherein the at least one processor is configured to apply the set of scrubber rules to the at least a subset of the data objects of the tenant data by executing the computer-executable instructions to conditionally apply the first scrubber rule to the at least a subset of data objects of the tenant data.
The system of claim 18, wherein a first scrubber rule in the set of scrubber rules further comprises a filter, and wherein the at least one processor is configured to apply the set of scrubber rules to the at least a subset of the data objects of the tenant data by executing the computer-executable instructions to conditionally apply the first scrubber rule to the at least a subset of data objects of the tenant data.
21
The system of claim 20, wherein the at least one processor is configured to conditionally apply the first scrubber rule to the at least a subset of data objects by executing the computer-executable instructions to, for each data object in the set of data objects: determine, based on the filter, whether the first scrubber rule is applicable to the data object; and when it is determined that the first scrubber rule is applicable to the data object, apply the respective predefined transformation of the first scrubber rule to an input value contained in the object field to be scrubbed in the data object to obtain a corresponding scrubbed data object having a scrubbed value in the object field.
20
The system of claim 19, wherein the at least one processor is configured to conditionally apply the first scrubber rule to the at least a subset of data objects by executing the computer-executable instructions to, for each data object in the set of data objects: determine, based on the filter, whether the first scrubber rule is applicable to the data object; and when it is determined that the first scrubber rule is applicable to the data object, apply the respective predefined transformation of the first scrubber rule to an input value contained in the object field to be scrubbed in the data object to obtain a corresponding scrubbed data object having a scrubbed value in the object field.
22
The system of claim 14, wherein the system is a centralized data transformation system located remotely from the source data system and the target data system.
21
The system of claim 13, wherein the system is a centralized data transformation system located remotely from the source data system and the target data system.
23
A computer program product for scrubbing tenant data in a multi-tenant system, the computer program product comprising a non-transitory computer-readable medium readable by a processing circuit, the non-transitory computer-readable medium storing instructions executable by the processing circuit to cause a method to be performed, the method comprising: accessing tenant data exported from a source data system, the tenant data being in a normalized data format, the tenant data including data objects; identifying at least a subset of the data objects of the tenant data associated with a particular data object type; identifying a set of scrubber rules associated with the particular data object type, each scrubber rule configured to apply a respective predefined transformation to an input value of a data object of the at least a subset of the data objects of the particular data object type to obtain a corresponding scrubbed value, a particular scrubber rule configured to perform a service to ensure sensitive information is removed from the at least a subset of the data objects of the tenant data; applying the set of scrubber rules to the at least a subset of the data objects to obtain scrubbed tenant data; and facilitating importing of the scrubbed tenant data into a target data system.
22
A computer program product for scrubbing tenant data in a multi-tenant system, the computer program product comprising a non-transitory computer-readable medium readable by a processing circuit, the non-transitory computer-readable medium storing instructions executable by the processing circuit to cause a method to be performed, the method comprising: accessing tenant data exported from a source data system, the tenant data being in a normalized data format, the tenant data including data objects; identifying at least a subset of the data objects of the tenant data associated with a particular data object type; identifying a set of scrubber rules associated with the particular data object type, each scrubber rule configured to apply a respective predefined transformation to an input value of a data object of the at least a subset of the data objects of the particular data object type to obtain a corresponding scrubbed value, a particular scrubber rule configured to perform a service to ensure referential integrity of the at least a subset of the data objects of the tenant data; applying the set of scrubber rules to the at least a subset of the data objects to obtain scrubbed tenant data; and facilitating importing of the scrubbed tenant data into a target data system.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7, 12-17 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Hossain et al. (US PGPUB. # US 2011/0276610, hereinafter “Hossain”), and further in view of Williams et al. (US PGPUB. # US 2021/0327009, hereinafter “Williams”).
Referring to Claims 1, 14 and 23:
Regarding Claim 1, Hossain teaches,
A method of scrubbing tenant data in a multi-tenant system, the method comprising:
accessing tenant data exported from a source data system, [the tenant data being in a normalized data format, the tenant data including data objects]; (Fig. 4(408), ¶60, “step 408, the heap dump data is rendered for navigation via a user interface, such as a web browser”, Fig. 5B, ¶100, “FIG. 5B shows an example of a view 550 of a heap dump displayed in a heap dump analysis tool, prior to scrubbing, as generated according to step 408”, i.e. heap dump data (tenant data) is accessed)
identifying at least a subset of the data objects of the tenant data associated with a particular data object type; (Fig. 5A(506), ¶80, “the data of a class dump may be used to identify a subset of objects within an instance dump as being objects of the class type Tenant, and another subset of objects may be identified within an instance dump as being objects of the class type EndUser”, ¶81, i.e. subset of tenant object associated with an object data type is identified)
identifying a set of scrubber rules associated with the particular data object type, each scrubber rule configured to apply a respective predefined transformation to an input value of a data object of the at least a subset of the data objects of the particular data object type to obtain a corresponding scrubbed value, a particular scrubber rule configured to perform a service to ensure sensitive information is removed from the at least a subset of the data objects of the tenant data; (Abstract, ¶11, Fig. 3, ¶53-¶55, “the user may enter a list of classes that are expected to contain sensitive information, and certain string variables or any string variable in that class may be scrubbed. Object inheritance 304 includes one or more rules that determine that an object may need to be scrubbed based on an inheritance from a class that is flagged for scrubbing “, “the user may enter in a set of names of objects that need to be scrubbed. Alternatively or additionally, the system may automatically look for variables and/or objects containing certain strings, such as "credit," "address," and "name." Object type 310 may allow objects to be scrubbed based on the object's type, and may consider an object for scrubbing or allow an object to be scrubbed if the object is of a certain type”, Fig. 5A(510), ¶91, ¶92, “one rule may identify object elements of the type string that containing 30 characters as having data to be scrubbed”, i.e. set of scrubber rules associated with a particular data object type are identified and a particular scrubber rule is applied to remove sensitive information from tenant data)
applying the set of scrubber rules to the at least a subset of the data objects to obtain scrubbed tenant data; (Fig. 3, ¶55, “the user may enter in a set of names of objects that need to be scrubbed. Alternatively or additionally, the system may automatically look for variables and/or objects containing certain strings, such as "credit," "address," and "name." Object type 310 may allow objects to be scrubbed based on the object's type, and may consider an object for scrubbing or allow an object to be scrubbed if the object is of a certain type”, Fig. 5A(510), ¶91, “In step 510, heuristic rules (rules that are heuristic in nature) are applied for determining whether an object being mapped contains data that should be scrubbed (in this specification the term heuristic rules refers to rules that are heuristic in nature)”, i.e. scrubbing rules are applied) and
facilitating importing of the scrubbed tenant data into a target data system. (Fig. 6(618), ¶110, “In step 618, the method for heuristic scrubbing of sensitive and secure customer data from production application heap dump files in a database network system may be implemented”, i.e. scrubbed data are imported into a database network system (target data environment).
Hossain does not teach explicitly,
[accessing tenant data exported from a source data system], the tenant data being in a normalized data format, the tenant data including data objects;
However, Williams teaches,
[accessing tenant data exported from a source data system], the tenant data being in a normalized data format, the tenant data including data objects; (Fig. 1, Fig. 2, ¶56, “The data transformation module 230 can be configured to use customized rules and processes to aggregate the raw district data into a common format. Because each school district can uniquely set up and store their data, this normalization and data transformation process is specific for each district or school. The normalized data is then uploaded into host databases, such as host database 105”, ¶57, the data transformation module 230 can also aggregate the set of raw district data stored in district raw database 104 and implement district-specific data transformation rules to normalize the data and store the processed district data into the host database 105”, Fig. 8(820,830,840), ¶144, “performing data transformation and normalization operations on the site data to convert the site data to a common format”, i.e. data is normalized).
As per KSR vs Teleflex, combining prior art elements according to known methods (device, product) to yield predictable results may be used to create a prima facie case of obviousness.
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the teachings of Williams with the invention of Hossain.
Hossain teaches, accessing tenant data and identifying objects for scrubbing the data based on matching scrubbing rules. Williams teaches, normalizing the exported data from various databases. Therefore, it would have been obvious to normalize the exported data from various databases of Williams with accessing tenant data and identifying objects for scrubbing the data based on matching scrubbing rules with Hossain to comply with privacy laws prior to sharing tenant data with third parties.
KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007).
Regarding Claim 14, it is a system claim of above method claim 1 and therefore Claim 14 is rejected with the same rationale as applied against Claim 1 above.
Hossain teaches a memory and a processor (¶27, ¶36).
Regarding Claim 23, it is a computer program product claim of above method claim 1 and therefore Claim 23 is rejected with the same rationale as applied against Claim 1 above.
Hossain teaches a computer program product (¶36).
Referring to Claims 2 and 15:
Regarding Claim 2, rejection of Claim 1 is included and for the same motivation Hossain teaches,
The method of claim 1, wherein accessing the tenant data comprises accessing a data repository storing the tenant data (Fig. 4(408), “step 408, the heap dump data is rendered for navigation via a user interface, such as a web browser”, Fig. 5B, ¶100, “FIG. 5B shows an example of a view 550 of a heap dump displayed in a heap dump analysis tool, prior to scrubbing, as generated according to step 408”, i.e. heap dump data (tenant data) is accessed) [in the normalized data format], and wherein facilitating importing of the scrubbed tenant data comprises making the scrubbed tenant data available to the target data system by storing the scrubbed tenant data in the data repository (Fig. 6(618), ¶110, “In step 618, the method for heuristic scrubbing of sensitive and secure customer data from production application heap dump files in a database network system may be implemented”, i.e. scrubbed data are imported into a database network system (target data environment)) [in the normalized data format].
Hossain does not teach explicitly,
The method of claim 1, [wherein accessing the tenant data comprises accessing a data repository storing the tenant data] in the normalized data format, [and wherein facilitating importing of the scrubbed tenant data comprises making the scrubbed tenant data available to the target data system by storing the scrubbed tenant data in the data repository] in the normalized data format.
However Williams teaches,
The method of claim 1, [wherein accessing the tenant data comprises accessing a data repository storing the tenant data] in the normalized data format, (Fig. 1, Fig. 2, ¶56, “The data transformation module 230 can be configured to use customized rules and processes to aggregate the raw district data into a common format. Because each school district can uniquely set up and store their data, this normalization and data transformation process is specific for each district or school. The normalized data is then uploaded into host databases, such as host database 105”, ¶57, the data transformation module 230 can also aggregate the set of raw district data stored in district raw database 104 and implement district-specific data transformation rules to normalize the data and store the processed district data into the host database 105”, Fig. 8(820,830,840), ¶144, “performing data transformation and normalization operations on the site data to convert the site data to a common format”, i.e. data is normalized) [and wherein facilitating importing of the scrubbed tenant data comprises making the scrubbed tenant data available to the target data system by storing the scrubbed tenant data in the data repository] in the normalized data format. (Fig. 1, Fig. 2, ¶56, “The data transformation module 230 can be configured to use customized rules and processes to aggregate the raw district data into a common format. Because each school district can uniquely set up and store their data, this normalization and data transformation process is specific for each district or school. The normalized data is then uploaded into host databases, such as host database 105”, ¶57, the data transformation module 230 can also aggregate the set of raw district data stored in district raw database 104 and implement district-specific data transformation rules to normalize the data and store the processed district data into the host database 105”, Fig. 8(820,830,840), ¶144, “performing data transformation and normalization operations on the site data to convert the site data to a common format”, i.e. data is normalized).
Regarding Claim 15, rejection of Claim 14 is included and Claim 15 is rejected with the same rationale as applied against Claim 2 above.
Regarding Claim 3, rejection of Claim 2 is included and for the same motivation Hossain does not teach explicitly,
The method of claim 2, wherein the tenant data is converted from a first native data format associated with the source data system to the normalized data format prior to being exported to the data repository.
However, Williams teaches,
The method of claim 2, wherein the tenant data is converted from a first native data format associated with the source data system to the normalized data format prior to being exported to the data repository. (¶38, Fig. 1, Fig. 2, ¶56, “The data transformation module 230 can be configured to use customized rules and processes to aggregate the raw district data into a common format. Because each school district can uniquely set up and store their data, this normalization and data transformation process is specific for each district or school. The normalized data is then uploaded into host databases, such as host database 105”, , i.e. data is converted from a first native format to the normalized data format and then uploaded (exported) to the database).
Regarding Claim 4, rejection of Claim 3 is included and for the same motivation Hossain teaches,
The method of claim 3, wherein the scrubbed tenant data is converted from [the normalized data format] to a second native data format associated with the target data system prior to importing the scrubbed tenant data into the target data system. (Fig. 6(618), ¶110, “In step 618, the method for heuristic scrubbing of sensitive and secure customer data from production application heap dump files in a database network system may be implemented”, i.e. scrubbed data are imported into a database network system (target data environment) and heap data are converted to a database format).
Hossain does not teach explicitly,
The method of claim 3, [wherein the scrubbed tenant data is converted from] the normalized data format [to a second native data format associated with the target data system prior to importing the scrubbed tenant data into the target data system].
However, Williams teaches,
The method of claim 3, [wherein the scrubbed tenant data is converted from] the normalized data format (Fig. 1, Fig. 2, ¶56, “The data transformation module 230 can be configured to use customized rules and processes to aggregate the raw district data into a common format. Because each school district can uniquely set up and store their data, this normalization and data transformation process is specific for each district or school. The normalized data is then uploaded into host databases, such as host database 105”, ¶57, the data transformation module 230 can also aggregate the set of raw district data stored in district raw database 104 and implement district-specific data transformation rules to normalize the data and store the processed district data into the host database 105”, Fig. 8(820,830,840), ¶144, “performing data transformation and normalization operations on the site data to convert the site data to a common format”, i.e. normalized data format) [to a second native data format associated with the target data system prior to importing the scrubbed tenant data into the target data system].
Regarding Claim 5, rejection of Claim 1 is included and for the same motivation Hossain teaches,
The method of claim 1, further comprising:
applying a respective additional set of scrubber rules to each of one or more additional subsets of the data objects of the tenant data to obtain additional scrubbed tenant data, wherein each additional subset of the data objects is associated with a respective corresponding additional data object type; (Fig. 3, ¶55, Fig. 5A, ¶92, i.e. Examiner submits that there are different rules for different objects. Thus for a second object an additional set of scrubber rules applies) and
facilitating importing of the additional scrubbed tenant data into the target data system. (Fig. 6(618), ¶110, “In step 618, the method for heuristic scrubbing of sensitive and secure customer data from production application heap dump files in a database network system may be implemented”, i.e. scrubbed data are imported into a database network system (target data environment)).
Referring to Claims 6 and 17:
Regarding Claim 6, rejection of Claim 1 is included and for the same motivation Hossain teaches,
The method of claim 1, wherein the tenant data is associated with a particular tenant of a plurality of tenants in the multi-tenant system, wherein an existing copy of the tenant data is maintained in the target data system, and wherein the scrubbed tenant data is an additional copy of the tenant data that is imported into the target data system. (Fig. 1, ¶29, Fig. 2, ¶38-¶40, ¶95-¶98, “The new, scrubbed heap dump created”, i.e. Examiner submit that new scrubbed dump is an additional copy of the original tenant data).
Regarding Claim 17, rejection of Claim 14 is included and Claim 17 is rejected with the same rationale as applied against Claim 6 above.
Regarding Claim 7, rejection of Claim 1 is included and for the same motivation Hossain teaches,
The method of claim 1, wherein the respective predefined transformation comprises replacing the sensitive information in the tenant data with cleaned tenant data. (Fig. 3, ¶55, “At the conclusion of step 320, the scrubbed heap dump is identical to the original heap dump, except the sensitive information has been replaced with nonsensitive information (e.g., nonsense information such as a sting of zeroes)”, Fig. 5, ¶93, Fig. 5B, Fig. 5C, ¶109).
Regarding Claim 12, rejection of Claim 1 is included and for the same motivation Hossain teaches,
The method of claim 1, wherein the set of scrubber rules is applied to the at least a subset of the data objects of the tenant data as the tenant data is being replicated from the source data system to the target data system. (Fig. 5A, ¶90-¶95).
Regarding Claim 13, rejection of Claim 1 is included and for the same motivation Hossain teaches,
The method of claim 1, wherein the sensitive information includes personally identifiable information. (Abstract, ¶11, Fig. 5B, ¶100).
Regarding Claim 16, rejection of Claim 15 is included and for the same motivation Hossain teaches,
The system of claim 15, [wherein the tenant data is converted from a first native data format associated with the source data environment to the normalized data format prior to being exported to the data repository], and wherein the scrubbed tenant data is converted from [the normalized data format] to a second native data format associated with the target data system prior to importing the scrubbed tenant data into the target data system. (Fig. 6(618), ¶110, “In step 618, the method for heuristic scrubbing of sensitive and secure customer data from production application heap dump files in a database network system may be implemented”, i.e. scrubbed data are imported into a database network system (target data environment) and heap data are converted to a database format)
Hossain does not teach explicitly,
The system of claim 15, wherein the tenant data is converted from a first native data format associated with the source data environment to the normalized data format prior to being exported to the data repository, [and wherein the scrubbed tenant data is converted from] the normalized data format [to a second native data format associated with the target data system prior to importing the scrubbed tenant data into the target data system].
However, Williams teaches,
The system of claim 15, wherein the tenant data is converted from a first native data format associated with the source data environment to the normalized data format prior to being exported to the data repository, (¶38, Fig. 1, Fig. 2, ¶56, “The data transformation module 230 can be configured to use customized rules and processes to aggregate the raw district data into a common format. Because each school district can uniquely set up and store their data, this normalization and data transformation process is specific for each district or school. The normalized data is then uploaded into host databases, such as host database 105”, i.e. data is converted from a first native format to the normalized data format and then uploaded (exported) to the database) [and wherein the scrubbed tenant data is converted from] the normalized data format (Fig. 1, Fig. 2, ¶56, “The data transformation module 230 can be configured to use customized rules and processes to aggregate the raw district data into a common format. Because each school district can uniquely set up and store their data, this normalization and data transformation process is specific for each district or school. The normalized data is then uploaded into host databases, such as host database 105”, ¶57, the data transformation module 230 can also aggregate the set of raw district data stored in district raw database 104 and implement district-specific data transformation rules to normalize the data and store the processed district data into the host database 105”, Fig. 8(820,830,840), ¶144, “performing data transformation and normalization operations on the site data to convert the site data to a common format”, i.e. normalized data format) [to a second native data format associated with the target data system prior to importing the scrubbed tenant data into the target data system].
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Hossain et al. (US PGPUB. # US 2011/0276610, hereinafter “Hossain”), and further in view of Williams et al. (US PGPUB. # US 2021/0327009, hereinafter “Williams”), and further in view of Raju et al. (US PGPUB. # US 2021/0124842, hereinafter “Raju”).
Regarding Claim 8, rejection of claim 7 is included and combination of Hossain and Williams does not teach explicitly,
The method of claim 7, further comprising maintaining, at least in part, referential integrity of the cleaned tenant data.
However, Raju teaches,
The method of claim 7, further comprising maintaining, at least in part, referential integrity of the cleaned tenant data. (Fig. 2, ¶54, i.e. referential integrity is maintained).
As per KSR vs Teleflex, combining prior art elements according to known methods (device, product) to yield predictable results may be used to create a prima facie case of obviousness.
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the teachings of Raju with the invention of Hossain in view of Williams.
Hossain in view of Williams teaches, accessing tenant data and identifying objects for scrubbing the data based on matching scrubbing rules and normalizing the exported data from various databases. Raju teaches, maintaining referential integrity of plain data. Therefore, it would have been obvious to maintain referential integrity of plain data of Raju into the teachings of Hossain to provide an end-to-end solution for importing data from production applications, automatically identifying NPI in the production data, sanitizing the NPI and staging the sanitized production data for subsequent loading/seeding of data to the testing and development applications while maintaining referential integrity.
KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007).
Regarding Claim 18, rejection of Claim 14 is included and Hossain teaches,
The system of claim 14, wherein the respective predefined transformation comprises replacing the sensitive information in the tenant data with cleaned tenant data, (Fig. 3, ¶55, “At the conclusion of step 320, the scrubbed heap dump is identical to the original heap dump, except the sensitive information has been replaced with nonsensitive information (e.g., nonsense information such as a sting of zeroes)”, Fig. 5, ¶93, Fig. 5B, Fig. 5C, ¶109), [and wherein referential integrity of the cleaned tenant data is maintained].
As per KSR vs Teleflex, combining prior art elements according to known methods (device, product) to yield predictable results may be used to create a prima facie case of obviousness.
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the teachings of Williams with the invention of Hossain.
Hossain teaches, accessing tenant data and identifying objects for scrubbing the data based on matching scrubbing rules. Williams teaches, normalizing the exported data from various databases. Therefore, it would have been obvious to normalize the exported data from various databases of Williams with accessing tenant data and identifying objects for scrubbing the data based on matching scrubbing rules with Hossain to comply with privacy laws prior to sharing tenant data with third parties.
KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007).
Combination of Hossain and Williams does not teach explicitly
The system of claim 14, [wherein the respective predefined transformation comprises replacing the sensitive information in the tenant data with cleaned tenant data], and wherein referential integrity of the cleaned tenant data is maintained.
However, Raju teaches,
The system of claim 14, [wherein the respective predefined transformation comprises replacing the sensitive information in the tenant data with cleaned tenant data], and wherein referential integrity of the cleaned tenant data is maintained. (Fig. 2, ¶54, i.e. referential integrity is maintained).
As per KSR vs Teleflex, combining prior art elements according to known methods (device, product) to yield predictable results may be used to create a prima facie case of obviousness.
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the teachings of Raju with the invention of Hossain in view of Williams.
Hossain in view of Williams teaches, accessing tenant data and identifying objects for scrubbing the data based on matching scrubbing rules and normalizing the exported data from various databases. Raju teaches, maintaining referential integrity of plain data. Therefore, it would have been obvious to maintain referential integrity of plain data of Raju into the teachings of Hossain to provide an end-to-end solution for importing data from production applications, automatically identifying NPI in the production data, sanitizing the NPI and staging the sanitized production data for subsequent loading/seeding of data to the testing and development applications while maintaining referential integrity.
KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007).
Claims 9-11 and 19-22 are rejected under 35 U.S.C. 103 as being unpatentable over Hossain et al. (US PGPUB. # US 2011/0276610, hereinafter “Hossain”), and further in view of Williams et al. (US PGPUB. # US 2021/0327009, hereinafter “Williams”), and further in view of Bilodeau et al. (US PGPUB. # US 2015/0213288, hereinafter “Bilodeau”),
Referring to Claims 9 and 19:
Regarding Claim 9, rejection of Claim 1 is included and Hossain teaches,
The method of claim 1, wherein each scrubber rule in the set of scrubber rules comprises a file path for an object field to be scrubbed, [an identifier of a scrubber], and a scrubber parameter comprising an input value to be transformed by the scrubber. (Fig. 5A, ¶87, ¶89, “The mapped relationships may be used for heuristic based scrubbing, as described in step 512, as only data belonging to a particular class and linked to a particular object may be scrubbed”, ¶93-¶94).
As per KSR vs Teleflex, combining prior art elements according to known methods (device, product) to yield predictable results may be used to create a prima facie case of obviousness.
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the teachings of Williams with the invention of Hossain.
Hossain teaches, accessing tenant data and identifying objects for scrubbing the data based on matching scrubbing rules. Williams teaches, normalizing the exported data from various databases. Therefore, it would have been obvious to normalize the exported data from various databases of Williams with accessing tenant data and identifying objects for scrubbing the data based on matching scrubbing rules with Hossain to comply with privacy laws prior to sharing tenant data with third parties.
KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007).
Combination of Hossain and Williams does not teach explicitly,
The method of claim 1,[ wherein each scrubber rule in the set of scrubber rules comprises a file path for an object field to be scrubbed], an identifier of a scrubber, [and a scrubber parameter comprising an input value to be transformed by the scrubber].
However, Bilodeau teaches,
The method of claim 1,[ wherein each scrubber rule in the set of scrubber rules comprises a file path for an object field to be scrubbed], an identifier of a scrubber, [and a scrubber parameter comprising an input value to be transformed by the scrubber].
As per KSR vs Teleflex, combining prior art elements according to known methods (device, product) to yield predictable results may be used to create a prima facie case of obviousness.
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the teachings of Bilodeau with the invention of Hossain in view of Williams.
Hossain in view of Williams teaches, accessing tenant data and identifying objects for scrubbing the data based on matching scrubbing rules and normalizing the exported data from various databases. Bilodeau teaches, an identifier of a scrubber to identify the scrubbing rules. Therefore, it would have been obvious to have an identifier of a scrubber to identify the scrubbing rules of Bilodeau into the teachings of Hossain to protect personally identifiable information according to government laws and/or regulations.
KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007).
Regarding Claim 19, rejection of Claim 14 is included and Claim 19 is rejected with the same rationale as applied against Claim 9 above.
Referring to Claims 10 and 20:
Regarding Claim 10, rejection of Claim 9 is included and for the same motivation Hossain teaches,
The method of claim 9, wherein a first scrubber rule in the set of scrubber rules further comprises a filter, and wherein applying the set of scrubber rules to the at least a subset of the data objects of the tenant data comprises conditionally applying the first scrubber rule to the at least a subset of data objects of the tenant data. Fig. 3, ¶55, Fig. 5A, ¶92-¶93).
Regarding Claim 20, rejection of Claim 19 is included and Claim 20 is rejected with the same rationale as applied against Claim 10 above.
Referring to Claims 11 and 21:
Regarding Claim 11, rejection of Claim 10 is included and for the same motivation Hossain teaches,
The method of claim 10, wherein the conditionally applying the first scrubber rule to the at least a subset of data objects comprises, for each data object:
determining, based on the filter, whether the first scrubber rule is applicable to the data object; ; (Fig. 3, ¶55, “the user may enter a list of classes that are expected to contain sensitive information, and certain string variables or any string variable in that class may be scrubbed”), and
when it is determined that the first scrubber rule is applicable to the data object, applying the respective predefined transformation of the first scrubber rule to an input value contained in the object field to be scrubbed in the data object to obtain a corresponding scrubbed data object having a scrubbed value in the object field. (Fig. 5A, ¶91-¶93, Fig. 5B, Fig. 5C, ¶108).
Regarding Claim 21, rejection of Claim 20 is included and Claim 21 is rejected with the same rationale as applied against Claim 11 above.
Regarding Claim 22, rejection of Claim 14 is included and combination of Hossain and Williams does not teach explicitly,
The system of claim 14, wherein the system is a centralized data transformation system located remotely from the source data system and the target data system.
However, Bilodeau teaches,
The system of claim 14, wherein the system is a centralized data transformation system located remotely from the source data system and the target data system. (Fig. 1, ¶32-¶37).
As per KSR vs Teleflex, combining prior art elements according to known methods (device, product) to yield predictable results may be used to create a prima facie case of obviousness.
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the teachings of Bilodeau with the invention of Hossain in view of Williams.
Hossain in view of Williams teaches, accessing tenant data and identifying objects for scrubbing the data based on matching scrubbing rules and normalizing the exported data from various databases. Bilodeau teaches, an identifier of a scrubber to identify the scrubbing rules. Therefore, it would have been obvious to have an identifier of a scrubber to identify the scrubbing rules of Bilodeau into the teachings of Hossain to protect personally identifiable information according to government laws and/or regulations.
KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Refer to PTO-892, Notice of References Cited for a listing of analogous art.
Iyoob et al. (US PGPUB. # US 2021/0133557) discloses, systems, methods and program products for automating pseudonymization of personal identifying information (PII) using machine learning, metadata, and crowdsourcing patterns to identify and replace PII. Machine learning models are trained for classifying known column names or key names for processing, using metadata. Column or key names are classified to be unprocessed, anonymized or pseudonymized by a pseudonymizer without revealing PII or scrubbing data into a useless format. A library of crowdsourced patterns are utilized for matching PII to data values within column or key names and PII is mapped to replacement methods. Feedback from user annotations retrains the algorithms to improve classification accuracy and Deep Learning algorithms automate the identification of PII using regular expression generation to concisely articulate how pseudonymizers search for PII patterns within a data set. PII replacement is mapped consistently across entire data packages and the crowdsourced pattern library is updated with generated regular expressions.
Jayawardena et al. (US PGPUB. # US 2021/0067489) discloses, technologies directed to scrubbed internet protocol domain for enhanced cloud security are disclosed herein. In various aspects, a system can include a processor and memory storing instructions that, upon execution, cause performance of operations. The operations can include exposing an application to a service provider network that provides an internet connection, where the application is provided by a datacenter that communicates with the service provider network. The operations can include monitoring traffic flows to the application during an observation time period, where the traffic flows includes probe traffic that attempts to reach the application. The operations can include constructing a scrubbed internet protocol domain such that detected probe traffic is prevented from reaching a plurality of virtual machines provided by the datacenter.
Patton et al. (US PGPUB. # US 2020/0241991) discloses, computer program products for event detection removing private information. In one aspect, an event detection infrastructure determines that characteristics of multiple signals, when considered collectively, indicate an event of interest to one or more parties. In another aspect, an evaluation module determines that characteristics of one or more signals indicate a possible event of interest to one or more parties. A validator then determines that characteristics of one or more other signals validate the possible event as an actual event of interest to the one or more parties. Signal features can be used to compute probabilities of events occurring. A privacy infrastructure spans signal ingestion, event detection, and event notification and protects the integrity of private information.
Brooks et al. (US PGPUB. # US 2017/0003947) discloses, methods for creating, exporting, viewing and testing, and importing custom applications in a multitenant database environment. These mechanisms and methods can enable embodiments to provide a vehicle for sharing applications across organizational boundaries. The ability to share applications across organizational boundaries can enable tenants in a multi-tenant database system, for example, to easily and efficiently import and export, and thus share, applications with other tenants in the multi-tenant environment.
John Peter Brinnand (US PGPUB. # US 2016/0321308) discloses, a method of ingesting data in an enterprise server environment. A configuration file is accessed. The configuration file specifies a blueprint for constructing a data adaptor that includes a data adaptor source, a data adaptor sink, and a data adaptor channel coupled between the data adaptor source and the data adaptor sink. The data adaptor is constructed based on the configuration file. Via the data adaptor, data is retrieved from a first entity. Also via the data adaptor, the retrieved data is written to a second entity different from the first entity. The accessing, the constructing, the retrieving, and the writing are performed by one or more electronic processors.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DARSHAN I DHRUV whose telephone number is (571)272-4316. The examiner can normally be reached M-F 9:00 AM-5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Yin-Chen Shaw can be reached at 571-272-8878. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DARSHAN I DHRUV/Primary Examiner, Art Unit 2498
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