DETAILED ACTION
This Office Action is in response to the original application filed 04/07/2025 and the preliminary amendment filed on 08/14/2025. Claim 1 is cancelled; and claims 2-20 are added; therefore claims 2-20 are pending in the application, of which, claims 2, 14, and 20 are presented in independent form.
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 .
Priority
This application is a CON that claims the benefit of U.S. Patent Application No. 18/760,961 filed on 07/01/2024, which has since been issued as U.S. Patent No. 12,277,099, which claims the benefit of U.S. Patent Application No. 18/328,337 filed on 06/02/2023, which has since been issued as U.S. Patent No. 12,050,572.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/14/2025 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings submitted on 04/07/2025 are accepted.
Specification
The specification submitted on 04/07/2025 is accepted.
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 2-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 7, 8, 11-14, 16, and 17 of U.S. Patent No. 12,050,572. Although the claims at issue are not identical, they are not patentably distinct from each other because of the mapping presented below.
Present Application 19/172,535
Patent No. 12,050,572
Analysis
2. A computer-implemented method for merging records from a plurality of database systems, comprising:
receiving, by one or more processors and from the plurality of database systems, a plurality of records comprising a first record and a merged record,
wherein each of the plurality of records is a patient record or an employee record and
wherein the merged record comprises two or more records and forms a person database entity associated with a unique entity identifier;
determining, by the one or more processors, that a threshold match exists between the first record and the merged record based upon a set of threshold matching criteria; and
merging, by the one or more processors, the first record, based upon determining that the threshold match exists, into the person database entity.
1. A computer-implemented method for merging records from a plurality of database systems, comprising:
receiving, by one or more processors and from the plurality of database systems, two or more database tables comprising a plurality of records;
each of the plurality of records is a patient record or an employee record;
merging… the two or more record… into a person database entity, wherein a unique entity identifier is associated with the person database entity;
determining, by the one or more processors, that a threshold match exists between the two or more records based upon a set of threshold matching criteria;
merging, by the one or more processors, the two or more records, based upon determining that the threshold match exists, into a person database entity
Same preamble.
Both receive a plurality of records from a plurality of database systems.
Same limitation.
Both have merged records from two or more records forming a person database entity associated with a unique entity identifier.
Both determine matches between records based on threshold matching criteria.
Both merge records into a person database entity based on determining that the threshold match exists.
Independent claims 14 and 20 are essentially just different embodiments of the same claimed limitations.
3. The computer-implemented method of claim 2, wherein the threshold match exists when a threshold number of data points of demographic information of the first record matches data points of demographic information of the two or more records match.
2. The computer-implemented method of claim 1, wherein each record of the two or more records comprises one or more data points of demographic information, and wherein the threshold match exists when a threshold number of the one or more data points of demographic information of the two or more records match.
Essentially the same limitation.
4. The computer-implemented method of claim 3, wherein the data points of demographic information comprise at least one of a name, a date of birth, or a zip code.
3. The computer-implemented method of claim 2, wherein the one or more data points of demographic information comprises at least one of a name, a date of birth, or a zip code.
Essentially the same limitation.
5. The computer-implemented method of claim 2, wherein the set of threshold matching criteria comprises a number of one or more data points of demographic information that can be used to determine the threshold match at a particular confidence rate.
4. The computer-implemented method of claim 1, wherein the set of threshold matching criteria comprises a number of one or more data points of demographic information that can be used to determine the threshold match at a particular confidence rate.
Same limitation.
Claim 16 is essentially just a different embodiment of the same claimed limitation.
6. The computer-implemented method of claim 2, further comprising recording the merging of the first record into the person database entity in a history of record changes and merges.
7. The computer-implemented method of claim 1, wherein merging the two or more records comprises storing a history of record changes and merges.
Essentially the same limitation.
7. The computer-implemented method of claim 2, wherein the person database entity is associated with a set of records pertaining to a single person, the set of records including the two or more records.
8. The computer-implemented method of claim 1, wherein the person database entity is associated with a set of records pertaining to a single person, the set of records including the two or more records.
Same limitation.
8. The computer-implemented method of claim 7, wherein the single person is both a patient and an employee and the set of records comprise at least one patient record and at least one employee record.
11. The computer-implemented method of claim 8, wherein the single person is both a patient and an employee and the set of records comprise at least one patient record and at least one employee record.
Same limitation.
9. The computer-implemented method of claim 7, further comprising:
determining, by the one or more processors, one or more anomalous activities based on data pertaining to the set of records.
12. The computer-implemented method of claim 8, further comprising:
determining, by the one or more processors and using a machine learning model, one or more anomalous activities based on data pertaining to the set of records.
Essentially the same limitation.
10. The computer-implemented method of claim 9, wherein the one or more anomalous activities comprise:
unauthorized access to patient data; and/or
drug diversion activity.
13. The computer-implemented method of claim 12, wherein the one or more anomalous activities comprise:
unauthorized access to patient data; and/or
drug diversion activity.
Same limitation.
11. The computer-implemented method of claim 2, wherein the unique entity identifier facilitates access to the person database entity by a plurality of platforms, each of the plurality of platforms configured to analyze the plurality of records.
14. The computer-implemented method of claim 1, wherein the unique entity identifier facilitates access to the person database entity by a plurality of platforms, each of the plurality of platforms configured to analyze the plurality of records.
Same limitation.
Claim 19 is essentially just a different embodiment of the same claimed limitation.
12. The computer-implemented method of claim 2, furthering comprising causing, by the one or more processors, a graphical user interface to be displayed on a computing device of a user for updating the person database entity,
wherein the graphical user interface is configured to enable the user to at least one of:
add a new record to the person database entity using one or more first graphical elements of the graphical user interface; or
remove records from the person database entity using one or more second graphical elements of the graphical user interface.
16. The computer-implemented method of claim 1, furthering comprising causing, by the one or more processors, a graphical user interface to be displayed on a computing device of a user for updating the person database entity,
wherein the graphical user interface is configured to enable the user to at least one of:
add a new record to the person database entity using one or more first graphical elements of the graphical user interface; or
remove a record of the two or more records from the person database entity using one or more second graphical elements of the graphical user interface.
Same limitation.
Same limitation.
Same limitation.
Essentially the same limitations.
13. The computer-implemented method of claim 2, further comprising causing, by the one or more processors, a graphical user interface to be displayed on a computing device of a user,
wherein the graphical user interface is configured to enable the user to at least one of:
select records to be merged into the person database entity independent of whether the threshold match exists between the records; or
select records to be not merged into the person database entity independent of whether the threshold match exists between the records.
17. The computer-implemented method of claim 1, further comprising causing, by the one or more processors, a graphical user interface to be displayed on a computing device of a user,
wherein the graphical user interface is configured to enable the user to at least one of:
select records to be merged into a person database entity independent of whether a threshold match exists between the records; or
select records to be not merged into a person database entity independent of whether a threshold match exists between the records.
Same limitation.
Same limitation.
Same limitation.
Same limitation.
15. The non-transitory computer readable medium of claim 14,
wherein the threshold match exists when a threshold number of data points of demographic information of the first record matches data points of demographic information of the two or more records match,
wherein the data points of demographic information comprise at least one of a name, a date of birth, or a zip code.
2. wherein the threshold match exists when a threshold number of the one or more data points of demographic information of the two or more records match.
3. wherein the one or more data points of demographic information comprises at least one of a name, a date of birth, or a zip code.
Essentially the same limitations.
Essentially the same limitations.
18. The non-transitory computer readable medium of claim 17, wherein the instructions further cause the one or more processors to determine one or more anomalous activities based on data pertaining to the set of records,
wherein the one or more anomalous activities comprise:
unauthorized access to patient data; and/or
drug diversion activity.
12. determining, by the one or more processors and using a machine learning model, one or more anomalous activities based on data pertaining to the set of records.
13. wherein the one or more anomalous activities comprise:
unauthorized access to patient data; and/or
drug diversion activity.
Essentially the same limitations.
Same limitations.
Claims 2-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 7, 8, 11-14, 16, and 17 of U.S. Patent No. 12,277,099. Although the claims at issue are not identical, they are not patentably distinct from each other because of the mapping presented below.
Present Application 19/172,535
Patent No. 12,277,099
Analysis
2. A computer-implemented method for merging records from a plurality of database systems, comprising:
receiving, by one or more processors and from the plurality of database systems, a plurality of records comprising a first record and a merged record,
wherein each of the plurality of records is a patient record or an employee record and
wherein the merged record comprises two or more records and forms a person database entity associated with a unique entity identifier;
determining, by the one or more processors, that a threshold match exists between the first record and the merged record based upon a set of threshold matching criteria; and
merging, by the one or more processors, the first record, based upon determining that the threshold match exists, into the person database entity.
1. A computer-implemented method for merging records from a plurality of database systems, comprising:
comparing, by one or more processors, two or more records of a plurality of records, wherein the plurality of records are from a plurality of database systems and
each of the plurality of records is a patient record or an employee record;
merging… the two or more record… into a person database entity, wherein a unique entity identifier is associated with the person database entity;
determining, by the one or more processors, that a threshold match exists between the two or more records based upon a set of threshold matching criteria;
merging, by the one or more processors, the two or more records, based upon determining that the threshold match exists, into a person database entity,
Same preamble.
Both process a plurality of records from a plurality of database systems.
Same limitation.
Both have merged records from two or more records forming a person database entity associated with a unique entity identifier.
Both determine matches between records based on threshold matching criteria.
Both merge records into a person database entity based on determining that the threshold match exists.
Independent claims 14 and 20 are essentially just different embodiments of the same claimed limitations.
3. The computer-implemented method of claim 2, wherein the threshold match exists when a threshold number of data points of demographic information of the first record matches data points of demographic information of the two or more records match.
2. The computer-implemented method of claim 1, wherein each record of the two or more records comprises one or more data points of demographic information, and wherein the threshold match exists when a threshold number of the one or more data points of demographic information of the two or more records match.
Essentially the same limitation.
4. The computer-implemented method of claim 3, wherein the data points of demographic information comprise at least one of a name, a date of birth, or a zip code.
3. The computer-implemented method of claim 2, wherein the one or more data points of demographic information comprises at least one of a name, a date of birth, or a zip code.
Essentially the same limitation.
5. The computer-implemented method of claim 2, wherein the set of threshold matching criteria comprises a number of one or more data points of demographic information that can be used to determine the threshold match at a particular confidence rate.
4. The computer-implemented method of claim 1, wherein the set of threshold matching criteria comprises a number of one or more data points of demographic information that can be used to determine the threshold match at a particular confidence rate.
Same limitation.
Claim 16 is essentially just a different embodiment of the same claimed limitation.
6. The computer-implemented method of claim 2, further comprising recording the merging of the first record into the person database entity in a history of record changes and merges.
7. The computer-implemented method of claim 1, wherein merging the two or more records comprises storing a history of record changes and merges.
Essentially the same limitation.
7. The computer-implemented method of claim 2, wherein the person database entity is associated with a set of records pertaining to a single person, the set of records including the two or more records.
8. The computer-implemented method of claim 1, wherein the person database entity is associated with a set of records pertaining to a single person, the set of records including the two or more records.
Same limitation.
8. The computer-implemented method of claim 7, wherein the single person is both a patient and an employee and the set of records comprise at least one patient record and at least one employee record.
11. The computer-implemented method of claim 8, wherein the single person is both a patient and an employee and the set of records comprise at least one patient record and at least one employee record.
Same limitation.
9. The computer-implemented method of claim 7, further comprising:
determining, by the one or more processors, one or more anomalous activities based on data pertaining to the set of records.
12. The computer-implemented method of claim 8, further comprising:
determining, by the one or more processors and using a machine learning model, one or more anomalous activities based on data pertaining to the set of records.
Essentially the same limitation.
10. The computer-implemented method of claim 9, wherein the one or more anomalous activities comprise:
unauthorized access to patient data; and/or
drug diversion activity.
13. The computer-implemented method of claim 12, wherein the one or more anomalous activities comprise:
unauthorized access to patient data; and/or
drug diversion activity.
Same limitation.
11. The computer-implemented method of claim 2, wherein the unique entity identifier facilitates access to the person database entity by a plurality of platforms, each of the plurality of platforms configured to analyze the plurality of records.
14. The computer-implemented method of claim 1, wherein the unique entity identifier facilitates access to the person database entity by a plurality of platforms, each of the plurality of platforms configured to analyze the plurality of records.
Same limitation.
Claim 19 is essentially just a different embodiment of the same claimed limitation.
12. The computer-implemented method of claim 2, furthering comprising causing, by the one or more processors, a graphical user interface to be displayed on a computing device of a user for updating the person database entity,
wherein the graphical user interface is configured to enable the user to at least one of:
add a new record to the person database entity using one or more first graphical elements of the graphical user interface; or
remove records from the person database entity using one or more second graphical elements of the graphical user interface.
16. The computer-implemented method of claim 1, furthering comprising causing, by the one or more processors, a graphical user interface to be displayed on a computing device of a user for updating the person database entity,
wherein the graphical user interface is configured to enable the user to at least one of:
add a new record to the person database entity using one or more first graphical elements of the graphical user interface; or
remove a record of the two or more records from the person database entity using one or more second graphical elements of the graphical user interface.
Same limitation.
Same limitation.
Same limitation.
Essentially the same limitations.
13. The computer-implemented method of claim 2, further comprising causing, by the one or more processors, a graphical user interface to be displayed on a computing device of a user,
wherein the graphical user interface is configured to enable the user to at least one of:
select records to be merged into the person database entity independent of whether the threshold match exists between the records; or
select records to be not merged into the person database entity independent of whether the threshold match exists between the records.
17. The computer-implemented method of claim 1, further comprising causing, by the one or more processors, a graphical user interface to be displayed on a computing device of a user,
wherein the graphical user interface is configured to enable the user to at least one of:
select records to be merged into a person database entity independent of whether a threshold match exists between the records; or
select records to be not merged into a person database entity independent of whether a threshold match exists between the records.
Same limitation.
Same limitation.
Same limitation.
Same limitation.
15. The non-transitory computer readable medium of claim 14,
wherein the threshold match exists when a threshold number of data points of demographic information of the first record matches data points of demographic information of the two or more records match,
wherein the data points of demographic information comprise at least one of a name, a date of birth, or a zip code.
2. wherein the threshold match exists when a threshold number of the one or more data points of demographic information of the two or more records match.
3. wherein the one or more data points of demographic information comprises at least one of a name, a date of birth, or a zip code.
Essentially the same limitations.
Essentially the same limitations.
18. The non-transitory computer readable medium of claim 17, wherein the instructions further cause the one or more processors to determine one or more anomalous activities based on data pertaining to the set of records,
wherein the one or more anomalous activities comprise:
unauthorized access to patient data; and/or
drug diversion activity.
12. determining, by the one or more processors and using a machine learning model, one or more anomalous activities based on data pertaining to the set of records.
13. wherein the one or more anomalous activities comprise:
unauthorized access to patient data; and/or
drug diversion activity.
Essentially the same limitations.
Same limitations.
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 2-8, 11, 14-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (U.S. Pub. No. 2021/0294797, cited in IDS), hereinafter Gupta, in view of Gallivan et al. (U.S. Pub. No. 2013/0304506, cited in IDS), hereinafter Gallivan.
Regarding independent claim 2, Gupta teaches a computer-implemented method for merging records from a plurality of database systems, (Gupta, [0012], discloses "…managing conflation of and access to data associated with multiple data providers… datasets from two or more organizations or data providers. The data is joined, merged, or otherwise conflated to produce one or more new datasets, which are stored in distributed environments that allow the datasets to be queried, analyzed, and/or served to users authorized by the data providers…". Gupta, [0043], discloses "…conflation of data includes joining, merging, or otherwise combining fields or records from two or more datasets.") comprising:
receiving, by one or more processors and from the plurality of database systems, a plurality of records comprising a first record and a merged record, (Gupta, Fig. 5 and [0111], discloses a computer system with a processor. Gupta, [0015], discloses producing a joined dataset from two or more input datasets [e.g. a plurality of records comprising records and merged records], with each record in the joined dataset [i.e. merged record] representing a match across entities in the input datasets and includes one or more fields from each of the input datasets. Examiner interprets that one of the input datasets could be itself a joined dataset containing merged records.) wherein each of the plurality of records is a patient record or an employee record (Gupta, [0058]-[0059] and [0049], discloses merging of employee records from a dataset with records of other datasets from other data providers.) and wherein the merged record comprises two or more records and forms a person database entity (Gupta, Fig. 5 and [0111], discloses a computer system with a processor. Gupta, [0015], discloses producing a joined dataset from two or more input datasets [e.g. a plurality of records comprising records and merged records], with each record in the joined dataset [i.e. merged record] representing a match across entities in the input datasets and includes one or more fields from each of the input datasets. Gupta, [0043], discloses "…conflation of data includes joining, merging, or otherwise combining fields or records from two or more datasets." Gupta, [0058]-[0059], discloses the conflation apparatus applies a machine learning model to features that include fields in the user and employee records and/or comparisons of fields in the user records with corresponding fields in the employee records. In response to the inputted features, the machine learning model outputs, for a given pair of records that include a user record from the first dataset and an employee record from the second dataset, a match score representing the level of confidence that the user record and employee record represent the same user. When the match score exceeds a threshold, the conflation apparatus establishes a match between the user record and employee record and joins the records in the input datasets based on a configurable requirement or threshold for the level of confidence in matches between entities represented by the records. Gupta, [0104], discloses data in the joined dataset may be stored in one or more data stores within the platform.)
determining, by the one or more processors, that a threshold match exists between the first record and the merged record based upon a set of threshold matching criteria; (Gupta, [0058]-[0059], discloses the conflation apparatus applies a machine learning model to features that include fields in the user and employee records and/or comparisons of fields in the user records with corresponding fields in the employee records. In response to the inputted features, the machine learning model outputs, for a given pair of records that include a user record from the first dataset and an employee record from the second dataset, a match score representing the level of confidence that the user record and employee record represent the same user. When the match score exceeds a threshold, the conflation apparatus establishes a match between the user record and employee record and joins the records in the input datasets based on a configurable requirement or threshold for the level of confidence in matches between entities represented by the records.) and
merging, by the one or more processors, the first record, based upon determining that the threshold match exists, into the person database entity. (Gupta, [0043], discloses "…conflation of data includes joining, merging, or otherwise combining fields or records from two or more datasets." Gupta, [0058]-[0059], discloses the conflation apparatus applies a machine learning model to features that include fields in the user and employee records and/or comparisons of fields in the user records with corresponding fields in the employee records. In response to the inputted features, the machine learning model outputs, for a given pair of records that include a user record from the first dataset and an employee record from the second dataset, a match score representing the level of confidence that the user record and employee record represent the same user. When the match score exceeds a threshold, the conflation apparatus establishes a match between the user record and employee record and joins the records in the input datasets based on a configurable requirement or threshold for the level of confidence in matches between entities represented by the records. Gupta, [0104], discloses data in the joined dataset may be stored in one or more data stores within the platform.)
However, Gupta does not explicitly teach wherein the merged record comprises two or more records and forms a person database entity associated with a unique entity identifier;
On the other hand, Gallivan teaches wherein the merged record comprises two or more records and forms a person database entity associated with a unique entity identifier; (Gallivan, [0014], discloses aggregating [e.g. merging/combining] health care related data associated with patients from various sources and generating a unique identifier for each patient.)
Gallivan further teaches wherein each of the plurality of records is a patient record or an employee record (Gallivan, [0027], discloses receiving data from health care data sources such as medical databases, dental records, pharmaceutical, vision, and/or health risk assessment databases, and other sources such as employee attendance/absence records.)
The health related data from various sources of Gallivan can be the datasets from data providers of Gupta and the aggregating of data of Gallivan can be the conflation of data of Gupta. It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have modified the data conflation system of Gupta to incorporate the teachings of healthcare related data of Gallivan because both address the same field of consolidated data management, and by incorporating Gallivan into Gupta to allows the conflation of healthcare and non-healthcare related data associated with a person.
One of ordinary skill in the art would be motivated to do so as to provide an inexpensive way of aggregation of multiple healthcare sources that allows for large amounts of data for analysis, as taught by Gallivan [0008].
Independent claims 14 and 20 recite substantially the same limitations as independent claim 2 and therefore are rejected for substantially the same reasons. Independent claims 14 and 20 further recite a non-transitory computer readable medium storing instructions which, when executed by one or more processors and a record consolidation system for merging records from a plurality of database systems, comprising: a non-transitory computer readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to which are taught by Gupta, [0012], which discloses "…managing conflation of and access to data associated with multiple data providers… datasets from two or more organizations or data providers. The data is joined, merged, or otherwise conflated to produce one or more new datasets, which are stored in distributed environments that allow the datasets to be queried, analyzed, and/or served to users authorized by the data providers…" Gupta, [0043], which discloses "…conflation of data includes joining, merging, or otherwise combining fields or records from two or more datasets." and Gupta, Fig. 5 and [0111] and [0115], which disclose a computer system that includes a processor and memory and/or storage that stores code and/or data for use by the computer system.
Regarding claim 3, Gupta, in view of Gallivan, teaches the computer-implemented method of claim 2, wherein the threshold match exists when a threshold number of data points of demographic information of the first record matches data points of demographic information of the two or more records match. (Gupta, [0014] and [0057]-[0059], discloses conflating records from input datasets from two or more data providers by matching entities in the datasets based on similarity and/or overlap in fields related to the entities from the datasets like first name, last name, location, etc. A match score representing the level of confidence that the record records from the input datasets represent the same user is generated and conflating the records when the match score exceeds a threshold.)
Regarding claim 4, Gupta, in view of Gallivan, teaches the computer-implemented method of claim 3, wherein the data points of demographic information comprise at least one of a name, a date of birth, or a zip code. (Gupta, [0014] and [0057]-[0059], discloses conflating records from input datasets from two or more data providers by matching entities in the datasets based on similarity and/or overlap in fields related to the entities from the datasets like first name, last name, location [e.g., zip code], etc.)
Regarding claim 5, Gupta, in view of Gallivan, teaches the computer-implemented method of claim 2, wherein the set of threshold matching criteria comprises a number of one or more data points of demographic information that can be used to determine the threshold match at a particular confidence rate. (Gupta, [0014] and [0057]-[0059], discloses conflating records from input datasets from two or more data providers by matching entities in the datasets based on similarity and/or overlap in fields related to the entities from the datasets like first name, last name, location, etc. A match score representing the level of confidence that the record records from the input datasets represent the same user is generated and conflating the records when the match score exceeds a threshold.)
Claim 16 recites substantially the same limitations as claim 5, and is rejected for substantially the same reasons.
Regarding claim 6, Gupta, in view of Gallivan, teaches the computer-implemented method of claim 2, further comprising recording the merging of the first record into the person database entity in a history of record changes and merges. (Gupta, [0078], discloses the platform includes functionality to support auditing and maintaining logs of accesses to the input datasets, joined dataset, and/or other data managed by platform. Entries in the logs represent reads, writes, and/or transformations [e.g. changes and merges] of the data.)
Regarding claim 7, Gupta, in view of Gallivan, teaches the computer-implemented method of claim 2, wherein the person database entity is associated with a set of records pertaining to a single person, the set of records including the two or more records. (Gallivan, [0014], discloses aggregating [e.g. merging/combining] health care related data associated with patients from various sources and generating a unique identifier for each patient. In combination, Gupta, [0058]-[0059], discloses the conflation apparatus applies a machine learning model to records from datasets, and a match records that represent the same user.)
Regarding claim 8, Gupta, in view of Gallivan, teaches the computer-implemented method of claim 7, wherein the single person is both a patient and an employee and the set of records comprise at least one patient record and at least one employee record. (Gupta, [0058]-[0059] and [0049], discloses merging of employee records from a dataset with records of other datasets from other data providers. In combination, Gallivan, [0027], discloses receiving data from health care data sources and other sources such as employee attendance/absence records.)
Regarding claim 11, Gupta, in view of Gallivan, teaches the computer-implemented method of claim 2, wherein the unique entity identifier facilitates access to the person database entity by a plurality of platforms, each of the plurality of platforms configured to analyze the plurality of records. (Gallivan, [0014] and [0027], discloses aggregating [e.g. merging/combining] health care related data and other non-healthcare related data associated with patients from various sources and generating a unique identifier for each patient that contains no personal health information (PHI) or personally identifiable information (PII) and matching the data to the unique identifiers. In combination, Gupta, [0048]-[0049], discloses generating a derived dataset from data in a joined dataset, where each record in the derived dataset includes an identifier (ID) of a user that has records in datasets from various data providers. The joined dataset and/or derived dataset allow disparate input datasets from different data providers to be combined and/or transformed in a way that improves understanding of data in the datasets, reveals insights associated with the data, and/or increases the usability or applicability of the data to various types of applications, platforms, and/or distributed systems.)
Claim 19 recites substantially the same limitations as claim 11, and is rejected for substantially the same reasons.
Regarding claim 15, Gupta, in view of Gallivan, teaches the non-transitory computer readable medium of claim 14, wherein the threshold match exists when a threshold number of data points of demographic information of the first record matches data points of demographic information of the two or more records match, (Gupta, [0014] and [0057]-[0059], discloses conflating records from input datasets from two or more data providers by matching entities in the datasets based on similarity and/or overlap in fields related to the entities from the datasets like first name, last name, location, etc. A match score representing the level of confidence that the record records from the input datasets represent the same user is generated and conflating the records when the match score exceeds a threshold.)
wherein the data points of demographic information comprise at least one of a name, a date of birth, or a zip code.(Gupta, [0014] and [0057]-[0059], discloses conflating records from input datasets from two or more data providers by matching entities in the datasets based on similarity and/or overlap in fields related to the entities from the datasets like first name, last name, location [e.g., zip code], etc.)
Regarding claim 17, Gupta, in view of Gallivan, teaches the non-transitory computer readable medium of claim 14, wherein the instructions further cause the one or more processors to record the merging of the first record into the person database entity in a history of record changes and merges, (Gupta, [0078], discloses the platform includes functionality to support auditing and maintaining logs of accesses to the input datasets, joined dataset, and/or other data managed by platform. Entries in the logs represent reads, writes, and/or transformations [e.g. changes and merges] of the data.) wherein the person database entity is associated with a set of records pertaining to a single person, the set of records including the two or more records. (Gallivan, [0014], discloses aggregating [e.g. merging/combining] health care related data associated with patients from various sources and generating a unique identifier for each patient. In combination, Gupta, [0058]-[0059], discloses the conflation apparatus applies a machine learning model to records from datasets, and a match records that represent the same user.)
Claims 9, 10, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta, in view of Gallivan, and further in view of Wang (U.S. Pub. No. 2023/0245651, cited in IDS).
Regarding claim 9, Gupta, in view of Gallivan, teaches all the limitations as set forth in the rejection of claim 7 above. However, Gupta, in view of Gallivan, does not explicitly teach the computer-implemented method of claim 7, further comprising: determining, by the one or more processors, one or more anomalous activities based on data pertaining to the set of records.
On the other hand, Wang teaches determining, by the one or more processors, one or more anomalous activities based on data pertaining to the set of records. (Wang, [0057], discloses an AI system may employ ML algorithms to analyze the interaction data and identify patterns and trends in the user’s behavior and preferences. Wang, [0229]-[0231], discloses the AI system continuously monitors user activity to detect any suspicious behavior [i.e. anomalous activity] or potential security breaches. The AI system monitors and logs all user activities involving sensitive data to detect and prevent unauthorized access or misuse. Sensitive data as any information that, if exposed, accessed, or misused, could cause harm or negative consequences to an individual, organization, or system. Some common types of sensitive data consist of: (1) Personal Identifiable Information (PII) [e.g. employee data], (2) Financial information, (3) Health information [i.e. patient data], (4) Confidential business information, (5) User credentials, and (6) Legal information. Wang, [0404], discloses the AI systems can use various types of contextual information to generate confidence scores, such as user profile data, historical behavior, etc. User profile data [i.e. person database entity] can include age, gender, occupation, and interests, while historical behavior can include search and purchase history.)
Wang [0226] discloses the AI system collects patient data from various sources. The patient data from various sources of Wang can be the datasets from different data providers of Gupta. It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have further modified the data conflation system of Gupta to incorporate the teachings of AI based authorized access of sensitive data of Wang because both address the same field of data management, and by incorporating Wang into Gupta provides the conflation data system a way to detect and prevent unauthorized access and misuse of sensitive data using machine learning.
One of ordinary skill in the art would be motivated to do so as to ensure that the AI model remains secure and confidential even after deployment and can help protect against unauthorized access or misuse of sensitive data, as taught by Wang [0098].
Regarding claim 10, Gupta, in view of Gallivan and Wang, teaches the computer-implemented method of claim 9, wherein the one or more anomalous activities comprise: unauthorized access to patient data; and/or drug diversion activity. (Wang, [0229]-[0231], discloses the AI system continuously monitors user activity to detect any suspicious behavior or potential security breaches. The AI system monitors and logs all user activities involving sensitive data to detect and prevent unauthorized access or misuse. Sensitive data as any information that, if exposed, accessed, or misused, could cause harm or negative consequences to an individual, organization, or system. Some common types of sensitive data consist of: (1) Personal Identifiable Information (PII), (2) Financial information, (3) Health information [i.e. patient data], (4) Confidential business information, (5) User credentials, and (6) Legal information.)
Regarding claim 18, Gupta, in view of Gallivan, teaches all the limitations as set forth in the rejection of claim 17 above. However, Gupta, in view of Gallivan, does not explicitly teach the non-transitory computer readable medium of claim 17, wherein the instructions further cause the one or more processors to determine one or more anomalous activities based on data pertaining to the set of records, wherein the one or more anomalous activities comprise:
unauthorized access to patient data; and/or
drug diversion activity.
On the other hand, Wang teaches determine one or more anomalous activities based on data pertaining to the set of records, (Wang, [0057], discloses an AI system may employ ML algorithms to analyze the interaction data and identify patterns and trends in the user’s behavior and preferences. Wang, [0229]-[0231], discloses the AI system continuously monitors user activity to detect any suspicious behavior [i.e. anomalous activity] or potential security breaches. The AI system monitors and logs all user activities involving sensitive data to detect and prevent unauthorized access or misuse. Sensitive data as any information that, if exposed, accessed, or misused, could cause harm or negative consequences to an individual, organization, or system. Some common types of sensitive data consist of: (1) Personal Identifiable Information (PII) [e.g. employee data], (2) Financial information, (3) Health information [i.e. patient data], (4) Confidential business information, (5) User credentials, and (6) Legal information. Wang, [0404], discloses the AI systems can use various types of contextual information to generate confidence scores, such as user profile data, historical behavior, etc. User profile data [i.e. person database entity] can include age, gender, occupation, and interests, while historical behavior can include search and purchase history.)
wherein the one or more anomalous activities comprise: unauthorized access to patient data; and/or drug diversion activity. (Wang, [0229]-[0231], discloses the AI system continuously monitors user activity to detect any suspicious behavior or potential security breaches. The AI system monitors and logs all user activities involving sensitive data to detect and prevent unauthorized access or misuse. Sensitive data as any information that, if exposed, accessed, or misused, could cause harm or negative consequences to an individual, organization, or system. Some common types of sensitive data consist of: (1) Personal Identifiable Information (PII), (2) Financial information, (3) Health information [i.e. patient data], (4) Confidential business information, (5) User credentials, and (6) Legal information.)
Wang [0226] discloses the AI system collects patient data from various sources. The patient data from various sources of Wang can be the datasets from different data providers of Gupta. It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have further modified the data conflation system of Gupta to incorporate the teachings of AI based authorized access of sensitive data of Wang because both address the same field of data management, and by incorporating Wang into Gupta provides the conflation data system a way to detect and prevent unauthorized access and misuse of sensitive data using machine learning.
One of ordinary skill in the art would be motivated to do so as to ensure that the AI model remains secure and confidential even after deployment and can help protect against unauthorized access or misuse of sensitive data, as taught by Wang [0098].
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Gupta, in view of Gallivan, and further in view of Mikhailov et al. (U.S. Pub. No. 2019/0258731, cited in IDS), hereinafter Mikhailov.
Regarding claim 12, Gupta, in view of Gallivan, teaches all the limitations as set forth in the rejection of claim 2 above. However, Gupta, in view of Gallivan, does not explicitly teach the computer-implemented method of claim 2, furthering comprising causing, by the one or more processors, a graphical user interface to be displayed on a computing device of a user for updating the person database entity, wherein the graphical user interface is configured to enable the user to at least one of:
add a new record to the person database entity using one or more first graphical elements of the graphical user interface; or
remove records from the person database entity using one or more second graphical elements of the graphical user interface.
On the other hand, Mikhailov teaches causing, by the one or more processors, a graphical user interface to be displayed on a computing device of a user for updating the person database entity, wherein the graphical user interface is configured to enable the user to at least one of: add a new record to the person database entity using one or more first graphical elements of the graphical user interface; or remove records from the person database entity using one or more second graphical elements of the graphical user interface. (Mikhailov, Fig. 4 and [0019] and [0025], discloses a data collection system utilizes a number of data collection user interfaces, allowing a user to enter data into a database to populate a number data records.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have further modified the data conflation system of Gupta to incorporate the teachings of user interface to add records to the database of Mikhailov because both address the same field of data collection and management, and by incorporating Mikhailov into Gupta provides the data system a way to allow users to manually add records to the person database.
One of ordinary skill in the art would be motivated to do so as to provide improved facilities for updating database records created by separate data collection systems, as taught by Mikhailov [0005].
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Gupta, in view of Gallivan, and further in view of Deshpande et al. (U.S. Pub. No. 2013/0325882, cited in IDS), hereinafter Deshpande.
Regarding claim 13, Gupta, in view of Gallivan, teaches all the limitations as set forth in the rejection of claim 2 above. However, Gupta, in view of Gallivan, does not explicitly teach the computer-implemented method of claim 2, further comprising causing, by the one or more processors, a graphical user interface to be displayed on a computing device of a user, wherein the graphical user interface is configured to enable the user to at least one of:
select records to be merged into the person database entity independent of whether the threshold match exists between the records; or
select records to be not merged into the person database entity independent of whether the threshold match exists between the records.
On the other hand, Deshpande teaches causing, by the one or more processors, a graphical user interface to be displayed on a computing device of a user, wherein the graphical user interface is configured to enable the user to at least one of: select records to be merged into the person database entity independent of whether the threshold match exists between the records; or select records to be not merged into the person database entity independent of whether the threshold match exists between the records. (Deshpande, [0014] and [0047], discloses a master data management (MDM) system that determines records that refer to the same entity by comparing corresponding attribute values in each record and determining an overall matching score between the records. If the matching score is above a certain threshold, the MDM system automatically merges two or more records into a single entity. However, records that belong to the same entity may not match sufficiently for an automatic merge, when two records score sufficiently close to the matching threshold, the MDM system marks the two records for manual inspection, and the merger occurs manually when a user determines that the records belong to the same entity. Examiner interprets that a user manually determining that records belong to the same entity so they are merged to be select records to be merged independent of whether a threshold match exists between the records.)
The record threshold matching of Deshpande can be the match score thresholds for record conflation of Gupta. It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have further modified the data conflation system of Gupta to incorporate the teachings of user interface to select records to merge of Deshpande because both address the same field of data record merging based on record match thresholds, and by incorporating Deshpande into Gupta provides the conflation data system to allow users to manually select records to merge together.
One of ordinary skill in the art would be motivated to do so as to provide ways to improve entity resolution and relationship discovery for existing structured master data within a master data management (MDM) system, as taught by Deshpande [0014].
Conclusion
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/Eddy Cheung/Primary Examiner, Art Unit 2165