DETAILED ACTION
This Office action is in response to original application filed on 02/03/2025.
Claims 3-22 are pending.
Claims 1-2 are canceled. Claims 3-22 are new.
Claims 3-22 are rejected.
Notice of 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 .
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 07/22/2025 was filed prior to this Office action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Statutory Review under 35 USC § 101
Claims 3-8 are directed toward a system and have been reviewed.
Claims 3-8 initially appear to be statutory, as the system includes hardware (one or more processors) as disclosed in ¶ 0064 of the applicant’s specification, “The processor can be, for example, a general-purpose central processing unit (CPU), such as a microprocessor, or a special-purpose processor, such as a microcontroller.”
However, claims 3-8 perform the method of claims 9-14, which appear to not be patent-eligible because the claimed invention is directed to an abstract idea without significantly more.
Claims 9-14 are directed towards a method and have been reviewed.
Claims 9-14 appear to not be patent-eligible because the claimed invention is directed to an abstract idea without significantly more.
Claims 15-18 are directed toward a system and have been reviewed.
Claims 15-18 initially appear to be statutory, as the system includes hardware (one or more processors) as disclosed in ¶ 0064 of the applicant’s specification, “The processor can be, for example, a general-purpose central processing unit (CPU), such as a microprocessor, or a special-purpose processor, such as a microcontroller.”
However, claims 15-18 perform the method of claims 19-22, which appear to not be patent-eligible because the claimed invention is directed to an abstract idea without significantly more.
Claims 19-22 are directed towards a method and have been reviewed.
Claims 19-22 appear to not be patent-eligible because the claimed invention is directed to an abstract idea without significantly more
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 3-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 3 recites decomposing an enterprise into a plurality of different context-based domains, generating a first context-based domain dataset, generating a first data product from the first context-based domain dataset, generating a second context-based domain dataset, generating a second data product from the second context-based domain dataset, identifying a first data record, identifying a second data record, determining a first entity and a second entity are the same entity, and merging the first data record and the second data record, which is a mental process (including an observation, evaluation, judgment, opinion).
Decomposing an enterprise into a plurality of different context-based domains, identifying a first data record, identifying a second data record, and determining a first entity and a second entity are the same entity are mental processes.
At first glance, generating a first context-based domain dataset, generating a first data product from the first context-based domain dataset, generating a second context-based domain dataset, generating a second data product from the second context-based domain dataset, and merging the first data record and the second data record may not appear to be a mental process; however, MPEP 2106.04(a)(2), Section III. Mental Processes, refers to a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016).
As a result, generating a first dataset within a first domain, generating a first data product from the first dataset, generating a second dataset within a second domain, and generating a second data product from that second dataset are considered a mental process.
¶ 0024 of the specification shows merging can include “creating a new authoritative data record (e.g., golden record) and/or promoting one of the existing data records to the authoritative data record,” and promoting can practically be performed in the human mind.
As a result, merging the first data record and the second data record is considered a mental process.
Step 2A, Prong Two
This judicial exception of decomposing an enterprise into a plurality of different context-based domains, generating a first context-based domain dataset, generating a first data product from the first context-based domain dataset, generating a second context-based domain dataset, generating a second data product from the second context-based domain dataset, identifying a first data record, identifying a second data record, determining a first entity and a second entity are the same entity, and merging the first data record and the second data record is not integrated into a practical application despite the generically recited computer elements shown below:
one or more processors;
The generically recited computer elements amount to implementing the abstract idea on a computer, merely using a computer as a tool to perform an abstract idea, or generally linking the use of a judicial exception to a particular technological environment or field of use as seen below.
one or more processors
This additional element merely uses a computer as a tool to perform an abstract idea (see MPEP 2160.05(f)).
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception despite the additional elements shown below:
memory storing instructions
These elements store and retrieve information in memory, which are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
Claim 4 recites each context-based domain produces a respective data product owned by the respective context-based domain; this is akin to storing and retrieving information in memory, which are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
Claim 5 specifies that the first data record is associated with the first entity; similarly, claim 6 specifies that the second data record is associated with the second entity.
Claims 5-6 do not add meaningful limitations as these are merely nominal or token extra-solution components of the claim and serves only as an attempt to generally link the product of nature to a further particular technological environment (see MPEP 2106.05(h)).
Claim 7 provides further detail on the merging, that it is performed in real time; the merging step is currently being considered a mental process at this time.
Claim 8 provides further detail on the merging, that it is based on one or more global interface rules. The merging remains considered a mental process at this time.
Claims 9-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 9 recites decomposing an enterprise into a plurality of different context-based domains, generating a first context-based domain dataset, generating a first data product from the first context-based domain dataset, generating a second context-based domain dataset, generating a second data product from the second context-based domain dataset, identifying a first data record, identifying a second data record, determining a first entity and a second entity are the same entity, and merging the first data record and the second data record, which is a mental process (including an observation, evaluation, judgment, opinion).
Decomposing an enterprise into a plurality of different context-based domains, identifying a first data record, identifying a second data record, and determining a first entity and a second entity are the same entity are mental processes.
At first glance, generating a first context-based domain dataset, generating a first data product from the first context-based domain dataset, generating a second context-based domain dataset, generating a second data product from the second context-based domain dataset, and merging the first data record and the second data record may not appear to be a mental process; however, MPEP 2106.04(a)(2), Section III. Mental Processes, refers to a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016).
As a result, generating a first dataset within a first domain, generating a first data product from the first dataset, generating a second dataset within a second domain, and generating a second data product from that second dataset are considered a mental process.
¶ 0024 of the specification shows merging can include “creating a new authoritative data record (e.g., golden record) and/or promoting one of the existing data records to the authoritative data record,” and promoting can practically be performed in the human mind.
As a result, merging the first data record and the second data record is considered a mental process.
Step 2A, Prong Two
This judicial exception of decomposing an enterprise into a plurality of different context-based domains, generating a first context-based domain dataset, generating a first data product from the first context-based domain dataset, generating a second context-based domain dataset, generating a second data product from the second context-based domain dataset, identifying a first data record, identifying a second data record, determining a first entity and a second entity are the same entity, and merging the first data record and the second data record is not integrated into a practical application.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 10 recites each context-based domain produces a respective data product owned by the respective context-based domain; this is akin to storing and retrieving information in memory, which are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
Claim 11 specifies that the first data record is associated with the first entity; similarly, claim 12 specifies that the second data record is associated with the second entity.
Claims 11-12 do not add meaningful limitations as these are merely nominal or token extra-solution components of the claim and serves only as an attempt to generally link the product of nature to a further particular technological environment (see MPEP 2106.05(h)).
Claim 13 provides further detail on the merging, that it is performed in real time; the merging step is currently being considered a mental process at this time.
Claim 14 provides further detail on the merging, that it is based on one or more global interface rules. The merging remains considered a mental process at this time.
Claims 15-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 15 recites decomposing an enterprise into a plurality of different context-based domains, generating a first context-based domain dataset, generating a first data product from the first context-based domain dataset, generating a second context-based domain dataset, generating a second data product from the second context-based domain dataset, identifying a first data record, identifying a second data record, determining a first entity and a second entity are the same entity, and merging in real time the first data record and the second data record based on rules, which is a mental process (including an observation, evaluation, judgment, opinion).
Decomposing an enterprise into a plurality of different context-based domains, identifying a first data record, identifying a second data record, and determining a first entity and a second entity are the same entity are mental processes.
At first glance, generating a first context-based domain dataset, generating a first data product from the first context-based domain dataset, generating a second context-based domain dataset, generating a second data product from the second context-based domain dataset, and merging the first data record and the second data record may not appear to be a mental process; however, MPEP 2106.04(a)(2), Section III. Mental Processes, refers to a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016).
As a result, generating a first dataset within a first domain, generating a first data product from the first dataset, generating a second dataset within a second domain, and generating a second data product from that second dataset are considered a mental process.
¶ 0024 of the specification shows merging can include “creating a new authoritative data record (e.g., golden record) and/or promoting one of the existing data records to the authoritative data record,” and promoting can practically be performed in the human mind.
As a result, merging in real time the first data record and the second data record based on rules is considered a mental process.
Step 2A, Prong Two
This judicial exception of decomposing an enterprise into a plurality of different context-based domains, generating a first context-based domain dataset, generating a first data product from the first context-based domain dataset, generating a second context-based domain dataset, generating a second data product from the second context-based domain dataset, identifying a first data record, identifying a second data record, determining a first entity and a second entity are the same entity, and merging in real time the first data record and the second data record based on rules is not integrated into a practical application despite the generically recited computer elements shown below:
one or more processors;
The generically recited computer elements amount to implementing the abstract idea on a computer, merely using a computer as a tool to perform an abstract idea, or generally linking the use of a judicial exception to a particular technological environment or field of use as seen below.
one or more processors
This additional element merely uses a computer as a tool to perform an abstract idea (see MPEP 2160.05(f)).
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception despite the additional elements shown below:
memory storing instructions
These elements store and retrieve information in memory, which are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
Claim 16 recites each context-based domain produces a respective data product owned by the respective context-based domain; this is akin to storing and retrieving information in memory, which are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
Claim 17 specifies that the first data record is associated with the first entity; similarly, claim 18 specifies that the second data record is associated with the second entity.
Claims 17-18 do not add meaningful limitations as these are merely nominal or token extra-solution components of the claim and serves only as an attempt to generally link the product of nature to a further particular technological environment (see MPEP 2106.05(h)).
Claims 19-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 19 recites decomposing an enterprise into a plurality of different context-based domains, generating a first context-based domain dataset, generating a first data product from the first context-based domain dataset, generating a second context-based domain dataset, generating a second data product from the second context-based domain dataset, identifying a first data record, identifying a second data record, determining a first entity and a second entity are the same entity, and merging in real time the first data record and the second data record based on rules, which is a mental process (including an observation, evaluation, judgment, opinion).
Decomposing an enterprise into a plurality of different context-based domains, identifying a first data record, identifying a second data record, and determining a first entity and a second entity are the same entity are mental processes.
At first glance, generating a first context-based domain dataset, generating a first data product from the first context-based domain dataset, generating a second context-based domain dataset, generating a second data product from the second context-based domain dataset, and merging the first data record and the second data record may not appear to be a mental process; however, MPEP 2106.04(a)(2), Section III. Mental Processes, refers to a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016).
As a result, generating a first dataset within a first domain, generating a first data product from the first dataset, generating a second dataset within a second domain, and generating a second data product from that second dataset are considered a mental process.
¶ 0024 of the specification shows merging can include “creating a new authoritative data record (e.g., golden record) and/or promoting one of the existing data records to the authoritative data record,” and promoting can practically be performed in the human mind.
As a result, merging in real time the first data record and the second data record based on rules is considered a mental process.
Step 2A, Prong Two
This judicial exception of decomposing an enterprise into a plurality of different context-based domains, generating a first context-based domain dataset, generating a first data product from the first context-based domain dataset, generating a second context-based domain dataset, generating a second data product from the second context-based domain dataset, identifying a first data record, identifying a second data record, determining a first entity and a second entity are the same entity, and merging in real time the first data record and the second data record based on rules is not integrated into a practical application.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 20 recites each context-based domain produces a respective data product owned by the respective context-based domain; this is akin to storing and retrieving information in memory, which are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
Claim 21 specifies that the first data record is associated with the first entity; similarly, claim 22 specifies that the second data record is associated with the second entity.
Claims 21-22 do not add meaningful limitations as these are merely nominal or token extra-solution components of the claim and serves only as an attempt to generally link the product of nature to a further particular technological environment (see MPEP 2106.05(h)).
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 3-22 are rejected under 35 U.S.C. 103 as being unpatentable over Tenner et al., U.S. Patent No. 7,200,619 (published April 3, 2007; hereinafter Tenner) in view of Meyerzon et al., U.S. Patent Application Publication No. 2022/0019905 (X reference provided in the IDS of 07/22/2025; hereinafter Meyerzon).
Regarding claim 3, Tenner teaches:
A system comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the system to perform: (Tenner col. 8, lines 40-44: Computer system 110 is shown comprising at least one processor 112, which obtains instructions, or operation codes, (also known as opcodes), and data via a bus 114 from a main memory 116. The processor 112 could be any processor adapted to support the methods of the invention)
…
identifying a first data record of [a] first data product; (Tenner FIG. 5A, col. 12, line 64-col. 13, line 17: in step 510 the first plurality of data records is received … In step 520, for each data record of the first plurality of data records, an associated internal identifier is retrieved from the data record; see also Tenner FIGs. 1, 4, col. 11, lines 13-59: The data warehouse 432 comprises a first plurality of tables 434 comprising data loaded from the first data source 410 ... The data from the first data source 410 is loaded first to the first plurality of tables 434 of data warehouse 432, whereby the first plurality of tables 434 is populated. For each data record in the first data source 410)
identifying a second data record of [a] second data product; (Tenner FIG. 5A, col. 13, lines 18-37: In step 550 the second plurality of data records is received … In step 560, for each data record of the second plurality of data records, at least one external identifier is retrieved from the data record; see also Tenner FIGs. 1, 4, col. 11, line 13-col. 12, line 4: The data warehouse 432 further comprises a second plurality of tables 436 comprising data loaded from the second data source 410 ... data from at least one second data source 420 is loaded to the second plurality of tables 436 of data warehouse 432, whereby the second plurality of tables 436 is populated. Each data record of the second data source 420 may comprise an internal key and at least one external key it maps to)
determining a first entity and a second entity are the same entity; (Tenner FIGs. 5A-5B, col. 13, lines 18-48: In step 580, a determination is made whether the at least one retrieved external identifier matches the at least one external identifier of a mapping data record in the mapping data structure. Accordingly, the determination is made for each data record in the second data record)
merging the first data record and the second data record without changing either the first context-based domain dataset or the second context-based domain dataset. (Tenner FIGs. 5A-5B, col. 12, lines 50-64: correlating at least a first plurality of data records and a second plurality of data records; Tenner col. 13, lines 18-48: if the retrieved external identifier of the second data source matches an external identifier of a specific mapping data record in the mapping data structure, an internal identifier associated with the data record of the second plurality of data records is retrieved from the data record in step 582 ... In step 592, the retrieved internal identifier and an indication of the corresponding data source is copied to the matching mapping data record in the mapping data structure)
Tenner does not expressly disclose:
decomposing an enterprise into a plurality of different context-based domains;
generating a first context-based domain dataset owned by a first context-based domain of the plurality of context-based domains;
generating a first data product from the first context-based domain dataset;
generating a second context-based domain dataset owned by a second context-based domain of the plurality of context-based domains;
generating a second data product from the second context-based domain dataset;
However, Meyerzon addresses this by teaching:
decomposing an enterprise into a plurality of different context-based domains; (Meyerzon FIG. 4, ¶ 0131-0133, ¶ 0136-0138, see primarily ¶ 0131: an example mining process 400 analyzes templates 410 and extracts 412 to generate entities to add to knowledge graph 470 … An entity is an instance of an entity type, and is also referred to herein as an entity record. There are typically many templates per entity type; see Meyerzon ABST: A mining of a set of enterprise source documents within an enterprise intranet is performed using singular value decomposition (SVD) to determine a plurality of entity names)
generating a first context-based domain dataset owned by a first context-based domain of the plurality of context-based domains; (Meyerzon FIG. 12, ¶ 0192-0194: At block 1210, the method 1200 includes comparing enterprise source documents within an enterprise intranet to a plurality of templates defining potential entity attributes to identify extracts of the enterprise source documents matching at least one of the plurality of templates ... At block 1220, the method 1200 includes parsing the extracts according to respective templates of the plurality of templates that match the extracts to determine instances ... At block 1230, the method 1200 includes performing clustering on a number of the instances to determine potential entity names [shows ownership by a first domain])
generating a first data product from the first context-based domain dataset; (Meyerzon FIG. 12, ¶ 0192-0193: At block 1220, the method 1200 includes parsing the extracts according to respective templates of the plurality of templates that match the extracts to determine instances … the template matching process 540 parses the extracts 412 according to respective templates 410 of the plurality of templates that match the extracts to determine instances [shows generating a first data product] … The template matching process 540 stores the instances in the topic match shard 544 via, for example, the substrate bus 542; see this in light of Meyerzon FIG. 4, ¶ 0131-0135: An entity is an instance of an entity type, and is also referred to herein as an entity record. There are typically many templates per entity type ... partitioning process 440 would group instances having the terms “Project Valkyrie,” “Valkyrie” and “Valkyrie Leader”)
generating a second context-based domain dataset owned by a second context-based domain of the plurality of context-based domains; (Meyerzon FIG. 12, ¶ 0192-0194: At block 1210, the method 1200 includes comparing enterprise source documents within an enterprise intranet to a plurality of templates defining potential entity attributes to identify extracts of the enterprise source documents matching at least one of the plurality of templates ... At block 1220, the method 1200 includes parsing the extracts according to respective templates of the plurality of templates that match the extracts to determine instances ... At block 1230, the method 1200 includes performing clustering on a number of the instances to determine potential entity names [can also show ownership by a second domain])
generating a second data product from the second context-based domain dataset; (Meyerzon FIG. 12, ¶ 0192-0193: At block 1220, the method 1200 includes parsing the extracts according to respective templates of the plurality of templates that match the extracts to determine instances … the template matching process 540 parses the extracts 412 according to respective templates 410 of the plurality of templates that match the extracts to determine instances [shows generating a second data product] … The template matching process 540 stores the instances in the topic match shard 544 via, for example, the substrate bus 542; see this in light of Meyerzon FIG. 4, ¶ 0131-0135: An entity is an instance of an entity type, and is also referred to herein as an entity record. There are typically many templates per entity type ... partitioning process 440 would group instances having the terms “Project Valkyrie,” “Valkyrie” and “Valkyrie Leader”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the functioning of the data extraction and correlation of Tenner with the functioning of the document mining records of Meyerzon.
In addition, both of the references (Tenner and Meyerzon) disclose features that are directed to analogous art, and they are directed to the same field of endeavor, such as data extraction and reconciliation.
Motivation to do so would be to improve the functioning of Tenner performing data correlation over extracted data with the functioning in similar reference Meyerzon also performing data correlation over extracted data but with the improvement of parsing, clustering, and pre-processing of datasets.
Motivation to do so would also be the teaching, suggestion, or motivation for a person of ordinary skill in the art to allow useful machine learning applications to be generated as seen in Meyerzon ¶ 0108.
Regarding claim 9, Tenner teaches:
A method comprising: … identifying a first data record of [a] first data product; (Tenner FIG. 5A, col. 12, line 64-col. 13, line 17: in step 510 the first plurality of data records is received … In step 520, for each data record of the first plurality of data records, an associated internal identifier is retrieved from the data record; see also Tenner FIGs. 1, 4, col. 11, lines 13-59: The data warehouse 432 comprises a first plurality of tables 434 comprising data loaded from the first data source 410 ... The data from the first data source 410 is loaded first to the first plurality of tables 434 of data warehouse 432, whereby the first plurality of tables 434 is populated. For each data record in the first data source 410)
identifying a second data record of [a] second data product; (Tenner FIG. 5A, col. 13, lines 18-37: In step 550 the second plurality of data records is received … In step 560, for each data record of the second plurality of data records, at least one external identifier is retrieved from the data record; see also Tenner FIGs. 1, 4, col. 11, line 13-col. 12, line 4: The data warehouse 432 further comprises a second plurality of tables 436 comprising data loaded from the second data source 410 ... data from at least one second data source 420 is loaded to the second plurality of tables 436 of data warehouse 432, whereby the second plurality of tables 436 is populated. Each data record of the second data source 420 may comprise an internal key and at least one external key it maps to)
determining a first entity and a second entity are the same entity; (Tenner FIGs. 5A-5B, col. 13, lines 18-48: In step 580, a determination is made whether the at least one retrieved external identifier matches the at least one external identifier of a mapping data record in the mapping data structure. Accordingly, the determination is made for each data record in the second data record)
merging the first data record and the second data record without changing either the first context-based domain dataset or the second context-based domain dataset. (Tenner FIGs. 5A-5B, col. 12, lines 50-64: correlating at least a first plurality of data records and a second plurality of data records; Tenner col. 13, lines 18-48: if the retrieved external identifier of the second data source matches an external identifier of a specific mapping data record in the mapping data structure, an internal identifier associated with the data record of the second plurality of data records is retrieved from the data record in step 582 ... In step 592, the retrieved internal identifier and an indication of the corresponding data source is copied to the matching mapping data record in the mapping data structure)
Tenner does not expressly disclose:
decomposing an enterprise into a plurality of different context-based domains;
generating a first context-based domain dataset owned by a first context-based domain of the plurality of context-based domains;
generating a first data product from the first context-based domain dataset;
generating a second context-based domain dataset owned by a second context-based domain of the plurality of context-based domains;
generating a second data product from the second context-based domain dataset;
However, Meyerzon addresses this by teaching:
decomposing an enterprise into a plurality of different context-based domains; (Meyerzon FIG. 4, ¶ 0131-0133, ¶ 0136-0138, see primarily ¶ 0131: an example mining process 400 analyzes templates 410 and extracts 412 to generate entities to add to knowledge graph 470 … An entity is an instance of an entity type, and is also referred to herein as an entity record. There are typically many templates per entity type; see Meyerzon ABST: A mining of a set of enterprise source documents within an enterprise intranet is performed using singular value decomposition (SVD) to determine a plurality of entity names)
generating a first context-based domain dataset owned by a first context-based domain of the plurality of context-based domains; (Meyerzon FIG. 12, ¶ 0192-0194: At block 1210, the method 1200 includes comparing enterprise source documents within an enterprise intranet to a plurality of templates defining potential entity attributes to identify extracts of the enterprise source documents matching at least one of the plurality of templates ... At block 1220, the method 1200 includes parsing the extracts according to respective templates of the plurality of templates that match the extracts to determine instances ... At block 1230, the method 1200 includes performing clustering on a number of the instances to determine potential entity names [shows ownership by a first domain])
generating a first data product from the first context-based domain dataset; (Meyerzon FIG. 12, ¶ 0192-0193: At block 1220, the method 1200 includes parsing the extracts according to respective templates of the plurality of templates that match the extracts to determine instances … the template matching process 540 parses the extracts 412 according to respective templates 410 of the plurality of templates that match the extracts to determine instances [shows generating a first data product] … The template matching process 540 stores the instances in the topic match shard 544 via, for example, the substrate bus 542; see this in light of Meyerzon FIG. 4, ¶ 0131-0135: An entity is an instance of an entity type, and is also referred to herein as an entity record. There are typically many templates per entity type ... partitioning process 440 would group instances having the terms “Project Valkyrie,” “Valkyrie” and “Valkyrie Leader”)
generating a second context-based domain dataset owned by a second context-based domain of the plurality of context-based domains; (Meyerzon FIG. 12, ¶ 0192-0194: At block 1210, the method 1200 includes comparing enterprise source documents within an enterprise intranet to a plurality of templates defining potential entity attributes to identify extracts of the enterprise source documents matching at least one of the plurality of templates ... At block 1220, the method 1200 includes parsing the extracts according to respective templates of the plurality of templates that match the extracts to determine instances ... At block 1230, the method 1200 includes performing clustering on a number of the instances to determine potential entity names [can also show ownership by a second domain])
generating a second data product from the second context-based domain dataset; (Meyerzon FIG. 12, ¶ 0192-0193: At block 1220, the method 1200 includes parsing the extracts according to respective templates of the plurality of templates that match the extracts to determine instances … the template matching process 540 parses the extracts 412 according to respective templates 410 of the plurality of templates that match the extracts to determine instances [shows generating a second data product] … The template matching process 540 stores the instances in the topic match shard 544 via, for example, the substrate bus 542; see this in light of Meyerzon FIG. 4, ¶ 0131-0135: An entity is an instance of an entity type, and is also referred to herein as an entity record. There are typically many templates per entity type ... partitioning process 440 would group instances having the terms “Project Valkyrie,” “Valkyrie” and “Valkyrie Leader”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the functioning of the data extraction and correlation of Tenner with the functioning of the document mining records of Meyerzon.
In addition, both of the references (Tenner and Meyerzon) disclose features that are directed to analogous art, and they are directed to the same field of endeavor, such as data extraction and reconciliation.
Motivation to do so would be to improve the functioning of Tenner performing data correlation over extracted data with the functioning in similar reference Meyerzon also performing data correlation over extracted data but with the improvement of parsing, clustering, and pre-processing of datasets.
Motivation to do so would also be the teaching, suggestion, or motivation for a person of ordinary skill in the art to allow useful machine learning applications to be generated as seen in Meyerzon ¶ 0108.
Regarding claim 15, Tenner teaches:
A system comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the system to perform: (Tenner col. 8, lines 40-44: Computer system 110 is shown comprising at least one processor 112, which obtains instructions, or operation codes, (also known as opcodes), and data via a bus 114 from a main memory 116. The processor 112 could be any processor adapted to support the methods of the invention)
…
identifying a first data record of [a] first data product; (Tenner FIG. 5A, col. 12, line 64-col. 13, line 17: in step 510 the first plurality of data records is received … In step 520, for each data record of the first plurality of data records, an associated internal identifier is retrieved from the data record; see also Tenner FIGs. 1, 4, col. 11, lines 13-59: The data warehouse 432 comprises a first plurality of tables 434 comprising data loaded from the first data source 410 ... The data from the first data source 410 is loaded first to the first plurality of tables 434 of data warehouse 432, whereby the first plurality of tables 434 is populated. For each data record in the first data source 410)
identifying a second data record of [a] second data product; (Tenner FIG. 5A, col. 13, lines 18-37: In step 550 the second plurality of data records is received … In step 560, for each data record of the second plurality of data records, at least one external identifier is retrieved from the data record; see also Tenner FIGs. 1, 4, col. 11, line 13-col. 12, line 4: The data warehouse 432 further comprises a second plurality of tables 436 comprising data loaded from the second data source 410 ... data from at least one second data source 420 is loaded to the second plurality of tables 436 of data warehouse 432, whereby the second plurality of tables 436 is populated. Each data record of the second data source 420 may comprise an internal key and at least one external key it maps to)
determining a first entity and a second entity are the same entity; (Tenner FIGs. 5A-5B, col. 13, lines 18-48: In step 580, a determination is made whether the at least one retrieved external identifier matches the at least one external identifier of a mapping data record in the mapping data structure. Accordingly, the determination is made for each data record in the second data record)
merging, in real time … the first data record and the second data record without changing either the first context-based domain dataset or the second context-based domain dataset. (Tenner FIGs. 5A-5B, col. 12, lines 50-64: correlating at least a first plurality of data records and a second plurality of data records; Tenner col. 13, lines 18-48: if the retrieved external identifier of the second data source matches an external identifier of a specific mapping data record in the mapping data structure, an internal identifier associated with the data record of the second plurality of data records is retrieved from the data record in step 582 ... In step 592, the retrieved internal identifier and an indication of the corresponding data source is copied to the matching mapping data record in the mapping data structure)
Tenner does not expressly disclose:
decomposing an enterprise into a plurality of different context-based domains;
generating a first context-based domain dataset owned by a first context-based domain of the plurality of context-based domains;
generating a first data product from the first context-based domain dataset;
generating a second context-based domain dataset owned by a second context-based domain of the plurality of context-based domains;
generating a second data product from the second context-based domain dataset;
merging … based on one or more global interface rules,
However, Meyerzon addresses this by teaching:
decomposing an enterprise into a plurality of different context-based domains; (Meyerzon FIG. 4, ¶ 0131-0133, ¶ 0136-0138, see primarily ¶ 0131: an example mining process 400 analyzes templates 410 and extracts 412 to generate entities to add to knowledge graph 470 … An entity is an instance of an entity type, and is also referred to herein as an entity record. There are typically many templates per entity type; see Meyerzon ABST: A mining of a set of enterprise source documents within an enterprise intranet is performed using singular value decomposition (SVD) to determine a plurality of entity names)
generating a first context-based domain dataset owned by a first context-based domain of the plurality of context-based domains; (Meyerzon FIG. 12, ¶ 0192-0194: At block 1210, the method 1200 includes comparing enterprise source documents within an enterprise intranet to a plurality of templates defining potential entity attributes to identify extracts of the enterprise source documents matching at least one of the plurality of templates ... At block 1220, the method 1200 includes parsing the extracts according to respective templates of the plurality of templates that match the extracts to determine instances ... At block 1230, the method 1200 includes performing clustering on a number of the instances to determine potential entity names [shows ownership by a first domain])
generating a first data product from the first context-based domain dataset; (Meyerzon FIG. 12, ¶ 0192-0193: At block 1220, the method 1200 includes parsing the extracts according to respective templates of the plurality of templates that match the extracts to determine instances … the template matching process 540 parses the extracts 412 according to respective templates 410 of the plurality of templates that match the extracts to determine instances [shows generating a first data product] … The template matching process 540 stores the instances in the topic match shard 544 via, for example, the substrate bus 542; see this in light of Meyerzon FIG. 4, ¶ 0131-0135: An entity is an instance of an entity type, and is also referred to herein as an entity record. There are typically many templates per entity type ... partitioning process 440 would group instances having the terms “Project Valkyrie,” “Valkyrie” and “Valkyrie Leader”)
generating a second context-based domain dataset owned by a second context-based domain of the plurality of context-based domains; (Meyerzon FIG. 12, ¶ 0192-0194: At block 1210, the method 1200 includes comparing enterprise source documents within an enterprise intranet to a plurality of templates defining potential entity attributes to identify extracts of the enterprise source documents matching at least one of the plurality of templates ... At block 1220, the method 1200 includes parsing the extracts according to respective templates of the plurality of templates that match the extracts to determine instances ... At block 1230, the method 1200 includes performing clustering on a number of the instances to determine potential entity names [can also show ownership by a second domain])
generating a second data product from the second context-based domain dataset; (Meyerzon FIG. 12, ¶ 0192-0193: At block 1220, the method 1200 includes parsing the extracts according to respective templates of the plurality of templates that match the extracts to determine instances … the template matching process 540 parses the extracts 412 according to respective templates 410 of the plurality of templates that match the extracts to determine instances [shows generating a second data product] … The template matching process 540 stores the instances in the topic match shard 544 via, for example, the substrate bus 542; see this in light of Meyerzon FIG. 4, ¶ 0131-0135: An entity is an instance of an entity type, and is also referred to herein as an entity record. There are typically many templates per entity type ... partitioning process 440 would group instances having the terms “Project Valkyrie,” “Valkyrie” and “Valkyrie Leader”)
merging … based on one or more global interface rules, (Meyerzon ¶ 0130: Curation actions include adding or removing attributes of an entity record including relationships to other entity records. Curation actions may also include adding or removing an entity record, creating a new topic, deleting an existing topic, and merging or splitting topics ... Topic pages and relationships serve as authoritative data to train the set of topics for clustering, which may allow the machine learning process (i.e., clustering) to link more data (e.g., people, files, sites) to the entity than only a mined entity name ['authoritative data' addresses 'global interface rules' based on instant specification ¶ 00144 and ¶ 00149-00150])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the functioning of the data extraction and correlation of Tenner with the functioning of the document mining records of Meyerzon.
In addition, both of the references (Tenner and Meyerzon) disclose features that are directed to analogous art, and they are directed to the same field of endeavor, such as data extraction and reconciliation.
Motivation to do so would be to improve the functioning of Tenner performing data correlation over extracted data with the functioning in similar reference Meyerzon also performing data correlation over extracted data but with the improvement of parsing, clustering, and pre-processing of datasets.
Motivation to do so would also be the teaching, suggestion, or motivation for a person of ordinary skill in the art to allow useful machine learning applications to be generated as seen in Meyerzon ¶ 0108.
Regarding claim 19, Tenner teaches:
A method comprising: … identifying a first data record of [a] first data product; (Tenner FIG. 5A, col. 12, line 64-col. 13, line 17: in step 510 the first plurality of data records is received … In step 520, for each data record of the first plurality of data records, an associated internal identifier is retrieved from the data record; see also Tenner FIGs. 1, 4, col. 11, lines 13-59: The data warehouse 432 comprises a first plurality of tables 434 comprising data loaded from the first data source 410 ... The data from the first data source 410 is loaded first to the first plurality of tables 434 of data warehouse 432, whereby the first plurality of tables 434 is populated. For each data record in the first data source 410)
identifying a second data record of [a] second data product; (Tenner FIG. 5A, col. 13, lines 18-37: In step 550 the second plurality of data records is received … In step 560, for each data record of the second plurality of data records, at least one external identifier is retrieved from the data record; see also Tenner FIGs. 1, 4, col. 11, line 13-col. 12, line 4: The data warehouse 432 further comprises a second plurality of tables 436 comprising data loaded from the second data source 4