DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1, 3-12, and 14-24 are pending.
Response to Arguments
101 Rejection: Applicant’s arguments, filed 10/17/2025, with respect to claims 1, 3-12, and 14-24 have been fully considered and are persuasive. The amended claim limitation “removing one or more attributes from the classified plurality of attributes of the source data to generate a set of classified attributes” integrates the judicial exception into the technological improvement disclosed in the specification (Para 0028, Another reason for using data deduplication techniques may be regulatory compliance with regulations such as Anti-Money Laundering (AML) or Know Your Customer (KYC) where it may be necessary to concisely identify clients by removing duplicated data entries and reconciling them to a golden record view). Accordingly, the 101 rejection of claims 1, 3-12, and 14-24 has been withdrawn.
103 Rejection: Applicant’s arguments with respect to claims 1, 3-12, and 14-24 have been fully considered and are persuasive. Upon further consideration, and in view of applicant’s amendments, a new grounds of rejection is made in view of newly cited reference Wu.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-12, 14-24 are rejected under 35 U.S.C. 103 as being unpatentable over Panuganty et al (US 20200210647 A1) hereafter Panuganty in view of Goldenberg et al (US 20090089630 A1) hereafter Goldenberg further in view of Wu et al (US 20100138370 A1) hereafter Wu
Regarding claim 1, Panuganty teaches a computer-implemented method comprising: receiving source data (Para 0313, the curation engine module 110 takes user data 3402 as input and generates curated data 3404 based on the user data 3402); analyzing the source data(Para 0314, The insight engine module 116 then takes the curated data 3404 as input and generates insight data), wherein the analyzing of the source data includes generating data profiling statistics from the source data and classifying a plurality of attributes of the source data for identifying data classes of the source data(Para 0184, Curation engine module 1200 alternately or additionally includes metrics generation module 1220 to generate other types of statistics and/or metrics not generated via domain KPI generation module); determining an intersection of the identified data classes and the set of classified attributes with an ontology graph(Para 0175, the curated attributes include knowledge graph attributes)(“knowledge graph” teaches “ontology graph”); based on the intersection and the data profiling statistics, determining at least one data domain associated with the source data(Para 0177, Curation engine module includes an entity-relationship (ER) model generation module 1204 that identifies a domain or topic of interest, and then specifies a relationship model for the domain or topic); determining, for the at least one data domain associated with the source data, required matching algorithms for a data matching engine to execute data deduplication within the source data(Para 0177, Curation engine module includes an entity-relationship (ER) model generation module 1204 that identifies a domain or topic of interest, and then specifies a relationship model for the domain or topic); determining, for each of the required matching algorithms, a mapping of the set of classified attributes of the source data to matching engine algorithm functions, the mapping of the set of classified attributes of the source data including an alignment of attributes of a source record to attributes of a target record, the matching engine algorithm functions including a determination component outputting a numerical value describing a similarity between the source record and the target record(Para 0411, the deduplicator module 3304 can perform a direct comparison of headline content between headline candidates, such as by comparing text content, images, and so forth, to determine if headline candidates include duplicate content. If multiple headline candidates include a threshold amount of duplicate content (e.g., 80% or more), the headline candidates are identified as duplicates); and merging the source record and the target record based on the numerical value that is above an auto-link threshold value (Para 0334, segmentation is performed by grouping data samples (e.g., from the curated data 3404) which are similar to each other).
Panuganty does not appear to explicitly teach determining, for the at least one data domain associated with the source data, a numerical variable representing a total amount of required matching algorithms.
In analogous art, Goldenberg teaches determining, for the at least one data domain associated with the source data, a numerical variable representing a total amount of required matching algorithms (Para 0067, Identity Hub 32 utilizes a plurality of algorithms to compare and score member attribute similarities and differences). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Goldenberg to include the teaching of Panuganty. One of ordinary skill in the art would be motivated to implement this modification in order to match large sets of data records, as taught by Goldenberg (Para 0025, embodiments disclosed herein can analyze in real time the configuration and performance of an identity hub capable of processing and matching large sets of data records).
Panuganty in view of Goldenberg does not appear to explicitly teach removing one or more attributes from the classified plurality of attributes of the source data to generate a set of classified attributes, wherein the removing of the one or more attributes is based on at least one of: a completeness score of each of the one or more attributes that is below a mandatory completeness score, a distinct value score of each of the one or more attributes that is below a mandatory distinctiveness score, or a timestamp associated with each of the one or more attributes that is older than a threshold value, and the data profiling statistics includes the completeness score and the distinct value score.
In analogous art, Wu teaches removing one or more attributes from the classified plurality of attributes of the source data to generate a set of classified attributes, wherein the removing of the one or more attributes is based on at least one of: a completeness score of each of the one or more attributes that is below a mandatory completeness score, a distinct value score of each of the one or more attributes that is below a mandatory distinctiveness score, or a timestamp associated with each of the one or more attributes that is older than a threshold value, and the data profiling statistics includes the completeness score and the distinct value score (Para 0028, When the most recent timestamp 422 in a text-timestamp record 420 is older than the pre-determined threshold, the text-timestamp record 420 is removed from the fine grain category activities 415). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Panuganty in view of Goldenberg to include the teaching of Wu. One of ordinary skill in the art would be motivated to implement this modification in order to maintain relevant information, as taught by Wu (Para 0028, The significance of the text-timestamp records 420 decays over time).
Regarding claim 3, Panuganty in view of Goldenberg further in view of Wu teaches the computer-implemented method of claim 1, wherein the matching engine algorithm functions are selected from the group consisting of: determining at least one standardizer considering a plurality of source data attributes; determining at least one comparison function considering a plurality of source data attributes; and determining bucket groups of source data records (Panuganty, Para 0177, Curation engine module includes an entity-relationship (ER) model generation module 1204 that identifies a domain or topic of interest, and then specifies a relationship model for the domain or topic).
Regarding claim 4, Panuganty in view of Goldenberg further in view of Wu teaches the computer-implemented method of claim 1, wherein determining the at least one data domain associated with the source data is further based, at least in part, on: configuring, for each detectable data domain, a domain detection threshold value for the data matching engine, the domain detection threshold value being indicative of a domain being detected as a separate domain; configuring a sub-class threshold value for a detection of the domain, the sub-class threshold value being indicative of a minimum number of detected sub-classes in a record of the source data; and determining a confidence threshold value indicative of an average value of confidence values of detected sub-classes to determine a detected class (Panuganty, Para 0079, some implementations utilize similarity comparison algorithms to compare similarity scores between various subsets of data. However, it is to be appreciated that alternate or additional algorithms can be utilized as well, such as those further described herein with respect to at least the insight engine module).
Regarding claim 5, Panuganty in view of Goldenberg further in view of Wu teaches the computer-implemented method of claim 4, further comprising: determining a detected data domain if the required matching algorithm of the data matching engine has to be configured (Panuganty, Para 0079, In various scenarios, curation engine module 110 curates data by applying machine learning algorithms, data mining algorithms, and/or Principal Component Analysis (PCA) algorithms to identify data relationships between the acquired and/or curated data).
Regarding claim 6, Panuganty in view of Goldenberg further in view of Wu teaches the computer-implemented method of claim 1, further comprising: configuring the auto-link threshold value depending on at least one of a detected false positive result or a detected false negative result during a matching of the source record and the target record; and configuring a clerical review rate threshold value depending on a number of clerical tasks to be performed (Goldenberg, Para 0245, the CR and AL thresholds are indicative of tolerance of Identity Hub 32 to false positive and false negative rates in matching a set of data records).
Regarding claim 7, Panuganty in view of Goldenberg further in view of Wu teaches the computer-implemented method of claim 6, further comprising: determining the source record and the target record to be duplicates if their combined matching score value is greater than the auto-link threshold value (Panuganty, Para 0079, some implementations utilize similarity comparison algorithms to compare similarity scores between various subsets of data).
Regarding claim 8, Panuganty in view of Goldenberg further in view of Wu teaches the computer-implemented method of claim 6, further comprising: determining the source record and the target record to not be duplicates if their combined matching score value is smaller than the clerical review rate threshold value (Panuganty, Para 0079, some implementations utilize similarity comparison algorithms to compare similarity scores between various subsets of data).
Regarding claim 9, Panuganty in view of Goldenberg further in view of Wu teaches the computer-implemented method of claim 6, further comprising: determining the source record and the target record to be assessed clerically if the source record and the target record are determined to be duplicates (Panuganty, Para 0311, If duplicate headline candidates are identified, the deduplicator module 3304 can remove the duplicate headline candidates prior to the headline candidates being marked as headlines for output).
Regarding claim 10, Panuganty in view of Goldenberg further in view of Wu teaches the computer-implemented method of claim 1, wherein the data profiling statistics from the source data and the classified attributes of the source data includes one or more of: technical metadata of the received source data; data quality metric values per attribute of the source data; relationship descriptors between sets of the source data; and a data classification per attribute, and thereby a linkage of the attributes and their relationships (Panuganty, Para 0080, the curation engine module 110 generates relational data models based on the curated data, and then stores the curated data in a database, such as in databases 124, according to the relational data models).
Regarding claim 11, Panuganty in view of Goldenberg further in view of Wu teaches the computer-implemented method of claim 1, wherein the data matching engine is at least one of a probabilistic data matching engine, a machine-learning based data matching engine and a deterministic data matching engine (Panuganty, Para 0311, the deduplicator module 3304 compares headline candidates generated by the summarization module 3300 to determine if any of the headline candidates are duplicates of one another).
Claim 12 is the system claim corresponding to the method claim 1, and is analyzed and rejected accordingly.
Claim 14 is the system claim corresponding to the method claim 3, and is analyzed and rejected accordingly.
Claim 15 is the system claim corresponding to the method claim 4, and is analyzed and rejected accordingly.
Claim 16 is the system claim corresponding to the method claim 5, and is analyzed and rejected accordingly.
Claim 17 is the system claim corresponding to the method claim 6, and is analyzed and rejected accordingly.
Claim 18 is the system claim corresponding to the method claim 7, and is analyzed and rejected accordingly.
Claim 19 is the system claim corresponding to the method claim 8, and is analyzed and rejected accordingly.
Regarding claim 20, Panuganty in view of Goldenberg further in view of Wu teaches the computer system of claim 16, wherein the program code portions further enable the processor to: determine the source record and the target record to be assessed clerically if the source record and the target record are determined to be duplicates (Panuganty, Para 0311, the deduplicator module 3304 compares headline candidates generated by the summarization module 3300 to determine if any of the headline candidates are duplicates of one another).
Claim 21 is the system claim corresponding to the to the method claim 10, and is analyzed and rejected accordingly.
Claim 22 is the system claim corresponding to the method claim 11, and is analyzed and rejected accordingly.
Claim 23 is the computer program product claim corresponding to the method claim 1, and is analyzed and rejected accordingly.
Regarding claim 24, Panuganty in view of Goldenberg further in view of Wu teaches the computer-implemented method of claim 1, wherein the determining of the mapping of the set of classified attributes further comprises: determining that an attribute of the set of classified attributes is associated with at least two matching engine algorithm functions of the matching engine algorithm functions; determining, based on the data profiling statistics, that the attribute includes multi- byte data; and removing, based on the determining that the attribute is associated with the at least two matching engine algorithm functions and the determining that the attribute includes the multi-byte data, a single-byte matching engine algorithm function of the at least two matching engine algorithm functions (Goldenberg, Para 0067, Identity Hub 32 utilizes a plurality of algorithms to compare and score member attribute similarities and differences. More specifically, Identity Hub 32 applies the algorithms to data to create tasks and to support search functionality). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Panuganty in view of Goldenberg to include the teaching of Wu. One of ordinary skill in the art would be motivated to implement this modification in order to maintain relevant information, as taught by Wu (Para 0028, The significance of the text-timestamp records 420 decays over time).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/B.T.H./Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166