DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office action is in response to the amendments, arguments and remarks, filed on 3/23/2026, in which claim(s) 1, 2, 4-9, 11-16 and 18-20 is/are presented for further examination.
Claim(s) 1, 2, 8, 9, 15 and 16 has/have been amended.
Claim(s) 3, 10 and 17 has/have been previously cancelled.
Claim(s) 7 (and similarly claim(s) 14 and 20) is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Amendments
Applicant’s amendment(s) to claim(s) 1, 2, 8, 9, 15 and 16 has/have been accepted.
The examiner thanks applicant’s representative for pointing out where s/he believes there is support for the amendment(s).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 2, 4-9, 11-16 and 18-20, filed on 9/23/2026, have been fully considered but they are not persuasive. Accordingly, this action has been made FINAL.
Applicant’s arguments with respect to the rejection(s) of claim(s) 1, 2, 4, 5, 8, 9, 11, 12, 15, 16 and 18, under 35 U.S.C. 103, see the middle of page 13 to the middle of page 15 and the bottom of page 16 to page 17 of applicant’s remarks, filed on 3/23/2026, have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
In response to applicant's argument that Lin is nonanalogous art, see the bottom of page 15 to page 16 of applicant’s remarks, filed on 3/23/2026, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, Lin deals with determining to what degree nodes are connected to one another. This connection is similar to how nodes are connected in the graphs in Kapoor and the semantic graphs in Tacchi showing edges connecting nodes in the graphs. As such, Lin is pertinent to the particular problems of Kapoor and Tacchi.
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.
Claim(s) 1, 2, 4, 5, 8, 9, 11, 12, 15, 16 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kapoor et al., US 2004/0249789 A1 (hereinafter “Kapoor”) in view of Moerk et al., US 2018/0234416 A1 (hereinafter “Moerk”) in further view of Tacchi et al., US 2017/0228435 A1 (hereinafter “Tacchi”) in further view of Lin et al., CN 102291203 A (hereinafter “Lin”; Note: Citations are from the English translation attached/provided).
Claims 1, 8 and 15
Kapoor discloses a method that includes one or more processing devices performing operations comprising:
accessing, via a public data network or a private data network, a set of data records stored in a data repository corresponding to the query record (Kapoor, [0021], see FIG. 1 depicts a system for practicing the disclosed invention utilizing a general purpose computer 20. A data mining software component that executes on the computer 20 accesses a database to extract data records stored within that database. An application program 36 either executing on the computer 20 or in communications with the computer 20 by means of a communications link such as a network 51 makes requests of a data mining engine);
identifying a list of candidate records from the set of data records stored in the data repository for merging (Kapoor, [0013], see “The system finds similar data records [i.e., “list of candidate records for merging”] from a set of data records. In one embodiment the records are provided from a database table [i.e., “database repository”] from which one or more canonical data records are identified. Scores are assigned to records or tuples based on their similarity to other records. If for example, a record is very similar to another specified record it is assigned a high score with respect to that other record.”);
determining a matching decision and a matching score for each pair of candidate records in the list, the matching score indicating a level of confidence in the matching decision (Kapoor, [0014], see “After assigning the scores [i.e., “matching scores”], data records are grouped together [i.e., “determining a matching decision”] based on the similarity score if that score is greater than a threshold [i.e., “level of confidence”]. The one or more groups of data records form nodes of a graph wherein edges between nodes represent a similarity score between records or tuples that make up a group. Within each group a canonical record is identified based on the similarity of data records to each other within the group.”);
generating a graph based on the matching scores and the matching decisions, the graph comprising nodes representing respective candidate records and edges connecting the nodes, each edge representing a match between a pair of nodes connected by the edge according to the corresponding matching decision and assigned a value based on the corresponding matching score (Kapoor, [0014], see “After assigning the scores, data records are grouped together based on the similarity score if that score is greater than a threshold. The one or more groups of data records form nodes of a graph wherein edges between nodes represent a similarity score between records or tuples that make up a group. Within each group a canonical record is identified based on the similarity of data records to each other within the group.”; and Kapoor, [0049], see “… That is, if one imagines tuples and their duplication relationship to be described by a graph where each node corresponds to a tuple and two nodes are connected by an edge if they are duplicates then the (sub-)graph induced by a group of duplicates has to have a certain structural form. …”; and Kapoor, [0071], see “… A first neighborhood graph generation phase identifies pairs of tuples that are considered duplicates according to the duplication function - the thresholded similarity function. Based on the output of the first phase, a second partitioning phase partitions the set of all tuples into groups such that each group consists of fuzzy duplicates (and satisfies either the star or almost-clique properties). Further, the second phase also returns a canonical representative tuple for each group);
identifying a connected component in the graph (Kapoor, [0071], see “… A first neighborhood graph generation phase identifies pairs of tuples that are considered duplicates [i.e., “connected component”] according to the duplication function - the thresholded similarity function. Based on the output of the first phase, a second partitioning phase partitions the set of all tuples into groups such that each group consists of fuzzy duplicates (and satisfies either the star or almost-clique properties). Further, the second phase also returns a canonical representative tuple for each group);
identifying a qualified connected component comprising a plurality of nodes from the connected component based on at least three or more nodes of the connected component having a minimum connectivity equal to or higher than a threshold number of connectivity for each node of the three or more nodes (Kapoor, Fig. 4, see node “Tulane University Medical Center” connected to nodes “Tulane University Medical Center Hospital”, “Tulane University”, “Tulane Univ Medical Center” and “Tulane University Medical CTR” [i.e., “Tulane University Medical Center” connected to 4 other nodes], see noted “Tulane University Medical Center Hospital” connected to nodes “Tulane University Medical Center”, “Tulane Univ Medical Center” and “Tulane Univ” [i.e., “Tulane University Medical Center Hospital” connected to 3 other nodes] and see node “Tulane Univ Medical Center” connected to nodes “Tulane University Medical CTR”, “Tulane University Medical Center”, “Tulane University Medical Center Hospital” and “Tulane Univ” [i.e., “Tulane Univ Medical Center” connected to 4 other nodes]; Kapoor, [0078], see “For each node v in the neighborhood graph, define its score as the sum of all edges incident on v. That is, the score represents the weight of the star formed with v as the center and all tuples that are duplicates of v as the fringe of star. In the greedy algorithm, identify the node v with the highest score. Then remove the star (all the nodes and all edges emanating from these nodes) formed around this node v as the center from the neighborhood graph. The node v is the canonical tuple for this set of duplicate tuples. Then continue with the residual graph, picking the node with the highest score, removing its group and so on until the residual graph is empty. The underlying assumption is that all nodes which belong in the same group would have been identified to be close to the canonical node and hence would have an edge to that node.”),
reducing a size of the data repository by reducing the set of data records stored in the data repository by merging candidate records represented by the nodes in the qualified connected component (Kapoor, [0003], see identifying and eliminating duplicated data [i.e., reducing”]; Kapoor, [0071], see “The process for solving the duplicate identification problem is performed in two phases. A first neighborhood graph generation phase identifies pairs of tuples that are considered duplicates according to the duplication function—the thresholded similarity function. Based on the output of the first phase, a second partitioning phase partitions the set of all tuples into groups such that each group consists of fuzzy duplicates (and satisfies either the star or almost-clique properties). Further, the second phase also returns a canonical representative tuple for each group [i.e., “merged candidate records”].”; and Kapoor, [0078], see, for each node v in the neighborhood graph, define its score as the sum of all edges incident on v. That is, the score represents the weight of the star formed with v as the center and all tuples that are duplicates of v as the fringe of star. In the greedy algorithm, identify the node v with the highest score. Then remove the star (all the nodes and all edges emanating from these nodes) [i.e., “reducing the set of data records”, where each data record is depicted as a node] formed around this node v as the center from the neighborhood graph. The node v is the canonical tuple for this set of duplicate tuples [i.e., “merged candidate records”]. Then continue with the residual graph, picking the node with the highest score, removing its group and so on until the residual graph is empty [i.e., where removing the groups reducing the data stored and the memory used for that storage]. The underlying assumption is that all nodes which belong in the same group would have been identified to be close to the canonical node and hence would have an edge to that node).
Kapoor does not appear to explicitly disclose receiving, via a client computing system, a request to input a query record into a data repository;
authorizing access to the data repository based on an identifier of the client computing system, and responsive to determining access is authorized:
generating a semantic graph with semantic connected components;
wherein the threshold number of connectivity is set to be a number of nodes in the three or more nodes of the semantic connected component minus one, such that each node of the three or more nodes defining the semantic qualified connected component has a connectivity greater than or equal to the number of the three or more nodes minus one.
Moerk discloses receiving, via a client computing system, a request to input a query record into a data repository (Moerk, [0029], see the server 20 may assign session identifiers to incoming requests [i.e., “request to input a query record”] and route incoming request to the authenticator 20 along with that session identifier, which may be carried through as context through an authentication session and used by the server 20 to identify a network address and port to which to send the reply, e.g., based on a session record mapping the session identifier to the address/port; and Moerk, [0039], see query [i.e., “request to input a query record”] to the database [i.e., “data repository”]);
authorizing access to the data repository based on an identifier of the client computing system, and responsive to determining access is authorized (Moerk, [0032], see a request to authenticate a user includes a user identifier (such as one that is unique to the user among a user base of the identity-provider computer system 12), a user password, and an identifier of the service-provider computer system 16 to which the user seeks access (such as an identifier that is unique to the service-provider computer system among all service-provider computer systems supported by the identity-provider computer system 12). …, once a user is authenticated for a given service provider, other request from other service providers during a given (i.e., any particular one) authenticated session involving the identity provider and the user may be deemed authenticated without the user needing to resupply their credentials).
Kapoor and Moerk are analogous art because they are from the problem-solving area of accessing records.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, having the teachings of Kapoor and Moerk before him/her, to modify the matching of Kapoor to include the authentication of Moerk because it would allow securing of sensitive information.
The suggestion/motivation for doing so would have been to reduce the bandwidth and computing resources needed to authenticate users, see Moerk, [0018].
Therefore, it would have been obvious to combine Moerk with Kapoor to obtain the invention as specified in the instant claim(s).
The combination of Kapoor and Moerk does not appear to explicitly disclose generating a semantic graph with semantic connected components;
wherein the threshold number of connectivity is set to be a number of nodes in the three or more nodes of the semantic connected component minus one, such that each node of the three or more nodes defining the semantic qualified connected component has a connectivity greater than or equal to the number of the three or more nodes minus one.
Tacchi discloses generating a semantic graph with semantic connected components (Tacchi,[0018], see a semantic graph (or other type of graph, like those described below) of documents, where edges are given by pairwise semantic similarities between each couples of documents in the corpus; and Tacchi, [0021], see some embodiments collect information about the connectivity around single documents (corresponding to nodes in the semantic graph) and create an endogenous representation of the documents based on the nodes in their neighborhood (e.g., sharing an edge with the node) [i.e., corresponds to the “semantic connected components”]).
Kapoor, Moerk and Tacchi are analogous art because they are from the problem-solving area of accessing records.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, having the teachings of Kapoor, Moerk and Tacchi before him/her, to modify the matching and authentication of the combination of Kapoor and Moerk to include the semantic graph of Tacchi because it would improve matching.
The suggestion/motivation for doing so would have been to make inferences on relatively large collections of documents, see Tacchi, [0006].
Therefore, it would have been obvious to combine Tacchi with the combination of Kapoor and Moerk to obtain the invention as specified in the instant claim(s).
The combination of Kapoor, Moerk and Tacchi does not appear to explicitly disclose wherein the threshold number of connectivity is set to be a number of nodes in the three or more nodes of the semantic connected component minus one, such that each node of the three or more nodes defining the semantic qualified connected component has a connectivity greater than or equal to the number of the three or more nodes minus one.
Lin discloses wherein the threshold number of connectivity is set to be a number of nodes in the three or more nodes of the connected component minus one, such that each node of the three or more nodes defining the qualified connected component has a connectivity greater than or equal to the number of the three or more nodes minus one (Lin, [0038], see placing group size of group network | c | (if the size of each group are the same [i.e., “threshold number of connectivity”]), this network is Cn = (N-M) /I Cl group, counting variable connection established after selecting one qualifying source node Si in the search set h of relay node h, first. then the source node availability of Si (Si) minus 1).
Kapoor, Moerk, Tacchi and Lin are analogous art because they are from the problem-solving area of accessing records.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, having the teachings of Kapoor, Moerk, Tacchi and Lin before him/her, to modify the semantic matching and authentication of the combination of Kapoor, Moerk and Tacchi to include the connectivity determination of Lin because it improve performance.
The suggestion/motivation for doing so would have been to eliminate the short cycle in the network and improve performance, see Lin, [0005].
Therefore, it would have been obvious to combine Lin with the combination of Kapoor, Moerk and Tacchi to obtain the invention as specified in the instant claim(s).
Claim(s) 8 and 15 recite(s) similar limitations to claim 1 and is/are rejected under the same rationale.
With respect to claim 8, Kapoor discloses a non-transitory computer-readable storage medium (Kapoor, [0023], see system memory).
With respect to claim 15, Kapoor discloses a computing system comprising:
a processing device (Kapoor, [0022], see computer);
a data repository for storing data records, wherein each data record comprises one or more identifiers (Kapoor, [021], see the database with data records).
a non-transitory computer-readable storage medium (Kapoor, [0023], see system memory).
Claims 2, 9 and 16
With respect to claims 2, 9 and 16, the combination of Kapoor, Moerk, Tacchi and Lin discloses further comprising:
updating the graph based on the merged candidate records (See citation below);
identifying a second semantic connected component in the updated graph (See citation below);
identifying a second semantic qualified connected component from the second semantic connected component based on a portion of the second semantic qualified connected component having a minimum connectivity exceeding the threshold number of connectivity (See citation below); and
further reducing the set of reduced data records stored in the data repository by merging the candidate records represented by the nodes in the second semantic qualified connected component (Kapoor, [0003], see identifying and eliminating duplicated data [i.e., reducing”]; Kapoor, [0071], see “The process for solving the duplicate identification problem is performed in two phases. A first neighborhood graph generation phase identifies pairs of tuples that are considered duplicates according to the duplication function—the thresholded similarity function. Based on the output of the first phase, a second partitioning phase partitions the set of all tuples into groups such that each group consists of fuzzy duplicates (and satisfies either the star or almost-clique properties). Further, the second phase also returns a canonical representative tuple for each group [i.e., “merged candidate records”].”; Kapoor, [0078], see, for each node v in the neighborhood graph, define its score as the sum of all edges incident on v. That is, the score represents the weight of the star formed with v as the center and all tuples that are duplicates of v as the fringe of star. In the greedy algorithm, identify the node v with the highest score. Then remove the star (all the nodes and all edges emanating from these nodes) [i.e., “reducing the set of data records”, where each data record is depicted as a node] formed around this node v as the center from the neighborhood graph. The node v is the canonical tuple for this set of duplicate tuples [i.e., “merged candidate records”]. Then continue with the residual graph, picking the node with the highest score, removing its group and so on until the residual graph is empty. The underlying assumption is that all nodes which belong in the same group would have been identified to be close to the canonical node and hence would have an edge to that node; Tacchi,[0018], see a semantic graph (or other type of graph, like those described below) of documents, where edges are given by pairwise semantic similarities between each couples of documents in the corpus; and Tacchi, [0021], see some embodiments collect information about the connectivity around single documents (corresponding to nodes in the semantic graph) and create an endogenous representation of the documents based on the nodes in their neighborhood (e.g., sharing an edge with the node) [i.e., corresponds to the “semantic connected components”]).
Claims 4, 11 and 18
With respect to claims 4, 11 and 18, the combination of Kapoor, Moerk, Tacchi and Lin discloses wherein identifying the list of candidate records from the set of data records comprises:
accessing a candidate search criterion (See citation below);
searching the set of data records to find records that satisfy the candidate search criterion (See citation below); and
returning the records that satisfy the candidate search criterion as the list of candidate records (Kapoor, [0013], see “The system finds similar data records [i.e., whatever criteria is used to find similarity is the “candidate search criterions”] from a set of data records. In one embodiment the records are provided from a database table from which one or more canonical data records are identified. Scores are assigned to records or tuples based on their similarity to other records. If for example, a record is very similar to another specified record it is assigned a high score with respect to that other record [i.e., where the high scoring data records based on the similarity criteria are selected].”).
Claims 5 and 12
With respect to claims 5 and 12, the combination of Kapoor, Moerk, Tacchi and Lin discloses wherein the candidate search criterion comprises values of two or more identifiers to be matched by the returned records (Kapoor, [0013], see “The system finds similar data records [i.e., whatever criteria is used to find similarity is the “candidate search criterions”] from a set of data records. In one embodiment the records are provided from a database table from which one or more canonical data records are identified. Scores are assigned to records or tuples based on their similarity to other records. If for example, a record is very similar to another specified record it is assigned a high score with respect to that other record.”).
Claim(s) 6, 13 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kapoor in view of Moerk in further view of Tacchi in further view of Lin in further view of Seth et al., US 2021/0279604 A1 (hereinafter “Seth”).
Claims 6, 13 and 19
Claims 6, 13 and 19 incorporate all of the limitations above.
With respect to claims 6, 13 and 19, the combination of Kapoor, Moerk, Tacchi and Lin discloses wherein determining the matching decision and the matching score for each pair of candidate records in the list comprises:
generating matching attributes for the pair of candidate records, wherein the matching attributes comprise one or more of:
a numerical identifier score measuring a degree of matching between numerical identifiers of the pair of candidate records,
a name identifier score measuring a degree of matching between name identifiers of the pair of candidate records (Kapoor, [0032], see “… The edit distance èd(s 1, s2) between two strings s1 and S2 is the minimum number of character edit operations (delete, insert, and substitute) required to transform s1 into s2, normalized by the maximum of the lengths of s1, and S2. For example, the edit distance between the strings ‘company’ and ‘corporation’ (FIG. 2) is 7/11≈0.64, and the sequence of edit operations is shown. Vertical lines indicate either exact matches (cost is 0) or substitutions (cost is 1). Characters in italics are deleted or inserted and always have a unit cost.”; Kapoor, [0046], see “…: Now define the fuzzy match similarity function fms(u, v) between an input tuple u and a reference tuple v in terms of the transformation cost tc(u, v). Let w(u) be the sum of weights of all tokens in the token set tok(u) of the input tuple u. …”; and Kapoor, Figs. 3 & 4),
an address identifier score measuring a degree of matching between address identifiers of the pair of candidate records,
a date identifier score measuring a degree of matching between date identifiers of the pair of candidate records, or
a compound score generated based on two or more of the numerical identifier score, the address identifier score, the address identifier score, and the date identifier score; and
The combination of Kapoor, Moerk, Tacchi and Lin does not appear to explicitly disclose determining, using a machine learning model, the matching decision and the matching score for the pair of candidate records based on the matching attributes.
Seth discloses determining, using a machine learning model, the matching decision and the matching score for the pair of candidate records based on the matching attributes (Seth, [0013], see “… provide techniques to apply machine learning (ML) to entity resolution, in order to significantly improve results. In some embodiments of the present disclosure, attribute comparison functions check for a variety of matching conditions including exact match, edit distance, n-gram techniques, phonetic approaches, and/or partial matching. Scores and/or vectors can be generated based on the outcome of these comparisons, and sub-scores from each attribute may be combined based on statistically determined relative weights.”; and Seth, [0020], see “…, the Scoring Component 110 also generates one or more comparison scores for each pair of Data Records 105. For example, the Scoring Component 110 may use one or more default configurations to score and weight various attributes in each record, in order to generate an overall matching score. In many existing systems, this score is used to determine whether the records are matched (e.g., by comparing the scores to one or more thresholds) and should therefore be linked. In some embodiments, this score is included as part of the Comparison Vector 115.”).
Kapoor, Moerk, Tacchi, Lin and Seth are analogous art because they are from the problem-solving area of accessing records.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, having the teachings of Kapoor, Moerk, Tacchi, Lin and Seth before him/her, to modify the semantic matching and authentication of the combination of Kapoor, Moerk and Lin to include the machine learning matching of Seth because of improved entity resolution that reduces manual effort, improves accuracy, and reduces resources required to perform the resolution.
The suggestion/motivation for doing so would have been to apply machine learning (ML) to entity resolution, in order to significantly improve results, see Seth, [0013].
Therefore, it would have been obvious to combine Seth with the combination of Kapoor, Moerk, Tacchi and Lin to obtain the invention as specified in the instant claim(s).
Allowable Subject Matter
Claim(s) 7 (and similarly claim(s) 14 and 20) is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
– Ciulla et al., 9645999 for adjustment of document relationship graphs;
– Ciulla et al., 2018/0039620 for adjustment of document relationship graphs;
– Valerio et al., 2012/0166378 for forwarding chaining as an orchestration mechanism for analytics; and
– Srinivas et al., 2020/0257731 for disambiguation of massive graph databases.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Point of Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUBERT G CHEUNG whose telephone number is (571) 270-1396. The examiner can normally be reached M-R 8:00A-5:00P EST; alt. F 8:00A-4:00P EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached at (571) 272-4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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HUBERT G. CHEUNG
Assistant Examiner
Art Unit 2161
Examiner: Hubert Cheung
/Hubert Cheung/Assistant Examiner, Art Unit 2161Date: May 21, 2026
/APU M MOFIZ/Supervisory Patent Examiner, Art Unit 2161
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