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 .
Status of the Claims
Claims 1-20 are pending for examination.
Claims 1, 8 and 15 are independent Claims.
Claims 1-20 are rejected under 35 U.S.C. §101.
Claims 1-6, 8-13 and 15-20 are rejected under 35 U.S.C. §102.
Claims 7 and 14 are rejected under 35 U.S.C. §103.
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) 1-6, 8-13 and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Christodoulopoulos (U.S. 10,896,222) in view of Roychoudhuri et al. (U.S. 2008/0263010) in view of Chang (U.S. 2007/0208693 hereinafter Chang).
As Claim 1, Christodoulopoulos teaches a method comprising:
receiving unstructured data as a data stream of data comprising an unstructured transaction or a payment data from a business production application via an event streaming platform in real or near-real time (Christodoulopoulos (col. 7 line 24-26), “The speech controlled appliance 113 is for example arranged to capture a spoken utterance of a user via an audio capture device such as a microphone or a microphone array.”);
persisting the unstructured data in a data lake as data objects in a native format without altering the data's native format (Roy (¶0053 line 3-7), “the content captured by the interactive recording, access and playback system is kept in its native format or ported to an equivalent format that can be played back in a browser, with minimal loss in fidelity in the conversion process.”);
extracting, by an extraction engine including one or more natural language processing (NLP) machine learning (ML) models (Christodoulopoulos (col. 17 line 43-48), “a supervised training process of the machine learning system may be performed, for example using a number of different sentences of text, annotated, tagged or flagged to identify portions of the text corresponding to the first and second related entity, each represented by output text data.”), a fact from the data object, wherein the fact includes a plurality of components, and wherein the plurality of components include a subject, a predicate, and an object (Christodoulopoulos (col. 10 line 45-48), “Based on identification of first and second related entities in the unstructured text, a fact triple 136 can be obtained, which in this example is in the form predicate (subject, object).”);
performing an update check by querying the knowledge graph to determine one or more of the plurality of components is not present in the knowledge graph (Christodoulopoulos (col. 18 line 27-34), “Similarly, if the machine learning system identifies that an existing entity of the knowledge database is involved in a relationship with a new entity, which is not included in the knowledge database, 30 new entity data (and, in examples, new text data associated with the new entity) may be added to the knowledge database, for example corresponding with a new node where the knowledge database is in the form of a graph”, new entity is construed as “not present in a knowledge graph”);
generating, via an ontology update engine, a first new node, a second new node (Christodoulopoulos (col. 28 line 16-22), “the knowledge database 226 includes a fact triple including a subject, a predicate and an object. In such cases, the first entity data may represent one of the subject or the object, the subject entity data may represent the other of the subject or the object and the relationship data representative of the relationship may represent the predicate.”), and a first new edge (Christodoulopoulos (col. 28 line 12-16), “nodes connected to the second node (corresponding to the subject entity 228a) with respective edges that satisfy the relationship condition may be identified as corresponding to related entities related to the subject entity 228a.”) from the one or more of the plurality of components not present in the knowledge graph (Christodoulopoulos (col. 18 line 27-34), “Similarly, if the machine learning system identifies that an existing entity of the knowledge database is involved in a relationship with a new entity, which is not included in the knowledge database, 30 new entity data (and, in examples, new text data associated with the new entity) may be added to the knowledge database, for example corresponding with a new node where the knowledge database is in the form of a graph”, new entity is construed as “not present in a knowledge graph”), wherein the ontology update engine determines a type of each of the first new node and the second new node and a label for the first new edge, wherein the first new node is generated from the subject, the second new node is generated from the object, and the first new edge is generated from the predicate (Christodoulopoulos (col. 10 line 48-58), “the predicate or relationship between the subject and object has been identified as located in, the subject is McGill University (the first related entity) and the object is Montreal Quebec (the second related entity). For example, the fact triple 136 may be obtained by identifying a semantic relationship between the first and second related entities. Such a semantic relationship may be identified by identifying one or more intervening words between the first text and the second text, such as one or more intervening words that satisfy certain semantic or other criteria.”);
assigning, by a property checker, properties to the first new node, the second new node, and the first new edge (Christodoulopoulos (col. 10 line 48-58), “the predicate or relationship between the subject and object has been identified as located in, the subject is McGill University (the first related entity) and the object is Montreal Quebec (the second related entity). For example, the fact triple 136 may be obtained by identifying a semantic relationship between the first and second related entities. Such a semantic relationship may be identified by identifying one or more intervening words between the first text and the second text, such as one or more intervening words that satisfy certain semantic or other criteria.”), wherein the property checker retrieves the properties from a registered property graph repository that is queried and updated using the knowledge graph compares received properties from extracted facts to the registered property graph repository (Christodoulopoulos (col. 18 line 27-34), “Similarly, if the machine learning system (the property checker) identifies that an existing entity of the knowledge database (a registered property graph repository) is involved in a relationship with a new entity, which is not included in the knowledge database, new entity data (and, in examples, new text data associated with the new entity) may be added to the knowledge database, for example corresponding with a new node where the knowledge database is in the form of a graph”), assigns retrieved properties to the first new node, the second new node, and the first new edge (Christodoulopoulos (col. 18 line 11-15), “These sentences have been processed using the machine learning system of FIG. 8 to determine that they participate in an instance of relationship between the first related entity and the second related entity”), and persists new properties to the registered property graph repository for a future iteration (Christodoulopoulos (col. 18 line 18-22), “For example, if a new relationship between existing entities of the knowledge database is identified by the machine learning system, the knowledge database may be updated to include this new relationship.”);
predicting, by a link predictor using a graph neural network (GNN) model, a second new edge, wherein the second new edge connects one of the first new node or the second new node to an existing node in the knowledge graph (Christodoulopoulos (col. 18 line 23-26), “a new edge may be added to the graph between the nodes corresponding to the entities of the knowledge database that are involved in the new relationship”);
preparing a graph update, wherein the graph update includes the first new node, the second new node, the first new edge (Christodoulopoulos (col. 18 line 11-15), “These sentences have been processed using the machine learning system of FIG. 8 to determine that they participate in an instance of relationship between the first related entity and the second related entity”, first entity and second entity are related through the first new edge. Christodoulopoulos (col. 2 line 59-62), “Such fact triples can be expressed in the form of predicate (subject, object), where the predicate is a relationship or relation and the subject and object may be considered to be entities that participate in that relationship”), and the second new edge (Christodoulopoulos (col. 18 line 23-26), “a new edge may be added to the graph between the nodes corresponding to the entities of the knowledge database that are involved in the new relationship”);
wherein the pushing includes pushing the first new node, the second new node, the first new edge, properties assigned to the first new node, the second new node, and the first new edge, and the second new edge to the knowledge graph (Christodoulopoulos (col. 10 line 48-58), “the predicate or relationship between the subject and object has been identified as located in, the subject is McGill University (the first related entity) and the object is Montreal Quebec (the second related entity). For example, the fact triple 136 may be obtained by identifying a semantic relationship between the first and second related entities. Such a semantic relationship may be identified by identifying one or more intervening words between the first text and the second text, such as one or more intervening words that satisfy certain semantic or other criteria.”).
Christodoulopoulos may not explicitly disclose:
persisting the unstructured data in a data lake as data objects in a native format without altering the data's native format;
Roy teaches:
persisting the unstructured data in a data lake as data objects in a native format without altering the data's native format (Roy (¶0053 line 3-7), “the content captured by the interactive recording, access and playback system is kept in its native format or ported to an equivalent format that can be played back in a browser, with minimal loss in fidelity in the conversion process.”);
Christodoulopoulos discloses a system/method to record user utterance and extract knowledge graph from the utterance. Roy discloses a system and method to store media in its native format. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify knowledge graph of Christodoulopoulos instead be native format content taught by Roy, with a reasonable expectation of success. The motivation would be so that “content will retain greatest fidelity in its most native state and web playback provides the greatest reach and audience coverage” (Roy (¶0053 line 1-3)).
Christodoulopoulos in view of Roy may not explicitly disclose:
Chang teaches:
performing, by a conflict engine, a conflict check on the graph update by comparing the graph update with a current state of the knowledge graph, attempting to rectify a determined conflict programmatically (Chang (¶0087), “Check to see if a Data Node already exists with the new Data Node value”); and
pushing the graph update if no conflicts are detected, or once all conflicts have been resolved, pushing the graph update to the knowledge graph, (Chang (¶0088), “If a matching Data Node does not exist, create one and a new ID, also create a corresponding DAG Node and a new ID for the DAG Node.”)
Christodoulopoulos discloses a system/method to record user utterance and extract knowledge graph from the utterance. Roy discloses graph conflict detection module. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify knowledge graph of Christodoulopoulos instead be a conflict detection module taught by Chang, with a reasonable expectation of success. The motivation would to “reduce the number of substring comparison needed to access a generalized DAG stored in a database and can maintain efficient DAG representation and access as the average number of parents of DAG nodes increases.” (Chang (¶0018 last 5 lines))
As Claim 3, besides Claim 1, Christodoulopoulos teaches wherein the first new edge connects that first new node and the second new node (Christodoupoulos (col. 28 line 12-16), “nodes connected to the second node (corresponding to the subject entity 228a) with respective edges that satisfy the relationship condition may be identified as corresponding to related entities related to the subject entity 228a.”).
As Claim 4, besides Claim 1, Christodoulopoulos teaches wherein the properties are from a registered property repository (Christodoulopoulos (col. 28 line 16-22), “the knowledge database (a registered property repository) 226 includes a fact triple including a subject, a predicate and an object. In such cases, the first entity data may represent one of the subject (first new node) or the object (second new node), the subject entity data may represent the other of the subject or the object and the relationship data representative of the relationship (the first new edge) may represent the predicate.”).
As Claim 5, besides Claim 1, Christodoulopoulos teaches wherein the unstructured data is received as a data stream from an event streaming platform (Christodouloupolos (col. 23 line 13-14), “Feature vectors may be streamed or combined into a matrix that represents a time period of the spoken utterance.”).
As Claim 6, besides Claim 1, Christodoulopoulos teaches wherein the knowledge graph is a labelled property graph (Christodoulopoulos (col. 28 line 16-22), “the knowledge database (a registered property repository) 226 includes a fact triple including a subject, a predicate and an object. In such cases, the first entity data may represent one of the subject (first new node) or the object (second new node), the subject entity data may represent the other of the subject or the object and the relationship data representative of the relationship (the first new edge) may represent the predicate.”).
As Claim 8-13, the Claims are rejected for the same reasons as Claim 1-6, respectively.
As Claim 15-20, the Claims are rejected for the same reasons as Claim 1-6, respectively.
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) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Christodoulopoulos and Roy in view of Chang in further view of Lee et al. (U.S. 2021/0117402 hereinafter Lee).
As Claim 7, besides Claim 1, Christodoulopoulos in view of Roy in further view of Chang may not explicitly disclose:
wherein the knowledge graph is a Resource Description Framework (RDF) graph.
Lee teaches:
wherein the knowledge graph is a Resource Description Framework (RDF) graph (Lee (¶0073 last 5 lines), Resource Description Framework (RDF)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify knowledge graph of Christodoulopoulos in view of Roy in further view of Chang instead be a RDF graph taught by Lee, with a reasonable expectation of success. The motivation would be to allow “for updating a server knowledge graph by using a device knowledge graph generated by a device” (Lee (¶0005)).
As Claim 14, the Claim is rejected for the same reasons as Claim 7.
Response to Arguments
Claims 1-20 rejection under 35 U.S.C. §101:
Applicant’s arguments are persuasive; therefore, 35 U.S.C. §101 rejections are respectfully withdrawn.
Claims Rejections under 35 U.S.C. §§ 102, 103:
As per Christodoulopoulos, Applicant argues that the references does not disclose “ receiving unstructured transaction …, persist that unstructured data in a data lake as native format data object …” (last paragraph of page 12 in the remarks).
Applicant’s arguments are not persuasive because Christodoulopoulos disclose “receiving unstructured transaction …”. See current rejection for detail. Limitation “persisting …” is taught by new reference Roy.
As per Christodoulopoulos, Applicant argues that the references does not disclose “ a property checker” (last paragraph of page 13 in the remarks).
Christodoulopoulos teaches the limitation as below:
assigning, by a property checker, properties to the first new node, the second new node, and the first new edge (Christodoulopoulos (col. 10 line 48-58), “the predicate or relationship between the subject and object has been identified as located in, the subject is McGill University (the first related entity) and the object is Montreal Quebec (the second related entity). For example, the fact triple 136 may be obtained by identifying a semantic relationship between the first and second related entities. Such a semantic relationship may be identified by identifying one or more intervening words between the first text and the second text, such as one or more intervening words that satisfy certain semantic or other criteria.”), wherein the property checker retrieves the properties from a registered property graph repository that is queried and updated using the knowledge graph compares received properties from extracted facts to the registered property graph repository (Christodoulopoulos (col. 18 line 27-34), “Similarly, if the machine learning system (the property checker) identifies that an existing entity of the knowledge database (a registered property graph repository) is involved in a relationship with a new entity, which is not included in the knowledge database, new entity data (and, in examples, new text data associated with the new entity) may be added to the knowledge database, for example corresponding with a new node where the knowledge database is in the form of a graph”), assigns retrieved properties to the first new node, the second new node, and the first new edge (Christodoulopoulos (col. 18 line 11-15), “These sentences have been processed using the machine learning system of FIG. 8 to determine that they participate in an instance of relationship between the first related entity and the second related entity”), and persists new properties to the registered property graph repository for a future iteration (Christodoulopoulos (col. 18 line 18-22), “For example, if a new relationship between existing entities of the knowledge database is identified by the machine learning system, the knowledge database may be updated to include this new relationship.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Hernandez (U.S. 2026/0155228) teaches a system/method for persist data in auditable log.
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.
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/NHAT HUY T NGUYEN/Primary Examiner, Art Unit 2147
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