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 Non-Final Office Action is in response to the application 18/774,891 filed on 11/10/2025.
Status of Claims:
Claims 6, 13, 20, and 27 are canceled in this Office Action.
Claims 1-5, 7-12, 14-19, 21-26, and 28 are pending in this Office Action.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/05/2026 has been entered.
Response to Arguments
CLAIM REJECTIONS UNDER 35 U.S.C. § 112(b)
Applicant’s arguments filed on 05/05/2026 (page 13) regarding claim rejections under 35 U.S.C 112(b) have been fully considered. However, the Examiner respectfully submits that the arguments are not persuasive.
The applicant provided that paragraph [77] of the specification as-filed states "In some implementations, the knowledge graph is updated in real-time with current device data to reflect the current operating state of an organization 's cyber-physical system".
Although the paragraph discloses that “the knowledge graph is updated in real-time”, it is still does not provide any definition or description of the term “near real-time” as claimed in claims 1, 8, 15, and 22. The term “near real-time” is not defined by the claim and the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The applied term “near real-time” in limitation “the additional data clumps are added into the knowledge graph in near real-time” does not allow one of ordinary skills in the art to determine a range of time that could be considered as near real-time when adding data into a knowledge graph. One of ordinary skills in the art can understand that “near real-time” describes a short delay of data processing, but the term can be used in various situations that can describe a few seconds, minutes, or even hours after an event happens.
Thus, claims 1-5, 7-12, 14-19, 21-26, and 28 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
CLAIM REJECTIONS UNDER 35 U.S.C. § 103
After closely reviewing the Applicant’s arguments filed on 05/15/2026 (page 14-18) regarding claim regarding to claims 1, 8, 15, and 22, the Examiner respectfully submits that the arguments are not persuasive.
Regarding claims 1, 8, 15, and 22:
The applicant argued that the prior arts of record do not teach “determining whether each data clump of the plurality of data clumps complies with the defined schema, wherein compliance with the defined schema is determined based on whether the ingestion of the data clump will completely succeed”. The examiner respectfully disagrees with the Applicant; the Examiner respectfully submits that Bhatti discloses “[0022]: In some embodiments, the process 10 begins with obtaining a node to be stored in a graph database, as indicated by block 12… [0032]: Next, some embodiments may determine whether the node satisfies criteria of the polymorphic schema, as indicated by block 24. In some embodiments, the polymorphic schema may include a plurality of criteria for determining whether nodes or other data entries are valid… [0033] Upon determining that the node does not satisfy the polymorphic schema, i.e., that the child that is not a valid entry in the graph database of the type of the node, some embodiments may proceed to block 28 and emit (e.g., log or throw) a validation error… [0034] Alternatively, upon determining that the node does satisfy the criteria of the polymorphic schema, some embodiments may proceed to block 26 and store the node and related edges in the graph database”. The system of Bhatti is directed to obtaining data such as a node to be stored and validate the data based on criteria of the a schema before storing the data in a graph database. The node of data can correspond to data clump wherein a node can have data relating to entity, link, and attribute records. The data is verified against the schema and if the data satisfies the criteria of the schema, the system stores the node and related edges in the graph database. The data that satisfies the criteria of the schema can correspond to compliance with the defined schema that data will completely succeed and the data is stored after determining that the node does satisfy the criteria of the schema.
The applicant also argued that “Paragraph [0032] of Bhatti furthermore notes how "the node may be stored first, and then edges may be stored after storing the node," which further highlights the distinction between the nodes of Bhatti and clumps of write instructions, ingestion of which must completely succeed before they are added”. However, the examiner struggles to read where in paragraph [0032] or anywhere in the specification that describes "the node may be stored first, and then edges may be stored after storing the node,". Even assuming arguendo that the Bhatti discloses "the node may be stored first, and then edges may be stored after storing the node", the examiner struggles to understand how the provided portion would hinder Bhatti from teaching the limitation. Therefore, Trim in view of Bhatti at least teaches “determining whether each data clump of the plurality of data clumps complies with the defined schema, wherein compliance with the defined schema is determined based on whether the ingestion of the data clump will completely succeed”.
The applicant argued that Trim teaches away from "determining whether each data clump of the plurality of data clumps complies with the defined schema, wherein compliance with the defined schema is determined based on whether the ingestion of the data clump will completely succeed".
The examiner respectfully disagrees with the Applicant; the Examiner respectfully submits that Trim discloses “[0026] Based on the analysis, server 104, using a clustering algorithm, divides ingested data 100 into a plurality of clusters and adds one additional cluster. Each of the plurality of clusters corresponds to an existing entity in the graph schema. If server 104 detects that the additional cluster is populated with data, then server 104 determines that an entity type is missing from the graph schema corresponding to the graph database of graph database server 106. Server 104 then adds the missing entity type to the graph schema. By ensuring that the graph schema is updated and complete and that the graph database of graph database server 106 complies with the updated graph schema, server 104 is able to increase the query performance and efficiency of graph database server 106… [0036] Graph database 220 complies with graph schema 222. Graph schema 222 includes root entities 224 and entity types 226.”. The system of Trim is directed to ingesting data into appropriate entity of a graph database based on a graph schema. The graph schema ensure that the data is complied with the graph database and store data accordingly with appropriate entity type. The system of Trim provides a structure where data can be processed by a schema and the data can be stored properly in a graph database. Although Trim also discloses that the schema structure of the system could be modified by adding the missing entity type to the schema, this additional feature does not hinder the system of Trim from verifying data against the schema to determine the data’s entity and for storage in a graph database. Bhatti discloses “[0032]: … determine whether the node satisfies criteria of the polymorphic schema, as indicated by block 24. In some embodiments, the polymorphic schema may include a plurality of criteria for determining whether nodes or other data entries are valid… [0033] Upon determining that the node does not satisfy the polymorphic schema, i.e., that the child that is not a valid entry in the graph database of the type of the node, some embodiments may proceed to block 28 and emit (e.g., log or throw) a validation error… [0034] Alternatively, upon determining that the node does satisfy the criteria of the polymorphic schema, some embodiments may proceed to block 26 and store the node and related edges in the graph database.”. The system of Bhatti is directed to validating data based on criteria of the a schema before storing the data in a graph database. Bhatti makes a determination whether the data is satisfied against the criteria of the schema, and produce binary outputs such that if the data is satisfied against the criteria of the schema the system stores the node and related edges in the graph database and if the data is not satisfied against the criteria of the schema the system emit a validation error. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Bhatti teachings in the Trim system. Skilled artisan would have been motivated to incorporate determining data compliance with the defined schema taught by Bhatti in the Trim system to ensure data is verified for compliance and determines proper actions in cases when data is complied or not complied with the schema, thus can enhance security, improve data organization, improve the overall efficiency of the system. Therefore, a POSITA would have been motivated to modify Trim in line with the teachings of Bhatti.
Applicant’s remaining arguments filed on arguments filed on 05/05/2026 (page 14-16) regarding claim regarding to claims 1, 8, 15, and 22 have been fully considered. However, after further examination, new grounds of rejection are presented.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-5, 7-12, 14-19, 21-26, and 28 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “near real-time” in claims 1, 8, 15, and 22 is a relative term which renders the claim indefinite. The term “near real-time” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The applied term “near real-time” in limitation “the additional data clumps are added into the knowledge graph in near real-time” does not allow one of ordinary skills in the art to determine a range of time that could be considered as near real-time when adding data into a knowledge graph.
Claims 2-5, 7, 9-12, 14, 16-19, 21, 23-26, and 28 are rejected because they inherit the deficiencies of claims 1, 8, 15, and 22, from which they depend, respectively, with respect to 35 U.S.C. 112(b).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1-2, 7-9, 14-16, 21-23, and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Trim et al. (US PGPUB 20200019647) “Trim” in view of Bhatti et al. (US PGPUB 20180232403) “Bhatti” and Brisimi et al. (US PGPUB 20200125659) “Brisimi” and Long et al. (US PGPUB 20180089331) “Long”.
Regarding claim 1, Trim teaches a computing system for dynamically specifiable knowledge graphs, the computing system comprising: one or more hardware processors configured for: defining a schema for data, wherein the schema is a data object that defines one or more entity definitions, one or more link definitions, and one or more attribute definitions ([0036]: Graph database complies with graph schema. Graph schema includes root entities and entity types . Root entities represent a set of one or more entities modeled in graph schema . Root entities have no parent entities. In other words, root entities represent root nodes in graph database. Entity types represent a plurality of sub-entities that are in a child relationship with one or more of root entities . In other words, entity types represent child nodes of parent nodes associated with root entities in graph database… Examiner’s note: The system comprises a graph schema where it includes information such as root entities and entity types that can correspond to entity definitions, link definitions, and attribute definitions); ingesting a plurality of data clumps, wherein each data clump comprises a block of write instructions described using entity, link, and attribute (ELA) records ([0025] Server 104 ingests and analyzes data 110 of domain storage 108… Server 104 analyzes ingested data 110 using, for example, machine learning…[0026] Based on the analysis, server 104, using a clustering algorithm, divides ingested data 100 into a plurality of clusters and adds one additional cluster. Each of the plurality of clusters corresponds to an existing entity in the graph schema… Examiner’s note: The system accepts data that corresponds to data clumps where data is processed according to the graph schema thus properties such as entity, link, and attribute of data can used to process); instantiating a knowledge graph in a distributed associative array using only the data clumps that are determined to comply with the defined schema (Fig. 2 & [0026]: Based on the analysis, server , using a clustering algorithm, divides ingested data into a plurality of clusters and adds one additional cluster. Each of the plurality of clusters corresponds to an existing entity in the graph schema… [0035]: Graph database represents a listing of a set of one or more domain knowledge graph databases residing in the graph database server. Graph database 220 contains information corresponding to a particular domain of knowledge… [0036]: Graph database complies with graph schema. Graph schema includes root entities and entity types… Examiner’s note: The knowledge graph database is stored in a particular memory and it complies with a schema wherein the schema is associated with data that are clustered to existing entity. Thus, the graph database’s use of data with existing entity found in schema can correspond to using only the data clumps that are determined to comply with the defined schema), wherein the distributed associative array is instantiated as a service in plurality of containerized environments across multiple computing nodes to enable automatic deployment, scaling, and management of the knowledge graph (Fig. 2 & [0024]: “Server 104 and server 106 connect to network 102, along with domain storage 108. Server 104 and server 106 may be, for example, server computers with high-speed connections to network 102. In addition, it should be noted that server 104 and server 106 may each represent clusters of servers in one or more data centers. Alternatively, server 104 and server 106 may each represent multiple computing nodes in a cloud environment”… Examiner’s note: Thus, the graph databases residing in the graph database server wherein the graph database server can be a separate device that is connected to a network. The server can be equivalent to a service in a containerized service management application).
Trim does not explicitly teach wherein distributed associative array is a distributed in-memory associative array; determining whether each data clump of the plurality of data clumps complies with the defined schema, wherein compliance with the defined schema is determined based on whether the ingestion of the data clump will completely succeed; in response to receiving further additional data clumps, adding the additional data clumps into the knowledge graph in the distributed associative array only when the additional data clumps are determined to comply with the defined schema.
Bhatti teaches determining whether each data clump of the plurality of data clumps complies with the defined schema, wherein compliance with the defined schema is determined based on whether the ingestion of the data clump will completely succeed ([0032] Next, some embodiments may determine whether the node satisfies criteria of the polymorphic schema, as indicated by block 24. In some embodiments, the polymorphic schema may include a plurality of criteria for determining whether nodes or other data entries are valid… [0033]: Upon determining that the node does not satisfy the polymorphic schema, i.e., that the child that is not a valid entry in the graph database of the type of the node, some embodiments may proceed to block 28 and emit (e.g., log or throw) a validation error… [0034] Alternatively, upon determining that the node does satisfy the criteria of the polymorphic schema, some embodiments may proceed to block 26 and store the node and related edges in the graph database.); in response to receiving further additional data clumps, adding the additional data clumps into the knowledge graph in the distributed associative array only when the additional data clumps are determined to comply with the defined schema ([0032]: Next, some embodiments may determine whether the node satisfies criteria of the polymorphic schema, as indicated by block 24. In some embodiments, the polymorphic schema may include a plurality of criteria for determining whether nodes or other data entries are valid…[0034]: upon determining that the node does satisfy the criteria of the polymorphic schema, some embodiments may proceed to block 26 and store the node and related edges in the graph database. In some embodiments, the node may be stored first, and then edges may be stored after storing the node. In some cases, storing edges may include executing queries upon the graph database to identify responsive nodes that edges will link to the node.). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Bhatti teachings in the Trim system. Skilled artisan would have been motivated to incorporate determining data compliance with the defined schema taught by Bhatti in the Trim system to enhance security, improve data organization, thus improves the overall efficiency of the system. This close relation between both of the references highly suggests an expectation of success.
Trim in view of Bhatti does not explicitly teach wherein distributed associative array is a distributed in-memory associative array; wherein the additional data clumps are added in near real-time.
Brisimi teaches the additional data clumps are added in near real-time ([0065] : The compliance evaluation component can receive new operational data regarding one or more operations of the enterprise (e.g., in real-time as the operations are being performed, in an evaluation report, in a submitted claim, etc.), and apply the structured policy information to determine whether one or more of the operations comply or fail to comply with the policy. The compliance evaluation component 602 can further generate compliance review data 608 based on the evaluation that can include information identifying whether one or more operations comply with the policy or whether (and optionally why) the one or more operations fail to comply with the policy.). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Brisimi teachings in the Trim and Bhatti system. Skilled artisan would have been motivated to incorporate retrieving data in real-time taught by Brisimi in the Trim and Bhatti system so data can be used to process in a faster time, which can enhance data-driven decisions and increase operational efficiency. This close relation between both of the references highly suggests an expectation of success.
Trim in view of Bhatti and Brisimi does not explicitly teach wherein distributed associative array is a distributed in-memory associative array.
Long teaches wherein distributed associative array is a distributed in-memory associative array ([0056]: To initialize the graph database, records may be obtained from a source of truth providing graph. For example, records may include a subset of nodes, edges, and/or predicates in the graph and/or synthetic data that is generated for use in testing specific features or aspects of the graph database. A schema that includes one or more rules for defining specific types of edges and/or complex structures in the graph may also be obtained from the source of truth. The graph database may read the records, schema, and other portions of the graph into memory and use the in-memory representation to process queries. As a result, the graph database may be used in flexible and efficient querying of data in graph.). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Long teachings in the Trim, Bhatti, and Brisimi system. Skilled artisan would have been motivated to incorporate a graph database having associations with an in-memory storage taught by Long in the Trim, Bhatti, and Brisimi system so the graph database can be utilized for fast data retrieval, reduce system latency, and simplify data management for high-demand applications. This close relation between the references highly suggests an expectation of success.
Regarding claim 2, Trim in view of Bhatti, Brisimi, and Long teaches all of the limitations of claim 1. Trim further teaches wherein a graph access control subsystem assigns access rights to a user, the access rights allowing the user to interact with at least one node and its associated edges, of the knowledge graph ([0036]: “Graph database complies with graph schema. Graph schema includes root entities and entity types. Entity types represent child nodes of parent nodes associated with root entities in graph database”…[0064]: “The computer adds the missing entity type to the graph schema. By adding the missing entity type to the graph schema, the computer transforms the graph schema into a new and modified graph schema. In an alternative illustrative embodiment, instead of, or in addition to, adding the missing entity type to the graph schema, the computer generates and sends a notification to a user, such as a database administrator, regarding the missing entity type for review and possible action”… Examiner’s note: Thus, the system can allow a user to access at least an entity type wherein the entity type represents child nodes of parent nodes associated with root entities in graph database which can be equivalent to at least one node and its associated edges, of the knowledge graph).
Regarding claim 7, Trim in view of Bhatti, Brisimi, and Long teaches all of the limitations of claim 1. Trim further teaches wherein separate instances of the distributed in-memory associative array are used for each client as services in the containerized environment ([0024]: “Server 104 and server 106 connect to network 102, along with domain storage 108. Server 104 and server 106 may be, for example, server computers with high-speed connections to network 102. In addition, it should be noted that server 104 and server 106 may each represent clusters of servers in one or more data centers. Alternatively, server 104 and server 106 may each represent multiple computing nodes in a cloud environment”…Thus, the graph databases residing in the graph database server wherein the graph database server can be a separate device that is connected to a network. Also, a server can represent clusters of servers in one or more data centers and this can be equivalent to separate instances in a containerized service management subsystem).
Regarding claim 8, note the rejections of claim 1. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 9, note the rejections of claim 2. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 14, Trim in view of Bhatti, Brisimi, and Long teaches all of the limitations of claim 8. Trim further teaches wherein separate instances of the distributed in-memory associative array are used for each client as services in the containerized environment ([0024]: “Server 104 and server 106 connect to network 102, along with domain storage 108. Server 104 and server 106 may be, for example, server computers with high-speed connections to network 102. In addition, it should be noted that server 104 and server 106 may each represent clusters of servers in one or more data centers. Alternatively, server 104 and server 106 may each represent multiple computing nodes in a cloud environment”… Examiner’s note: Thus, the graph databases residing in the graph database server wherein the graph database server can be a separate device that is connected to a network. Also, a server can represent clusters of servers in one or more data centers and this can be equivalent to separate instances in a containerized service management subsystem ).
Regarding claim 15, note the rejections of claim 1. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 16, note the rejections of claim 2. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 21, note the rejections of claim 14. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 22, note the rejections of claim 1. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 23, note the rejections of claim 2. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 28, note the rejections of claim 14. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Claims 3, 10, 17, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Trim et al. (US PGPUB 20200019647) “Trim” in view of Bhatti et al. (US PGPUB 20180232403) “Bhatti” and Brisimi et al. (US PGPUB 20200125659) “Brisimi”, Long et al. (US PGPUB 20180089331) “Long”, Voigt et al. (US Patent 8719299) “Voigt” and Myhre et al. (US PGPUB 20200005159) “Myhre”.
Regarding claim 3, Trim in view of Bhatti, Brisimi, and Long teaches all of the limitations of claim 1. Trim in view of Bhatti, Brisimi, and Long does not explicitly teach retrieving one or more known schemas from a database; applying a known schema to the one or more data clumps; identifying any errors in the application of the known schema to the data clumps and computing an error rate based on any identified errors; wherein if the error rate is below a predetermined threshold value the known schema is added to a list; and displaying the list to a user, wherein the user can optionally select a known schema from a plurality of known schemas on the list.
Voigt teaches retrieving one or more known schemas from a database (Col 8 line 42-46: “Schema matching process includes, schema to be matched/transformed are received. Stored schemas (known schemas) are accessed from repository. Schemas are selected from according to their applicability to the received schema”… Examiner’s note: Thus, selections of known schemas from a database such as repository are collected for subsequent processing); applying a known schema to the one or more data clumps (Col 8 line 45-46: “Schema (known schema) are selected from according to their applicability to the received schema (data clumps)”… Examiner’s note: Thus, schemas from repository are selected to further map the schemas to the received data clumps such as received schema ); identifying any errors in the application of the known schema to the data clumps and computing an error rate based on any identified errors; wherein if the error rate is below a predetermined threshold value the known schema is added to a list (Col 8 line 47-55: “Applicability can be determined by a combined measure of semantic and a structural similarity. A semantic similarity score is calculated, which represents a measure for potential matches based on the names of elements. A structural similarity score based on path length and neighbour relations is calculated to filter out scattered concepts. After selecting relevant concepts, schema covering computes the coverage of the concepts in the received schema”… Examiner’s note: Thus, selected schemas are further calculated to determine their applicability. Semantic similarity score and structural similarity score are calculated to determine an applicable schema and this can be equivalent to identifying any errors in the application of the known schema and schema with error rate below a predetermined threshold value is selected). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Voigt teachings in the Trim, Bhatti and Brisimi and Long system. Skilled artisan would have been motivated to incorporate selecting schemas from a repository of available schemas for processing taught by Voigt in the Trim, Bhatti and Brisimi and Long system to identify the most fitting schemas for processing thus improves the system’s performance. This close relation between both of the references highly suggests an expectation of success.
Trim in view of Voigt does not explicitly teach displaying the list to a user, wherein the user can optionally select a known schema from a plurality of known schemas on the list.
Myhre teaches displaying the list to a user, wherein the user can optionally select a known schema from a plurality of known schemas on the list ([0067]: “As shown in FIG. 2, an activity graph can include a number of nodes 202A-202J, including leaf nodes ”…[0134]: “The activity schema includes other types of data used to construct interactive activity-specific UIs”... [0152]: “Although the AI engine can select an activity schema for a specific activity, a user can select a different schema for the activity. The newly selected schema can define layout options for UI elements and provide an indication of the relevant activity-specific content 504 and/or the data sources that are selected for obtaining the relevant activity-specific content . When a user selects a new activity schema, the AI engine may update relevancy scores in the AI model to improve the accuracy of the system in selecting an activity schema 1102 for an activity in the future… Examiner’s note: Thus, a user can have access to different schemas and able to select a schema to be used). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Myhre teachings in the Trim, Bhatti, Brisimi, Long, and Voigt system. Skilled artisan would have been motivated to incorporate user selections of schemas taught by Myhre in the Trim, Bhatti, Brisimi, Long and Voigt system to improve user’s involvements within the system and ensure that schemas can be best satisfied based on the user’s selection. This close relation between both of the references highly suggests an expectation of success.
Regarding claim 10, note the rejections of claim 3. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 17, note the rejections of claim 3. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 24, note the rejections of claim 3. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Claims 4-5, 11-12, 18-19, and 25-26 are rejected under 35 U.S.C. 103 as being unpatentable over Trim et al. (US PGPUB 20200019647) “Trim” in view of Bhatti et al. (US PGPUB 20180232403) “Bhatti” and Brisimi et al. (US PGPUB 20200125659) “Brisimi”, Long et al. (US PGPUB 20180089331) “Long” and Park et al. (US PGPUB 20180159876) “Park”.
Regarding claim 4, Trim in view of Bhatti, Brisimi, and Long teaches all of the limitations of claim 1. Trim in view of Bhatti, Brisimi, and Long does not explicitly teach receiving a network event associated with an observed event; applying a known schema to create a constrained knowledge graph; analyzing the constrained knowledge graph to identify a operational risk; generating one or more subgraphs from the constrained knowledge graph based on the identified cybersecurity threat, wherein each subgraph maps the identified operational risk to a threat scenario; performing graph fusion on the subgraphs to form a fused knowledge graph; storing the fused knowledge graph in a database; and presenting the fused knowledge graph to a user for graph analysis.
Park teaches receiving a network event associated with an observed event ([0049]: “In one embodiment, security event data (network event) is being processed in association with a cybersecurity knowledge graph (“KG”)”); applying a known schema to create a constrained knowledge graph ([0073] : “The initial data model may be developed using requirements retrieved or obtained from a security application such as a SIEM or other network security device or system. The data model may be represented as a schema in a database, or in some equivalent format. An initial knowledge graph (KG) is constructed from the initial data model and the security and threat intelligence information retrieved the structured data sources”… Examiner’s note: Thus, a knowledge graph can be constructed based on a schema and further data such as security and threat intelligence information retrieved); analyzing the constrained knowledge graph to identify a operational risk ([0073]: “An initial knowledge graph (KG) is constructed from the initial data model and the security and threat intelligence information retrieved the structured data sources . Typically, step 706 is carried out by identifying domain entities (e.g., without limitation, IP addresses, URLs, hashes, etc.), and representing the underlying relationships between and among those entities. The building of an entity-relationship graph according to a data model and based on retrieved (or otherwise available) information is known in the art. The structured data retrieved from the structured data sources is used to construct the initial KG. As noted above, cybersecurity experts and tools rely on such data sources because they are carefully curated by domain experts”... Examiner’s note: Thus, the knowledge graph is constructed with security and threat intelligence information retrieved the structured data sources… [0079]: “The composite knowledge graph thus represents both structured and unstructured security and threat intelligence information that may be then be used to facilitate cognitive security analysis”… Examiner’s note: Thus, a knowledge graph can be used to identify data relating to security and threat which can be equivalent to risk); generating one or more subgraphs from the constrained knowledge graph based on the identified cybersecurity threat, wherein each subgraph maps the identified operational risk to a threat scenario ([0074]: “Unstructured text from an unstructured data source is searched and collected for one or more entities and relationships that are present in the initial KG”…[0076]: “The extraction of entities and relationships (subgraph) can be carried out using rule/pattern matching tools, or supervised machine learning (ML) models”… Examiner’s note: Thus, extracted entities and relationships are based on the initial KG so the extracted entities and relationships can be equivalent to subgraphs generated from a knowledge graph wherein the information is related to cybersecurity and threat); performing graph fusion on the subgraphs to form a fused knowledge graph; storing the fused knowledge graph in a database ([0078]: “As depicted in FIG. 7, the extracted and normalized entities and relationships (subgraphs) are then added back into the KG. This addition (or “augmentation,” “supplementation” or “modification”) is carried out at step 724 and results in a composite knowledge graph (fused knowledge graph)”… [0084]: “Multiple knowledge graphs derived from one or more unstructured data sources may be merged with a knowledge graph derived from one or more structured data sources to build a large scale cybersecurity knowledge graph”); and presenting the fused knowledge graph to a user for graph analysis ([0079]: “The composite knowledge graph 726 thus represents both structured and unstructured security and threat intelligence information (i.e. knowledge) that may be then be used to facilitate cognitive security analysis as previously described”... [0084]: “Different portions of the large scale cybersecurity knowledge graph may be hosted in different computing entities and/or data stores. During a security analysis, and in response to a user query, multiple subgraphs may be identified and then merged to provide a response to the information query”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Park teachings in the Trim in view of Bhatti and Brisimi and Long system. Skilled artisan would have been motivated to incorporate applying schema to knowledge graphs associated with security and threat intelligence information and merging of graphs taught by Park in the Trim in view of Bhatti and Brisimi and Long system to improve data analysis and improve responses to queries that are related to information on security and threat intelligence. This close relation between both of the references highly suggests an expectation of success.
Regarding claim 5, Trim in view of Bhatti, Brisimi, and Long teaches all of the limitations of claim 1. Trim in view of Bhatti, Brisimi, and Long does not explicitly teach wherein the knowledge graph is a cyber-physical graph representing an enterprises cyber-physical system.
Park teaches the knowledge graph is a cyber-physical graph representing an enterprises cyber-physical system ([0073]: “An initial knowledge graph is constructed from the initial data model and the security and threat intelligence information retrieved the structured data sources”… [0079]: “The composite knowledge graph 726 thus represents both structured and unstructured security and threat intelligence information (i.e. knowledge) that may be then be used to facilitate cognitive security analysis”). Please refer to claim 4 for the motivational statement.
Regarding claim 11, note the rejections of claim 4. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 12, note the rejections of claim 5. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 18, note the rejections of claim 4. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 19, note the rejections of claim 5. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 25, note the rejections of claim 4. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 26, note the rejections of claim 5. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Prior Art
The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure.
Song et al. (US PGPUB 20180239796) is directed to a system for performing multi-tenancy distribution of a graph database cache. During operation, the system obtains a cache of a set of records in a graph database storing a graph, wherein the graph includes a set of nodes, a set of edges between pairs of nodes in the set of nodes, and a set of predicates. Next, the system identifies one or more records in the cache that fail a validation test. The system then merges a remainder of the cache with online updates to the subset of records to produce a merged version of the cache. Finally, the system distributes the merged version to one or more instances of the graph database for use in processing queries of the graph database.
Conclusion
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/C.D.V./Examiner, Art Unit 2153 08/05/2026
/KAVITA STANLEY/Supervisory Patent Examiner, Art Unit 2153
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