Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination
2. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/20/2026 has been entered.
DETAILED ACTION
3. This Office Action is in response to the filing with the office dated 05/20/2026.
Claims 1 and 14 have been amended. Claim 7 has been cancelled. Claims 1 and 14 are independent claims. Claims 1-6 and 8-20 are presented in this office action.
Response to amendment/arguments
4. Applicant’s arguments with respect to the rejection of claims under 35 U.S.C. § 102 (a)(i) and 103(a) have been fully considered but are moot in view of the new grounds of rejection. Please see the rejection below.
Claim Rejections - 35 U.S.C. § 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.
5. Claims 1, 3, 4, 8, 9, 12, 14, 15, 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Estes; Timothy Wayne (US 9348815 B1) in view of Puttagunta; Shanmukha Sravan (US 20180057030 A1), Morse; Charles W. (US 4122523 A) and in further view of Baldini Soares; Ioana Monica (US 20180189389 A1).
Regarding independent claim 1, Estes; Timothy Wayne (US 9348815 B1) teaches, a system for defining data analytics pipelines including at least one processor and at least one memory (Fig. 9 Col 21, Lines 7-20 (125) As shown, the computer 900 includes a processing unit 902, a system memory 904, and a system bus 906 that couples the memory 904 to the processing unit 902. Also see Col 2, Lines 1-9 (3)), the system comprising: a plurality of data sources (Fig. 1, Col 6, Lines 3-5 (48) discloses system comprising unstructured data) including
a semantic data lake (Col 6, Lines 61-67 (50), Col 7, Lines 8-24 (51) discloses storing semantic annotations/ annotated message are stored in the Knowledge Base), and a data integration module, wherein the data integration module is configured via computer executable instructions to create semantic annotations that describe capabilities and structures of the train related raw data (Col 2, Lines 1-9 (3) In another aspect, the present disclosure relates to a system. In one embodiment, the system includes one or more processors and a memory that is operatively coupled to the one or processors. The memory stores computer-executable instructions that, when executed by the one or more processors, cause the system to perform functions that include reading text data corresponding to one or more messages and creating one or more semantic annotations to the text data to generate one or more annotated messages. Also see Col 6, Lines, 3-15 (48) (Examiner interprets capability of data source as functional process and raw data as unstructured data). Train data is taught by Estes (Paragraph [0114]), create or modify a knowledge graph utilizing the semantic annotations (Col 16, Lines, 28-67, Col 17, Lines 1-21 (105)-(108) discloses, creating or modifying the knowledge graph utilizing the semantic annotations. Also see (Col 1, Lines 45-67 (2), Claim 1), and integrate the train related raw data and the semantic annotations into the semantic data lake, wherein the train related raw data are interpretable via the knowledge graph and the semantic annotations (Col 19, Lines, 60-67, Col 20, Lines 1-10 (119) discloses, integrating the raw data via knowledge graph and storing the semantic annotations in the persistent storage. Also see Col 5, Lines 58-67 (47), Col 6, Lines, 55-67 (51),(106), Claim 1. Train data is taught by Puttagunta et al (Paragraph [0114])).
Estes et al fails to explicitly teach, comprising train related raw data, infrastructure data, onboard train data, signal data and plan/timetable data; wherein the
Puttagunta; Shanmukha Sravan (US 20180057030 A1) teaches, a plurality of data sources comprising train related raw data (Paragraph [0009] A remote database and processor stores and processes data collected from multiple sources) including infrastructure data, onboard train data, signal data, and plan/timetable data ([0114] FIG. 9. (Paragraph [0114] discloses, infrastructure such as track identity, onboard train data such as (speed acceleration….), signal data such as signal lights, and plan/timetable data such as trains route from source to destination. Also see [0045] and [0053] for plan/ train routs from source to destination);
wherein the train related raw data provide train tracking and semantic annotations for the train tracking include localization capability and generate output including train ID and train positions within a railroad network (Paragraph [0151] discloses, localization capability. Paragraph [0127] discloses, monitoring the location and behavior of the train in that route. Also see [0114]. Examiner interprets localization capability as determining trains location);
wherein the(Paragraph [0151] discloses, localization capability. Paragraph [0127] discloses, monitoring the location and behavior of the train in that route. Also see [0114]. (Examiner interprets localization capability as determining trains location). Semantic annotations are taught by Baldini et al (Paragraph [0071]);
and wherein the outputs are updated in real-time based on real-time information transmitted by trains travelling in the rail network (Paragraphs [0125], [0127] discloses, updating the data in real time. Also see [0144]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Estes et al by providing train related raw data including infrastructure data, onboard train data, signal data and plan/timetable data; wherein the train related raw data provide train tracking and semantic annotations for the train tracking include localization capability and generate output including train ID and train positions within a railroad network, as taught by Puttagunta et al (Fig. 9, Paragraph [0114]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, raw data in railways provides the foundational, detailed information (from sensors, GPS, operations) that enables crucial benefits like predictive maintenance, bottleneck avoidance, improved safety, better asset utilization, and real-time decision-making, leading to significant cost savings and enhanced efficiency, though it requires processing to become actionable insights.
Estes et al and Puttagunta et al fails to explicitly teach, semantic annotations; wherein the semantic annotations include conflict detection capability and generate output describe a conflict detection service that provides a conflict detection capability, wherein the conflict detection service generates output including conflict identification, conflict location and train ID of trains involved in the conflict.
Morse; Charles W. (US 4122523 A) teaches, wherein the semantic annotations include conflict detection capability and generate output describe a conflict detection service that provides a conflict detection capability, wherein the conflict detection service generates output including conflict identification, conflict location and train ID of trains involved in the conflict (Fig. 4 Col 6, Lines, 52-64 (21) discloses, identifying a conflict and generating an output including, train identifications are known as well as the location of the conflict, type of conflict) Semantic annotations are taught by Baldini et al (Paragraph [0071]).
Morse et al also teaches, wherein the train tracking service generates output including train ID and train positions within a railroad network (See Fig 4 and related paragraphs).
Estes et al, Puttagunta et al and Morse et al fails to explicitly teach, semantic annotations.
Baldini Soares; Ioana Monica (US 20180189389 A1) teaches, the(Paragraph [0071] discloses, generating an output based on matching the annotated graph to a set of semantic labels in a knowledge database resulting in a semantic flow graph (e.g., semantic graph 500). (Examiner interprets capability service, as the following semantic concepts and properties are formally annotated within the ontology). Also see Abstract. Train data is taught by Puttagunta et al (Paragraph [0114])).
Baldini et al also teaches, create or modify a knowledge graph utilizing the semantic annotations and integrate the train related raw data and the semantic annotations into the semantic data lake, wherein the train related raw data are interpretable via the knowledge graph and the semantic annotations (Paragraph [0071] discloses, generating an output based on matching the annotated graph to a set of semantic labels in a knowledge database resulting in a semantic flow graph (e.g., semantic graph 500). (Examiner interprets localization-aware service, the following semantic concepts and properties are formally annotated within the ontology). Also see Abstract. Semantic annotations are taught by Baldini et al (Paragraph [0071]). rain data is taught by Puttagunta et al (Paragraph [0114])).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Estes et al, Puttagunta et al and Morse et al by providing semantic annotations, as taught by Baldini et al (Paragraph [0071]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, the system can then semantically label the annotated flow graph by aligning the annotated graph with a knowledge base of data analysis concepts to provide context for the operations being performed by the data analysis program as taught by Baldini et al (Paragraph [0038]).
Regarding dependent claim 3, Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, the system of claim 1.
Estes et al further teaches, wherein the knowledge graph comprises common identifiers to connect the raw data of the plurality of data sources (Col 8, Lines 1, 50 (57)- (59) discloses, collecting data from plurality of sites/sources in knowledge graph. Also see Col 14, Lines, 51-67, Col 15, Lines 1-26 (96)-(98), Col 17, Lines 42-53 (109)).
Regarding dependent claim 4, Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, the system of claim 3.
Estes et al further teaches, wherein the data integration module is configured to create the semantic annotations for each data source, and create or modify the knowledge graph for the plurality data sources based on the semantic annotations and the common identifiers (Col 7, 45-67 (56) discloses, creating semantic annotations and creating/ modifying/ updating the knowledge graph based on the semantic annotations. Also see Col 19. Lines, 12-26 (116)).
Regarding dependent claim 8, Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, the system of claim 1.
Estes et al further teaches, further comprising: a knowledge reasoning engine configured to interface with the knowledge graph (Col 6, Lines 20-39 (49) discloses the knowledge graph is associated with reasoning to resolve concept resolution. Also see Col 11, Lines 15-34 (78)).
Regarding dependent claim 9, Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, the system of claim 8.
Estes et al further teaches, wherein the knowledge reasoning engine is configured to receive user inputs for creating or modifying the knowledge graph, and validate and check consistency of the inputs in view of existing connections and semantic annotations of the knowledge graph (Col 16, Lines 63-67, Col 17, Lines 1-20 (107) end users can provide input 516 (“user feedback”) to correct values and relationships in the Knowledge Graph 508 via a user interface such as a graphical user interface, to provide for user feedback-driven correction of the Knowledge Graph 508).
Regarding dependent claim 12, Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, the system of claim 8.
Estes et al further teaches, wherein the knowledge reasoning engine is configured to discover and define relationships between data sources utilizing artificial intelligence (AI)-algorithms (Col 16, Lines 14-67, (104)-(107) discloses. Knowledge graph is used to discover and define relationships utilizing statistical language model. (Examiner interprets statistical language model as artificial intelligence (AI) model).
Regarding independent claim 14, Estes; Timothy Wayne (US 9348815 B1) teaches, a method for defining data analytics pipelines, the method comprising through at least one processor and at least one memory (Fig. 9 Col 21, Lines 7-20 (125) As shown, the computer 900 includes a processing unit 902, a system memory 904, and a system bus 906 that couples the memory 904 to the processing unit 902. Also see Col 2, Lines 1-9 (3)): receiving train related raw data of multiple data sources… creating semantic annotations describing capabilities and structures of the train related raw data of each data source (Fig. 7 Col 19, Lines, 27-45 (117) discloses receiving natural language content and creating semantic annotations. (Col 2, Lines 1-9 (3) In another aspect, the present disclosure relates to a system. In one embodiment, the system includes one or more processors and a memory that is operatively coupled to the one or processors. The memory stores computer-executable instructions that, when executed by the one or more processors, cause the system to perform functions that include reading text data corresponding to one or more messages and creating one or more semantic annotations to the text data to generate one or more annotated messages. Also see Col 6, Lines, 3-15 (48). Examiner interprets capability of data source as functional process and raw data as unstructured data) Semantic annotations are taught by Baldini et al (Paragraph [0071]);
creating or modifying a knowledge graph utilizing the semantic annotations (Col 16, Lines, 28-67, Col 17, Lines 1-21 (105)-(108) discloses, creating or modifying the knowledge graph utilizing the semantic annotations. Also see (Col 1, Lines 45-67 (2), Claim 1), and integrating the train related raw data and the semantic annotations into a semantic data lake, wherein the train related raw data of the multiple data sources are interpretable via the knowledge graph and the semantic annotations (Col 19, Lines, 60-67, Col 20, Lines 1-10 (119) discloses, integrating the raw data via knowledge graph and storing the semantic annotations in the persistent storage. Also see Col 5, Lines 58-67 (47), Col 6, Lines, 55-67 (51),(106), Claim 1. Train data is taught by Puttagunta et al (Paragraph [0114])).
Estes et al fails to explicitly teach, comprising train related raw data, infrastructure data, onboard train data, signal data and plan/timetable data; wherein the semantic annotations for the train tracking include localization capability and generate output describe a train tracking service that provides a localization capability, wherein the train tracking service generates output including train ID and train positions within a railroad network, wherein the semantic annotations include conflict detection capability and generate output describe a conflict detection service that provides a conflict detection capability, wherein the conflict detection service generates output including conflict identification, conflict location and train ID of trains involved in the conflict.
Puttagunta; Shanmukha Sravan (US 20180057030 A1) teaches, a plurality of data sources comprising train related raw data (Paragraph [0009] A remote database and processor stores and processes data collected from multiple sources) including infrastructure data, onboard train data, signal data, and plan/timetable data ([0114] FIG. 9. (Paragraph [0114] discloses, infrastructure such as track identity, onboard train data such as (speed acceleration….), signal data such as signal lights, and plan/timetable data such as trains route from source to destination. Also see [0045] and [0053] for plan/ train routs from source to destination);
wherein the train related raw data provide train tracking and semantic annotations for the train tracking include localization capability and generate output including train ID and train positions within a railroad network (Paragraph [0151] discloses, localization capability. Paragraph [0127] discloses, monitoring the location and behavior of the train in that route. Also see [0114]. Examiner interprets localization capability as determining trains location);
wherein the(Paragraph [0151] discloses, localization capability. Paragraph [0127] discloses, monitoring the location and behavior of the train in that route. Also see [0114]. (Examiner interprets localization capability as determining trains location). Semantic annotations are taught by Baldini et al (Paragraph [0071]);
and wherein the outputs are updated in real-time based on real-time information transmitted by trains travelling in the rail network (Paragraphs [0125], [0127] discloses, updating the data in real time. Also see [0144]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Estes et al by providing train related raw data including infrastructure data, onboard train data, signal data and plan/timetable data; wherein the train related raw data provide train tracking and semantic annotations for the train tracking include localization capability and generate output including train ID and train positions within a railroad network, as taught by Puttagunta et al (Fig. 9, Paragraph [0114]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, raw data in railways provides the foundational, detailed information (from sensors, GPS, operations) that enables crucial benefits like predictive maintenance, bottleneck avoidance, improved safety, better asset utilization, and real-time decision-making, leading to significant cost savings and enhanced efficiency, though it requires processing to become actionable insights.
Estes et al and Puttagunta et al fails to explicitly teach, semantic annotations; wherein the semantic annotations include conflict detection capability and generate output describe a conflict detection service that provides a conflict detection capability, wherein the conflict detection service generates output including conflict identification, conflict location and train ID of trains involved in the conflict.
Morse; Charles W. (US 4122523 A) teaches, wherein the semantic annotations include conflict detection capability and generate output describe a conflict detection service that provides a conflict detection capability, wherein the conflict detection service generates output including conflict identification, conflict location and train ID of trains involved in the conflict (Fig. 4 Col 6, Lines, 52-64 (21) discloses, identifying a conflict and generating an output including, train identifications are known as well as the location of the conflict, type of conflict) Semantic annotations are taught by Baldini et al (Paragraph [0071]).
Morse et al also teaches, wherein the train tracking service generates output including train ID and train positions within a railroad network (See Fig 4 and related paragraphs).
Estes et al, Puttagunta et al and Morse et al fails to explicitly teach, semantic annotations.
Baldini Soares; Ioana Monica (US 20180189389 A1) teaches, the(Paragraph [0071] discloses, generating an output based on matching the annotated graph to a set of semantic labels in a knowledge database resulting in a semantic flow graph (e.g., semantic graph 500). (Examiner interprets capability service as the following semantic concepts and properties are formally annotated within the ontology). Also see Abstract. Train data is taught by Puttagunta et al (Paragraph [0114])).
Baldini et al also teaches, create or modify a knowledge graph utilizing the semantic annotations and integrate the train related raw data and the semantic annotations into the semantic data lake, wherein the train related raw data are interpretable via the knowledge graph and the semantic annotations (Paragraph [0071] discloses, generating an output based on matching the annotated graph to a set of semantic labels in a knowledge database resulting in a semantic flow graph (e.g., semantic graph 500). (Examiner interprets localization-aware service, the following semantic concepts and properties are formally annotated within the ontology). Also see Abstract. Semantic annotations are taught by Baldini et al (Paragraph [0071]). rain data is taught by Puttagunta et al (Paragraph [0114])).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Estes et al, Puttagunta et al and Morse et al by providing semantic annotations, as taught by Baldini et al (Paragraph [0071]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, the system can then semantically label the annotated flow graph by aligning the annotated graph with a knowledge base of data analysis concepts to provide context for the operations being performed by the data analysis program as taught by Baldini et al (Paragraph [0038]).
Regarding dependent claim 15, Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, the method of claim 14.
Estes et al further teaches, creating or modifying the knowledge graph based on common identifiers that connect the train related raw data of the multiple data sources (Paragraphs (57)- (59) discloses, collecting data from plurality of sites/sources in knowledge graph. Also see Paragraphs (96)-(98), (109). Train related data is taught by Puttagunta (Paragraph [0114]).
Regarding dependent claim 17, Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, the method of claim 14.
Estes et al further teaches, further comprising: interfacing with the knowledge graph via a knowledge reasoning engine (Col 6, Lines 20-39 (49) discloses the knowledge graph is associated with reasoning to resolve concept resolution. Also see Col 11, Lines 15-34 (78)).
Regarding dependent claim 18, Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, the method of claim 17.
Estes et al further teaches, further comprising, via the knowledge reasoning engine, receiving user inputs for creating or modifying the knowledge graph, and validating and checking consistency of the user inputs in view of existing connections and semantic annotations of the knowledge graph (Col 16, Lines 63-67, Col 17, Lines 1-20 (107) end users can provide input 516 (“user feedback”) to correct values and relationships in the Knowledge Graph 508 via a user interface such as a graphical user interface, to provide for user feedback-driven correction of the Knowledge Graph 508).
Regarding dependent claim 19, Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, the method of claim 17.
Estes et al further teaches, further comprising, via the knowledge reasoning engine, discovering and defining relationships between the multiple data sources utilizing artificial intelligence (AI)-algorithms (Col 16, Lines 14-67 (104)-(107) discloses. Knowledge graph is used to discover and define relationships utilizing statistical language model. (Examiner interprets statistical language model as artificial intelligence (AI) model).
Regarding dependent claim 20, Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, a non-transitory computer readable medium storing executable instructions that when executed by a computer perform a method for defining data analytics pipelines as claimed in claim 14 (Col 2, Lines 26-50 (4) a non-transitory computer-readable medium. In one embodiment, the computer-readable medium stores instructions that, when executed by one or more processors, cause a computer to perform functions that include reading text data corresponding to one or more messages and creating one or more semantic annotations to the text data to generate one or more annotated messages. See the rejection of claim 14).
6. Claims 2, 5, 6, 10, 11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Estes; Timothy Wayne (US 9348815 B1) in view of Puttagunta; Shanmukha Sravan (US 20180057030 A1), Morse; Charles W. (US 4122523 A), Baldini Soares; Ioana Monica (US 20180189389 A1) and in further view of Seetharaman; Ganesh (US 20180052861 A1).
Regarding dependent claim 2, Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, the system of claim 1.
Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, fails to explicitly teach, wherein the semantic annotations are created according to a pre-defined schema.
Seetharaman; Ganesh (US 20180052861 A1) teaches, wherein the semantic annotations are created according to a pre-defined schema (Paragraph [0073] In accordance with an embodiment, the system provides a programmatic interface, referred to herein in some embodiments as a foreign function interface, by which a user or third-party can define a service, functional and business types, semantic actions, and patterns or predefined complex data flows based on functional and business types, in a declarative manner, to extend the functionality of the system. Also see Paragraph [0095]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, by providing wherein the semantic annotations are created according to a pre-defined schema, as taught by Seetharaman et al (Paragraph [0425]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, providing real-time recommendations for performing semantic actions on data accessed from an input HUB, based on an understanding of the meaning or semantics associated with the data as taught by Seetharaman et al (Paragraph [007]).
Regarding dependent claim 5, Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, the system of claim 1.
Estes et al, Puttagunta et al and Morse et al teach, fails to explicitly teach, wherein the semantic annotations are based on attributes that describe the capability and/or behavior of the data source.
Seetharaman; Ganesh (US 20180052861 A1) teaches, wherein the semantic annotations are based on attributes that describe the capability and/or behavior of the data source (Paragraph [0425] discloses, semantic annotations are based on ontology. Paragraph [0072] discloses, ontology is defined based on relationships between datasets or entities, and their attributes. Therefore the semantic annotations are based on attributes that describe the capability and/or behavior of the data source (Examiner interprets capability as functional type). Also see Paragraphs [0082], [0252]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, by providing wherein the semantic annotations are based on attributes that describe the capability and/or behavior of the data source, as taught by Seetharaman et al (Paragraph [0425]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, providing real-time recommendations for performing semantic actions on data accessed from an input HUB, based on an understanding of the meaning or semantics associated with the data as taught by Seetharaman et al (Paragraph [007]).
Regarding dependent claim 6, Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, the system of claim 1.
Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, fails to explicitly teach, wherein the semantic annotations are based on inputs and/or outputs and a data type of the data source.
Seetharaman; Ganesh (US 20180052861 A1) teaches, wherein the semantic annotations are based on inputs and/or outputs and a data type of the data source (Paragraph [0088] discloses, semantic annotations are based on inputs and/or outputs and a data type of the data source (including for example, structured, semi-structured, or unstructured data)).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, by providing wherein the semantic annotations are based on inputs and/or outputs and a data type of the data source, as taught by Seetharaman et al (Paragraph [0425]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, providing real-time recommendations for performing semantic actions on data accessed from an input HUB, based on an understanding of the meaning or semantics associated with the data as taught by Seetharaman et al (Paragraph [007]).
Regarding dependent claim 10, Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, the system of claim 9.
Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, fails to explicitly teach, wherein the knowledge reasoning engine is configured to solve first order logic rules based on inputs and/or outputs of each data source and associated capability.
Seetharaman; Ganesh (US 20180052861 A1) teaches, wherein the knowledge reasoning engine is configured to solve first order logic rules based on inputs and/or outputs of each data source and associated capability (Paragraph [0235] As illustrated in FIG. 15, in this example 490, in accordance with an embodiment, a sales dataset should be evaluated as a cube functional type by the rules engine. Similarly the product, customer, and time should be evaluated as dimensions and levels (for example by age group, gender. Also see Paragraphs [0413], [0414]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Estes et al, Puttagunta et al and Morse et al teach, by providing wherein the knowledge reasoning engine is configured to solve first order logic rules based on inputs and/or outputs of each data source and associated capability, as taught by Seetharaman et al (Paragraph [0235]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, providing real-time recommendations for performing semantic actions on data accessed from an input HUB, based on an understanding of the meaning or semantics associated with the data as taught by Seetharaman et al (Paragraph [007]).
Regarding dependent claim 11, Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, the system of claim 10.
Estes et al further teaches, wherein the knowledge reasoning engine is configured to create or modify the knowledge graph and associated workflows based on received and validated user inputs (Paragraph (0107), (0108) discloses, modifying the knowledge graph based on user feedback.
Seetharaman et al also further teaches, wherein the knowledge reasoning engine is configured to create or modify the knowledge graph and associated workflows based on received and validated user inputs (Paragraph [0089] A data AI subsystem can analyze amounts of input data, and continuously update a domain-knowledge model. During the processing of a dataflow application (e.g., pipeline), each stage of the, e.g., pipeline, can proceed, based on recommended alternatives or options provided by the data AI subsystem, the updated domain model, and inputs from users, e.g., to accept or reject a recommended semantic action).
Regarding dependent claim 13, Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, the system of claim 12.
Estes et al, Puttagunta et al and Morse et al teach, fails to explicitly teach, wherein the AI-algorithms comprise random walk, path-recurrent neural network, and/or reinforcement learning algorithms.
Seetharaman; Ganesh (US 20180052861 A1) teaches, wherein the AI-algorithms comprise random walk, path-recurrent neural network, and/or reinforcement learning algorithms (Paragraph [0387] In accordance with an embodiment, the pattern of transformation can be determined in a crowd sourcing manner based on passive analysis of data flows for different applications. The pattern can be determined using machine learning (e.g., deep reinforcement learning)).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, by providing herein the AI-algorithms comprise random walk, path-recurrent neural network, and/or reinforcement learning algorithms, as taught by Seetharaman et al (Paragraph [0387]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, providing real-time recommendations for performing semantic actions on data accessed from an input HUB, based on an understanding of the meaning or semantics associated with the data as taught by Seetharaman et al (Paragraph [007]).
Regarding dependent claim 16, Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, the method of claim 14.
Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, fails to explicitly teach, wherein creating the semantic annotations comprises creating attributes that describe the capability and/or behavior, and inputs and/or outputs of each data source.
Seetharaman; Ganesh (US 20180052861 A1) teaches, wherein creating the semantic annotations comprises creating attributes that describe the capability and/or behavior, and inputs and/or outputs of each data source (Paragraph [0425] discloses, semantic annotations are based on ontology. Paragraph [0072] discloses, ontology is defined based on relationships between datasets or entities, and their attributes. Therefore the semantic annotations are based on attributes that describe the capability and/or behavior of the data source (Examiner interprets capability as functional type). Also see Paragraphs [0082], [0252]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Estes et al, Puttagunta et al, Morse et al and Baldini et al teach, by providing wherein the semantic annotations are based on attributes that describe the capability and/or behavior of the data source, as taught by Seetharaman et al (Paragraph [0425]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, providing real-time recommendations for performing semantic actions on data accessed from an input HUB, based on an understanding of the meaning or semantics associated with the data as taught by Seetharaman et al (Paragraph [007]).
Closest Prior Art
7. The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
Boegl, Andreas (US 20090234640 A1) teaches, An apparatus and a method for automated semantic annotation of a process model having model elements named by natural language expressions, wherein said apparatus comprises at least one semantic pattern analyser which analyses the textual structure of each natural language expression on the basis of predefined semantic pattern descriptions to establish a semantic linkage between each model element to classes and instances of a reference process ontology for generating a semantically annotated process model (Abstract).
Riley; Thomas Patrick (US 20150012288 A1) teaches, [0047] Under one embodiment, semantic annotation techniques are used to arrange and configure keywords with symptoms. Medical concepts often have alternate names (synonyms), one of which may be easier for the patient to understand than others. A first linking may be provided by linking a term with synonyms and, more specifically, providing its more comprehensible synonym. For example, "pyrexia" is easier to understand if "perspiration," "sweating" and/or "whole body ache" is also provided in parallel. Similarly the medical abbreviation "OD" in medication intake contexts is easier to understand if "once daily" is also provided. These semantic annotations are advantageous in overcoming vocabulary differences and to provide user-friendly synonyms. Under one embodiment, the keyword lexicography of the present system may be based off of a Consumer Health Vocabulary (CHV) tied to a semantic annotation platform residing in 101, which may be part of a knowledge information management (KIM) platform. Semantic annotations may be performed in the KIM by creating and/or generating semantic annotations for each target vocabulary word or term (keyword) using a predefined ontology. The annotations stored in the system maintain semantic descriptions of the vocabulary in the form of instances, relations and attributes. The ontology may be extended to include "preferred" words or terms and aliases, compared to other, less relevant, words to represent classes within the vocabulary. Thus, for each target vocabulary word and subsets thereof, classes of preferred terms, synonyms and aliases may be linked. As an example, a symptom defined as "pyrexia" would be identified as an instance of "perspiration," "chills" and "whole body ache" in the metadata (tags), where any further aliases or synonyms may be further linked as related data.
8. Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968))).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUMAN RAJAPUTRA whose telephone number is (571) 272-4669. The examiner can normally be reached between 8:00 AM - 5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi (571) 272-4078 can be reached. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/ patents/ apply/ patent-center for more information about Patent Center and https://www.uspto.gov/ patents/ docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/S. R./
Examiner, Art Unit 2163
/ALEX GOFMAN/Primary Examiner, Art Unit 2163
Prosecution Insights