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 .
Response to Arguments
Applicant’s arguments, see Applicants Remarks pages 7-8, filed 12/27/25, with respect to the rejection(s) of claim(s) 1-5, 7-14 and 16-18 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Hu et al US 20210406779.
Regarding claim 1, Examiner previously indicated the limitations of claim 6 as allowable, but upon further search and consideration, the limitations are now rejected. Hu et al teaches feature knowledge graph system 210 may use the intelligent logic module 213 to learn new knowledge through graph learning based on the knowledge graph. In particular embodiments, the intelligent logic 213 (neural network) may include one or more rule-based algorithms, statistic algorithms, or ML models (e.g., convolutional neural networks, graph neural networks) (computational units). The intelligent logic module 213 may identify new relationships and discover hidden relationships (hidden layers) between features and ML models (plurality of input features) in the knowledge graph. The new knowledge (e.g., new relationships) learned by the intelligent logic module 213 may be sent back to the graph engine 216 (knowledge matching engine) to update the knowledge graph (paragraph 0026). This reads on wherein said neural network comprises a knowledge matching engine that receives input features from a plurality of sources, and processes said input features using one or more hidden layers and computational units to generate output predictions that are used for updating said one or more knowledge graphs.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 1, 7, 9, 10, 16 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rao US 2021/0342723 in view of Barrett et al US 2017/0230312 further in view of Hu et al US 20210406779.
Regarding claim 1, Rao teaches a system for bot processing automation (paragraph 0028 and abstract), said system comprising:
a processor (processor paragraph 0026);
a memory comprising a set of instructions (memory paragraph 0026), which when executed by the processor cause the processor to:
receive, from a repository, an image, and process said image using optical character recognition (OCR) to generate a first output (A user may upload an image, a text document, a spreadsheet, a presentation, a PDF or any other data file. The uploaded data may then be made searchable by performing OCR and indexing the data (paragraph 0044) Note: the image being uplodaded is uploaded from a memory which can read on a repository;
process any or a part of said image and said generated first output using contextual artificial intelligence (Al) to generate a second output (After parsing and indexing the data from the document, it is then fed into the NER module 124, where a machine learning algorithm then identifies or classifies elements within the document which are an organization, a person, an entity or other relevant labels such as financial terms (paragraph 0044), said contextual Al being based on a combination of a neural network, one or more knowledge graphs, and natural language processing (NLP) (AI & ML core technology platform 120 may comprise a natural language processing (NLP) module 121, bots module 122, language understanding intelligent service (LUIS) module 123, named-entity recognition (NER) module 124, GraphQL module 125, vision module 126, cognitive search module 127 and optical character recognition (OCR) module 128 (paragraph 0037);
Rao fails to teach facilitate use of said second output and said one or more knowledge graphs for bot automation to validate and build interdependency among one or more fields that form part of said image.
Barrett et al teaches facilitate use of said second output and said one or more knowledge graphs for bot automation to validate and build interdependency among one or more fields that form part of said image (information determined from chat input 11 can provide information sources, such when chats progress to a point where the chat input query includes feedback or more depth of information. The chatbot 102 can include logic to extract private or confidential information from the contents of the chat 101, and the extracted information (second output) can be used by the graph module 158 to develop and refine the knowledge graph 155 (paragraph 0068). Note: the extracted information of the chat input 11 is included in the chatbot 102, which would read on second output because it comes from the logic of the chatbot 102 which can also read on contextual AI. the chatbot 102 (bot automation) can access the private portion of the knowledge graph 155 in order to determine (validate) the user's address, login information, password (validate), and further implement a plug-in or process that navigates the website of a financial institution and recognizes the fields of the online interface of the financial institution, so as to insert the relevant information for the user in the appropriate field (build interdependency among one or more fields) (paragraph 0070-0071) Note: the extracted information that is included in the chatbot 102 as read in paragraph 0068 (see Note above) and knowledge graph 155 is used by the chatbot 102 to determine (validate) the fields of financial institution
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Rao to include: facilitate use of said second output and said one or more knowledge graphs for bot automation to validate and build interdependency among one or more fields that form part of said image.
The reason of doing so would be to accurately process image data.
Rao in view of Barret et al fails to teach wherein said neural network comprises a knowledge matching engine that receives input features from a plurality of sources, and processes said input features using one or more hidden layers and computational units to generate output predictions that are used for updating said one or more knowledge graphs
Hu et al teaches wherein said neural network comprises a knowledge matching engine that receives input features from a plurality of sources, and processes said input features using one or more hidden layers and computational units to generate output predictions that are used for updating said one or more knowledge graphs (feature knowledge graph system 210 may use the intelligent logic module 213 to learn new knowledge through graph learning based on the knowledge graph. In particular embodiments, the intelligent logic 213 (neural network) may include one or more rule-based algorithms, statistic algorithms, or ML models (e.g., convolutional neural networks, graph neural networks) (computational units). The intelligent logic module 213 may identify new relationships and discover hidden relationships (hidden layers) between features and ML models (plurality of input features) in the knowledge graph. The new knowledge (e.g., new relationships) learned by the intelligent logic module 213 may be sent back to the graph engine 216 (knowledge matching engine) to update the knowledge graph (paragraph 0026)
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Rao in view of Barret et al to include: wherein said neural network comprises a knowledge matching engine that receives input features from a plurality of sources, and processes said input features using one or more hidden layers and computational units to generate output predictions that are used for updating said one or more knowledge graphs.
The reason of doing so would be to greatly improve the accuracy of predictions of the neural network.
Regarding claim 7, Rao teaches wherein any or both of said first and second outputs comprise textual data retrieved from said image (the NLP module 121 may also use the OCR module 128 to perform optical character recognition on text before being analyzed in a natural language understanding process. NER module 124 may also be used to determine proper names and types in the text (paragraph 0039).
Regarding claim 9, Rao teaches wherein the processing of said image based on the OCR comprises:
pre-processing image data associated with said received image (The uploaded data may then be made searchable by performing OCR and indexing the data. After parsing and indexing the data from the document, it is then fed into the NER module 124 (paragraph 0044);
detecting any or a combination of lines, words or characters (The uploaded data may then be made searchable by performing OCR and indexing the data (paragraph 0044);
post-processing detected characters; and rendering text as output based on the post-processed detected characters (it is then fed into the NER module 124, where a machine learning algorithm then identifies or classifies elements within the document which are an organization, a person, an entity or other relevant labels such as financial terms (paragraph 0044).
Regarding claim 10, Rao teaches a method for bot processing automation (paragraph 0028 and abstract), said method comprising:
receiving, from a repository, through a processor, an image, and processing said image using optical character recognition (OCR) to generate a first output (A user may upload an image, a text document, a spreadsheet, a presentation, a PDF or any other data file. The uploaded data may then be made searchable by performing OCR and indexing the data (paragraph 0044) Note: the image being uplodaded is uploaded from a memory which can read on a repository;
processing any or a part of said image and said generated first output using contextual artificial intelligence (AI) to generate a second output (After parsing and indexing the data from the document, it is then fed into the NER module 124, where a machine learning algorithm then identifies or classifies elements within the document which are an organization, a person, an entity or other relevant labels such as financial terms (paragraph 0044), said contextual Al being based on a combination of a neural network, one or more knowledge graphs, and natural language processing (NLP) (AI & ML core technology platform 120 may comprise a natural language processing (NLP) module 121, bots module 122, language understanding intelligent service (LUIS) module 123, named-entity recognition (NER) module 124, GraphQL module 125, vision module 126, cognitive search module 127 and optical character recognition (OCR) module 128 (paragraph 0037); and
Rao fails to teach facilitating use of said second output and said one or more knowledge graphs for bot automation to validate and build interdependency among one or more fields that form part of said image.
Barrett et al teaches facilitating use of said second output and said one or more knowledge graphs for bot automation to validate and build interdependency among one or more fields that form part of said image ((information determined from chat input 11 can provide information sources, such when chats progress to a point where the chat input query includes feedback or more depth of information. The chatbot 102 can include logic to extract private or confidential information from the contents of the chat 101, and the extracted information (second output) can be used by the graph module 158 to develop and refine the knowledge graph 155 (paragraph 0068). Note: the extracted information of the chat input 11 is included in the chatbot 102, which would read on second output because it comes from the logic of the chatbot 102 which can also read on contextual AI. the chatbot 102 (bot automation) can access the private portion of the knowledge graph 155 in order to determine (validate) the user's address, login information, password (validate), and further implement a plug-in or process that navigates the website of a financial institution and recognizes the fields of the online interface of the financial institution, so as to insert the relevant information for the user in the appropriate field (build interdependency among one or more fields) (paragraph 0070-0071) Note: the extracted information that is included in the chatbot 102 as read in paragraph 0068 (see Note above) and knowledge graph 155 is used by the chatbot 102 to determine (validate) the fields of financial institution
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Rao to include: facilitating use of said second output and said one or more knowledge graphs for bot automation to validate and build interdependency among one or more fields that form part of said image.
The reason of doing so would be to accurately process image data.
Rao in view of Barret et al fails to teach wherein said neural network comprises a knowledge matching engine that receives input features from a plurality of sources, and processes said input features using one or more hidden layers and computational units to generate output predictions that are used for updating said one or more knowledge graphs
Hu et al teaches wherein said neural network comprises a knowledge matching engine that receives input features from a plurality of sources, and processes said input features using one or more hidden layers and computational units to generate output predictions that are used for updating said one or more knowledge graphs (feature knowledge graph system 210 may use the intelligent logic module 213 to learn new knowledge through graph learning based on the knowledge graph. In particular embodiments, the intelligent logic 213 (neural network) may include one or more rule-based algorithms, statistic algorithms, or ML models (e.g., convolutional neural networks, graph neural networks) (computational units). The intelligent logic module 213 may identify new relationships and discover hidden relationships (hidden layers) between features and ML models (plurality of input features) in the knowledge graph. The new knowledge (e.g., new relationships) learned by the intelligent logic module 213 may be sent back to the graph engine 216 (knowledge matching engine) to update the knowledge graph (paragraph 0026)
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Rao in view of Barret et al to include: wherein said neural network comprises a knowledge matching engine that receives input features from a plurality of sources, and processes said input features using one or more hidden layers and computational units to generate output predictions that are used for updating said one or more knowledge graphs.
The reason of doing so would be to greatly improve the accuracy of predictions of the neural network.
Regarding claim 16, Rao teaches wherein any or both of said first and second outputs comprise textual data retrieved from said image (the NLP module 121 may also use the OCR module 128 to perform optical character recognition on text before being analyzed in a natural language understanding process. NER module 124 may also be used to determine proper names and types in the text (paragraph 0039).
Regarding claim 18, Rao teaches wherein the processing of said image based on the OCR comprises:
pre-processing image data associated with said received image (The uploaded data may then be made searchable by performing OCR and indexing the data. After parsing and indexing the data from the document, it is then fed into the NER module 124 (paragraph 0044);
detecting any or a combination of lines, words or characters (The uploaded data may then be made searchable by performing OCR and indexing the data (paragraph 0044);
post-processing detected characters; and rendering text as output based on the post-processed detected characters (it is then fed into the NER module 124, where a machine learning algorithm then identifies or classifies elements within the document which are an organization, a person, an entity or other relevant labels such as financial terms (paragraph 0044).
Claim(s) 2 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rao US 2021/0342723 in view of Barrett et al US 2017/0230312 further in view of Hu et al US 20210406779 further in view of Jia et al US 2019/0236205.
Regarding claim 2, Rao in view of Barrett et al further in view of Hu et al teaches all of the limitations of claim 1
Rao in view of Barrett et al further in view of Hu et al fails to teach wherein based on the validation and built interdependency, any or a combination of said neural network and said one or more knowledge graphs are updated to provision intelligence to said bot automation.
Jia et al teaches wherein based on the validation and built interdependency, any or a combination of said neural network and said one or more knowledge graphs are updated to provision intelligence to said bot automation (machine learning to continuously improve the conversational knowledge graph powered APM virtual assistant as disclosed herein. The process 220 includes using machine learning to dynamically and continuously update and improve the conversational knowledge graph (paragraph 0039)
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Rao n view of Barrett et al further in view of Hu et al to include: wherein based on the validation and built interdependency, any or a combination of said neural network and said one or more knowledge graphs are updated to provision intelligence to said bot automation.
The reason of doing so would be to accurately train bot to accurately process image data.
Regarding claim 11, Rao in view of Barrett et al further in view of Hu et al teaches all of the limitations of claim 10
Rao in view of Barrett et al in view of Hu et al fails to teach wherein based on the validation and built interdependency, any or a combination of said neural network and said one or more knowledge graphs are updated to provision intelligence to said bot automation.
Jia et al teaches wherein based on the validation and built interdependency, any or a combination of said neural network and said one or more knowledge graphs are updated to provision intelligence to said bot automation (machine learning to continuously improve the conversational knowledge graph powered APM virtual assistant as disclosed herein. The process 220 includes using machine learning to dynamically and continuously update and improve the conversational knowledge graph (paragraph 0039)
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Rao in view of Barrett et al in view of Hu et al to include: wherein based on the validation and built interdependency, any or a combination of said neural network and said one or more knowledge graphs are updated to provision intelligence to said bot automation.
The reason of doing so would be to accurately train bot to accurately process image data.
Claim(s) 3, 4, 12, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rao US 2021/0342723 in view of Barrett et al US 2017/0230312 further in view of Hu et al US 20210406779 further in view Wang et al US 20220335307.
Regarding claim 3, Rao in view of Barrett et al further in view of Hu et al teach all of the limitations of claim 1
Rao in view of Barrett et al further in view of Hu et al fails to teach wherein at least one of said one or more knowledge graphs represents knowledge in an area of interest using a graph-like structure, said atleast one knowledge graph being built on top of a plurality of existing databases so as to link data together by combining structured and unstructured information.
Wang et al teaches wherein at least one of said one or more knowledge graphs represents knowledge in an area of interest using a graph-like structure, said atleast one knowledge graph being built on top of a plurality of existing databases so as to link data together by combining structured and unstructured information (A knowledge graph can be built on top of one or more databases and serves to connect the data from the one or more databases together. Such data may include both structured and unstructured data (e.g., text, numbers, and geometries) (paragraph 0022)
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Rao in view of Barrett et al in view Hu et al to include: wherein at least one of said one or more knowledge graphs represents knowledge in an area of interest using a graph-like structure, said atleast one knowledge graph being built on top of a plurality of existing databases so as to link data together by combining structured and unstructured information.
The reason of doing so would be to accurately train the graph.
Regarding claim 4, Rao in view of Barrett et al further in view of Hu et al further in view of Wang et al teach wherein said at least one knowledge graph is generated and updated based on deep learning enabled through said neural network, said NLP, said OCR, and said bot automation (Rao: AI & ML core technology platform 120 may comprise a natural language processing (NLP) module 121, bots module 122, language understanding intelligent service (LUIS) module 123, named-entity recognition (NER) module 124, GraphQL module 125, vision module 126, cognitive search module 127 and optical character recognition (OCR) module 128. The AI & ML core technology platform 120 may use machine learning and AI algorithms such as petri nets, neural networks, deep belief networks, random forest, or other algorithms to tag, label, classify or identify elements from data sources, as well as learn, predict and validate data related to the process being automated (paragraph 0037), wherein said at least one knowledge graph is processed to indicate any or a combination of geo-location of a user, demographic information associated with said user, a health attribute associated with said user, and one or more parameters pertaining to said user (Rao: the user may provide the model type and algorithm or combination of algorithms to use, as well as adjustments to individual parameters of the algorithms. The process models generated may be used to simulate user activity (one or more parameters pertaining to said user) and system behavior (paragraph 0035).
Regarding claim 12, Rao in view of Barrett et al further in view of Hu et al teach all of the limitations of claim 10
Rao in view of Barrett et al further in view of Hu et al fails to teach wherein at least one of said one or more knowledge graphs represents knowledge in an area of interest using a graph-like structure, said atleast one knowledge graph being built on top of a plurality of existing databases so as to link data together by combining structured and unstructured information.
Wang et al teaches wherein at least one of said one or more knowledge graphs represents knowledge in an area of interest using a graph-like structure, said atleast one knowledge graph being built on top of a plurality of existing databases so as to link data together by combining structured and unstructured information (A knowledge graph can be built on top of one or more databases and serves to connect the data from the one or more databases together. Such data may include both structured and unstructured data (e.g., text, numbers, and geometries) (paragraph 0022)
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Rao in view of Barrett et al in view Hu et al to include: wherein at least one of said one or more knowledge graphs represents knowledge in an area of interest using a graph-like structure, said atleast one knowledge graph being built on top of a plurality of existing databases so as to link data together by combining structured and unstructured information.
The reason of doing so would be to accurately train the graph.
Regarding claim 13, Rao in view of Barrett et al further in view of Hu et al further in view of Wang et al teach wherein said at least one knowledge graph is generated and updated based on deep learning enabled through said neural network, said NLP, said OCR, and said bot automation (Rao: AI & ML core technology platform 120 may comprise a natural language processing (NLP) module 121, bots module 122, language understanding intelligent service (LUIS) module 123, named-entity recognition (NER) module 124, GraphQL module 125, vision module 126, cognitive search module 127 and optical character recognition (OCR) module 128. The AI & ML core technology platform 120 may use machine learning and AI algorithms such as petri nets, neural networks, deep belief networks, random forest, or other algorithms to tag, label, classify or identify elements from data sources, as well as learn, predict and validate data related to the process being automated (paragraph 0037), wherein said at least one knowledge graph is processed to indicate any or a combination of geo-location of a user, demographic information associated with said user, a health attribute associated with said user, and one or more parameters pertaining to said user (Rao: the user may provide the model type and algorithm or combination of algorithms to use, as well as adjustments to individual parameters of the algorithms. The process models generated may be used to simulate user activity (one or more parameters pertaining to said user) and system behavior (paragraph 0035).
Claim(s) 5 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rao US 2021/0342723 in view of Barrett et al US 2017/0230312 further in view of Hu et al US 20210406779 further in view of Reumann et al US 2021/0319858.
Regarding claim 5, Rao in view of Barrett et al further in view of Hu et al teaches all of the limitations of claims 1-3
Rao in view of Barrett et al further in view of Hu et al fails to teach wherein the at least one knowledge graph is generated for or associated with a healthcare patient, and comprises interrelationships between name of the patient, location of the patient, and a unique subscriber identifier associated with said patient.
Reumann et al teaches wherein the at least one knowledge graph is generated for or associated with a healthcare patient, and comprises interrelationships between name of the patient, location of the patient, and a unique subscriber identifier associated with said patient (the knowledge graph comprises as nodes case identifiers, diagnosis codes and related procedure codes, and secondary diagnosis codes and related secondary procedure codes. here may be nodes relating to a case identifier that, by its nature, specifies a predefined relationship. Node types like “gender” and “length of stay”—i.e., a length of a stay of a patient in the hospital or in a specific department of a hospital—are treated similarly (paragraph 0053) Note: the case nodes describe an interrelationship between a patient and identifiers like gender, age, length of stay, etc
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Rao in view of Barrett et al to further in view of Hu et al to include: wherein the at least one knowledge graph is generated for or associated with a healthcare patient, and comprises interrelationships between name of the patient, location of the patient, and a unique subscriber identifier associated with said patient.
The reason of doing so would be to accurately label a graph.
Regarding claim 14, Rao in view of Barrett et al further in view of Hu et al teaches all of the limitations of claims 10-12
Rao in view of Barrett et al further in view of Hu et al fails to teach wherein the at least one knowledge graph is generated for or associated with a healthcare patient, and comprises interrelationships between name of the patient, location of the patient, and a unique subscriber identifier associated with said patient.
Reumann et al teaches wherein the at least one knowledge graph is generated for or associated with a healthcare patient, and comprises interrelationships between name of the patient, location of the patient, and a unique subscriber identifier associated with said patient (the knowledge graph comprises as nodes case identifiers, diagnosis codes and related procedure codes, and secondary diagnosis codes and related secondary procedure codes. here may be nodes relating to a case identifier that, by its nature, specifies a predefined relationship. Node types like “gender” and “length of stay”—i.e., a length of a stay of a patient in the hospital or in a specific department of a hospital—are treated similarly (paragraph 0053) Note: the case nodes describe an interrelationship between a patient and identifiers like gender, age, length of stay, etc
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Rao in view of Barrett et al to further in view of Hu et al to include: wherein the at least one knowledge graph is generated for or associated with a healthcare patient, and comprises interrelationships between name of the patient, location of the patient, and a unique subscriber identifier associated with said patient.
The reason of doing so would be to accurately label a graph.
Claim(s) 8 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rao US 2021/0342723 in view of Barrett et al US 2017/0230312 further in view of Hu et al US 20210406779 further in view of Pal et al US 2017/0053461.
Regarding claim 8, Rao in view of Barrett et al further in view of Hu et al teaches all of the limitations of claim 1
Rao in view of Barrett et al Hu et al fails to teach wherein said bot automation is used for processing insurance claims.
Pal et al teach wherein said bot automation is used for processing insurance claims (virtual assistant preferably aids with tasks related to vehicular accident events, including communications with services police reporting, insurance processing (paragraph 0132)
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Rao in view of Barrett et al further in view of Hu et al to include: wherein said bot automation is used for processing insurance claims.
The reason of doing so would be to accurately process insurance claims.
Regarding claim 17, Rao in view of Barrett et al in view of Hu et al teaches all of the limitations of claim 10
Rao in view of Barrett et al further in view Hu et al fails to teach wherein said bot automation is used for processing insurance claims.
Pal et al teach wherein said bot automation is used for processing insurance claims (virtual assistant preferably aids with tasks related to vehicular accident events, including communications with services police reporting, insurance processing (paragraph 0132)
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Rao in view of Barrett et al further in view of Hu et al to include: wherein said bot automation is used for processing insurance claims.
The reason of doing so would be to accurately process insurance claims.
Conclusion
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Michael Burleson
Patent Examiner
Art Unit 2681
Michael Burleson
March 30, 2026
/MICHAEL BURLESON/