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 .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 4, 7-9, 11, 14-16, 18, 20 are rejected under 35 U.S.C. 102(a)(1) as being taught by Rath (US 20210328888 A1).
Regarding Claim 1, Rath teaches a system (“A system trains a machine learning model to identify topic sequences for support ticket communications, identify topic sequences that are classified as similar, and predict subsequent topics for multiple support ticket communications, in response to receiving the support ticket communications.”; See Para. 12) for generative artificial intelligence ("machine learning model"; See Para. 12) that dynamically summarizes text including support tickets ("support ticket summarizer"; See Paragraph 12), the system comprising:
one or more processors (Processor; See Fig. 5 502);
and a non-transitory computer readable medium storing a plurality of instructions (“It is noted that the methods described herein may be embodied in executable instructions stored in a computer readable medium for use by or in connection with an instruction execution machine, apparatus, or device, such as a computer-based or processor-containing machine, apparatus, or device”; See Para. 108), which when executed, cause the one or more processors (Processor) to:
fine-tune at least one generative artificial intelligence model ("A system trains a machine learning model"; See Para. 12) to summarize at least text from a historical support ticket ("The machine-learning model uses the historical sequences of topics to predict at least one subsequent topic for the sequence of topics. The system outputs the at least one subsequent topic."; See Para. 12);
generate, by the at least one generative artificial intelligence model ("machine learning model"; See Para. 12), a summary of at least text from a support ticket assigned to a support agent to assist a customer, wherein the summary comprises an overview of the at least the text from the support ticket ("The natural language processor machine-learning models 220 and/or 226 can determine broad categories of data for a support ticket's communications, such as the content data, the metadata, and the context data. The content data that is determined for any type of support ticket communications refers to the language used by a support agent and a customer when they discuss a problem. The natural language processor machine-learning models 220 and/or 226 automatically determine the relevant content data for identifying a sequence of topics for a support ticket's communications, which are unstructured text, as a summary of the support ticket. Examples of topics identified from content data include a technical topic under discussion, evolving problem diagnosis and problem resolution, a problem type, and a product type. Communications between a customer and a support agent during the lifetime of a support ticket may contain a wide variety of relevant data that may be used to identify a sequence of topics for the summary of a support ticket."; See Para. 35) and which includes some text that is absent from the at least the text from the support ticket (“In addition to identifying historical sequences of topics, for historical support tickets, which are similar to the support ticket's sequence of topics, a type of information is optionally identified which is associated with a topic that is in the historical sequences of topics and that is absent from the sequence of topics, block 410. Having identified a type of information that is missing from the support ticket's sequence of topics, a request for the type of information is optionally output, block 412. The system can request information that is missing from the support ticket to forecast a support ticket's resolution.”; See Para. 93-94), in response to receiving a request from a user interface to generate the summary (“A historical support ticket can be a request which was logged on a work tracking system, and which described a problem to be addressed.”; See Para. 23; Rath later teaches a work tracking system with a UI. (“Such a query-able interface can include, but is not limited to, a REST (Representational State Transfer) Application Programming Interface (API), Python API, Web API, or a web user interface.”; See Para. 27));
and output the summary to the user interface, wherein the summary enables a user to efficiently understand the at least the text from the support ticket("The ticket summarizer, classifier, and forecaster systems 218 and/or 224 exposes a query-able interface to accept input data points for periodically determining data for support tickets to generate summaries of support tickets, identify similar summaries of support tickets, and use the similar summaries to forecast the resolutions of support tickets. Such a query-able interface can include, but is not limited to, a REST (Representational State Transfer) Application Programming Interface (API), Python API, Web API, or a web user interface"; See Para 27.).
Regarding Claim 2, most of the limitations of this claim have been noted in the
rejection of claim 1 (and thus the rejections of claim 1 are incorporated), Rath Teaches wherein the request to generate the summary (summary of the support ticket; See Para. 5) is based on text associated with at least one of the following: at least one other support ticket (historical support tickets; See Para. 12), at least one support agent, at least one customer (“Communications between a customer and a support agent during the lifetime of a support ticket may contain a wide variety of relevant data that may be used to identify a sequence of topics for the summary of a support ticket."; See Para. 35), at least one topic of the support ticket ("identifies a sequence of topics for the communication for the support ticket"; See Para. 12), and at least one point in time (“support ticket age”; See Para. 5) .
Regarding Claim 4, most of the limitations of this claim have been noted in the
rejection of claim 1 (and thus the rejections of claim 1 are incorporated), Rath teaches wherein generating the summary (“create a summary of the support ticket.”; See Para. 5) is based on at least one of features (“the natural language processor machine-learning model 226 can use content data to identify the product that is causing the problem and can use historical data and/or trends to indicate which products or product features tend to experience critical issues without requiring intimate knowledge of the nature of those products and product features.”; See Para. 36), communications (" Such a summary can provide support agents with an enriched informational representation of a support ticket and its evolution through time", See Para. 5), descriptive statistics ("Using statistical as well as natural language processing-based methods to determine such pieces of information for a support ticket can enable the natural language processor machine-learning models 220 and/or 226 to focus on processing natural language in the support ticket's communications to infer specific topics and power downstream topic inference, without being distracted by machine-generated text."; See Para 45), or metrics derived by machine-learning ("The ticket summarizer, classifier, and forecaster systems 218 and/or 224 can use a dedicated machine learning classifier to identify log messages, error messages, command-line instructions and various categories of machine text based on statistical differences between human-generated text and machine-generated text"; See Para 45), associated with the support ticket.
Regarding Claim 7, most of the limitations of this claim have been noted in the
rejection of claim 1 (and thus the rejections of claim 1 are incorporated), Rath teaches wherein the summary (summary of the support ticket; See Para. 5) is also used for at least one of a management of multiple support agents assigned to a support ticket (“The ticket summarizer, classifier, and forecaster systems 218 and/or 224 can also be queried for support ticket topics in an ad hoc manner for individual support tickets and/or groups of support tickets, whether open or closed during any time frame, as well as queried for support agents, teams of support agents, customers, products, and the support organization, in part or as a whole.”; See Para. 27) , a hand-off between support agents (“The ticket summarizer, classifier, and forecaster systems 218 and/or 224 can also be queried for support ticket topics in an ad hoc manner for individual support tickets and/or groups of support tickets, whether open or closed during any time frame, as well as queried for support agents, teams of support agents, customers, products, and the support organization, in part or as a whole.”; See Para. 27), an evaluation of at least one of a customer account or a support ticket (“The ticket summarizer, classifier, and forecaster machine-learning models 222 and/or 228 can additionally identify a sequence of topics for a support ticket by using the support ticket's context data, such as information about time gaps between support ticket communications, each-customer's prior support ticket histories, each support agent's support ticket histories, etc. For example, if a customer's prior support ticket histories indicate that the customer greatly exaggerates the severity of his problems when emotional, the ticket summarizer, classifier, and forecaster machine-learning model 228 would not identify the topic of an assigned high priority if the customer's communication is automatically identified as emotional and specifies that the support ticket's priority should be high.”; See Para. 38), a generation of one of another support ticket related to the support ticket ("The system identifies similar support tickets to forecast a support ticket's resolution."; See Para. 92), or of a summary of the summary and other summaries ("The ticket summarizer, classifier, and forecaster machine-learning model 228 can automatically use the results of the real-time analysis of subsequent communications to update a support ticket's summary and resolution forecast periodically and then output this updated summary and forecast to end users to ensure that the support ticket's summary and resolution forecast continues to accurately reflect any changes in the support ticket's circumstances."; See Para 20), or a performance review associated with at least one of a support agent or a support manager (“The ticket summarizer, classifier, and forecaster machine-learning models 222 and/or 228 can identify cross-communication similarity using models such as Siamese networks, which may be used to identify frequent communication patterns in responses from support agents and identify those support agents who frequently provide responses that are devoid of substance.”; See Para. 55).
Regarding Claim 8, Rath teaches a computer-implemented method (“It is noted that the methods described herein may be embodied in executable instructions stored in a computer readable medium for use by or in connection with an instruction execution machine, apparatus, or device, such as a computer-based or processor-containing machine, apparatus, or device”; See Para. 108) for generative artificial intelligence ("machine learning model"; See Para. 12) that dynamically summarizes text including support tickets ("support ticket summarizer"; See Paragraph 12), the computer-implemented method comprising:
fine-tuning at least one generative artificial intelligence model ("A system trains a machine learning model"; See Para. 12) to summarize at least text from a historical support ticket ("The machine-learning model uses the historical sequences of topics to predict at least one subsequent topic for the sequence of topics. The system outputs the at least one subsequent topic."; See Para. 12);
generate, by the at least one generative artificial intelligence model ("machine learning model"; See Para. 12), a summary of at least text from a support ticket assigned to a support agent to assist a customer, wherein the summary comprises an overview of the at least the text from the support ticket ("The natural language processor machine-learning models 220 and/or 226 can determine broad categories of data for a support ticket's communications, such as the content data, the metadata, and the context data. The content data that is determined for any type of support ticket communications refers to the language used by a support agent and a customer when they discuss a problem. The natural language processor machine-learning models 220 and/or 226 automatically determine the relevant content data for identifying a sequence of topics for a support ticket's communications, which are unstructured text, as a summary of the support ticket. Examples of topics identified from content data include a technical topic under discussion, evolving problem diagnosis and problem resolution, a problem type, and a product type. Communications between a customer and a support agent during the lifetime of a support ticket may contain a wide variety of relevant data that may be used to identify a sequence of topics for the summary of a support ticket."; See Para. 35) and which includes some text that is absent from the at least the text from the support ticket (“In addition to identifying historical sequences of topics, for historical support tickets, which are similar to the support ticket's sequence of topics, a type of information is optionally identified which is associated with a topic that is in the historical sequences of topics and that is absent from the sequence of topics, block 410. Having identified a type of information that is missing from the support ticket's sequence of topics, a request for the type of information is optionally output, block 412. The system can request information that is missing from the support ticket to forecast a support ticket's resolution.”; See Para. 93-94), in response to receiving a request from a user interface to generate the summary (“A historical support ticket can be a request which was logged on a work tracking system, and which described a problem to be addressed.”; See Para. 23; Rath later teaches a work tracking system with a UI. (“Such a query-able interface can include, but is not limited to, a REST (Representational State Transfer) Application Programming Interface (API), Python API, Web API, or a web user interface.”; See Para. 27));
and output the summary to the user interface, wherein the summary enables a user to efficiently understand the at least the text from the support ticket("The ticket summarizer, classifier, and forecaster systems 218 and/or 224 exposes a query-able interface to accept input data points for periodically determining data for support tickets to generate summaries of support tickets, identify similar summaries of support tickets, and use the similar summaries to forecast the resolutions of support tickets. Such a query-able interface can include, but is not limited to, a REST (Representational State Transfer) Application Programming Interface (API), Python API, Web API, or a web user interface"; See Para 27.).
Regarding Claim 9, most of the limitations of this claim have been noted in the
rejection of Claim 8 (and thus the rejections of claim 8 are incorporated), Rath Teaches wherein the request to generate the summary (summary of the support ticket; See Para. 5) is based on text associated with at least one of the following: at least one other support ticket (historical support tickets; See Para. 12), at least one support agent, at least one customer (“Communications between a customer and a support agent during the lifetime of a support ticket may contain a wide variety of relevant data that may be used to identify a sequence of topics for the summary of a support ticket."; See Para. 35), at least one topic of the support ticket ("identifies a sequence of topics for the communication for the support ticket"; See Para. 12), and at least one point in time (“support ticket age”; See Para. 5) .
Regarding Claim 11, most of the limitations of this claim have been noted in the
rejection of claim 8 (and thus the rejections of claim 8 are incorporated), Rath wherein generating the summary (“create a summary of the support ticket.”; See Para. 5) is based on at least one of features (“the natural language processor machine-learning model 226 can use content data to identify the product that is causing the problem and can use historical data and/or trends to indicate which products or product features tend to experience critical issues without requiring intimate knowledge of the nature of those products and product features.”; See Para. 36), communications (" Such a summary can provide support agents with an enriched informational representation of a support ticket and its evolution through time", See Para. 5), descriptive statistics ("Using statistical as well as natural language processing-based methods to determine such pieces of information for a support ticket can enable the natural language processor machine-learning models 220 and/or 226 to focus on processing natural language in the support ticket's communications to infer specific topics and power downstream topic inference, without being distracted by machine-generated text."; See Para 45), or metrics derived by machine-learning ("The ticket summarizer, classifier, and forecaster systems 218 and/or 224 can use a dedicated machine learning classifier to identify log messages, error messages, command-line instructions and various categories of machine text based on statistical differences between human-generated text and machine-generated text"; See Para 45), associated with the support ticket.
Regarding Claim 14, most of the limitations of this claim have been noted in the
rejection of claim 8 (and thus the rejections of claim 8 are incorporated), Rath teaches wherein generating the summary (“create a summary of the support ticket.”; See Para. 5) is based on at least one of features (“the natural language processor machine-learning model 226 can use content data to identify the product that is causing the problem and can use historical data and/or trends to indicate which products or product features tend to experience critical issues without requiring intimate knowledge of the nature of those products and product features.”; See Para. 36), communications (" Such a summary can provide support agents with an enriched informational representation of a support ticket and its evolution through time", See Para. 5), descriptive statistics ("Using statistical as well as natural language processing-based methods to determine such pieces of information for a support ticket can enable the natural language processor machine-learning models 220 and/or 226 to focus on processing natural language in the support ticket's communications to infer specific topics and power downstream topic inference, without being distracted by machine-generated text."; See Para 45), or metrics derived by machine-learning ("The ticket summarizer, classifier, and forecaster systems 218 and/or 224 can use a dedicated machine learning classifier to identify log messages, error messages, command-line instructions and various categories of machine text based on statistical differences between human-generated text and machine-generated text"; See Para 45), associated with the support ticket.
Regarding Claim 15, Rath teaches a computer program product, comprising a non-transitory computer-readable medium having a computer-readable program code embodied therein (“It is noted that the methods described herein may be embodied in executable instructions stored in a computer readable medium for use by or in connection with an instruction execution machine, apparatus, or device, such as a computer-based or processor-containing machine, apparatus, or device”; See Para. 108) to be executed by one or more processors (Processor; See Fig. 5 502), the program code including instructions to:
fine-tune at least one generative artificial intelligence model ("A system trains a machine learning model"; See Para. 12) to summarize at least text from a historical support ticket ("The machine-learning model uses the historical sequences of topics to predict at least one subsequent topic for the sequence of topics. The system outputs the at least one subsequent topic."; See Para. 12);
generate, by the at least one generative artificial intelligence model ("machine learning model"; See Para. 12), a summary of at least text from a support ticket assigned to a support agent to assist a customer, wherein the summary comprises an overview of the at least the text from the support ticket ("The natural language processor machine-learning models 220 and/or 226 can determine broad categories of data for a support ticket's communications, such as the content data, the metadata, and the context data. The content data that is determined for any type of support ticket communications refers to the language used by a support agent and a customer when they discuss a problem. The natural language processor machine-learning models 220 and/or 226 automatically determine the relevant content data for identifying a sequence of topics for a support ticket's communications, which are unstructured text, as a summary of the support ticket. Examples of topics identified from content data include a technical topic under discussion, evolving problem diagnosis and problem resolution, a problem type, and a product type. Communications between a customer and a support agent during the lifetime of a support ticket may contain a wide variety of relevant data that may be used to identify a sequence of topics for the summary of a support ticket."; See Para. 35) and which includes some text that is absent from the at least the text from the support ticket (“In addition to identifying historical sequences of topics, for historical support tickets, which are similar to the support ticket's sequence of topics, a type of information is optionally identified which is associated with a topic that is in the historical sequences of topics and that is absent from the sequence of topics, block 410. Having identified a type of information that is missing from the support ticket's sequence of topics, a request for the type of information is optionally output, block 412. The system can request information that is missing from the support ticket to forecast a support ticket's resolution.”; See Para. 93-94), in response to receiving a request from a user interface to generate the summary (“A historical support ticket can be a request which was logged on a work tracking system, and which described a problem to be addressed.”; See Para. 23; Rath later teaches a work tracking system with a UI. (“Such a query-able interface can include, but is not limited to, a REST (Representational State Transfer) Application Programming Interface (API), Python API, Web API, or a web user interface.”; See Para. 27));
and output the summary to the user interface, wherein the summary enables a user to efficiently understand the at least the text from the support ticket("The ticket summarizer, classifier, and forecaster systems 218 and/or 224 exposes a query-able interface to accept input data points for periodically determining data for support tickets to generate summaries of support tickets, identify similar summaries of support tickets, and use the similar summaries to forecast the resolutions of support tickets. Such a query-able interface can include, but is not limited to, a REST (Representational State Transfer) Application Programming Interface (API), Python API, Web API, or a web user interface"; See Para 27.).
Regarding claim 16, most of the limitations of this claim have been noted in the
rejection of claim 15 (and thus the rejections of claim 15 are incorporated), Rath Teaches wherein the request to generate the summary (summary of the support ticket; See Para. 5) is based on text associated with at least one of the following: at least one other support ticket (historical support tickets; See Para. 12), at least one support agent, at least one customer (“Communications between a customer and a support agent during the lifetime of a support ticket may contain a wide variety of relevant data that may be used to identify a sequence of topics for the summary of a support ticket."; See Para. 35), at least one topic of the support ticket ("identifies a sequence of topics for the communication for the support ticket"; See Para. 12), and at least one point in time (“support ticket age”; See Para. 5) .
Regarding Claim 18, most of the limitations of this claim have been noted in the
rejection of claim 15 (and thus the rejections of claim 15 are incorporated), Rath teaches wherein generating the summary (“create a summary of the support ticket.”; See Para. 5) is based on at least one of features (“the natural language processor machine-learning model 226 can use content data to identify the product that is causing the problem and can use historical data and/or trends to indicate which products or product features tend to experience critical issues without requiring intimate knowledge of the nature of those products and product features.”; See Para. 36), communications (" Such a summary can provide support agents with an enriched informational representation of a support ticket and its evolution through time", See Para. 5), descriptive statistics ("Using statistical as well as natural language processing-based methods to determine such pieces of information for a support ticket can enable the natural language processor machine-learning models 220 and/or 226 to focus on processing natural language in the support ticket's communications to infer specific topics and power downstream topic inference, without being distracted by machine-generated text."; See Para 45), or metrics derived by machine-learning ("The ticket summarizer, classifier, and forecaster systems 218 and/or 224 can use a dedicated machine learning classifier to identify log messages, error messages, command-line instructions and various categories of machine text based on statistical differences between human-generated text and machine-generated text"; See Para 45), associated with the support ticket.
Regarding Claim 20, most of the limitations of this claim have been noted in the
rejection of claim 8 (and thus the rejections of claim 8 are incorporated), Rath wherein generating the summary (“create a summary of the support ticket.”; See Para. 5) is based on at least one of features (“the natural language processor machine-learning model 226 can use content data to identify the product that is causing the problem and can use historical data and/or trends to indicate which products or product features tend to experience critical issues without requiring intimate knowledge of the nature of those products and product features.”; See Para. 36), communications (" Such a summary can provide support agents with an enriched informational representation of a support ticket and its evolution through time", See Para. 5), descriptive statistics ("Using statistical as well as natural language processing-based methods to determine such pieces of information for a support ticket can enable the natural language processor machine-learning models 220 and/or 226 to focus on processing natural language in the support ticket's communications to infer specific topics and power downstream topic inference, without being distracted by machine-generated text."; See Para 45), or metrics derived by machine-learning ("The ticket summarizer, classifier, and forecaster systems 218 and/or 224 can use a dedicated machine learning classifier to identify log messages, error messages, command-line instructions and various categories of machine text based on statistical differences between human-generated text and machine-generated text"; See Para 45), associated with the support ticket.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3, 5, 10, 12, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Rath (US 20210328888 A1, hereinafter referred to as Rath) within view of Gadot et al. (US 20220261816 A1, hereinafter referred to as Gadot).
Regarding Claim 3, most of the limitations of this claim have been noted in the rejection of claim 1 (and thus the rejections of claim 1 are incorporated), Rath teaches wherein the at least the text from the support ticket (“Therefore, the ticket summarizer, classifier, and forecaster machine-learning models 222 and/or 228 can generate retrospective analytics reports of support ticket summaries grouped by similar sequences of topics over a given time frame. Retrospective summaries can then be viewed over different time windows, and distinct sequences of topics that represent support tickets over distinct time slices may be identified, thereby powering support teams with insights into the evolving nature of their support tickets.”; See Para. 66) further comprises at least one of a question ("a problem resolution may involve responding appropriately to a customer's request or question..."; See Para. 3), a message ("A communication can be a message."; See Para. 22), a support ticket comment ("Most of the interactions between support agents and the customers they serve occur in the form of a stream of communications or comments, which are recorded within a support ticket."; See Para 3), a support ticket note ("...a support ticket note."; See paragraph 5), a chat message ("A communication can be a message."; See Para. 22), another support ticket (historical support tickets; See Para. 12).
Rath does not teach wherein the at least the text from the support ticket further comprises an email, a knowledge-based article, or a customer satisfaction survey. Gadot teaches wherein the at least the text from the support ticket comprises an email (“It will also be appreciated that either side may initiate the communication, the client (e.g., a client calling a customer support line) or supported system 100 (e.g., a sales organization making an outgoing call to potential clients). An initiated communication may be synchronous (e.g., video conference), asynchronous (emails going back and forth), or both. The communication may also be able to switch both mode and/or direction. For example, an incoming customer email may trigger an outgoing video call.”; See Para. 58; The support ticket itself may be an email, since text is extracted from the support ticket communications that mean the text could come from the email.), a knowledge-based article (linked knowledge base articles; See Para. 76), or a customer satisfaction survey (“A further such interaction may be a post-contact questionnaire to gather feedback on the provided support (or other contact or service). This may support implementation of surveys and data collections.”; See Para. 108).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to combine the method Rath teaches with the method Gadot teaches of text from the support ticket further comprising an email, a knowledge-based article, or a customer satisfaction survey. The motivation to combine what Gadot teaches with Rath would be to provide relevant information from the active forms of communication between the customer and support agent asynchronously (“An initiated communication may be synchronous (e.g., video conference), asynchronous (emails going back and forth), or both. The communication may also be able to switch both mode and/or direction. For example, an incoming customer email may trigger an outgoing video call.”; See Para. 58). The motivation to combine Rath with what Gadot teaches about the text in the support ticket containing a knowledge-based article or its text is to provide relevant information to the customer to allow further and easier topic discussion between the customer and support agent, and easier topic identification. The motivation to combine Rath’s method with what Gadot teaches about customer satisfaction surveys would be to receive valuable feedback from customers which could be used to further improve the machine-learning model and systems, to gauge the customer and support agent relationship, and to profile customers for future use in other tickets or use in their own future tickets.
Regarding Claims 5, most of the limitations of this claim have been noted in the rejection of claims 1 and 4 (and thus the rejections of claims 1 and 4 are incorporated), Rath teaches wherein the features of the support ticket comprise at least one of a status (Rath anticipates although they do not specifically state they use a status, a support ticket should or will always have a status associated with it on at least one end of them system as support agents need to know if a ticket is completed or not.), a priority (assigned support ticket priority; See Para. 37), a customer contact, or a customer account ("the author of a communication"; See Para. 37; Later they specify it’s used for determining the difference of customer or support agent, which can or cannot be logged in on the website giving them access to see and use the account.), and a communication comprises at least one of an inbound message ("...and whether an individual communication is inbound or outbound."; See Para. 37), an outbound message ("...and whether an individual communication is inbound or outbound."; See Para. 37), an internal comment, an external comment ("interactions between support agents and the customers they serve occur in the form of a stream of communications or comments, which are recorded within a support ticket."; See Para. 3; Rath anticipates both internal and external comments because comments left by customers or ones sent to the customer would be external, while any one that only the support agents can see would be internal.), a voice recording ("a well-performing voice-to-text application would render the natural language processor machine-learning models 220 and/or 226 as useful for voice calls as well."; See Para. 29), a voice transcription ("a well-performing voice-to-text application would render the natural language processor machine-learning models 220 and/or 226 as useful for voice calls as well."; See Para. 29), a video recording ("input devices such as a video camera, a still camera, etc. The data entry module 508 may be configured to receive input from one or more users of the hardware device"; See Para. 110), a transcript (“Most of the interactions between support agents and the customers they serve occur in the form of a stream of communications or comments, which are recorded within a support ticket.”; See Para. 3; Rath anticipates a transcript because the support ticket records all the communications and stores them in the support ticket for later use, as in a transcript of communications.), or an external source of content (" Other external input devices (not shown) are connected to the hardware device 500 via an external data entry interface "; See Para. 110).
Rath does not teach, wherein the features of the support ticket comprise at least one of an email. Gadot teaches wherein the features of the support ticket comprise at least one of an email (“An initiated communication may be synchronous (e.g., video conference), asynchronous (emails going back and forth), or both. The communication may also be able to switch both mode and/or direction. For example, an incoming customer email may trigger an outgoing video call.”; See Para. 58; The support ticket itself may be an email or be initiated by an email).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention to combine the features of the support ticket that Rath teaches, with the email feature that Gadot teaches. The motivation to do so would be to allow asynchronous communication between customers and support agents allowing them to respond or comment whenever they are free and allows planning of future synchronous communication (“An initiated communication may be synchronous (e.g., video conference), asynchronous (emails going back and forth), or both. The communication may also be able to switch both mode and/or direction. For example, an incoming customer email may trigger an outgoing video call.”; See Para. 58), this also provides text which allows the machine-learning model to easier identify topics and text that evolve over the emails.
Regarding claim 10, most of the limitations of this claim have been noted in the rejection of claim 8 (and thus the rejections of claim 8 are incorporated), Rath teaches wherein the at least the text from the support ticket (“Therefore, the ticket summarizer, classifier, and forecaster machine-learning models 222 and/or 228 can generate retrospective analytics reports of support ticket summaries grouped by similar sequences of topics over a given time frame. Retrospective summaries can then be viewed over different time windows, and distinct sequences of topics that represent support tickets over distinct time slices may be identified, thereby powering support teams with insights into the evolving nature of their support tickets.”; See Para. 66) further comprises at least one of a question ("a problem resolution may involve responding appropriately to a customer's request or question..."; See Para. 3), a message ("A communication can be a message."; See Para. 22), a support ticket comment ("Most of the interactions between support agents and the customers they serve occur in the form of a stream of communications or comments, which are recorded within a support ticket."; See Para 3), a support ticket note ("...a support ticket note."; See paragraph 5), a chat message ("A communication can be a message."; See Para. 22), another support ticket (historical support tickets; See Para. 12).
Rath does not teach wherein the at least the text from the support ticket further comprises an email, a knowledge-based article, or a customer satisfaction survey. Gadot teaches wherein the at least the text from the support ticket comprises an email (“It will also be appreciated that either side may initiate the communication, the client (e.g., a client calling a customer support line) or supported system 100 (e.g., a sales organization making an outgoing call to potential clients). An initiated communication may be synchronous (e.g., video conference), asynchronous (emails going back and forth), or both. The communication may also be able to switch both mode and/or direction. For example, an incoming customer email may trigger an outgoing video call.”; See Para. 58; The support ticket itself may be an email, since text is extracted from the support ticket communications that mean the text could come from the email.), a knowledge-based article (linked knowledge base articles; See Para. 76), or a customer satisfaction survey (“A further such interaction may be a post-contact questionnaire to gather feedback on the provided support (or other contact or service). This may support implementation of surveys and data collections.”; See Para. 108).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to combine the method Rath teaches with the method Gadot teaches of text from the support ticket further comprising an email, a knowledge-based article, or a customer satisfaction survey. The motivation to combine what Gadot teaches with Rath would be to provide relevant information from the active forms of communication between the customer and support agent asynchronously (“An initiated communication may be synchronous (e.g., video conference), asynchronous (emails going back and forth), or both. The communication may also be able to switch both mode and/or direction. For example, an incoming customer email may trigger an outgoing video call.”; See Para. 58). The motivation to combine Rath with what Gadot teaches about the text in the support ticket containing a knowledge-based article or its text is to provide relevant information to the customer to allow further and easier topic discussion between the customer and support agent, and easier topic identification. The motivation to combine Rath’s method with what Gadot teaches about customer satisfaction surveys would be to receive valuable feedback from customers which could be used to further improve the machine-learning model and systems, to gauge the customer and support agent relationship, and to profile customers for future use in other tickets or use in their own future tickets.
Regarding Claim 12, most of the limitations of this claim have been noted in the rejection of claim 8 and 11 (and thus the rejections of claim 8 and 11 are incorporated), Rath teaches wherein the features of the support ticket comprise at least one of a status (Rath anticipates although they do not specifically state they use a status, a support ticket should or will always have a status associated with it on at least one end of them system as support agents need to know if a ticket is completed or not.), a priority (assigned support ticket priority; See Para. 37), a customer contact, or a customer account ("the author of a communication"; See Para. 37; Later they specify it’s used for determining the difference of customer or support agent, which can or cannot be logged in on the website giving them access to see and use the account.), and a communication comprises at least one of an inbound message ("...and whether an individual communication is inbound or outbound."; See Para. 37), an outbound message ("...and whether an individual communication is inbound or outbound."; See Para. 37), an internal comment, an external comment ("interactions between support agents and the customers they serve occur in the form of a stream of communications or comments, which are recorded within a support ticket."; See Para. 3; Rath anticipates both internal and external comments because comments left by customers or ones sent to the customer would be external, while any one that only the support agents can see would be internal.), a voice recording ("a well-performing voice-to-text application would render the natural language processor machine-learning models 220 and/or 226 as useful for voice calls as well."; See Para. 29), a voice transcription ("a well-performing voice-to-text application would render the natural language processor machine-learning models 220 and/or 226 as useful for voice calls as well."; See Para. 29), a video recording ("input devices such as a video camera, a still camera, etc. The data entry module 508 may be configured to receive input from one or more users of the hardware device"; See Para. 110), a transcript (“Most of the interactions between support agents and the customers they serve occur in the form of a stream of communications or comments, which are recorded within a support ticket.”; See Para. 3; Rath anticipates a transcript because the support ticket records all the communications and stores them in the support ticket for later use, as in a transcript of communications.), or an external source of content (" Other external input devices (not shown) are connected to the hardware device 500 via an external data entry interface "; See Para. 110).
Rath does not teach, wherein the features of the support ticket comprise at least one of an email. Gadot teaches wherein the features of the support ticket comprise at least one of an email (“An initiated communication may be synchronous (e.g., video conference), asynchronous (emails going back and forth), or both. The communication may also be able to switch both mode and/or direction. For example, an incoming customer email may trigger an outgoing video call.”; See Para. 58; The support ticket itself may be an email or be initiated by an email).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention to combine the features of the support ticket that Rath teaches, with the email feature that Gadot teaches. The motivation to do so would be to allow asynchronous communication between customers and support agents allowing them to respond or comment whenever they are free and allows planning of future synchronous communication (“An initiated communication may be synchronous (e.g., video conference), asynchronous (emails going back and forth), or both. The communication may also be able to switch both mode and/or direction. For example, an incoming customer email may trigger an outgoing video call.”; See Para. 58), this also provides text which allows the machine-learning model to easier identify topics and text that evolve over the emails.
Regarding Claim 17, most of the limitations of this claim have been noted in the rejection of claim 15 (and thus the rejections of claim 15 are incorporated), Rath teaches wherein the at least the text from the support ticket (“Therefore, the ticket summarizer, classifier, and forecaster machine-learning models 222 and/or 228 can generate retrospective analytics reports of support ticket summaries grouped by similar sequences of topics over a given time frame. Retrospective summaries can then be viewed over different time windows, and distinct sequences of topics that represent support tickets over distinct time slices may be identified, thereby powering support teams with insights into the evolving nature of their support tickets.”; See Para. 66) further comprises at least one of a question ("a problem resolution may involve responding appropriately to a customer's request or question..."; See Para. 3), a message ("A communication can be a message."; See Para. 22), a support ticket comment ("Most of the interactions between support agents and the customers they serve occur in the form of a stream of communications or comments, which are recorded within a support ticket."; See Para 3), a support ticket note ("...a support ticket note."; See paragraph 5), a chat message ("A communication can be a message."; See Para. 22), another support ticket (historical support tickets; See Para. 12).
Rath does not teach wherein the at least the text from the support ticket further comprises an email, a knowledge-based article, or a customer satisfaction survey. Gadot teaches wherein the at least the text from the support ticket comprises an email (“It will also be appreciated that either side may initiate the communication, the client (e.g., a client calling a customer support line) or supported system 100 (e.g., a sales organization making an outgoing call to potential clients). An initiated communication may be synchronous (e.g., video conference), asynchronous (emails going back and forth), or both. The communication may also be able to switch both mode and/or direction. For example, an incoming customer email may trigger an outgoing video call.”; See Para. 58; The support ticket itself may be an email, since text is extracted from the support ticket communications that mean the text could come from the email.), a knowledge-based article (linked knowledge base articles; See Para. 76), or a customer satisfaction survey (“A further such interaction may be a post-contact questionnaire to gather feedback on the provided support (or other contact or service). This may support implementation of surveys and data collections.”; See Para. 108).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to combine the method Rath teaches with the method Gadot teaches of text from the support ticket further comprising an email, a knowledge-based article, or a customer satisfaction survey. The motivation to combine what Gadot teaches with Rath would be to provide relevant information from the active forms of communication between the customer and support agent asynchronously (“An initiated communication may be synchronous (e.g., video conference), asynchronous (emails going back and forth), or both. The communication may also be able to switch both mode and/or direction. For example, an incoming customer email may trigger an outgoing video call.”; See Para. 58). The motivation to combine Rath with what Gadot teaches about the text in the support ticket containing a knowledge-based article or its text is to provide relevant information to the customer to allow further and easier topic discussion between the customer and support agent, and easier topic identification. The motivation to combine Rath’s method with what Gadot teaches about customer satisfaction surveys would be to receive valuable feedback from customers which could be used to further improve the machine-learning model and systems, to gauge the customer and support agent relationship, and to profile customers for future use in other tickets or use in their own future tickets.
Claims 6, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Rath within view of Gadot and in further view of Murti et al. (US 20240161121 A1, hereinafter referred to as Murti.) and Gayde et al (US 20080209564 A1, hereinafter referred to as Gayde).
Regarding Claim 6 most of the limitations of this claim have been noted in the rejection of claim 1 and 4 (and thus the rejections of claim 1 and 4 are incorporated), Rath teaches wherein the descriptive statistic ("Using statistical as well as natural language processing-based methods to determine such pieces of information for a support ticket can enable the natural language processor machine-learning models 220 and/or 226 to focus on processing natural language in the support ticket's communications to infer specific topics and power downstream topic inference, without being distracted by machine-generated text."; See Para 45) comprises at least one of an age ("An age of a support ticket can be an elapsed period of time since a request was logged on a work tracking system and described a problem to be addressed."; See Para. 23), a resolution rate for support tickets (“resolution forecasts based on preferred outcomes, such as customer satisfaction, support ticket resolution time, or customer sentiment.”; See Para. 61), a total number of support agents assigned to a support ticket ("The ticket summarizer, classifier, and forecaster systems 218 and/or 224 can also be queried for support ticket topics in an ad hoc manner for individual support tickets and/or groups of support tickets, whether open or closed during any time frame, as well as queried for support agents, teams of support agents..."; See Para 27), a level associated with one of a priority, a severity, or an urgency (“assigned support ticket priority”; See Para. 37), or a mean-time associated with one of a resolution time for support tickets (support ticket resolution time; See Para. 61), or a response by one of a customer or a support agent (“Communications between a customer and a support agent during the lifetime of a support ticket may contain a wide variety of relevant data that may be used to identify a sequence of topics for the summary of a support ticket."; See Para. 35). and a metric derived by machine-learning comprises, a churn risk ("By generating a more accurate representation of a customer's problems through rich representations of support tickets, the ticket summarizer, classifier, and forecaster machine-learning models 222 and/or 228 can facilitate the designing of customer-centric solutions to enhance customer engagement and minimize customer churn."; See Para. 84), or a customer sentiment (customer sentiment; See Para. 37; Customer sentiment is a combination of effort, satisfaction, and other related customer variables that might affect how a support agent communicates with a customer), a score associated with one of a customer effort (customer sentiment; See Para 61; Customer sentiment is a combination of effort, satisfaction, and other related customer variables that might affect how a support agent communicates with a customer), a customer satisfaction (“Although examples describe the use of a preferred outcome filter that is based on unresolved problems, such a filter could be based on other factors such as an expressed customer satisfaction”; See Para. 77),
Rath does not teach wherein the descriptive statistic
Gadot teaches wherein the descriptive statistic comprises at least one of a first response time of a support agent (“It will be further appreciated that ticket 5 may include a variety of time-related elements (which may be instantaneous or occur over a period of time). Such elements may include (for example): interaction activities (such as an email or a video-conference)”; See Para. 86).
It would have been obvious before the filing date of the invention to combine the descriptive statistics of the support ticket that Rath teaches with the first response time of a support agent that Gadot teaches. The motivation to do so would be to increase timeliness of the completion of the support ticket and to help customers and other support agents understand the timeline of the support agents’ help, and for further identification of support topics with similar response times.
Murti teaches a metric (customer-centric health score; See Para. 16) derived by machine-learning (A modeling framework; See Para. 16; They are using a machine-learning model) comprises at least one of an escalation (customer account escalation; See Para. 16), a needs attention (“Customer satisfaction, as it relates to a software product or service, may be a direct measure of customer health, and might be defined as the difference between performance of a product or service that a customer utilizes and the customer's expectations and needs relative to the product or service.”; See Para. 04; The Needs attention (score) is a factor or a part of the calculation of the Customer Satisfaction score.), or a net promoter (“Some of the most widely used industry metrics which are utilized to measure customer health/satisfaction are Net Promoter Score (NPS)”; See Para. 5).
It would have been obvious to combine what Rath teaches with the metric that Murti teaches. The motivation to do so would be to help support agents understand and evaluate a customer’s possible reaction or interaction about a support ticket.
Gayde teaches a ratio between inbound comments and outbound comments (“Maximum ratio of inbound messages to outbound messages”; See Para. 46).
It would have been obvious before the filing date of the invention to combine what Rath teaches with what Murti teaches. The motivation to do so would be to help identify and profile helpful or unhelpful support agents and customers, as generally you would want an even ratio of communication between both, this would reveal the effort of both sides.
Regarding Claim 13, most of the limitations of this claim have been noted in the rejection of claim 8 and 11 (and thus the rejections of claim 8 and 11 are incorporated), Rath teaches wherein the descriptive statistic ("Using statistical as well as natural language processing-based methods to determine such pieces of information for a support ticket can enable the natural language processor machine-learning models 220 and/or 226 to focus on processing natural language in the support ticket's communications to infer specific topics and power downstream topic inference, without being distracted by machine-generated text."; See Para. 45) comprises at least one of an age ("An age of a support ticket can be an elapsed period of time since a request was logged on a work tracking system and described a problem to be addressed."; See Para. 23), a resolution rate for support tickets (“resolution forecasts based on preferred outcomes, such as customer satisfaction, support ticket resolution time, or customer sentiment.”; See Para. 61), a total number of support agents assigned to a support ticket ("The ticket summarizer, classifier, and forecaster systems 218 and/or 224 can also be queried for support ticket topics in an ad hoc manner for individual support tickets and/or groups of support tickets, whether open or closed during any time frame, as well as queried for support agents, teams of support agents..."; See Para 27), a level associated with one of a priority, a severity, or an urgency (“assigned support ticket priority”; See Para. 37), or a mean-time associated with one of a resolution time for support tickets (support ticket resolution time; See Para. 61), or a response by one of a customer or a support agent (“Communications between a customer and a support agent during the lifetime of a support ticket may contain a wide variety of relevant data that may be used to identify a sequence of topics for the summary of a support ticket."; See Para. 35). and a metric derived by machine-learning comprises, a churn risk ("By generating a more accurate representation of a customer's problems through rich representations of support tickets, the ticket summarizer, classifier, and forecaster machine-learning models 222 and/or 228 can facilitate the designing of customer-centric solutions to enhance customer engagement and minimize customer churn."; See Para. 84), or a customer sentiment (customer sentiment; See Para. 37; Customer sentiment is a combination of effort, satisfaction, and other related customer variables that might affect how a support agent communicates with a customer), a score associated with one of a customer effort (customer sentiment; See Para 61; Customer sentiment is a combination of effort, satisfaction, and other related customer variables that might affect how a support agent communicates with a customer), a customer satisfaction (“Although examples describe the use of a preferred outcome filter that is based on unresolved problems, such a filter could be based on other factors such as an expressed customer satisfaction”; See Para. 77),
Rath does not teach wherein the descriptive statistic
Gadot teaches wherein the descriptive statistic comprises at least one of a first response time of a support agent (“It will be further appreciated that ticket 5 may include a variety of time-related elements (which may be instantaneous or occur over a period of time). Such elements may include (for example): interaction activities (such as an email or a video-conference)”; See Para. 86).
It would have been obvious before the filing date of the invention to combine the descriptive statistics of the support ticket that Rath teaches with the first response time of a support agent that Gadot teaches. The motivation to do so would be to increase timeliness of the completion of the support ticket and to help customers and other support agents understand the timeline of the support agents’ help, and for further identification of support topics with similar response times.
Murti teaches a metric (customer-centric health score; See Para. 16) derived by machine-learning (A modeling framework; See Para. 16; They are using a machine-learning model) comprises at least one of an escalation (customer account escalation; See Para. 16), a needs attention (“Customer satisfaction, as it relates to a software product or service, may be a direct measure of customer health, and might be defined as the difference between performance of a product or service that a customer utilizes and the customer's expectations and needs relative to the product or service.”; See Para. 04; The Needs attention (score) is a factor or a part of the calculation of the Customer Satisfaction score.), or a net promoter (“Some of the most widely used industry metrics which are utilized to measure customer health/satisfaction are Net Promoter Score (NPS)”; See Para. 5).
It would have been obvious to combine what Rath teaches with the metric that Murti teaches. The motivation to do so would be to help support agents understand and evaluate a customer’s possible reaction or interaction about a support ticket.
Gayde teaches a ratio between inbound comments and outbound comments (“Maximum ratio of inbound messages to outbound messages”; See Para. 46).
It would have been obvious before the filing date of the invention to combine what Rath teaches with what Murti teaches. The motivation to do so would be to help identify and profile helpful or unhelpful support agents and customers, as generally you would want an even ratio of communication between both, this would reveal the effort of both sides.
Regarding Claim 19, most of the limitations of this claim have been noted in the rejection of claim 15 and 18 (and thus the rejections of claim 15 and 18 are incorporated), Rath teaches wherein the descriptive statistic ("Using statistical as well as natural language processing-based methods to determine such pieces of information for a support ticket can enable the natural language processor machine-learning models 220 and/or 226 to focus on processing natural language in the support ticket's communications to infer specific topics and power downstream topic inference, without being distracted by machine-generated text."; See Para 45) comprises at least one of an age ("An age of a support ticket can be an elapsed period of time since a request was logged on a work tracking system and described a problem to be addressed."; See Para. 23), a resolution rate for support tickets (“resolution forecasts based on preferred outcomes, such as customer satisfaction, support ticket resolution time, or customer sentiment.”; See Para. 61), a total number of support agents assigned to a support ticket ("The ticket summarizer, classifier, and forecaster systems 218 and/or 224 can also be queried for support ticket topics in an ad hoc manner for individual support tickets and/or groups of support tickets, whether open or closed during any time frame, as well as queried for support agents, teams of support agents..."; See Para 27), a level associated with one of a priority, a severity, or an urgency (“assigned support ticket priority”; See Para. 37), or a mean-time associated with one of a resolution time for support tickets (support ticket resolution time; See Para. 61), or a response by one of a customer or a support agent (“Communications between a customer and a support agent during the lifetime of a support ticket may contain a wide variety of relevant data that may be used to identify a sequence of topics for the summary of a support ticket."; See Para. 35). and a metric derived by machine-learning comprises, a churn risk ("By generating a more accurate representation of a customer's problems through rich representations of support tickets, the ticket summarizer, classifier, and forecaster machine-learning models 222 and/or 228 can facilitate the designing of customer-centric solutions to enhance customer engagement and minimize customer churn."; See Para. 84), or a customer sentiment (customer sentiment; See Para. 37; Customer sentiment is a combination of effort, satisfaction, and other related customer variables that might affect how a support agent communicates with a customer), a score associated with one of a customer effort (customer sentiment; See Para 61; Customer sentiment is a combination of effort, satisfaction, and other related customer variables that might affect how a support agent communicates with a customer), a customer satisfaction (“Although examples describe the use of a preferred outcome filter that is based on unresolved problems, such a filter could be based on other factors such as an expressed customer satisfaction”; See Para. 77),
Rath does not teach wherein the descriptive statistic
Gadot teaches wherein the descriptive statistic comprises at least one of a first response time of a support agent (“It will be further appreciated that ticket 5 may include a variety of time-related elements (which may be instantaneous or occur over a period of time). Such elements may include (for example): interaction activities (such as an email or a video-conference)”; See Para. 86).
It would have been obvious before the filing date of the invention to combine the descriptive statistics of the support ticket that Rath teaches with the first response time of a support agent that Gadot teaches. The motivation to do so would be to increase timeliness of the completion of the support ticket and to help customers and other support agents understand the timeline of the support agents’ help, and for further identification of support topics with similar response times.
Murti teaches a metric (customer-centric health score; See Para. 16) derived by machine-learning (A modeling framework; See Para. 16; They are using a machine-learning model) comprises at least one of an escalation (customer account escalation; See Para. 16), a needs attention (“Customer satisfaction, as it relates to a software product or service, may be a direct measure of customer health, and might be defined as the difference between performance of a product or service that a customer utilizes and the customer's expectations and needs relative to the product or service.”; See Para. 04; The Needs attention (score) is a factor or a part of the calculation of the Customer Satisfaction score.), or a net promoter (“Some of the most widely used industry metrics which are utilized to measure customer health/satisfaction are Net Promoter Score (NPS)”; See Para. 5).
It would have been obvious to combine what Rath teaches with the metric that Murti teaches. The motivation to do so would be to help support agents understand and evaluate a customer’s possible reaction or interaction about a support ticket.
Gayde teaches a ratio between inbound comments and outbound comments (“Maximum ratio of inbound messages to outbound messages”; See Para. 46).
It would have been obvious before the filing date of the invention to combine what Rath teaches with what Murti teaches. The motivation to do so would be to help identify and profile helpful or unhelpful support agents and customers, as generally you would want an even ratio of communication between both, this would reveal the effort of both sides.
Conclusion
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/BRETT DAVID KOLB JR/Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145
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