DETAILED ACTION
This communication is in response to the Application filed on 28 October 2024. Claims 1-20 are pending and have been examined.
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 .
Information Disclosure Statement
The IDS dated 28 October 2024 has been considered and placed in the application file.
Claim Objections
Claims 8 and 15 are objected to because of the following informalities:
Claim 8, line 4, should be “a hardware processor”
Claim 15 is objected to for similar reasons to claim 8.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. All of the claims are method claims (8-14), apparatus/machine claims (1-7, 15-20) or manufacture claim under (Step 1), but under Step 2A all of these claims recite abstract ideas and specifically mental processes. These mental processes are more particularly recited in claims 1, 8, and 15 as:
determining, via the hardware processor executing computer-readable code instructions of an automatic predictive capability recommendation system trained neural network, a recommendation intervention action at an information handling system…
generating, via the hardware processor executing computer-readable code instructions of a text generation module, a natural language description of the recommendation intervention action…
generating, via the hardware processor executing computer-readable program code instructions, an intervention recommendation input intent value for the natural language description of the recommendation intervention action…
performing a semantic similarity search, via the hardware processor, comparing the intervention recommendation input intent value to a plurality of capability intent values…
determining that the best match responsive capability requires action on behalf of an information technology decision maker (ITDM) for an enterprise…
automatically generating and transmitting a service IT ticket requesting the action to the ITDM at a remote enterprise management system…
Under Step 2A Prong One, claims 1, 8, and 15 are directed to an abstract idea and specifically a mental process. As detailed above, the steps of determining, generating, performing, transmitting, etc. may be practically performed in the human mind with the use of a physical aid such as a pen and paper. For example, a human agent could notice a malfunction in a software application, brainstorm actions to resolve the malfunction, create a description of the malfunction using the brainstormed actions, assign an intent value to the description, compare the intent value with a list of predetermined intent values in order to determine a best match intent, determine that the best match intent requires authorization from an ITDM, and create an IT ticket for the best match intent that they then transmit to the IT department of their enterprise.
Under Step 2A Prong Two, this judicial exception is not integrated into a practical application because claims 1-20 do not recite additional elements that integrate the exception into a practical application. In particular, claims 1, 8, and 15 recite the additional elements of a processor (¶ [0050]), artificial intelligence productivity tools (¶ [0011]), and a neural network (¶ [0027], [0074]). These additional elements are recited at a high level of generality and merely equate to “apply it” or otherwise merely uses a generic computer as a tool to perform an abstract which are not indicative of integration into a practical application as per MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Under Step 2B, the claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using a computer is noted as a general computer {processor (¶ [0050])}. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
With respect to claims 2, the claim relates to the ITDM granting a software license. This relates to a human ITDM granting permission to the agent to run the software application. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 3, the claim relates to inserting a natural language description of a best match capability within an IT ticket template. This relates to the human agent populating a summary field of an IT ticket template with their own summary of the malfunction. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 4 and 5, the claim relates to requesting second user input in order to populate an IT ticket template. This relates to the human agent asking a user about additional details regarding the software malfunction in order to fill out an IT ticket template. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 6, 13, and 17, the claim relates to including the natural language description, the user query, and the chat history in the IT ticket. This relates to the human agent including the natural language description of the malfunction, a user query describing the malfunction, and a chat history with the user in the IT ticket. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 7 and 14, the claim relates to an ITDM directing execution of the best match capability in order to address the user query. This relates to a human ITDM giving step-by-step instructions to the human agent about how to fix the software malfunction. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 9-12 and 19-20, the claim relates to specific errors and associated best match capabilities. This relates to a human agent dynamically assessing each malfunction in a computing system in order to recommend the most appropriate solution on a case-by-case basis. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 16, the claim relates to monitoring telemetries including system analytics and event error logs. The limitation of monitoring telemetries is considered insignificant extra-solution activities which are not indicative of integration into a practical application as per MPEP 2106.05(g). No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 18 , the claim relates to requesting user confirmation. This relates to a human agent confirming with a user the details of an IT ticket before transmitting it to the IT department. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
For all of the above reasons, taken alone or in combination, claims 1-20 recite a non-statutory mental process.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7 are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 20250355947 A1 (Wiggins) in view of US Patent Publication 20180218374 A1 (Shah et al.) in view of US Patent Publication 20200097563 A1 (Alexander et al.).
Claim 1
Regarding claim 1, Wiggins et al. disclose an information handling system executing computer readable code instructions for an on the box (OTB) artificial intelligence (AI) productivity tool comprising:
a hardware processor executing computer-readable program code instructions (Wiggins ¶ [0087], "a non-transitory computer readable medium can comprise instructions, that when executed by one or more processors, cause a computing device to perform the acts of FIG. 9.") for generating a query input intent value for a user query input received via text or audio (Wiggins ¶ [0052], "the intent analysis system 102 determines an intent 312 of the query 300 based on the content analysis performed by the digital content analysis model(s) 306. For instance, the intent analysis system 102 determines the intent 312 to include one or more domains/topics, data types, operation types, purposes, etc., of the query 300 based on text analysis and/or image analysis." ¶ [0018], " the query is a latent feature of the query's content (e.g., the text, imagery, or other information or data comprising the query).") requesting an action to be taken by one of a plurality of AI productivity tool-enableable software applications or one of a plurality of AI productivity tool-enableable platform tools executing on the information handling system (Wiggins ¶ [0052], "In an illustrative example, the intent analysis system 102 can determine that the query 300 is intended to cause the AI system to generate or produce a specific data type, answer, or series of operations."); and
the hardware processor executing computer-readable program code instructions for performing a semantic similarity search comparing a plurality of capability intent values generated from a plurality of natural language descriptions of capabilities associated with each of a plurality of AI productivity tool enableable software applications or a plurality of hardware components stored via a natural language capabilities database (Wiggins ¶ [0058]-[0060], "the intent analysis system 102 utilizes digital content analysis model(s) 408 to analyze the digital documents 402. ... For example, as described above with respect to FIG. 3, the intent analysis system 102 can determine the intended use 410 by analyzing text and/or image content in the digital documents 402" ¶ [0064], “the intent analysis system 102 generates the similarity score 508 by determining a semantic similarity between the intent 504 and the intended use 506, such as by using a language processing neural network to compare one or more words or phrases in the intent 504 to one or more words or phrases in the intended use 506.” Digital documents 402 are considered analogous to a plurality of natural language descriptions of capabilities associated with each of a plurality of AI productivity tools. Intended use is thus considered analogous to a plurality of capability intent values) to the query input intent value to identify a best match responsive capability for the received user query input having a capability intent value that generates a [highest] semantic similarity search score (Wiggins ¶ [0046], "In response to determining the intent 208 of the query 202 and the intended use 210 of the AI system 204, the intent analysis system 102 ... determines whether the intent 208 aligns with the intended use 210 by generating a similarity score.")….
Wiggins et al. do not explicitly disclose all of requesting assistance from an ITDM or generating a service IT ticket.
However, Shah et al. disclose the hardware processor executing computer-readable program code instructions (Shah et al. ¶ [0031], "the processor 202 is capable of executing the stored platform instructions") for generating a query input intent value for a user query input received via text or audio (Shah et al. ¶ [0041], "the processor 202 is configured to, with the content of the memory 204, cause the system 200 to provision a user interface (UI) to a user to enable the user to provide a query to a virtual agent.") [requesting an action to be taken by one of a plurality of AI productivity tool-enableable software applications or one of a plurality of AI productivity tool-enableable platform tools executing on the information handling system];
the hardware processor executing computer-readable program code instructions for performing a semantic similarity search comparing a plurality of capability intent values generated from a plurality of natural language descriptions of capabilities associated with each of a plurality of AI productivity tool enableable software applications or a plurality of hardware components stored via a natural language capabilities database to the query input intent value to identify a best match responsive capability for the received user query input having a capability intent value that generates a [highest] semantic similarity search score (Shah et al. ¶ [0044], "The virtual agent may request the user to confirm if a system query from among the one or more system queries substantially matches the user requirements. The term ‘substantially matches’ as used herein implies a system query matches at least the content (for example, a sequence of words) or implied intention from the user. If the user identifies one system query from among the displayed system queries to match the user's query, the processor 102 may be able to interpret the user's query (for example, may relate the user's query to stored interpretation of the matching system query) and accordingly determine the system query to be a service request or an incident.");
the hardware processor executing computer-readable program code instructions for determining that execution of the best match responsive capability requires assistance from an information technology decision maker (ITDM) for an enterprise (Shah et al. ¶ [0030], "various embodiments of the present technology disclose an operator less service desk that uses virtual agents to determine whether a user query is a service request or an incident. ... If the query is an incident, then a response to the query is identified from the knowledge base and provided to the user. If the user is not satisfied by the response, then a complete and accurately filled ticket is generated based on the interaction and routed to an appropriate resolver queue of the service desk for resolution." ¶ [0041], "the term ‘service desk’ refers to a single point of contact between users and Information Technology Service Management (ITSM) of the enterprise. In an example embodiment, the service desk may include a call center or an IT help desk supported by human agents."); and
the hardware processor executing computer-readable program code instructions for automatically generating and transmitting a service IT ticket requesting the assistance to the ITDM at a remote enterprise management system (Shah et al. ¶ [0047], "system 200 [may] generate a ticket corresponding to the query if the reply provided to the user is deemed to be unsatisfactory by the user. The ticket is generated based on the query and the interaction between the user and the virtual agent. More specifically, processor 102, using machine learning and AI routines may be configured to interpret the interaction and summarize the query and the interaction to generate an accurate and complete ticket.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Wiggins’ intent classification system to incorporate Shah et al.’s IT ticket generation.
The suggestion/motivation for doing so would have been that, “there is a need to overcome the drawbacks of conventional query resolution mechanisms at service desks of the enterprises and facilitate resolution of queries of enterprise users in an efficient manner,” as noted by the Shah et al. disclosure in paragraph [0006].
Wiggins et al. in view of Shah et al. do not disclose all of a highest similarity score match for intent classification.
However, Alexander et al. disclose identifying a best match responsive capability for the received user query input having a capability intent value that generates a highest semantic similarity search score (Alexander et al. ¶ [0049], "The intent classification procedure includes generation of a conversation input vector at 555 based on the conversation input.... At 560, the data processing system 510 calculates similarity scores between the conversation input vector and each vector of the set of intent vectors. At 565, the data processing system 510 identifies an intent category of the set of intent categories corresponding to the intent vector having the highest similarity score with the conversation input vector.")
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Wiggins in view of Shah et al. to include Alexander et al.’s highest similarity score because such a modification is the result of simple substitution of one known element for another producing a predictable result. More specifically, Wiggins’ similarity threshold and Alexander et al.’s highest similarity score perform the same general and predictable function, the predictable function being identifying intent categories that are relevant to the user query. Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself - that is in the substitution of Wiggins’ similarity threshold by replacing it with Alexander et al.’s highest similarity score. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Claim 2
Regarding claim 2, the rejection of claim 1 is incorporated.
Shah et al. further disclose wherein the determined assistance required from the ITDM includes granting a software license to the information handling system for execution of a software application (Shah et al. ¶ [0057], "for the service request relating to “How to get a login access to a printer”, a service request form pertaining to printer login access may be selected as the relevant service request form. ... a human agent associated with the resolver queue to which the service request form may be routed, may resolve the query by rendering appropriate service as requested in the service request form viz. providing the necessary login access to the user for accessing the local printer." Granting login access to a printer service is considered analogous to granting a software license).
Claim 3
Regarding claim 3, the rejection of claim 1 is incorporated.
Shah et al. further disclose the hardware processor executing machine readable code instructions to automatically insert a natural language description of the best match responsive capability (Shah et al. ¶ [0063], " The user may ask a query 504b, such as for example “How do I pay by cash?”, to which the virtual agent may display a corresponding output message 506b including system queries related to the query 504b and may request the user to select a system query that is similar to the query 504b. The system queries likely to match the query 504b may be identified from the knowledge base 210 of the memory 204 as explained with reference to FIGS. 2 and 3 and is not explained again herein.") within an IT ticket template to generate the service IT ticket (Shah et al. ¶ [0064], "the virtual agent is caused to offer generation of ticket as depicted in response 506d. The response 506d is depicted to offer generation of a ticket along with a summary of the ticket to be raised. It is noted that the ticket summary is configured to include the details of the chat interaction 502 conducted so far. More specifically, the ticket may include interaction lines 504b, 506b, 504c, 506c and 504d." See Figure 5B, which illustrates the generated ticket including a natural language description of the responsive capability ("How do I pay using cash?")).
Claim 4
Regarding claim 4, the rejection of claim 1 is incorporated.
Shah et al. further disclose the hardware processor executing machine readable code instructions to automatically generate a natural language text descriptor of required information fields within an IT ticket template for generating the service IT ticket that requires user input for requesting required information from a user via a second user input (Shah et al. ¶ [0064], "The response 506d is depicted to offer generation of a ticket along with a summary of the ticket to be raised. It is noted that the ticket summary is configured to include the details of the chat interaction 502 conducted so far. More specifically, the ticket may include interaction lines 504b, 506b, 504c, 506c and 504d." Including multiple interaction lines from the user in order to generate an IT ticket is considered analogous to requesting required information via second user input).
Claim 5
Regarding claim 5, the rejection of claim 1 is incorporated.
Shah et al. further disclose the hardware processor executing machine readable code instructions of a universal user conversational interface software application to request and receive natural language responses for required information fields for the service IT ticket, based on an IT ticket template, from a user via a second user input (Shah et al. ¶ [0072], "In at least one example embodiment, the prompts for added information as well as the Yes/No checks guide the virtual agent for further diagnosis. As explained above, the virtual agent may leverage NLP and ML capabilities to engage in natural language interactions with users. ... the virtual agent may be configured to create a ticket (i.e. register a case) as depicted by a statement 606c from the virtual agent: “Ok. Would you like me to create a case for this issue with the service desk?”." See Figure 6, which illustrates a natural language conversation to fill required information field for a service IT ticket).
Claim 6
Regarding claim 6, the rejection of claim 1 is incorporated.
Shah et al. further disclose the hardware processor executing machine readable code instructions to automatically enter required information for an IT ticket template (Shah et al. ¶ [0050], "Subsequent to determining the query to be the service request, in at least one example embodiment, the processor 202 is configured to, with the content of the memory 204, cause the system 200 to update a service request form. The service request form may be identified from among a plurality of service request forms based on the information obtained from the interaction between the user and the virtual agent.") describing in natural language the request for execution of the determined best match responsive capability (Shah et al. ¶ [0047], "processor 102, using machine learning and AI routines may be configured to interpret the interaction and summarize the query and the interaction to generate an accurate and complete ticket."), the user query input prompting generation of the service IT ticket (Shah et al. ¶ [0029], "The ticket 106 may include a description of the query as provided by the user 102."), and a chat history with a user from a universal user conversational interface software application (Shah et al. ¶ [0064], "It is noted that the ticket summary is configured to include the details of the chat interaction 502 conducted so far. More specifically, the ticket may include interaction lines 504b, 506b, 504c, 506c and 504d.").
Claim 7
Regarding claim 7, the rejection of claim 1 is incorporated.
Shah et al. further disclose the hardware processor executing machine readable code instructions of the OTB AI productivity tool to allow the ITDM to direct execution of the best match responsive capability at the AI productivity tool enableable software application or hardware or firmware for the information handling system in response to the user query input (Shah et al. ¶ [0057], "for the service request relating to “How to get a login access to a printer”, a service request form pertaining to printer login access may be selected as the relevant service request form. ... a human agent associated with the resolver queue to which the service request form may be routed, may resolve the query by rendering appropriate service as requested in the service request form viz. providing the necessary login access to the user for accessing the local printer.").
Claims 8, 13-15, and 17-18 are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 20250378005 A1 (Harris) in view of US Patent Publication 20250355947 A1 (Wiggins) in view of US Patent Publication 20180218374 A1 (Shah et al.).
Claim 8
Regarding claim 8, Harris discloses determining, via a hardware processor executing computer-readable code instructions (Harris ¶ [0028], "The processing device 202 can execute instructions 206 stored in the memory 204 to perform operations.") of an automatic predictive capability recommendation system trained neural network (Harris ¶ [0017], "some or all techniques described herein such as detecting or predicting errors 118 or generating recommendations 136 may be performed using a machine learning model 120. The machine learning model 120 may include any suitable type or combination of machine-learning models. For example, the machine learning model 120 may include a neural network, such as a generative pre-trained transformer (GPT) model. "), a recommendation intervention action at an information handling system (Harris ¶ [0036], "the processing device 202 may generate, by executing the machine learning model 120, a recommendation 136 associated with the error 118 based on determining that the error 118 is associated with the text input 116 or the technical issue 208.") to avoid occurrence of an error indicating a software, firmware, or hardware failure or malfunction identified within received failure operational telemetries monitored for the information handling system (Harris ¶ [0036], "If the error 118 is associated with a technical issue 208, the recommendation 136 may involve switching a network or a web browser used by the client device 108 or restarting the application 102. In some examples, in response to detecting that the error 118 is a technical issue 208, the processing device 202 may automatically restart the application 102."); and
generating, via the hardware processor executing computer-readable code instructions of a text generation module, a natural language description of the recommendation intervention action to avoid occurrence of the error (Harris ¶ [0036], "the processing device 202 may execute the machine learning model 120, which may include a natural language processing (NLP) model, to generate a prompt 138 describing the error 118 and the recommendation 136 in a natural language format.")….
Harris do not explicitly disclose all of generating an intent value for the natural language description.
However, Wiggins discloses generating, via the hardware processor executing computer-readable program code instructions, an intervention recommendation input intent value for the natural language description of the recommendation intervention action to avoid occurrence of the error (Wiggins ¶ [0052], "the intent analysis system 102 determines an intent 312 of the query 300 based on the content analysis performed by the digital content analysis model(s) 306. For instance, the intent analysis system 102 determines the intent 312 to include one or more domains/topics, data types, operation types, purposes, etc., of the query 300 based on text analysis and/or image analysis."); and
performing a semantic similarity search, via the hardware processor, comparing the intervention recommendation input intent value to a plurality of capability intent values (Wiggins ¶ [0046], "In response to determining the intent 208 of the query 202 and the intended use 210 of the AI system 204, the intent analysis system 102 ... determines whether the intent 208 aligns with the intended use 210 by generating a similarity score." ¶ [0064], “the intent analysis system 102 generates the similarity score 508 by determining a semantic similarity between the intent 504 and the intended use 506, such as by using a language processing neural network to compare one or more words or phrases in the intent 504 to one or more words or phrases in the intended use 506.”) generated from a plurality of natural language descriptions of capabilities associated with each of a plurality of AI productivity tool enableable software applications or a plurality of hardware components stored via a natural language capabilities database to identify a best match responsive capability for the received natural language description of the recommendation intervention action (Wiggins ¶ [0058]-[0060], "the intent analysis system 102 utilizes digital content analysis model(s) 408 to analyze the digital documents 402. ... For example, as described above with respect to FIG. 3, the intent analysis system 102 can determine the intended use 410 by analyzing text and/or image content in the digital documents 402" Digital documents 402 are considered analogous to a plurality of natural language descriptions of capabilities associated with each of a plurality of AI productivity tools. Intended use is thus considered analogous to a plurality of capability intent values generated from a plurality of natural language descriptions of capabilities)….
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Harris’ error detection system to incorporate Wiggins’ intent classification because such a modification is the result of combining prior art elements according to known methods to yield predictable results. More specifically, Harris’ error detection system as modified by Wiggins’ intent classification can yield a predictable result of extending system function capabilities since the ability to classify intents associated with natural language descriptions of errors would allow the system to alternatively function on user query inputs. Thus, a person of ordinary skill would have appreciated including in Harris’ error detection system the ability to do Wiggins’ intent classification since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Harris in view of Wiggins does not explicitly disclose all of generating an IT ticket.
However, Shah et al. disclose determining that the best match responsive capability requires action on behalf of an information technology decision maker (ITDM) for an enterprise (Shah et al. ¶ [0030], "various embodiments of the present technology disclose an operator less service desk that uses virtual agents to determine whether a user query is a service request or an incident. ... If the query is an incident, then a response to the query is identified from the knowledge base and provided to the user. If the user is not satisfied by the response, then a complete and accurately filled ticket is generated based on the interaction and routed to an appropriate resolver queue of the service desk for resolution." ¶ [0041], "the term ‘service desk’ refers to a single point of contact between users and Information Technology Service Management (ITSM) of the enterprise. In an example embodiment, the service desk may include a call center or an IT help desk supported by human agents."); and
automatically generating and transmitting a service IT ticket requesting the action to the ITDM at a remote enterprise management system (Shah et al. ¶ [0047], "system 200 [may] generate a ticket corresponding to the query if the reply provided to the user is deemed to be unsatisfactory by the user. The ticket is generated based on the query and the interaction between the user and the virtual agent. More specifically, processor 102, using machine learning and AI routines may be configured to interpret the interaction and summarize the query and the interaction to generate an accurate and complete ticket.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Harris in view of Wiggins to incorporate Shah et al.’s IT ticket generation.
The suggestion/motivation for doing so is similar to the suggestion/motivation described above with respect to claim 1.
Claim 13
Regarding claim 13, the rejection of claim 8 is incorporated.
Shah et al. further disclose retrieving an IT ticket template for requesting execution of the best match responsive capability from an enterprise management system (Shah et al. ¶ [0050], "Subsequent to determining the query to be the service request, in at least one example embodiment, the processor 202 is configured to, with the content of the memory 204, cause the system 200 to update a service request form. The service request form may be identified from among a plurality of service request forms based on the information obtained from the interaction between the user and the virtual agent."); and
automatically entering required information for the IT ticket template describing in natural language the request for execution of the determined best match responsive capability (Shah et al. ¶ [0047], "processor 102, using machine learning and AI routines may be configured to interpret the interaction and summarize the query and the interaction to generate an accurate and complete ticket."), the user query input prompting generation of the service IT ticket (Shah et al. ¶ [0029], "The ticket 106 may include a description of the query as provided by the user 102."), and a chat history with a user from a universal user conversational interface software application (Shah et al. ¶ [0064], "It is noted that the ticket summary is configured to include the details of the chat interaction 502 conducted so far. More specifically, the ticket may include interaction lines 504b, 506b, 504c, 506c and 504d.").
Claim 14
Regarding claim 14, the rejection of claim 8 is incorporated.
Shah et al. further disclose executing machine readable code instructions of an on the box AI productivity tool to allow the ITDM to direct execution of the best match responsive capability at the AI productivity tool enableable software application or hardware or firmware at the information handling system (Shah et al. ¶ [0057], "for the service request relating to “How to get a login access to a printer”, a service request form pertaining to printer login access may be selected as the relevant service request form. ... a human agent associated with the resolver queue to which the service request form may be routed, may resolve the query by rendering appropriate service as requested in the service request form viz. providing the necessary login access to the user for accessing the local printer.").one
Claim 15
Regarding claim 15, Harris discloses an information handling system executing computer readable code instructions for an on the box (OTB) artificial intelligence (AI) productivity tool comprising:
hardware processor executing computer-readable code instructions of an automatic predictive capability recommendation system trained neural network (Harris ¶ [0017], "some or all techniques described herein such as detecting or predicting errors 118 or generating recommendations 136 may be performed using a machine learning model 120. The machine learning model 120 may include any suitable type or combination of machine-learning models. For example, the machine learning model 120 may include a neural network, such as a generative pre-trained transformer (GPT) model. ") to determine a recommendation intervention action (Harris ¶ [0036], "the processing device 202 may generate, by executing the machine learning model 120, a recommendation 136 associated with the error 118 based on determining that the error 118 is associated with the text input 116 or the technical issue 208.") that is an adjustment to an adjustable hardware, firmware, or software configuration associated with an error indicating a software, firmware, or hardware failure or malfunction within received failure operational telemetries monitored for the information handling system (Harris ¶ [0036], "If the error 118 is associated with a technical issue 208, the recommendation 136 may involve switching a network or a web browser used by the client device 108 or restarting the application 102. In some examples, in response to detecting that the error 118 is a technical issue 208, the processing device 202 may automatically restart the application 102."); and
the hardware processor executing computer-readable code instructions of a text generation module to generate a natural language description of the recommendation intervention action to perform the determined adjustment to the adjustable hardware, firmware, or software configuration (Harris ¶ [0036], "the processing device 202 may execute the machine learning model 120, which may include a natural language processing (NLP) model, to generate a prompt 138 describing the error 118 and the recommendation 136 in a natural language format.")….
Harris do not explicitly disclose all of generating an intent value for the natural language description.
However, Wiggins discloses generating an intervention recommendation input intent value for the natural language description of the recommendation intervention action to perform the determined adjustment (Wiggins ¶ [0052], "the intent analysis system 102 determines an intent 312 of the query 300 based on the content analysis performed by the digital content analysis model(s) 306. For instance, the intent analysis system 102 determines the intent 312 to include one or more domains/topics, data types, operation types, purposes, etc., of the query 300 based on text analysis and/or image analysis.") and performing a semantic similarity search comparing the intervention recommendation input intent value to a plurality of capability intent values (Wiggins ¶ [0046], "In response to determining the intent 208 of the query 202 and the intended use 210 of the AI system 204, the intent analysis system 102 ... determines whether the intent 208 aligns with the intended use 210 by generating a similarity score.") generated from a plurality of natural language descriptions of capabilities associated with each of a plurality of AI productivity tool enableable software applications or a plurality of hardware components stored at a natural language capabilities database to identify a best match responsive capability for the received natural language description of the recommendation intervention action (Wiggins ¶ [0058]-[0060], "the intent analysis system 102 utilizes digital content analysis model(s) 408 to analyze the digital documents 402. ... For example, as described above with respect to FIG. 3, the intent analysis system 102 can determine the intended use 410 by analyzing text and/or image content in the digital documents 402" Digital documents 402 are considered analogous to a plurality of natural language descriptions of capabilities associated with each of a plurality of AI productivity tools. Intended use is thus considered analogous to a plurality of capability intent values generated from a plurality of natural language descriptions of capabilities) that generates a threshold level semantic similarity search score (Wiggins ¶ [0065], "the intent analysis system 102 can compare the similarity score 508 to a similarity threshold 510 to determine whether the intent 504 aligns with the intended use 506.")….
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Harris’ error detection system to incorporate Wiggins’ intent classification.
The suggestion/motivation for doing so is similar to the suggestion/motivation described above with respect to claim 8.
Harris in view of Wiggins does not explicitly disclose all of generating an IT ticket.
However, Shah et al. disclose determining that the best match responsive capability requires action on behalf of an information technology decision maker (ITDM) for an enterprise (Shah et al. ¶ [0030], "various embodiments of the present technology disclose an operator less service desk that uses virtual agents to determine whether a user query is a service request or an incident. ... If the query is an incident, then a response to the query is identified from the knowledge base and provided to the user. If the user is not satisfied by the response, then a complete and accurately filled ticket is generated based on the interaction and routed to an appropriate resolver queue of the service desk for resolution." ¶ [0041], "the term ‘service desk’ refers to a single point of contact between users and Information Technology Service Management (ITSM) of the enterprise. In an example embodiment, the service desk may include a call center or an IT help desk supported by human agents."); and
…generating and transmitting a service IT ticket requesting the action to the ITDM at a remote enterprise management system (Shah et al. ¶ [0047], "system 200 [may] generate a ticket corresponding to the query if the reply provided to the user is deemed to be unsatisfactory by the user. The ticket is generated based on the query and the interaction between the user and the virtual agent. More specifically, processor 102, using machine learning and AI routines may be configured to interpret the interaction and summarize the query and the interaction to generate an accurate and complete ticket.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Harris in view of Wiggins to incorporate Shah et al.’s IT ticket generation.
The suggestion/motivation for doing so is similar to the suggestion/motivation described above with respect to claim 1.
Claim 17
Regarding claim 17, the rejection of claim 15 is incorporated.
Harris further discloses failure operational telemetries (Harris ¶ [0036], "If the error 118 is associated with a technical issue 208, the recommendation 136 may involve switching a network or a web browser used by the client device 108 or restarting the application 102. In some examples, in response to detecting that the error 118 is a technical issue 208, the processing device 202 may automatically restart the application 102.").
Shah et al. further disclose the hardware processor executing machine readable code instructions to automatically enter required information for an IT ticket template (Shah et al. ¶ [0050], "Subsequent to determining the query to be the service request, in at least one example embodiment, the processor 202 is configured to, with the content of the memory 204, cause the system 200 to update a service request form. The service request form may be identified from among a plurality of service request forms based on the information obtained from the interaction between the user and the virtual agent.") describing in natural language a request for execution of the determined best match responsive capability [and the failure operational telemetries] prompting generation of the service IT ticket (Shah et al. ¶ [0047], "processor 102, using machine learning and AI routines may be configured to interpret the interaction and summarize the query and the interaction to generate an accurate and complete ticket.").
Claim 18
Regarding claim 18, the rejection of claim 15 is incorporated.
Shah et al. further disclose generating a natural language text recommendation user confirmation request presented to a user requesting user approval confirmation, via a universal user conversational interface software application, to automatically transmit the generated service IT ticket to the ITDM for execution of the best match responsive capability (Shah et al. ¶ [0064], "the system 200 may be caused to generate a ticket in response to such a query. ... The response 506d is depicted to offer generation of a ticket along with a summary of the ticket to be raised. ... The user is also offered options to change a summary of the ticket, to ratify the generation of the ticket, and the like." Ratifying the generation of a ticket is considered analogous to requesting user approval).
Claim 9 is rejected under 35 U.S.C. 103 as obvious over Harris in view of Wiggins in view of Shah et al. as applied to claim 8 above, and further in view of US Patent Publication 20230341822 A1 (Maitra et al.).
Claim 9
Regarding claim 9, the rejection of claim 8 is incorporated.
Harris in view of Wiggins in view of Shah et al. does not explicitly disclose all of adjusting thermal tables in response to temperatures reading above a thermal warning level.
However, Maitra et al. disclose wherein the error includes a temperature reading above a thermal warning level (Maitra et al. ¶ [0032], "one error metric 128 can capture a temperature difference between a datacenter aisle and the outside air. The error metrics 128 can be compared against various threshold error metrics 130 defined by the failure conditions 126 to determine if an operational failure is likely.") and the best match responsive capability includes adjusting thermal tables (Maitra et al. ¶ [0033], "the selector agent 114 can choose to deploy the backup actions 124 generated by the redundancy agent 118 to address the specific circumstances causing the failure condition 126. In this example, the backup action 124 can define temperature setpoints for air exchange units to return the temperature difference to normal ranges and prevent overheating.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Harris in view of Wiggins in view of Shah et al. to incorporate Maitra et al.’s temperature maintenance.
The suggestion/motivation for doing so would have been that, “there is a need for methods to increase redundancy in autonomous control systems to minimize and mitigate operational failures,” as noted by the Maitra et al. disclosure in paragraph [0005].
Claims 10-11 are rejected under 35 U.S.C. 103 as obvious over Harris in view of Wiggins in view of Shah et al. as applied to claim 8 above, and further in view of US Patent Publication 20250068507 A1 (Mehta et al.).
Claim 10
Regarding claim 10, the rejection of claim 8 is incorporated.
Harris in view of Wiggins in view of Shah et al. does not explicitly disclose all of throwing an error for exceeding a maximum processor capacity threshold.
However, Mehta et al. disclose wherein an error includes usage of the hardware processor exceeding a maximum processor capacity percentage threshold (Mehta et al. ¶ [0040], "NMS 130 is configured to determine, for each of the one or more of switches 146, whether the overall processor usage of the network switch across the time window exceeds a baseline processor usage threshold. In some examples, the baseline threshold may be a specified percentage of processor utilization, such as 80% CPU utilization. ") and a best match responsive capability includes terminating execution of a background application (Mehta et al. ¶ [0053], "if the anomaly detection model detects that a certain user space process executing at the processor of a network switch is the root cause of high processor usage of the network switch, NMS 130 may be configured to auto-terminate the user space process or to restart the user space process to resolve the high processor usage.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Harris in view of Wiggins in view of Shah et al. to incorporate Mehta et al.’s load utilization thresholds.
The suggestion/motivation for doing so would have been that, “[detecting] high processor usage of network devices in a network…, [determining] the root causes of the high processor usage, and … automatically [taking] actions to remediate the anomalous behavior of the network devices… may therefore reduce the amount of time during which network devices experiencing high processor CPU usage may degrade the performance of the network, thereby improving the performance and reliability of the network,” as noted by the Maitra et al. disclosure in paragraph [0007].
Claim 11
Regarding claim 11, the rejection of claim 8 is incorporated.
Harris in view of Wiggins in view of Shah et al. does not explicitly disclose all of throwing an error for exceeding a maximum memory capacity threshold.
However, Mehta et al. disclose wherein the error includes usage of a memory exceeding a maximum memory capacity percentage threshold (Mehta et al. ¶ [0040], "NMS 130 is configured to determine, for each of the one or more of switches 146, whether the overall processor usage of the network switch across the time window exceeds a baseline processor usage threshold. In some examples, the baseline threshold may be a specified percentage of processor utilization, such as 80% CPU utilization." ¶ [0056], "While the techniques of this disclosure are described with respect to detecting high CPU usage of network switches, the techniques described herein may similarly be applied to detecting high memory usage of network switches and/or high temperature of network switches.") and the best match responsive capability includes terminating execution of a background application (Mehta et al. ¶ [0053], "if the anomaly detection model detects that a certain user space process executing at the processor of a network switch is the root cause of high processor usage of the network switch, NMS 130 may be configured to auto-terminate the user space process or to restart the user space process to resolve the high processor usage.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Harris in view of Wiggins in view of Shah et al. to incorporate Mehta et al.’s load utilization thresholds.
The suggestion/motivation for doing so is similar to the suggestion/motivation described above with respect to claim 10.
Claims 12 and 16 are rejected under 35 U.S.C. 103 as obvious over Harris in view of Wiggins in view of Shah et al. as applied to claims 8 and 15 above, and further in view of US Patent Publication 20240176726 A1 (Lemberg et al.).
Claim 12
Regarding claim 12, the rejection of claim 8 is incorporated.
Harris in view of Wiggins in view of Shah et al. does not explicitly disclose all of throwing an error for an operating system encountering a critical error and performing an automatic shutdown.
However, Lemberg et al. disclose wherein an error includes a warning that the operating system encountered a critical error and performed an automatic shutdown (Lemberg et al. ¶ [0088], "Some embodiments of error detector 262 comprise an operating system service, such as an operating system error handling service, which monitors aspects of the operating environment 260 computer system and checks for errors. ... For example, the monitor or observation service may detect ... computer system restarts, system events, or other data from operating environment 260 computer system indicating possible error.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Harris in view of Wiggins in view of Shah et al. to incorporate Lemberg et al.’s computer system restart monitoring.
The suggestion/motivation for doing so would have been that, “embodiments of these technologies reduce the need for manual analysis and troubleshooting by technicians having extensive understanding of the operational state of the computer system on which the computer application is operating, as well as an understanding of the computer application and its design,” as noted by the Lemberg et al. disclosure in paragraph [0013].
Claim 16
Regarding claim 16, the rejection of claim 15 is incorporated.
Harris in view of Wiggins in view of Shah et al. does not explicitly disclose all of failure operational telemetries including platform level analytics, operating system analytics, or event view error logs.
However, Lemberg et al. disclose wherein failure operational telemetries monitored for the information handling system include platform level analytics (Lemberg et al. ¶ [0088], "telemetry data comprises one or more metrics, events, activity logs, traces, or other similar data regarding the state of the computer system or network, such as the use or performance of applications or application components, and may include information regarding computer applications operating thereon. In some instances, the telemetry data comprises timestamped or time-associated data regarding particular events, metrics, activity logs, and other telemetry data features, and may further comprise a sequence, burst, trajectory, or pattern of events, a metric or activity data with respect to time."), operating system (OS) level analytics (Lemberg et al. ¶ [0088], "operating system error handling service, which monitors aspects of the operating environment 260 computer system and checks for errors. "), and event viewer error logs (Lemberg et al. ¶ [0067], "a log of system events that can include application and system messages, warnings, errors, or other similar events, is generated by an operating system service of the testing environment 230 computer system.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Harris in view of Wiggins in view of Shah et al. to incorporate Lemberg et al.’s failure operational telemetries because such a modification is the result of combining prior art elements according to known methods to yield predictable results. More specifically, Harris’ error detection system as modified by Lemberg et al.’s failure operational telemetries can yield a predictable result of improving error detection capabilities since Lemberg et al.’s failure operational telemetries would provide the system with a greater amount of information, making error diagnosis more comprehensive. Thus, a person of ordinary skill would have appreciated including in Harris’ error detection system the ability to do Lemberg et al.’s failure operational telemetries since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 19 is rejected under 35 U.S.C. 103 as obvious over Harris in view of Wiggins in view of Shah et al. as applied to claim 15 above, and further in view of US Patent Publication 20190173396 A1 (Arao et al.).
Claim 19
Regarding claim 19, the rejection of claim 15 is incorporated.
Harris in view of Wiggins in view of Shah et al. does not explicitly disclose all of monitoring the number of restarts within a preset period of time.
However, Arao et al. disclose wherein the error includes multiple forced restarts of the operating system within a preset time period (Arao et al. ¶ [0046], "The sign diagnostic unit 115 acquires the restart data stored in the restart recording unit 114 (S301). The sign diagnostic unit 115 counts the number of errors occurred within a first predetermined period for each restart type (S302). The first predetermined period may be a long period such as a month or a half year or may be a unit of a day or a week. … When the respective numbers of error restarts exceeds a predetermined number (S303), the sign diagnostic unit 115 outputs a warning based on the exceeded error.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Harris in view of Wiggins in view of Shah et al. to incorporate Arao et al.’s error restart monitoring because such a modification is based on the use of known techniques to improve similar devices in the same way. More specifically, Harris’ error detection method is comparable to Arao et al.’s error restart monitoring. Therefore, it is within the capabilities of one of ordinary skill in the art to modify Harris’ error detection method to include Arao et al.’s error restart monitoring with the predictable result of throwing an error if the monitored number of forced restarts exceeds a threshold amount within a preset time period.
Claim 20 is rejected under 35 U.S.C. 103 as obvious over Harris in view of Wiggins in view of Shah et al. as applied to claim 15 above, and further in view of US Patent Publication 20140184124 A1 (Chiang et al.) in view of US Patent Publication 20250068507 A1 (Mehta et al.).
Claim 20
Regarding claim 20, the rejection of claim 15 is incorporated.
Harris in view of Wiggins in view of Shah et al. does not explicitly disclose all of monitoring fan power draw.
However, Chiang et al. disclose wherein the error includes a fan drawing power above a preset fan power draw maximum (Chiang et al. ¶ [0019]-[0020], " In block S11, the determining module 102 determines whether the current supplied to the fan 16 is more than or equal to a predefined current. In the embodiment, the predefined current is a maximum rated current of the fan 16. ... If the current supplied to the fan 16 is more than or equal to the predefined current... the controlling module 104 controls the BMC 20 to send a PWM signal to the fan 16 to decrease the duty cycle of the fan 16 by a predefined proportion, so as to decrease the current supplied to the fan 16.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Harris in view of Wiggins in view of Shah et al. to incorporate Chiang et al.’s fan power draw monitoring because such a modification is the result of combining prior art elements according to known methods to yield predictable results. More specifically, Harris’ error detection system as modified by Chiang et al.’s fan power draw monitoring can yield a predictable result of improving error detection since Chiang et al.’s fan power draw monitoring would provide a more telemetric information for the error detection system to diagnose from. Thus, a person of ordinary skill would have appreciated including in Harris’ error detection system the ability to do Chiang et al.’s fan power draw monitoring since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Harris in view of Wiggins in view of Shah et al. in view of Chiang et al. does not explicitly disclose all of terminating background applications.
However, Mehta et al. disclose wherein … the best match responsive capability includes terminating execution of background applications (Mehta et al. ¶ [0053], "if the anomaly detection model detects that a certain user space process executing at the processor of a network switch is the root cause of high processor usage of the network switch, NMS 130 may be configured to auto-terminate the user space process or to restart the user space process to resolve the high processor usage.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Harris in view of Wiggins in view of Shah et al. in view of Chiang et al. to incorporate Mehta et al.’s termination action because such a modification is the result of combining prior art elements according to known methods to yield predictable results. More specifically, Shah et al.’s IT ticket generation as modified by Mehta et al.’s automatic termination of background processes can yield a predictable result of reducing ticket queue strain in an IT department since the system could use Mehta et al.’s automatic termination of background processes to resolve software malfunctions without having to submit an IT ticket. Thus, a person of ordinary skill would have appreciated including in Shah et al.’s IT ticket generation the ability to do Mehta et al.’s automatic termination of background processes since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Patent Publication 20260017137 A1 to Dasari et al. discloses detecting and resolving application errors as well as automatically generating an IT ticket if the resolution is not satisfactory.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACOB B VOGT whose telephone number is (571)272-7028. The examiner can normally be reached Monday - Friday 9:30am - 7pm EST.
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/JACOB B VOGT/Examiner, Art Unit 2653
/Paras D Shah/ Supervisory Patent Examiner, Art Unit 2653
06/09/2026
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