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 .
DETAILED ACTION
This action is responsive to communications regarding the applicant’s amendments and arguments, filed on 01/09/2026.
Claims 8, 18, 20-21 have been canceled.
Claims 23-24 have been added.
Claims 1-7, 9-17, 19, and 22-24 are pending.
Notice of Pre-AIA or AIA Status
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Response to Arguments and Amendments
Applicant's arguments filed on 01/09/2026 have been fully considered but they are not persuasive for the following reasons:
Applicant’s main argument is that claims 11-7, 9-17, 19 and 22 direct toward “significantly more” than abstract idea (Argument 1, Remark, pages 8-11); Gupta fails to teach various features as claimed in the amended independent claims, and the motivation to combine the references is insufficient (Argument 2, Remark, pages 11-15).
Examiner respectfully disagrees with the above argument.
In response to Applicant’ Argument 1, it is noted that the representative claim 11 claims features “A computing system comprising: a memory storing a computer program of an extraction engine for being part of a process mining and discovery suite and adapting a large language model (LLM) to accurately process unstructured data from communications to execute action and task mining on the unstructured data without identifying intent in the unstructured data; and
at least one processor executing the computer program to cause the extraction engine to perform:
receiving a communication comprising unstructured data defining an action, wherein the communication comprises one or more of an email, a chat, a comment within a ticket, a post, or a micro-blog thread;
automatically processing the communication by:
utilizing at least one generative artificial intelligence (AI) model comprising the LLM to extract details of the action being performed in the unstructured data,
identifying a set of activities as a variation on an activity from the details of the action,
recording the variation of how work is performed, and inferring via historical learning the activity an activity or a task from the details of the action without identifying the intent in the unstructured data, wherein the LLM is trained to identify specific actions being done in the communication and utilizes numerous parameters of data to infer the activity or the task to include historical learning;
automatically converting the action into the activity of a process associated with the communication based on the automatic processing of the communication comprising an automatic conversion of the unstructured data from into structured; and
combining the details of the action with at least one of metadata, a timestamp, case information, user information, data from process mining operations, or data from task mining operations to form a comprehensive picture of the activity or the task for process mining.” The processor in performing the steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of the steps) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Further, using a generative artificial intelligence (AI) is a step of generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Specifically:
A computing system comprising: a memory storing a computer program of an extraction engine for being part of a process mining and discovery suite and adapting a large language model (LLM) to accurately process unstructured data from communications to execute action and task mining on the unstructured data without identifying intent in the unstructured data; (generic computer components) and
at least one processor executing the computer program to cause the extraction engine to perform (generic computer components):
receiving a communication comprising unstructured data defining an action, wherein the communication comprises one or more of an email, a chat, a comment within a ticket, a post, or a micro-blog thread; (The limitation is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion). Human can receive a communication.).
automatically processing the communication by (Human can process a communication):
utilizing at least one generative artificial intelligence (AI) model comprising the LLM to extract details of the action being performed in the unstructured data, (The limitation is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion). Human can perform simple LLM to extract details of the action being performed in the unstructured data).
identifying a set of activities as a variation on an activity from the details of the action, (The limitation is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion). Human can perform the step of identifying).
recording the variation of how work is performed, and inferring via historical learning the activity an activity or a task from the details of the action without identifying the intent in the unstructured data, wherein the LLM is trained to identify specific actions being done in the communication and utilizes numerous parameters of data to infer the activity or the task to include historical learning; (The limitation is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion). Human can mentally or physically perform recording).
automatically converting the action into the activity of a process associated with the communication based on the automatic processing of the communication comprising an automatic conversion of the unstructured data from into structured; (The limitation is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion). Human can perform the step of converting); and
combining the details of the action with at least one of metadata, a timestamp, case information, user information, data from process mining operations, or data from task mining operations to form a comprehensive picture of the activity or the task for process mining (The limitation is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion). Human can combine data from multiple sources of information).
It is noted that other than using a processor, memory to automatically perform the tasks, all the processes or steps can be performed mentally or manually with pen/pencil and paper.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor and generative artificial intelligence (AI) model to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component and generic computation. Mere instructions to apply an exception using a generic computer component and generic computation cannot provide an inventive concept. The claims are not patent eligible.
In response to Applicant’ Argument 2, it is noted that the amended claim features are addressed in the rejection sections. Further, in response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, 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 teaching of Gupta with the teaching of Devaux because they are in the same field of endeavor. One of ordinary skill in the art at the time of the invention would have been motivated to do so because the teaching of Devaux would allow Gupta to “ improve the technological efficiency and computational and communication resource utilization across system 100 (and its variants, such as system 100a) by making more efficient use of network and processing resources in system 100, as well as more efficient use of travel actor engines 112. At least one technical problem addressed by the present teachings includes the dynamic of repetitive network searching that consumes processing resources and bandwidth. Such repetitive searching arises in many contexts including travel searches. Enabling real-time access to the internet is generally incompatible with the static nature of large language model datasets, and different architecture and continuous updating, which would be computationally expensive and challenging to manage. At the same time, existing chat functionality does not address the problem of collecting rich and structured travel queries that can be used to provide meaningful searches” (Devaux, par. 0003, 0279).
For the above reasons, Examiner believed that rejection of the last Office action was proper and within their broadest reasonable interpretation in light of the specification. See MPEP 2111 [R-1] Interpretation of Claims-Broadest Reasonable Interpretation.
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 1-7, 9-17, 19 and 22-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
(Step 1) The claim(s) 1-20 recite(s) recite(s) a method, system and are directed toward statutory subject matter.
(Step 2A1-does the claim recite an abstract idea, law of nature, or natural phenomenon?)
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
The limitation of representative claim the representative claim 11 claims features “A computing system comprising: a memory storing a computer program of an extraction engine for being part of a process mining and discovery suite and adapting a large language model (LLM) to accurately process unstructured data from communications to execute action and task mining on the unstructured data without identifying intent in the unstructured data; and
at least one processor executing the computer program to cause the extraction engine to perform:
receiving a communication comprising unstructured data defining an action, wherein the communication comprises one or more of an email, a chat, a comment within a ticket, a post, or a micro-blog thread;
automatically processing the communication by:
utilizing at least one generative artificial intelligence (AI) model comprising the LLM to extract details of the action being performed in the unstructured data,
identifying a set of activities as a variation on an activity from the details of the action,
recording the variation of how work is performed, and inferring via historical learning the activity an activity or a task from the details of the action without identifying the intent in the unstructured data, wherein the LLM is trained to identify specific actions being done in the communication and utilizes numerous parameters of data to infer the activity or the task to include historical learning;
automatically converting the action into the activity of a process associated with the communication based on the automatic processing of the communication comprising an automatic conversion of the unstructured data from into structured; and
combining the details of the action with at least one of metadata, a timestamp, case information, user information, data from process mining operations, or data from task mining operations to form a comprehensive picture of the activity or the task for process mining.” The processor in performing the steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of the steps) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Further, using a generative artificial intelligence (AI) is a step of generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Specifically:
A computing system comprising: a memory storing a computer program of an extraction engine for being part of a process mining and discovery suite and adapting a large language model (LLM) to accurately process unstructured data from communications to execute action and task mining on the unstructured data without identifying intent in the unstructured data; (generic computer components) and
at least one processor executing the computer program to cause the extraction engine to perform (generic computer components):
receiving a communication comprising unstructured data defining an action, wherein the communication comprises one or more of an email, a chat, a comment within a ticket, a post, or a micro-blog thread; (The limitation is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion). Human can receive a communication.).
automatically processing the communication by (Human can process a communication):
utilizing at least one generative artificial intelligence (AI) model comprising the LLM to extract details of the action being performed in the unstructured data, (The limitation is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion). Human can perform simple LLM to extract details of the action being performed in the unstructured data).
identifying a set of activities as a variation on an activity from the details of the action, (The limitation is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion). Human can perform the step of identifying).
recording the variation of how work is performed, and inferring via historical learning the activity an activity or a task from the details of the action without identifying the intent in the unstructured data, wherein the LLM is trained to identify specific actions being done in the communication and utilizes numerous parameters of data to infer the activity or the task to include historical learning; (The limitation is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion). Human can mentally or physically perform recording).
automatically converting the action into the activity of a process associated with the communication based on the automatic processing of the communication comprising an automatic conversion of the unstructured data from into structured; (The limitation is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion). Human can perform the step of converting); and
combining the details of the action with at least one of metadata, a timestamp, case information, user information, data from process mining operations, or data from task mining operations to form a comprehensive picture of the activity or the task for process mining (The limitation is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion). Human can combine data from multiple sources of information).
It is noted that other than using a processor, memory to automatically perform the tasks, all the processes or steps can be performed mentally or manually with pen/pencil and paper.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor and generative artificial intelligence (AI) model to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component and generic computation. Mere instructions to apply an exception using a generic computer component and generic computation cannot provide an inventive concept. The claims are not patent eligible.
The claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:
a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);
claims to “comparing BRCA sequences and determining the existence of alterations,” where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 763, 113 USPQ2d 1241, 1246 (Fed. Cir. 2014);
a claim to collecting and comparing known information, which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011); and
Further, if a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. In this case, except for using generic elements such as processor, all other element can be performed by human mind as a mental process and/or performed manually using pencil and paper (The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another. For instance, in CyberSource, the court determined that the step of "constructing a map of credit card numbers" was a limitation that was able to be performed "by writing down a list of credit card transactions made from a particular IP address." In making this determination, the court looked to the specification, which explained that the claimed map was nothing more than a listing of several (e.g., four) credit card transactions. The court concluded that this step was able to be performed mentally with a pen and paper, and therefore, it qualified as a mental process. 654 F.3d at 1372-73, 99 USPQ2d at 1695. See also Flook, 437 U.S. at 586, 198 USPQ at 196 (claimed "computations can be made by pencil and paper calculations"); University of Florida Research Foundation, Inc. v. General Electric Co., 916 F.3d 1363, 1367, 129 USPQ2d 1409, 1411-12 (Fed. Cir. 2019) (relying on specification’s description of the claimed analysis and manipulation of data as being performed mentally "‘using pen and paper methodologies, such as flowsheets and patient charts’"); Symantec, 838 F.3d at 1318, 120 USPQ2d at 1360 (although claimed as computer-implemented, steps of screening messages can be "performed by a human, mentally or with pen and paper").) (MPEP 2106.04(a)(2).)
Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind and/or manually performed, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
(Step 2A2-Practical Application?)This judicial exception is not integrated into a practical application.
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
In particular, the claim only recites one additional element – using a processor and generative artificial intelligence (AI) to perform “converting the action into an activity or a task of a process associated with the communication”. The processor in performing the steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of the steps) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Further, using a generative artificial intelligence (AI) is a step of generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
(Step 2B- does the claim recite additional elements that amount to significantly more than the judicial exception?)
Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor and generative artificial intelligence (AI) model to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component and generic computation. Mere instructions to apply an exception using a generic computer component and generic computation cannot provide an inventive concept. The claims are not patent eligible.
“As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978). In Flook, the Court reasoned that “[t]he notion that post-solution activity, no matter how conventional or obvious in itself, can transform an unpatentable principle into a patentable process exalts form over substance. A competent draftsman could attach some form of post-solution activity to almost any mathematical formula”. 437 U.S. at 590; 198 USPQ at 197; Id. (holding that step of adjusting an alarm limit variable to a figure computed according to a mathematical formula was “post-solution activity”). “
As to claims 12-17, 19 and 22, the claim further recites additional variations and characteristics of unstructured data and generative AI models . The additional limitation further detailing with non-functional descriptive features of data and the variances of AI models and add insignificant extra-solution activity. Refining the abstract idea and/or add insignificant extra-solution activity does not make an abstract idea beyond the abstract idea itself. The claim recites mental process and/or manual process including steps that are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). Thus, the claim does not mount to significantly more than the abstract idea.
As to claims 1-7, 9-10, and 23-24, all limitations of these claims have been addressed in the analysis of claims 12-17, 19 and 22 above, and these claims are rejected on that basis.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-7, 9-17, 19 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 20250077581 to Gupta et al. (hereinafter “Gupta”), and further in view of U.S. Patent Application Publication No. 20240370432 to Devaux et al. (hereinafter “Devaux”).
As to claim 1, Gupta teaches a method executed by an extraction engine implemented as a computer program within a computing environment, the extraction engine being part of a process mining and discovery suite and adapting a large language model (LLM) to accurately process unstructured data from communications to execute action and task mining on the unstructured data without identifying intent in the unstructured data, the method comprising (computer implemented method in a system comprising processor and non-transitory computer readable storage medium using one or more large language models (LLMs), par. 0005-0007, 0035, Fig. 4A-5):
receiving a communication comprising unstructured data defining an action (Fig. 4A, par. 0005-0007, 0061, unstructured data defining an action such as natural language query of User’s First Question), wherein the communication comprises one or more of an email, a chat, a comment within a ticket, a post, or a micro-blog thread (par. 0026, i.e. “The present disclosure enables recording of the entire conversation history, including queries, text, relevant links, responses, and user click-through activity, in a search database for powerful search analytics and user journey mapping. This data can be utilized to gain insights into user behavior, improve search performance, and understand user interactions across the enterprise application ecosystem. The dynamic recommending the set of potential courses of actions (NBAs) application discovery is not limited to search interactions alone. It can also be extended to other user interactions, such as chat conversations, where application links can be offered to assist users or provide up-selling and cross-selling opportunities based on user intent.”);
automatically processing the communication by: utilizing at least one generative artificial intelligence (AI) model comprising the LLM to extract details of the action being performed in the unstructured data (par. 0005-0007, 0035, i.e. “the system generates one or more responses and one or more clickable elements corresponding to the one or more search queries, using one or more large language models (LLMs), based on the retrieved the one or more recommending the set of potential courses of actions for the user, the one or more applications and the deep integration parameters. The LLMs are associated with at least one of a generative artificial intelligence (AI) environment, and a conversation AI environment”);
identifying a set of activities as a variation on an activity from the details of the action (par. 0025-0026, 0073, set of activities as a variation such as existing automations to the activity or task or NBAs, i.e. “…relevant information from the search and conversation history is automatically passed along to the recommending the set of potential courses of actions (NBAs) applications. This eliminates the need for users to re-enter the same information within the application, saving time and effort while providing a seamless user experience…”),
recording the variation of how work is performed (par. 0065-0070, i.e. “[0065] Further, the database 104 may include a completion of initiated user action via recommended set of potential courses of actions click (i.e., action sequence completion data 308). There may be a user action feedback loop from the recommended set of potential courses of actions application to the database 104, to indicate if the user-initiated action via the recommended set of potential courses of actions link was completed or abandoned. This may be used by the recommended set of potential courses of actions generation algorithm in future to rank/select the NBA for similar user queries. Furthermore, the database 104 may include feedback data 304 provided by the users for the recommended set of potential courses of action(s). The clickable elements such as thumbs up/down will allow the search user to indicate relevance of the presented recommended set of potential courses of actions. This may be used by the recommended set of potential courses of actions generation algorithm in future to rank/select the recommended set of potential courses of actions.”), and
inferring via historical leaning the activity an activity or a task from the details of the action without identifying the intent in the unstructured data (par. 0025-0026, 0036-0037, 0065-0070, i.e. “For initiating the process, a search client 352 may provide the user message and conversation history to LLMs 356. The LLMs 356 then analyzes the data, deriving contextual variables, rephrased search queries, and relevant source URLs. Subsequently, a NBA discovery module 358 utilize this information to predict the most relevant Next Best Actions (NBAs) from a list stored in a NBA repository 360.”);
wherein the LLM is trained to identify specific actions being done in the communication and utilizes numerous parameters of data to infer the activity or the task to include historical learning (par. 0069-0071, training and learning, i.e. “The dynamic active learning methodology enables the system 102 to learn and adapt to seasonal patterns and user behavior specific to a particular brand and use-case. By constantly monitoring user interactions and incorporating feedback, the system 102 delivers a personalized experience tailored to individual users and brand preferences. This personalized approach significantly enhances the user journey by providing targeted and relevant recommendations that address specific user needs and expectations. [0071] Further, the discovery of recommending set of potential courses of actions (e.g., next best actions (NBAs)) using AI models 300C, such as LLMs, represents a significant advancement in conversational AI. By integrating AI models and utilizing active learning methodologies, organizations can leverage AI studios to empower their AI systems with enhanced predictive capabilities.”);
automatically converting the action into the activity of a process associated with the communication based on the automatic processing of the communication [0060] In an exemplary embodiment, for determining the one or more type of one or more recommending the set of potential courses of actions (NBAs), the type determining module 218 may determine at least one of an auto-executed type one or more recommending the set of potential courses of actions and a user triggered type of one or more recommending the set of potential courses of actions”); and
combining the details of the action with at least one of metadata, a timestamp, case information, user information, data from process mining operations, or data from task mining operations to form a comprehensive picture of the activity or the task for process mining (Fig. 5, par. 0044, 0087-0096, combining user query at step 502 with metadata and/or user information such as user preferences for the user profile to determining and generating one or more actions/responses ).
It is noted that Gupta does not explicitly teach an automatic conversion of the unstructured data from into structured as claimed.
Devaux teaches an automatic conversion of the unstructured data from into structured (Fig 4., par. 0120-0131, 0140-0142, “[0125] At block 412, a determination is made as to whether the message 504-1 includes a travel query. Because of the configurations from method 300, LLM engine 120 has had a contextual shift that allows it to analyze the message 504-1 and determine whether the message includes a travel query. Based on the example message 504-1, “Hey, I need to book a flight to Paris.”, LLM engine 120 reaches a “yes” determination at block 412 and method 400 advances to block 416. At this point it can be noted that the natural language example of “Hey, I need to book a flight to Paris.” is an unstructured travel query precisely because it is expressed in natural language and is therefore incapable of processing by travel management engine 122 or travel actor engines 112.[0126] Block 416 comprises iterating a natural language conversation via the LLM engine 120 towards generation of a structured travel query building on the input message 504-1 from block 404. Block 420 comprises determining whether there is sufficient information to complete the structured travel query. [0127] Because of the configuration from method 300, (specifically, per Table 228-7) LLM engine 120 can analyze the message 504-1 from block 404 and, via an iterative conversation between LLM engine 120 and user 124-1 (per block 416 and block 420), LLM engine 120 can direct questions to user 124-1 and receive further input from user 124-1 until a fully structured travel query can be generated.”) based on recording the variation of how work is performed (par. 0275-0277, feedback learning and improvement, i.e. “[0275] In another variant, machine learning feedback can be used to further improve the context shifts and/or train the LLM Engine 120 in providing its dialogue with the user 124. The conversations between been users 124 and LLM Engine 120 can be archived and fed into a machine learning studio platform. The studio allows to train a machine learning algorithm. The machine learning algorithm, can for example, generate a new version of the orchestrator prompt engineering from Table 228-1, or any of the other Tables 228. Then the updated model can be deployed into method 300 via an application via a workflow from the machine learning studio platform to LLM Engine 120.” )
Therefore, 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 teaching of Gupta with the teaching of Devaux because they are in the same field of endeavor. One of ordinary skill in the art at the time of the invention would have been motivated to do so because the teaching of Devaux would allow Gupta to “ improve the technological efficiency and computational and communication resource utilization across system 100 (and its variants, such as system 100a) by making more efficient use of network and processing resources in system 100, as well as more efficient use of travel actor engines 112. At least one technical problem addressed by the present teachings includes the dynamic of repetitive network searching that consumes processing resources and bandwidth. Such repetitive searching arises in many contexts including travel searches. Enabling real-time access to the internet is generally incompatible with the static nature of large language model datasets, and different architecture and continuous updating, which would be computationally expensive and challenging to manage. At the same time, existing chat functionality does not address the problem of collecting rich and structured travel queries that can be used to provide meaningful searches” (Devaux, par. 0003, 0279).
As to claim 2, combination of Gupta and Devaux teaches the method of claim 1, wherein the unstructured data is configured in natural language and comprises a request of a user (Fig. 4A, par. 0005-0007, 0061, unstructured data defining an action/request of a user such as natural language query of User’s First Question).
As to claim 3, combination of Gupta and Devaux teaches the method of claim 1, wherein the details of the action are associated with at least one of application windows, graphical elements, mouse click locations, keyboard inputs, or timestamps for interactions from task mining operations (par. 0044, details of the action such as clickable elements as mouse click locations, i.e. “The one or more clickable elements may include, but are not limited to, links, descriptions, source of information, directions, maps, website address, universal resource locator (URL), share, like button, dislike button, copy button, alternative links/buttons, and the like.”).
As to claim 4, combination of Gupta and Devaux teaches the method of claim 1, wherein the generative AI model comprises one or more of a generative pre-trained transform (GPT), an AI agent, and a large language models (LLM) (par. 0005-0007, large language models).
As to claim 5, combination of Gupta and Devaux teaches the method of claim 1, wherein the activity is represented in machine language, computer code, or business process notation model (par. 0030, machine language or computer code, i.e. “The system 102 may be a hardware device including the hardware processor 110 executing machine-readable program instructions for dynamically recommending the set of potential courses of actions for the user within a search query. Execution of the machine-readable program instructions by the hardware processor 110 may enable the proposed system 102 to dynamically recommend the set of potential courses of actions for the user, within a search query. The “hardware” may comprise a combination of discrete components, an integrated circuit, an application-specific integrated circuit, a field-programmable gate array, a digital signal processor, or other suitable hardware. The “software” may comprise one or more objects, agents, threads, lines of code, subroutines, separate software applications, two or more lines of code, or other suitable software structures operating in one or more software applications or on one or more processors.”).
As to claim 6, combination of Gupta and Devaux teaches the method of claim 1, wherein the extraction engine processes the activity to match one or more of a plurality of existing automations to the activity (par. 0025-0026, 0073, match existing automations to the activity or task, i.e. “…relevant information from the search and conversation history is automatically passed along to the recommending the set of potential courses of actions (NBAs) applications. This eliminates the need for users to re-enter the same information within the application, saving time and effort while providing a seamless user experience…”).
As to claim 7, combination of Gupta and Devaux teaches the method of claim 1, wherein the extraction engine generates one or more new automations or robotic process automations (RPAs) to match the activity (par. 0028, 0041-0049, i.e. “…generated recommending the set of potential courses of actions, one or more clickable elements, completion status of initiated user action through recommended the set of potential courses of actions (next best actions (NBAs)), feedback loops, feedback from users, query parameters, additional query parameters, deep integration parameters, up-sell/x-sell product links, tracked user click-through rates, any other data, and combinations thereof…”).
As to claim 9, combination of Gupta and Devaux teaches the method of claim 1, wherein the large language models (LLM) utilizes the numerous parameters of data (par. 0005-0007, 0035-0040, i.e. “the system 102 may analyze the one or more search queries. The analysis of the one or more search queries may entail integrating contextual details, rectifying misspelled words, and handling grammatical errors. These tasks are achieved through prompt-based Large Language Model (LLM) inference. For example, consider a search query such as a “Sullivan park ticket price”, which may be analyzed by the system 102 to rephrase as “price of Sullivan park ticket.” Similarly, the next search query may include “opening time?” may be analyzed by the system 102 to rephrase as “what is the opening time of Sullivan park”… the system 102 may identify one or more query parameters and additional query parameters related to the one or more query parameters, based on the determined context variables”).
As to claim 10, combination of Gupta and Devaux teaches the method of claim 1, wherein the large language models (LLM) increases a quality of an extraction engine to process the communication to understand context and ability (par. 0070, increases a quality, i.e. “The recommended set of potential courses of actions algorithms leverage rich contextual information to make accurate predictions, ensuring that the recommended set of potential courses of actions closely aligns with the user's needs and preferences. Over time, the AI model 300C such as the system 102 may employ an active learning methodology to continuously enhance its recommendations by tracking user click-through rates on the recommended set of potential courses of actions. This creates a feedback loop that influences the AI model's predictions, reinforcing their accuracy and guiding them to make similar potential courses of actions recommendations in similar use-cases. This iterative process allows the system 102 to adapt and learn from user feedback, continuously improving its predictive capabilities and enhancing the user experience.”).
Regarding claims 11-17, 19, are essentially the same as claims 1-7, 9, except that it sets forth the claimed invention as computing system rather than method and rejected for the same reasons as applied hereinabove.
As to claim 22, the rejection of claim 1 is hereby incorporated by reference, the combination of Gupta and Devaux teaches the method of claim 1, wherein the extraction engine utilizes communication mining and task mining to turn the unstructured data that can be associated with other structured data in process mining into structured data before being ingested by process mining (Devaux, Fig 4., par. 0120-0131, 0140-0142, 0275, “[0125] At block 412, a determination is made as to whether the message 504-1 includes a travel query. Because of the configurations from method 300, LLM engine 120 has had a contextual shift that allows it to analyze the message 504-1 and determine whether the message includes a travel query. Based on the example message 504-1, “Hey, I need to book a flight to Paris.”, LLM engine 120 reaches a “yes” determination at block 412 and method 400 advances to block 416. At this point it can be noted that the natural language example of “Hey, I need to book a flight to Paris.” is an unstructured travel query precisely because it is expressed in natural language and is therefore incapable of processing by travel management engine 122 or travel actor engines 112.[0126] Block 416 comprises iterating a natural language conversation via the LLM engine 120 towards generation of a structured travel query building on the input message 504-1 from block 404. Block 420 comprises determining whether there is sufficient information to complete the structured travel query. [0127] Because of the configuration from method 300, (specifically, per Table 228-7) LLM engine 120 can analyze the message 504-1 from block 404 and, via an iterative conversation between LLM engine 120 and user 124-1 (per block 416 and block 420), LLM engine 120 can direct questions to user 124-1 and receive further input from user 124-1 until a fully structured travel query can be generated.”)
Claim(s) 23-24 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 20250077581 to Gupta et al. (hereinafter “Gupta”), U.S. Patent Application Publication No. 20240370432 to Devaux et al. (hereinafter “Devaux”), and further in view of U.S. Patent Application Publication No. 20240338710 to Mico et al. (hereinafter “Mico”).
As to claim 23, the rejection of claim 1 is hereby incorporated by reference, the combination of Gupta and Devaux teaches the method of claim 1. The combination of Gupta and Devaux does not explicitly teach wherein the extraction engine operates with at least one of a ticketing system or a helpdesk system to observe email exchanges, comments on tickets, and chat message exchanges between customers and users of the ticketing system or helpdesk system as claimed.
Mico teaches wherein the extraction engine operates with at least one of a ticketing system or a helpdesk system to observe email exchanges, comments on tickets, and chat message exchanges between customers and users of the ticketing system or helpdesk system (Fig. 2, 3-5, par. 0017-0019, 0028- 0036, 0039, 0042, helpdesk system with chat message exchanges and extracting customer information, including order information).
Therefore, 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 teaching of combination of Gupta and Devaux with the teaching of Mico because they are in the same field of endeavor. One of ordinary skill in the art at the time of the invention would have been motivated to do so because the teaching of Mico would allow combination of Gupta and Devaux to “…dynamically generate a relevant smart indicator for the customer during an interactional event in realtime for an improved user experience…” (Mico, par. 0017-0019, 0034.)
As to claim 24, , the rejection of claim 1 is hereby incorporated by reference, the combination of Gupta and Devaux teaches the method of claim 1. The combination of Gupta and Devaux does not explicitly teach wherein the extraction engine extracts a business identifier from the communication and associates subsequent actions with a business process, wherein the business identifier comprises at least one of a purchase order number or an invoice identifier as claimed.
Mico teaches wherein the extraction engine extracts a business identifier from the communication and associates subsequent actions with a business process, wherein the business identifier comprises at least one of a purchase order number or an invoice identifier (Fig. 2, 3-5, par. 0017-0019, 0028- 0036, 0039, 0042, helpdesk system with chat message exchanges and extracting customer information, including order information with associated actions, i.e. “[0031] In some embodiments, the artificial intelligence based configurator of the disclosed system may allow the system to create and/or modify a workflow. For example, there may be a track order flow in the disclosed system. The intelligent indicator generation algorithm of the conversational software application may allow the user to take action in internal and/or external business systems such as an order management system.”).
Therefore, 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 teaching of combination of Gupta and Devaux with the teaching of Mico because they are in the same field of endeavor. One of ordinary skill in the art at the time of the invention would have been motivated to do so because the teaching of Mico would allow combination of Gupta and Devaux to “…dynamically generate a relevant smart indicator for the customer during an interactional event in realtime for an improved user experience…” (Mico, par. 0017-0019, 0034.)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ANHTAI V TRAN/Primary Examiner, Art Unit 2168
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