DETAILED ACTION
This office action is in response to Applicant’s submission filed on 3/16/2026. Claims 1, 7, and 13 were amended. Claims 5, 6, 11, 12, 17, and 18 were canceled. Claims 1-4, 7-10 and 13-16 are pending in the application of which Claims 1, 7, and 13 are independent 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 .
Response to Arguments
Applicant’s arguments filed in the Amendment filed 3/16/2026 (herein “Amendment”) with respect to the 35 USC §101 rejection raised in the previous office action have been fully considered but they are persuasive. As such 101 rejection is withdrawn.
Applicant’s arguments filed in the Amendment with respect to USC §103 rejection raised in the previous office action have been fully considered but they are not persuasive.
Applicant set forth on page 12: the cited portions of Ping do not teach or suggest "the language model is trained using training data of one or more automation platforms, one or more process mining platforms, and one or more task mining platforms"
Examiner disagrees and it is a common knowledge that any model training requires training data, furthermore, Ping teaches the usage of training data to train the model as recited below:
Ping, Page 3:”acquiring training data, wherein the training data comprises sensitive data samples and sensitive data categories corresponding to the sensitive data samples; based on training data, training a model to be trained to obtain a sensitive data classification model, wherein the model to be trained is a neural network model based on machine learning or a neural network model based on deep learning.”, and Page 8:”Step S202, training a model to be trained through the training data to obtain a sensitive data classification model; the model to be trained is a neural network model based on machine learning or a neural network model based on deep learning.”)
Applicant furthers:” claim 1 requires that "the language model is trained using training data of one or more automation platforms, one or more process mining platforms, and one or more task mining platforms."
Examiner, restates that the “training teaching” provided by Ping as noted earlier provide sufficient mapping for a PHOSITA. Furthermore, classification models can be mapped to and implemented by language models (LMs), particularly Large Language Models (LLMs), by transforming classification tasks into text-generation or sequence-modeling problems.
Applicant still furthers:” As noted above, the cited portions of Ping do not teach or suggest "wherein the language model is trained using training data of one or more automation platforms, one or more process mining platforms, and one or more task mining platforms" let alone "automatically receiving at least one of system event logs or recorded user interaction data generated by at least one of the automation platform, the process mining platform, or the task mining platform, the system event logs recording execution of one or more processes; processing the at least one of the system event logs or the recorded user interaction data to generate one or more process graphs representing execution flows of the one or more processes; and training the language model based on the one or more process graphs."
Examiner refute by: as per as-filed specification (Par. 0125) an automation platform is a platform or system for implementing automation. As such under BRI, any software system which execute repetitive task can be considered as an automation platform. As an example, when Ping teaches: Page 6:” … performing data desensitization on the sensitive data … perform specific analysis tasks when the device of the external institution issues a data acquisition request to the desensitization server.” can be mapped to an automation platform. Or when Ping teaches page 1:” … process mining and task mining often need to acquire the service log or the operation log of the user to perform analysis so as to generate a flowchart.”, and Page 2:” The foregoing Process Mining is a Process model, such as a form of a flowchart, from which a Process log in a user service system is mined and can reflect the execution Process of the enterprise real service Process. The user can diagnose or optimize according to the original business process of the process model.” Therefore, automation platform is disclosed sufficiently well. However, examiner would like to remind the applicant that the rejection is done per obviousness rationale and as such one cannot state arguments against the references individually, in other words, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Applicant continues on page 13 and 14:” Ping do not teach or suggest at least "receiving one or more requests for insights on data of at least one of an automation platform, a process mining platform, or a task mining platform" or "in response to receiving the one or more requests, generating a response comprising the insights using a language model" as recited in claim 1”
Examiner disagrees and repeat that the rejection is based on combination s of references. Furthermore, as filed spec. par. 0123 states:” The insights represent an analysis or understanding of the data. “ As such when Ping disclose on page 10:” request to acquire sensitive information, a stricter desensitization method can be adopted on the premise of not losing the availability of the information. For the time stamp when the data acquisition request is initiated, different desensitization methods can be executed for the same staff in the same department to acquire the same sensitive information at different times under the same application scene.” all point toward analysis to understand the data.
Applicant still furthers on page 14:” Singh relates to generative artificial intelligence/machine learning models to determine sequences of user interactions with computing systems, extract common processes, and generate robotic process automation (RPA) robots.”
Examiner would like to point out that each reference is chosen to teach a specific aspect of the instant claim language and not the entire claim language.
Examiner would like to make the following statement for the records:
One cannot state arguments against the references individually, in other words, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
The test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
MPEP 2141.01(a) Analogous and Non-analogous Art [R-01.2024] where it recites:” In order for a reference to be proper for use in an obviousness rejection under 35 U.S.C. 103, the reference must be analogous art to the claimed invention. In re Bigio, 381 F.3d 1320, 1325, 72 USPQ2d 1209, 1212 (Fed. Cir. 2004). A reference is analogous art to the claimed invention if: (1) the reference is from the same field of endeavor as the claimed invention (even if it addresses a different problem); or (2) the reference is reasonably pertinent to the problem faced by the inventor (even if it is not in the same field of endeavor as the claimed invention). Note that "same field of endeavor" and "reasonably pertinent" are two separate tests for establishing analogous art; it is not necessary for a reference to fulfill both tests in order to qualify as analogous art. See Bigio, 381 F.3d at 1325, 72 USPQ2d at 1212. .... When more than one prior art reference is used as the basis of an obviousness rejection, it is not required that the references be analogous art to each other. See Sanofi-Aventis Deutschland GMbH v. Mylan Pharms. Inc., 66 F.4th 1373, 1380, 2023 USPQ2d 552 (Fed. Cir. 2023) and Corephotonics, Ltd. v. Apple Inc., 84 F.4th 990, 1007, 2023 USPQ2d 1202 (Fed. Cir. 2023).
Therefore, Examiner does not find the Applicant’s argument persuasive. Each and every limitation as presented in the previous/present Office action is mapped to teach a certain feature of the instant application. That does not mean that the entire body of the given prior art MUST teach the applicant’s disclosed invention. Also, the mapping/rejection is based on a nonobviousness (35 U.S.C. §103) rejection, which means the invention would have been obvious to a person skilled in the art at the time of invention. Therefore, one cannot attack references individually where the rejections are based on combinations of references.
To overcome the rejections, Applicant is advised to amend the claims/limitations appropriately such that the application of prior arts render moot.
Therefore, while all of the Applicant’s arguments filed in the Amendment have been fully considered, they are not persuasive. Please see below for more detail including updated citations and obviousness rationale.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 7 - 10, and 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over Ping (CN117010017A), and in further view of Singh (US 20230385085A1), Ramesh (US 20250164978 A1), and Madugula (US20240126737A1).
Ping, Singh, Ramesh, and Madugula were applied in the previous Office Action.
Regarding claims 1, 7 and 13 Ping teaches receiving one or more requests for insights on data of at least one of an automation platform, a process mining platform, or a task mining platform; (Ping, Page 10:” … When staff members … request to acquire sensitive information, a stricter desensitization method can be adopted on the premise of not losing the availability of the information. For the time stamp when the data acquisition request is initiated, different desensitization methods can be executed for the same staff in the same department to acquire the same sensitive information at different times under the same application scene.”, and Page 6:” … performing data desensitization on the sensitive data … perform specific analysis tasks when the device of the external institution issues a data acquisition request to the desensitization server. The external organization may be other departments within the company, other than the financial department, or an external technical service company, such as a company that provides process mining or task mining technical services.”)
in response to receiving the one or more requests, generating a response comprising the insights using a language model (Ping, Page 1:” … process mining and task mining often need to acquire the service log or the operation log of the user to perform analysis so as to generate a flowchart.”, and Page 2:” The foregoing Process Mining is a Process model, such as a form of a flowchart, from which a Process log in a user service system is mined and can reflect the execution Process of the enterprise real service Process. The user can diagnose or optimize according to the original business process of the process model.”, and Page 2:” The Task Mining (Task Mining) is to mine operation log data of a pointer to a user desktop, and a flow model capable of reflecting the operation process of the user to a specific Task is mined. For example, a flow chart with an operation window as a view angle may be generated, and a flow chart with an operation event as a view angle may be generated. Optimization of existing task operations may be achieved based on the flow chart.”, and Page 5:” the method can automatically identify the existence of sensitive data rows or sensitive data columns in the desensitization data based on the preset sensitive data classifier, and execute different desensitization processes for different types of sensitive data, so that the requirement of rapidly desensitizing a large number of task logs or operation logs in the process mining and task mining scenes can be met.”)
wherein the language model is trained using training data of one or more automation platforms, one or more process mining platforms, and one or more task mining platforms by: (Ping, Page 3:”acquiring training data, wherein the training data comprises sensitive data samples and sensitive data categories corresponding to the sensitive data samples; based on training data, training a model to be trained to obtain a sensitive data classification model, wherein the model to be trained is a neural network model based on machine learning or a neural network model based on deep learning.”, and Page 8:”Step S202, training a model to be trained through the training data to obtain a sensitive data classification model; the model to be trained is a neural network model based on machine learning or a neural network model based on deep learning.”)
Ping, does not teach, however Singh teaches [A computer-implemented method comprising: - claim 1], [A system comprising: a memory storing computer program instructions; and at least one processor configured to execute the computer program instructions, the computer program instructions configured to cause the at least one processor to perform operations of: - claim 7], and [A non-transitory computer-readable medium storing computer program instructions, the computer program instructions, when executed on at least one processor, cause the at least one processor to perform operations comprising: -claim 13] (Singh, Par. 0006:” … a non-transitory computer-readable medium stores a computer program. The computer program is configured to cause at least one processor to analyze recorded real user interactions …”, and Par. 0007:” … a system includes memory storing computer program instructions and at least one processor configured to execute the computer program instructions. The computer program instructions are configured to cause the at least one processor to analyze recorded real user interactions of a plurality of users …”, and Par. 0118:” … a memory 515 for storing information and instructions to be executed by processor(s) 510. … non-transitory computer-readable media or combinations thereof. Non-transitory computer-readable media may be any available media that can be accessed by processor(s) 510 …”).
outputting the response, (Singh, Par. 0073:” … The analytics software may present results in a dashboard format for better understanding by human users.”)
automatically receiving at least one of system event logs or recorded user interaction data generated by at least one of the automation platform, (Singh, ABS:” … Recorded real user interactions may be analyzed, and matching sequences may be implemented as corresponding activities in an RPA workflow.”, and Par. 0005:” … to recognize related sequences of user interactions that pertain to tasks in the time-ordered sequences of user interactions by comparing n-grams of sequences of user interactions in recorded data from the computing systems over a sliding window to find the related sequences.”)
Singh is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ping further in view of Singh to teach a method, a system, and a computer-readable medium, and output the response, automatically receiving at least one of system event logs or recorded user interaction data generated by at least one of the automation platform. Motivation to do so would allow users and organizations to efficiently and effectively discover, understand, and scale automations (Singh, Par. 0049).
Ping, as modified above, does not teach, however Ramesh teaches the process mining platform, or the task mining platform, the system event logs recording execution of one or more processes; (Ramesh, Par. 0043:” … The computing device repeats this process for each row in the table to generate multiple sentences.”, and Par. 0054:” … The processing device 902 executes program code that configures the computing system 900 to perform one or more of the operations described herein. The program code includes, for example, analytics application 102 or other suitable applications that perform one or more operations described herein.”)
training the language model based on the one or more process graphs. (Ramesh, Par. 0020:” … The analytics application can also define edges of the graph, where each edge represents an action corresponding to a relationship between products represented by the nodes between which the edge is defined. The graph can be used to train the LLM with respect to the product dependencies.”)
Ramesh is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ping, as modified above, further in view of Ramesh to process mining platform, or the task mining platform, the system event logs recording execution of one or more processes; training the language model based on the one or more process graphs. Motivation to do so would provide analytical understanding aiding the model's reasoning (Ramesh, Par. 0028).
Ping, as modified above, does not teach, however, Madugula teaches processing the at least one of the system event logs or the recorded user interaction data to generate one or more process graphs representing execution flows of the one or more processes; and (Madugula, Par. 0060:” … receiving message data from a natural language conversation [event logs] among participants in a project, identifying at least two tasks mentioned in the message data, determining a dependency between the at least two tasks based on the output of a sequential language model, where the messages associated with the at least two tasks are inputs to the sequential language model, generating a directed graph depicting the at least two tasks and the determined dependency of the at least two tasks, sharing a directed graph across participants and notifying participants who are blocked when dependent tasks are complete.”) Note: directed graph is mapped to process graph.
Madugula is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ping, as modified above, further in view of Madugula to process the at least one of the system event logs or the recorded user interaction data to generate one or more process graphs representing execution flows of the one or more processes. Motivation to do so would improve task tracking in a project by digesting natural language message data (Madugula, Par. 0060).
Regarding claims 2, 8, and 14, Ping, as modified above, teaches the method, the system, and the non-transitory computer-readable medium of claims 1, 7, and 13 respectively.
Ping, as modified above, further teaches wherein the language model is a large language model. (Ping, Page 11:” … the above-mentioned extracted action can also adopt a pre-trained sensitive data extraction model, and the sensitive data extraction model can adopt a text classification model in a traditional natural language processing task, such as a fasttext model and a textCNN model, and can also use a large-scale language model based on a transformer for extraction.”)
Regarding claims 3, 9, and 15, Ping, as modified above, teaches the method, the system, and the non-transitory computer-readable medium of claims 1, 7, and 13 respectively.
Ping, as modified above, does not teach, however, Singh further teaches wherein the language model receives as input the one or more requests and generates as output the response. (Singh, Par. 0081:” … various types of generative AI models may be used, including, but not limited to, large language models (LLMs), generative adversarial networks (GANs), variational autoencoders (VAEs), transformers, etc. … where generative AI model(s) 172 are remotely hosted, server 130 can be configured to integrate with third-party APIs, which allow server 130 to send a request to generative AI model(s) 172 including the requisite input information and receive a response in return (e.g., sequences of user interactions, extracted common processes, generated RPA robots, recognized applications, screens, and/or UI elements, semantic matches of fields between application versions and/or screens, a classification of the type of the application on the screen, etc.).”)
Regarding claims 4, 10, and 16, Ping, as modified above, teaches the method, the system, and the non-transitory computer-readable medium of claims 1, 7, and 13 respectively.
Ping, as modified above, does not teach, however, Singh further teaches wherein the one or more automation platforms comprise an RPA (robotic process automation) platform. (
Singh, Par. 0049:" … For instance, RPA may be used at the core of a hyper-automation system in some embodiments, and in certain embodiments, automation capabilities may be expanded with AI/ML, process mining, analytics, and/or other advanced tools.")
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Ghosh et al. (US20200065151A1) teaches in Par. 0022:” … reference numbers 125 and 130, automation platform 110 may train a ticket analysis model, and may use the ticket analysis model to classify tickets and generate an automation plan for automatically classifying groups of tickets. For example, automation platform 110 may process the ticket data to generate the ticket analysis model, which may be a clustering based natural language analysis model of classifying tickets based on natural language text, such as ticket descriptions. In some implementations, automation platform 110 may perform a set of data manipulation procedures to process the ticket data to generate the ticket analysis model, such as a data preprocessing procedure, a model training procedure, a model verification procedure, and/or the like.”
Examiner's Note: Examiner has cited particular columns and line numbers and/or paragraph numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DARIOUSH AGAHI, P.E. whose telephone number is (408)918-7689. The examiner can normally be reached Monday - Thursday and alternate Fridays, 7:30-4:30 PT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh Mehta can be reached at 571-272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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DARIOUSH AGAHI, P.E.
Primary Examiner
/DARIOUSH AGAHI/Primary Examiner, Art Unit 2656
Prosecution Insights