DETAILED ACTION
This final Office action is responsive to Applicant’s amendment filed June 16, 2026. Claims 1-20 have been cancelled. Claims 21-38 have been added. Claims 21-38 are presented for examination.
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 June 16, 2026 have been fully considered but they are not persuasive.
The previously pending objections and rejections are rendered moot by Applicant’s cancellation of all previously pending claims.
Regarding the rejection under 35 U.S.C. § 101, Applicant argues that the claims present a “concrete, computer-implemented method for automatically triggering execution of a workflow by one or more computers.” (Page 6 of Applicant’s response) As explained in the rejection, the general idea of the concept of a solution alone is not sufficient to present an improvement that would overcome the rejection. Even though claims 21 and 30 trigger execution of a workflow by the one or more computers to generally perform the recited operations and claims 25 and 34 implement a Customer Relationship Management (CRM) application by the one or more computers, the claims present no specific technical details as to how workflow execution is performed or how a CRM application is implemented. These limitations, at best, each present the general idea of the concept of a solution. MPEP § 2106.05(a) states, “An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP § 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.”
Applicant argues that the claims “provide a specific technological improvement to computer-implemented workflow management.” (Page 6 of Applicant’s response) The Examiner respectfully disagrees. The claims only generally apply the additional elements and use them simply as tools to generally perform the abstract ideas. For example, a human user can process unstructured data and generate workflows based on the processed data. The Generative AI system is only generally applied to facilitate such an operation. No specific technical details as to how the Generative AI system performs the translation of unstructured data into workflows are recited in the claims, for example.
Applicant’s arguments regarding the prior art rejections are moot in view of the new grounds of rejection, necessitated by Applicant’s amendments.
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 21-38 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 21-38 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claimed invention is directed to workflow management (Spec: p. 1) without significantly more.
Step
Analysis
1: Statutory Category?
Yes – The claims fall within at least one of the four categories of patent eligible subject matter. Process (claims 21-29), Article of Manufacture (claims 30-38)
Independent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claims 21, 30] a method, the method comprising the steps of:
a. receiving unstructured data;
b. outputting a request to associate the unstructured data with a workflow from among a set of workflows, wherein each workflow in the set of workflows is characterized by a series of data processing steps, wherein the request conveys:
i. the unstructured data;
ii. an identification of each workflow in the set of workflows, wherein the unstructured data and the set of workflows defining a semantic relationship, wherein the request further includes instructions to generate an output mapping the unstructured data to one of the workflows in the set of workflows that best aligns semantically with the unstructured data;
c. receiving an identification of the one of the workflows in the set of workflows;
d. triggering execution of the one workflow, in response to the output.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. Aside from the generic processing elements, including a GUI to receive input and present output and the use of a Generative AI system (presented at a high level of generality), a human user could receive unstructured data (including text and audio-related data), output a request to associate the unstructured data to a workflow characterized by a series of steps, present information on a display, make workflow and application recommendations, and extract workflow data from records. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
Claim 21 recites a method executed by one or more computers for automatically triggering execution of a workflow by the one or more computers to generally perform the recited operations.
Claim 30 recites a non-transitory computer-readable storage medium storing instructions that, when executed by one or more computers, configure the one or more computers to perform a method for automatically triggering execution of a workflow by the one or more computers to generally perform the recited operations.
Claims 21 and 30 receive, at an input of the one or more computers, unstructured data.
Claims 21 and 30 output, at an interface of the one or more computers, a request to a Generative Al system to associate the unstructured data with a workflow from among a set of workflows.
Claims 21 and 30 recite wherein the request further includes instructions causing the Generative Al system to generate an output mapping the unstructured data to one of the workflows in the set of workflows that best aligns semantically with the unstructured data.
Claims 21 and 30 receive, from the interface as a result of processing by the Generative Al system, an identification of the one of the workflows in the set of workflows.
Claims 21 and 30 trigger execution by the one or more computers of the one workflow, in response to the output.
Claims 21 and 30 recite that the set of workflows is characterized by a series of data processing steps executable by the one or more computers, which just defines the nature of the data processing steps and presents a general link to a field of use.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: p. 12, including the following statement: “The hardware associated with the CRM software application 12 is not being discussed here in detail because it is an aspect known in the art.”).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
Considering that the implementation of the machine learning model and/or the training of the model (e.g., the integration of a Generative AI system) is performed using generic processing elements, such an implementation is presented as a generic recitation of machine learning in the claims and as a general link to technology. The machine learning-based processing elements are simply tools to generally automate the underlying process that could be performed by a human. It is further noted that, as described in Applicant’s Specification, the machine learning operations are generic machine learning operations (Spec: p. 1: “The invention relates to computer implemented methodologies and systems for workflow management utilizing a Generative AI framework, notably one that employs a language model like a Large Language Model (LLM).”; p. 3: “A Generative AI system, also known as a Generative model, is a type of artificial intelligence that is designed to generate new data samples that resemble a given dataset. It is a class of AI models capable of learning the underlying patterns and structures of the training data and then using that knowledge to produce new, synthetic data that resembles the original data distribution.”). The Specification presents no assertion that there is any improvement in the automated machine learning process itself. Such a generic recitation of machine learning, as recited in the claims, is little more than automating an analogous process that can be performed by a human.
It is noted that the general idea of the concept of a solution alone is not sufficient to present an improvement that would overcome the rejection. Even though claims 21 and 30 trigger execution of a workflow by the one or more computers to generally perform the recited operations, the claims present no specific technical details as to how workflow execution is performed. This limitation, at best, presents the general idea of the concept of a solution. MPEP § 2106.05(a) states, “An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP § 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.”
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claims 22, 31] wherein the unstructured data comprises at least one of text and audio.
[Claims 23, 32] wherein the unstructured data is derived from audio conveying an utterance by a user.
[Claims 25, 34] implementing a Customer Relationship Management (CRM) application.
[Claims 26, 35] wherein the CRM application includes a plurality of functional segments corresponding to respective ones of the workflows in the set of workflows, including activating in response to the output a functional segment of the CRM application corresponding to the one workflow.
[Claims 27, 36] wherein the CRM application includes a CRM record, the method further comprising processing the CRM record to extract from the CRM record the set of workflows.
[Claims 28, 37] receive a user input indicative that the one workflow is valid.
[Claims 29, 38] receive a user input indicative that the one workflow is invalid.
The dependent claims further present details of the abstract ideas identified in regard to the independent claims.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. Aside from the generic processing elements, including a GUI to receive input and present output, a human user could receive unstructured data (including text and audio-related data), output a request to associate the unstructured data to a workflow characterized by a series of steps, present information on a display, make workflow and application recommendations, and extract workflow data from records. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
The dependent claims include the additional elements of their independent claims.
Claim 21 recites a method executed by one or more computers for automatically triggering execution of a workflow by the one or more computers to generally perform the recited operations.
Claim 30 recites a non-transitory computer-readable storage medium storing instructions that, when executed by one or more computers, configure the one or more computers to perform a method for automatically triggering execution of a workflow by the one or more computers to generally perform the recited operations.
Claims 21 and 30 receive, at an input of the one or more computers, unstructured data.
Claims 21 and 30 output, at an interface of the one or more computers, a request to a Generative Al system to associate the unstructured data with a workflow from among a set of workflows.
Claims 21 and 30 recite wherein the request further includes instructions causing the Generative Al system to generate an output mapping the unstructured data to one of the workflows in the set of workflows that best aligns semantically with the unstructured data.
Claims 21 and 30 receive, from the interface as a result of processing by the Generative Al system, an identification of the one of the workflows in the set of workflows.
Claims 21 and 30 trigger execution by the one or more computers of the one workflow, in response to the output.
Claims 21 and 30 recite that the set of workflows is characterized by a series of data processing steps executable by the one or more computers, which just defines the nature of the data processing steps and presents a general link to a field of use.
Claims 24 and 33 recite wherein the input comprises a Graphical User Interface (GUI) including a control configured to capture the unstructured data from the user.
Claims 25 and 34 implement a Customer Relationship Management (CRM) application by the one or more computers.
Claims 28 and 37 recite wherein the control is a first control, the GUI comprising a second control, which is a validation control configured to receive a user input indicative that the one workflow is valid.
Claims 29 and 38 recite wherein the GUI further comprises a third control configured to receive a user input indicative that the one workflow is invalid.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: p. 12, including the following statement: “The hardware associated with the CRM software application 12 is not being discussed here in detail because it is an aspect known in the art.”).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
Considering that the implementation of the machine learning model and/or the training of the model (e.g., the integration of a Generative AI system) is performed using generic processing elements, such an implementation is presented as a generic recitation of machine learning in the claims and as a general link to technology. The machine learning-based processing elements are simply tools to generally automate the underlying process that could be performed by a human. It is further noted that, as described in Applicant’s Specification, the machine learning operations are generic machine learning operations (Spec: p. 1: “The invention relates to computer implemented methodologies and systems for workflow management utilizing a Generative AI framework, notably one that employs a language model like a Large Language Model (LLM).”; p. 3: “A Generative AI system, also known as a Generative model, is a type of artificial intelligence that is designed to generate new data samples that resemble a given dataset. It is a class of AI models capable of learning the underlying patterns and structures of the training data and then using that knowledge to produce new, synthetic data that resembles the original data distribution.”). The Specification presents no assertion that there is any improvement in the automated machine learning process itself. Such a generic recitation of machine learning, as recited in the claims, is little more than automating an analogous process that can be performed by a human.
It is noted that the general idea of the concept of a solution alone is not sufficient to present an improvement that would overcome the rejection. Even though claims 21 and 30 trigger execution of a workflow by the one or more computers to generally perform the recited operations and claims 25 and 34 implement a Customer Relationship Management (CRM) application by the one or more computers, the claims present no specific technical details as to how workflow execution is performed or how a CRM application is implemented. These limitations, at best, each present the general idea of the concept of a solution. MPEP § 2106.05(a) states, “An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP § 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.”
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 21-38 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by
Dines et al. (US 2024/0220581).
[Claim 21] Dines discloses a method executed by one or more computers for automatically triggering execution of a workflow by the one or more computers (¶ 73 – “”Once a workflow is developed in designer 210, execution of business processes is orchestrated by conductor 220, which orchestrates one or more robots 230 that execute the workflows developed in designer 210.), the method comprising the steps of:
a. receiving, at an input of the one or more computers, unstructured data (¶ 179 – “FIG. 11 is a flowchart illustrating a process 1100 for performing AI-driven, semantic, automatic data transfer between a source and a target using task mining, according to an embodiment of the present invention. The process beings with performing task mining on user computing systems at 1105 (e.g., by a listener). Task mining may include monitoring user interactions with the computing systems, which applications are running, which windows are open, which UI element is the active element, etc. However, in some embodiments, task mining is not performed prior to training and deployment of an initial semantic matching AI/ML model.”; ¶ 181 – “In some embodiments, the source, the target, or both, may be a web form, a document, whether digital or scanned (such as an invoice, a receipt, a report, handwritten information, etc.), an image, an application GUI, a spreadsheet, or any other suitable information format without deviating from the scope of the invention, whether structured or unstructured. OCR may be performed on the source and/or the target before performing semantic copy-and-paste functionality. In certain embodiments, the source and the target may be of different types. For instance, the source may be a web form and the target may be a spreadsheet.”);
b. outputting, at an interface of the one or more computers, a request to a Generative Al system to associate the unstructured data with a workflow from among a set of workflows, wherein each workflow in the set of workflows is characterized by a series of data processing steps executable by the one or more computers (¶ 35 – “Some embodiments use a large semantic language model that maps the similarity of the fields based on semantic meaning in high dimensionality vector space. For instance, a natural language processing (NLP) model such as word2vec or a more advanced semantic NLP model such as Bidirectional Encoder Representations from Transformers (BERT) or Generative Pre-trained Transformer 3 (GPT-3) may be used. Such models build a vector representation of the screen and may learn that two different labels are similar using learned language understanding.”; ¶ 37 – “Similar to the example in the previous paragraph, a sentence from the source and a sentence from the target may be provided to a BERT model or a GPT-3 model as input.”; ¶ 181 – “In some embodiments, the source, the target, or both, may be a web form, a document, whether digital or scanned (such as an invoice, a receipt, a report, handwritten information, etc.), an image, an application GUI, a spreadsheet, or any other suitable information format without deviating from the scope of the invention, whether structured or unstructured.“; ¶ 56 – “Task capture (e.g., via UiPath Automation Cloud™ and/or UiPath AI Center™) automatically documents attended processes as users work or provides a framework for unattended processes. Such documentation may include desired tasks to automate in the form of process definition documents (PDDs), skeletal workflows, capturing actions for each part of a process, recording user actions and automatically generating a comprehensive workflow diagram including the details about each step, Microsoft Word® documents, XAML files, and the like. Build-ready workflows may be exported directly to a designer application in some embodiments, such as UiPath Studio™. Task capture may simplify the requirements gathering process for both subject matter experts explaining a process and Center of Excellence (CoE) members providing production-grade automations.”; ¶ 46 – “Automation processes may execute the logic developed in workflows during design time. In the case of RPA, workflows may include a set of steps, defined herein as “activities,” that are executed in a sequence or some other logical flow. Each activity may include an action, such as clicking a button, reading a file, writing to a log panel, etc. In some embodiments, workflows may be nested or embedded.”; ¶¶ 30-31 – “[0030] Images captured during task mining recording and the associated API call information can be time synchronized, and the API information can be used to provide further understanding regarding what the user is doing in the screens. This may facilitate better understanding by matching sets of user actions to an activity, such as via image comparison techniques. For instance, CV (including OCR) may be used to extract information about a given screen and then a clustering algorithm may be used to match the extracted information to similar screens. This allows the type of the user action to be more accurately identified. [0031] A more complete picture of what a user is doing may be understood by combining image analysis and API information collection. For example, screenshots, user interaction events, API events, operating system (OS)/document object model (DOM) events, user interaction/input types (e.g., mouse click versus typing), location data (e.g., where was the mouse clicked or text was entered on the screen), which mouse button was pressed, application/process name (e.g., a universal resource locator (URL) for the screen or the application that is currently running and active for a window), a UI descriptor, any combination thereof, etc. may be collected. This provides further context than image analysis alone. For instance, the API may provide information regarding which sheet and cell in an Excel® spreadsheet was modified, the format of that cell (e.g., currency, string, etc.), and various other API-facilitated information.”), wherein the request conveys:
i. the unstructured data (¶ 181 – “In some embodiments, the source, the target, or both, may be a web form, a document, whether digital or scanned (such as an invoice, a receipt, a report, handwritten information, etc.), an image, an application GUI, a spreadsheet, or any other suitable information format without deviating from the scope of the invention, whether structured or unstructured.“; ¶¶ 30-31 – “[0030] Images captured during task mining recording and the associated API call information can be time synchronized, and the API information can be used to provide further understanding regarding what the user is doing in the screens. This may facilitate better understanding by matching sets of user actions to an activity, such as via image comparison techniques. For instance, CV (including OCR) may be used to extract information about a given screen and then a clustering algorithm may be used to match the extracted information to similar screens. This allows the type of the user action to be more accurately identified. [0031] A more complete picture of what a user is doing may be understood by combining image analysis and API information collection. For example, screenshots, user interaction events, API events, operating system (OS)/document object model (DOM) events, user interaction/input types (e.g., mouse click versus typing), location data (e.g., where was the mouse clicked or text was entered on the screen), which mouse button was pressed, application/process name (e.g., a universal resource locator (URL) for the screen or the application that is currently running and active for a window), a UI descriptor, any combination thereof, etc. may be collected. This provides further context than image analysis alone. For instance, the API may provide information regarding which sheet and cell in an Excel® spreadsheet was modified, the format of that cell (e.g., currency, string, etc.), and various other API-facilitated information.”);
ii. an identification of each workflow in the set of workflows, wherein the unstructured data and the set of workflows defining a semantic relationship, wherein the request further includes instructions causing the Generative Al system to generate an output mapping the unstructured data to one of the workflows in the set of workflows that best aligns semantically with the unstructured data (¶ 146 – “Over time, information pertaining to user interactions with the computing system is collected. Information from interactions of other users with their respective computing systems may be collected as well. This information may be analyzed to search for copy-and-paste operations. For instance, cloud-based analysis may be performed on the collected data to search for instances where values from one or more screens or forms appeared in one or more other screens or forms. The relationship may be one-to-multiple, multiple-to-one, or multiple-to-multiple. In such instances, one or more data structures may be used to capture the information of the collective source and/or the information of the collective target. Identified copy-and-paste tasks in the task mining data may be treated as accurate, per the above. This information is then used to train a mapping model that maps.”; ¶¶ 153 -154 – “[0153] The information to create the RPA workflow for the copy-and-paste task may be derived from task mining and the semantic matching AI/ML model. By watching the fields that the user completes and the matches from the semantic matching AI/ML model, the matching fields in the source and the target may be determined and activities may be automatically created that enter the desired information into the appropriate fields in the target. This functionality may be similar to that provided by an RPA designer application such as UiPath Studio™, for example. The RPA designer application may be launched and the workflow generated for review by the user therein. Alternatively, the workflow may be automatically generated and the associated automation may be created without a graphical display for the user using underlying functionality of the RPA designer application. In some embodiments, the workflow and automation may be generated by a different computing system than that of the user. In certain embodiments, the workflow may be provided to an RPA developer for verification prior to generation and deployment of the automation. However, as the semantic matching AI/ML model becomes more accurate with further training over time, the proposed RPA workflow may become accurate enough that it can be deployed as an automation for execution by an RPA robot automatically without developer review. [0154] Data pertaining to copy-and-paste operations from task mining may be used to train or retrain a semantic matching AI/ML model. The data may include corresponding labels for fields from the source and the target. In some embodiments, UI descriptor/selector information may be useful to get precise fields, labels of fields, screenshots, etc. In virtual environments, there may be no descriptors/selectors since the user's computing system is receiving a stream of images from a server. However, fields and labels may still be obtained using CV and its descriptors. This may also be useful to get the actual data in the specific fields for the source and destination so the mapping of which data went where can be obtained. In certain embodiments, this could theoretically be done based only on the actions themselves, but this may add another layer of certainty. This information may become part of the training dataset. Human labeling may not be required in some embodiments since the information may be considered to be validated by the user(s) that entered the information.”);
c. receiving, from the interface as a result of processing by the Generative Al system, an identification of the one of the workflows in the set of workflows (¶¶ 146, 153-154 (reproduced above); ¶ 156 – “FIG. 9 illustrates an RPA designer application 900 with an automatically generated RPA workflow 910, according to an embodiment of the present invention. In this example, RPA workflow 910 is automatically generated based on the task mining and semantic matching discussed above. RPA workflow 910 is populated with activities that enter the source values into the target. For instance, the workflow includes activities for clicking on the “Billing System” target form, clicking on the “Cust. Num.” field, typing the appropriate value into the “Cust. Num.” field, etc.”);
d. triggering execution by the one or more computers of the one workflow, in response to the output (¶ 156 – “FIG. 9 illustrates an RPA designer application 900 with an automatically generated RPA workflow 910, according to an embodiment of the present invention. In this example, RPA workflow 910 is automatically generated based on the task mining and semantic matching discussed above. RPA workflow 910 is populated with activities that enter the source values into the target. For instance, the workflow includes activities for clicking on the “Billing System” target form, clicking on the “Cust. Num.” field, typing the appropriate value into the “Cust. Num.” field, etc.”; ¶ 46 – “Automation processes may execute the logic developed in workflows during design time. In the case of RPA, workflows may include a set of steps, defined herein as “activities,” that are executed in a sequence or some other logical flow. Each activity may include an action, such as clicking a button, reading a file, writing to a log panel, etc. In some embodiments, workflows may be nested or embedded.”; ¶ 63 – “Performance of the AI/ML models may be monitored, and the AI/ML models may be trained and improved using human-validated data, such as that provided by data review center 160. Human reviewers may provide labeled data to core hyper-automation system 120 via a review application 152 on computing systems 154. For instance, human reviewers may validate that predictions by AI/ML models 132 are accurate or provide corrections otherwise. This dynamic input may then be saved as training data for retraining AI/ML models 132, and may be stored in a database such as database 140, for example. The AI center may then schedule and execute training jobs to train the new versions of the AI/ML models using the training data. Both positive and negative examples may be stored and used for retraining of AI/ML models 132.”).
[Claim 22] Dines discloses wherein the unstructured data comprises at least one of text and audio (¶ 31 – “A more complete picture of what a user is doing may be understood by combining image analysis and API information collection. For example, screenshots, user interaction events, API events, operating system (OS)/document object model (DOM) events, user interaction/input types (e.g., mouse click versus typing), location data (e.g., where was the mouse clicked or text was entered on the screen), which mouse button was pressed, application/process name (e.g., a universal resource locator (URL) for the screen or the application that is currently running and active for a window), a UI descriptor, any combination thereof, etc. may be collected. This provides further context than image analysis alone. For instance, the API may provide information regarding which sheet and cell in an Excel® spreadsheet was modified, the format of that cell (e.g., currency, string, etc.), and various other API-facilitated information.”; ¶ 50 – “AI/ML models 132 may perform or assist with CV (including OCR), document processing and/or understanding, semantic learning and/or analysis, analytical predictions, process discovery, task mining, testing, automatic RPA workflow generation, sequence extraction, clustering detection, audio-to-text translation, any combination thereof, etc.”; ¶ 181 – “In some embodiments, the source, the target, or both, may be a web form, a document, whether digital or scanned (such as an invoice, a receipt, a report, handwritten information, etc.), an image, an application GUI, a spreadsheet, or any other suitable information format without deviating from the scope of the invention, whether structured or unstructured. OCR may be performed on the source and/or the target before performing semantic copy-and-paste functionality. In certain embodiments, the source and the target may be of different types. For instance, the source may be a web form and the target may be a spreadsheet.”; ¶ 67 – “Conversations between people may be readily automated, as with other processes. Trigger RPA robots kicked off in this manner may perform operations such as checking an order status, posting data in a CRM, etc., potentially using plain language commands.”).
[Claim 23] Dines discloses wherein the unstructured data is derived from audio conveying an utterance by a user (¶ 67 – “Conversations between people may be readily automated, as with other processes. Trigger RPA robots kicked off in this manner may perform operations such as checking an order status, posting data in a CRM, etc., potentially using plain language commands.”).
[Claim 24] Dines discloses wherein the input comprises a Graphical User Interface (GUI) including a control configured to capture the unstructured data from the user (¶ 71 – “The automation project enables automation of rule-based processes by giving the developer control of the execution order and the relationship between a custom set of steps developed in a workflow, defined herein as “activities” per the above. One commercial example of an embodiment of designer 210 is UiPath Studio™. Each activity may include an action, such as clicking a button, reading a file, writing to a log panel, etc. In some embodiments, workflows may be nested or embedded.”; ¶ 86 – “In some embodiments, data labeling may be performed by a user of the computing system on which a robot is executing or on another computing system that the robot provides information to. For instance, if a robot calls an AI/ML model that performs CV on images for VM users, but the AI/ML model does not correctly identify a button on the screen, the user may draw a rectangle around the misidentified or non-identified component and potentially provide text with a correct identification. This information may be provided to core hyper-automation system 240 and then used later for training a new version of the AI/ML model.”).
[Claim 25] Dines discloses implementing a Customer Relationship Management (CRM) application by the one or more computers (¶ 54 – “Process mining (e.g., via UiPath Automation Cloud™ and/or UiPath AI Center™) refers to the process of gathering and analyzing the data from applications (e.g., enterprise resource planning (ERP) applications, customer relation management (CRM) applications, email applications, call center applications, etc.) to identify what end-to-end processes exist in an organization and how to automate them effectively, as well as indicate what the impact of the automation will be. This data may be gleaned from user computing systems 102, 104, 106 by listeners, for example, and processed by servers, such as server 130. One or more AI/ML models 132 may be employed for this purpose in some embodiments. This information may be exported to the automation hub to speed up implementation and avoid manual information transfer. The goal of process mining may be to increase business value by automating processes within an organization. Some examples of process mining goals include, but are not limited to, increasing profit, improving customer satisfaction, regulatory and/or contractual compliance, improving employee efficiency, etc.”).
[Claim 26] Dines discloses wherein the CRM application includes a plurality of functional segments corresponding to respective ones of the workflows in the set of workflows, including activating in response to the output a functional segment of the CRM application corresponding to the one workflow (¶ 146 – “Over time, information pertaining to user interactions with the computing system is collected. Information from interactions of other users with their respective computing systems may be collected as well. This information may be analyzed to search for copy-and-paste operations. For instance, cloud-based analysis may be performed on the collected data to search for instances where values from one or more screens or forms appeared in one or more other screens or forms. The relationship may be one-to-multiple, multiple-to-one, or multiple-to-multiple. In such instances, one or more data structures may be used to capture the information of the collective source and/or the information of the collective target. Identified copy-and-paste tasks in the task mining data may be treated as accurate, per the above. This information is then used to train a mapping model that maps.”; ¶¶ 153 -154 – “[0153] The information to create the RPA workflow for the copy-and-paste task may be derived from task mining and the semantic matching AI/ML model. By watching the fields that the user completes and the matches from the semantic matching AI/ML model, the matching fields in the source and the target may be determined and activities may be automatically created that enter the desired information into the appropriate fields in the target. This functionality may be similar to that provided by an RPA designer application such as UiPath Studio™, for example. The RPA designer application may be launched and the workflow generated for review by the user therein. Alternatively, the workflow may be automatically generated and the associated automation may be created without a graphical display for the user using underlying functionality of the RPA designer application. In some embodiments, the workflow and automation may be generated by a different computing system than that of the user. In certain embodiments, the workflow may be provided to an RPA developer for verification prior to generation and deployment of the automation. However, as the semantic matching AI/ML model becomes more accurate with further training over time, the proposed RPA workflow may become accurate enough that it can be deployed as an automation for execution by an RPA robot automatically without developer review. [0154] Data pertaining to copy-and-paste operations from task mining may be used to train or retrain a semantic matching AI/ML model. The data may include corresponding labels for fields from the source and the target. In some embodiments, UI descriptor/selector information may be useful to get precise fields, labels of fields, screenshots, etc. In virtual environments, there may be no descriptors/selectors since the user's computing system is receiving a stream of images from a server. However, fields and labels may still be obtained using CV and its descriptors. This may also be useful to get the actual data in the specific fields for the source and destination so the mapping of which data went where can be obtained. In certain embodiments, this could theoretically be done based only on the actions themselves, but this may add another layer of certainty. This information may become part of the training dataset. Human labeling may not be required in some embodiments since the information may be considered to be validated by the user(s) that entered the information.”; ¶ 54 – “Process mining (e.g., via UiPath Automation Cloud™ and/or UiPath AI Center™) refers to the process of gathering and analyzing the data from applications (e.g., enterprise resource planning (ERP) applications, customer relation management (CRM) applications, email applications, call center applications, etc.) to identify what end-to-end processes exist in an organization and how to automate them effectively, as well as indicate what the impact of the automation will be.).
[Claim 27] Dines discloses wherein the CRM application includes a CRM record, the method further comprising processing the CRM record to extract from the CRM record the set of workflows (¶ 54 – “Process mining (e.g., via UiPath Automation Cloud™ and/or UiPath AI Center™) refers to the process of gathering and analyzing the data from applications (e.g., enterprise resource planning (ERP) applications, customer relation management (CRM) applications, email applications, call center applications, etc.) to identify what end-to-end processes exist in an organization and how to automate them effectively, as well as indicate what the impact of the automation will be. This data may be gleaned from user computing systems 102, 104, 106 by listeners, for example, and processed by servers, such as server 130. One or more AI/ML models 132 may be employed for this purpose in some embodiments. This information may be exported to the automation hub to speed up implementation and avoid manual information transfer. The goal of process mining may be to increase business value by automating processes within an organization. Some examples of process mining goals include, but are not limited to, increasing profit, improving customer satisfaction, regulatory and/or contractual compliance, improving employee efficiency, etc.”).
[Claim 28] Dines discloses wherein the control is a first control, the GUI comprising a second control, which is a validation control configured to receive a user input indicative that the one workflow is valid (¶ 63 – “Performance of the AI/ML models may be monitored, and the AI/ML models may be trained and improved using human-validated data, such as that provided by data review center 160. Human reviewers may provide labeled data to core hyper-automation system 120 via a review application 152 on computing systems 154. For instance, human reviewers may validate that predictions by AI/ML models 132 are accurate or provide corrections otherwise. This dynamic input may then be saved as training data for retraining AI/ML models 132, and may be stored in a database such as database 140, for example. The AI center may then schedule and execute training jobs to train the new versions of the AI/ML models using the training data. Both positive and negative examples may be stored and used for retraining of AI/ML models 132.”; ¶ 65 – “An action center (e.g., UiPath Action Center™) provides a straightforward and efficient mechanism to hand off processes from automations to humans, and vice versa. Humans may provide approvals or escalations, make exceptions, etc. The automation may then perform the automatic functionality of a given workflow.”; ¶ 66 – “A local assistant may be provided as a launchpad for users to launch automations (e.g., UiPath Assistant™). This functionality may be provided in a tray provided by an operating system, for example, and may allow users to interact with RPA robots and RPA robot-powered applications on their computing systems. An interface may list automations approved for a given user and allow the user to run them.”; ¶ 27 – “For instance, most or all fields may have associated values in the last screen(s) for the target prior to when the user presses a “Submit” button, but the labels remain the same. This “completed” state may be indicative of a completed manual copy-and-paste operation regardless of the order in which the user populated the fields.”; ¶ 178 – “After the target has been automatically filled in, an associated RPA automation may be created by automation module 1050. This may be responsive to user approval or automatic with no user involvement. The automation may be deployed to the user computing system or otherwise be made available to the user so the same copy-and-paste task may be performed automatically in the future.”).
[Claim 29] Dines discloses wherein the GUI further comprises a third control configured to receive a user input indicative that the one workflow is invalid (¶ 63 – “Performance of the AI/ML models may be monitored, and the AI/ML models may be trained and improved using human-validated data, such as that provided by data review center 160. Human reviewers may provide labeled data to core hyper-automation system 120 via a review application 152 on computing systems 154. For instance, human reviewers may validate that predictions by AI/ML models 132 are accurate or provide corrections otherwise. This dynamic input may then be saved as training data for retraining AI/ML models 132, and may be stored in a database such as database 140, for example. The AI center may then schedule and execute training jobs to train the new versions of the AI/ML models using the training data. Both positive and negative examples may be stored and used for retraining of AI/ML models 132.”; ¶ 86 – “In some embodiments, data labeling may be performed by a user of the computing system on which a robot is executing or on another computing system that the robot provides information to. For instance, if a robot calls an AI/ML model that performs CV on images for VM users, but the AI/ML model does not correctly identify a button on the screen, the user may draw a rectangle around the misidentified or non-identified component and potentially provide text with a correct identification. This information may be provided to core hyper-automation system 240 and then used later for training a new version of the AI/ML model.”).
[Claims 30-38] Claims 30-38 recite limitations already addressed by the rejections of claims 21-29 above; therefore, the same rejections apply.
Furthermore, Dines discloses a non-transitory computer-readable storage medium storing instructions that, when executed by one or more computers, configure the one or more computers to perform a method for automatically triggering execution of a workflow by the one or more computers (¶ 113 – hardware, software, memory; ¶¶ 46, 63, 156 – workflow execution).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUSANNA M DIAZ whose telephone number is (571)272-6733. The examiner can normally be reached M-F, 8 am-4:30 pm.
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/SUSANNA M. DIAZ/
Primary Examiner
Art Unit 3625A
Prosecution Insights