DETAILED ACTION
The following is a Final Office action. In response to Examiner’s communication of 8/21/25, Applicant, on 11/20/25, amended claims 1, 6, 8, 13, 15, and 20, cancelled claims 4, 5, 11, 12, 18, and 19, and added new claims 21-26. Claims 1-3, 6-10, 13-17, and 20-26 are now pending and have been rejected as indicated below.
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 Amendments
Applicant’s amendments are acknowledged.
The 35 USC 101 rejection of claims 1-3, 6, 8-10, 13, 15-17, and 20-26 in regard to abstract ideas has been maintained in light of Applicant’s amendments and explanations.
New 35 USC § 103 rejections of claims 1-3, 6-10, 13-17, and 20-26 are applied in light of Applicant’s amendments and explanations.
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 1-3, 6, 8-10, 13, 15-17, and 20-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Here, under considerations of the broadest reasonable interpretation of the claimed invention, Examiner finds that the Applicant invented a method and system for process management including extracting collaborative sentiment from shared workspaces. Examiner formulates an abstract idea analysis, following the framework described in the MPEP as follows:
Step 1: The claims are directed to a statutory category, namely a "method" (claims 1-3, 6, and 21-26) and "system" (claims 8-10, 13, 15-17, and 20).
Step 2A - Prong 1: The claims are found to recite limitations that set forth the abstract idea(s), namely, regarding claim 1:
identifying a process to be analyzed, wherein the process is comprised of one or more activities…
generating a list of relevant individuals corresponding to the process;
and requesting access to collaborative communications between one or more individuals from the list of relevant individuals;
generating one or more scores based on the collaborative communications between the one or more individuals, wherein the one or more scores correspond to the one or more activities comprising the process;
…
improve a sentiment associated with the one or more scores generated based on the collaborative communications,
…
providing one or more recommendations
updating the process within the user interface using the one or more scores to enrich the process with additional contextual data.
Independent claims 8 and 15 recites substantially similar claim language.
Dependent claims 2, 3, 6, 9, 10, 13, 16, 17, and 20-26 recite the same or similar abstract idea(s) as independent claims 1, 8, and 15 with merely a further narrowing of the abstract idea(s) to particular data characterization and/or additional data analyses performed as part of the abstract idea.
The limitations in claims 1-3, 6, 8-10, 13, 15-17, and 20-26 above falling well-within the groupings of subject matter identified by the courts as being abstract concepts, specifically the claims are found to correspond to the category of:
"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)" as the limitations identified above are directed to process management including extracting collaborative sentiment from shared workspaces and thus is a method of organizing human activity including at least commercial or business interactions or relations and/or a management of user personal behavior; and/or
"Mental processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)" as the limitations identified above include mere data observations, evaluations, judgements, and/or opinions, e.g. including process management including extracting collaborative sentiment from shared workspaces, which is capable of being performed mentally and/or using pen and paper.
Step 2A - Prong 2: Claims 1-3, 6, 8-10, 13, 15-17, and 20-26 are found to clearly be directed to the abstract idea identified above because the claims, as a whole, fail to integrate the claimed judicial exception into a practical application, specifically the claims recite the additional elements of:
"wherein the process is identified based on a selection made by a user within a user interface" (claim 1, 8, and 15) "wherein the additional contextual data enables the user to visualize, within the user interface, which of the one or more activities comprising the process provides further automation opportunities" (claims 2, 9, and 16), “presenting one or more prompts to the user within the user interface, wherein the one or more prompts are designed to gather feedback from the user with respect to the at the at least one of the one or more recommendations,” (claims 6, 13, and 20), however the aforementioned elements directed to the receiving of user input/selection of data to view via a dashboard and displaying corresponding data via the dashboard merely amount to generic GUI elements of a general purpose computer used to "apply" the abstract idea (MPEP 2106.05(f)) and/or is merely an attempt at limiting the abstract idea of process management including extracting collaborative sentiment from shared workspaces to a particular field of use/technological environment of a GUI dashboard (MPEP 2106.05(h)) and therefore the GUI dashboard input and display of data fails to integrate the abstract idea into a practical application;
"identifying, using a machine learning model, one or more recommendations …wherein the machine learning model utilizes one or more simulation methods" (claims 1, 8, and 15), “retraining the machine learning model to generate improved recommendations in the future specific to the user,” (claims 6, 13, and 20), however the aforementioned elements merely amount to generic components of a general purpose computer used to "apply" the abstract idea (MPEP 2106.0S(f)) and thus fails to integrate the recited abstract idea into a practical application, furthermore the high-level recitation of receiving data from a generic "building system" is at most an attempt to limit the abstract to a particular field of use (MPEP 2106.0S(h), e.g.: "For instance, a data gathering step that is limited to a particular data source (such as the Internet) or a particular type of data (such as power grid data or XML tags) could be considered to be both insignificant extra-solution activity and a field of use limitation. See, e.g., Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (limiting use of abstract idea to the Internet); Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data); Intellectual Ventures I LLC v. Erie lndem. Co., 850 F.3d 1315, 1328-29, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017) (limiting use of abstract idea to use with XML tags).") and/or merely insignificant extra-solution activity (MPE 2106.05(g)) and thus further fails to integrate the abstract idea into a practical application;
Step 2B: Claims 1-3, 6, 8-10, 13, 15-17, and 20-26 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 merely amount to a general purpose computer that attempts to apply the abstract idea in a technological environment (MPEP 2106.0S(f)), including merely limiting the abstract idea to a particular field of use of process management including extracting collaborative sentiment from shared workspaces via a GUI "dashboard" and “machine learning,” as explained above, and/or performs insignificant extra-solution activity, e.g. data gathering or output, (MPEP 2106.0S(g)), as identified above, which is further found under step 2B to be merely well-understood, routine, and conventional activities as evidenced by MPEP 2106.0S(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser's back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to process management including extracting collaborative sentiment from shared workspaces.
Claims 1-3, 6, 8-10, 13, 15-17, and 20-26 are accordingly rejected under 35 USC§ 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
Note: The analysis above applies to all statutory categories of invention. As such, the presentment of any claim otherwise styled as a machine or manufacture, for example, would be subject to the same analysis
For further authority and guidance, see:
MPEP § 2106
https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility
In regard to claims 7 and 14, Examiner notes that these claims recite eligible subject matter in that a recommendation of a specific automated process is implemented by an RPA bot in response to feedback received during a machine learning process. Examiner notes that this constitutes a practical application under step 2A of the above analysis and accordingly find the claim language to recite eligible subject matter.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-3, 6-10, 13-17, 20, and 22-26 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2024/0176960 to Maurer et al. (hereafter referred to as Maurer) in view of U.S. Patent Application Publication Number 2022/0129257 to Touati et al. (hereafter referred to as Touati).
As per claim 1, Maurer teaches:
A method for process mining and optimization, the method comprising: identifying a process to be analyzed, wherein the process is comprised of one or more activities (Paragraph Number [0035] teaches the audio/video component 118 can be configured to cause presentation of the transcript in association with a virtual space with which the audio and/or video conversation is associated. For example, a first user can initiate an audio and/or video conversation in association with a communication channel. The audio/video component 118 can process audio and/or video data between attendees of the audio and/or video conversation, and further can generate a transcript of the audio and/or video data. In response to generating the transcript, the audio/video component 118 can cause the transcript to be published or otherwise presented via the communication channel. In at least one example, the audio/video component 118 can render one or more sections of the transcript selectable for commenting, such as to enable members of the communication channel to comment on, or further contribute to, the conversation. In some examples, the audio/video component 118 can update the transcript based on the comments).
wherein the process is identified based on a selection made by a user within a user interface (Paragraph Number [0082] teaches the navigation pane 206 can include a sub-section that is a personalized sub-section associated with a team of which the user is a member. That is, the “team” sub-section can include affordance(s) of one or more virtual spaces that are associated with the team, such as communication channels, collaborative documents, direct messaging instances, audio or video synchronous or asynchronous meetings, and/or the like. In at least one example, the user can associate selected virtual spaces with the team sub-section, such as by dragging and dropping, pinning, or otherwise associating selected virtual spaces with the team sub-section).
generating a list of relevant individuals corresponding to the process (Paragraph Number [0030] teaches the messaging component 116 can receive a message transmitted in association with a virtual space (e.g., direct message instance, communication channel, canvas, collaborative document, etc.). In various examples, the messaging component 116 can identify one or more users associated with the virtual space and can cause a rendering of the message in association with instances of the virtual space on respective user computing devices 104. Paragraph Number [0148] teaches the ML model(s) 142 may be trained to interpret keystroke data in order to identify a participant in a synchronous multimedia collaboration session, (See also Paragraph Number [0097])).
and requesting access to collaborative communications between one or more individuals from the list of relevant individuals (Paragraph Number [0073] teaches a virtual space can be associated with one or more boards or collaborative documents with which the user is associated. In at least one example, a document can include a collaborative document configured to be accessed and/or edited by two or more users with appropriate permissions (e.g., viewing permissions, editing permissions, etc.). In at least one example, if the user requests to access the virtual space associated with one or more documents with which the user is associated, the one or more documents can be presented via the user interface 200. In at least one example, the documents, as described herein, can be associated with an individual (e.g., private document for a user), a group of users (e.g., collaborative document), and/or one or more communication channels (e.g., members of the communication channel rendered access permissions to the document), such as to enable users of the communication platform to create, interact with, and/or view data associated with such documents).
generating one or more scores based on the collaborative communications between the one or more individuals, wherein the one or more scores correspond to the one or more activities comprising the process (Paragraph Number [0141] teaches the output data 415 may comprise a summary document 415A that summarizes the content posted and communication that occurred within the virtual space during the synchronous multimedia collaboration session. In this manner, the communication platform can treat the summary document 415A as a synopsis or outline of the synchronous multimedia collaboration session and the tasks, deadlines, etc. that were discussed. As such, functionalities of the virtual space may be imbued in the summary document. The summary document 415A may accordingly identify action(s), upload(s), conversation(s) (such as conversation threads), and the like that transpired in the virtual space. In at least some examples, the ML model(s) 142 may be trained to rank identified topics of conversation according to one or more relevance regimes. The ML model(s) 142 may implement any appropriate ranking metric including mean average precision, discounted cumulative gain or the like. Accordingly, the summary document 415A may classify text or other content by a relevance score or other indicator of overall significance).
identifying, using a machine learning model, one or more recommendations (Paragraph Number [0097] teaches a list of recommended active users may include a plurality of group-based communication system users recommended based on at least one of user activity, user interaction, or other user information. For example, the list of recommended active users may be selected based on an active status of the users within the group-based communication system; historic, recent, or frequent user interaction with the instant user (such as communicating within the group-based communication channel); or similarity between the recommended users and the instant user (such as determining that a recommended user shares common membership in channels with the instant user). In some examples, machine learning techniques such as cluster analysis can be used to determine recommended users. The list of recommended active users may include status user information for each recommended user, such as whether the recommended user is active, in a meeting, idle, in a synchronous multimedia collaboration session, or offline. In some examples, the list of recommended active users further comprises a plurality of actuatable buttons corresponding to some of or all the recommended users (for example, those recommended users with a status indicating availability) that, when selected, may be configured to initiate at least one of a text-based communication session (such as a direct message conversation) or a synchronous multimedia collaboration session).
providing one or more recommendations (Paragraph Number [0097] teaches a list of recommended active users may include a plurality of group-based communication system users recommended based on at least one of user activity, user interaction, or other user information. For example, the list of recommended active users may be selected based on an active status of the users within the group-based communication system; historic, recent, or frequent user interaction with the instant user (such as communicating within the group-based communication channel); or similarity between the recommended users and the instant user (such as determining that a recommended user shares common membership in channels with the instant user). In some examples, machine learning techniques such as cluster analysis can be used to determine recommended users. The list of recommended active users may include status user information for each recommended user, such as whether the recommended user is active, in a meeting, idle, in a synchronous multimedia collaboration session, or offline. In some examples, the list of recommended active users further comprises a plurality of actuatable buttons corresponding to some of or all the recommended users (for example, those recommended users with a status indicating availability) that, when selected, may be configured to initiate at least one of a text-based communication session (such as a direct message conversation) or a synchronous multimedia collaboration session).
updating the process within the user interface using the one or more scores to enrich the process with additional contextual data (Paragraph Number [0157] teaches contents presented in the first section 602 may have been added or uploaded from another virtual space, such as a canvas, communication channel, workspace, collaborative document, etc. associated with the communication platform. Contents uploaded or pulled from a virtual space may include textual data, audio data, video data, images, files and/or any other type of data that may be configured to be presented in the first section 602 of the synchronous multimedia collaboration session. In some examples, the content items presented in the first section 602 may be entered and/or edited during the teleconferencing meeting by one or more users. Alternatively, or in addition to, one or more of the content items in the first section 602 may be generated and/or updated using a ML model. Paragraph Number [0179] teaches the thread summary may be updated in instances where additional communications occur within the thread. For example, an additional reply may be posted to the thread 722 after the thread summary 728 has been generated and, in response, the summarization engine may utilize ML model(s) to generate a second thread summary or update the original thread summary. In some examples, the user(s) that requested the generation of the thread summary may be notified (e.g., receive an automatic message, email, etc.) that a new thread summary is available for review. In some examples, prior to generating a new thread summary, the communication platform may send a notification to the user(s) confirming that the user(s) would like an updated thread summary).
Maurer teaches process management including extracting collaborative sentiment from shared workspaces but does not explicitly teach utilizing machine learning to determine improvements to a process that can be implemented via a RPA bot as described by the following citations from Touati:
which will improve a sentiment associated with the one or more scores generated based on the collaborative communications (Paragraph Number [0131] teaches the method may perform recommendations at step S270. In exemplary embodiments, a recommendation service may observe use behavior and recommend appropriate next actions leveraging deep learning technologies. The recommendation service may leverage sentiment analysis to ensure users are having an optimal experience. The method may also perform virtual development at step S280. In exemplary embodiments, a bot responds to user requests and automatically configure records according to the users ‘natural language requests. The method may further perform integration building at step S290. In exemplary embodiments, the integration builder service is leveraged by end users to construct new integrations out of connected systems, events, and actions).
wherein the machine learning model utilizes one or more simulation methods (Paragraph Number [0124] teaches the User-Facing services include a Virtual Developer 1, a Wizard 2 and a Recommender 3 all connected via a Bus 4. The Virtual Developer 1 includes a ML- and AI-powered NLP bot configured to answer user questions, fill out forms, and build integrations and bots on behalf of the users. The Virtual Developer 1 may respond to user requests and may automatically configure records according to the user's natural language requests. In exemplary embodiments, the Wizard 2, or low-code integration builder service, may be leveraged by end users to construct new integrations and robotic process automations (RPAs). The Wizard 2 may include a serverless front end and an operational data store, and may store configurations made by users through either the Virtual Developer 1 or the low-code interfaces. In exemplary embodiments, the Recommender 3 is configured to observe user behavior and to recommend appropriate next actions leveraging deep learning technologies. The Recommender 3 may leverage sentiment analysis to ensure that users are having an optimal experience. The Recommender 3 may reach out to multiple different NLP services in order to obtain the most accurate predictions for any scenario. In exemplary embodiments, the Virtual Developer 1, Wizard 2, and Recommender 3 may all share information to stay in sync with respect to user inputs. Paragraph Number [0105] teaches at runtime, both the rules based decision tree and ML methods may be used simultaneously or contemporaneously, and a confidence score may be calculated for each based on previous user experiences).
Both Maurer and Touati are directed to process management. Maurer discloses process management including extracting collaborative sentiment from shared workspaces. Touati improves upon Maurer by disclosing utilizing machine learning to determine improvements to a process that can be implemented via a RPA bot. One of ordinary skill in the art would be motivated to further include utilizing machine learning to determine improvements to a process that can be implemented via a RPA bot, to efficiently make and implement automation recommendations to optimize workflows and workloads in a collaborative work project. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of process management including extracting collaborative sentiment from shared workspaces in Maurer to further utilize machine learning to determine improvements to a process that can be implemented via a RPA bot as disclosed in Touati, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 8, Maurer teaches:
A computer system for process mining and optimization, comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising: program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more memories (Paragraph Numbers [0055]-[0058] teach the user computing device 104 can include one or more processors 130, computer-readable media 132, one or more communication interfaces 134, and input/output devices 136. Each processor of the processor(s) 130 can be a single processing unit or multiple processing units, and further can include single or multiple computing units or multiple processing cores. The processor(s) 130 can comprise any of the types of processors described above with reference to the processor(s) 108 and may be the same as or different than the processor(s) 108. the computer-readable media 132 can comprise any of the types of computer-readable media 132 described above with reference to the computer-readable media 110 and may be the same as or different than the computer-readable media 110. Functional components stored in the computer-readable media can optionally include at least one application 138 and an operating system 140. In at least one example, the application 138 can be a mobile application, a web application, or a desktop application, which can be provided by the communication platform, or which can be an otherwise dedicated application. In some examples, individual user computing devices associated with the environment 100 can have an instance or versioned instance of the application 138, which can be downloaded from an application store, accessible via the Internet, or otherwise executable by the processor(s) 130 to perform operations as described herein).
The remainer of the claim limitations are substantially similar to those found in claim 1 and are rejected for the same reasons put forth in regard to claim 1.
As per claim 15, Maurer teaches:
A computer program product for process mining and optimization, comprising: one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising: program instructions, stored on at least one of the one or more computer-readable storage media (Paragraph Numbers [0055]-[0058] teach the user computing device 104 can include one or more processors 130, computer-readable media 132, one or more communication interfaces 134, and input/output devices 136. Each processor of the processor(s) 130 can be a single processing unit or multiple processing units, and further can include single or multiple computing units or multiple processing cores. The processor(s) 130 can comprise any of the types of processors described above with reference to the processor(s) 108 and may be the same as or different than the processor(s) 108. the computer-readable media 132 can comprise any of the types of computer-readable media 132 described above with reference to the computer-readable media 110 and may be the same as or different than the computer-readable media 110. Functional components stored in the computer-readable media can optionally include at least one application 138 and an operating system 140. In at least one example, the application 138 can be a mobile application, a web application, or a desktop application, which can be provided by the communication platform, or which can be an otherwise dedicated application. In some examples, individual user computing devices associated with the environment 100 can have an instance or versioned instance of the application 138, which can be downloaded from an application store, accessible via the Internet, or otherwise executable by the processor(s) 130 to perform operations as described herein).
The remainer of the claim limitations are substantially similar to those found in claim 1 and are rejected for the same reasons put forth in regard to claim 1.
As per claims 2, 9, and 16, Maurer teaches each of the limitations of claims 1, 8, and 15 respectively.
In addition, Maurer teaches:
wherein the additional contextual data enables the user to visualize, within the user interface, which of the one or more activities comprising the process provides further automation opportunities (Paragraph Number [0122] teaches a user interface 300 for automation in the group-based communication system. Automation, also referred to as workflows, allow users to automate functionality within the group-based communication system. Workflow builder 302 is depicted which allows a user to create new workflows, modify existing workflows, and review the workflow activity. Workflow builder 302 may comprise a workflow tab 304, an activity tab 306, and/or a settings tab 308. In some examples, workflow builder may include a publish button 314 which permits a user to publish a new or modified workflow. Paragraph Number [0123] teaches the workflow tab 304 may be selected to enable a user to create a new workflow or to modify an existing workflow. For example, a user may wish to create a workflow to automatically welcome new users who join a channel. A workflow may comprise workflow steps 310. Workflow steps 310 may comprise at least one trigger which initiates the workflow and at least one function which takes an action once the workflow is triggered. For example, a workflow may be triggered when a user joins a channel, and a function of the workflow may be to post within the channel welcoming the new user. In some examples, workflows may be triggered from a user action, such as a user reacting to a message, joining a channel, or collaborating in a collaborative document, from a scheduled date and time, or from a web request from a third-party application or service. In further examples, workflow functionality may include sending messages or forms to users, channels, or any other virtual space, modifying collaborative documents, or interfacing with applications. Workflow functionality may include workflow variables 312. For example, a welcome message may include a user's name via a variable to allow for a customized message. Users may edit existing workflow steps or add new workflow steps depending on the desired workflow functionality. Once a workflow is complete, a user may publish the workflow using publish button 314. A published workflow will wait until it is triggered, at which point the functions will be executed.).
As per claims 3, 10, and 17, Maurer teaches each of the limitations of claims 1, 8, and 15 respectively.
In addition, Maurer teaches:
wherein the one or more scores are a numerical value corresponding to a sentiment derived from the collaborative communications between the one or more individuals (Paragraph Number [0140] teaches the machine-learning model 142 may comprise a singular ML model that is trainable to fuse the various data types comprising the input data 405. For example, the machine learning model 142 may comprise a deep neural network (DNN), capable of multimodal fusion of audio, video, and text data. Accordingly, the machine-learning model 142 of some examples may comprise a DNN architecture including separate fully connected layers per data modality. That is, the ML model(s) 142 may be trained to learn relationships between instances of input data of the same type (such as for example, by implementing pairs of fully connected layers per data modality). The outputs of each fully connected layer may then be merged (such as by concatenation). Alternatively, the ML model 142 may comprise a first model and second model (not shown), each model being trainable to learn relationships between input data of different types and transcripts. That is, the first model and second model comprising the ML model 142 of some examples may implement ensemble learning algorithms. In some examples, the ML model(s) 142 may include a Generative Pre-trained Transformer 3 (GPT-3) model, a neural model for summarization, such as an abstractive or a generative summarization model, a machine-learned summarization model, natural language processing, machine learning, and/or other techniques that identify meaning and/or sentiment in messages within the virtual space and/or the communication session).
As per claims 6, 13, and 20, Maurer teaches each of the limitations of claims 1, 8, and 15 respectively.
Maurer teaches process management including extracting collaborative sentiment from shared workspaces but does not explicitly teach utilizing machine learning to determine improvements to a process that can be implemented via a RPA bot as described by the following citations from Touati:
monitoring an implementation of at least one of the one or more recommendations provided to the user into the process using at least one or more new process event logs and one or more performance metrics. (Paragraph Number [0126] teaches mining service 11 may perform various functions such as, e.g., monitor external event logs to identify business processes by correlating the timings and dependencies of various events, identify portions of identified processes that can be easily automated by the platform and inform end-users of this discovery, and build automations/integrations for these identified portions. Paragraph Number [0131] teaches the method may perform recommendations at step S270. In exemplary embodiments, a recommendation service may observe use behavior and recommend appropriate next actions leveraging deep learning technologies. The recommendation service may leverage sentiment analysis to ensure users are having an optimal experience. The method may also perform virtual development at step S280. In exemplary embodiments, a bot responds to user requests and automatically configure records according to the users ‘natural language requests. The method may further perform integration building at step S290. In exemplary embodiments, the integration builder service is leveraged by end users to construct new integrations out of connected systems, events, and actions. The method may also use machine learning at step S295. In exemplary embodiments, the system may host its own machine learning algorithms to power the recommendation engine and virtual developer and also reach out to external machine learning commodities to enhance performance. In exemplary embodiments, through the event log and immutable blockchain ledger, the system may also be configured to service as a source of truth for audits and data integrity purposes).
presenting one or more prompts to the user within the user interface, wherein the one or more prompts are designed to gather feedback from the user with respect to the at the at least one of the one or more recommendations (Paragraph Number [0131] the method may perform recommendations at step S270. In exemplary embodiments, a recommendation service may observe use behavior and recommend appropriate next actions leveraging deep learning technologies. The recommendation service may leverage sentiment analysis to ensure users are having an optimal experience. The method may also perform virtual development at step S280. In exemplary embodiments, a bot responds to user requests and automatically configure records according to the users ‘natural language requests. The method may further perform integration building at step S290. In exemplary embodiments, the integration builder service is leveraged by end users to construct new integrations out of connected systems, events, and actions. The method may also use machine learning at step S295. In exemplary embodiments, the system may host its own machine learning algorithms to power the recommendation engine and virtual developer and also reach out to external machine learning commodities to enhance performance. In exemplary embodiments, through the event log and immutable blockchain ledger, the system may also be configured to service as a source of truth for audits and data integrity purposes).
retraining the machine learning model to generate improved recommendations in the future specific to the user (Paragraph Number [0062] teaches NLP is coupled with Chatbot technology to enable text and/or voice-based interaction with the system. Many traditional enterprise platforms offer the ability to switch between low code and traditional coding views. This instead offers the ability to switch between conversational and low code views. One advantage of the system is that it may improve in its NLP over time both in aggregate and as well as improving individual integration performance. The system can learn from low-code activities and use the interactions with NLP as compared to low-code interactions to offer additional training to the ML system and through the use of feedback generated from the data regarding the disparity between the NLP interpretation and the low-code specifications, system training and feedback may occur. Paragraph Number [0107] teaches added flexibility due to the use of speech to interrogate the actual end-user data itself and its ability to work with a wide range of application APIs that can be auto-discovered and exposed to the speech/bot interface. In exemplary embodiments, the controlled vocabulary and use of ML allows to refine the relationship between intent and entity extraction on an aggregate basis, e.g. to optimize both a single tenant system or globally using these insights across all systems, as well as enhance individual profiles by learning from integration success and proxies for success, sentiment analysis of communications and human-based training and audit review).
A person with ordinary skill would have been motivated to combine these references as described in regard to claim 5.
As per claims 7 and 14, the combination of Maurer and Touati teaches each of the limitations of claims 1 and 6, and 8 and 13 respectively.
Maurer teaches process management including extracting collaborative sentiment from shared workspaces but does not explicitly teach utilizing machine learning to determine improvements to a process that can be implemented via a RPA bot as described by the following citations from Touati:
wherein the improved recommendations includes updating instructions provided to a Robotic Process Automation (RPA) bot, wherein the instructions enable the RPA bot to perform one or more activities of the process which were previously not automated. (Paragraph Number [0039] teaches a flowchart illustrating a method of dynamic software integration in accordance with exemplary embodiments. In exemplary embodiments, the method starts at step S110 where the integration of a plurality of services is performed. In exemplary embodiments, integration and/or automation is performed by connecting a plurality of software systems via connectors, the connectors being, e.g., applications, application programming interfaces (APIs), or conversational/robotic process automation (RPA) bots, allowing users to string together and orchestrate bots with their applications. Paragraph Number [0062] teaches the combination of low-code and chatbot interfaces may allow users to rapidly bring about hyper automation transformations to their businesses by delivering RPA and integration technologies. Paragraph [0124] teaches the User-Facing services include a Virtual Developer 1, a Wizard 2 and a Recommender 3 all connected via a Bus 4. The Virtual Developer 1 includes a ML- and AI-powered NLP bot configured to answer user questions, fill out forms, and build integrations and bots on behalf of the users. The Virtual Developer 1 may respond to user requests and may automatically configure records according to the user's natural language requests. In exemplary embodiments, the Wizard 2, or low-code integration builder service, may be leveraged by end users to construct new integrations and robotic process automations (RPAs). The Wizard 2 may include a serverless front end and an operational data store, and may store configurations made by users through either the Virtual Developer 1 or the low-code interfaces).
A person with ordinary skill would have been motivated to combine these references as described in regard to claim 1.
As per claim 22, the combination of Maurer and Touati teaches each of the limitations of claim 1.
Maurer teaches process management including extracting collaborative sentiment from shared workspaces but does not explicitly teach utilizing machine learning to determine improvements to a process that can be implemented via a RPA bot as described by the following citations from Touati:
wherein the machine learning model is utilized to simulate the one or more recommendations prior to providing the one or more recommendations to the user, wherein a simulation for each of the one or more recommendations includes a projected metric improvement (Paragraph Number [0124] teaches the User-Facing services include a Virtual Developer 1, a Wizard 2 and a Recommender 3 all connected via a Bus 4. The Virtual Developer 1 includes a ML- and AI-powered NLP bot configured to answer user questions, fill out forms, and build integrations and bots on behalf of the users. The Virtual Developer 1 may respond to user requests and may automatically configure records according to the user's natural language requests. In exemplary embodiments, the Wizard 2, or low-code integration builder service, may be leveraged by end users to construct new integrations and robotic process automations (RPAs). The Wizard 2 may include a serverless front end and an operational data store, and may store configurations made by users through either the Virtual Developer 1 or the low-code interfaces. In exemplary embodiments, the Recommender 3 is configured to observe user behavior and to recommend appropriate next actions leveraging deep learning technologies. The Recommender 3 may leverage sentiment analysis to ensure that users are having an optimal experience. The Recommender 3 may reach out to multiple different NLP services in order to obtain the most accurate predictions for any scenario. In exemplary embodiments, the Virtual Developer 1, Wizard 2, and Recommender 3 may all share information to stay in sync with respect to user inputs. Paragraph Number [0105] teaches at runtime, both the rules based decision tree and ML methods may be used simultaneously or contemporaneously, and a confidence score may be calculated for each based on previous user experiences. Paragraph Number [0131] teaches the method may perform recommendations at step S270. In exemplary embodiments, a recommendation service may observe use behavior and recommend appropriate next actions leveraging deep learning technologies. The recommendation service may leverage sentiment analysis to ensure users are having an optimal experience. The method may also perform virtual development at step S280. In exemplary embodiments, a bot responds to user requests and automatically configure records according to the users ‘natural language requests. The method may further perform integration building at step S290. In exemplary embodiments, the integration builder service is leveraged by end users to construct new integrations out of connected systems, events, and actions).
A person with ordinary skill would have been motivated to combine these references as described in regard to claim 1.
As per claim 23, the combination of Maurer and Touati teaches each of the limitations of claim 1.
Maurer teaches process management including extracting collaborative sentiment from shared workspaces but does not explicitly teach utilizing machine learning to determine improvements to a process that can be implemented via a RPA bot as described by the following citations from Touati:
further comprising: identifying hot zones within the process based on the one or more activities with a low corresponding score (Paragraph Number [0062] teaches NLP is coupled with Chatbot technology to enable text and/or voice based interaction with the system. Many traditional enterprise platforms offer the ability to switch between low code and traditional coding views. This instead offers the ability to switch between conversational and low code views. One advantage of the system is that it may improve in its NLP over time both in aggregate and as well as improving individual integration performance. The system can learn from low-code activities and use the interactions with NLP as compared to low-code interactions to offer additional training to the ML system and through the use of feedback generated from the data regarding the disparity between the NLP interpretation and the low-code specifications, system training and feedback may occur. Hence, the platform according to exemplary embodiments may better translate the user's instructions using ML with more accurate interpretation of the user's intent. Hand-tuning and interpretation of the platform can further advance its accuracy. The combination of low-code and chatbot interfaces may allow users to rapidly bring about hyper automation transformations to their businesses by delivering RPA and integration technologies).
and evaluating an effectiveness of existing Robot Process Automations (RPAs) being utilized in the hot zones (Paragraph Number [0124] teaches the User-Facing services include a Virtual Developer 1, a Wizard 2 and a Recommender 3 all connected via a Bus 4. The Virtual Developer 1 includes a ML- and AI-powered NLP bot configured to answer user questions, fill out forms, and build integrations and bots on behalf of the users. The Virtual Developer 1 may respond to user requests and may automatically configure records according to the user's natural language requests. In exemplary embodiments, the Wizard 2, or low-code integration builder service, may be leveraged by end users to construct new integrations and robotic process automations (RPAs). The Wizard 2 may include a serverless front end and an operational data store, and may store configurations made by users through either the Virtual Developer 1 or the low-code interfaces).
A person with ordinary skill would have been motivated to combine these references as described in regard to claim 1.
As per claim 24, the combination of Maurer and Touati teaches each of the limitations of claims 1 and 23.
Maurer teaches process management including extracting collaborative sentiment from shared workspaces but does not explicitly teach utilizing machine learning to determine improvements to a process that can be implemented via a RPA bot as described by the following citations from Touati:
further comprising: replacing at least one of the existing RPAs being utilized in the hot zones and providing updated or new instructions to at least one of the existing RPAs being utilized in the hot zones (Paragraph Number [0124] teaches the User-Facing services include a Virtual Developer 1, a Wizard 2 and a Recommender 3 all connected via a Bus 4. The Virtual Developer 1 includes a ML- and AI-powered NLP bot configured to answer user questions, fill out forms, and build integrations and bots on behalf of the users. The Virtual Developer 1 may respond to user requests and may automatically configure records according to the user's natural language requests. In exemplary embodiments, the Wizard 2, or low-code integration builder service, may be leveraged by end users to construct new integrations and robotic process automations (RPAs). The Wizard 2 may include a serverless front end and an operational data store, and may store configurations made by users through either the Virtual Developer 1 or the low-code interfaces. In exemplary embodiments, the Recommender 3 is configured to observe user behavior and to recommend appropriate next actions leveraging deep learning technologies. The Recommender 3 may leverage sentiment analysis to ensure that users are having an optimal experience. The Recommender 3 may reach out to multiple different NLP services in order to obtain the most accurate predictions for any scenario. In exemplary embodiments, the Virtual Developer 1, Wizard 2, and Recommender 3 may all share information to stay in sync with respect to user inputs).
A person with ordinary skill would have been motivated to combine these references as described in regard to claim 1.
As per claim 25, the combination of Maurer and Touati teaches each of the limitations of claim 1.
Maurer teaches process management including extracting collaborative sentiment from shared workspaces but does not explicitly teach utilizing machine learning to determine improvements to a process that can be implemented via a RPA bot as described by the following citations from Touati:
wherein an enriched process includes visual representations enabling the user to identify areas of the enriched process to be improved (Paragraph Number [0092] teaches auto-mapping recommender 116 may suggest mappings to user 102 in a graphical representation. Such a graphical representation may demonstrate connections between the fields of one asset to the fields of the second asset with lines, arrows, or other visual connectors. Auto-mapping recommender 116 may further translate recommended mappings into a script in an expression or scripting language. Such translation is advantageous because user 102 may view an alternate, textual form of the recommendation and make modifications therein. Moreover, the script may subsequently be executed at runtime to link the source fields and target fields. Additionally, the script may be updated, either manually by user 102 or by auto-mapping recommender 116, to perform additional data transformations. For example, a small date time field may be translated to a date field, a long integer transformed into a floating point number, etc. Paragraph Number [0117] teaches extracting Analytics Workflows using Conversational artificial intelligence (AI)—The platform according to exemplary embodiments can also provide bi-directional insights/communication through automated testing, recommendations and other data that is informed by system status and analysis of meta-data that is generated from the platform according to exemplary embodiments and the enterprise use. The platform according to exemplary embodiments, is also configured to show analytics workflows both of the platform itself as well as the constituent enterprise software packages that make up the integrated system of an organization).
A person with ordinary skill would have been motivated to combine these references as described in regard to claim 1.
As per claim 26, the combination of Maurer and Touati teaches each of the limitations of claim 1 and 25.
In addition, Maurer teaches:
wherein the identified areas are ranked as automation candidates which prioritize impact on the process (Paragraph Number [0122] teaches a user interface 300 for automation in the group-based communication system. Automation, also referred to as workflows, allow users to automate functionality within the group-based communication system. Workflow builder 302 is depicted which allows a user to create new workflows, modify existing workflows, and review the workflow activity. Workflow builder 302 may comprise a workflow tab 304, an activity tab 306, and/or a settings tab 308. In some examples, workflow builder may include a publish button 314 which permits a user to publish a new or modified workflow. Paragraph Number [0123] teaches the workflow tab 304 may be selected to enable a user to create a new workflow or to modify an existing workflow. For example, a user may wish to create a workflow to automatically welcome new users who join a channel. A workflow may comprise workflow steps 310. Workflow steps 310 may comprise at least one trigger which initiates the workflow and at least one function which takes an action once the workflow is triggered. For example, a workflow may be triggered when a user joins a channel, and a function of the workflow may be to post within the channel welcoming the new user. In some examples, workflows may be triggered from a user action, such as a user reacting to a message, joining a channel, or collaborating in a collaborative document, from a scheduled date and time, or from a web request from a third-party application or service. In further examples, workflow functionality may include sending messages or forms to users, channels, or any other virtual space, modifying collaborative documents, or interfacing with applications. Workflow functionality may include workflow variables 312. For example, a welcome message may include a user's name via a variable to allow for a customized message. Users may edit existing workflow steps or add new workflow steps depending on the desired workflow functionality. Once a workflow is complete, a user may publish the workflow using publish button 314. A published workflow will wait until it is triggered, at which point the functions will be executed. (See also Paragraph Number [0186] in regard to importance or priority of actions that affect the workflow)).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2024/0176960 to Maurer et al. (hereafter referred to as Maurer) in view of U.S. Patent Application Publication Number 2022/0129257 to Touati et al. (hereafter referred to as Touati) in further view of U.S. Patent Application Publication Number 2017/0357893 to Dexter et al. (hereafter referred to as Dexter) and in even further view of U.S. Patent Application Publication Number 2008/0313110 to Kreamer et al. (hereafter referred to as Kreamer).
As per claim 21, the combination of Maurer and Touati teaches each of the limitations of claim 1.
Maurer teaches process management including extracting collaborative sentiment from shared workspaces but does not explicitly teach utilizing a Monte Carlo simulation process as an input as described by the following citations from Dexter:
where the one or more simulation methods include an Artificial Intelligence (AI) powered simulation of the process using a Monte Carlo simulation process... and the one or more scores as input (Paragraph Number [0011] teaches systems and methods for using an artificial intelligence (AI) controller to select an AI algorithm for use in a software application component. In the context of this disclosure, an AI controller is a simulated intelligence incorporating algorithms to satisfy conditions. AI in general in this disclosure may include aspects of machine learning, optimization using an objective function, incorporating feedback, or applying an algorithm to determine an output or set of outputs from an input or set of inputs. In this disclosure, an AI algorithm includes algorithms to solve particular technical problems based on state information and at least one end condition of a software application component. The AI algorithms may include, but are not limited to, a minmax algorithm, a Monte Carlo algorithm, a neural net algorithm, a decision tree algorithm, a Q-learning algorithm, or the like. Paragraph Number [0014] teaches the AI controller 106 may simulate AI algorithms from the plurality of AI algorithms applied to the software application component 104 or aspects of the software application component 104 to select an AI algorithm. For example, an AI algorithm may include a minmax algorithm, which is computationally complex but guaranteed to give an optimal solution or a Monte Carlo algorithm, which is computationally cheap, but gives a potentially suboptimal solution. In an example, if the AI controller 106 determines from the state information that there are many potential state changes and a solution is needed quickly, the AI controller 106 may select the Monte Carlo algorithm. In another example, if the AI controller 106 determines from the state information that an optimal solution is desirable, the AI controller 106 may select the minmax algorithm. For example, an optimal solution would be desirable when possible (e.g., a short computational time to determine the optimal solution), or when accuracy is preferable over timeliness (e.g., when a user selects this preference or when the AI controller 106 determines the current state information implies at a particularly important decision)).
Both the combination of Maurer and Touati and Dexter are directed to process management. The combination of Maurer and Touati discloses process management including extracting collaborative sentiment from shared workspaces. Dexter improves upon the combination of Maurer and Touati by disclosing utilizing a Monte Carlo simulation process as an input. One of ordinary skill in the art would be motivated to further include utilizing a Monte Carlo simulation process as an input, to efficiently utilize a ready-made and conventional algorithm to process data in a specific way. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of utilizing a Monte Carlo simulation process as an input in the combination of Maurer and Touati to further utilize utilizing a Monte Carlo simulation process as an input as disclosed in Dexter, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Maurer teaches process management including extracting collaborative sentiment from shared workspaces but does not explicitly teach utilizing Component Business Models as an input as described by the following citations from Kreamer:
Component Business Models (Paragraph Number [0180] teaches component business modeling (CBM) is being utilized by the consulting industry to understand and transform businesses. A component business model represents the entire business in a simple framework that fits on a single page. CBM is an evolution of traditional views of a business, such as ones through business units, functions, geography, processes or workflow. The component business model methodology helps identify basic building blocks of business, where each building block includes the people, processes and technology needed by this component to act as a standalone entity and deliver value to the organization. This single page perspective provides a view of the business, which is not constricted by barriers that could potentially hamper the ability to make a meaningful business transformation. The component business model facilitates to identify which components of the business create differentiation and value. It also helps identify where the business has capability gaps that need to be addressed, as well as opportunities to improve efficiency and lower costs across the entire enterprise).
Both the combination of Maurer, Touati, and Dexter and Kreamer are directed to process management. The combination of Maurer, Touati, and Dexter discloses process management including extracting collaborative sentiment from shared workspaces. Kreamer improves upon the combination of Maurer, Touati, and Dexter by disclosing utilizing Component Business Models as an input. One of ordinary skill in the art would be motivated to further include utilizing Component Business Models as an input, to efficiently utilize a ready-made and conventional algorithm to process data in a specific way. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of process management including extracting collaborative sentiment from shared workspaces in the combination of Maurer, Touati, and Dexter to further utilize Component Business Models as an input as disclosed in Kreamer, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Response to Arguments
Applicant’s arguments filed 11/20/2025 have been fully considered but they are not fully persuasive.
Applicant argues that the claims are eligible under 35 USC 101. (See Applicant’s Remarks, 11/20/2025, pgs. 10-11). Examiner respectfully disagrees. As noted in the 35 USC 101 analysis presented above, the claims recite an abstract concept that is encapsulated by decision making analogous to a method of organizing human activity. Examiner notes that each of the limitations that encapsulate the abstract concepts are recited in the above 35 USC 101. Implementing a knowledge graph and improving its functionality are abstract concepts. Being able to understand, add to, and manipulate a knowledge graph is additionally an abstract concept. Other than storing the knowledge graph in a computer database, the knowledge graph and its associated manipulations are wholly independent from computer technology. Additionally, the claims do not recite a practical application of the abstract concepts in that there is no specific use or application of the method steps other than to make conclusory determinations and provide for direction for either a person or machine to follow at some future time. The claims do not recite any particular use for these determinations and directions that improve upon the underlying computer technology (in this instance the computer software, processor, and memory). Instead, Examiner asserts that the additional elements in the claim language are only used as implementation of the abstract concepts utilizing technology. The concepts described in the limitations when taken both as a whole and individually are not meaningfully different than those found by the courts to be abstract ideas and are similarly considered to be certain methods of organizing human activity such as managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions. The steps are then encapsulated into a particular technological environment by executing these steps upon a computer processor and utilizing features such as a computer interface or sending and receiving data over a network or displaying information via a computerized graphical user interface. However, sending and receiving of information over a network and execution of algorithms on a computer are utilized only to facilitate the abstract concepts (i.e. selecting data on an interface, publishing/displaying information, etc.). As such, Examiner asserts that the implementation of the abstract concepts recited by the claims utilize computer technology in a way that is considered to be generally linking the use of the judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)). Accordingly, Examiner does not find that the claims recite a practical application of the abstract concepts recited by the claims.
Applicant argues that the previously cited reference does not teach the newly amended portions including the new limitations recited by the independent claims. (See Applicant’s Remarks, 11/20/2025, pgs. 12-13). Examiner respectfully disagrees. Examiner notes that new citations from the previously cited references have been applied to the newly presented claim limitations as indicated in the above in the new 35 USC 103 rejection. Examiner has added and emphasized specific portions of the Maurer and Touati references to read on the new independent claims. As such, Applicant’s arguments directed towards the previous rejection are moot. In response to Applicant’s arguments, Examiner directs Applicant to review the new citations and explanations provided in the new 35 USC 103 rejection presented above.
Conclusion
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW H. DIVELBISS whose telephone number is (571) 270-0166. The fax phone number is 571-483-7110. The examiner can normally be reached on M-Th, 7:00 - 5:00. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached on (571) 272-6787.
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/M.H.D/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624