MULTI-PARTY CROSS-PLATFORM QUERY AND CONTENT CREATION SERVICE AND INTERFACE FOR COLLABORATION PLATFORMS

§101 §103

DETAILED ACTION 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 . Status of Claims The following is a final office action. Claims 1-6 and 8-20 are currently pending and have been examined on their merits. Claims 1, 10, and 15 are newly amended see REMARKS June 25, 2026. Claim 7 is currently cancelled see REMARKS June 25, 2026. 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 1-6 and 8-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1: Claims 1-6 and 8-20 recite a method (i.e. a series of steps), and therefore each claim falls within one of the four statutory categories. Step 2A prong 1 (Is a judicial exception recited?): The representative claim 1 recites: a method comprising: in response to a user input, instantiating a generative service, the generative service, receive a natural language input at an input region; in response to user input provided to the input region, analyzing the user input to determine an action intent; evaluating a first degree of correlation between the action intent and a first subject-matter expertise of a first automated assistant service by analyzing a first semantic similarity of the action intent with text representing the first subject-matter expertise of the first automated assistant service and a second degree of correlation between the action intent and a second subject-matter expertise of a second automated assistant service by analyzing a second semantic similarity of the action intent with text representing the second subject-matter expertise of the second automated assistant service; in response to the first degree of correlation being greater than the second degree of correlation, causing the first automated assistant service to generate a prompt comprising: predefined query prompt text associated with the first subject-matter expertise; at least a portion of the natural language user input; and text extracted from the content displayed in the content panel; providing the prompt and obtaining a generative response; and causing (presentation) of a result based on the generative response. Claim 10: a method comprising: receive a natural language input at an input region; in response to a first user input provided to the input region, analyzing the first user input to determine a first action intent; in response to determining the first action intent corresponds to a first subject-matter expertise of a first automated assistant service by analyzing a first semantic similarity of the action intent with text representing the first subject matter expertise of the first automated assistant service, causing the first automated assistant service to generate a first prompt comprising: first predefined query prompt text associated with the first subject-matter expertise; and text extracted from the content displayed in the content panel; providing the first prompt and obtaining a first generative response; causing (presentation) of a first result based on the first generative response; in response to a second user input provided to the input region, analyzing the second user input to determine a second action intent; in response to determining the second action intent corresponds to a second subject-matter expertise of a second automated assistant service by analyzing a second semantic similarity of the action intent with text representing the second subject-matter expertise of the second automated assistant service, causing the second automated assistant service to generate a second prompt comprising second predefined query prompt text associated with the second subject-matter expertise; providing the second prompt and obtaining a second generative response; and causing (presentation) of a second result based on the second generative response. Claim 15: a method comprising: in response to a first user input provided to the input region, analyzing the first user input to determine an action intent; in response to determining the action intent corresponds to a first subject-matter expertise of a first automated assistant service by analyzing a first semantic similarity of the action intent with text representing the first subject-matter expertise of the first automated assistant service, causing the first automated assistant service to generate a first prompt comprising: first predefined query prompt text associated with the first subject-matter expertise; and at least a portion of the natural language user input; providing the first prompt and obtaining a first generative response; causing (presentation) of a first result based on the first generative response; causing a second automated assistant service to generate a second prompt comprising: second predefined query prompt text associated with a second subject-matter expertise; and at least a portion of the first generative response; providing the second prompt and obtaining a second generative response; and causing (presentation) of a second result based on the second generative response. The claims recite a certain method of organizing human activity. The claims are found to be considered a method of organizing human activity as they relate towards managing personal behavior or relationships or interactions between people. As the claims recite a method for receiving a user prompt, identifying a user intent of the prompt, identifying an assistant for generating a response based on the intent, and generating a response to the prompt. The method merely recites a series of steps for receiving a user input and identifying an assistant service that can perform a desired response based on a user’s intent. Alternatively, the claims also recite a mental process. The claims recite merely receiving and analyzing user input such as receiving a user prompt and determining a desired intent, based on the identified intent identifying an assistant, and generating a response to the prompt. The examiner finds these limitations to merely be observations, evaluations, judgements, and opinions. As the claims recite receiving and evaluating a user prompt to determine an intent and evaluate the intent to identify an assistant service for generating a response to the prompt. Furthermore, the examiner finds that a user could mentally or with the aid of a “pen and paper” perform the steps of receiving and analyzing user input to determine an intent and identifying an assistant service based on the intent for generating a result. Therefore, the examiner finds the claims to recite an abstract idea. Step 2A Prong 2 (Is the exception integrated into a practical application?): The claims additionally recite; Claim 1: A computer, a multi-participant interface for a content collaboration platform, causing display of a graphical user interface having a content panel depicting content of a content item managed by a content collaboration system, the graphical user interface displayed on a display of a client device; providing the prompt to an external generative output engine and obtaining a generative response from the external generative output engine. Claim 10: A computer, a multi-participant interface for a content collaboration platform, causing display of a graphical user interface having a content panel depicting content of a content item managed by a content collaboration system, the graphical user interface displayed on a display of a client device; providing the prompt to an external generative output engine and obtaining a generative response from the external generative output engine. Claim 15: A computer, a multi-participant interface for a content collaboration platform, causing display of a graphical user interface having a content panel depicting content of a content item managed by a content collaboration system, the graphical user interface displayed on a display of a client device; providing the prompt to an external generative output engine and obtaining a generative response from the external generative output engine. However, the limitations merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Merely utilizing a generic computer system to perform the claim limitations of receiving and analyzing user input prompt to identify an assistant service and generate a response is not an improvement in a technology or technical field. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Step 2B (Does the claim recite additional elements that amount to significantly more that the judicial exception?): As discussed above, the additional imitations amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Therefore, the additional elements do not integrate the judicial exception into a practical application and do not amount to significantly more. Claims 2-9, 11-14, and 16-20 are further narrowing the abstract idea of receiving and analyzing user prompt to determine an assistant service. The independent claims recite the following additional elements: Claims 9 and 20: an issue tracking plugin Claim 8-9, 11-12, and 18-20: a first and second set of plugins. However, the additional elements are directed to merely “apply it” or applying generic computer elements to perform the abstract idea. Therefore, claims 1-6 and 8-20 are rejected under U.S.C. 101. Claim Rejections - 35 USC § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. Claim(s) 1-6 and 8-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grinberg (US 2025/0061285) in view of Bansal (US 2019/0149488). Claim 1: Grinberg discloses a computer-implemented method for operating a multi-participant interface for a content collaboration platform, the method comprising: causing display of a graphical user interface having a content panel depicting content of a content item managed by a content collaboration system, the graphical user interface displayed on a display of a client device (Paragraph [0007-0008]; [0080-0083]; [0099]; [0120]; Fig. 5, embodiments consistent with the present disclosure involve systems, methods, and computer readable medium for building an application incorporating AI functionality. Exemplary operations may include receiving a selection of an AI assistant add-on and at least one of a plurality of SaaS platform elements, enabling implementation of permission for providing access to data from the at least one of the plurality of linked SaaS platform elements. Some embodiments involve performing selection operations for a plurality of distinct artificial intelligence agents. Exemplary embodiments include sending via the application, a prompt to a plurality of distinct AI agents, and receiving from each of the plurality of distinct AI agents a response to the prompt. The operation may further include comparing information associated with each of the received responses, and selecting at least one AI agent from the plurality of distinct AI agents. A software application may include a user interface enabling users to interact with and access features and functionalities of the software application. User interface is consistent with use of the term as described herein. For example, displaying a user interface may be done by showing a message on a screen, such as a pop-up box, or a list of selectable options); in response to a user input, instantiating a generative service, the generative service causing display of a generative interface panel within the graphical user interface, the generative interface panel configured to receive a natural language input at an input region (Paragraph [0117-0121]; Fig. 4, in some embodiments disclosed herein, processes are described that explain ways to select an AI agent from a pool of AI agents. Embodiments may involve selection operations for a plurality of distinct AI agent. An AI agent may be distinct if it provides at least one functions, purpose, solution, task, or use that distinguishes it from other agents. For example, one AI agent may be used to analyze text, while another may be used to analyze audio. Based on a developer’s needs or requests. Some embodiments may involve accessing an application that employes AI functionality. Some examples involve sending a prompt to a plurality of distinct Ai agents. A prompt is a message, question, or indication presented to elicit a response or action. It can be a textual message, a dialog box, an input field where an entity is pinged); in response to user input provided to the input region of the generative interface panel, analyzing the user input to determine an action intent (Paragraph [0117-0121]; Fig. 4, the operations further include analyzing a context associated with the prompt. A context refers to circumstances, conditions, environment, or background in which something exists. Analyzing a context for a prompt refers to examination or consideration of surrounding information and/or relevant factors to better understand the meaning and/or intent of the prompt. For example, a context may refer to any kind of information that associates a query with a specific topic or sub-element. Increasing the context of the query may lead to a more accurate selection of an AI agent); in response to the first degree of correlation being greater than the second degree of correlation, causing the first automated assistant service to generate a prompt comprising: predefined query prompt text associated with the first subject-matter expertise; at least a portion of the natural language user input; and text extracted from the content displayed in the content panel (Paragraph [0123-0126]; [0129-0131]; Fig. 4, receiving from each of a plurality of distinct AI agents a response to the prompt. A response refers to a piece of information that is sent as a reply to a request, query, or preliminary information. The queried AI agents send responses that may provide instructions, information, or data to be further analyzed in the application. Some embodiments may involve comparing information associated with each of the received responses. Comparing information refers to examining two or more sets of data, facts, or details to identify similarities, differences, patterns, and/or relationships. A comparison could be made by analyzing segments or portions of information associated with each of the received responses or by analyzing the entirety of the data. Each AI agent may send differing sets of information which are then compared to determine the most suitable. The information may contain data in an answer to a user query. Analyzing information associated with each of the received responses include evaluating qualify of content of each of the response, determining a response time, or combination thereof. Following ranking of the plurality of AI agents according to the determining scores, the operation further include saving the determined scores in a database. Selecting at least one Ai agent from the plurality of distinct AI agents based on the comparison. Upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user); providing the prompt to an external generative output engine and obtaining a generative response from the external generative output engine; and causing display of a result based on the generative response in the generative interface panel (Paragraph [0129-0131]; [0138-0139]; Fig. 4, upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user. An AI agent may generate and/or provide automatic replies, or rephrasing of text, for a conversation, such as a chat box or emails. The operations further include outputting the response of the at least one selected AI agent. The output may be received by a system or device associated with a user or sender of the query). Gringberg discloses a system of managing a plurality of artificial intelligences in an application and allowing a user to find an AI agent for performing a task based on a query. Whoever, Grinberg does not specifically disclose the following claim limitations: evaluating a first degree of correlation between the action intent and a first subject-matter expertise of a first automated assistant service by analyzing a first semantic similarity of the action intent with text representing the first subject-matter expertise of the firs automated assistant service and a second degree of correlation between the action intent and a second subject-matter expertise of a second automated assistant service by analyzing a second semantic similarity of the action intent with text representing the second subject-matter expertise of the second automated assistant service. In the same field of endeavor of identifying an AI bot for performing an action based on a user’s intent Bansal teaches evaluating a first degree of correlation between the action intent and a first subject-matter expertise of a first automated assistant service by analyzing a first semantic similarity of the action intent with text representing the first subject-matter expertise of the firs automated assistant service and a second degree of correlation between the action intent and a second subject-matter expertise of a second automated assistant service by analyzing a second semantic similarity of the action intent with text representing the second subject-matter expertise of the second automated assistant service (Paragraph [0003-0004]; [0083]; [0093]; [0095-0097]; [0106]; Fig. 9, a search engine or a recommendation engine can use implicit quality signals to help identify an appropriate “BOT” for user by a current user. The recommendation engine initiatives its operation when it receives a BOT recommendation event from a component. That event inform the recommendation engine that a current user may benefit from a suggestion that identifies another BOT that can be used to complete the user’s task at hand. A signal providing component can provide information which describes the intent that the user was attempting to accomplish. The BOT selection logic can use any type of score. Such as a model can map an input vector associated with the input query into a first vector in a low-dimensioned semantic space, and then map an input vector associated with the BOT under consideration into a second vector. The model can then compute the score based on the relation of these two vectors in the low-dimensional semantic space, using any metric of similarity. The score measures an extent to which the candidate BOT matches the input query. BOT selection logic generates the recommendation results that identifies the recommended BOT(s). More specifically, the BOT selection logic can generate a score for each candidate BOT based on features generated by a feature providing component). Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of managing and selecting artificial intelligence functionalities in an application for performing a desired task as disclosed by Grinberg with the system of evaluating a first degree of correlation between the action intent and a first subject-matter expertise of a first automated assistant service by analyzing a first semantic similarity of the action intent with text representing the first subject-matter expertise of the firs automated assistant service and a second degree of correlation between the action intent and a second subject-matter expertise of a second automated assistant service by analyzing a second semantic similarity of the action intent with text representing the second subject-matter expertise of the second automated assistant service as taught by Bansal (Bansal [0106]). With the motivation of helping to identify an ideal candidate BOT or assistant for performing a desired user action (Bansal [0001]). Claim 2: Modified Grinberg discloses the computer-implemented method as per claim 1. Grinberg further discloses wherein: the generative response is a first generative response; the prompt is a first prompt; the result is a first result; the predefined query prompt text is a first predefined query prompt text; in response to the action intent corresponding to a compound action: subsequent to obtaining the first generative response, causing the second automated assistant service to generate a second prompt comprising: second predefined query prompt text associated with the second subject-matter expertise; and text extracted from the first generative response; providing the second prompt to the external generative output engine and obtaining a second generative response from the external generative output engine; and causing display of a second result based on the second generative response in the generative interface panel (Paragraph [0123-0126]; [0129-0131]; [0135-0137]; Fig. 4, receiving from each of a plurality of distinct AI agents a response to the prompt. A response refers to a piece of information that is sent as a reply to a request, query, or preliminary information. The queried AI agents send responses that may provide instructions, information, or data to be further analyzed in the application. Some embodiments may involve comparing information associated with each of the received responses. Comparing information refers to examining two or more sets of data, facts, or details to identify similarities, differences, patterns, and/or relationships. A comparison could be made by analyzing segments or portions of information associated with each of the received responses or by analyzing the entirety of the data. Each Ai agent may send differing sets of information which are then compared to determine the most suitable. The information may contain data in an answer to a user query. Analyzing information associated with each of the received responses include evaluating qualify of content of each of the response, determining a response time, or combination thereof. Following ranking of the plurality of AI agents according to the determining scores, the operation further include saving the determined scores in a database. Selecting at least one Ai agent from the plurality of distinct AI agents based on the comparison. Upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user. Consistent with some embodiments, selecting at least one AI agent from the plurality of distinct AI agents includes selecting at least two AI agents from the plurality of AI agents, and merging responses of the at least two selected AI agents to generate a merged response). Claim 3: Modified Grinberg discloses the computer-implemented method as per claim 2. Grinberg further discloses wherein: the first predefined query prompt text includes content extracted from a first corpus of knowledge base electronic documents directed to the first subject-matter expertise; and the second predefined query prompt text includes content extracted from a second corpus of knowledge base electronic documents directed to the second subject-matter expertise (Paragraph [0123-0126]; [0129-0131]; Fig. 4, receiving from each of a plurality of distinct AI agents a response to the prompt. A response refers to a piece of information that is sent as a reply to a request, query, or preliminary information. The queried AI agents send responses that may provide instructions, information, or data to be further analyzed in the application. Some embodiments may involve comparing information associated with each of the received responses. Comparing information refers to examining two or more sets of data, facts, or details to identify similarities, differences, patterns, and/or relationships. A comparison could be made by analyzing segments or portions of information associated with each of the received responses or by analyzing the entirety of the data. Each Ai agent may send differing sets of information which are then compared to determine the most suitable. The information may contain data in an answer to a user query. Analyzing information associated with each of the received responses include evaluating qualify of content of each of the response, determining a response time, or combination thereof. Following ranking of the plurality of AI agents according to the determining scores, the operation further include saving the determined scores in a database. Selecting at least one Ai agent from the plurality of distinct AI agents based on the comparison. Upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user). Claim 4: Modified Grinberg discloses the computer-implemented method as per claim 1. Grinberg further discloses wherein: the user input is a first user input; the generative response is a first generative response; and the method further comprises storing a set of user inputs, including the first user input, provided to the generative interface panel and a corresponding set of generative responses, including the first generative response, provided by the external generative output engine in a persistence module of the generative service (Paragraph [0123-0126]; [0129-0131]; Fig. 4, receiving from each of a plurality of distinct AI agents a response to the prompt. A response refers to a piece of information that is sent as a reply to a request, query, or preliminary information. The queried AI agents send responses that may provide instructions, information, or data to be further analyzed in the application. Some embodiments may involve comparing information associated with each of the received responses. Comparing information refers to examining two or more sets of data, facts, or details to identify similarities, differences, patterns, and/or relationships. A comparison could be made by analyzing segments or portions of information associated with each of the received responses or by analyzing the entirety of the data. Each Ai agent may send differing sets of information which are then compared to determine the most suitable. The information may contain data in an answer to a user query. Analyzing information associated with each of the received responses include evaluating qualify of content of each of the response, determining a response time, or combination thereof. Following ranking of the plurality of AI agents according to the determining scores, the operation further include saving the determined scores in a database. Selecting at least one Ai agent from the plurality of distinct AI agents based on the comparison. Upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user). Claim 5: Modified Grinberg discloses the computer-implemented method as per claim 4. Grinberg further discloses wherein: the prompt is a first prompt; in response to a second user input provided to the input region, causing the first automated assistant service to generate a second prompt comprising: the predefined query prompt text associated with the first subject-matter expertise; and at least a portion of the set of user inputs or the set of generative responses; providing the second prompt to the external generative output engine and obtaining a second generative response from the external generative output engine; and causing display of a second result based on the second generative response in the generative interface panel (Paragraph [0123-0126]; [0129-0131]; [0135-0137]; Fig. 4, receiving from each of a plurality of distinct AI agents a response to the prompt. A response refers to a piece of information that is sent as a reply to a request, query, or preliminary information. The queried AI agents send responses that may provide instructions, information, or data to be further analyzed in the application. Some embodiments may involve comparing information associated with each of the received responses. Comparing information refers to examining two or more sets of data, facts, or details to identify similarities, differences, patterns, and/or relationships. A comparison could be made by analyzing segments or portions of information associated with each of the received responses or by analyzing the entirety of the data. Each Ai agent may send differing sets of information which are then compared to determine the most suitable. The information may contain data in an answer to a user query. Analyzing information associated with each of the received responses include evaluating qualify of content of each of the response, determining a response time, or combination thereof. Following ranking of the plurality of AI agents according to the determining scores, the operation further include saving the determined scores in a database. Selecting at least one Ai agent from the plurality of distinct AI agents based on the comparison. Upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user. Consistent with some embodiments, selecting at least one AI agent from the plurality of distinct AI agents includes selecting at least two AI agents from the plurality of AI agents, and merging responses of the at least two selected AI agents to generate a merged response). Claim 6: Modified Grinberg discloses the computer-implemented method as per claim 4. Grinberg further discloses wherein: the action intent is a first action intent; in response to a second user input provided to a second input region, analyzing the second user input to determine a second action intent; and evaluating a third degree of correlation between the second action intent and the first subject-matter expertise of the first automated assistant service, evaluation of the third degree of correlation including an analysis of one or more of the set of user inputs or the set of generative responses (Paragraph [0123-0126]; [0129-0131]; [0135-0137]; Fig. 4, receiving from each of a plurality of distinct AI agents a response to the prompt. A response refers to a piece of information that is sent as a reply to a request, query, or preliminary information. The queried AI agents send responses that may provide instructions, information, or data to be further analyzed in the application. Some embodiments may involve comparing information associated with each of the received responses. Comparing information refers to examining two or more sets of data, facts, or details to identify similarities, differences, patterns, and/or relationships. A comparison could be made by analyzing segments or portions of information associated with each of the received responses or by analyzing the entirety of the data. Each Ai agent may send differing sets of information which are then compared to determine the most suitable. The information may contain data in an answer to a user query. Analyzing information associated with each of the received responses include evaluating qualify of content of each of the response, determining a response time, or combination thereof. Following ranking of the plurality of AI agents according to the determining scores, the operation further include saving the determined scores in a database. Selecting at least one Ai agent from the plurality of distinct AI agents based on the comparison. Upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user. Consistent with some embodiments, selecting at least one AI agent from the plurality of distinct AI agents includes selecting at least two AI agents from the plurality of AI agents, and merging responses of the at least two selected AI agents to generate a merged response). Claim 8: Modified Grinberg discloses the computer-implemented method as per claim 1. Grinberg further discloses wherein: the first automated assistant service includes a first set of plugins, each plugin configured to extract content from content items of a respective platform; and the second automated assistant service includes a second set of plugins different than the first set of plugins (Paragraph [0123-0126]; [0129-0131]; [0135-0137]; Fig. 4, receiving from each of a plurality of distinct AI agents a response to the prompt. A response refers to a piece of information that is sent as a reply to a request, query, or preliminary information. The queried AI agents send responses that may provide instructions, information, or data to be further analyzed in the application. Some embodiments may involve comparing information associated with each of the received responses. Comparing information refers to examining two or more sets of data, facts, or details to identify similarities, differences, patterns, and/or relationships. A comparison could be made by analyzing segments or portions of information associated with each of the received responses or by analyzing the entirety of the data. Each Ai agent may send differing sets of information which are then compared to determine the most suitable. The information may contain data in an answer to a user query. Analyzing information associated with each of the received responses include evaluating qualify of content of each of the response, determining a response time, or combination thereof. Following ranking of the plurality of AI agents according to the determining scores, the operation further include saving the determined scores in a database. Selecting at least one Ai agent from the plurality of distinct AI agents based on the comparison. Upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user. Consistent with some embodiments, selecting at least one AI agent from the plurality of distinct AI agents includes selecting at least two AI agents from the plurality of AI agents, and merging responses of the at least two selected AI agents to generate a merged response). Claim 9: Modified Grinberg discloses the computer-implemented method as per claim 8. Grinberg further discloses wherein: the first set of plugins includes an issue tracking plugin configured to extract content from issues managed by an issue tracking platform; and the second set of plugins includes a documentation plugin configured to extract content from pages managed by a documentation platform (Paragraph [0117-0121]; Fig. 4, in some embodiments disclosed herein, processes are described that explain ways to select an AI agent from a pool of AI agents. Embodiments may involve selection operations for a plurality of distinct AI agent. An AI agent may be distinct if it provides at least one functions, purpose, solution, task, or use that distinguishes it from other agents. For example, one AI agent may be used to analyze text, while another may be used to analyze audio. Based on a developer’s needs or requests. Some embodiments may involve accessing an application that employes AI functionality. Some examples involve sending a prompt to a plurality of distinct Ai agents. A prompt is a message, question, or indication presented to elicit a response or action. It can be a textual message, a dialog box, an input field where an entity is pinged). Claim 10: Modified Grinberg discloses a computer-implemented method for operating a cross-platform multi-participant interface for a content collaboration platform, the method comprising: causing display of a graphical user interface having a content panel depicting content of a content item managed by a content collaboration system, the graphical user interface displayed on a display of a client device; causing display of a generative interface panel within the graphical user interface, the generative interface panel configured to receive a natural language input at an input region; in response to a first user input provided to the input region of the generative interface panel, analyzing the first user input to determine a first action intent (Paragraph [0007-0008]; [0080-0083]; [0099]; [0120]; Fig. 5, embodiments consistent with the present disclosure involve systems, methods, and computer readable medium for building an application incorporating AI functionality. Exemplary operations may include receiving a selection of an AI assistant add-on and at least one of a plurality of SaaS platform elements, enabling implementation of permission for providing access to data from the at least one of the plurality of linked SaaS platform elements. Some embodiments involve performing selection operations for a plurality of distinct artificial intelligence agents. Exemplary embodiments include sending via the application, a prompt to a plurality of distinct AI agents, and receiving from each of the plurality of distinct AI agents a response to the prompt. The operation may further include comparing information associated with each of the received responses, and selecting at least one AI agent from the plurality of distinct AI agents. A software application may include a user interface enabling users to interact with and access features and functionalities of the software application. User interface is consistent with use of the term as described herein. For example, displaying a user interface may be done by showing a message on a screen, such as a pop-up box, or a list of selectable options); causing the first automated assistant service to generate a first prompt comprising: first predefined query prompt text associated with the first subject-matter expertise; and text extracted from the content displayed in the content panel; providing the first prompt to an external generative output engine and obtaining a first generative response from the external generative output engine (Paragraph [0117-0121]; Fig. 4, in some embodiments disclosed herein, processes are described that explain ways to select an AI agent from a pool of AI agents. Embodiments may involve selection operations for a plurality of distinct AI agent. An AI agent may be distinct if it provides at least one functions, purpose, solution, task, or use that distinguishes it from other agents. For example, one AI agent may be used to analyze text, while another may be used to analyze audio. Based on a developer’s needs or requests. Some embodiments may involve accessing an application that employes AI functionality. Some examples involve sending a prompt to a plurality of distinct Ai agents. A prompt is a message, question, or indication presented to elicit a response or action. It can be a textual message, a dialog box, an input field where an entity is pinged); causing display of a first result based on the first generative response in the generative interface panel; in response to a second user input provided to the input region of the generative interface panel, analyzing the second user input to determine a second action intent (Paragraph [0117-0121]; Fig. 4, the operations further include analyzing a context associated with the prompt. A context refers to circumstances, conditions, environment, or background in which something exists. Analyzing a context for a prompt refers to examination or consideration of surrounding information and/or relevant factors to better understand the meaning and/or intent of the prompt. For example, a context may refer to any kind of information that associates a query with a specific topic or sub-element. Increasing the context of the query may lead to a more accurate selection of an AI agent); providing the second prompt to the external generative output engine and obtaining a second generative response from the external generative output engine; and causing display of a second result based on the second generative response in the generative interface panel (Paragraph [0123-0126]; [0129-0131]; [0135-0137]; Fig. 4, receiving from each of a plurality of distinct AI agents a response to the prompt. A response refers to a piece of information that is sent as a reply to a request, query, or preliminary information. The queried AI agents send responses that may provide instructions, information, or data to be further analyzed in the application. Some embodiments may involve comparing information associated with each of the received responses. Comparing information refers to examining two or more sets of data, facts, or details to identify similarities, differences, patterns, and/or relationships. A comparison could be made by analyzing segments or portions of information associated with each of the received responses or by analyzing the entirety of the data. Each Ai agent may send differing sets of information which are then compared to determine the most suitable. The information may contain data in an answer to a user query. Analyzing information associated with each of the received responses include evaluating qualify of content of each of the response, determining a response time, or combination thereof. Following ranking of the plurality of AI agents according to the determining scores, the operation further include saving the determined scores in a database. Selecting at least one Ai agent from the plurality of distinct AI agents based on the comparison. Upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user. Consistent with some embodiments, selecting at least one AI agent from the plurality of distinct AI agents includes selecting at least two AI agents from the plurality of AI agents, and merging responses of the at least two selected AI agents to generate a merged response). Gringberg discloses a system of managing a plurality of artificial intelligences in an application and allowing a user to find an AI agent for performing a task based on a query. Whoever, Grinberg does not specifically disclose the following claim limitations: in response to determining the first action intent corresponds to a first subject-matter expertise of a first automated assistant service by analyzing a first semantic similarity of the action intent with text representing the first subject-matter expertise of the first automated assistant service, causing the first automated assistant service to generate a first prompt comprising: first predefined query prompt text associated with the first subject-matter expertise; in response to determining the second action intent corresponds to a second subject-matter expertise of a second automated assistant service by analyzing a second semantic similarity of the action intent with text representing the second subject-matter expertise of the second automated assistant service, causing the second automated assistant service to generate a second prompt comprising second predefined query prompt text associated with the second subject-matter expertise. In the same field of endeavor of identifying an AI bot for performing an action based on a user’s intent Bansal teaches in response to determining the first action intent corresponds to a first subject-matter expertise of a first automated assistant service by analyzing a first semantic similarity of the action intent with text representing the first subject-matter expertise of the first automated assistant service, causing the first automated assistant service to generate a first prompt comprising: first predefined query prompt text associated with the first subject-matter expertise; in response to determining the second action intent corresponds to a second subject-matter expertise of a second automated assistant service by analyzing a second semantic similarity of the action intent with text representing the second subject-matter expertise of the second automated assistant service, causing the second automated assistant service to generate a second prompt comprising second predefined query prompt text associated with the second subject-matter expertise (Paragraph [0003-0004]; [0083]; [0093]; [0095-0097]; [0106]; Fig. 9, a search engine or a recommendation engine can use implicit quality signals to help identify an appropriate “BOT” for user by a current user. The recommendation engine initiatives its operation when it receives a BOT recommendation event from a component. That event inform the recommendation engine that a current user may benefit from a suggestion that identifies another BOT that can be used to complete the user’s task at hand. A signal providing component can provide information which describes the intent that the user was attempting to accomplish. The BOT selection logic can use any type of score. Such as a model can map an input vector associated with the input query into a first vector in a low-dimensioned semantic space, and then map an input vector associated with the BOT under consideration into a second vector. The model can then compute the score based on the relation of these two vectors in the low-dimensional semantic space, using any metric of similarity. The score measures an extent to which the candidate BOT matches the input query. BOT selection logic generates the recommendation results that identifies the recommended BOT(s). More specifically, the BOT selection logic can generate a score for each candidate BOT based on features generated by a feature providing component). Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of managing and selecting artificial intelligence functionalities in an application for performing a desired task as disclosed by Grinberg with the system of in response to determining the first action intent corresponds to a first subject-matter expertise of a first automated assistant service by analyzing a first semantic similarity of the action intent with text representing the first subject-matter expertise of the first automated assistant service, causing the first automated assistant service to generate a first prompt comprising: first predefined query prompt text associated with the first subject-matter expertise; in response to determining the second action intent corresponds to a second subject-matter expertise of a second automated assistant service by analyzing a second semantic similarity of the action intent with text representing the second subject-matter expertise of the second automated assistant service, causing the second automated assistant service to generate a second prompt comprising second predefined query prompt text associated with the second subject-matter expertise as taught by Bansal (Bansal [0106]). With the motivation of helping to identify an ideal candidate BOT or assistant for performing a desired user action (Bansal [0001]). Claim 11: Modified Grinberg discloses the computer-implemented method as per claim 10. Grinberg further discloses wherein: the first automated assistant service includes a first set of plugins, each plugin configured to extract content from content items of a respective platform; a first plugin of the first set of plugins extracts the text from the content used for the first prompt; and the second automated assistant service includes a second set of plugins different than the first set of plugins (Paragraph [0123-0126]; [0129-0131]; [0135-0137]; Fig. 4, receiving from each of a plurality of distinct AI agents a response to the prompt. A response refers to a piece of information that is sent as a reply to a request, query, or preliminary information. The queried AI agents send responses that may provide instructions, information, or data to be further analyzed in the application. Some embodiments may involve comparing information associated with each of the received responses. Comparing information refers to examining two or more sets of data, facts, or details to identify similarities, differences, patterns, and/or relationships. A comparison could be made by analyzing segments or portions of information associated with each of the received responses or by analyzing the entirety of the data. Each Ai agent may send differing sets of information which are then compared to determine the most suitable. The information may contain data in an answer to a user query. Analyzing information associated with each of the received responses include evaluating qualify of content of each of the response, determining a response time, or combination thereof. Following ranking of the plurality of AI agents according to the determining scores, the operation further include saving the determined scores in a database. Selecting at least one Ai agent from the plurality of distinct AI agents based on the comparison. Upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user. Consistent with some embodiments, selecting at least one AI agent from the plurality of distinct AI agents includes selecting at least two AI agents from the plurality of AI agents, and merging responses of the at least two selected AI agents to generate a merged response). Claim 12: Modified Grinberg discloses the computer-implemented method as per claim 11. Grinberg further discloses wherein: a second plugin of the second set of plugins extracts source code from a source code management platform; and the second prompt includes the source code extracted by the second plugin (Paragraph [0123-0126]; [0129-0131]; [0135-0137]; Fig. 4, receiving from each of a plurality of distinct AI agents a response to the prompt. A response refers to a piece of information that is sent as a reply to a request, query, or preliminary information. The queried AI agents send responses that may provide instructions, information, or data to be further analyzed in the application. Some embodiments may involve comparing information associated with each of the received responses. Comparing information refers to examining two or more sets of data, facts, or details to identify similarities, differences, patterns, and/or relationships. A comparison could be made by analyzing segments or portions of information associated with each of the received responses or by analyzing the entirety of the data. Each Ai agent may send differing sets of information which are then compared to determine the most suitable. The information may contain data in an answer to a user query. Analyzing information associated with each of the received responses include evaluating qualify of content of each of the response, determining a response time, or combination thereof. Following ranking of the plurality of AI agents according to the determining scores, the operation further include saving the determined scores in a database. Selecting at least one Ai agent from the plurality of distinct AI agents based on the comparison. Upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user. Consistent with some embodiments, selecting at least one AI agent from the plurality of distinct AI agents includes selecting at least two AI agents from the plurality of AI agents, and merging responses of the at least two selected AI agents to generate a merged response). Claim 13: Modified Grinberg discloses the computer-implemented method as per claim 10. Grinberg further discloses wherein causing the first automated assistant service to generate the first prompt is based on a determination that a first correlation between the first action intent and the first subject-matter expertise satisfies a selection criteria (Paragraph [0123-0126]; [0129-0131]; Fig. 4, receiving from each of a plurality of distinct AI agents a response to the prompt. A response refers to a piece of information that is sent as a reply to a request, query, or preliminary information. The queried AI agents send responses that may provide instructions, information, or data to be further analyzed in the application. Some embodiments may involve comparing information associated with each of the received responses. Comparing information refers to examining two or more sets of data, facts, or details to identify similarities, differences, patterns, and/or relationships. A comparison could be made by analyzing segments or portions of information associated with each of the received responses or by analyzing the entirety of the data. Each Ai agent may send differing sets of information which are then compared to determine the most suitable. The information may contain data in an answer to a user query. Analyzing information associated with each of the received responses include evaluating qualify of content of each of the response, determining a response time, or combination thereof. Following ranking of the plurality of AI agents according to the determining scores, the operation further include saving the determined scores in a database. Selecting at least one Ai agent from the plurality of distinct AI agents based on the comparison. Upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user); Claim 14: Modified Grinberg discloses the computer-implemented method as per claim 10. Grinberg further discloses wherein the second prompt further comprises at least a portion of the first generative response (Paragraph [0123-0126]; [0129-0131]; Fig. 4, receiving from each of a plurality of distinct AI agents a response to the prompt. A response refers to a piece of information that is sent as a reply to a request, query, or preliminary information. The queried AI agents send responses that may provide instructions, information, or data to be further analyzed in the application. Some embodiments may involve comparing information associated with each of the received responses. Comparing information refers to examining two or more sets of data, facts, or details to identify similarities, differences, patterns, and/or relationships. A comparison could be made by analyzing segments or portions of information associated with each of the received responses or by analyzing the entirety of the data. Each Ai agent may send differing sets of information which are then compared to determine the most suitable. The information may contain data in an answer to a user query. Analyzing information associated with each of the received responses include evaluating qualify of content of each of the response, determining a response time, or combination thereof. Following ranking of the plurality of AI agents according to the determining scores, the operation further include saving the determined scores in a database. Selecting at least one Ai agent from the plurality of distinct AI agents based on the comparison. Upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user); Claim 15: Modified Grinberg discloses a computer-implemented method for operating a cross-platform multi-participant interface for a content collaboration platform, the method comprising: causing display of a graphical user interface having a content panel depicting content of a content item managed by a content collaboration system, the graphical user interface displayed on a display of a client device (Paragraph [0007-0008]; [0080-0083]; [0099]; [0120]; Fig. 5, embodiments consistent with the present disclosure involve systems, methods, and computer readable medium for building an application incorporating AI functionality. Exemplary operations may include receiving a selection of an AI assistant add-on and at least one of a plurality of SaaS platform elements, enabling implementation of permission for providing access to data from the at least one of the plurality of linked SaaS platform elements. Some embodiments involve performing selection operations for a plurality of distinct artificial intelligence agents. Exemplary embodiments include sending via the application, a prompt to a plurality of distinct AI agents, and receiving from each of the plurality of distinct AI agents a response to the prompt. The operation may further include comparing information associated with each of the received responses, and selecting at least one AI agent from the plurality of distinct AI agents. A software application may include a user interface enabling users to interact with and access features and functionalities of the software application. User interface is consistent with use of the term as described herein. For example, displaying a user interface may be done by showing a message on a screen, such as a pop-up box, or a list of selectable options); causing display of a generative interface panel within the graphical user interface, the generative interface panel configured to receive a natural language input at an input region (Paragraph [0123-0126]; [0129-0131]; Fig. 4, receiving from each of a plurality of distinct AI agents a response to the prompt. A response refers to a piece of information that is sent as a reply to a request, query, or preliminary information. The queried AI agents send responses that may provide instructions, information, or data to be further analyzed in the application. Some embodiments may involve comparing information associated with each of the received responses. Comparing information refers to examining two or more sets of data, facts, or details to identify similarities, differences, patterns, and/or relationships. A comparison could be made by analyzing segments or portions of information associated with each of the received responses or by analyzing the entirety of the data. Each Ai agent may send differing sets of information which are then compared to determine the most suitable. The information may contain data in an answer to a user query. Analyzing information associated with each of the received responses include evaluating qualify of content of each of the response, determining a response time, or combination thereof. Following ranking of the plurality of AI agents according to the determining scores, the operation further include saving the determined scores in a database. Selecting at least one Ai agent from the plurality of distinct AI agents based on the comparison. Upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user); in response to a first user input provided to the input region of the generative interface panel, analyzing the first user input to determine an action intent; in response to the action intent corresponding to a first subject-matter expertise of a first automated assistant service, causing the first automated assistant service to generate a first prompt comprising: first predefined query prompt text associated with the first subject-matter expertise; and at least a portion of the natural language user input (Paragraph [0123-0126]; [0129-0131]; Fig. 4, receiving from each of a plurality of distinct AI agents a response to the prompt. A response refers to a piece of information that is sent as a reply to a request, query, or preliminary information. The queried AI agents send responses that may provide instructions, information, or data to be further analyzed in the application. Some embodiments may involve comparing information associated with each of the received responses. Comparing information refers to examining two or more sets of data, facts, or details to identify similarities, differences, patterns, and/or relationships. A comparison could be made by analyzing segments or portions of information associated with each of the received responses or by analyzing the entirety of the data. Each Ai agent may send differing sets of information which are then compared to determine the most suitable. The information may contain data in an answer to a user query. Analyzing information associated with each of the received responses include evaluating qualify of content of each of the response, determining a response time, or combination thereof. Following ranking of the plurality of AI agents according to the determining scores, the operation further include saving the determined scores in a database. Selecting at least one Ai agent from the plurality of distinct AI agents based on the comparison. Upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user); providing the first prompt to an external generative output engine and obtaining a first generative response from the external generative output engine; causing display of a first result based on the first generative response in the generative interface panel (Paragraph [0129-0131]; [0138-0139]; Fig. 4, upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user. An AI agent may generate and/or provide automatic replies, or rephrasing of text, for a conversation, such as a chat box or emails. The operations further include outputting the response of the at least one selected AI agent. The output may be received by a system or device associated with a user or sender of the query); causing a second automated assistant service to generate a second prompt comprising: second predefined query prompt text associated with a second subject-matter expertise; and at least a portion of the first generative response; providing the second prompt to the external generative output engine and obtaining a second generative response from the external generative output engine; and causing display of a second result based on the second generative response in the generative interface panel (Paragraph [0123-0126]; [0129-0131]; [0135-0137]; Fig. 4, receiving from each of a plurality of distinct AI agents a response to the prompt. A response refers to a piece of information that is sent as a reply to a request, query, or preliminary information. The queried AI agents send responses that may provide instructions, information, or data to be further analyzed in the application. Some embodiments may involve comparing information associated with each of the received responses. Comparing information refers to examining two or more sets of data, facts, or details to identify similarities, differences, patterns, and/or relationships. A comparison could be made by analyzing segments or portions of information associated with each of the received responses or by analyzing the entirety of the data. Each Ai agent may send differing sets of information which are then compared to determine the most suitable. The information may contain data in an answer to a user query. Analyzing information associated with each of the received responses include evaluating qualify of content of each of the response, determining a response time, or combination thereof. Following ranking of the plurality of AI agents according to the determining scores, the operation further include saving the determined scores in a database. Selecting at least one Ai agent from the plurality of distinct AI agents based on the comparison. Upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user. Consistent with some embodiments, selecting at least one AI agent from the plurality of distinct AI agents includes selecting at least two AI agents from the plurality of AI agents, and merging responses of the at least two selected AI agents to generate a merged response). Gringberg discloses a system of managing a plurality of artificial intelligences in an application and allowing a user to find an AI agent for performing a task based on a query. Whoever, Grinberg does not specifically disclose the following claim limitations: in response to determining the action intent corresponds to a first subject- matter expertise of a first automated assistant service by analyzing a first semantic similarity of the action intent with text representing the first subject-matter expertise of the first automated assistant service, In the same field of endeavor of identifying an AI bot for performing an action based on a user’s intent Bansal teaches in response to determining the action intent corresponds to a first subject- matter expertise of a first automated assistant service by analyzing a first semantic similarity of the action intent with text representing the first subject-matter expertise of the first automated assistant service, (Paragraph [0003-0004]; [0083]; [0093]; [0095-0097]; [0106]; Fig. 9, a search engine or a recommendation engine can use implicit quality signals to help identify an appropriate “BOT” for user by a current user. The recommendation engine initiatives its operation when it receives a BOT recommendation event from a component. That event inform the recommendation engine that a current user may benefit from a suggestion that identifies another BOT that can be used to complete the user’s task at hand. A signal providing component can provide information which describes the intent that the user was attempting to accomplish. The BOT selection logic can use any type of score. Such as a model can map an input vector associated with the input query into a first vector in a low-dimensioned semantic space, and then map an input vector associated with the BOT under consideration into a second vector. The model can then compute the score based on the relation of these two vectors in the low-dimensional semantic space, using any metric of similarity. The score measures an extent to which the candidate BOT matches the input query. BOT selection logic generates the recommendation results that identifies the recommended BOT(s). More specifically, the BOT selection logic can generate a score for each candidate BOT based on features generated by a feature providing component). Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of managing and selecting artificial intelligence functionalities in an application for performing a desired task as disclosed by Grinberg with the system of in response to determining the action intent corresponds to a first subject- matter expertise of a first automated assistant service by analyzing a first semantic similarity of the action intent with text representing the first subject-matter expertise of the first automated assistant service, as taught by Bansal (Bansal [0106]). With the motivation of helping to identify an ideal candidate BOT or assistant for performing a desired user action (Bansal [0001]). Claim 16: Modified Grinberg discloses the computer-implemented method as per claim 15. Grinberg further discloses wherein: the action intent indicates a compound request; a first portion of the compound request corresponds to the first subject-matter expertise; and a second portion of the compound request corresponds to the second subject-matter expertise (Paragraph [0123-0126]; [0129-0131]; [0135-0137]; Fig. 4, receiving from each of a plurality of distinct AI agents a response to the prompt. A response refers to a piece of information that is sent as a reply to a request, query, or preliminary information. The queried AI agents send responses that may provide instructions, information, or data to be further analyzed in the application. Some embodiments may involve comparing information associated with each of the received responses. Comparing information refers to examining two or more sets of data, facts, or details to identify similarities, differences, patterns, and/or relationships. A comparison could be made by analyzing segments or portions of information associated with each of the received responses or by analyzing the entirety of the data. Each Ai agent may send differing sets of information which are then compared to determine the most suitable. The information may contain data in an answer to a user query. Analyzing information associated with each of the received responses include evaluating qualify of content of each of the response, determining a response time, or combination thereof. Following ranking of the plurality of AI agents according to the determining scores, the operation further include saving the determined scores in a database. Selecting at least one Ai agent from the plurality of distinct AI agents based on the comparison. Upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user. Consistent with some embodiments, selecting at least one AI agent from the plurality of distinct AI agents includes selecting at least two AI agents from the plurality of AI agents, and merging responses of the at least two selected AI agents to generate a merged response). Claim 17: Modified Grinberg discloses the computer-implemented method as per claim 15. Grinberg further discloses wherein the first user input includes a first reference to the first automated assistant service and a second reference to the second automated assistant service (Paragraph [0123-0126]; [0129-0131]; [0135-0137]; Fig. 4, receiving from each of a plurality of distinct AI agents a response to the prompt. A response refers to a piece of information that is sent as a reply to a request, query, or preliminary information. The queried AI agents send responses that may provide instructions, information, or data to be further analyzed in the application. Some embodiments may involve comparing information associated with each of the received responses. Comparing information refers to examining two or more sets of data, facts, or details to identify similarities, differences, patterns, and/or relationships. A comparison could be made by analyzing segments or portions of information associated with each of the received responses or by analyzing the entirety of the data. Each Ai agent may send differing sets of information which are then compared to determine the most suitable. The information may contain data in an answer to a user query. Analyzing information associated with each of the received responses include evaluating qualify of content of each of the response, determining a response time, or combination thereof. Following ranking of the plurality of AI agents according to the determining scores, the operation further include saving the determined scores in a database. Selecting at least one Ai agent from the plurality of distinct AI agents based on the comparison. Upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user. Consistent with some embodiments, selecting at least one AI agent from the plurality of distinct AI agents includes selecting at least two AI agents from the plurality of AI agents, and merging responses of the at least two selected AI agents to generate a merged response). Claim 18: Modified Grinberg discloses the computer-implemented method as per claim 15. Grinberg further discloses wherein: the first automated assistant service includes a first set of plugins, each plugin configured to extract content from content items of a respective platform; and the second automated assistant service includes a second set of plugins different than the first set of plugins (Paragraph [0123-0126]; [0129-0131]; [0135-0137]; Fig. 4, receiving from each of a plurality of distinct AI agents a response to the prompt. A response refers to a piece of information that is sent as a reply to a request, query, or preliminary information. The queried AI agents send responses that may provide instructions, information, or data to be further analyzed in the application. Some embodiments may involve comparing information associated with each of the received responses. Comparing information refers to examining two or more sets of data, facts, or details to identify similarities, differences, patterns, and/or relationships. A comparison could be made by analyzing segments or portions of information associated with each of the received responses or by analyzing the entirety of the data. Each Ai agent may send differing sets of information which are then compared to determine the most suitable. The information may contain data in an answer to a user query. Analyzing information associated with each of the received responses include evaluating qualify of content of each of the response, determining a response time, or combination thereof. Following ranking of the plurality of AI agents according to the determining scores, the operation further include saving the determined scores in a database. Selecting at least one Ai agent from the plurality of distinct AI agents based on the comparison. Upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user. Consistent with some embodiments, selecting at least one AI agent from the plurality of distinct AI agents includes selecting at least two AI agents from the plurality of AI agents, and merging responses of the at least two selected AI agents to generate a merged response). Claim 19: Modified Grinberg discloses the computer-implemented method as per claim 18. Grinberg further discloses wherein: the content collaboration platform is a documentation platform; the content item is a page managed by the documentation platform; the first set of plugins includes a content extraction plugin configured to extract content from pages managed by the documentation platform; the first automated assistant service generates the first prompt by extracting content from the page using the content extraction plugin; and the first prompt includes the content extracted from the page using the content extraction plugin (Paragraph [0123-0126]; [0129-0131]; Fig. 4, receiving from each of a plurality of distinct AI agents a response to the prompt. A response refers to a piece of information that is sent as a reply to a request, query, or preliminary information. The queried AI agents send responses that may provide instructions, information, or data to be further analyzed in the application. Some embodiments may involve comparing information associated with each of the received responses. Comparing information refers to examining two or more sets of data, facts, or details to identify similarities, differences, patterns, and/or relationships. A comparison could be made by analyzing segments or portions of information associated with each of the received responses or by analyzing the entirety of the data. Each Ai agent may send differing sets of information which are then compared to determine the most suitable. The information may contain data in an answer to a user query. Analyzing information associated with each of the received responses include evaluating qualify of content of each of the response, determining a response time, or combination thereof. Following ranking of the plurality of AI agents according to the determining scores, the operation further include saving the determined scores in a database. Selecting at least one Ai agent from the plurality of distinct AI agents based on the comparison. Upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user). Claim 20: Modified Grinberg discloses the computer-implemented method as per claim 18. Grinberg further discloses wherein: the second set of plugins includes an issue tracking plugin configured to extract content from issues of an issue tracking platform; the second automated assistant service generates the second prompt by extracting content from an issue using the issue tracking plugin; and the second prompt includes the content extracted from the issue using the issue tracking plugin (Paragraph [0123-0126]; [0129-0131]; [0135-0137]; Fig. 4, receiving from each of a plurality of distinct AI agents a response to the prompt. A response refers to a piece of information that is sent as a reply to a request, query, or preliminary information. The queried AI agents send responses that may provide instructions, information, or data to be further analyzed in the application. Some embodiments may involve comparing information associated with each of the received responses. Comparing information refers to examining two or more sets of data, facts, or details to identify similarities, differences, patterns, and/or relationships. A comparison could be made by analyzing segments or portions of information associated with each of the received responses or by analyzing the entirety of the data. Each Ai agent may send differing sets of information which are then compared to determine the most suitable. The information may contain data in an answer to a user query. Analyzing information associated with each of the received responses include evaluating qualify of content of each of the response, determining a response time, or combination thereof. Following ranking of the plurality of AI agents according to the determining scores, the operation further include saving the determined scores in a database. Selecting at least one Ai agent from the plurality of distinct AI agents based on the comparison. Upon selection the Ai agent may be assigned to further process and address one or more tasks of the query. For example, a prompt may be “complete task” and after the AI agent send responses to this query, one or more agent may generate and/or provide a response to the query or prompt to a device associated with the user. Consistent with some embodiments, selecting at least one AI agent from the plurality of distinct AI agents includes selecting at least two AI agents from the plurality of AI agents, and merging responses of the at least two selected AI agents to generate a merged response). Therefore, claim 1-6 and 8-20 are rejected under U.S.C. 103. Response to arguments Applicant’s arguments, see REMARKS, filed July 08, 2026, with respect to the rejections of claims 1-3, 6-16, and 18-23 under U.S.C. 101 have been fully considered but are not persuasive. The applicant argues that the claims do not recite an abstract idea as they recite are performed on a machine and recite the limitation of evaluating a first degree of correlation between an action intent and a first servicer’s expertise by analyzing a first semantic similarity of the action intent with text representing that expertise, and concurrently a second degree of correlation via a second semantic similarity, then acting on which degree is greater. However, the examiner respectfully disagrees as the claims recite a method for identifying an automated assistant service based on a user’s action intent. The claims recite receiving a natural language input from a user, analyzing the user input to determine an action intent, evaluating a first degree of correlation between the action intent an a first subject matter expertise of a first automated assistant service by analyzing a first semantic similarity of the action intent with text representing the first subject matter expertise of the first automated assistant service and a second degree of correlation between the action intent and a second subject matter expertise of a second automated assistant service any analyzing a second semantic similarity of the action intent with text representing the second subject-matter expertise of the second automated assistant service. The examiner finds that merely receiving and analyzing a user input by determining a semantic similarity between the text of the user intent and a text representation of the expertise of an assistant service is a mental process. Merely receiving and analyzing user input to determine a result such as performing a semantic similarity between text to generate an output for evaluating which automated assistant service is the best match for a user can be practically performed in the human mind. As the courts have stated that concepts that are performed in the human mind include observation, evaluation, judgement, and opinion. Additionally, the courts have stated that claims that recite using a computer to perform a mental process or merely performing a mental process in a computer environment are still considered to recite a mental process. Therefore, the examiner finds that merely analyzing and comparing the semantic similarity of a user’s request or “action intent” to a text pertaining to the expertise of an automated assistant service to help identify a service for performing the desired action is a mental process. Furthermore, the additional elements of generic computer elements of a GUI to receive and display information to a user and an automated assistant service to receive a user input to generate an output are directed to merely “apply it” or applying generic computer elements to perform the abstract idea. Therefore, the claims are not directed to a practical application. Therefore, the examiner maintains the current 101 rejection. Applicant argues that claims 2-6, 8-9, 11-14, and 16-20 are allowable as being dependent on claims 1, 10, and 15 and therefore are rejected under the same rejection. Applicant’s arguments, see REMARKS, filed July 25, 2026, with respect to the rejections of Claim(s) 1-6 and 8-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grinberg (US 2025/0061285) in view of Bansal (US 2019/0149488) are not persuasive as amendments require further search and consideration and new art was applied. Claims 1, 10, and 15: Applicant argues that the combination of prior art does not disclose the amended claim limitations of “evaluating a first degree of correlation between the action intent and a first subject- matter expertise of a first automated assistant service by analyzing a first semantic similarity of the action intent with text representing the first subject-matter expertise of the first automated assistant service and a second degree of correlation between the action intent and a second subject-matter expertise of a second automated assistant service by analyzing a second semantic similarity of the action intent with text representing the second subject-matter expertise of the second automated assistant service.” However, upon further search and consideration the examiner finds that the combination of Grinberg and Bansal teaches the newly amended claim limitations. Grinberg discloses a system of providing a plurality of AI agents for performing various tasks in an application workspace accessed by a user. And identifying an ideal AI agent for performing a task based on user input such as a query. Grinberg can be combined with Bansal to teach the amended claim limitations as Bansal further teaches a system of identifying a user’s intent based on passive interactions and performing semantic analysis to determine the similarity between a BOT or AI assistant capabilities and a user’s intent for performing an action. Therefore, the combination of prior art is found to teach the newly amended claim limitations. Therefore, claims 1, 10, and 15 are newly rejected under U.S.C. 103. Claims 2-6, 8-9, 11-14, and 16-20 were argued as being allowable only as being dependent on claims 1, 10, and 15. Therefore, they are also rejected under the same rejection as above. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Kush (US 2025/0193133) Automated bot switching according to dynamic intent analysis. Brown (US 2015/0186154) Virtual assistant team customization. Sukhija (US 2025/0036887) Method and system for generative AI based unified virtual assistant. Padgett (US 2024/0160902) Similarity based generative AI output filtering. Gollareddy (US 2023/0353512) Method and system for identifying ideal virtual assistant bots for providing response to user queries. 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 COREY RUSS whose telephone number is (571)270-5902. The examiner can normally be reached on M-F 7:30-4:30. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /COREY RUSS/Primary Examiner, Art Unit 3629