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 .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 25 March 2026 has been entered.
3. Accordingly, claims 1-5, 7-9 and 11-20 are pending in this application. Claims 1, 15 and 19-20 are currently amended; Claims 2-5, 7-9, 11-14 and 16-18 are original; and claims 6 and 10 are cancelled.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
4. Claims 1-5, 7-9 and 11-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1 recites the limitation "the client machine" in line 19. There is insufficient antecedent basis for this limitation in the claim.
Claim 15 recites the limitation "the client machine" in line 16. There is insufficient antecedent basis for this limitation in the claim.
Claim 20 recites the limitation "the generative language model interface" and “the client machine” in lines 9-10 and 16 respectively. There is insufficient antecedent basis for this limitation in the claim.
The remaining claims are rejected for fully incorporating the deficiencies of the base claim(s) from which they depend.
Claim Rejections - 35 USC § 103
5. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
6. Claims 1-5, 7-9 and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gutzeit et al. (previously presented) (US 11,928,426 B1) hereinafter Gutzeit, in view of deLevie (previously presented) (US 2025/0061279 A1) and further in view of Boyd et al. (US 2023/0376175 A1) hereinafter Boyd.
As to claim 1, Gutzeit discloses a computing services environment comprising: a database system storing a plurality of database records for a plurality of client organizations accessing computing services via the computing services environment (Col. 2 lines 52-63, “a method for providing machine learning-based analysis of communications in a central communication platform comprises receiving a plurality of messages for a plurality of internal users of an organization from a plurality of external users, constructing a plurality of embedding representations of the plurality of messages by converting each message of the plurality of messages into one or more embedding representations, and storing the plurality of embeddings in an embedding database. Each embedding of the plurality of embeddings is associated with a corresponding sender identity or a corresponding recipient identity.”. Col. 3 lines 61-67, “The application framework can provide various machine-learning models to perform a variety of analysis tasks to analyze enterprise data such as communications between one or more employees of an organization and one or more clients of the organization and provide intelligence and insights for a user in the organization (e.g., an employee of the organization).”. Thus, a database system storing a plurality of database records for a plurality of client organizations accessing computing services via the computing services environment.), the computing services including a conversational chat assistant (Col. 12 lines 25-28, “The analysis task may be obtained based on a query of the internal user of the organization. For example, the query is made via the internal user's conversation with a personal assistant (e.g., a chatbot).”. Thus, the computing services including a conversational chat assistant.);
an application server receiving user input for the conversational chat assistant via the Internet (Col. 4 lines 61-65, The application framework may identify an analysis task for the employee, for example, based on the employee's input, i.e., user input, to the framework via a graphical user interface or a triggering event, such as receiving a message suggesting a need for the task. Col. 12 lines 25-28, “The analysis task may be obtained based on a query of the internal user of the organization. For example, the query is made via the internal user's conversation with a personal assistant (e.g., a chatbot)”.);
a generative language model interface providing access to one or more generative language models (Col. 4 lines 24-29, “to perform an analysis task, the application framework automatically provides to the machine learning model(s) information in accordance with the enterprise's data sharing and access control requirements to prevent inappropriate access and use of sensitive information”. Col. 6 lines 12-17, “the framework comprises a delivery mechanism of insights generated via the applications, one or more machine-learning models used by the applications for generating the insights, and a control framework to enforce data access policies and access control requirements on data provided to the machine-learning models.”. Thus, a generative language model interface providing access to one or more generative language models.).
Gutzeit further discloses [Col. 16 lines 42-60], “the architecture 200 comprises orchestration logic 210, which ensures that the appropriate applications have access to the correct models and data and poses the appropriate queries. For example, the orchestration logic 210 helps shuttle relevant information from external users (e.g., client inputs) to the appropriate model while abiding by the access controls (e.g., determined based on client access control requirements or enterprise data sharing policies). In some embodiments, the orchestration logic 210 is configured to communicate an application's needs, such as one or more appropriate models for the application, in response to a request for executing an analysis task. The model's use may be changed. In some embodiments, the orchestration logic 210 initially uses one model for the task, and then based on an output from this model, the orchestration logic 210 uses a second model for the task, such that more than one models may be combined to provide a better outcome”.
Gutzeit does not explicitly disclose an orchestration and planning service configured: to identify one or more actions based on the user input by transmitting a plan identification input prompt to a generative language model via the generative language model interface and determining the one or more actions based on novel text generated by the generative language model, the novel text including unique identifiers corresponding to the one or more actions, execute the one or more actions to determine a natural language response message, and determine a recommended user-selectable action identified in the novel text, wherein determining the user-selectable recommended action includes (1) identifying a context associated with an interaction between the client machine and the conversational chat assistant and (2) determining that the context satisfies a triggering condition associated with the user-selectable recommended action; and a communication interface configured to transmit the natural language response message and a user interface generation instruction to a client machine via the application server, the user interface generation instruction being executable by the client machine to provide a selection affordance in a conversational chat interface to receive additional user input selecting the user-selectable recommended action, the computing services environment being configured to execute the recommended action upon receipt of the user input selecting the user-selectable recommended action the user-selectable recommended action including executing a database query to update a database record accessible via the computing services environment.
However, in the same field of endeavor, deLevie discloses an orchestration and planning service configured: to identify one or more actions based on the user input by transmitting a plan identification input prompt to a generative language model via the generative language model interface and determining the one or more actions based on novel text generated by the generative language model (Fig. 2, Para. 46, “the orchestrator 230 facilitates the implementation of one or more skills, such as the skills 232 through 234. A skill may act as a collection of interfaces, prompts, actions, data, and/or metadata that collectively provide a type of functionality to the client machine. For instance, a skill may involve receiving information from a client machine, transmitting one or more requests to the text generation modeling system 270, processing one or more response received form the text generation modeling system 270, performing one or more searches, and the like.”. Para. 138, The text generation prompt is transmitted to a large language model, i.e., a generative language model, for completion at 910. A text generation response message is received from the large language model at 912. The text generation response message is parsed at 914 to identify a novel text portion. Parsing the text generation response message may involve, for instance, separating the novel text portion generated by the large language model from the rest of the completed text generation response message. Para. 36, “A novel text is determined at 104 based on the query and the one or more source documents. According to various embodiments, determining the novel text may involve determining one or more query response prompts based on a text generation prompt template, the query, and a subset of the enumerated source text passages.”. Thus, an orchestration and planning service configured: to identify one or more actions based on the user input by transmitting a plan identification input prompt to a generative language model via the generative language model interface and determining the one or more actions based on novel text generated by the generative language model.),
the novel text including unique identifiers corresponding to the one or more actions (Fig. 10, Para. 140, The novel text passage may include text generated by the large language model in response to the query, along with one or more identifiers, i.e., unique identifiers, linking the generated text to the enumerated source text passages. Para. 116, “any suitable enumeration scheme may be used for creating the unique identifiers. For example, text passages may be identified by combining a document identifier with a sequentially generated text passage identifier. For instance, "D12-T14" may identify the 14th text passage in the 12th document of a set of source documents.”. Thus, the novel text including unique identifiers corresponding to the one or more actions.),
execute the one or more actions to determine a natural language response message (Fig. 8, Para. 76, One or more text response messages, i.e., a natural language response message, are received from the remote computing system at 414. According to various embodiments, the one or more text response messages include one or more novel text portions generated by a text generation model implemented at the remote computing system. The novel text portions may be generated based at least in part on the prompt received at the text generation modeling system, including the instructions and the input text. Para. 121, one or more of the operations, i.e., one or more actions, shown in FIG. 8 may be performed based on interaction with a large language model. For instance, a text passage identification prompt template may include natural language instructions that when executed by a large language model cause the large language model to perform operations such as identifying text passages, enumerating text passages, and/or evaluating text passages for relevance. Thus, the one or more actions being executed to determine a natural language response message.).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Gutzeit such that the user input of Gutzeit can be used in the environment of deLevie by transmitting one or more text generation prompts to the large language model to determine identify the novel text as disclosed by deLevie (Para. 36). A large language model may be employed to produce accurate summaries of legal texts, to perform legal research tasks, to generate legal documents, to generate questions for legal depositions, and the like (deLevie, Para. 27). One of the ordinary skills in the art would have motivated to make this modification by linking a large language model with a legal research database which allows the large language model to automatically determine appropriate searches to perform and then ground its responses to a source of truth so that it does not "hallucinate" a response that is inaccurate as suggested by deLevie (Para. 30).
Combination of Gutzeit and deLevie do not explicitly disclose determine a recommended user-selectable action identified in the novel text, wherein determining the user-selectable recommended action includes (1) identifying a context associated with an interaction between the client machine and the conversational chat assistant and (2) determining that the context satisfies a triggering condition associated with the user-selectable recommended action; and a communication interface configured to transmit the natural language response message and a user interface generation instruction to a client machine via the application server, the user interface generation instruction being executable by the client machine to provide a selection affordance in a conversational chat interface to receive additional user input selecting the user selectable recommended action, the computing services environment being configured to execute the recommended action upon receipt of the user input selecting the user-selectable recommended action, the user-selectable recommended action including executing a database query to update a database record accessible via the computing services environment.
However, in the same field of endeavor, Boyd discloses determine a recommended user-selectable action identified in the novel text (Fig. 5-7, Para. 68, The interaction pattern management system 234 is responsible for detecting determinable interaction patterns by users of the interaction system 100, and initiating predefined responses based on respective determinable interaction patterns. For example, and as described further below, the interaction pattern management system 234 may work with the messaging system 210 or the collection management system 222 to identify that a user has accessed a content item multiple times and, in response thereto, cause activation, presentation and/or highlighting of a specific action graphical user interface element to enable the user to perform an action, i.e., a recommended user-selectable action, related to the content item.), wherein determining the user-selectable recommended action includes (1) identifying a context associated with an interaction between the client machine and the conversational chat assistant (Para. 199, the action graphical user interface element comprises a tray of user selectable elements, i.e., the user-selectable recommended action, presented within a context of a viewing user interface of an interaction client of the interaction system, i.e., a context associated with an interaction between the client machine and the conversational chat assistant. Para. 67, “The AI/ML system 232 may also provide chatbot functionality to message interactions 120 between user systems 102 and between a user system 102 and the interaction server system 110.”. Para. 73, the content item is an image or video, the interaction client 104 may detect that the viewing user performs multiple viewings (e.g., re-watches or re-views) of the relevant image or video using the interaction client 104, i.e., the client machine.) and (2) determining that the context satisfies a triggering condition associated with the user-selectable recommended action (Para. 73, The determinable interaction pattern may comprise a series of access actions, e.g., performed within a determinable time (such as, for example, 24 hours, 48 hours, or one week), with respect to a content item published on the interaction system 100 by a publishing user. For example, where the content item is an image or video, the interaction client 104 may detect that the viewing user performs multiple viewings (e.g., re-watches or re-views), i.e., the context satisfies a triggering condition, of the relevant image or video using the interaction client 104. Para. 183, detecting a determinable interaction pattern of interactions by a first user, via a user system, with a content item communicated on an interaction system by a second user, the determinable interaction pattern comprising multiple access actions with respect to the content item by the first user; and responsive to detecting the determinable interaction pattern, i.e., the context satisfies a triggering condition, automatically activating an action graphical user interface element that is user selectable to enable the first user to perform an action related to the content item, i.e., the user-selectable recommended action.); and
a communication interface configured to transmit the natural language response message and a user interface generation instruction to a client machine via the application server (Para. 67, “The AI/ML system 232 may also provide chatbot functionality to message interactions 120 between user systems 102 and between a user system 102 and the interaction server system 110. The AI/ML system 232 may also work with the audio communication system 216 to provide speech recognition and natural language processing capabilities, allowing users to interact with the interaction system 100 using voice commands.”. Thus, a communication interface configured to transmit the natural language response message and a user interface generation instruction to a client machine via the application server.), the user interface generation instruction being executable by the client machine to provide a selection affordance in a conversational chat interface to receive additional user input selecting the user-selectable recommended action (Para. 74, the interaction client 104, i.e., the client machine, responsive to detecting the determinable interaction pattern, automatically activates an action graphical user interface element, i.e., the user-selectable recommended action. The action graphical user interface element is user selectable to enable the viewing user to perform an action, i.e., to receive additional user input, related to the content item.), the computing services environment being configured to execute the recommended action upon receipt of the user input selecting the user-selectable recommended action, the user-selectable recommended action including executing a database query to update a database record accessible via the computing services environment (Fig. 8, Para. 31, The API server 122 receives and transmits interaction data (e.g., commands and message payloads) between the interaction servers 124 and the user systems 102 (and, for example, interaction clients 104 and other application 106) and the third-party server 112. Specifically, the API server 122 provides a set of interfaces (e.g., routines and protocols) that can be called or queried, i.e., a database query, by the interaction client 104 and other applications 106 to invoke functionality of the interaction servers 124. The API server 122 exposes various functions supported by the interaction servers 124, including account registration; login functionality; the sending of interaction data, via the interaction servers 124, from a particular interaction client 104 to another interaction client 104; the communication of media files (e.g., images or video) from an interaction client 104 to the interaction servers 124; the settings of a collection of media data (e.g., a story); the retrieval of a list of friends of a user of a user system 102; the retrieval of messages and content; the addition and deletion of entities (e.g., friends) to an entity graph (e.g., a social graph), where “addition and deletion of entities” indicate “update a database record” in response to a database query; the location of friends within a social graph; and opening an application event (e.g., relating to the interaction client 104). Para. 30, “The interaction servers 124 are communicatively coupled to a database server 126, facilitating access to a database 128 that stores data associated with interactions processed by the interaction servers 124.”. Thus, the computing services environment being configured to execute the recommended action upon receipt of the user input selecting the user-selectable recommended action, the user-selectable recommended action including executing a database query to update a database record accessible via the computing services environment.).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Boyd into the combined method of Gutzeit and deLevie by providing the specific action graphical user interface element to enable the user to perform an action related to the content item as disclosed by Boyd (Para. 68). The interaction pattern management system is responsible for detecting determinable interaction patterns by users of the interaction system, and initiating predefined responses such as the recommended action based on respective determinable interaction patterns. One of the ordinary skills in the art would have motivated to make this modification in order to enable the viewing user to conveniently reply to the publishing user, or send a chat message to the publishing user, related to the content item by using the selectable graphical user interface element as suggested by Boyd (Para. 85).
As to claim 15, Gutzeit discloses a method performed at a computing services environment, the method comprising: storing in a database system a plurality of database records for a plurality of client organizations accessing computing services via the computing services environment (Col. 2 lines 52-63, “a method for providing machine learning-based analysis of communications in a central communication platform comprises receiving a plurality of messages for a plurality of internal users of an organization from a plurality of external users, constructing a plurality of embedding representations of the plurality of messages by converting each message of the plurality of messages into one or more embedding representations, and storing the plurality of embeddings in an embedding database. Each embedding of the plurality of embeddings is associated with a corresponding sender identity or a corresponding recipient identity.”. Col. 3 lines 61-67, “The application framework can provide various machine-learning models to perform a variety of analysis tasks to analyze enterprise data such as communications between one or more employees of an organization and one or more clients of the organization and provide intelligence and insights for a user in the organization (e.g., an employee of the organization).”. Thus, a database system storing a plurality of database records for a plurality of client organizations accessing computing services via the computing services environment.), the computing services including a conversational chat assistant (Col. 12 lines 25-28, “The analysis task may be obtained based on a query of the internal user of the organization. For example, the query is made via the internal user's conversation with a personal assistant (e.g., a chatbot).”. Thus, the computing services including a conversational chat assistant.);
receiving user input for the conversational chat assistant at an application server via the Internet (Col. 4 lines 61-65, The application framework may identify an analysis task for the employee, for example, based on the employee's input, i.e., user input, to the framework via a graphical user interface or a triggering event, such as receiving a message suggesting a need for the task. Col. 12 lines 25-28, “The analysis task may be obtained based on a query of the internal user of the organization. For example, the query is made via the internal user's conversation with a personal assistant (e.g., a chatbot)”.).
Gutzeit further discloses [Col. 16 lines 42-60], “the architecture 200 comprises orchestration logic 210, which ensures that the appropriate applications have access to the correct models and data and poses the appropriate queries. For example, the orchestration logic 210 helps shuttle relevant information from external users (e.g., client inputs) to the appropriate model while abiding by the access controls (e.g., determined based on client access control requirements or enterprise data sharing policies). In some embodiments, the orchestration logic 210 is configured to communicate an application's needs, such as one or more appropriate models for the application, in response to a request for executing an analysis task. The model's use may be changed. In some embodiments, the orchestration logic 210 initially uses one model for the task, and then based on an output from this model, the orchestration logic 210 uses a second model for the task, such that more than one models may be combined to provide a better outcome”.
Gutzeit does not explicitly disclose identifying one or more actions based on the user input by transmitting a plan identification input prompt to a generative language model via a generative language model interface and determining the one or more actions based on novel text generated by the generative language model, the novel text including unique identifiers corresponding to the one or more actions; executing the one or more actions to determine a natural language response message; determining a recommended user-selectable action identified in the novel text, wherein determining the user-selectable recommended action includes (1) identifying a context associated with an interaction between the client machine and the conversational chat assistant and (2) determining that the context satisfies a triggering condition associated with the user-selectable recommended action; and transmitting the natural language response message and a user interface generation instruction to a client machine via the application server, the user interface generation instruction being executable by the client machine to provide a selection affordance in a conversational chat interface to receive additional user input selecting the user-selectable recommended action, the computing services environment being configured to execute the recommended action upon receipt of the user input selecting the user-selectable recommended action, the user-selectable recommended action including executing a database query to update a database record accessible via the computing services environment..
However, in the same field of endeavor, deLevie discloses identifying one or more actions based on the user input by transmitting a plan identification input prompt to a generative language model via a generative language model interface and determining the one or more actions based on novel text generated by the generative language model (Fig. 2, Para. 46, “the orchestrator 230 facilitates the implementation of one or more skills, such as the skills 232 through 234. A skill may act as a collection of interfaces, prompts, actions, data, and/or metadata that collectively provide a type of functionality to the client machine. For instance, a skill may involve receiving information from a client machine, transmitting one or more requests to the text generation modeling system 270, processing one or more response received form the text generation modeling system 270, performing one or more searches, and the like.”. Para. 138, The text generation prompt is transmitted to a large language model, i.e., a generative language model, for completion at 910. A text generation response message is received from the large language model at 912. The text generation response message is parsed at 914 to identify a novel text portion. Parsing the text generation response message may involve, for instance, separating the novel text portion generated by the large language model from the rest of the completed text generation response message. Para. 36, “A novel text is determined at 104 based on the query and the one or more source documents. According to various embodiments, determining the novel text may involve determining one or more query response prompts based on a text generation prompt template, the query, and a subset of the enumerated source text passages.”. Thus, an orchestration and planning service configured: to identify one or more actions based on the user input by transmitting a plan identification input prompt to a generative language model via the generative language model interface and determining the one or more actions based on novel text generated by the generative language model.),
the novel text including unique identifiers corresponding to the one or more actions (Fig. 10, Para. 140, The novel text passage may include text generated by the large language model in response to the query, along with one or more identifiers, i.e., unique identifiers, linking the generated text to the enumerated source text passages. Para. 116, “any suitable enumeration scheme may be used for creating the unique identifiers. For example, text passages may be identified by combining a document identifier with a sequentially generated text passage identifier. For instance, "D12-T14" may identify the 14th text passage in the 12th document of a set of source documents.”. Thus, the novel text including unique identifiers corresponding to the one or more actions.),
executing the one or more actions to determine a natural language response message (Fig. 8, Para. 76, One or more text response messages, i.e., a natural language response message, are received from the remote computing system at 414. According to various embodiments, the one or more text response messages include one or more novel text portions generated by a text generation model implemented at the remote computing system. The novel text portions may be generated based at least in part on the prompt received at the text generation modeling system, including the instructions and the input text. Para. 121, one or more of the operations, i.e., one or more actions, shown in FIG. 8 may be performed based on interaction with a large language model. For instance, a text passage identification prompt template may include natural language instructions that when executed by a large language model cause the large language model to perform operations such as identifying text passages, enumerating text passages, and/or evaluating text passages for relevance. Thus, the one or more actions being executed to determine a natural language response message.).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Gutzeit such that the user input of Gutzeit can be used in the environment of deLevie by transmitting one or more text generation prompts to the large language model to determine identify the novel text as disclosed by deLevie (Para. 36). A large language model may be employed to produce accurate summaries of legal texts, to perform legal research tasks, to generate legal documents, to generate questions for legal depositions, and the like (deLevie, Para. 27). One of the ordinary skills in the art would have motivated to make this modification by linking a large language model with a legal research database which allows the large language model to automatically determine appropriate searches to perform and then ground its responses to a source of truth so that it does not "hallucinate" a response that is inaccurate as suggested by deLevie (Para. 30).
Combination of Gutzeit and deLevie do not explicitly disclose determining a recommended user-selectable action identified in the novel text, wherein determining the user-selectable recommended action includes (1) identifying a context associated with an interaction between the client machine and the conversational chat assistant and (2) determining that the context satisfies a triggering condition associated with the user-selectable recommended action; and transmitting the natural language response message and a user interface generation instruction to a client machine via the application server, the user interface generation instruction being executable by the client machine to provide a selection affordance in a conversational chat interface to receive additional user input selecting the user-selectable recommended action, the computing services environment being configured to execute the recommended action upon receipt of the user input selecting the user-selectable recommended action, the user-selectable recommended action including executing a database query to update a database record accessible via the computing services environment.
However, in the same field of endeavor, Boyd discloses determining a recommended user-selectable action identified in the novel text (Fig. 5-7, Para. 68, The interaction pattern management system 234 is responsible for detecting determinable interaction patterns by users of the interaction system 100, and initiating predefined responses based on respective determinable interaction patterns. For example, and as described further below, the interaction pattern management system 234 may work with the messaging system 210 or the collection management system 222 to identify that a user has accessed a content item multiple times and, in response thereto, cause activation, presentation and/or highlighting of a specific action graphical user interface element to enable the user to perform an action, i.e., a recommended user-selectable action, related to the content item.), wherein determining the user-selectable recommended action includes (1) identifying a context associated with an interaction between the client machine and the conversational chat assistant (Para. 199, the action graphical user interface element comprises a tray of user selectable elements, i.e., the user-selectable recommended action, presented within a context of a viewing user interface of an interaction client of the interaction system, i.e., a context associated with an interaction between the client machine and the conversational chat assistant. Para. 67, “The AI/ML system 232 may also provide chatbot functionality to message interactions 120 between user systems 102 and between a user system 102 and the interaction server system 110.”. Para. 73, the content item is an image or video, the interaction client 104 may detect that the viewing user performs multiple viewings (e.g., re-watches or re-views) of the relevant image or video using the interaction client 104, i.e., the client machine.) and (2) determining that the context satisfies a triggering condition associated with the user-selectable recommended action (Para. 73, The determinable interaction pattern may comprise a series of access actions, e.g., performed within a determinable time (such as, for example, 24 hours, 48 hours, or one week), with respect to a content item published on the interaction system 100 by a publishing user. For example, where the content item is an image or video, the interaction client 104 may detect that the viewing user performs multiple viewings (e.g., re-watches or re-views), i.e., the context satisfies a triggering condition, of the relevant image or video using the interaction client 104. Para. 183, detecting a determinable interaction pattern of interactions by a first user, via a user system, with a content item communicated on an interaction system by a second user, the determinable interaction pattern comprising multiple access actions with respect to the content item by the first user; and responsive to detecting the determinable interaction pattern, i.e., the context satisfies a triggering condition, automatically activating an action graphical user interface element that is user selectable to enable the first user to perform an action related to the content item, i.e., the user-selectable recommended action.); and
transmitting the natural language response message and a user interface generation instruction to a client machine via the application server (Para. 67, “The AI/ML system 232 may also provide chatbot functionality to message interactions 120 between user systems 102 and between a user system 102 and the interaction server system 110. The AI/ML system 232 may also work with the audio communication system 216 to provide speech recognition and natural language processing capabilities, allowing users to interact with the interaction system 100 using voice commands.”. Thus, a communication interface configured to transmit the natural language response message and a user interface generation instruction to a client machine via the application server.), the user interface generation instruction being executable by the client machine to provide a selection affordance in a conversational chat interface to receive additional user input selecting the user-selectable recommended action (Para. 74, the interaction client 104, i.e., the client machine, responsive to detecting the determinable interaction pattern, automatically activates an action graphical user interface element, i.e., the user-selectable recommended action. The action graphical user interface element is user selectable to enable the viewing user to perform an action, i.e., to receive additional user input, related to the content item.), the computing services environment being configured to execute the recommended action upon receipt of the user input selecting the user-selectable recommended action, the user-selectable recommended action including executing a database query to update a database record accessible via the computing services environment (Fig. 8, Para. 31, The API server 122 receives and transmits interaction data (e.g., commands and message payloads) between the interaction servers 124 and the user systems 102 (and, for example, interaction clients 104 and other application 106) and the third-party server 112. Specifically, the API server 122 provides a set of interfaces (e.g., routines and protocols) that can be called or queried, i.e., a database query, by the interaction client 104 and other applications 106 to invoke functionality of the interaction servers 124. The API server 122 exposes various functions supported by the interaction servers 124, including account registration; login functionality; the sending of interaction data, via the interaction servers 124, from a particular interaction client 104 to another interaction client 104; the communication of media files (e.g., images or video) from an interaction client 104 to the interaction servers 124; the settings of a collection of media data (e.g., a story); the retrieval of a list of friends of a user of a user system 102; the retrieval of messages and content; the addition and deletion of entities (e.g., friends) to an entity graph (e.g., a social graph), where “addition and deletion of entities” indicate “update a database record” in response to a database query; the location of friends within a social graph; and opening an application event (e.g., relating to the interaction client 104). Para. 30, “The interaction servers 124 are communicatively coupled to a database server 126, facilitating access to a database 128 that stores data associated with interactions processed by the interaction servers 124.”. Thus, the computing services environment being configured to execute the recommended action upon receipt of the user input selecting the user-selectable recommended action, the user-selectable recommended action including executing a database query to update a database record accessible via the computing services environment.).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Boyd into the combined method of Gutzeit and deLevie by providing the specific action graphical user interface element to enable the user to perform an action related to the content item as disclosed by Boyd (Para. 68). The interaction pattern management system is responsible for detecting determinable interaction patterns by users of the interaction system, and initiating predefined responses such as the recommended action based on respective determinable interaction patterns. One of the ordinary skills in the art would have motivated to make this modification in order to enable the viewing user to conveniently reply to the publishing user, or send a chat message to the publishing user, related to the content item by using the selectable graphical user interface element as suggested by Boyd (Para. 85).
As to claim 20, Gutzeit discloses a method performed at a computing services environment, the method comprising: storing in a database system a plurality of database records for a plurality of client organizations accessing computing services via the computing services environment (Col. 2 lines 52-63, “a method for providing machine learning-based analysis of communications in a central communication platform comprises receiving a plurality of messages for a plurality of internal users of an organization from a plurality of external users, constructing a plurality of embedding representations of the plurality of messages by converting each message of the plurality of messages into one or more embedding representations, and storing the plurality of embeddings in an embedding database. Each embedding of the plurality of embeddings is associated with a corresponding sender identity or a corresponding recipient identity.”. Col. 3 lines 61-67, “The application framework can provide various machine-learning models to perform a variety of analysis tasks to analyze enterprise data such as communications between one or more employees of an organization and one or more clients of the organization and provide intelligence and insights for a user in the organization (e.g., an employee of the organization).”. Thus, a database system storing a plurality of database records for a plurality of client organizations accessing computing services via the computing services environment.), the computing services including a conversational chat assistant (Col. 12 lines 25-28, “The analysis task may be obtained based on a query of the internal user of the organization. For example, the query is made via the internal user's conversation with a personal assistant (e.g., a chatbot).”. Thus, the computing services including a conversational chat assistant.);
receiving user input for the conversational chat assistant at an application server via the Internet (Col. 4 lines 61-65, The application framework may identify an analysis task for the employee, for example, based on the employee's input, i.e., user input, to the framework via a graphical user interface or a triggering event, such as receiving a message suggesting a need for the task. Col. 12 lines 25-28, “The analysis task may be obtained based on a query of the internal user of the organization. For example, the query is made via the internal user's conversation with a personal assistant (e.g., a chatbot)”.).
Gutzeit further discloses [Col. 16 lines 42-60], “the architecture 200 comprises orchestration logic 210, which ensures that the appropriate applications have access to the correct models and data and poses the appropriate queries. For example, the orchestration logic 210 helps shuttle relevant information from external users (e.g., client inputs) to the appropriate model while abiding by the access controls (e.g., determined based on client access control requirements or enterprise data sharing policies). In some embodiments, the orchestration logic 210 is configured to communicate an application's needs, such as one or more appropriate models for the application, in response to a request for executing an analysis task. The model's use may be changed. In some embodiments, the orchestration logic 210 initially uses one model for the task, and then based on an output from this model, the orchestration logic 210 uses a second model for the task, such that more than one models may be combined to provide a better outcome”.
Gutzeit does not explicitly disclose identifying one or more actions based on the user input by transmitting a plan identification input prompt to a generative language model via the generative language model interface and determining the one or more actions based on novel text generated by the generative language model, the novel text including unique identifiers corresponding to the one or more actions; executing the one or more actions to determine a natural language response message; determining a recommended user-selectable action identified in the novel text, wherein determining the user-selectable recommended action includes (1) identifying a context associated with an interaction between the client machine and the conversational chat assistant and (2) determining that the context satisfies a triggering condition associated with the user-selectable recommended action; and transmitting the natural language response message and a user interface generation instruction to a client machine via the application server, the user interface generation instruction being executable by the client machine to provide a selection affordance in a conversational chat interface to receive additional user input selecting the user-selectable recommended action, the computing services environment being configured to execute the recommended action upon receipt of the user input selecting the user-selectable recommended action, the user-selectable recommended action including executing a database query to update a database record accessible via the computing services environment.
However, in the same field of endeavor, deLevie discloses identifying one or more actions based on the user input by transmitting a plan identification input prompt to a generative language model via the generative language model interface and determining the one or more actions based on novel text generated by the generative language model (Fig. 2, Para. 46, “the orchestrator 230 facilitates the implementation of one or more skills, such as the skills 232 through 234. A skill may act as a collection of interfaces, prompts, actions, data, and/or metadata that collectively provide a type of functionality to the client machine. For instance, a skill may involve receiving information from a client machine, transmitting one or more requests to the text generation modeling system 270, processing one or more response received form the text generation modeling system 270, performing one or more searches, and the like.”. Para. 138, The text generation prompt is transmitted to a large language model, i.e., a generative language model, for completion at 910. A text generation response message is received from the large language model at 912. The text generation response message is parsed at 914 to identify a novel text portion. Parsing the text generation response message may involve, for instance, separating the novel text portion generated by the large language model from the rest of the completed text generation response message. Para. 36, “A novel text is determined at 104 based on the query and the one or more source documents. According to various embodiments, determining the novel text may involve determining one or more query response prompts based on a text generation prompt template, the query, and a subset of the enumerated source text passages.”. Thus, an orchestration and planning service configured: to identify one or more actions based on the user input by transmitting a plan identification input prompt to a generative language model via the generative language model interface and determining the one or more actions based on novel text generated by the generative language model.),
the novel text including unique identifiers corresponding to the one or more actions (Fig. 10, Para. 140, The novel text passage may include text generated by the large language model in response to the query, along with one or more identifiers, i.e., unique identifiers, linking the generated text to the enumerated source text passages. Para. 116, “any suitable enumeration scheme may be used for creating the unique identifiers. For example, text passages may be identified by combining a document identifier with a sequentially generated text passage identifier. For instance, "D12-T14" may identify the 14th text passage in the 12th document of a set of source documents.”. Thus, the novel text including unique identifiers corresponding to the one or more actions.),
executing the one or more actions to determine a natural language response message (Fig. 8, Para. 76, One or more text response messages, i.e., a natural language response message, are received from the remote computing system at 414. According to various embodiments, the one or more text response messages include one or more novel text portions generated by a text generation model implemented at the remote computing system. The novel text portions may be generated based at least in part on the prompt received at the text generation modeling system, including the instructions and the input text. Para. 121, one or more of the operations, i.e., one or more actions, shown in FIG. 8 may be performed based on interaction with a large language model. For instance, a text passage identification prompt template may include natural language instructions that when executed by a large language model cause the large language model to perform operations such as identifying text passages, enumerating text passages, and/or evaluating text passages for relevance. Thus, the one or more actions being executed to determine a natural language response message.).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Gutzeit such that the user input of Gutzeit can be used in the environment of deLevie by transmitting one or more text generation prompts to the large language model to determine identify the novel text as disclosed by deLevie (Para. 36). A large language model may be employed to produce accurate summaries of legal texts, to perform legal research tasks, to generate legal documents, to generate questions for legal depositions, and the like (deLevie, Para. 27). One of the ordinary skills in the art would have motivated to make this modification by linking a large language model with a legal research database which allows the large language model to automatically determine appropriate searches to perform and then ground its responses to a source of truth so that it does not "hallucinate" a response that is inaccurate as suggested by deLevie (Para. 30).
Combination of Gutzeit and deLevie do not explicitly disclose determining a recommended user-selectable action identified in the novel text, wherein determining the user-selectable recommended action includes (1) identifying a context associated with an interaction between the client machine and the conversational chat assistant and (2) determining that the context satisfies a triggering condition associated with the user-selectable recommended action; and transmitting the natural language response message and a user interface generation instruction to a client machine via the application server, the user interface generation instruction being executable by the client machine to provide a selection affordance in a conversational chat interface to receive additional user input selecting the user-selectable recommended action, the computing services environment being configured to execute the recommended action upon receipt of the user input selecting the user-selectable recommended action, the user-selectable recommended action including executing a database query to update a database record accessible via the computing services environment.
However, in the same field of endeavor, Boyd discloses determining a recommended user-selectable action identified in the novel text (Fig. 5-7, Para. 68, The interaction pattern management system 234 is responsible for detecting determinable interaction patterns by users of the interaction system 100, and initiating predefined responses based on respective determinable interaction patterns. For example, and as described further below, the interaction pattern management system 234 may work with the messaging system 210 or the collection management system 222 to identify that a user has accessed a content item multiple times and, in response thereto, cause activation, presentation and/or highlighting of a specific action graphical user interface element to enable the user to perform an action, i.e., a recommended user-selectable action, related to the content item.), wherein determining the user-selectable recommended action includes (1) identifying a context associated with an interaction between the client machine and the conversational chat assistant (Para. 199, the action graphical user interface element comprises a tray of user selectable elements, i.e., the user-selectable recommended action, presented within a context of a viewing user interface of an interaction client of the interaction system, i.e., a context associated with an interaction between the client machine and the conversational chat assistant. Para. 67, “The AI/ML system 232 may also provide chatbot functionality to message interactions 120 between user systems 102 and between a user system 102 and the interaction server system 110.”. Para. 73, the content item is an image or video, the interaction client 104 may detect that the viewing user performs multiple viewings (e.g., re-watches or re-views) of the relevant image or video using the interaction client 104, i.e., the client machine.) and (2) determining that the context satisfies a triggering condition associated with the user-selectable recommended action (Para. 73, The determinable interaction pattern may comprise a series of access actions, e.g., performed within a determinable time (such as, for example, 24 hours, 48 hours, or one week), with respect to a content item published on the interaction system 100 by a publishing user. For example, where the content item is an image or video, the interaction client 104 may detect that the viewing user performs multiple viewings (e.g., re-watches or re-views), i.e., the context satisfies a triggering condition, of the relevant image or video using the interaction client 104. Para. 183, detecting a determinable interaction pattern of interactions by a first user, via a user system, with a content item communicated on an interaction system by a second user, the determinable interaction pattern comprising multiple access actions with respect to the content item by the first user; and responsive to detecting the determinable interaction pattern, i.e., the context satisfies a triggering condition, automatically activating an action graphical user interface element that is user selectable to enable the first user to perform an action related to the content item, i.e., the user-selectable recommended action.); and transmitting the natural language response message and a user interface generation instruction to a client machine via the application server (Para. 67, “The AI/ML system 232 may also provide chatbot functionality to message interactions 120 between user systems 102 and between a user system 102 and the interaction server system 110. The AI/ML system 232 may also work with the audio communication system 216 to provide speech recognition and natural language processing capabilities, allowing users to interact with the interaction system 100 using voice commands.”. Thus, a communication interface configured to transmit the natural language response message and a user interface generation instruction to a client machine via the application server.), the user interface generation instruction being executable by the client machine to provide a selection affordance in a conversational chat interface to receive additional user input selecting the user-selectable recommended action (Para. 74, the interaction client 104, i.e., the client machine, responsive to detecting the determinable interaction pattern, automatically activates an action graphical user interface element, i.e., the user-selectable recommended action. The action graphical user interface element is user selectable to enable the viewing user to perform an action, i.e., to receive additional user input, related to the content item.), the computing services environment being configured to execute the recommended action upon receipt of the user input selecting the user-selectable recommended action, the user-selectable recommended action including executing a database query to update a database record accessible via the computing services environment (Fig. 8, Para. 31, The API server 122 receives and transmits interaction data (e.g., commands and message payloads) between the interaction servers 124 and the user systems 102 (and, for example, interaction clients 104 and other application 106) and the third-party server 112. Specifically, the API server 122 provides a set of interfaces (e.g., routines and protocols) that can be called or queried, i.e., a database query, by the interaction client 104 and other applications 106 to invoke functionality of the interaction servers 124. The API server 122 exposes various functions supported by the interaction servers 124, including account registration; login functionality; the sending of interaction data, via the interaction servers 124, from a particular interaction client 104 to another interaction client 104; the communication of media files (e.g., images or video) from an interaction client 104 to the interaction servers 124; the settings of a collection of media data (e.g., a story); the retrieval of a list of friends of a user of a user system 102; the retrieval of messages and content; the addition and deletion of entities (e.g., friends) to an entity graph (e.g., a social graph), where “addition and deletion of entities” indicate “update a database record” in response to a database query; the location of friends within a social graph; and opening an application event (e.g., relating to the interaction client 104). Para. 30, “The interaction servers 124 are communicatively coupled to a database server 126, facilitating access to a database 128 that stores data associated with interactions processed by the interaction servers 124.”. Thus, the computing services environment being configured to execute the recommended action upon receipt of the user input selecting the user-selectable recommended action, the user-selectable recommended action including executing a database query to update a database record accessible via the computing services environment.).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Boyd into the combined method of Gutzeit and deLevie by providing the specific action graphical user interface element to enable the user to perform an action related to the content item as disclosed by Boyd (Para. 68). The interaction pattern management system is responsible for detecting determinable interaction patterns by users of the interaction system, and initiating predefined responses such as the recommended action based on respective determinable interaction patterns. One of the ordinary skills in the art would have motivated to make this modification in order to enable the viewing user to conveniently reply to the publishing user, or send a chat message to the publishing user, related to the content item by using the selectable graphical user interface element as suggested by Boyd (Para. 85).
As to claims 2 and 16, the claims are rejected for the same reasons as claims 1 and 15 above. In addition, Gutzeit discloses wherein the recommended action comprises retrieving one or more database records from the database system, the one or more database records being associated with a client organization of the plurality of client organizations (Col. 5 lines 22-31, The application framework retrieves, based on the ethical walls associated with the employee, a subset of embeddings from the embeddings stored in the database. The retrieved subset of embeddings, i.e., retrieving one or more database records, may comprise embeddings that users on a same side of an ethical wall (e.g., employees in a same department, employees in departments that do not conflict) can access, and the retrieved subset of embeddings may not comprise embeddings that the employee is not allowed to access based on data access policies and access control requirements. Col. 3 lines 61-67, “The application framework can provide various machine-learning models to perform a variety of analysis tasks to analyze enterprise data such as communications between one or more employees of an organization and one or more clients of the organization and provide intelligence and insights for a user in the organization (e.g., an employee of the organization).”. Thus, the recommended action comprises retrieving one or more database records from the database system, the one or more database records being associated with a client organization of the plurality of client organizations.).
As to claims 3 and 17, the claims are rejected for the same reasons as claims 1 and 15 above. In addition, Gutzeit discloses wherein the recommended action comprises generating a summary of one or more database records via a generative language model (Col. 8 lines 60-62, “the trained machine-learning model is configured to receive the subset of embeddings and output a result, such as a score, a summary, a text, or a recommendation.”. Col. 23 lines 57-64, “the graphical user interface object 908 comprises a suggestion for summarizing the conversation. The conversation summary may be suggested by the framework based on enterprise information associated with the external user and/or internal user. For example, accessible embeddings include information indicating the external user is detail-oriented or information indicating that internal user prefers to summarize client conversations.”. Thus, the recommended action comprises generating a summary of one or more database records via a generative language model.).
As to claim 4, the claim is rejected for the same reasons as claim 1 above. In addition, deLevie discloses wherein the recommended action comprises storing information to the database system (Para. 56, “the database system 214 is configured to store information determined based on natural language. For example, the database system 214 may be configured to store one or more database tables that include fields corresponding with information extracted from natural language documents. As another example, the database system 214 may be configured to store metadata information about documents based on information extracted from those documents. As yet another example, the database system 214 may be configured to store linkages between documents and document portions.”. Para. 158, “the novel text response message may be transmitted to a storage system for storing the novel text passage and/or the text verification information.”. Thus, the recommended action comprises storing information to the database system.).
As to claim 5, the claim is rejected for the same reasons as claim 1 above. In addition, Gutzeit discloses wherein the recommended action comprises generating a draft message by a generative language model (Col. 30 lines 31-43, The graphical user interface 2100 may also comprise graphical user interface object 2106, which includes a welcome message to the internal user, information about other users, and suggested actions related to the information. The welcome message and information about other users may be generated, i.e., generating a draft message, based on embeddings associated with the internal user and accessible embeddings about these other internal users. For example, the welcome message is determined based on an embedding associated with the internal user indicating a preference for a motivating welcome message, and the birthday is displayed based on an embedding associated with that the user's personal information, which is accessible to the internal user of this graphical user interface. Col. 31 lines 1-14, “As illustrated in this example, group 2110 of graphical user interface objects may comprise graphical user interfaces associated with notes, to-do list, and contacts. In some embodiments, at least a part of the notes is generated based on conversation history information (e.g., embeddings comprising information that Simon Hans followed up twice, embeddings comprising information that Claire Jasmine is retiring soon) between the internal user and a respective external user. In some embodiments, at least a part of the to-do list is generated based on conversation history information (e.g., embeddings comprising information indicating that a discussion with Tom is needed) between the internal user and an associated external user.”. Thus, the recommended action comprises generating a draft message by a generative language model.).
As to claim 7, the claim is rejected for the same reasons as claim 1 above. In addition, Boyd discloses wherein the selection affordance is a button presented in the conversational chat interface (Para. 74, The action graphical user interface element is user selectable to enable the viewing user to perform an action related to the content item. The action graphical user interface element may be a reply button, i.e., the selection affordance is a button, that is presented and activated within a viewing user interface by the messaging system 210 in response to the detection of the interaction pattern. Thus, the selection affordance is a button presented in the conversational chat interface.).
As to claim 8, the claim is rejected for the same reasons as claim 1 above. In addition, Boyd discloses wherein the conversational chat interface is included in a mobile application at the client machine (Fig. 10, Para. 77, “The method 400 is described with reference to examples of viewing user interfaces depicted in FIG. 5, FIG. 6, and FIG. 7, which may be presented to a viewing user by the interaction client 104 (as an example of an interaction application). Viewing user interfaces, such as those depicted in FIG. 5, FIG. 6, and FIG. 7, may be presented by an interaction client 104 executing on a device such as the mobile device 114.”. Para. 78, “interfaces in FIG. 5, FIG. 6, and FIG. 7, are described and shown as being presented on a touch screen, such as a screen of the mobile device 114, interfaces according to some examples may also be presented using other types of devices that can provide suitable user interfaces”. Thus, the conversational chat interface is included in a mobile application at the client machine.).
As to claim 9, the claim is rejected for the same reasons as claim 1 above. In addition, Boyd discloses wherein the conversational chat interface included in a web application at the client machine (Para. 30, “a web server 130 is coupled to the interaction servers 124 and provides web-based interfaces to the interaction servers 124.”. Para. 63, “The interaction servers 124 can add a visual representation (such as a box art or other graphic) of the web-based external resource in the interaction client 104. Once the user selects the visual representation or instructs the interaction client 104 through a GUI of the interaction client 104 to access features of the web-based external resource, the interaction client 104 obtains the HTML5 file and instantiates the resources to access the features of the web-based external resource.”. Thus, the conversational chat interface included in a web application at the client machine.).
As to claims 11 and 19, the claims are rejected for the same reasons as claims 1 and 15 above. In addition, Boyd discloses further comprising a conversational chat studio configured to customize the conversational chat assistant based on graphical user input provided via a graphical user interface (Para. 68, “the interaction pattern management system 234 may work with the messaging system 210 or the collection management system 222 to identify that a user has accessed a content item multiple times and, in response thereto, cause activation, presentation and/or highlighting of a specific action graphical user interface element to enable the user to perform an action related to the content item (or to facilitate the performance of such an action).”. Thus, a conversational chat studio configured to customize the conversational chat assistant based on graphical user input provided via a graphical user interface.).
As to claim 12, the claim is rejected for the same reasons as claim 11 above. In addition, Boyd discloses wherein the graphical user input includes identifying a triggering condition associated with the recommended action and one or more operations to perform within the computing services environment to execute the recommended action (Para. 73, The determinable interaction pattern may comprise a series of access actions, e.g., performed within a determinable time (such as, for example, 24 hours, 48 hours, or one week), with respect to a content item published on the interaction system 100 by a publishing user. For example, where the content item is an image or video, the interaction client 104 may detect that the viewing user performs multiple viewings (e.g., re-watches or re-views), i.e., the context satisfies a triggering condition, of the relevant image or video using the interaction client 104. Para. 183, detecting a determinable interaction pattern of interactions by a first user, via a user system, with a content item communicated on an interaction system by a second user, the determinable interaction pattern comprising multiple access actions with respect to the content item by the first user; and responsive to detecting the determinable interaction pattern, i.e., the context satisfies a triggering condition, automatically activating an action graphical user interface element that is user selectable to enable the first user to perform an action related to the content item, i.e., the recommended action.”. Thus, the graphical user input includes identifying a triggering condition associated with the recommended action and one or more operations to perform within the computing services environment to execute the recommended action.).
As to claim 13, the claim is rejected for the same reasons as claim 1 above. In addition, deLevie discloses wherein executing the recommended action involves transmitting an input prompt to a generative language model for completion via the generative language model interface (Para. 36, “determining the novel text may involve determining one or more query response prompts based on a text generation prompt template, the query, and a subset of the enumerated source text passages. The one or more text generation prompts may then be transmitted to a large language model for completion. The large language model may return one or more completed text generation prompts, which may be parsed by the text generation interface system to determine identify the novel text.”. Para. 158, “the novel text response message may be transmitted to a client machine. For instance, the novel text response message may be transmitted via a chat interface, application procedure interface, or other suitable communication medium.”. Thus, wherein executing the recommended action involves transmitting an input prompt to a generative language model for completion via the generative language model interface.).
As to claim 14, the claim is rejected for the same reasons as claim 1 above. In addition, Gutzeit discloses wherein the conversational chat assistant is one of a plurality of conversational chat assistants accessible via the computing services environment, and wherein the conversational chat assistant is specific to a client organization of the plurality of client organizations (Col. 2 lines 52-63, “a method for providing machine learning-based analysis of communications in a central communication platform comprises receiving a plurality of messages for a plurality of internal users of an organization from a plurality of external users, constructing a plurality of embedding representations of the plurality of messages by converting each message of the plurality of messages into one or more embedding representations, and storing the plurality of embeddings in an embedding database. Each embedding of the plurality of embeddings is associated with a corresponding sender identity or a corresponding recipient identity.”. Col. 3 lines 61-67, “The application framework can provide various machine-learning models to perform a variety of analysis tasks to analyze enterprise data such as communications between one or more employees of an organization and one or more clients of the organization and provide intelligence and insights for a user in the organization (e.g., an employee of the organization).”. Col. 12 lines 25-28, “The analysis task may be obtained based on a query of the internal user of the organization. For example, the query is made via the internal user's conversation with a personal assistant (e.g., a chatbot).”. Thus, the conversational chat assistant is one of a plurality of conversational chat assistants accessible via the computing services environment, and wherein the conversational chat assistant is specific to a client organization of the plurality of client organizations.).
As to claim 18, the claims are rejected for the same reasons as claim 15 above. In addition, Boyd discloses wherein determining the recommended action includes: identifying a context associated with an interaction between the client machine and the conversational chat assistant (Para. 199, the action graphical user interface element comprises a tray of user selectable elements, i.e., the recommended action, presented within a context of a viewing user interface of an interaction client of the interaction system, i.e., a context associated with an interaction between the client machine and the conversational chat assistant. Para. 67, “The AI/ML system 232 may also provide chatbot functionality to message interactions 120 between user systems 102 and between a user system 102 and the interaction server system 110.”. Para. 73, the content item is an image or video, the interaction client 104 may detect that the viewing user performs multiple viewings (e.g., re-watches or re-views) of the relevant image or video using the interaction client 104, i.e., the client machine.); and determining that the context satisfies a triggering condition associated with the recommended action (Para. 73, The determinable interaction pattern may comprise a series of access actions, e.g., performed within a determinable time (such as, for example, 24 hours, 48 hours, or one week), with respect to a content item published on the interaction system 100 by a publishing user. For example, where the content item is an image or video, the interaction client 104 may detect that the viewing user performs multiple viewings (e.g., re-watches or re-views), i.e., the context satisfies a triggering condition, of the relevant image or video using the interaction client 104. Para. 183, detecting a determinable interaction pattern of interactions by a first user, via a user system, with a content item communicated on an interaction system by a second user, the determinable interaction pattern comprising multiple access actions with respect to the content item by the first user; and responsive to detecting the determinable interaction pattern, i.e., the context satisfies a triggering condition, automatically activating an action graphical user interface element that is user selectable to enable the first user to perform an action related to the content item, i.e., the recommended action.).
Response to Arguments
7. Applicant’s arguments with respect to claims 1-5, 7-9 and 11-20 have been considered but are moot because of the new ground of rejection necessitated by the amendment to the claims. For Examiner's response, see discussion below:
Applicant's arguments, see pages 7-11, with respect to the rejections of claims 1-5, 7-9 and 11-20 under 35 USC §103 have been considered but are moot in view of the new ground(s) of rejection necessitated by applicant's amendments as set forth in the respective rejections of claims 1-5, 7-9 and 11-20 under 35 USC §103 above in view of the newly found reference.
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Heere et al. (US 2021/0089860 A1) teaches digital assistant with predictions, notifications, and recommendations.
Koukoumidis et al. (US 11,038,974 B1) teaches recommending content with assistant systems.
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD SOLAIMAN BHUYAN whose telephone number is (571)272-7843. The examiner can normally be reached on Monday - Friday 9:00am-5:00pm EST.
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/Mohammad S Bhuyan/Examiner, Art Unit 2168
/CHARLES RONES/Supervisory Patent Examiner, Art Unit 2168
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