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 .
DETAILED ACTION
This action is responsive to patent application as filed on 10/1/2024, which claims priority to U.S. Provisional Pat. App. No: 63/587,805 filed 10/4/2023.
This action is made Non-Final.
Claims 1 – 20 are pending in the case. Claims 1, 9, and 17 are independent claims.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6/10/2026, 1/12/2026, 9/10/2025, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings filed on 10/1/2024 have been accepted by the Examiner.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 4, 6, 9, 10, 12, 14 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sheikh (USPUB 20260228067 A1).
Claim 1:
Sheikh discloses A method comprising: responsive to a user presenting a request to a facility management system, accessing metadata associated with a content or context of the request; identifying metadata related to the content or context of the request; presenting the metadata to a large language model (LLM) configured to generate a suggestion based on the request and the metadata; receiving the suggestion from the LLM determined to be relevant to the content or context of the request; and presenting the suggestion to the user (0036-39 and 0174: The first agent receives or obtains a service request. The term “service request” as used herein refers to a specific action or communication made by a user, typically through a digitalized system, to seek a particular service or assistance. Optionally, the service request can take various forms, such as direct interactions with digital interfaces like voice assistants (such as Siri, Alexa, ChatGPT, and so forth), inputting information into dedicated applications, or entering appointments into personal calendars. Optionally, the service request may include metadata, which is additional information accompanying the request, and is utilized by at least one Large Language Model (LLM) to provide relevant inferences or responses…Optionally, the service request includes at least one of: a time needed for providing the service, a price associated with the service, a quality associated with the service, and/or at least one preference associated with the service. For example, the user specifies a parameter (such as, using the graphical user interface associated with a device including at least one of: time, price, quality and/or at least one preference that is required by the user in the provided service. In such an instance, the parameter is provided to the device with the generated service request. In one example, the service request includes the price associated with the service, such as a minimum and maximum price associated with the service…upon receiving the service request, the first agent is configured to generate one or more objectives associated with the service request. The first agent processes the service request (for example, using at least one processor configured to execute various instruction sets to achieve the required objective), to derive structured metadata and leveraging historical metadata corresponding to previously processed service requests stored in a memory…At step 302, a service request is received by a first agent of a plurality of agents. At step 304, at least one large language model (LLM) is called by the first agent to generate structured service data for execution of the service request. At step 306, at least one second agent of the plurality of agents is invoked, via a communication channel, by the first agent using the structured service data. At step 308, messages are exchanged, via the communication channel, between the first agent and the at least one second agent, to iteratively evaluate the structured service data and reach a consensus on execution of the service request. At step 310, at least one task corresponding to the structured service data is executed, by at least one execution agent of the plurality of agents, in response to the consensus. At step 312, an output is generated from the executed at least one task. At step 314, the communication channel is updated based on the output to record completion of the at least one task, thereby fulfilling the service request).
Claim 2:
Sheikh discloses the suggestion includes: a suggested answer; or a suggested course of action (0036-39 and 0174: The first agent receives or obtains a service request. The term “service request” as used herein refers to a specific action or communication made by a user, typically through a digitalized system, to seek a particular service or assistance. Optionally, the service request can take various forms, such as direct interactions with digital interfaces like voice assistants (such as Siri, Alexa, ChatGPT, and so forth), inputting information into dedicated applications, or entering appointments into personal calendars. Optionally, the service request may include metadata, which is additional information accompanying the request, and is utilized by at least one Large Language Model (LLM) to provide relevant inferences or responses…Optionally, the service request includes at least one of: a time needed for providing the service, a price associated with the service, a quality associated with the service, and/or at least one preference associated with the service. For example, the user specifies a parameter (such as, using the graphical user interface associated with a device including at least one of: time, price, quality and/or at least one preference that is required by the user in the provided service. In such an instance, the parameter is provided to the device with the generated service request. In one example, the service request includes the price associated with the service, such as a minimum and maximum price associated with the service…upon receiving the service request, the first agent is configured to generate one or more objectives associated with the service request. The first agent processes the service request (for example, using at least one processor configured to execute various instruction sets to achieve the required objective), to derive structured metadata and leveraging historical metadata corresponding to previously processed service requests stored in a memory…At step 302, a service request is received by a first agent of a plurality of agents. At step 304, at least one large language model (LLM) is called by the first agent to generate structured service data for execution of the service request. At step 306, at least one second agent of the plurality of agents is invoked, via a communication channel, by the first agent using the structured service data. At step 308, messages are exchanged, via the communication channel, between the first agent and the at least one second agent, to iteratively evaluate the structured service data and reach a consensus on execution of the service request. At step 310, at least one task corresponding to the structured service data is executed, by at least one execution agent of the plurality of agents, in response to the consensus. At step 312, an output is generated from the executed at least one task. At step 314, the communication channel is updated based on the output to record completion of the at least one task, thereby fulfilling the service request).
Claim 4:
Sheikh discloses the context of the request includes visual or audible data associated with the user or a facility associated with the facility management system, the facility including a home, business, yard, activity, device owned, hobby, pet, or other individual associated with the user (0036-40 and 0042: The first agent receives or obtains a service request. The term “service request” as used herein refers to a specific action or communication made by a user, typically through a digitalized system, to seek a particular service or assistance. Optionally, the service request can take various forms, such as direct interactions with digital interfaces like voice assistants (such as Siri, Alexa, ChatGPT, and so forth), inputting information into dedicated applications, or entering appointments into personal calendars. Optionally, the service request may include metadata, which is additional information accompanying the request, and is utilized by at least one Large Language Model (LLM) to provide relevant inferences or responses … the service request includes at least one of: a time needed for providing the service, a price associated with the service, a quality associated with the service, and/or at least one preference associated with the service. For example, the user specifies a parameter (such as, using the graphical user interface associated with a device including at least one of: time, price, quality and/or at least one preference that is required by the user in the provided service. In such an instance, the parameter is provided to the device with the generated service request … upon receiving the service request, the first agent is configured to generate one or more objectives associated with the service request. The first agent processes the service request (for example, using at least one processor configured to execute various instruction sets to achieve the required objective), to derive structured metadata and leveraging historical metadata corresponding to previously processed service requests stored in a memory. Herein, the objective is defined in the form of the one or more representations providing a machine-processable, context-enriched formulation of the service request suitable for downstream orchestration, task generation, and assignment to autonomous agents … The term “metadata” as used herein refers to structured descriptive information characterizing attributes of a service request, including but not limited to: a service type (e.g., travel booking, procurement, scheduling), a problem domain identifier, entities involved (e.g., vendor identifiers, destination identifiers), temporal constraints, geographic parameters, cost constraints, user preferences, device/account identifiers, and optionally historical execution outcomes and performance indicators …when the software application refers the service request to the LLM, the LLM interprets, using natural language processing techniques, unstructured data corresponding to the service request and transforms it into structured data to allow the method to be carried out. The LLM understands the context, identifies key information, and extracts relevant details from the service request).
Claim 6:
Sheikh discloses the context of the request includes historical search data including a previous request from the user and a user reaction to a previous suggestion or action by the facility management system (0039: The first agent processes the service request (for example, using at least one processor configured to execute various instruction sets to achieve the required objective), to derive structured metadata and leveraging historical metadata corresponding to previously processed service requests stored in a memory. Herein, the objective is defined in the form of the one or more representations providing a machine-processable, context-enriched formulation of the service request suitable for downstream orchestration, task generation, and assignment to autonomous agents).
Claim 9:
Sheikh discloses A system comprising (Claim 1): a request module configured to receive a request to a facility management system and from a user; a metadata module configured to access and identify metadata related to a content or context of the request; a large language model (LLM) module configured to receive the request and the metadata and to generate a suggestion relevant to one of the content or context of the request; and a suggestion module configured to present the suggestion to the user (0036-39 and 0174: The first agent receives or obtains a service request. The term “service request” as used herein refers to a specific action or communication made by a user, typically through a digitalized system, to seek a particular service or assistance. Optionally, the service request can take various forms, such as direct interactions with digital interfaces like voice assistants (such as Siri, Alexa, ChatGPT, and so forth), inputting information into dedicated applications, or entering appointments into personal calendars. Optionally, the service request may include metadata, which is additional information accompanying the request, and is utilized by at least one Large Language Model (LLM) to provide relevant inferences or responses…Optionally, the service request includes at least one of: a time needed for providing the service, a price associated with the service, a quality associated with the service, and/or at least one preference associated with the service. For example, the user specifies a parameter (such as, using the graphical user interface associated with a device including at least one of: time, price, quality and/or at least one preference that is required by the user in the provided service. In such an instance, the parameter is provided to the device with the generated service request. In one example, the service request includes the price associated with the service, such as a minimum and maximum price associated with the service…upon receiving the service request, the first agent is configured to generate one or more objectives associated with the service request. The first agent processes the service request (for example, using at least one processor configured to execute various instruction sets to achieve the required objective), to derive structured metadata and leveraging historical metadata corresponding to previously processed service requests stored in a memory…At step 302, a service request is received by a first agent of a plurality of agents. At step 304, at least one large language model (LLM) is called by the first agent to generate structured service data for execution of the service request. At step 306, at least one second agent of the plurality of agents is invoked, via a communication channel, by the first agent using the structured service data. At step 308, messages are exchanged, via the communication channel, between the first agent and the at least one second agent, to iteratively evaluate the structured service data and reach a consensus on execution of the service request. At step 310, at least one task corresponding to the structured service data is executed, by at least one execution agent of the plurality of agents, in response to the consensus. At step 312, an output is generated from the executed at least one task. At step 314, the communication channel is updated based on the output to record completion of the at least one task, thereby fulfilling the service request).
Claim 10:
Sheikh discloses the suggestion includes: a suggested answer; or a suggested course of action (0036-39 and 0174: The first agent receives or obtains a service request. The term “service request” as used herein refers to a specific action or communication made by a user, typically through a digitalized system, to seek a particular service or assistance. Optionally, the service request can take various forms, such as direct interactions with digital interfaces like voice assistants (such as Siri, Alexa, ChatGPT, and so forth), inputting information into dedicated applications, or entering appointments into personal calendars. Optionally, the service request may include metadata, which is additional information accompanying the request, and is utilized by at least one Large Language Model (LLM) to provide relevant inferences or responses…Optionally, the service request includes at least one of: a time needed for providing the service, a price associated with the service, a quality associated with the service, and/or at least one preference associated with the service. For example, the user specifies a parameter (such as, using the graphical user interface associated with a device including at least one of: time, price, quality and/or at least one preference that is required by the user in the provided service. In such an instance, the parameter is provided to the device with the generated service request. In one example, the service request includes the price associated with the service, such as a minimum and maximum price associated with the service…upon receiving the service request, the first agent is configured to generate one or more objectives associated with the service request. The first agent processes the service request (for example, using at least one processor configured to execute various instruction sets to achieve the required objective), to derive structured metadata and leveraging historical metadata corresponding to previously processed service requests stored in a memory…At step 302, a service request is received by a first agent of a plurality of agents. At step 304, at least one large language model (LLM) is called by the first agent to generate structured service data for execution of the service request. At step 306, at least one second agent of the plurality of agents is invoked, via a communication channel, by the first agent using the structured service data. At step 308, messages are exchanged, via the communication channel, between the first agent and the at least one second agent, to iteratively evaluate the structured service data and reach a consensus on execution of the service request. At step 310, at least one task corresponding to the structured service data is executed, by at least one execution agent of the plurality of agents, in response to the consensus. At step 312, an output is generated from the executed at least one task. At step 314, the communication channel is updated based on the output to record completion of the at least one task, thereby fulfilling the service request).
Claim 12:
Sheikh discloses the context of the request includes visual or audible data associated with the user or a facility associated with the facility management system, the facility including a home, business, yard, activity, device owned, hobby, pet, or other individual associated with the user (0036-40 and 0042: The first agent receives or obtains a service request. The term “service request” as used herein refers to a specific action or communication made by a user, typically through a digitalized system, to seek a particular service or assistance. Optionally, the service request can take various forms, such as direct interactions with digital interfaces like voice assistants (such as Siri, Alexa, ChatGPT, and so forth), inputting information into dedicated applications, or entering appointments into personal calendars. Optionally, the service request may include metadata, which is additional information accompanying the request, and is utilized by at least one Large Language Model (LLM) to provide relevant inferences or responses … the service request includes at least one of: a time needed for providing the service, a price associated with the service, a quality associated with the service, and/or at least one preference associated with the service. For example, the user specifies a parameter (such as, using the graphical user interface associated with a device including at least one of: time, price, quality and/or at least one preference that is required by the user in the provided service. In such an instance, the parameter is provided to the device with the generated service request … upon receiving the service request, the first agent is configured to generate one or more objectives associated with the service request. The first agent processes the service request (for example, using at least one processor configured to execute various instruction sets to achieve the required objective), to derive structured metadata and leveraging historical metadata corresponding to previously processed service requests stored in a memory. Herein, the objective is defined in the form of the one or more representations providing a machine-processable, context-enriched formulation of the service request suitable for downstream orchestration, task generation, and assignment to autonomous agents … The term “metadata” as used herein refers to structured descriptive information characterizing attributes of a service request, including but not limited to: a service type (e.g., travel booking, procurement, scheduling), a problem domain identifier, entities involved (e.g., vendor identifiers, destination identifiers), temporal constraints, geographic parameters, cost constraints, user preferences, device/account identifiers, and optionally historical execution outcomes and performance indicators …when the software application refers the service request to the LLM, the LLM interprets, using natural language processing techniques, unstructured data corresponding to the service request and transforms it into structured data to allow the method to be carried out. The LLM understands the context, identifies key information, and extracts relevant details from the service request).
Claim 14:
Sheikh discloses the context of the request includes historical search data including a previous request from the user and a user reaction to a previous suggestion or action by the facility management system (0039: The first agent processes the service request (for example, using at least one processor configured to execute various instruction sets to achieve the required objective), to derive structured metadata and leveraging historical metadata corresponding to previously processed service requests stored in a memory. Herein, the objective is defined in the form of the one or more representations providing a machine-processable, context-enriched formulation of the service request suitable for downstream orchestration, task generation, and assignment to autonomous agents).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
Claim(s) 3, 11, 17, 18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sheikh in view of Cardona (USPUB 20250048111 filed Jan. 8, 2024 which claims priority to Provisional App. No: 63/517018).
Claims 3 and 11:
Sheikh discloses every feature of claims 1 and 10.
Sheikh, by itself, does not seem to completely teach determining that the user requires assistance in submitting the request by detecting: an engaging of a help function; a delay in completing the request for longer than a specified interval of time after beginning the request; an indication that a previous response of the facility management system was unsatisfactory; or a history of interaction with the facility management system indicative of an inability of the user to secure a desired action.
The Examiner maintains that these features were previously well-known as taught by Cardona.
Cardona teaches determining that the user requires assistance in submitting the request by detecting: an engaging of a help function; a delay in completing the request for longer than a specified interval of time after beginning the request; an indication that a previous response of the facility management system was unsatisfactory; or a history of interaction with the facility management system indicative of an inability of the user to secure a desired action (0112: determining that the user requires assistance in submitting the request by detecting: an engaging of a help function; a delay in completing the request for longer than a specified interval of time after beginning the request; an indication that a previous response of the facility management system was unsatisfactory; or a history of interaction with the facility management system indicative of an inability of the user to secure a desired action).
Sheikh and Cardona are analogous art because they are from the same problem-solving area, smart device process management.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sheikh and Cardona before him or her, to combine the teachings of Sheikh and Cardona. The rationale for doing so would have been to provide a user with assistance when necessary to operate the smart device.
Therefore, it would have been obvious to combine Sheikh and Cardona to obtain the invention as specified in the instant claim(s).
Claim 17:
Sheikh teaches A system for facility management comprising: one or more input devices configured to collect data and receive a user request; one or more control devices configured to respond to instructions based on the user request; and a device interface configured to receive the user request from the one or more input devices; a personalized suggestion manager including a metadata module configured to access and identify metadata related to a content or context of the request; a large language model (LLM) module configured to generate, based on the request and the metadata, a suggestion relevant to the content or context of the request; and a suggestion module configured to present the suggestion to the user (0036-39 and 0174: The first agent receives or obtains a service request. The term “service request” as used herein refers to a specific action or communication made by a user, typically through a digitalized system, to seek a particular service or assistance. Optionally, the service request can take various forms, such as direct interactions with digital interfaces like voice assistants (such as Siri, Alexa, ChatGPT, and so forth), inputting information into dedicated applications, or entering appointments into personal calendars. Optionally, the service request may include metadata, which is additional information accompanying the request, and is utilized by at least one Large Language Model (LLM) to provide relevant inferences or responses…Optionally, the service request includes at least one of: a time needed for providing the service, a price associated with the service, a quality associated with the service, and/or at least one preference associated with the service. For example, the user specifies a parameter (such as, using the graphical user interface associated with a device including at least one of: time, price, quality and/or at least one preference that is required by the user in the provided service. In such an instance, the parameter is provided to the device with the generated service request. In one example, the service request includes the price associated with the service, such as a minimum and maximum price associated with the service…upon receiving the service request, the first agent is configured to generate one or more objectives associated with the service request. The first agent processes the service request (for example, using at least one processor configured to execute various instruction sets to achieve the required objective), to derive structured metadata and leveraging historical metadata corresponding to previously processed service requests stored in a memory…At step 302, a service request is received by a first agent of a plurality of agents. At step 304, at least one large language model (LLM) is called by the first agent to generate structured service data for execution of the service request. At step 306, at least one second agent of the plurality of agents is invoked, via a communication channel, by the first agent using the structured service data. At step 308, messages are exchanged, via the communication channel, between the first agent and the at least one second agent, to iteratively evaluate the structured service data and reach a consensus on execution of the service request. At step 310, at least one task corresponding to the structured service data is executed, by at least one execution agent of the plurality of agents, in response to the consensus. At step 312, an output is generated from the executed at least one task. At step 314, the communication channel is updated based on the output to record completion of the at least one task, thereby fulfilling the service request)..
Sheikh, by itself, does not seem to completely teach a request module configured to determine that the user requires assistance in submitting the request.
The Examiner maintains that these features were previously well-known as taught by Cardona.
Cardona teaches a request module configured to determine that the user requires assistance in submitting the request (0112: determining that the user requires assistance in submitting the request by detecting: an engaging of a help function; a delay in completing the request for longer than a specified interval of time after beginning the request; an indication that a previous response of the facility management system was unsatisfactory; or a history of interaction with the facility management system indicative of an inability of the user to secure a desired action).
Sheikh and Cardona are analogous art because they are from the same problem-solving area, smart device process management.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sheikh and Cardona before him or her, to combine the teachings of Sheikh and Cardona. The rationale for doing so would have been to provide a user with assistance when necessary to operate the smart device.
Therefore, it would have been obvious to combine Sheikh and Cardona to obtain the invention as specified in the instant claim(s).
Claim 18:
Sheikh teaches the context of the request includes visual or audible data associated with the user or a facility, the facility including a home, business, yard, activity, device owned, hobby, pet, or other individual associated with the user (0036-40 and 0042: The first agent receives or obtains a service request. The term “service request” as used herein refers to a specific action or communication made by a user, typically through a digitalized system, to seek a particular service or assistance. Optionally, the service request can take various forms, such as direct interactions with digital interfaces like voice assistants (such as Siri, Alexa, ChatGPT, and so forth), inputting information into dedicated applications, or entering appointments into personal calendars. Optionally, the service request may include metadata, which is additional information accompanying the request, and is utilized by at least one Large Language Model (LLM) to provide relevant inferences or responses … the service request includes at least one of: a time needed for providing the service, a price associated with the service, a quality associated with the service, and/or at least one preference associated with the service. For example, the user specifies a parameter (such as, using the graphical user interface associated with a device including at least one of: time, price, quality and/or at least one preference that is required by the user in the provided service. In such an instance, the parameter is provided to the device with the generated service request … upon receiving the service request, the first agent is configured to generate one or more objectives associated with the service request. The first agent processes the service request (for example, using at least one processor configured to execute various instruction sets to achieve the required objective), to derive structured metadata and leveraging historical metadata corresponding to previously processed service requests stored in a memory. Herein, the objective is defined in the form of the one or more representations providing a machine-processable, context-enriched formulation of the service request suitable for downstream orchestration, task generation, and assignment to autonomous agents … The term “metadata” as used herein refers to structured descriptive information characterizing attributes of a service request, including but not limited to: a service type (e.g., travel booking, procurement, scheduling), a problem domain identifier, entities involved (e.g., vendor identifiers, destination identifiers), temporal constraints, geographic parameters, cost constraints, user preferences, device/account identifiers, and optionally historical execution outcomes and performance indicators …when the software application refers the service request to the LLM, the LLM interprets, using natural language processing techniques, unstructured data corresponding to the service request and transforms it into structured data to allow the method to be carried out. The LLM understands the context, identifies key information, and extracts relevant details from the service request).
Claim 20:
Sheikh teaches the context of the request includes historical search data including a previous request from the user and a user reaction to previous suggestion or action by the facility management system (0039: The first agent processes the service request (for example, using at least one processor configured to execute various instruction sets to achieve the required objective), to derive structured metadata and leveraging historical metadata corresponding to previously processed service requests stored in a memory. Herein, the objective is defined in the form of the one or more representations providing a machine-processable, context-enriched formulation of the service request suitable for downstream orchestration, task generation, and assignment to autonomous agents).
Claim(s) 5 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sheikh in view of Letort (USPUB 20250007988) filed Feb. 26, 2024 which is a CIP of U.S. Pat. App. No: 17890712 filed Aug. 18, 2022).
Claims 5 and 13:
Sheikh discloses every feature of claims 1 and 12.
Sheikh, by itself, does not seem to completely teach the visual or audible data associated with the user are maintained in a multimodal embedding model indexable for access by a natural language search algorithm associated with the LLM.
The Examiner maintains that these features were previously well-known as taught by Letort.Letort teaches the visual or audible data associated with the user are maintained in a multimodal embedding model indexable for access by a natural language search algorithm associated with the LLM (0116: In some embodiments, the new training datasets may be utilized as part of RAG data training, to add new indexed data. For example, in the case of a large foundational model (e.g., multimodal generative AI/ML model that includes an LLM), this may include building an index of internal data and loading it to a vector database. Then, when the LLM is prompted, it may query both the vector database and the large foundational model. In this manner, the vector data grounds the foundational model to enable it to provide fine-tuned and specific answers).
Sheikh and Letort are analogous art because they are from the same problem-solving area, LLM training and utilization.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sheikh and Cardona before him or her, to combine the teachings of Sheikh and Cardona. The rationale for doing so would have been to enable the model to provide fine-tuned and specific answers, as discussed in the citation of Cardona.
Therefore, it would have been obvious to combine Sheikh and Cardona to obtain the invention as specified in the instant claim(s).
Claim(s) 7, 8, 15 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sheikh in view of Fan (USPUB 20250078823 A1).
Claims 7 and 15:
Sheikh discloses every feature of claims 1 and 9.
Sheikh, by itself, does not seem to completely teach ranking multiple suggestions according to a relative relevance of each of the multiple suggestions; and presenting the multiple suggestions to the user in an order according to the ranking.
The Examiner maintains that these features were previously well-known as taught by Fan.
Fan teaches ranking multiple suggestions according to a relative relevance of each of the multiple suggestions; and presenting the multiple suggestions to the user in an order according to the ranking (0036 and 0116: The LLM shortlister component 140 receives and processes the action response data 158a-n and generates potential response data 143a-n representing the potential response(s) (e.g., relevant potential responses, selected potential responses, ranked potential responses, etc.) for further processing (e.g., as described in detail herein below with respect to FIG. 5). If the LLM shortlister component 140 determines that there are no remaining tasks to generate potential responses for, the LLM shortlister component 140 may send the potential response data 143a-n to the response arbitration component… the shortlister language model 540 may be configured to filter and/or rank the action response data 158a-n based on how relevant the action response data 158a-n is to the current task. In some embodiments, the shortlister language model 540 may be configured to filter and/or rank the action response data 158a-n based on a confidence level of the component that provided the action response data, where the confidence level may indicate a likelihood of the component being able to respond (e.g., within a period of time), the component being able to perform a potential action that corresponds to the current task, etc. In some embodiments, the action response data 158a-n may indicate whether or not the corresponding component is able to respond (e.g., the action response data 158a may include a Boolean value such as “yes” or “no” or other similar indications). In some embodiments, the shortlister language model 540 may filter and/or rank the action response data 158a-n based on information included in the prompt data).
Sheikh and Fan are analogous art because they are from the same problem-solving area, LLM utilization in smart facility management systems.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sheikh and Fan before him or her, to combine the teachings of Sheikh and Fan. The rationale for doing so would have been to enable a user to be provided options to select from rather than just one response to the query.
Therefore, it would have been obvious to combine Sheikh and Fan to obtain the invention as specified in the instant claim(s).
Claims 8 and 16:
Sheikh, by itself, does not seem to completely teach collecting feedback to the one or more suggestions indicative of relevancy of the one or more of the suggestions.
The Examiner maintains that these features were previously well-known as taught by Fan.
Fan teaches collecting feedback to the one or more suggestions indicative of relevancy of the one or more of the suggestions (0036: The LLM shortlister component 140 receives and processes the action response data 158a-n and generates potential response data 143a-n representing the potential response(s) (e.g., relevant potential responses, selected potential responses, ranked potential responses, etc.) for further processing (e.g., as described in detail herein below with respect to FIG. 5). If the LLM shortlister component 140 determines that there are no remaining tasks to generate potential responses for, the LLM shortlister component 140 may send the potential response data 143a-n to the response arbitration component).
Sheikh and Fan are analogous art because they are from the same problem-solving area, LLM utilization in smart facility management systems.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sheikh and Fan before him or her, to combine the teachings of Sheikh and Fan. The rationale for doing so would have been to enable a user to be provided options to select from rather than just one response to the query.
Therefore, it would have been obvious to combine Sheikh and Fan to obtain the invention as specified in the instant claim(s).
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sheikh and Cardano and further in view of Letort (USPUB 20250007988) filed Feb. 26, 2024 which is a CIP of U.S. Pat. App. No: 17890712 filed Aug. 18, 2022).
Claim 19:
Sheikh and Cardano teach every feature of claim 17.
Sheikh, by itself, does not seem to completely teach the visual or audible data associated with the user are maintained in a multimodal embedding model indexable for access by a natural language search algorithm associated with the LLM.
The Examiner maintains that these features were previously well-known as taught by Letort.Letort teaches the visual or audible data associated with the user are maintained in a multimodal embedding model indexable for access by a natural language search algorithm associated with the LLM (0116: In some embodiments, the new training datasets may be utilized as part of RAG data training, to add new indexed data. For example, in the case of a large foundational model (e.g., multimodal generative AI/ML model that includes an LLM), this may include building an index of internal data and loading it to a vector database. Then, when the LLM is prompted, it may query both the vector database and the large foundational model. In this manner, the vector data grounds the foundational model to enable it to provide fine-tuned and specific answers).
Sheikh and Letort are analogous art because they are from the same problem-solving area, LLM training and utilization.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sheikh and Cardona before him or her, to combine the teachings of Sheikh and Cardona. The rationale for doing so would have been to enable the model to provide fine-tuned and specific answers, as discussed in the citation of Cardona.
Therefore, it would have been obvious to combine Sheikh and Cardona to obtain the invention as specified in the instant claim(s).
Note
The Examiner cites particular columns, line numbers and/or paragraph numbers in the references as applied to the claims below for the convenience of the Applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. See MPEP 2123.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is listed in the attached PTOL-892 form.
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/MOHAMMED H ZUBERI/ Primary Examiner, Art Unit 2178
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