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 .
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 06/20/2024, 10/09/2024, and 05/22/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
At step 1, the claim recites a method comprising series of steps (receiving, determining, generating), and therefore is a process, which is a statutory category of invention.
At step 2A, prong one, the claim recites “determining…an anomaly of the one or more building subsystems, based on the subject matter expert data and the building subsystem data”, “generating… a recommendation to resolve the anomaly based on fault detection and diagnostic (FDD) data related to a fault determined in the one or more building subsystems”, and “generating, by the one or more processors using a generative large language model, a response to the query based on the recommendation, the FDD data, and the subject matter expert data”.
The limitations of “determining… an anomaly of the one or more building subsystems, based on the subject matter expert data and the building subsystem data”, “generating… a recommendation to resolve the anomaly based on fault detection and diagnostic (FDD) data related to a fault determined in the one or more building subsystems”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. Determining anomaly, and generating recommendation to resolve the anomaly in the context of this claim encompasses an observation, evaluation, judgement, and/or opinion.
The limitation of “generating, by the one or more processors using a generative large language model, a response to the query based on the recommendation, the FDD data, and the subject matter expert data” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation performed using mathematical concepts. That is, nothing in the claim element precludes the step from practically being performed using mathematical concepts. Generating a response based on data generative large language model, in the context of this claim encompasses mathematical calculations.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, and of mathematical calculations, then it falls within the “Mental Processes”, and “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
At step 2A, prong two, this judicial exception is not integrated into a practical application. In particular, the claim recites “receiving, by one or more processors via a conversational interface, a query from a user”, “receiving, by the one or more processors, building subsystem data for one or more building subsystems in a building”, “retrieving, by the one or more processors, subject matter expert data”, “one or more processors”, “conversational interface”.
The limitations of “one or more processors”, and “conversational interface” is recited at a high level of generality and recited so generically that it represents no more than mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f)). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)).
The limitations of “receiving, by one or more processors via a conversational interface, a query from a user”, “receiving, by the one or more processors, building subsystem data for one or more building subsystems in a building”, “retrieving, by the one or more processors, subject matter expert data”, represents mere data gathering (obtaining a query, building subsystem data, and subject matter expert data) that is necessary for use of the recited judicial exception, as the obtained information is used in the abstract mental process of determining an anomaly, generating a recommendation, and generating a response to query. The receiving of a query, receiving building subsystem data, and retrieving subject matter expert data is recited at a high level of generality. Therefore, it is insignificant extra-solution activity (see MPEP 2106.05(g)). Even when viewed in combination, the additional elements in this claim do no more than automate the mental and mathematical processes that the method uses to determine an anomaly, a recommendation, and a response, using computer components as a tool.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
At step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “one or more processors”, and “a conversational interface” amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
The receiving of a query, building subsystem data, and subject matter expert data, represents mere data gathering and is insignificant extra-solution activity. Further, these elements are well-understood, routine, and conventional.
With respect to receiving data, the courts have found limitation directed to obtaining information electronically, as recited at high level of generality, to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II), “storing and retrieving information in memory”.
Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible.
Regarding Independent Claim 8, this claim recites substantively the same abstract idea identified in claim 1 above; and recites substantively similar additional elements (a system for generating a response to a query using a generative artificial intelligence model, the system comprising: one or more memory devices having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations comprising performing the abstract idea) and is ineligible for the same reasons as those indicated in the analysis of claim 1 above.
Regarding Independent Claim 15, this claim recites substantively the same abstract idea identified in claim 1 above; and recites substantively similar additional elements (one or more non-transitory computer-readable media storing program instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising performing the abstract idea) and is ineligible for the same reasons as those indicated in the analysis of claim 1 above.
Regarding Dependent Claim 2, the additional limitations of “wherein retrieving the subject matter expert data comprises: obtaining, by the one or more processors, previous report data, wherein the report data comprises use cases for the one or more building subsystems in an unstructured format; and formatting, by the one or more building subsystems, the use cases into a standardized format” merely defines subject matter expert data, thus the limitation is part of insignificant extra-solution activity.
Regarding Dependent Claim 3, the additional limitations of “wherein the formatted use cases are embedded into a vector and stored in a vector database” if further defining formatting data, thus the limitation is part of insignificant extra-solution activity.
Regarding Dependent Claim 4, the additional limitations of “wherein the FDD data comprises at least one of one or more FDD rules, work order data, or cost analysis data” further defines FDD data, thus the limitation is part of insignificant extra-solution activity.
Regarding Dependent Claim 5, the additional limitations of “wherein the FDD data is generated based on data from a third-party source”, further defines FDD data, thus the limitation is part of insignificant extra-solution activity.
Regarding Dependent Claim 6, the additional limitations of “wherein the recommendation is a whole system level recommendation which is a recommendation for resolving a fault in a first building subsystem based on data from one or more other building subsystems in the building” merely defines abstract query information, and/or are further observations, evaluation, judgements and/or opinions practicably performable mentally by a human mind, and/or further comprises mathematical calculations; and accordingly further limitations that are part of the abstract idea.
Regarding Dependent Claim 7, the additional limitations of “wherein generating, using the generative large language model, the response to the query further comprises generating action steps to implement the recommendation based on a building knowledge base” merely defines abstract query information, and/or are further observations, evaluation, judgements and/or opinions practicably performable mentally by a human mind, and/or further comprises mathematical calculations; and accordingly further limitations that are part of the abstract idea.
Regarding Dependent Claims 9-14, the additional limitations are rejected for the same reasons as those indicated in the analysis of claims 2-7 above.
Regarding Dependent Claims 16-20, the additional limitations are rejected for the same reasons as those indicated in the analysis of claims 2-6 above.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 8-11, 13-17, and 19-21 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sharma et al. USPGPUB 2020/0142365 (hereinafter “Sharma”).
Regarding claim 1, Sharma teaches a method comprising: receiving, by one or more processors via a conversational interface, a query from a user (Paragraph [0007] “ the one or more processing circuits are configured to receive a natural language input from a user of a user device including a request for generation of analytics for the physical building device, perform natural language processing to determine the request for the generation of analytics for the physical building device, and generate the recommendation in response to determining the request for the generation of analytics”, Paragraph [0174] “the user of the user device 1190 can interact with the interfaces via a natural language input (e.g., spoken words, entered text, etc.). In this regard, the interface generator 1188 can receive various user inputs for navigating the interfaces generated by the interface generator 1188 via natural language user inputs”, and Paragraph [0175], wherein examiner interpreted user inputting natural language including a request for generation of analytics, performing language processing to request generation of analytics as receiving a query from a user, wherein examiner interpreted user device as one or more processors including a conversational interface);
receiving, by the one or more processors, building subsystem data for one or more building subsystems in a building (Paragraph [0103] “The data generated by building subsystems 428 can include measured or calculated values that exhibit statistical characteristics and provide information about how the corresponding system or process (e.g., a temperature control process, a flow control process, etc.) is performing in terms of error from its setpoint. These processes can be examined by FDD layer 416 to expose when the system begins to degrade in performance and alarm a user to repair the fault before it becomes more severe”, wherein examiner interpreted data generated by building subsystems, which the FDD layer uses to identify faults as receiving building subsystem data for one or more building subsystems in a building);
retrieving, by the one or more processors, subject matter expert data (Paragraph [0103] “FDD layer 416 can be configured to store or access a variety of different system data stores (or data points for live data). FDD layer 416 can use some content of the data stores to identify faults at the equipment level (e.g., specific chiller, specific AHU, specific terminal unit, etc.) and other content to identify faults at component or subsystem levels”, wherein examiner interpreted storing or accessing variety of different system data that can be used to identify faults in equipment as retrieving subject matter expert data);
determining, by the one or more processors, an anomaly of the one or more building subsystems, based on the subject matter expert data and the building subsystem data (Paragraph [0101] “Fault detection and diagnostics (FDD) layer 416 can be configured to provide on-going fault detection for building subsystems 428, building subsystem devices (i.e., building equipment), and control algorithms used by demand response layer 414 and integrated control layer 418. FDD layer 416 can receive data inputs from integrated control layer 418, directly from one or more building subsystems or devices, or from another data source. FDD layer 416 can automatically diagnose and respond to detected faults. The responses to detected or diagnosed faults can include providing an alarm message to a user, a maintenance scheduling system, or a control algorithm configured to attempt to repair the fault or to work-around the fault”, and Paragraph [0103] “FDD layer 416 can be configured to store or access a variety of different system data stores (or data points for live data). FDD layer 416 can use some content of the data stores to identify faults at the equipment level (e.g., specific chiller, specific AHU, specific terminal unit, etc.) and other content to identify faults at component or subsystem levels. For example, building subsystems 428 can generate temporal (i.e., time-series) data indicating the performance of BAS 400 and the various components thereof. The data generated by building subsystems 428 can include measured or calculated values that exhibit statistical characteristics and provide information about how the corresponding system or process (e.g., a temperature control process, a flow control process, etc.) is performing in terms of error from its setpoint. These processes can be examined by FDD layer 416 to expose when the system begins to degrade in performance and alarm a user to repair the fault before it becomes more severe”, wherein examiner interpreted determining fault based on stored data and temporal data indicating performance of BAS as determining, by the one or more processors, an anomaly of the one or more building subsystems, based on the subject matter expert data and the building subsystem data);
generating, by the one or more processors, a recommendation to resolve the anomaly based on fault detection and diagnostic (FDD) data related to a fault determined in the one or more building subsystems (Paragraph [0171] “The recommendation generator 1184 can be configured to generate a recommendation 1186 based on the predicted device performance 1182. For example, if the predicted device performance 1182 is indicative of the physical device 1166 deteriorating or failing in the future, then the recommendation 1186 generated by the recommendation generator 1184 may indicate that maintenance should be performed on the physical device 1166 to prevent or reduce a chance of the physical device 1166 from failing. In some embodiments, the recommendation 1186 is to adjust future designs of the physical device 1166. The recommendation 1186 may indicate that certain components or features of the physical device 1166 may need improvement in the future and/or may recommend various changes that could be made to the design of the physical device 1166 that would improve the physical device 1166”, wherein examiner interpreted generating recommendation based on device performance that is indicative of device deteriorating or failing the future, where the recommendation is to perform maintenance to prevent or reduce chance of failing, as generating, by the one or more processors, a recommendation to resolve the anomaly based on fault detection and diagnostic (FDD) data related to a fault determined in the one or more building subsystems); and
generating, by the one or more processors using a generative large language model (Paragraph [0148] “The cloud platform 502 is shown to include artificial intelligence 1028 and cognitive engines 1032. The artificial intelligence 1028 and/or cognitive engines 1032 can be configured to implement various forms of machine learning, data mining, pattern recognition, natural language processing (NLP), and/or the like”), a response to the query based on the recommendation, the FDD data, and the subject matter expert data (Paragraph [0172] “Based on the recommendation 1186 generated by the recommendation generator 1184, the interface generator 1188 can be configured to generate an interface and cause the interface to be displayed on a display device of user device 1190. The interface generator 1188 is shown to receive user input from a natural language processing (NLP) manager 1192, the recommendation 1186 from the recommendation generator 1184, and data (e.g., the environmental inputs 1170, the environmental outputs 1172, and/or the like) from the cloud database 1142. Based on this received information, the interface generator 1188 can generate one or more interfaces for the user device 1190”, wherein examiner interpreted recommendation being displayed on a display device of user device using natural language processing (NPL) manager as generating, by the one or more processors using a generative large language model, a response to the query based on the recommendation, the FDD data, and the subject matter expert data).
Regarding claim 2, Sharma teaches wherein retrieving the subject matter expert data comprises: obtaining, by the one or more processors, previous report data, wherein the report data comprises use cases for the one or more building subsystems in an unstructured format (Paragraph [0101] “FDD layer 416 can receive data inputs from integrated control layer 418, directly from one or more building subsystems or devices, or from another data source”, Paragraph [0103] “FDD layer 416 can be configured to store or access a variety of different system data stores (or data points for live data). FDD layer 416 can use some content of the data stores to identify faults at the equipment level (e.g., specific chiller, specific AHU, specific terminal unit, etc.) and other content to identify faults at component or subsystem levels”, wherein examiner interpreted storing data from one or more building subsystems or devices or other data source as obtaining, by the one or more processors, previous report data, wherein the report data comprises use cases for the one or more building subsystems in an unstructured format); and
formatting, by the one or more building subsystems, the use cases into a standardized format (Paragraph [0119] “In various embodiments, the data manager 540 can be configured to manage, organize, integrate, and store data, and/or send data to various components of the cloud platform 502. For example, the data manager 544 can generate various reports and/or combine (e.g., integrate) data from disparate devices in the building 10, various components of the cloud platform 502, and/or the Internet”, wherein examiner interpreted data manager managing, organizing, integrating, and storing data as formatting, by the one or more building subsystems, the use cases into a standardized format).
Regarding claim 4, Sharma teaches wherein the FDD data comprises at least one of one or more FDD rules, work order data, or cost analysis data (Paragraph [0103] “FDD layer 416 can be configured to store or access a variety of different system data stores (or data points for live data). FDD layer 416 can use some content of the data stores to identify faults at the equipment level (e.g., specific chiller, specific AHU, specific terminal unit, etc.) and other content to identify faults at component or subsystem levels”, wherein examiner interpreted data stored to identify faults as FDD data comprising at least one of one or more FDD rules, work order data, or cost analysis data).
Regarding claim 5, Sharma teaches wherein the FDD data is generated based on data from a third-party source (Paragraph [0101] “FDD layer 416 can receive data inputs from integrated control layer 418, directly from one or more building subsystems or devices, or from another data source”, wherein examiner interpreted receiving data inputs from one or more building subsystems or devices or from another data source as FDD data generated based on data from a third-party source).
Regarding claim 6, Sharma teaches wherein the recommendation is a whole system level recommendation which is a recommendation for resolving a fault in a first building subsystem based on data from one or more other building subsystems in the building (Paragraph [0171] “The recommendation generator 1184 can be configured to generate a recommendation 1186 based on the predicted device performance 1182. For example, if the predicted device performance 1182 is indicative of the physical device 1166 deteriorating or failing in the future, then the recommendation 1186 generated by the recommendation generator 1184 may indicate that maintenance should be performed on the physical device 1166 to prevent or reduce a chance of the physical device 1166 from failing. In some embodiments, the recommendation 1186 is to adjust future designs of the physical device 1166. The recommendation 1186 may indicate that certain components or features of the physical device 1166 may need improvement in the future and/or may recommend various changes that could be made to the design of the physical device 1166 that would improve the physical device 1166”, Paragraph [0101] “Fault detection and diagnostics (FDD) layer 416 can be configured to provide on-going fault detection for building subsystems 428, building subsystem devices (i.e., building equipment), and control algorithms used by demand response layer 414 and integrated control layer 418. FDD layer 416 can receive data inputs from integrated control layer 418, directly from one or more building subsystems or devices, or from another data source”, wherein examiner interpreted generating recommendation based on device performance that is indicative of device deteriorating or failing the future, where the recommendation is to perform maintenance to prevent or reduce chance of failing of device based on data from one or more building subsystems, as the recommendation being a whole system level recommendation which is a recommendation for resolving a fault in a first building subsystem based on data from one or more other building subsystems in the building).
Regarding claim 7, Sharma teaches wherein generating, using the generative large language model, the response to the query further comprises generating action steps to implement the recommendation based on a building knowledge base (Paragraph [0171] “The recommendation generator 1184 can be configured to generate a recommendation 1186 based on the predicted device performance 1182. For example, if the predicted device performance 1182 is indicative of the physical device 1166 deteriorating or failing in the future, then the recommendation 1186 generated by the recommendation generator 1184 may indicate that maintenance should be performed on the physical device 1166 to prevent or reduce a chance of the physical device 1166 from failing. In some embodiments, the recommendation 1186 is to adjust future designs of the physical device 1166. The recommendation 1186 may indicate that certain components or features of the physical device 1166 may need improvement in the future and/or may recommend various changes that could be made to the design of the physical device 1166 that would improve the physical device 1166”, and Paragraph [0172] “Based on the recommendation 1186 generated by the recommendation generator 1184, the interface generator 1188 can be configured to generate an interface and cause the interface to be displayed on a display device of user device 1190. The interface generator 1188 is shown to receive user input from a natural language processing (NLP) manager 1192, the recommendation 1186 from the recommendation generator 1184, and data (e.g., the environmental inputs 1170, the environmental outputs 1172, and/or the like) from the cloud database 1142. Based on this received information, the interface generator 1188 can generate one or more interfaces for the user device 1190”, wherein examiner interpreted generating recommendation that indicates maintenance should be performed using natural language processing (NPL) manager as generating, using the generative large language model, the response to the query further comprises generating action steps to implement the recommendation based on a building knowledge base, wherein examiner interpreted maintenance that should be performed, and components or features should be improved or changes that could improve design as the steps to implement the recommendation based on a building knowledge base).
Regarding claim 8, Sharma teaches a system for generating a response to a query using a generative artificial intelligence model (Paragraph [0007] “the one or more processing circuits are configured to receive a natural language input from a user of a user device including a request for generation of analytics for the physical building device, perform natural language processing to determine the request for the generation of analytics for the physical building device, and generate the recommendation in response to determining the request for the generation of analytics”, and Paragraph [0148] “The cloud platform 502 is shown to include artificial intelligence 1028 and cognitive engines 1032. The artificial intelligence 1028 and/or cognitive engines 1032 can be configured to implement various forms of machine learning, data mining, pattern recognition, natural language processing (NLP), and/or the like”, wherein examiner interpreted user inputting natural language input from user device to request for analytics for physical building device as a system for generating a response to a query using a generative artificial intelligence model), the system comprising:
one or more memory devices having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations comprising (Paragraph [0088] “According to an exemplary embodiment, memory 408 is communicably connected to processor 406 via processing circuit 404 and includes computer code for executing (e.g., by processing circuit 404 and/or processor 406) one or more processes described herein”):
receiving, via a conversational interface, a query from a user (Paragraph [0007] “ the one or more processing circuits are configured to receive a natural language input from a user of a user device including a request for generation of analytics for the physical building device, perform natural language processing to determine the request for the generation of analytics for the physical building device, and generate the recommendation in response to determining the request for the generation of analytics”, Paragraph [0174] “the user of the user device 1190 can interact with the interfaces via a natural language input (e.g., spoken words, entered text, etc.). In this regard, the interface generator 1188 can receive various user inputs for navigating the interfaces generated by the interface generator 1188 via natural language user inputs”, and Paragraph [0175], wherein examiner interpreted user inputting natural language including a request for generation of analytics, performing language processing to request generation of analytics as receiving a query from a user, wherein examiner interpreted user device as a conversational interface);
receiving building subsystem data for one or more building subsystems in a building (Paragraph [0103] “The data generated by building subsystems 428 can include measured or calculated values that exhibit statistical characteristics and provide information about how the corresponding system or process (e.g., a temperature control process, a flow control process, etc.) is performing in terms of error from its setpoint. These processes can be examined by FDD layer 416 to expose when the system begins to degrade in performance and alarm a user to repair the fault before it becomes more severe”, wherein examiner interpreted data generated by building subsystems, which the FDD layer uses to identify faults as receiving building subsystem data for one or more building subsystems in a building);
retrieving subject matter expert data (Paragraph [0103] “FDD layer 416 can be configured to store or access a variety of different system data stores (or data points for live data). FDD layer 416 can use some content of the data stores to identify faults at the equipment level (e.g., specific chiller, specific AHU, specific terminal unit, etc.) and other content to identify faults at component or subsystem levels”, wherein examiner interpreted storing or accessing variety of different system data that can be used to identify faults in equipment as retrieving subject matter expert data);
determining an anomaly of the one or more building subsystems, based on the subject matter expert data and the building subsystem data (Paragraph [0101] “Fault detection and diagnostics (FDD) layer 416 can be configured to provide on-going fault detection for building subsystems 428, building subsystem devices (i.e., building equipment), and control algorithms used by demand response layer 414 and integrated control layer 418. FDD layer 416 can receive data inputs from integrated control layer 418, directly from one or more building subsystems or devices, or from another data source. FDD layer 416 can automatically diagnose and respond to detected faults. The responses to detected or diagnosed faults can include providing an alarm message to a user, a maintenance scheduling system, or a control algorithm configured to attempt to repair the fault or to work-around the fault”, and Paragraph [0103] “FDD layer 416 can be configured to store or access a variety of different system data stores (or data points for live data). FDD layer 416 can use some content of the data stores to identify faults at the equipment level (e.g., specific chiller, specific AHU, specific terminal unit, etc.) and other content to identify faults at component or subsystem levels. For example, building subsystems 428 can generate temporal (i.e., time-series) data indicating the performance of BAS 400 and the various components thereof. The data generated by building subsystems 428 can include measured or calculated values that exhibit statistical characteristics and provide information about how the corresponding system or process (e.g., a temperature control process, a flow control process, etc.) is performing in terms of error from its setpoint. These processes can be examined by FDD layer 416 to expose when the system begins to degrade in performance and alarm a user to repair the fault before it becomes more severe”, wherein examiner interpreted determining fault based on stored data and temporal data indicating performance of BAS as determining an anomaly of the one or more building subsystems, based on the subject matter expert data and the building subsystem data);
generating a recommendation to resolve the anomaly based on fault detection and diagnostic (FDD) data related to a fault determined in the one or more building subsystems (Paragraph [0171] “The recommendation generator 1184 can be configured to generate a recommendation 1186 based on the predicted device performance 1182. For example, if the predicted device performance 1182 is indicative of the physical device 1166 deteriorating or failing in the future, then the recommendation 1186 generated by the recommendation generator 1184 may indicate that maintenance should be performed on the physical device 1166 to prevent or reduce a chance of the physical device 1166 from failing. In some embodiments, the recommendation 1186 is to adjust future designs of the physical device 1166. The recommendation 1186 may indicate that certain components or features of the physical device 1166 may need improvement in the future and/or may recommend various changes that could be made to the design of the physical device 1166 that would improve the physical device 1166”, wherein examiner interpreted generating recommendation based on device performance that is indicative of device deteriorating or failing the future, where the recommendation is to perform maintenance to prevent or reduce chance of failing, as generating a recommendation to resolve the anomaly based on fault detection and diagnostic (FDD) data related to a fault determined in the one or more building subsystems); and
generating, using a generative large language model (Paragraph [0148] “The cloud platform 502 is shown to include artificial intelligence 1028 and cognitive engines 1032. The artificial intelligence 1028 and/or cognitive engines 1032 can be configured to implement various forms of machine learning, data mining, pattern recognition, natural language processing (NLP), and/or the like”), a response to the query based on the recommendation, the FDD data, and the subject matter expert data (Paragraph [0172] “Based on the recommendation 1186 generated by the recommendation generator 1184, the interface generator 1188 can be configured to generate an interface and cause the interface to be displayed on a display device of user device 1190. The interface generator 1188 is shown to receive user input from a natural language processing (NLP) manager 1192, the recommendation 1186 from the recommendation generator 1184, and data (e.g., the environmental inputs 1170, the environmental outputs 1172, and/or the like) from the cloud database 1142. Based on this received information, the interface generator 1188 can generate one or more interfaces for the user device 1190”, wherein examiner interpreted recommendation being displayed on a display device of user device using natural language processing (NPL) manager as generating, by the one or more processors using a generative large language model, a response to the query based on the recommendation, the FDD data, and the subject matter expert data).
Regarding claim 9, Sharma teaches wherein generating the subject matter expert data comprises: obtaining previous report data, wherein the report data comprises use cases for the one or more building subsystems in an unstructured format (Paragraph [0101] “FDD layer 416 can receive data inputs from integrated control layer 418, directly from one or more building subsystems or devices, or from another data source”, Paragraph [0103] “FDD layer 416 can be configured to store or access a variety of different system data stores (or data points for live data). FDD layer 416 can use some content of the data stores to identify faults at the equipment level (e.g., specific chiller, specific AHU, specific terminal unit, etc.) and other content to identify faults at component or subsystem levels”, wherein examiner interpreted storing data from one or more building subsystems or devices or other data source as obtaining, by the one or more processors, previous report data, wherein the report data comprises use cases for the one or more building subsystems in an unstructured format); and
formatting the use cases into a standardized format (Paragraph [0119] “In various embodiments, the data manager 540 can be configured to manage, organize, integrate, and store data, and/or send data to various components of the cloud platform 502. For example, the data manager 544 can generate various reports and/or combine (e.g., integrate) data from disparate devices in the building 10, various components of the cloud platform 502, and/or the Internet”, wherein examiner interpreted data manager managing, organizing, integrating, and storing data as formatting, by the one or more building subsystems, the use cases into a standardized format).
Regarding claim 11, Sharma teaches wherein the FDD data comprises at least one of one or more FDD rules, work order data, or cost analysis data (Paragraph [0103] “FDD layer 416 can be configured to store or access a variety of different system data stores (or data points for live data). FDD layer 416 can use some content of the data stores to identify faults at the equipment level (e.g., specific chiller, specific AHU, specific terminal unit, etc.) and other content to identify faults at component or subsystem levels”, wherein examiner interpreted data stored to identify faults as FDD data comprising at least one of one or more FDD rules, work order data, or cost analysis data).
Regarding claim 12, Sharma teaches wherein the FDD data is generated based on data from a third-party source (Paragraph [0101] “FDD layer 416 can receive data inputs from integrated control layer 418, directly from one or more building subsystems or devices, or from another data source”, wherein examiner interpreted receiving data inputs from one or more building subsystems or devices or from another data source as FDD data generated based on data from a third-party source).
Regarding claim 13, Sharma teaches wherein the recommendation is a whole system level recommendation which is a recommendation for resolving a fault in a first building subsystem based on data from one or more other building subsystems in the building (Paragraph [0171] “The recommendation generator 1184 can be configured to generate a recommendation 1186 based on the predicted device performance 1182. For example, if the predicted device performance 1182 is indicative of the physical device 1166 deteriorating or failing in the future, then the recommendation 1186 generated by the recommendation generator 1184 may indicate that maintenance should be performed on the physical device 1166 to prevent or reduce a chance of the physical device 1166 from failing. In some embodiments, the recommendation 1186 is to adjust future designs of the physical device 1166. The recommendation 1186 may indicate that certain components or features of the physical device 1166 may need improvement in the future and/or may recommend various changes that could be made to the design of the physical device 1166 that would improve the physical device 1166”, Paragraph [0101] “Fault detection and diagnostics (FDD) layer 416 can be configured to provide on-going fault detection for building subsystems 428, building subsystem devices (i.e., building equipment), and control algorithms used by demand response layer 414 and integrated control layer 418. FDD layer 416 can receive data inputs from integrated control layer 418, directly from one or more building subsystems or devices, or from another data source”, wherein examiner interpreted generating recommendation based on device performance that is indicative of device deteriorating or failing the future, where the recommendation is to perform maintenance to prevent or reduce chance of failing of device based on data from one or more building subsystems, as the recommendation being a whole system level recommendation which is a recommendation for resolving a fault in a first building subsystem based on data from one or more other building subsystems in the building).
Regarding claim 14, Sharma teaches wherein generating, using the generative large language model, the response to the query further comprises generating action steps to implement the recommendation based on a building knowledge base (Paragraph [0171] “The recommendation generator 1184 can be configured to generate a recommendation 1186 based on the predicted device performance 1182. For example, if the predicted device performance 1182 is indicative of the physical device 1166 deteriorating or failing in the future, then the recommendation 1186 generated by the recommendation generator 1184 may indicate that maintenance should be performed on the physical device 1166 to prevent or reduce a chance of the physical device 1166 from failing. In some embodiments, the recommendation 1186 is to adjust future designs of the physical device 1166. The recommendation 1186 may indicate that certain components or features of the physical device 1166 may need improvement in the future and/or may recommend various changes that could be made to the design of the physical device 1166 that would improve the physical device 1166”, and Paragraph [0172] “Based on the recommendation 1186 generated by the recommendation generator 1184, the interface generator 1188 can be configured to generate an interface and cause the interface to be displayed on a display device of user device 1190. The interface generator 1188 is shown to receive user input from a natural language processing (NLP) manager 1192, the recommendation 1186 from the recommendation generator 1184, and data (e.g., the environmental inputs 1170, the environmental outputs 1172, and/or the like) from the cloud database 1142. Based on this received information, the interface generator 1188 can generate one or more interfaces for the user device 1190”, wherein examiner interpreted generating recommendation that indicates maintenance should be performed using natural language processing (NPL) manager as generating, using the generative large language model, the response to the query further comprises generating action steps to implement the recommendation based on a building knowledge base, wherein examiner interpreted maintenance that should be performed, and components or features should be improved or changes that could improve design as the steps to implement the recommendation based on a building knowledge base).
Regarding claim 15, Sharma teaches one or more non-transitory computer-readable media storing program instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising (Paragraph [0148] “The cloud platform 502 is shown to include artificial intelligence 1028 and cognitive engines 1032. The artificial intelligence 1028 and/or cognitive engines 1032 can be configured to implement various forms of machine learning, data mining, pattern recognition, natural language processing (NLP), and/or the like”, Paragraph [0144] “The cloud platform 502 is shown to include various components (e.g., pieces of software stored on transitory and/or non-transitory storage mediums) that may be implemented by processing circuits, such as logic circuits (e.g., ASICs), for example”):
receiving, via a conversational interface, a query from a user (Paragraph [0007] “ the one or more processing circuits are configured to receive a natural language input from a user of a user device including a request for generation of analytics for the physical building device, perform natural language processing to determine the request for the generation of analytics for the physical building device, and generate the recommendation in response to determining the request for the generation of analytics”, Paragraph [0174] “the user of the user device 1190 can interact with the interfaces via a natural language input (e.g., spoken words, entered text, etc.). In this regard, the interface generator 1188 can receive various user inputs for navigating the interfaces generated by the interface generator 1188 via natural language user inputs”, and Paragraph [0175], wherein examiner interpreted user inputting natural language including a request for generation of analytics, performing language processing to request generation of analytics as receiving a query from a user, wherein examiner interpreted user device as a conversational interface);
receiving building subsystem data for one or more building subsystems in a building (Paragraph [0103] “The data generated by building subsystems 428 can include measured or calculated values that exhibit statistical characteristics and provide information about how the corresponding system or process (e.g., a temperature control process, a flow control process, etc.) is performing in terms of error from its setpoint. These processes can be examined by FDD layer 416 to expose when the system begins to degrade in performance and alarm a user to repair the fault before it becomes more severe”, wherein examiner interpreted data generated by building subsystems, which the FDD layer uses to identify faults as receiving building subsystem data for one or more building subsystems in a building);
retrieving subject matter expert data (Paragraph [0103] “FDD layer 416 can be configured to store or access a variety of different system data stores (or data points for live data). FDD layer 416 can use some content of the data stores to identify faults at the equipment level (e.g., specific chiller, specific AHU, specific terminal unit, etc.) and other content to identify faults at component or subsystem levels”, wherein examiner interpreted storing or accessing variety of different system data that can be used to identify faults in equipment as retrieving subject matter expert data);
determining an anomaly of the one or more building subsystems, based on the subject matter expert data and the building subsystem data (Paragraph [0101] “Fault detection and diagnostics (FDD) layer 416 can be configured to provide on-going fault detection for building subsystems 428, building subsystem devices (i.e., building equipment), and control algorithms used by demand response layer 414 and integrated control layer 418. FDD layer 416 can receive data inputs from integrated control layer 418, directly from one or more building subsystems or devices, or from another data source. FDD layer 416 can automatically diagnose and respond to detected faults. The responses to detected or diagnosed faults can include providing an alarm message to a user, a maintenance scheduling system, or a control algorithm configured to attempt to repair the fault or to work-around the fault”, and Paragraph [0103] “FDD layer 416 can be configured to store or access a variety of different system data stores (or data points for live data). FDD layer 416 can use some content of the data stores to identify faults at the equipment level (e.g., specific chiller, specific AHU, specific terminal unit, etc.) and other content to identify faults at component or subsystem levels. For example, building subsystems 428 can generate temporal (i.e., time-series) data indicating the performance of BAS 400 and the various components thereof. The data generated by building subsystems 428 can include measured or calculated values that exhibit statistical characteristics and provide information about how the corresponding system or process (e.g., a temperature control process, a flow control process, etc.) is performing in terms of error from its setpoint. These processes can be examined by FDD layer 416 to expose when the system begins to degrade in performance and alarm a user to repair the fault before it becomes more severe”, wherein examiner interpreted determining fault based on stored data and temporal data indicating performance of BAS as determining an anomaly of the one or more building subsystems, based on the subject matter expert data and the building subsystem data);
generating a recommendation to resolve the anomaly based on fault detection and diagnostic (FDD) data related to a fault determined in the one or more building subsystems (Paragraph [0171] “The recommendation generator 1184 can be configured to generate a recommendation 1186 based on the predicted device performance 1182. For example, if the predicted device performance 1182 is indicative of the physical device 1166 deteriorating or failing in the future, then the recommendation 1186 generated by the recommendation generator 1184 may indicate that maintenance should be performed on the physical device 1166 to prevent or reduce a chance of the physical device 1166 from failing. In some embodiments, the recommendation 1186 is to adjust future designs of the physical device 1166. The recommendation 1186 may indicate that certain components or features of the physical device 1166 may need improvement in the future and/or may recommend various changes that could be made to the design of the physical device 1166 that would improve the physical device 1166”, wherein examiner interpreted generating recommendation based on device performance that is indicative of device deteriorating or failing the future, where the recommendation is to perform maintenance to prevent or reduce chance of failing, as generating a recommendation to resolve the anomaly based on fault detection and diagnostic (FDD) data related to a fault determined in the one or more building subsystems); and
generating, using a generative large language model (Paragraph [0148] “The cloud platform 502 is shown to include artificial intelligence 1028 and cognitive engines 1032. The artificial intelligence 1028 and/or cognitive engines 1032 can be configured to implement various forms of machine learning, data mining, pattern recognition, natural language processing (NLP), and/or the like”), a response to the query based on the recommendation, the FDD data, and the subject matter expert data (Paragraph [0172] “Based on the recommendation 1186 generated by the recommendation generator 1184, the interface generator 1188 can be configured to generate an interface and cause the interface to be displayed on a display device of user device 1190. The interface generator 1188 is shown to receive user input from a natural language processing (NLP) manager 1192, the recommendation 1186 from the recommendation generator 1184, and data (e.g., the environmental inputs 1170, the environmental outputs 1172, and/or the like) from the cloud database 1142. Based on this received information, the interface generator 1188 can generate one or more interfaces for the user device 1190”, wherein examiner interpreted recommendation being displayed on a display device of user device using natural language processing (NPL) manager as generating, by the one or more processors using a generative large language model, a response to the query based on the recommendation, the FDD data, and the subject matter expert data).
Regarding claim 16, Sharma teaches wherein generating the subject matter expert data comprises: obtaining previous report data, wherein the report data comprises use cases for the one or more building subsystems in an unstructured format (Paragraph [0101] “FDD layer 416 can receive data inputs from integrated control layer 418, directly from one or more building subsystems or devices, or from another data source”, Paragraph [0103] “FDD layer 416 can be configured to store or access a variety of different system data stores (or data points for live data). FDD layer 416 can use some content of the data stores to identify faults at the equipment level (e.g., specific chiller, specific AHU, specific terminal unit, etc.) and other content to identify faults at component or subsystem levels”, wherein examiner interpreted storing data from one or more building subsystems or devices or other data source as obtaining, by the one or more processors, previous report data, wherein the report data comprises use cases for the one or more building subsystems in an unstructured format); and
formatting the use cases into a standardized format (Paragraph [0119] “In various embodiments, the data manager 540 can be configured to manage, organize, integrate, and store data, and/or send data to various components of the cloud platform 502. For example, the data manager 544 can generate various reports and/or combine (e.g., integrate) data from disparate devices in the building 10, various components of the cloud platform 502, and/or the Internet”, wherein examiner interpreted data manager managing, organizing, integrating, and storing data as formatting, by the one or more building subsystems, the use cases into a standardized format).
Regarding claim 18, Sharma teaches wherein the FDD data comprises at least one of one or more FDD rules, work order data, or cost analysis data (Paragraph [0103] “FDD layer 416 can be configured to store or access a variety of different system data stores (or data points for live data). FDD layer 416 can use some content of the data stores to identify faults at the equipment level (e.g., specific chiller, specific AHU, specific terminal unit, etc.) and other content to identify faults at component or subsystem levels”, wherein examiner interpreted data stored to identify faults as FDD data comprising at least one of one or more FDD rules, work order data, or cost analysis data).
Regarding claim 19, Sharma teaches wherein the FDD data is generated based on data from a third-party source (Paragraph [0101] “FDD layer 416 can receive data inputs from integrated control layer 418, directly from one or more building subsystems or devices, or from another data source”, wherein examiner interpreted receiving data inputs from one or more building subsystems or devices or from another data source as FDD data generated based on data from a third-party source).
Regarding claim 20, Sharma teaches wherein the recommendation is a whole system level recommendation which is a recommendation for resolving a fault in a first building subsystem based on data from one or more other building subsystems in the building (Paragraph [0171] “The recommendation generator 1184 can be configured to generate a recommendation 1186 based on the predicted device performance 1182. For example, if the predicted device performance 1182 is indicative of the physical device 1166 deteriorating or failing in the future, then the recommendation 1186 generated by the recommendation generator 1184 may indicate that maintenance should be performed on the physical device 1166 to prevent or reduce a chance of the physical device 1166 from failing. In some embodiments, the recommendation 1186 is to adjust future designs of the physical device 1166. The recommendation 1186 may indicate that certain components or features of the physical device 1166 may need improvement in the future and/or may recommend various changes that could be made to the design of the physical device 1166 that would improve the physical device 1166”, Paragraph [0101] “Fault detection and diagnostics (FDD) layer 416 can be configured to provide on-going fault detection for building subsystems 428, building subsystem devices (i.e., building equipment), and control algorithms used by demand response layer 414 and integrated control layer 418. FDD layer 416 can receive data inputs from integrated control layer 418, directly from one or more building subsystems or devices, or from another data source”, wherein examiner interpreted generating recommendation based on device performance that is indicative of device deteriorating or failing the future, where the recommendation is to perform maintenance to prevent or reduce chance of failing of device based on data from one or more building subsystems, as the recommendation being a whole system level recommendation which is a recommendation for resolving a fault in a first building subsystem based on data from one or more other building subsystems in the building).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma et al. USPGPUB 2020/0142365 (hereinafter “Sharma”) as applied to claims 1-2, 4-9, 11-16, and 18-20 above, in view of Drees et al. USPGPUB 2020/0162354 (hereinafter “Drees”).
Regarding claim 3, Sharma teaches all of the features with respect to claim 2 as outlined above.
Sharma does not explicitly teach wherein the formatted use cases are embedded into a vector and stored in a vector database.
However, Drees teaches wherein the formatted use cases are embedded into a vector and stored in a vector database (Paragraph [0201] “the building cloud platform 502 collects the building device data from the BMS 504 and ingests the building device data into the timeseries database 910 for the timeseries generator 912 to perform processing on. In some embodiments, the smart equipment 602 collects building device data from the data system 902 and ingests the building device data into the timeseries database 910 for the timeseries generator 912 to perform processing on”, Paragraph [0202] “the timeseries data stream includes multiple samples each associated with a timestamp indicating a particular time or data order. The time correlated data stream, in some embodiments, may be a vector, a column, a data table, a matrix, etc.”, wherein examiner interpreted storing building device data into a timeseries database, wherein the timeseries database or correlated data stream maybe a vector as embedding into a vector and stored in a vector database).
Sharma, and Drees are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They relate to building system.
Therefore, before the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above formatting data, as taught by Sharma, and incorporating vector database, as taught by Drees.
One of ordinary skill in the art would have been motivated to improve storing correlated timeseries data in a vector, as suggested by Drees (see Paragraphs [0201-0202]).
Regarding claim 10, Sharma teaches all of the features with respect to claim 9 as outlined above.
Sharma does not explicitly teach wherein the formatted use cases are embedded into a vector and stored in a vector database.
However, Drees teaches wherein the formatted use cases are embedded into a vector and stored in a vector database (Paragraph [0201] “the building cloud platform 502 collects the building device data from the BMS 504 and ingests the building device data into the timeseries database 910 for the timeseries generator 912 to perform processing on. In some embodiments, the smart equipment 602 collects building device data from the data system 902 and ingests the building device data into the timeseries database 910 for the timeseries generator 912 to perform processing on”, Paragraph [0202] “the timeseries data stream includes multiple samples each associated with a timestamp indicating a particular time or data order. The time correlated data stream, in some embodiments, may be a vector, a column, a data table, a matrix, etc.”, wherein examiner interpreted storing building device data into a timeseries database, wherein the timeseries data stream or correlated data stream include a vector as embedding into a vector and stored in a vector database).
Sharma, and Drees are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They relate to building system.
Therefore, before the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above formatting data, as taught by Sharma, and incorporating vector database, as taught by Drees.
One of ordinary skill in the art would have been motivated to improve storing correlated timeseries data in a vector, as suggested by Drees (see Paragraphs [0201-0202]).
Regarding claim 17, Sharma teaches all of the features with respect to claim 16 as outlined above.
Sharma does not explicitly teach wherein the formatted use cases are embedded into a vector and stored in a vector database.
However, Drees teaches wherein the formatted use cases are embedded into a vector and stored in a vector database (Paragraph [0201] “the building cloud platform 502 collects the building device data from the BMS 504 and ingests the building device data into the timeseries database 910 for the timeseries generator 912 to perform processing on. In some embodiments, the smart equipment 602 collects building device data from the data system 902 and ingests the building device data into the timeseries database 910 for the timeseries generator 912 to perform processing on”, Paragraph [0202] “the timeseries data stream includes multiple samples each associated with a timestamp indicating a particular time or data order. The time correlated data stream, in some embodiments, may be a vector, a column, a data table, a matrix, etc.”, wherein examiner interpreted storing building device data into a timeseries database, wherein the timeseries data stream or correlated data stream include a vector as embedding into a vector and stored in a vector database).
Sharma, and Drees are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They relate to building system.
Therefore, before the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above formatting data, as taught by Sharma, and incorporating vector database, as taught by Drees.
One of ordinary skill in the art would have been motivated to improve storing correlated timeseries data in a vector, as suggested by Drees (see Paragraphs [0201-0202]).
Citation of Pertinent Prior Art
The prior art made of record and on the attached PTO Form 892 but not relied upon is considered pertinent to applicant's disclosure.
Hafernik et al. [USPGPUB 2021/0278833] teaches a support system and method for automated building management assistance.
Khurana et al. [USPGPUB 2021/0103260] teaches methods and systems for controlling a building.
Cohen et al. [USPGPUB 2018/0231967] teaches a computer-facilitated method and a computerized system for providing optimization or improvement measures for one or more buildings.
Conclusion
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/D.P./ Examiner, Art Unit 2119
/MOHAMMAD ALI/ Supervisory Patent Examiner, Art Unit 2119
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