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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6/20/2024 and 10/9/2024 is 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 he claimed invention is directed to an abstract idea without significantly more.
Independent claim 1 recites, “receive, from one or more devices having subscriptions with the BMS, a plurality of information that corresponds to at least one of a structured data format or a timeseries data format”, “generate, based at least on one or more rules, a data model to represent the plurality of information in a common format associated with the BMS”, “converting, responsive to generation of the data model, the plurality of information into natural language text, the natural language text including a plurality of segments that represents the plurality of information”, “generating, using a large language model (LLM), a plurality of vector embeddings that represent the plurality of segments, wherein a respective vector embedding of the plurality of vector embeddings represents a respective segment of the plurality of segments”, “receive, via a user interface displayed by a user device, a query that corresponds to a building associated with the BMS, the query including a request for first information associated with the building”, “identify a given vector embedding of the plurality of vector embeddings that correlates to the first information associated with the building”, and “generate, using the LLM based at least on a given segment of the plurality of segments represented by the given vector embedding of the plurality of vector embeddings, a response to the query that includes at least one of: a graphical representation of the first information associated with the building; or a textual summary of the first information associated with the building; and display, via the user interface, the response to the query”.
The limitation of receiving data, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “one or more memory devices” and “one or more processors”, nothing in the claim precludes the step from practically being performed in the mind. For example, “receive” in the context of this claim encompasses receiving data, which a human can do in the mind or with a pen and paper. Next, the limitation of generating a data model, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the computer components listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “generate” in the context of this claim encompasses developing a system governed by rules, which a human can do in the mind or with a pen and paper. Next, the limitation of converting information into natural language, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the computer components listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “converting” in the context of this claim encompasses converting data into text format, which a human can do in the mind or with a pen and paper. Next, the limitation of generating vector embeddings, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the computer components listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “generating” in the context of this claim encompasses generating different representation of text, which a human can do in the mind or with a pen and paper. Next, the limitation of receiving a query, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the computer components listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “receive” in the context of this claim encompasses receiving a request for information, which a human can do in the mind or with a pen and paper. Next, the limitation of identifying a vector embedding, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the computer components listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “identifying” in the context of this claim encompasses recognizing a textual representation, which a human can do in the mind or with a pen and paper. Lastly, the limitation of generating a graphical/text response, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the computer components listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “generate” in the context of this claim encompasses responding to a query in a particular manner, which a human can do in the mind or with a pen and paper.
The judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements, using “one or more memory devices” and “one or more processors” to perform the recited limitations. These elements in these steps are recited at a high-level of generality such that is amounts no more than mere instructions to apply the exception using generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
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 the integration of the abstract idea into a practical application, the additional elements of using “one or more memory devices” and “one or more processors” to perform the recited limitations amounts 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 claim is not patent eligible.
Dependent claims 2-8 are also rejected for the same reasons provided in independent claim 1 above. The dependent claim, including the further recited limitation, does not integrate the abstract idea into a practical application and the additional elements, taken individually and in combination do not contribute to an inventive concept. In other words, the dependent claim is directed to an abstract idea without significantly more.
Independent claim 9 recites, “receiving, by one or more processing circuits from one or more devices having subscriptions with a building management system (BMS), a plurality of information that corresponds to at least one of a structured data format or a timeseries data format”, “generating, by the one or more processing circuits, based at least on one or more rules, a data model to represent the plurality of information in a common format associated with the BMS”, “converting, by the one or more processing circuits, responsive to generation of the data model, the plurality of information into natural language text, the natural language text including a plurality of segments that represents the plurality of information”, “generating, by the one or more processing circuits, using a large language model (LLM), a plurality of vector embeddings that represent the plurality of segments, wherein a respective vector embedding of the plurality of vector embeddings represents a respective segment of the plurality of segments”, “receiving, by the one or more processing circuits, from a user device, a query that corresponds to a building associated with the BMS, the query including a request for first information associated with the building”, and “generating, by the one or more processing circuits, using the LLM based at least on a given segment of the plurality of segments represented by a given vector embedding of the plurality of vector embeddings, a response to the query that includes at least one of: a graphical representation of the first information associated with the building; or a textual summary of the first information associated with the building”.
The limitation of receiving data, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “one or more processors”, nothing in the claim precludes the step from practically being performed in the mind. For example, “receiving” in the context of this claim encompasses receiving data, which a human can do in the mind or with a pen and paper. Next, the limitation of generating a data model, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the computer components listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “generating” in the context of this claim encompasses developing a system governed by rules, which a human can do in the mind or with a pen and paper. Next, the limitation of converting information into natural language, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the computer components listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “converting” in the context of this claim encompasses converting data into text format, which a human can do in the mind or with a pen and paper. Next, the limitation of generating vector embeddings, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the computer components listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “generating” in the context of this claim encompasses generating different representation of text, which a human can do in the mind or with a pen and paper. Next, the limitation of receiving a query, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the computer components listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “receiving” in the context of this claim encompasses receiving a request for information, which a human can do in the mind or with a pen and paper. Lastly, the limitation of generating a graphical/text response, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the computer components listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “generating” in the context of this claim encompasses responding to a query in a particular manner, which a human can do in the mind or with a pen and paper.
The judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements, using “one or more processors” to perform the recited limitations. These elements in these steps are recited at a high-level of generality such that is amounts no more than mere instructions to apply the exception using generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
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 the integration of the abstract idea into a practical application, the additional elements of using “one or more processors” to perform the recited limitations amounts 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 claim is not patent eligible.
Dependent claims 10-17 are also rejected for the same reasons provided in independent claim 9 above. The dependent claim, including the further recited limitation, does not integrate the abstract idea into a practical application and the additional elements, taken individually and in combination do not contribute to an inventive concept. In other words, the dependent claim is directed to an abstract idea without significantly more.
Independent claim 18 recites, “receiving, from a user device, a query that corresponds to a building associated with a building management system (BMS), the query including a request for first information associated with the building”, “determining, responsive to receiving the query, correlations between a plurality of vector embeddings that represent a plurality of segments of natural language text”, and “generating, using a large language model (LLM) based at least on a given segment of the plurality of segments represented by a given vector embedding of the plurality of vector embeddings, a response to the query that includes at least one of a graphical representation of the first information associated with the building or a textual summary of the first information associated with the building”
First, the limitation of receiving a query, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “one or more processors”, nothing in the claim precludes the step from practically being performed in the mind. For example, “receiving” in the context of this claim encompasses receiving a request for information, which a human can do in the mind or with a pen and paper. Next, the limitation of determining correlations between vectors, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the elements listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “determining” in the context of this claim encompasses finding associations of textual representations, which a human can do in the mind or with a pen and paper. Lastly, the limitation of generating a graphical/text response, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the computer components listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “generating” in the context of this claim encompasses responding to a query in a particular manner, which a human can do in the mind or with a pen and paper.
The judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements, using “one or more processors” to perform the recited limitations. These elements in these steps are recited at a high-level of generality such that is amounts no more than mere instructions to apply the exception using generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
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 the integration of the abstract idea into a practical application, the additional elements of using “one or more processors” to perform the recited limitations amounts 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 claim is not patent eligible.
Dependent claims 19-20 are also rejected for the same reasons provided in independent claim 18 above. The dependent claim, including the further recited limitation, does not integrate the abstract idea into a practical application and the additional elements, taken individually and in combination do not contribute to an inventive concept. In other words, the dependent claim is directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4, 8-10, 12-13, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Williams et al. US 20240289851 A1 (hereinafter Williams) in view of Zhang et al. US 20230394188 A1 (hereinafter Zhang).
Regarding independent claims 1 and 9, Williams teaches a building management system (BMS) comprising one or more memory devices storing instructions thereon that, when executed by one or more processors, causes the one or more processors to / a method, comprising:
receive, from one or more devices having subscriptions with the BMS, a plurality of information that corresponds to at least one of a structured data format or a timeseries data format ([0038] The user data or user telematics data may also include home telematics data collected or otherwise generated by a home telematics app installed and/or running on the user's mobile device or other computing device. For instance, a home telematics app may be in communication with a smart home controller (e.g., for controlling a heating/HVAC system) and/or smart lights, smart appliances, or other smart devices situated about a home, and may collect data from the interconnected smart devices and/or smart home sensors.);
generate, based at least on one or more rules, a data model to represent the plurality of information in a common format associated with the BMS (FIG. 2A, 210, [0061] “the generator model 210 receives an input vector 205A to generate a generated example 215”);
execute a pre-processing routine, including: converting, responsive to generation of the data model, the plurality of information into natural language text, the natural language text including a plurality of segments that represents the plurality of information (FIG. 2B, [0071] “the self-attention block 252 may transform the input vector 205B into different sets (e.g., queries, keys, values, etc.)”); and
receive, via a user interface displayed by a user device, a query that corresponds to a building associated with the BMS, the query including a request for first information associated with the building (FIG. 1, 112, [0038] “The user data or user telematics data may also include home telematics data collected or otherwise generated by a home telematics app installed and/or running on the user's mobile device or other computing device…the user telematics data and/or the home telematics data may include information input by the user at a computing device or at another device associated with the user”);
identify a given vector embedding of the plurality of vector embeddings that correlates to the first information associated with the building ([0061] “the generator model 210 receives an input vector 205A to generate a generated example 215”);
generate, using the LLM based at least on a given segment of the plurality of segments represented by the given vector embedding of the plurality of vector embeddings, a response to the query that includes at least one of: a graphical representation of the first information associated with the building; or a textual summary of the first information associated with the building; and display, via the user interface, the response to the query ([0028] “The voice or chatbot may employ the techniques utilized for ChatGPT. The voice bot, chatbot, ChatGPT-based bot, ChatGPT bot, and/or other bots may generate audible or verbal output, text or textual output, visual or graphical output, output for use with speakers and/or display screens, and/or other types of output for user and/or other computer or bot consumption”).
Williams fails to teach generating, using a large language model (LLM), a plurality of vector embeddings that represent the plurality of segments, wherein a respective vector embedding of the plurality of vector embeddings represents a respective segment of the plurality of segments;
However, Zhang teaches generating, using a large language model (LLM), a plurality of vector embeddings that represent the plurality of segments, wherein a respective vector embedding of the plurality of vector embeddings represents a respective segment of the plurality of segments ([0100] “the feature extraction module 608 extracts a feature vector 612 from input data 604”; [0108] “Generative AI systems use generative models such as large language models to produce data based on the training data set that was used to create them”);
Williams in view of Zhang are considered to be analogous to the claimed invention because both are the same field of data retrieval in the field of smart home products. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the techniques of identifying potentially impactful factors based upon at least the internal database data, and generating an output dialogue of Williams with the technique of generating vector embeddings using a large language model taught by Zhang in order to improve smart devices that connect to a network for pairing smart home products for both convenience and safety (see Zhang [0002]).
Regarding claims 2 and 10, Williams in view of Zhang teaches all of the limitations of claims 1 and 9, upon which claims 2 and 10 depend.
Additionally, Williams teaches generating, prior to generating the plurality of vector embeddings, a plurality of summaries that describe the plurality of segments ([0111] “the dialogue output may include summaries and/or scripts for a user with regard to presenting information, such as information generated as part of the analysis at block 504”); and
constructing, responsive to generating the plurality of summaries, a plurality of keys to associate the plurality of summaries with the plurality of segments ([0071] “the self-attention block 252 may transform the input vector 205B into different sets (e.g., queries, keys, values, etc.)”).
Regarding claims 4 and 12, Williams in view of Zhang teaches all of the limitations of claims 1 and 9, upon which claims 4 and 12 depend.
Additionally, Williams teaches wherein the instructions cause the one or more processors to: identify, responsive to receipt of the query, using the LLM, a first agent of a plurality of agents to process the query, wherein the first agent is identified based on a context of the request for information associated with the building (FIG. 5, 502, 504, [0007] “receiving, by one or more processors, internal database information at a generative artificial intelligence (AI) model, wherein the internal database information includes data associated with interaction dialogue; (2) analyzing, by the one or more processors, the internal database information via the generative AI model to generate an internal database analysis, examiner interprets generative AI model as agents”);
input, using the LLM, the query and the given segment of the plurality of segments into the first agent to cause the first agent to generate an output ([0081] “Subsequently, the generative AI and/or ML model may further be trained via reinforcement learning. Here, further inputs are fed into the model, and the model then generates, based upon the policy learned during reward modeling, (i) outputs corresponding to the inputs”); and
generate, using the LLM based at least one the output of the first agent, the response to the query (FIG. 5, 508, [0007] “generating, by the one or more processors and based upon at least the one or more impact elements, a dialogue output (or visual or virtual output) regarding the data via the generative AI model”).
Regarding claims 8 and 17, Williams in view of Zhang teaches all of the limitations of claims 1 and 9, upon which claims 8 and 17 depend.
Additionally, Williams teaches wherein the LLM comprises a pre-trained generative transformer model ([0070] Advantageously, some embodiments overcome these drawbacks of the LSTM model by using transformers (e.g., by using a generative pre-trained transformer (GPT) model)).
Regarding claim 13, Williams in view of Zhang teaches all of the limitations of claim 9, upon which claim 13 depend.
Additionally, Williams teaches receiving, by the one or more processing circuits via a user interface displayed by the user device, the query; and displaying, by the one or more processing circuits via the user interface, the response to the query (FIG. 3 [0028] “The voice bot, chatbot, ChatGPT-based bot, ChatGPT bot, and/or other bots may generate audible or verbal output, text or textual output, visual or graphical output, output for use with speakers and/or display screens, and/or other types of output for user and/or other computer or bot consumption.”).
Regarding independent claim 18, Williams teaches One or more non-transitory storage media storing instructions thereon that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
receiving, from a user device, a query that corresponds to a building associated with a building management system (BMS), the query including a request for first information associated with the building (FIG. 1, 112, [0038] “The user data or user telematics data may also include home telematics data collected or otherwise generated by a home telematics app installed and/or running on the user's mobile device or other computing device…the user telematics data and/or the home telematics data may include information input by the user at a computing device or at another device associated with the user”);
generating, using a large language model (LLM) based at least on a given segment of the plurality of segments represented by a given vector embedding of the plurality of vector embeddings, a response to the query that includes at least one of a graphical representation of the first information associated with the building or a textual summary of the first information associated with the building ([0028] “The voice or chatbot may employ the techniques utilized for ChatGPT. The voice bot, chatbot, ChatGPT-based bot, ChatGPT bot, and/or other bots may generate audible or verbal output, text or textual output, visual or graphical output, output for use with speakers and/or display screens, and/or other types of output for user and/or other computer or bot consumption”).
Williams fails to teach determining, responsive to receiving the query, correlations between a plurality of vector embeddings that represent a plurality of segments of natural language text
However, Zhang teaches determining, responsive to receiving the query, correlations between a plurality of vector embeddings that represent a plurality of segments of natural language text ([0106] The ML system 600 applies ML techniques to train the ML model 616, that when applied to the feature vector 612, the ML model 616 outputs indications of whether the feature vector 612 has an associated desired property or properties, such as a probability that the feature vector 612 has a particular Boolean property, or an estimated value of a scalar property);
Williams in view of Zhang are considered to be analogous to the claimed invention because both are the same field of data retrieval in the field of smart home products. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the techniques of identifying potentially impactful factors based upon at least the internal database data, and generating an output dialogue of Williams with the technique of determining correlations between vector embeddings taught by Zhang in order to improve smart devices that connect to a network for pairing smart home products for both convenience and safety (see Zhang [0002]).
Regarding claim 19, Williams in view of Zhang teaches all of the limitations of claim 18, upon which claim 19 depends.
Additionally, Williams teaches wherein the instructions cause the one or more processors to perform operations comprising executing a pre-processing routine, wherein the pre-processing routine includes: converting, responsive to generation of a data model, a plurality of information into the natural language text, the natural language text including the plurality of segments that represents the plurality of information (FIG. 2B, [0071] “the self-attention block 252 may transform the input vector 205B into different sets (e.g., queries, keys, values, etc.)”);
generating a plurality of summaries that describe the plurality of segments; ([0111] “the dialogue output may include summaries and/or scripts for a user with regard to presenting information, such as information generated as part of the analysis at block 504”);
constructing, responsive to generating the plurality of summaries, a plurality of keys to associate the plurality of summaries with the plurality of segments ([0071] “the self-attention block 252 may transform the input vector 205B into different sets (e.g., queries, keys, values, etc.)”)
Additionally, Zhang teaches generating, using the LLM, the plurality of vector embeddings ([0100] “the feature extraction module 608 extracts a feature vector 612 from input data 604”; [0108] “Generative AI systems use generative models such as large language models to produce data based on the training data set that was used to create them”);
Claims 5-6 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Williams in view of Zhang, as shown in claim 1 above, in further view of Ploegert et al. US 20210200807 A1 (hereinafter Ploegert).
Regarding claims 5 and 14, Williams in view of Zhang teaches all of the limitations of claims 1 and 9, upon which claims 5 and 14 depend.
Williams in view of Zhang fails to teach wherein the data model comprises a digital twin of the building, and wherein the instructions cause the one or more processors to: generate, using the LLM, the digital twin of the building or a portion thereof using data related to a plurality of pieces of building equipment of the building, the LLM configured to generate the digital twin by at least one of: generating, using the data, at least one first new relationship for the digital twin between first building equipment of the plurality of pieces of building equipment and at least one of second building equipment of the plurality of pieces of building equipment or one or more entities associated with the building, the one or more entities comprising people associated with the building, locations within the building, events associated with the building, or assets of the building; or generating, using the data, at least one first new entity for the digital twin, the first new entity comprising a digital representation of a person associated with the building, a location within the building, an event associated with the building, or an asset of the building.
However, Ploegert teaches wherein the data model comprises a digital twin of the building, and wherein the instructions cause the one or more processors to: generate, using the LLM, the digital twin of the building or a portion thereof using data related to a plurality of pieces of building equipment of the building, the LLM configured to generate the digital twin by at least one of ([0193] “the instructions cause the one or more processors to generate a first update for the external digital twin of the building in the first format for the external digital twin of the building of the external system based on the change feed event…”):
generating, using the data, at least one first new relationship for the digital twin between first building equipment of the plurality of pieces of building equipment and at least one of second building equipment of the plurality of pieces of building equipment or one or more entities associated with the building, the one or more entities comprising people associated with the building, locations within the building, events associated with the building, or assets of the building ([0024] “the graph including nodes and edges between the nodes, the nodes representing entities of the building and the edges representing relationships between the entities of the building”; [0025] “the entities of the building are at least one of building equipment, locations of the building, users of the building, and events of the building.”); or
generating, using the data, at least one first new entity for the digital twin, the first new entity comprising a digital representation of a person associated with the building, a location within the building, an event associated with the building, or an asset of the building ([0338] “Data access 1446 and 1448 can provide access to assets, points, people, locations, and events”)
Williams in view of Zhang in view of Ploegert are considered to be analogous to the claimed invention because all are in the same field of building data management. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the techniques of home data monitoring systems of Williams in view of Zhang with the technique of generating a digital twin of a building taught by Ploegert in order to improve management of building systems of a building (see Ploegert [0002]).
Regarding claims 6 and 15, Williams in view of Zhang in view of Ploegert teaches all of the limitations of claim 5 and 14, upon which claims 6 and 15 depend.
Additionally, Ploegert teaches wherein the data comprises unstructured data conforming to a plurality of different predetermined formats and/or not conforming to a predetermined format, and wherein the LLM is configured to generate the digital twin from the unstructured data ([0192] “the second external digital twin in a second format different than the first format of the external digital twin and the format of the digital twin of the building” [0398] Referring now to FIG. 24, the tenant entitlement model 2200 shown in greater detail, according to an exemplary embodiment. In some embodiments, the tenant entitlement model 2200 is a graph data structure, one or more tables, or other data storage structures).
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Williams in view of Zhang in view of Ploegert in further view of Si et al. US 20250245260 A1 (hereinafter Si)
Regarding claims 7 and 16, Williams in view of Zhang in view of Ploegert teaches all of the limitations of claim 5 and 14, upon which claims 7 and 16 depend.
Williams in view of Zhang in view of Ploegert fails to teach wherein the LLM is configured to autonomously generate the digital twin from the unstructured data without requiring manual user intervention.
However, Si teaches wherein the LLM is configured to autonomously generate the digital twin from the unstructured data without requiring manual user intervention ([0020] “the invention further provides methods and systems to automatically generate a digital twin instantiating the next ontology that follows on from a sequence corresponding to user input, with that “next ontology” being predicted”; [0057] “a user is creating a series of digital twins on a computerized digital-twin platform, this automatic pre-creation of the next digital twin will tend to speed up the process of generating the digital twins on the platform”).
Williams in view of Zhang in view of Ploegert in view of Si are considered to be analogous to the claimed invention because all are in the same field of building data management. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the techniques of building data monitoring systems of Williams in view of Zhang in view of Ploegert with the technique of automatically generating digital twins taught by Si in order to improve systems and methods that anticipate the next digital-twin ontology following on from a sequence corresponding to user input, notably user input while creating digital twins or searching for digital twins. (see Si [0001]).
Allowable Subject Matter
Claim 3, 11, and 20 are objected to as being dependent upon a rejected base claim, but would be allowable in terms of the prior art rejection if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Taheri et al. (US 20230324860 A1) teaches systems and methods for estimating energy consumption data for a building. A system (600) for estimating an energy use of a building uses a dilated convolutional neural network architecture (610) to receive time-series data (620) for the building and to predict one or more time-series data points (650) representing an estimated energy consumption for the building. A method for estimating an energy use of a building includes obtaining time-series data for the building, providing the time-series data as input to a dilated convolutional neural network architecture, and predicting one or more time-series data points representing an estimated energy consumption for the building using the dilated convolutional neural network architecture. The systems and methods may be used to help users and building controllers reduce energy use within a building.
Orr et al. (US 11257504 B2) teaches systems and processes for using a virtual assistant to control electronic devices. In one example process, a user can speak an input in natural language form to a user device to control one or more electronic devices. The user device can transmit the user speech to a server to be converted into a textual representation. The server can identify the one or more electronic devices and appropriate commands to be performed by the one or more electronic devices based on the textual representation. The identified one or more devices and commands to be performed can be transmitted back to the user device, which can forward the commands to the appropriate one or more electronic devices for execution. In response to receiving the commands, the one or more electronic devices can perform the commands and transmit their current states to the user device.
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/ZEESHAN MAHMOOD SHAIKH/Examiner, Art Unit 2658
/RICHEMOND DORVIL/Supervisory Patent Examiner, Art Unit 2658