18Notice 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 06-24-2024 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 § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ranjan et al. (US 2020/0380623 A1) in view of Gao et al. (“Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System”).
Regarding claim 1, Ranjan explicitly discloses:
providing, by one or more processors, a digital occupant assistant application for occupants of a commercial building; and (Ranjan, ¶[0057]: “large percentage of the population or workforce spends a large amount of time working in offices within office buildings. Unfortunately, the office buildings may experience issues with their building management systems that can cause a downturn in work as employees are forced to take time off while technicians fix the issues… Thus, there is a need for a smart incident management solution which can help users report issues and automate certain processes to avoid human intervention and delays.”, ¶[0155]: “ The input may be a transcription of one or more utterances made in a phone call by a user and/or a description in an incident ticket received via an application associated with the building management system.”, ¶[0150]: “Processing circuit 1608 can be configured to implement any of the methods described herein and/or to cause such methods to be performed, e.g., by processor 1610.”)
dynamically generating, by the one or more processors using a generative artificial intelligence (AI) model, data to present to an occupant via the digital occupant assistant application, the generative AI model configured to dynamically generate the data based on at least one of: a prompt from the occupant provided via an input interface of the digital occupant assistant application; or context relating to at least one of the occupant, the commercial building, a space of the commercial building, an event relating to the commercial building, one or more other occupants, or building equipment or other assets of the commercial building. (Ranjan, ¶[0150]: “Memory 1612 is shown to include a building model 1614, a natural language processing (NLP) Module 1616, an incident identifier 1618, an entity matcher 1622, and an incident database 1624.”, ¶[0183]: “Referring now to FIG. 21, a graphical user interface 2100 including an incident ticket screen 2102 that a user can use to report an incident and an active incidents screen 2104 is shown, according to an exemplary embodiment. On incident ticket screen 2102, a user can manually input information about an incident that the user is experiencing or noticed in a building system. The user may input the location, space, category, issue, description, and priority of the incident. The user may also input a picture of the incident if possible… On active incidents screen 2104, currently active incidents ( e.g., incidents that have not been resolved yet) may be displayed. Active incidents screen 2104 may show a list of incidents and their current assignment status ( e.g., assigned or not assigned). Active incident screen 2104 may show an identification of the ticket that was used to report the incident and, in some embodiments, a picture of the incident. A user may toggle between currently active incidents and resolved incidents to view a history of incident that have been reported or resolved at the building system.”)
Ranjan fails to disclose:
using a generative artificial intelligence (AI) model
However, Gao explicitly discloses:
using a generative artificial intelligence (AI) model (Gao, Abstract: “To address these limitations, this paper proposes a novel paradigm called Chat-Rec (Chat- GPT Augmented Recommender System) that innovatively augments LLMs for building conversational recommender systems by converting user profiles and historical interactions into prompts”)
The combination of Ranjan and Gao are analogous art because they are in the same field of training time series data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, having the teachings of Ranjan and Gao before them, to modify the teachings of Ranjan to include the teachings of Gao to improve the assistant’s ability to understand varied natural language reports and provide more natural, interactive and context-relevant responses while reducing reliance on predefined templates.
Regarding claim 2, the combination of Ranjan and Gao discloses all the limitations of claim 1 (as shown in the rejections above).
Ranjan in view of Gao further discloses:
wherein the generative AI model comprises a pretrained generative transformer model. (Gao, Pg. 8, Section 3.4: “The LLMs-augmented recommender system introduced above can be used to address several challenging tasks, that are hard or even impossible to be addressed with conventional recommender systems, such as cross-domain recommendation [26] and cold-start recommendation [17]. In this part, we will first talk about how to use the LLMs-augmented recommender system for the cross-domain recommendation. LLMs pre-trained with information across the Internet actually can serve as the multi-perspective knowledge base [14]. Besides the target product in one do- main, such as movies, the LLMs not only has a broad knowledge about products many other domains, like music and books, but also understands the relations among the products across the domains mentioned above.”)
Regarding claim 3, the combination of Ranjan and Gao discloses all the limitations of claim 1 (as shown in the rejections above).
Ranjan in view of Gao further discloses:
wherein the prompt from the occupant comprises unstructured data conforming to a plurality of different predetermined formats and/or not conforming to the plurality of different predetermined formats, and (Ranjan, ¶[0171]: “Incident identifier 1618 may obtain information from the incident ticket that it did not identify from the transcription of the phone call (e.g., location, space, category, issue, priority, type, and photographs) or vice versa. For example, incident identifier 1618 may obtain a picture of a burnt outlet and determine features of the burnt outlet such as the outlet being burnt, a type of outlet, or a location of the burnt outlet using object recognition techniques. Incident identifier 1618 may also receive a transcript of a phone call regarding the same burnt outlet. Incident identifier 1618 may identify keywords, entities, and/or intents from the phone call and use the identified keywords, entities, and/or intents from the phone call in addition to the identified features from the picture of the burnt outlet to determine a template associated with the burnt outlet is satisfied. Incident identifier 1618 may identify any information from the incident ticket ( e.g., information from a description of the issue using natural language processing techniques such as those described herein, a category of the incident, a priority of the incident, a space of the incident, a building of the incident, etc.) from the incident ticket in addition to a transcript to determine an incident was satisfied”)
wherein the generative AI model is configured to generate the data using the unstructured data. (Gao, Pg. 4, Section 3.1: “To bridge recommender systems and LLMs, we propose an enhanced recommender system module based on ChatGPT, a large language model trained by OpenAI. As the Fig. 1 shows, the module takes as input user-item history interactions, user profile, user query Qi, and history of dialogue H<i (if available, and the notation <i denotes the dialogue history prior to the current query), and interfaces with any recommender system R.”, Pg. 5, ¶[6]: “History of dialogue H<i, which contains the previous conversation between the user and the system. This information is used to understand the context of the user's query and to provide a more personalized and relevant response.”) [Examiner’s note: Examiner is interpreting the “unstructured data” as the text input provided from the user in the previous conversation between the user and the system, which corresponds to the definition in the Instant Specification ¶[0049]]
Regarding claim 4, the combination of Ranjan and Gao discloses all the limitations of claim 3 (as shown in the rejections above).
Ranjan in view of Gao further discloses:
wherein the generative AI model is configured to autonomously generate the data from the unstructured data without requiring manual user intervention. (Ranjan, ¶[0057]: “Thus, there is a need for a smart incident management solution which can help users report issues and automate certain processes to avoid human intervention and delays.”)
Regarding claim 5, the combination of Ranjan and Gao discloses all the limitations of claim 1 (as shown in the rejections above).
Ranjan in view of Gao further discloses:
wherein dynamically generating the data comprises:
dynamically generating a recommendation to the occupant based on the at least one of the prompt or the context; (Ranjan, ¶0194]: “In yet another use case, the data processing system can maintain a live database that keeps track of the incidents in a table identifying solutions to different incidents. The table can be used for future reference by a second entity that is being assigned to a similar issue. The data processing system can detect incidents and aspects about the incidents based on entities, intents, context, and/or keywords that the data processing system extracts from the incident tickets and/or the phone call transcription using natural language processing techniques. Upon receiving an incident report or a phone call transcription, the data processing system can query the database to identify similar incidents that have been resolved. Responsive to the data processing system identifying a similar incident, the data processing system can send the second entity selected to resolve the incident along with information about how the identified similar incident was resolved and references to help the second entity resolve the incident.”)
wherein the recommendation comprises information aggregated by the generative AI model; and (Ranjan, ¶[0194]: “The data processing system can detect incidents and aspects about the incidents based on entities, intents, context, and/or keywords that the data processing system extracts from the incident tickets and/or the phone call transcription using natural language processing techniques.”)
wherein the information aggregated by the generative AI model was separate prior to training of the generative AI model. (Ranjan, ¶[0110]: “Natural language processing system 606 can train language processing service 638 by transmitting the identifications including the input text segments and the output selected domains and intents to language processing service 638”, ¶[0111]: “Process 700 can be conducted by a data processing system (e.g., natural language processing system 606). At a step 702, the data processing system can receive an input including text and an organization ( e.g., building management system) that is associated with the text. The organization can be an identification of which building management database to pull data to identify entities and intents from. At a step 704, the data processing system can separate words of the text into chunks based on the parts of speech of each word. At a step 706, the data processing system can filter entities extracted from the chunks to obtain entities that are likely being referenced in the text.”)
Regarding claim 6, the combination of Ranjan and Gao discloses all the limitations of claim 1 (as shown in the rejections above).
Ranjan in view of Gao further discloses:
wherein at least a portion of the data comprises new data generated by the generative AI model responsive to the at least one of the prompt or the context, (Gao, Pg. 8, Section 3.3, ¶[3]: “However, since the knowledge held by ChatGPT is limited to September 2021, ChatGPT does not cope well when encountering unknown items, such as a user requesting to recommend some new movies released in 2023 or content related to a movie that ChatGPT is not aware of, as shown in the top part of Fig. 3. To address this issue, we introduce external information about new items, utilizing large language models to generate corresponding embedding representations and cache them. When encountering new item recommendations, we calculate the similarity between item embeddings and embeddings of user requests and preferences, then retrieve the most relevant item information based on the similarity and construct a prompt to input to ChatGPT for recommendation”)
the new data not preexisting, at the time of receiving the prompt and/or the context, in a data set upon which the generative AI model has been trained. (Gao, Pg. 8, Section 3.3, ¶[3]: “Large language models can use the vast amount of knowledge they contain to help recommender systems alleviate the cold-start problem of new items, i.e., recommending items that lack a large number of user interactions. However, since the knowledge held by ChatGPT is limited to September 2021, ChatGPT does not cope well when encountering unknown items, such as a user requesting to recommend some new movies released in 2023 or content related to a movie that ChatGPT is not aware of,”)
Regarding claim 7, the combination of Ranjan and Gao discloses all the limitations of claim 1 (as shown in the rejections above).
Ranjan in view of Gao further discloses:
receiving, by the one or more processors, from one or more data sources of the commercial building, first information associated with a component of the commercial building; (Rajan, ¶[0091]: “Layers 410-420 may be configured to receive inputs from building subsystems 428 and other data sources, determine optimal control actions for building subsystems 428 based on the inputs, generate control signals based on the optimal control actions, and provide the generated control signals to building subsystems”)
dynamically generating, by the one or more processors by inputting the first information into the generative AI model, second data to present to the occupant via the digital occupant assistant application, the second data generated based on a persona of the occupant; and (Gao, Pg. 4, Section 3.1: “To bridge recommender systems and LLMs, we propose an enhanced recommender system module based on ChatGPT, a large language model trained by OpenAI. As the Fig. 1 shows, the module takes as input user-item history interactions, user profile, user query Qi, and history of dialogue H<i (if available, and the notation <i denotes the dialogue history prior to the current query), and interfaces with any recommender system R.”, Pg. 5, ¶[2]: “The prompt constructor module in the enhanced recommender system takes multiple inputs to generate a natural language paragraph that captures the user's query and recommendation information. The inputs are as follows:… User profile, which contains demographic and preference information about the user. This may include age, gender, location, and interests. The user profile helps the system understand the user's characteristics and preferences.”, Pg. 6, ¶[3]: “In the second round of Q&A, the user asks why the movie “Fargo" was recommended. The system determines that no recommendation task is needed and instead executes the explanation for the recommendation module, using the movie title, history interaction, and user profile as inputs… The answer A2 is then generated, which provides a brief explanation of the recommendation, including information about the user's general interests and the specific characteristics of the movie that may be appealing to the user”)
dynamically generating, by the one or more processors by inputting the first information into the generative AI model, third data to present to a second occupant via the digital occupant assistant application, the third data generated based on a persona of the second occupant; (Gao, Pg. 4, Section 3.1: “To bridge recommender systems and LLMs, we propose an enhanced recommender system module based on ChatGPT, a large language model trained by OpenAI. As the Fig. 1 shows, the module takes as input user-item history interactions, user profile, user query Qi, and history of dialogue H<i (if available, and the notation <i denotes the dialogue history prior to the current query), and interfaces with any recommender system R.”, Pg. 5, ¶[2]: “The prompt constructor module in the enhanced recommender system takes multiple inputs to generate a natural language paragraph that captures the user's query and recommendation information. The inputs are as follows:… User profile, which contains demographic and preference information about the user. This may include age, gender, location, and interests. The user profile helps the system understand the user's characteristics and preferences.”, Pg. 6, ¶[3]: “In the second round of Q&A, the user asks why the movie “Fargo" was recommended. The system determines that no recommendation task is needed and instead executes the explanation for the recommendation module, using the movie title, history interaction, and user profile as inputs..”) The answer A2 is then generated, which provides a brief explanation of the recommendation, including information about the user's general interests and the specific characteristics of the movie that may be appealing to the user
wherein the persona of the occupant is different than the persona of the second occupant; (Gao, Pg. 7, Fig. 2: “Case study of interactive recommendation. It shows two conversations between different users and LLM. Where the user profile and historical users are converted into corresponding prompts for personalized recommendations”)
wherein the second data is different than the third data. (Gao, Pg. 7, Fig. 2:
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) [Examiner’s note: Fig. 2 discloses different personalized conversations, recommendations, and explanations generated from the corresponding profiles]
Regarding claim 8, the combination of Ranjan and Gao discloses all the limitations of claim 1 (as shown in the rejections above).
Ranjan in view of Gao further discloses:
wherein the prompt from the occupant includes an indication of a status of a component of the commercial building, and comprising: retrieving, by the one or more processors responsive to receiving the prompt, information associated with the component of the commercial building; (Ranjan, ¶[0157]: “To extract an entity as a parameter, NLP module 1616 may identify entities from a text segment and identify the corresponding entities from incident data in incident database 1624. For example, NLP module 1616 can receive or obtain a transcript of a phone call from a transcription service or, in some embodiments, transcribe the phone call using transcription techniques. NLP module 1616 can use the natural language processing techniques described above with reference to FIGS. 5-15 to identify entities (e.g., representations of specific building devices or spaces stored in the BRICK data structure) of building management system 400 that are referenced in the text segments of the transcript of the phone call.”)
inputting, by the one or more processors, the information associated with the component of the commercial building into the generative AI model to cause the generative AI model to generate a response that addresses the status of the component of the commercial building; and (Ranjan, ¶[0113]: “At a step 802, the data processing system can receive an input including a string of words with the phrase "Show me the power consumption details of 200 George Str." The string of words may have been generated from voice data that was transcribed by a transcription service. In some cases, the input may be a transcribed command that an administrator speaks into an agent to obtain information about a building management system or to change a configuration of a building device of the building management system.”)
presenting, by the one or more processors via the digital occupant assistant application, the response to the occupant. (Ranjan, ¶[0183]: “Active incidents screen 2104 may show a list of incidents and their current assignment status ( e.g., assigned or not assigned). Active incident screen 2104 may show an identification of the ticket that was used to report the incident and, in some embodiments, a picture of the incident. A user may toggle between currently active incidents and resolved incidents to view a history of incident that have been reported or resolved at the building system”)
Regarding claim 9, the combination of Ranjan and Gao discloses all the limitations of claim 1 (as shown in the rejections above).
Ranjan in view of Gao further discloses:
receiving, by the one or more processors, from the occupant via the digital occupant assistant application, an indication to modify a component of the commercial building; (Ranjan, ¶[0055]: “In a speech recognition system (e.g., Natural language processing (NLP)), speech is converted to text using standard transcription techniques. The text is sent to cognitive services for intent and entity detection. The intent may be an intention of the speech and the entity may be the subject of the speech ( e.g., a space or a building device) or modify the intents. Unfortunately, standard transcription techniques do not always generate accurate text. This can be an issue when determining which domain entity of a building management system speech is referring to when asking questions about the domain entity or directing the domain entity to change configurations or states”, ¶[0113]: “In some cases, the input may be a transcribed command that an administrator speaks into an agent to obtain information about a building management system or to change a configuration of a building device of the building management system.”)
retrieving, by the one or more processors, information associated with the component of the commercial building; (Ranjan, ¶[0157]: “To extract an entity as a parameter, NLP module 1616 may identify entities from a text segment and identify the corresponding entities from incident data in incident database 1624. For example, NLP module 1616 can receive or obtain a transcript of a phone call from a transcription service or, in some embodiments, transcribe the phone call using transcription techniques. NLP module 1616 can use the natural language processing techniques described above with reference to FIGS. 5-15 to identify entities (e.g., representations of specific building devices or spaces stored in the BRICK data structure) of building management system 400 that are referenced in the text segments of the transcript of the phone call.”)
inputting, by the one or more processors, the information associated with component of the commercial building into the generative AI model to cause the generative AI model to generate one or more recommendations to modify the component of the commercial building; and (Ranjan, ¶[0113]: “At a step 802, the data processing system can receive an input including a string of words with the phrase "Show me the power consumption details of 200 George Str." The string of words may have been generated from voice data that was transcribed by a transcription service. In some cases, the input may be a transcribed command that an administrator speaks into an agent to obtain information about a building management system or to change a configuration of a building device of the building management system.”)
presenting, by the one or more processors via the digital occupant assistant application, the one or more recommendations to the occupant. (Ranjan, ¶0194]: “In yet another use case, the data processing system can maintain a live database that keeps track of the incidents in a table identifying solutions to different incidents. The table can be used for future reference by a second entity that is being assigned to a similar issue. The data processing system can detect incidents and aspects about the incidents based on entities, intents, context, and/or keywords that the data processing system extracts from the incident tickets and/or the phone call transcription using natural language processing techniques. Upon receiving an incident report or a phone call transcription, the data processing system can query the database to identify similar incidents that have been resolved. Responsive to the data processing system identifying a similar incident, the data processing system can send the second entity selected to resolve the incident along with information about how the identified similar incident was resolved and references to help the second entity resolve the incident.”)
Regarding claim 10, the combination of Ranjan and Gao discloses all the limitations of claim 1 (as shown in the rejections above).
Ranjan in view of Gao further discloses:
receiving, by the one or more processors, a first plurality of unstructured building reports for a first plurality of occupants of a plurality of buildings, (Ranjan, ¶[0183]: “Referring now to FIG. 21, a graphical user interface 2100 including an incident ticket screen 2102 that a user can use to report an incident and an active incidents screen 2104 is shown, according to an exemplary embodiment. On incident ticket screen 2102, a user can manually input information about an incident that the user is experiencing or noticed in a building system. The user may input the location, space, category, issue, description, and priority of the incident”)
the first plurality of unstructured building reports conforming to a plurality of different predetermined formats and/or comprising unstructured data not conforming to the plurality of different predetermined formats; (Ranjan, ¶[0171]: “Incident identifier 1618 may obtain information from the incident ticket that it did not identify from the transcription of the phone call (e.g., location, space, category, issue, priority, type, and photographs) or vice versa. For example, incident identifier 1618 may obtain a picture of a burnt outlet and determine features of the burnt outlet such as the outlet being burnt, a type of outlet, or a location of the burnt outlet using object recognition techniques. Incident identifier 1618 may also receive a transcript of a phone call regarding the same burnt outlet. Incident identifier 1618 may identify keywords, entities, and/or intents from the phone call and use the identified keywords, entities, and/or intents from the phone call in addition to the identified features from the picture of the burnt outlet to determine a template associated with the burnt outlet is satisfied. Incident identifier 1618 may identify any information from the incident ticket ( e.g., information from a description of the issue using natural language processing techniques such as those described herein, a category of the incident, a priority of the incident, a space of the incident, a building of the incident, etc.) from the incident ticket in addition to a transcript to determine an incident was satisfied”)
training, by the one or more processors, the generative AI model using the first plurality of unstructured building reports; and (Ranjan, ¶[0110]: “Natural language processing system 606 can train language processing service 638 by transmitting the identifications including the input text segments and the output selected domains and intents to language processing service 638”, ¶[0111]: “Process 700 can be conducted by a data processing system (e.g., natural language processing system 606). At a step 702, the data processing system can receive an input including text and an organization ( e.g., building management system) that is associated with the text. The organization can be an identification of which building management database to pull data to identify entities and intents from. At a step 704, the data processing system can separate words of the text into chunks based on the parts of speech of each word. At a step 706, the data processing system can filter entities extracted from the chunks to obtain entities that are likely being referenced in the text.”)
providing, by the one or more processors using the generative AI model, one or more responses with respect to one or more requests from occupants of a building via the digital occupant assistant application. (Ranjan, ¶[0183]: “On active incidents screen 2104, currently active incidents ( e.g., incidents that have not been resolved yet) may be displayed. Active incidents screen 2104 may show a list of incidents and their current assignment status ( e.g., assigned or not assigned). Active incident screen 2104 may show an identification of the ticket that was used to report the incident and, in some embodiments, a picture of the incident. A user may toggle between currently active incidents and resolved incidents to view a history of incident that have been reported or resolved at the building system.”)
Regarding claim 11, this claim is rejected under the same rationale with claim 10 (as shown in the rejections above), because they are analogous claims.
Regarding claim 12, this claim is rejected under the same rationale with claim 1 (as shown in the rejections above), because they are analogous claims.
Regarding claim 13, this claim is rejected under the same rationale with claim 1 (as shown in the rejections above), because they are analogous claims.
Regarding claim 14, this claim is rejected under the same rationale with claim 8 (as shown in the rejections above), because they are analogous claims.
Regarding claim 15, this claim is rejected under the same rationale with claim 9 (as shown in the rejections above), because they are analogous claims.
Regarding claim 16, this claim is rejected under the same rationale with claim 12 and claim 14 (as shown in the rejections above), because they are analogous claims.
Regarding claim 17, this claim is rejected under the same rationale with claim 2 (as shown in the rejections above), because they are analogous claims.
Regarding claim 18, this claim is rejected under the same rationale with claim 3 (as shown in the rejections above), because they are analogous claims.
Regarding claim 19, this claim is rejected under the same rationale with claim 4 (as shown in the rejections above), because they are analogous claims.
Regarding claim 20, the combination of Ranjan and Gao discloses all the limitations of claim 16 (as shown in the rejections above).
Ranjan in view of Gao further discloses:
(Ranjan, ¶0194]: “In yet another use case, the data processing system can maintain a live database that keeps track of the incidents in a table identifying solutions to different incidents. The table can be used for future reference by a second entity that is being assigned to a similar issue. The data processing system can detect incidents and aspects about the incidents based on entities, intents, context, and/or keywords that the data processing system extracts from the incident tickets and/or the phone call transcription using natural language processing techniques. Upon receiving an incident report or a phone call transcription, the data processing system can query the database to identify similar incidents that have been resolved. Responsive to the data processing system identifying a similar incident, the data processing system can send the second entity selected to resolve the incident along with information about how the identified similar incident was resolved and references to help the second entity resolve the incident.”)
Conclusion
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/AMY TRAN/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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