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 .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Goyal (US PgPub 2020/0159723).
Regarding claim 1, Goyal discloses a method (Figures 1-3), comprising: receiving, by one or more processors, from a device associated with a user identifier, a prompt for generating data regarding a building management system (Figure 2, 204, Figure 3, 304 and Paragraphs 0026, 0038, 0163, 0176, 0180, 0242 where an authorized user provides a user prompt for generating data regarding a building management system); generating, by the one or more processors, using at least one generative artificial intelligence (AI) model, a completion to the prompt based at least on the prompt and the user identifier, the completion indicating one or more actions corresponding to a performance target for the building management system (Figure 6, Figure 26, Figure 31 and Paragraphs 0130, 0134, 0135, 0197, 0216, 0221-0224, 0301, 0307, 0308, 0314, 0317, 0318, 0322 and 0378-0382 where the building semantic model system uses AI to generate nodes of the semantic building model and a plurality of relationships between the nodes where the nodes include information relative to performance targets for building operational systems/equipment); and presenting, by the one or more processors, the completion using at least one of a display device or an audio output device (Figure 3, Elements 316, 332 and Paragraphs 0049, 0084, 0174, 0192, 0262, 0286 and 0289 where the user interface manager provides display data to the user device to display building nodes and relationships therebetween).
Regarding claim 2, Goyal discloses identifying, by the one or more processors, the performance target by processing the prompt using the at least one generative AI model, wherein the performance target comprises at least one of an operating parameter of one or more items of equipment associated with the building management system or a key performance index (KPI) of the one or more items of equipment (Figure 26, Figure 31 and Paragraphs 0130, 0134, 0135, 0197, 0216, 0221-0224, 0301, 0307, 0308, 0314, 0317, 0318, 0322 and 0378-0382 where the building semantic model system uses AI to generate nodes of the semantic building model and a plurality of relationships between the nodes where the nodes include information relative to performance targets for building operational systems/equipment. The operational systems in the building correspond to building equipment that regulates lighting, temperature and the like).
Regarding claim 3, Goyal discloses wherein the at least one generative AI model is configured using training data comprising data of at least one other item of equipment or building previously modified to achieve a related KPI, wherein the related KPI is a measurable metric associated with at least one of (i) an operating parameter of the at least one other item of equipment or building or (ii) a value of the operating parameter, and wherein the generative AI model comprises at least one neural network comprising at least one transformer (Figure 26, Figure 31 and Paragraphs 0075, 0134, 0135, 0145, 0176, 0197, 0216, 0301, 0307, 0308, 0314, 0317, 0318, 0322 and 0378-0382 where the AI model, comprising a neural network, uses historical user input and corresponding outputs to determine performance metrics).
Regarding claim 4, Goyal discloses generating, by the one or more processors, skeleton code to execute the one or more actions, wherein the skeleton code comprises one or more code sections corresponding to the one or more actions, and wherein the one or more code sections comprises the skeleton code corresponding to at least one of adjusting an energy usage, modifying a temperature control setting, modifying a lighting condition, reconfiguring a space utilization, updating an air quality indicator, updating a security protocol, updating a maintenance schedule, or optimizing a waste management; receiving, by the one or more processors, input to update the skeleton code, wherein the input comprises at least one modification or at least one instruction to update at least one operating parameter of the building management system; and updating, by the one or more processors, the skeleton code based on the input, wherein updating the skeleton code comprises incorporating the at least one modification or implementing the at least one instruction into the skeleton code (Figure 3, Element 324 and Paragraphs 0063, 0129, 0152, 0161, 0180, 0181, 0183, 0196 and 0292 where the logic generator adjusts various building systems based on user input).
Regarding claim 5, Goyal discloses receiving, by the one or more processors, real-time operational data from the building management system, wherein the real-time operational data comprises at least one of energy usage data, temperature reading, lighting condition, space utilization metric, air quality indicator, security status, maintenance schedule, or waste management metric; and generating, by the one or more processors, updates to the completion based on the real-time operational data, wherein the updates to the completion comprise at least one modification to the one or more actions to align with the performance target (Figure 26, Figure 31 and Paragraphs 0130, 0134, 0135, 0161, 0197, 0216, 0221-0224, 0301, 0307, 0308, 0314, 0317, 0318, 0322 and 0378-0382 where the building semantic model system uses AI to generate nodes of the semantic building model and a plurality of relationships between the nodes where the nodes include real-time information relative to performance targets for building operational systems/equipment. The real-time information is gathered by sensors sensing temperature, humidity, pressure, lighting, occupancy and the like).
Regarding claim 6, Goyal discloses activating, by the one or more processors, a co-pilot model of the generative AI to facilitate executing the one or more actions related to the performance target, wherein activating the co-pilot model comprises: initiating a session with the co-pilot model through a user interface, wherein the user interface receives the real-time operational data and a plurality of prompts; modeling using the co-pilot model, the real-time operational data and the plurality of prompts to generate one or more actionable recommendations corresponding to the performance target; presenting the one or more actionable recommendations via the user interface; and updating the one or more actionable recommendations based on new real-time operational data or a new prompt (Figure 3, Element 324 and Paragraphs 0063, 0129, 0152, 0161, 0180, 0181, 0183, 0196 and 0292 where the logic generator adjusts various building systems based on user inputs relative to performance metrics. The AI model produces new outputs based on the updated performance standards).
Regarding claim 7, Goyal discloses wherein the user interface is presented on the display device comprising the completion, and wherein the user interface is personalized to the user identifier based on a user preference, a user interest, or a previous interaction associated with using the at least one generative AI model (Figure 3, Element 324 and Paragraphs 0063, 0129, 0152, 0161, 0180, 0181, 0183, 0196 and 0292 where the logic generator adjusts various building systems based on user inputs relative to performance metrics. The AI model produces new outputs based on the updated performance standards. The displayed outputs are actionable by the user).
Regarding claim 8, Goyal discloses wherein the one or more actions comprise customized natural language based on account information associated with the user identifier, wherein the customized natural language is customized, using the account information, according to at least one of a choice of vocabulary, a level of technicality, a depth of detail, or a preferred communication style, and wherein the generative AI model is configured using training data comprising the account information (Paragraphs 0026, 0033, 0037, 0085 and 0173 where natural language is used to train/prompt the AI model).
Regarding claim 9, Goyal discloses wherein the completion comprises a synthetization of one or more reports, wherein the presentation of the completion comprises a design customized to the user identifier or the building management system, and wherein the synthetization comprises: aggregating data and content of the one or more reports from a plurality of data sources; and generating insights corresponding with one of a plurality of performance targets or operational goals of the building management system (Paragraphs 0308, 0368 and 0377 where reports are generated for an end user to review and properly service faulty equipment and/or recommend optimizations in the system).
Regarding claim 10, Goyal discloses wherein the at least one generative AI model implements reinforcement learning, wherein the reinforcement learning comprises updating the at least one generative AI model based on receiving feedback on an effectiveness of generated completions indicating the one or more actions (Figure 26, Figure 31 and Paragraphs 0130, 0134, 0135, 0197, 0216, 0221-0224, 0301, 0307, 0308, 0314, 0317, 0318, 0322 and 0378-0382 where performance of the AI model is monitored and updated to increase efficiency).
System claims 11-19 are drawn to the system corresponding to the method of using same as claimed in claims 1-10. Therefore system claims 11-19 correspond to method claims 1-10 and are rejected for the same reasons of anticipation as used above.
Regarding claim 20, see rejection for claim 1 which discloses all of the claimed limitations.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS D ALUNKAL whose telephone number is (571)270-1127. The examiner can normally be reached M-F 9AM-5PM.
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/THOMAS D ALUNKAL/
Primary Examiner, Art Unit 2686
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