Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This action is responsive to the Application filed on 7/29/2024. A filing date 7/29/2024 is acknowledged. Claims 1-22 are pending in this application. Claims 1, 22 are independent claims.
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 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
3. Claims 1-8, 10-22 are rejected under 35 U.S.C. 103 as being unpatentable over Rajiv Ramanasankaran et al (US Publication 20240403343 A1, hereinafter Ramanasankaran), and in view of Sara Itani (US Publication 20250053732 A1, hereinafter Itani).
As for independent claim 1, Ramanasankaran discloses: A computer monitoring system for classifying one or more assets in an automated and industrial control system (AIC) ([0158], the data sources 612 can include building information model (BIM) or industry foundation classes (IFC) data) according to a classification standard ([0233], autonomously generate one or more tags for the new relationship and new entity in a pre-determined ontology or schema of the digital twin. The tag for the new relationship and/or new entity can include a label, characteristic, parameter, attribute, or category describing the new relationship and/or new entity), wherein each asset has a plurality of attribute variables in a computer database of the AIC ([0233], autonomously generate one or more tags for the new relationship and new entity in a pre-determined ontology or schema of the digital twin. The tag for the new relationship and/or new entity can include a label, characteristic, parameter, attribute, or category describing the new relationship and/or new entity), comprising:
one or more storage devices having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to (Abstract, A building management system (BMS) can include one or more memory devices storing instructions thereon that can, when executed by one or more processors, cause the one or more processors to receive a plurality of information, generate a data model to represent the plurality of information in a common format associated with the BMS): receive a classification query for an asset in the AIC (Abstract, receive a query that corresponds to a building associated with the BMS), and responsive to the query: 1) provide a listing of candidate ontology classes for the queried asset utilizing information received from a semantic data model of known assets ([0055], Systems and methods in accordance with the present disclosure can leverage the efficiency of language models (e.g., GPT-based models or other pre-trained LLMs) in extracting semantic information (e.g., semantic information identifying faults, causes of faults, and other accurate expert knowledge regarding equipment servicing) from the unstructured data in order to use both the unstructured data and the data relating to equipment operation to generate more accurate outputs regarding equipment servicing; [0141], The GPT model can process the modified input sequence to determine a next token in the sequence (e.g., to append to the end of the sequence), such as by determining probability scores indicating the likelihood of one or more candidate tokens being the next token, and selecting the next token according to the probability scores (e.g., selecting the candidate token having the highest probability scores as the next token). For example, the GPT model can apply various attention and/or transformer based operations or networks to the modified input sequence to identify relationships between tokens for detecting the next token to form the output sequence); 2) capture, from the AIC computer database, certain classification attribute variables associated with the queried asset ([0073], AHU controller 230 may provide BMS controller 266 with temperature measurements from temperature sensors 262-264, equipment on/off states, equipment operating capacities, and/or any other information that can be used by BMS controller 266 to monitor or control a variable state or condition within building zone; [0136], The items of equipment can operate in accordance with various qualitative and quantitative parameters, variables, setpoints, and/or thresholds or other criteria); 3) receive user information describing certain building information associated with the queried asset ([0250], the building data platform 300 may receive a reply from the user to the first response. The reply may add more details or further refine the initial query); and 4) generate a computer query configured for requesting results from a machine learning (ML) algorithm ([0052], AI and/or machine learning (ML) systems, including but not limited to LLMs, can be used to generate text data and data of other modalities in a more responsive manner to real-time conditions) indicative of [one or more classification standards for the queried asset], wherein the generated computer query includes the: a) provided candidate ontology classes ([0233], identify a new relationship or new entity of the digital twin and autonomously generate one or more tags for the new relationship and new entity in a pre-determined ontology or schema of the digital twin. The tag for the new relationship and/or new entity can include a label, characteristic, parameter, attribute, or category describing the new relationship and/or new entity), b) captured certain attribute variables ([0073], AHU controller 230 may provide BMS controller 266 with temperature measurements from temperature sensors 262-264, equipment on/off states, equipment operating capacities, and/or any other information that can be used by BMS controller 266 to monitor or control a variable state or condition within building zone; [0136], The items of equipment can operate in accordance with various qualitative and quantitative parameters, variables, setpoints, and/or thresholds or other criteria), and c) received user building information associated with the queried asset ([0250], the building data platform 300 may receive a reply from the user to the first response. The reply may add more details or further refine the initial query).
Ramanasankaran does not clearly disclose providing a list or result, in an analogous art of classifying data using AI technologies, Itani discloses: one or more classification standards for the queried asset (Itani: [0094], Global Industry Classification Standard (GICS®) industry category);
Ramanasankaran and Itani are analogous arts because they are in the same field of endeavor, classifying data using AI technologies. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Ramanasankaran using the teachings of Itani to include Global Industry Classification Standard. It would provide Ramanasankaran’s system with enhanced capabilities of providing specific classification data to user based on specific domain to better meet user’s interests.
As for claim 2, Ramanasankaran-Itani discloses: wherein the one or more processors are further caused to format the generated computer query as an artificial intelligence (AI) prompt that is electronically submitted to a ML algorithm, whereby the ML algorithm, responsive to receiving the generated AI prompt, provides the results response indicating one or more classification standards for the queried asset (Itani: [0094], the dialog 400 prompts the user to give a name 410 to a custom universe of, e.g., stocks having chosen aspects 420 such as a specified country, a market capitalization range, a liquidity minimum, or a Global Industry Classification Standard (GICS®) industry category).
As for claim 3, Ramanasankaran-Itani discloses: wherein the ML algorithm consists of large language model (LLM) (Ramanasankaran: [0004], using a generative large language model (LLM)).
As for claim 4, Ramanasankaran-Itani discloses: wherein each asset is one of a point or equipment (Ramanasankaran: [0152], The data can be control data of a control point or set point of a piece of building equipment such as a value of a fan speed, a value of a temperature setpoint, a value of a water setpoint, etc.).
As for claim 5, Ramanasankaran-Itani discloses: generate a user interface comprising indications of plurality of assets in the AIC computer database;
cause a user device to display the user interface; receive, via the user device, designation of the one or more assets to be queried for classification from the plurality of AIC assets; receive, via the user device, the user information describing certain building information associated with the queried asset; and
display, on the user interface via the user device, the results response indicating the one or more classification standards for the queried asset (Ramanasankaran: Fig. 21 and [0284]-[0285], user may input query in the user interface and the user interface displays the response to query).
As for claim 6, Ramanasankaran-Itani discloses: wherein providing a listing of candidate ontology classes includes: A) retrieve, from a database of BrickSchema assets (Ramanasankaran: [0120], the graph 529 uses the BRICK schema), a plurality of potential candidate ontology classes for the queried asset, responsive to the received classification query for the queried asset; B) retrieve, from the BrickSchema database, one or more attributes associated with the retrieved potential classifications for the queried asset;
C) concatenate into a first textual string the retrieved plurality of potential candidate ontology classes with the retrieved one or more attributes associated with potential classifications for the queried asset; D) retrieve comparison attribute variables from the AIC computer database associated with the queried asset; E) concatenate into a second textual string the retrieved attribute variables associated with the queried asset; F) determine semantic similarities between the first and second textual strings; and G) determine the listing of candidate ontology classes responsive to the determined semantic similarities between the first and second textual strings (Ramanasankaran: Abstract, generate 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; [0055], Systems and methods in accordance with the present disclosure can leverage the efficiency of language models (e.g., GPT-based models or other pre-trained LLMs) in extracting semantic information (e.g., semantic information identifying faults, causes of faults, and other accurate expert knowledge regarding equipment servicing) from the unstructured data in order to use both the unstructured data and the data relating to equipment operation to generate more accurate outputs regarding equipment servicing. As such, by implementing language models using various operations and processes described herein, building management and equipment servicing systems can take advantage of the causal/semantic associations between the unstructured data and the data relating to equipment operation, and the language models can allow these systems to more efficiently extract these relationships in order to more accurately predict targeted, useful information for servicing applications at inference-time/runtime.
As for claim 7, Ramanasankaran-Itani discloses: wherein the one or more processors are further caused to compute numerical embeddings for each of the first and second concatenated textual strings such that the numerical embeddings for each of the first and second concatenated textual strings are compared with one another to determine the similarities between the first and second textual strings (Ramanasankaran: [0141], The GPT model can process the modified input sequence to determine a next token in the sequence (e.g., to append to the end of the sequence), such as by determining probability scores indicating the likelihood of one or more candidate tokens being the next token, and selecting the next token according to the probability scores (e.g., selecting the candidate token having the highest probability scores as the next token); [0275], the orchestrator 1640 may identify that a given vector embedding 1635 correlates to information included in Q1 based on a correlation score between a given summary of a text chunk 1625 and Q1 (e.g., the given summary has the highest correlation score, the given summary has a correlation score that exceeds a threshold, etc.)).
As for claim 8, Ramanasankaran-Itani discloses: wherein the one or more processors utilize a large language model (LLM) to compute the numerical embeddings for each of the first and second concatenated textual strings (Ramanasankaran: [0275], the orchestrator 1640 may identify that a given vector embedding 1635 correlates to information included in Q1 based on a correlation score between a given summary of a text chunk 1625 and Q1 (e.g., the given summary has the highest correlation score, the given summary has a correlation score that exceeds a threshold, etc.)).
As for claim 10, Ramanasankaran-Itani discloses: wherein the retrieved one or more attributes associated with the retrieved potential classes for the queried asset, include at least one of a classification name, classification definition and equivalent class name for each of the retrieved potential classes (Ramanasankaran: [0233], The tag for the new relationship and/or new entity can include a label, characteristic, parameter, attribute, or category describing the new relationship and/or new entity).
As for claim 11, Ramanasankaran-Itani discloses: wherein the retrieved comparison attribute variables from the AIC computer database associated with the queried asset, include at least an object name, object path in the AIC database and an object description associated with the queried asset (Ramanasankaran: [0112], The context can be information that provides a contextual description of the data, e.g., what device the event is associated with, what control point should be updated based on the event, etc; [0167], The virtual assistant application can receive information regarding multiple buildings, a building, a portion of a building, or a piece of equipment for a building, such as sensor data, text descriptions, or camera images, and process the received information using the second model 616 to generate corresponding responses; [0233], The tag for the new relationship and/or new entity can include a label, characteristic, parameter, attribute, or category describing the new relationship and/or new entity).
As for claim 12, Ramanasankaran-Itani discloses: wherein the one or more processors are further configured to filter out or fix hallucinated classes from the results response using a similarity algorithm (Ramanasankaran: [0142], The denoising neural network can be configured by applying noise to one or more training data elements (e.g., images, video frames) to generate noised data, providing the noised data as input to a candidate denoising neural network, causing the candidate denoising neural network to modify the noised data according to a denoising schedule, evaluating a convergence condition based on comparing the modified noised data with the training data instances, and modifying the candidate denoising neural network according to the convergence condition (e.g., modifying weights and/or biases of one or more layers of the neural network); please note the denoising neural network may filter out noised data).
As for claim 13, Ramanasankaran-Itani discloses: wherein the certain classification attribute variables associated with the queried asset captured from the AIC computer database include at least an object name, object type identified, object path in the AIC database and an object description associated with the queried asset (Ramanasankaran: [0112], The context can be information that provides a contextual description of the data, e.g., what device the event is associated with, what control point should be updated based on the event, etc; [0167], The virtual assistant application can receive information regarding multiple buildings, a building, a portion of a building, or a piece of equipment for a building, such as sensor data, text descriptions, or camera images, and process the received information using the second model 616 to generate corresponding responses).
As for claim 14, Ramanasankaran-Itani discloses: wherein the queried asset is a point in the AIC database wherein the certain classification attribute variables associated with the queried point further includes a: unit name, unit description, and unit category associated with the queried asset (Ramanasankaran: [0112], The context can be information that provides a contextual description of the data, e.g., what device the event is associated with, what control point should be updated based on the event, etc; [0167], The virtual assistant application can receive information regarding multiple buildings, a building, a portion of a building, or a piece of equipment for a building, such as sensor data, text descriptions, or camera images, and process the received information using the second model 616 to generate corresponding responses).
As for claim 15, Ramanasankaran-Itani discloses: wherein the queried asset is an equipment defined by a plurality of certain points in the AIC database, wherein the certain classification attribute variables associated with the queried equipment further include information from children computer directory paths associated with each of the certain points refining a definition of the queried equipment (Ramanasankaran: Fig. 4 shows parent nodes and children nodes represents equipments from different compter directory paths; [0233], The tag for the new relationship and/or new entity can include a label, characteristic, parameter, attribute, or category describing the new relationship and/or new entity; please note the category describing may include sub-category).
As for claim 16, Ramanasankaran-Itani discloses: wherein the results generated from the ML algorithm further include building location information associated with the queried equipment (Ramanasankaran: [0020], locations within the building, events associated with the building, or assets of the building).
As for claim 17, Ramanasankaran-Itani discloses: wherein the received user information describing certain building information associated with the queried asset include a listing of possible locations and/or information about possible acronyms associated with the queried asset (Ramanasankaran: [0020], locations within the building, events associated with the building, or assets of the building).
As for claim 18, Ramanasankaran-Itani discloses: wherein the AIC is one of either a building management system (BMS) (Ramanasankaran: Abstract, A building management system (BMS)) or a supervisory control and data acquisition (SCADA) system.
As for claim 19, Ramanasankaran-Itani discloses: wherein the candidate ontology classes are defined by one or more semantic data models (Ramanasankaran: [0141], The GPT model can receive an input sequence, and can parse the input sequence to determine a sequence of tokens (e.g., words or other semantic units of the input sequence, such as by using Byte Pair Encoding tokenization)).
As for claim 20, Ramanasankaran-Itani discloses: wherein the one or more semantic data models are selected from one of Brick Schema and Haystack (Ramanasankaran: [0158], The data sources can include spatial context data in a variety of formats, e.g., BIM, IFC, BACnet, Haystack, LonMark, Modbus, etc.
As for claim 22, Ramanasankaran-Itani discloses: wherein the generated query further includes examples of queries associated with their expected answer (Ramanasankaran: [0168], The virtual assistant application 620 can use requests for information such as for unstructured text by which the user describes characteristics of the item of equipment or portion of the building relating to the issue; answers expected to correspond to different scenarios indicative of the issue; and/or image and/or video input (e.g., images of problems, equipment, spaces, etc. that can provide more context around the issue and/or configurations). For example, responsive to receiving a response via the virtual assistant application 620 indicating that the problem is with temperature in the space, the system 600 can request, via the virtual assistant application 620, information regarding HVAC-R equipment associated with the space, such as pictures of the space, an air handling unit, a chiller, or various combinations thereof).
As per claim 22, it recites features that are substantially same as those features claimed by claim 1, thus the rationales for rejecting claim 1 are incorporated herein.
4. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Ramanasankaran and Itani as applied on claim 1, and further in view of Santle Camilus Kulandai Samy et al (US Publication 20240086249 hereinafter Kulandai).
As for claim 9, Ramanasankaran-Itani does not clearly disclose using a cosine similarity algorithm, Kulandai discloses: wherein the one or more processors utilize a cosine similarity algorithm to compare the numerical embeddings of the first and second concatenated textual strings with one another (Kulandai: [0078], The embedding manager 145 can compare, with a comparison technique, the embedding vector for each tag against the embedding vectors of the classes of the graph schema to generate a similarity or similarity level. The comparison technique can be or include a cosine, a Euclidean distance, a dot product. The comparison technique can generate a value that is based, at least in part, on an angle formed between the two vectors).
Ramanasankaran and Itani and Kulandai are analogous arts because they are in the same field of endeavor, classifying data using AI technologies. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Ramanasankaran using the teachings of Kulandai to include using cosine similarity algorithm. It would provide Ramanasankaran’s system with enhanced capabilities of generating data tag more efficiently and accurately.
Examiner’s Note
Examiner has cited particular columns/paragraph and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. This will assist in expediting compact prosecution. MPEP 714.02 recites: “Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” Amendments not pointing to specific support in the disclosure may be deemed as not complying with provisions of 37 C.F.R. 1.131(b), (c), (d), and (h) and therefore held not fully responsive. Generic statements such as “Applicants believe no new matter has been introduced” may be deemed insufficient.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Applicants are required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Swanson (US Publication 20240394281) ARTIFICIAL INTELLIGENCE GENERATED CORRELATORS FOR BUILDING DIGITAL TWIN
Neilson (US Publication 20250306546) SYSTEMS AND METHODS FOR OPTIMIZATION OF A BUILDING MANAGEMENT SYSTEM
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hua Lu whose telephone number is 571-270-1410 and fax number is 571-270-2410. The examiner can normally be reached on Mon-Fri 9:00 am to 6:00 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman can be reached on 571-272-3644. The fax phone number for the organization where this application or proceeding is assigned is 703-273-8300.
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/Hua Lu/
Primary Examiner, Art Unit 2118
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