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 statements (IDSs) submitted on July 3, 2025, June, 3, 2025, March 31, 2025, December 4, 2024, October 15, 2024, September 13, 2024, July 24, 2024, June 7, 2024 and April 23, 2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDSs have been considered by the examiner.
Claim Status
In the April 11, 2024 submission, claims 1-20 were presented for consideration and are pending.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. The following title is suggested:
Building Management System using Generative AI-Based Coupling of System and Resource Data.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is not supported by either a specific and substantial asserted utility or a well-established utility.
Claims 1, 11 and 20 focuses on a method for receiving unstructured data, detecting identifiers based on the unstructured data, retrieving additional data associated with an environment separate from the unstructured data and training a generative AI model based on the unstructured and additional data retrieved. While the claims list a series of active steps used to capture, train an AI model and generate structured output data, the claim fails to present specific or substantial active utility in applying the processed data.
Several embodiments of the intended invention are noted in the specification. In at least one of the embodiments presented, paragraph 358 of the specification recites the following:
[0358] In some embodiments, step 1802 includes receiving outcome data indicating outcomes of the first service requests. Step 1802 may include training the AI model to identify one or more patterns or trends between the problems corresponding to the first service requests and the outcome data indicating the outcomes of the first service requests. The outcome data may indicate the root causes of the problems associated with the first service requests, the actions performed to address the root causes or respond to the service requests, or any other type of outcome data described throughout the present disclosure. The root causes can be identified based on service reports, information provided by a user or technician, or can be inferred based on whether a given action or outcome was successful in resolving the problem. For example, if a service action involved replacing a compressor in a chiller and the problem associated with the chiller was resolved, the system 100 or 200 can infer that the compressor was the cause of the problem. In some embodiments, step 1802 includes receiving outcome data indicating whether predicted root causes of the problems were determined to be actual root causes of the problems after performing service on the building equipment in response to the first service requests, and retraining the AI model using the outcome data.
The applicant notes that the AI model identifies one or more patterns or trends between the data corresponding to the service requests and outcome data, and indicates the outcomes of the first service requests, wherein the system provides outcome data via a report or system initiated corrective action used to further facilitate active steps used to resolve potential issues. In this instance and other embodiments noted in the specification, active steps are taken based on the processed data, to provide information and perform activity on the system based on processed outcomes. Independent claims 11 and 20 present similar attributes.
Several of the dependent claims recite language relative to the differing types of received data (e.g., engineering, operational, etc.), addressing steps taken by the system to compare processing parameters – “wherein training the generative AI model comprises training the generative AI model to identify correlations or patterns between the engineering data and the unstructured service data” – but fails to denote how the outcome of the correlations and determined patterns are employed to further support service activity and/or provide utility to the system. In an effort to cure the aforementioned 35 USC 101 issues, the specification points to several instances or embodiments wherein the processed output is used to provide a report, instructions for further service activity and/or system activity taken [based on output] to improve operations.
Claims 1-20 are also rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph. Specifically, because the claimed invention is not supported by either a specific and substantial asserted utility or a well-established utility for the reasons set forth above, one skilled in the art clearly would not know how to use the claimed invention.
Examiner notes that during the examination process, claim language is considered in broad terms, whereby careful consideration is taken to view claim language in light of the specification, and not read details of the specification into the submitted claim language (see MPEP 2111).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 11 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential steps and relationships, such omission amounting to a gap between the steps and relationships employed in training and generating structured data output based on unstructured and structured data using machine learning or generative AI models. See MPEP § 2172.01.
As noted above, the independent claims focus on receiving unstructured data, detecting identifiers based on the unstructured data, and retrieving additional data associated with an environment separate from the unstructured data. The captured data is subsequently used in training a generative AI model and generating structured data based on processing of the unstructured and additional data. Several of the embodiments presented in the specification, as well as noted limitations offered in the dependent claims, suggestion actionable steps taken by the method for processing and training a model based on the structured and unstructured data. Paragraph 354 of the specification cites the following:
[0354] The process 1700 is shown to include determining a response to the second service request based on characteristics of the second service request using the AI model (step 1708). In some embodiments the AI model is trained to identify patterns or trends between characteristics of the first service requests and outcome data indicating outcomes of the first service requests, and the response to the second service request is determined based on the patterns or trends identified using the AI model. Step 1708 may be the same as or similar to step 1608 of the process 1600 and may include determining any of the types of responses described with reference to step 1608.
In an effort to generate meaningful information respective of the unstructured and unstructured data, the method provides further actionable steps to determine correlations and/or recognized patterns in the data - “wherein training the generative AI model comprises training the generative AI model to identify correlations or patterns between the operational data and the unstructured service data”. Identifying patterns and/or trends in operational and/or system characteristics provides outcome data which can be used by the system or service personnel to perform corrective actions to resolve potential issues.
Examiner notes that during the examination process, claim language is considered in broad terms, whereby careful consideration is taken to view claim language in light of the specification, and not read details of the specification into the submitted claim language (see MPEP 2111).
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The combination of Huber et al. [US-2023/0145448] and Wenzel et al., [US-11,636,429] teach of using machine learning to analyze operational conditions of a building management system (BMS) and making equipment and operational predictions for service and energy procedures, while Ramanasankaran et al. [US-2025/0078648] focuses on using machine learning techniques to generate rule-based operations within a BMS.
Conclusion
The applicant is strongly encouraged to contact the examiner if further clarifications are needed with respect to interpretation of currently presented claims and/or cited prior art.
A reference to specific paragraphs, columns, pages, or figures in a cited prior art reference is not limited to preferred embodiments or any specific examples. It is well settled that a prior art reference, in its entirety, must be considered for all that it expressly teaches and fairly suggests to one having ordinary skill in the art. Stated differently, a prior art disclosure reading on a limitation of Applicant's claim cannot be ignored on the ground that other embodiments disclosed were instead cited. Therefore, the Examiner's citation to a specific portion of a single prior art reference is not intended to exclusively dictate, but rather, to demonstrate an exemplary disclosure commensurate with the specific limitations being addressed. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006,1009, 158 USPQ 275, 277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23 USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck& Co. v. BiocraftLabs., Inc., 874 F.2d 804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d 792,794 n.1,215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747, 750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163 USPQ 545, 549 (CCPA 1969).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KELVIN BOOKER whose telephone number is (571)272-7827. The examiner can normally be reached on M-F 9am-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached on (571) 272-4105. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/Kelvin Booker/
Examiner, Art Unit 2119
/MOHAMMAD ALI/Supervisory Patent Examiner, Art Unit 2119
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