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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: Reference number “224” in Figure. 2, reference number “3704” in Figure 37, and reference number “3110” in Figure 31. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
In paragraph 214, delete multiple instances of “pre-processor 400”, and insert --pre-processor 404-- as shown in Figure 4.
Appropriate correction is required.
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 directed to an abstract idea without significantly more.
Per step 1 of the Subject Matter Eligibility Test (See MPEP 2106), claim 1 is directed to a method, which is a process and falls within a statutory category (See MPEP 2106.03).
Per step 2A, prong 1, claim 1 recites creating, from subject matter expert knowledge, a broad set of analysis components that describe expected behaviors of the building equipment; combining multiple analysis components that satisfy a similarity criterion from the broad set of analysis components to form a concise set of analysis components; prompting the generative artificial intelligence model to generate a response based on the concise set of analysis components. These claim limitations require making observations, opinions and judgments that can be performed in the human mind and fall into the mental processes grouping (See MPEP 2106.04(a)(2)).
The additional element in claim 1 is performing an automated action for servicing the building equipment based on the response of the generative artificial intelligence model.
Per step 2A, prong 2, The abstract idea is not integrated into a practical application because the limitation for performing an automated action is recited at a high level of generality and is not more than merely generally linking the abstract idea to a field of use (See MPEP 2106.05(h)). Further, the automated action could simply be outputting instructions to a user or person performing maintenance, which would be insignificant extra solution activity (See MPEP 2106.05(g)).
Per step 2B, claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons. Further outputting the result of an abstract idea in various manners has been recognized by the courts as well-understood, routine and conventional (See MPEP 2106.05(d), subsection II).
Claims 2-7 depend from claim 1 and recite further details of the abstract idea. Claims 2-7 do not recite any additional elements. Since there are no recited additional elements, claims 2-7 are not integrated into a practical application and does not amount to significantly more than the abstract idea.
Per step 1 of the Subject Matter Eligibility Test (See MPEP 2106), claim 8 is directed to method, which is a process and falls within a statutory category (See MPEP 2106.03).
Per step 2A, prong 1, claim 8 recites receiving an indication of a potential anomaly related to the building equipment; determining, based on a functional knowledge graph of the building equipment, other equipment related to the potential anomaly; and generating a first prompt for the generative artificial intelligence model, the first prompt dependent on data related to the other equipment. These claim limitations require making observations, opinions and judgments that can be performed in the human mind and fall into the mental processes grouping (See MPEP 2106.04(a)(2)).
The additional element in claim 8 is performing an automated action for servicing the building equipment based on a response of the generative artificial intelligence model to the first prompt.
Per step 2A, prong 2, The abstract idea is not integrated into a practical application because the limitation for performing an automated action is recited at a high level of generality and is not more than merely generally linking the abstract idea to a field of use (See MPEP 2106.05(h)). Further, the automated action could simply be outputting instructions to a user or person performing maintenance, which would be insignificant extra solution activity (See MPEP 2106.05(g)).
Per step 2B, claim 8 does not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons. Further outputting the result of an abstract idea in various manners has been recognized by the courts as well-understood, routine and conventional (See MPEP 2106.05(d), subsection II).
Claims 9-14 depend from claim 8 and recite further details of the abstract idea. Claims 9-14 do not recite any additional elements. Since there are no recited additional elements, claims 9-14 are not integrated into a practical application and does not amount to significantly more than the abstract idea
Per step 1 of the Subject Matter Eligibility Test (See MPEP 2106), claim 15 is directed to system, which is a product and falls within a statutory category (See MPEP 2106.03).
Per step 2A, prong 1, claim 15 recites creating, from subject matter expert knowledge, a broad set of analysis components that describe expected behaviors of the building equipment; combining multiple analysis components that satisfy a similarity criterion from the broad set of analysis components to form a concise set of analysis components; receiving an indication of a potential anomaly related to the building equipment; determining, based on a functional knowledge graph of the building equipment, other equipment related to the potential anomaly; and prompting the generative artificial intelligence model to generate a response based on data related to the other equipment and the concise set of analysis components. These claim limitations require making observations, opinions and judgments that can be performed in the human mind and fall into the mental processes grouping (See MPEP 2106.04(a)(2)).
The additional elements in claim 15 are one or more memory devices having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations and performing an automated action for servicing the building equipment based on the response of the generative artificial intelligence model.
Per step 2A, prong 2, The abstract idea is not integrated into a practical application. The recitation of a memory and a processor amount to instructions to implement the abstract idea on a generic computer (See MPEP 2106.05(f)). The limitation for performing an automated action is recited at a high level of generality and is not more than merely generally linking the abstract idea to a field of use (See MPEP 2106.05(h)). Further, the automated action could simply be outputting instructions to a user or person performing maintenance, which would be insignificant extra solution activity (See MPEP 2106.05(g)).
Per step 2B, claim 15 does not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons. Further outputting the result of an abstract idea in various manners has been recognized by the courts as well-understood, routine and conventional (See MPEP 2106.05(d), subsection II).
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 8, 10, 11 and 14 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US Patent Application Publication 2023/0259821 to Travalini et al. (Travalini).
Claim 8
With regard to receiving an indication of a potential anomaly related to the building equipment; Travalini teaches receiving a description of a problem from an user (par. 43).
With regard to determining, based on a functional knowledge graph of the building equipment, other equipment related to the potential anomaly; Travalini teaches that the chatbot may infer the equipment where the problem exists (par. 43).
With regard to generating a first prompt for the generative artificial intelligence model, the first prompt dependent on data related to the other equipment; Travalini teaches that the chatbot may recognize the problem and use the determination to gather more information and make a diagnosis (pars. 43-45).
With regard to performing an automated action for servicing the building equipment based on a response of the generative artificial intelligence model to the first prompt; Travalini teaches that the chatbot may indicate a solution to the user or to a technician (pars. 45, 46).
Claim 10
Travalini teaches that determining, based on the functional knowledge graph of the building equipment, the other equipment related to the potential anomaly comprises using a relational schema of a building management system (par. 43).
Claim 11
Travalini teaches that receiving the data related to the other equipment comprises receiving available measurements available for the other equipment (par. 66).
Claim 14
Travalini teaches generating a second prompt dependent on the response to the first prompt and the functional knowledge graph of the building equipment (par. 43).
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-4, 15, 16 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Travalini in view of US Patent Application Publication 2023/0061096 to Mojtahedzadeh et al. (Mojtahedzadeh).
Claim 1
With regard to form a concise set of analysis components; Travalini teaches determining whether there is a threshold level of information including specifying components or inferring and item (par. 43).
With regard to prompting the generative artificial intelligence model to generate a response based on the concise set of analysis components; Travalini teaches that the chatbot may recognize the problem and use the determination to gather more information and make a diagnosis (pars. 43-45).
With regard to performing an automated action for servicing the building equipment based on the response of the generative artificial intelligence model; Travalini teaches that the chatbot may indicate a solution to the user or to a technician (pars. 45, 46).
Travalini does not teach creating, from subject matter expert knowledge, a broad set of analysis components that describe expected behaviors of the building equipment; and combining multiple analysis components that satisfy a similarity criterion from the broad set of analysis components. Mojtahedzadeh teaches loading a plurality of records from one or more different components and analyzing records for a selected type of component (Fig. 2, step 202; Fig. 3, aircraft component records 302A-302C; pars. 23, 49, 50). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the diagnosing and troubleshooting, as taught by Travalini, to include selecting specific records from a large number of records, because then specific components in a system including a large number of components would have been more quickly analyzed and diagnosed (Mojtahedzadeh, par. 3).
Claim 2
Travalini teaches generating a prompt for the generative artificial intelligence model, the prompt dependent on the concise set of analysis components, wherein prompting the generative artificial intelligence model comprises providing the prompt as an input to the generative artificial intelligence model (pars. 43-45).
Claim 3
Travalini teaches that generating the prompt for the generative artificial intelligence model comprises: receiving data related to the building equipment; determining an example analysis component from the broad set of analysis components or the concise set of analysis components that has a predetermined format; and combining the data related to the building equipment, the example analysis component, and the concise set of analysis components (pars. 43-45).
Claim 4
Travalini does not teach that creating the broad set of analysis components comprises using the generative artificial intelligence model to determine the expected behaviors of the building equipment. Mojtahedzadeh teaches loading a plurality of records from one or more different components and analyzing records for a selected type of component (Fig. 2, step 202; Fig. 3, aircraft component records 302A-302C; pars. 23, 49, 50). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the diagnosing and troubleshooting, as taught by Travalini, to include selecting specific records from a large number of records, because then specific components in a system including a large number of components would have been more quickly analyzed and diagnosed (Mojtahedzadeh, par. 3).
Claim 15
Travalini teaches one or more memory devices having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations (Fig. 1, memory 115, processor 103).
With regard to form a concise set of analysis components; Travalini teaches determining whether there is a threshold level of information including specifying components or inferring and item (par. 43).
With regard to receiving an indication of a potential anomaly related to the building equipment; Travalini teaches receiving a description of a problem from an user (par. 43).
With regard to determining, based on a functional knowledge graph of the building equipment, other equipment related to the potential anomaly; Travalini teaches that the chatbot may infer the equipment where the problem exists (par. 43).
With regard to prompting the generative artificial intelligence model to generate a response based on data related to the other equipment and the concise set of analysis components; Travalini teaches that the chatbot may recognize the problem and use the determination to gather more information and make a diagnosis (pars. 43-45).
With regard to performing an automated action for servicing the building equipment based on the response of the generative artificial intelligence model; Travalini teaches that the chatbot may indicate a solution to the user or to a technician (pars. 45, 46).
Travalini does not teach creating, from subject matter expert knowledge, a broad set of analysis components that describe expected behaviors of the building equipment; and combining multiple analysis components that satisfy a similarity criterion from the broad set of analysis components. Mojtahedzadeh teaches loading a plurality of records from one or more different components and analyzing records for a selected type of component (Fig. 2, step 202; Fig. 3, aircraft component records 302A-302C; pars. 23, 49, 50). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the diagnosing and troubleshooting, as taught by Travalini, to include selecting specific records from a large number of records, because then specific components in a system including a large number of components would have been more quickly analyzed and diagnosed (Mojtahedzadeh, par. 3).
Claim 16
Travalini does not teach that creating the broad set of analysis components comprises using the generative artificial intelligence model to determine the expected behaviors of the building equipment. Mojtahedzadeh teaches loading a plurality of records from one or more different components and analyzing records for a selected type of component (Fig. 2, step 202; Fig. 3, aircraft component records 302A-302C; pars. 23, 49, 50). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the diagnosing and troubleshooting, as taught by Travalini, to include selecting specific records from a large number of records, because then specific components in a system including a large number of components would have been more quickly analyzed and diagnosed (Mojtahedzadeh, par. 3).
Claim 18
Travalini teaches that determining, based on the functional knowledge graph of the building equipment, the other equipment related to the potential anomaly comprises using a relational schema of a building management system (par. 43).
Claim 19
Travalini teaches that prompting the generative artificial intelligence model comprises generating a tree of thoughts prompt based on the functional knowledge graph of the building equipment (par. 66).
Claim 20
Travalini does not teach that performing the automated action for servicing the building equipment based on the response of the generative artificial intelligence model to the prompt comprises adjusting a parameter of a building management system to cause the building equipment to exhibit the expected behaviors. Mojtahedzadeh teaches design changes (pars. 62, 68, 69). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the diagnosing and troubleshooting, as taught by Travalini, to include design changes because then future performance would have been improved.
Claim(s) 5 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Travalini in view of Mojtahedzadeh as applied to claims 1 and 15 above, and further in view of US Patent Application Publication 2023/0127651 to Cella et al. (Cella).
Claim 5
Travalini and Mojtahedzadeh teach all the limitations of claim 1 upon which claim 5 depends. Travalini and Mojtahedzadeh do not teach determining whether the multiple analysis components from the broad set of analysis components satisfy the similarity criterion by at least one of: performing k-means clustering on the broad set of analysis components; or clustering, using a directed acyclic graph, the broad set of analysis components. Cella teaches determining a subset of solutions using k-means clustering. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the diagnosing and troubleshooting combination, as taught by Travalini and Mojtahedzadeh, to include k-means clustering, as taught by Cella, because then a well known method of searching for common elements in a large set would have been available.
Claim 17
Travalini and Mojtahedzadeh teach all the limitations of claim 15 upon which claim 17 depends. Travalini and Mojtahedzadeh do not teach determining whether the multiple analysis components from the broad set of analysis components satisfy the similarity criterion by at least one of: performing k-means clustering on the broad set of analysis components; or clustering, using a directed acyclic graph, the broad set of analysis components. Cella teaches determining a subset of solutions using k-means clustering. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the diagnosing and troubleshooting combination, as taught by Travalini and Mojtahedzadeh, to include k-means clustering, as taught by Cella, because then a well known method of searching for common elements in a large set would have been available.
Claim(s) 6 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Travalini in view of Mojtahedzadeh as applied to claim 1 above, and further in view of US Patent Application Publication 2026/0030413 to Simmons et al. (Simmons).
Claim 6
Travalini and Mojtahedzadeh teach all the limitations of claim 1 upon which claim 6 depends. Travalini and Mojtahedzadeh do not teach that combining the multiple analysis components from the broad set of analysis components to form the concise set of analysis components comprises embedding one or more analysis components into a numerical vector representation. Simmons teaches using a convolutional neural network to map patterns to equipment types (par. 124). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the diagnosing and troubleshooting combination, as taught by Travalini and Mojtahedzadeh, to include a convolutional neural network as taught by Simmons, because then relevant equipment types would have been identified.
Claim 7
Travalini and Mojtahedzadeh teach all the limitations of claim 1 upon which claims 6 and 7 depend. Travalini and Mojtahedzadeh do not teach that embedding the one or more analysis components into the numerical vector representation comprises using a convolutional neural network. Simmons teaches using a convolutional neural network to map patterns to equipment types (par. 124). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the diagnosing and troubleshooting combination, as taught by Travalini and Mojtahedzadeh, to include a convolutional neural network as taught by Simmons, because then relevant equipment types would have been identified.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Travalini in view of US Patent Application Publication 2020/0351283 to Salunke et al. (Salunke).
Claim 9
Travalini teaches all the limitations of claim 8 upon which claim 9 depends. Travalini does not teach that receiving the indication of the potential anomaly comprises generating the indication of the potential anomaly in response to a plot shown on a user interface. Salunke teaches using plots for anomaly detection (par. 159). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the diagnosing and troubleshooting, as taught by Travalini, to include plots as taught by Salunke, because then anomalies would have easier to identify in a visual display.
Claim(s) 12 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Travalini in view of US Patent Application Publication 2025/0259144 to Crabtree et al. (Crabtree).
Claim 12
Travalini teaches all the limitations of claim 8 upon which claim 12 depends. Travalini does not teach that generating the first prompt for the generative artificial intelligence model comprises generating a tree-of-thoughts prompt based on the functional knowledge graph of the building equipment. Crabtree teaches generating a tree of thought prompt (par. 185). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the diagnosing and troubleshooting, as taught by Travalini, to include tree of thought prompts, as taught by Crabtree, because then an additional tool for creating prompts would have been available (Crabtree, par. 185).
Claim 13
Travalini teaches all the limitations of claim 8 upon which claim 12 depends. Travalini does not teach that generating the first prompt for the generative artificial intelligence model comprises generating a chain-of-thoughts prompt based on the functional knowledge graph of the building equipment. Crabtree teaches generating a chain of thought prompt (par. 185). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the diagnosing and troubleshooting, as taught by Travalini, to include chain of thought prompts, as taught by Crabtree, because then an additional tool for creating prompts would have been available (Crabtree, par. 185).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANUEL L BARBEE whose telephone number is (571)272-2212. The examiner can normally be reached M-F: 9-5:30..
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/MANUEL L BARBEE/Primary Examiner, Art Unit 2857
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