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 .
This office action is a response to an application filed 04/10/2024, in which claims 1-20 are pending and ready for examination.
Information Disclosure Statement
The Examiner has considered the references listed on the Information Disclosure Statement submitted on 04/15/2025, 05/21/2024, 06/11/2024, 08/22/2024, 10/01/2024, 11/06/2024, 03/31/2025, 06/03/2025, 07/02/2025, 08/29/2025, 01/28/2025, 04/02/2026, 05/05/2026, 07/29/2026, and 08/13/2026.
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.
At step 1, claim 1 recites “A method for servicing building equipment using generative artificial intelligence models,” which is a process, and therefore a statutory category. Meanwhile, claim 9 also “A method for servicing building equipment using generative artificial intelligence models,” and thus is also a statutory category. Claim 15 recites “A system for servicing building equipment using generative artificial intelligence models,” which is a system and a statutory category.
At Step 2A, prong one, claims 1 and 15 recite a series of limitations that involve associating related data portions from data input of multiple modes of input data to form a set of original analysis packages, generating a set of multiple mode artificial analysis packages based on the original analysis packages, and adjusting a model using the artificial analysis packages and original packages. Claim 9 recites a series of limitations that generate an analysis package from received multi-modal data input, use an output model to generate service relevant data output using the analysis package. The limitations can be broadly interpreted to comprise mentally associating received data to form a set of original data termed an original analysis package, mentally generating artificial analysis data packages using the formed original analysis package data, such as by calculation, estimation, etc., and mentally adjusting a model based on the generated artificial data. Meanwhile, claim 9 can also be broadly interpreted as mental generation of data from received input data, and mentally using a model (that can include the use of an aid) to generate service relevant data. This judicial exception is not integrated into a practical application because they are directed to the abstract idea of mental limitations capable of being performed in the mind, and thus directed to the mental processes grouping of abstract ideas.
Specifically, the abstract idea include the limitations of:
“associating, …, related data portions from multiple modes of a multi-modal data input to form a set of original analysis packages, …; using, …, the at least one data generator to generate a set of artificial analysis packages based on the original analysis packages, the artificial analysis packages comprising multiple modes of artificial data; and adjusting, …, an output model using the set of artificial analysis packages and the set of original analysis packages…” in claim 1. Analogous claim limitations are found in independent claim 15, and are analyzed the same. The claim 9 limitation include: “… generating, …, an analysis package from the multi-modal data input; using, …, an output model to generate a service relevant multi-modal data output dependent on the analysis package;”
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
At step 2A, prong 2, claim 1 recites “one or more processors,” “…the multi-modal data input characterizing operation of the building equipment using multiple modes of original data” and “… the output model configured to generate a service relevant multi-modal data output for use in servicing the building equipment.” Claim 15 recites similar limitations as claim 1. Meanwhile, claim 9 recites “receiving, at one or more processors, a multi-modal data input characterizing operation of the building equipment using multiple modes of data” and “performing, by the one or more processors, an automated action for servicing the building equipment based on the service relevant multi-modal data output.”
At Step 2B, while the claims include additional elements as noted above in Step 2A prong 2, they are not sufficient to amount to significantly more than the judicial exception. In particular, the recitation of processors, amounts to no more than mere instructions to apply the exception using a generic computer components. These are recited at a high level of generality and recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f)). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP2106.05(h)). Furthermore, the limitations of receiving data, amount to necessary data gathering, which the courts have found to be insignificant extra-solution activity, see MPEP 2106.05(g)(3). The limitations of what various types of data characterize, and the limitation of what a model is configured to generate, amount to intended use/field of use limitations that do not integrate the judicial exception or provide significantly more because the intent of the limitations are merely to generically highlight the field of use (see MPEP2106.05(b)III., MPEP2106.5(c) 5., MPEP2106.05(h)). Finally, the limitation of performing an automated action for servicing building equipment based on the service relevant multi-modal data output, amounts to extra-solution activity and field-of-use to the extent that it does not impose meaningful limits on the claim because this aspect contributes only nominally or insignificantly to the execution of the claimed subject matter. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The dependent claims 2-8, 10-14, and 16-20, similarly recite an abstract idea of mental limitations capable of being performed in the mind, without significantly more. Claim 2 recites further intended use/field of use limitations of the types of intended data received, claims 3, 11, and 17 teach mental generation of data by a specified algorithm, claims 4 and 18 recite intended use of a portion of data to use in mental generation, claims 5 and 12 recite limitations of mental generation of data and mentally combining data, claims 6, 13, and 19 recite limitations of mentally adjusting a model using specified data, claims 7 and 14 recite limitations of types of intended use data that can be received, claim 8 recites limitations of data gathering, mental association, and mentally updating data, claims 10 and 20 recite limitations of a mental judgement of whether data is sufficient and mentally generating or calculating data to augment a data set, and claim 16 recites limitations of data gathering, mental generation of data, mental use of a model to generate further data, and extra-solution activity, akin to that analyzed with respect to claim 9.
The claims are not patent eligible.
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 1-8 and 11-20 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.
Regarding claims 1 and 15, the claims respectively recite the limitation “using, by the one or more processors, the at least one data generator to generate a set of artificial analysis packages based on the original analysis packages, the artificial analysis packages comprising multiple modes of artificial data;” (or a variation thereof). There has been no prior mention of “an at least one data generator,” and it is not clear what this refers to. For the purpose of examination, the limitation is being broadly interpreted to include any form or source that provides data. There is insufficient antecedent basis for this limitation in the claim.
Regarding claims 11 and 12, the claims respectively recite the limitation “The method of claim 9, wherein using the data generator to generate the artificial analysis packages comprises…” (or a variation thereof). There has been no prior mention of “a data generator” or “artificial analysis packages” in the respective claims or in the claim from which these claims depend, and it is not clear what this refers to. While, the independent claim does allude to generation of “an analysis package,” these is no mention to specific artificial analysis packages, nor mention of a specific “data generator.” For the purpose of examination, the limitation is being broadly interpreted to include any form or source that provides data. There is insufficient antecedent basis for this limitation in the claim.
Further regarding claim 11, the claim recites the limitation “…to generate the artificial analysis packages comprises using a generative adversarial network framework to generate the representative data.” There has been no prior mention of “representative data,” it is not clear what this refers to. For the purpose of examination, the limitation is being broadly interpreted to include any data. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 13, the claim recites the limitation “…using a first model adjusted to generate service relevant text data dependent on the novel analysis package to generate service relevant text data; and using a second model adjusted to combine the service relevant multi-modal input data and the service relevant text data to generate a service plan of action dependent on the novel analysis package.” There has been no prior mention of “a novel analysis package” or “a service relevant multi-modal input data,” it is not clear what these refers to. For the purpose of examination, the limitations are being broadly interpreted to include any data as a package or service relevant multi-modal input data. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 14, the claim recites the limitation “…wherein the novel multi-modal data input comprises at least one of: an image of the building equipment; a video of the building equipment; an audio clip associated with the building equipment; or a time series from a building automation system associated with the building equipment.” There has been no prior mention of “a novel multi-modal data input,” it is not clear what this refers to. For the purpose of examination, the limitation is being broadly interpreted to include any data input data. There is insufficient antecedent basis for this limitation in the claim.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant 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.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication No. 2023/0259821 to Travalini et al., (hereinafter Travalini), in view of US Patent Publication No. 2020/0202221 to Wang et al., (hereinafter Wang).
Regarding claim 1, Travalini teaches a method for servicing building equipment using generative artificial intelligence models (Diagnosis and maintenance servicing used for components/equipment in a building, such as a dwelling, where generation of data, training, models, services, etc., are employed, see Abs, p21, p2, p43, p3, p58, 99, 94, 130-132, 135, 138, 92, 142, Travalini), the method comprising:
associating, by one or more processors (processor, server, computing devices, and various algorithms/models that process data, p32-p34, 35-38, 53-57, Travalini), related data portions from multiple modes of a multi-modal data input to form a set of original analysis packages, the multi-modal data input characterizing operation of the building equipment using multiple modes of original data (Data from multiple sources and formats (i.e. multiple modes of multi-modal input), such as user text input data, user voice input data, component video/images input, context data, sensor data, etc., are associated with an incident request for a component and form a set of data related for analysis of the incident/component, and characterize operation of the building component as in possible problem, etc., see p51-52, p54, 56-57, p65-66, 68, p75, p114, 106, 137, p24, 77, 47, Travalini);
using, by the one or more processors, the at least one data generator to generate a set of artificial analysis packages based on the original analysis packages, the artificial analysis packages comprising multiple modes of artificial data (Data can be generated from multiple obtained data forms that are related to a component, such as pre-processing data, analysis data, interpretation data, prediction data, etc., and generated via artificial intelligence, see p138, abs, 136, 133, 99, 103, 83, 61 p150, p51-52, p61, p65, p79, p42, p34, 26-27, p66, p138, abs, 136, 133, 99, 103, 83, 61, p21, p2, p43, p3, p58, 99, 94, 130-132, 135, 92, 142, Travalini);
and adjusting, by the one or more processors, an output model using the set of artificial analysis packages and the set of original analysis packages, the output model configured to generate a service relevant multi-modal data output for use in servicing the building equipment (Models for problem diagnosis, analysis and maintenance services can be modified and updated based on generated and obtained data, see p83, p114, 106-107, 102-103, 100, p32-38, 25, Travalini).
While Travakini teaches a form of generating analysis data,
Wang from the same or similar field of artificial intelligence machine learning fault and diagnosis detection, teaches a complimentary form of: at least one data generator to generate a set of artificial analysis packages based on original analysis packages (Virtual data is generated (i.e. a set of artificial analysis packages) based on original fault samples, see p7, p9-16, p2, p21, Wang).
It would have been obvious to a person of ordinary skill in the art before the filing date of the claimed invention to modify the building and artificial intelligence based services analysis as described by Travalini and incorporating generated artificial analysis packages, as taught by Wang.
One of ordinary skill in the art would have been motivated to do this modification in order to better obtain a more balanced collection of data where at least one form/type of data may be less than needed, and thereby use reasonably determined artificial/virtual samples that can better be used for intended service such as diagnosis analysis, and to form balanced classification data for training machine learning models for tuning/updating said models (see p2-4, p25. p7, p9-16, p2, p21, Wang; p128, Travalini).
Regarding claim 2, the combination of Travalini and Wang teaches all the limitations of the base claim as outlined above, and are analyzed as previously discussed with regard to that claim.
Travalini further teaches wherein associating related data portions from each mode of a multi-modal data input to form a set of original analysis packages comprises selecting the related data portions from each mode of a multi-modal input for an original analysis package dependent on at least one of: a similar time from which the related portions were collected; a similar equipment from which the related portions were collected; or a similar location from which the related portions were collected (Obtained data that is initially used for training an artificial intelligence generation model that produces output, can be composed of data that includes data from jobs of the same nature (i.e. similar equipment and time), see p102, p138, p150, p42, p34, 26-27, p66, p138, abs, 136, 133, 99, 103, 83, 61, p21, p2, p43, p3, p58, 99, 94, 130-132, 135, 92, 142, Travalini).
Regarding claim 3, the combination of Travalini and Wang teaches all the limitations of the base claim as outlined above, and are analyzed as previously discussed with regard to that claim.
Wang further teaches wherein training an at least one data generator to generate artificial analysis packages comprises using a generative adversarial network framework to generate the at least one data generator (Virtual data is generated via adversarial network and used in training the generator and services generator, see p7-15, p9-16, p2, p21, Wang).
It would have been obvious to a person of ordinary skill in the art before the filing date of the claimed invention to modify the building and artificial intelligence based services analysis as described by the combination that includes Travalini and incorporating an adversarial network, as taught by Wang.
One of ordinary skill in the art would have been motivated to do this modification in order to better use a learning method that can autonomously learn a lar of samples to generate virtual/artificial samples so as to better obtain a more balanced collection of data where at least one form/type of data may be less than needed, and thereby use reasonably determined artificial/virtual samples that can better be used for intended service such as diagnosis analysis, and to form balanced classification data for training machine learning models for tuning/updating said models (see p2-4, p5, p25. p7, p9-16, p2, p21, Wang).
Regarding claim 4, the combination of Travalini and Wang teaches all the limitations of the base claim as outlined above, and are analy zed as previously discussed with regard to that claim.
Travalini further teaches wherein training an at least one data generator to generate artificial analysis packages comprises generating the at least one data generator for a single mode of multi-modal data input (Data for training an artificial intelligence generation model that produces output, can be one user input using a mode, such as text, of a plurality of potential inputs, see p151, p42, p34, 129, 26-27, p66, 146, p138, abs, 136, 133, 99, 103, 83, 61, p21, p2, p43, p3, p58, 99, 94, 130-132, 135, 92, 142, Travalini).
Regarding claim 5, the combination of Travalini and Wang teaches all the limitations of the base claim as outlined above, and are analyzed as previously discussed with regard to that claim.
Travalini further teaches wherein using at least one data generator to generate a set of artificial analysis packages comprises: generating an artificial portion of a single mode of multi-modal data input (Artificial intelligence generated output, can be based on one user input mode, such as text, see p151, p42, p34, 26-27, p66, p138, abs, 136, 133, 146, 99, 103, 83, 61, p21, p2, p43, p3, p58, 99, 94, 130-132, 135, 92, 142, Travalini); and combining the artificial portion of the single mode of the multi-modal data input with an artificial portion of a different mode of the multi-modal data input or an original portion of a different mode of the multi-modal data input (Artificial intelligence generated output, can be based on additional data to the initial input data, for example in the form of images or video (i.e. a different mode) and/or feedback generated data, see p150, p51-52, 151, p61, p65, p79, p42, p34, 26-27, p66, p138, abs, 136, 133, 99, 103, 83, 61, p21, p2, p43, p3, p58, 99, 94, 130-132, 135, 92, 142, Travalini).
Regarding claim 6, the combination of Travalini and Wang teaches all the limitations of the base claim as outlined above, and are analyzed as previously discussed with regard to that claim.
Travalini further teaches further comprising: using a set of artificial analysis packages and a set of original analysis packages to adjust a first model that generates service relevant text data dependent on an analysis package (A model, such as used for language based model, conversation service text via a chatbox to interpret and guide a conversation with a users/technicians for problem determination, language, diagnosing, etc., and is adjusted based on user/technician original input and generated historical data to improve the model, see p21, p42, 51, p55-58, p65-66, p83, 79, p106-107, 137, Fig. 2A, Fig. 4, p102, p138, p150, p42, p34, 26-27, p66, p138, abs, 136, 133, 99, 103, 83, 61, p21, p2, p43, p3, p58, 99, 94, 130-132, 135, 92, 142, Travalini); and using the set of artificial analysis packages and the set of original analysis packages to adjust a second model that combines the service relevant multi-modal data output and service relevant text data into a service plan of action (A model, such as recommendation and/or work service model, is adjusted based on user/technician conversation interaction including various forms of input and feedback generated data for providing recommended and work service plans to deal with a component incident, see p108, p114, 118, p21, p42, 51, p55-58, p65, p83, 79, Fig. 2A, Fig. 4, p102, p138, p150, p42, p34, 26-27, p66, p138, abs, 136, 133, 99, 103, 83, 61, p21, p2, p43, p3, p58, 99, 94, 130-132, 135, 92, 142, Travalini).
Regarding claim 7, the combination of Travalini and Wang teaches all the limitations of the base claim as outlined above, and are analyzed as previously discussed with regard to that claim.
Travalini further teaches wherein a multi-modal data input comprises at least two of: an image of the building equipment; a video of building equipment; an audio clip associated with the building equipment; a time series from a building automation system associated with the building equipment; one or more equipment product manuals associated with the building equipment; or a technician analysis of building equipment (Input data can comprise a plurality of information, including audio associated with a component incident, images/video associated with component, technician analysis feedback, sensor data, etc., see p106-107, p77, 75, p54, 104, p51, 34, Travalini).
Regarding claim 8, the combination of Travalini and Wang teaches all the limitations of the base claim as outlined above, and are analyzed as previously discussed with regard to that claim.
Travalini further teaches further comprising: receiving an additional multi-modal data input from one or more field technicians; associating related data portions from each mode of the additional multi-modal data input to form a set of additional analysis packages; and using the set of additional analysis packages, a set of artificial analysis packages, and a set of original analysis packages to update an output model to generate updated service relevant multi-modal data output for use in servicing building equipment (Technician feedback input provides additional input data, and can be used in combination with associated original user and generated data to update service output model for increased accuracy and prediction for providing component incident servicing, see p106-108, p77,p75, p65-67, p54, 104, p51, 34, 75, Travalini)
Regarding claim 9, Travalini teaches a method for servicing building equipment using a generative artificial intelligence model (Diagnosis and maintenance servicing used for components/equipment in a building, such as a dwelling, where generation of data, training, models, services, etc., are employed, see Abs, p21, p2, p43, p3, p58, 99, 94, 130-132, 135, 138, 92, 142, Travalini), the method comprising:
receiving, at one or more processors (processor, server, computing devices, and various algorithms/models that process data, p32-p34, 35-38, 53-57, Travalini), a multi-modal data input characterizing operation of the building equipment using multiple modes of data (Data from multiple sources and formats (i.e. multiple modes of multi-modal input), such as user text input data, user voice input data, component video/images input, context data, sensor data, etc., can be received that characterize operation of the building component as in possible problem, etc., see p51-52, p54, 56-57, p65-66, 68, p75, p114, 106, 137, p24, 77, 47, Travalini);
generating, by the one or more processors, an analysis package from the multi-modal data input (Input data, user voice input data, component video/images input, context data, sensor data, etc., are associated with an incident request for a component and form a generated set of data related for analysis of the incident/component that can be interpreted as an analysis package, see p51-52, p54, 56-57, p65-66, 68, p75, p114, 106, 137, p24, 77, 47, Travalini);
using, by the one or more processors, an output model to generate a service relevant multi-modal data output dependent on the analysis package (Data can be generated from multiple obtained data forms that are related to a component, such as pre-processing data, analysis data, interpretation data, prediction data, etc., and generated via artificial intelligence, see p138, abs, 136, 133, 99, 103, 83, 61 p150, p51-52, p61, p65, p79, p42, p34, 26-27, p66, p138, abs, 136, 133, 99, 103, 83, 61, p21, p2, p43, p3, p58, 99, 94, 130-132, 135, 92, 142, Travalini);
and performing, by the one or more processors, an automated action for servicing the building equipment based on the service relevant multi-modal data output (System uses service relevant data output from algorithms and models such as natural language data, image analysis data, etc., to perform automated action for servicing a building component, such as automated user guidance/instructions or automated work order action service for providing maintenance by a technician, see p52, p100, p45, p79, p83, p114, 106-107, 102-103, 100, p32-38, 25, 56, 77, Travalini).
While Travakini teaches a form of generating analysis data,
Wang from the same or similar field of artificial intelligence machine learning fault and diagnosis detection, teaches a complimentary form of: at least one data generator to generate a set of artificial analysis packages based on original analysis packages (Virtual data is generated (i.e. a set of artificial analysis packages) based on original fault samples, see p7, p9-16, p2, p21, Wang).
It would have been obvious to a person of ordinary skill in the art before the filing date of the claimed invention to modify the building and artificial intelligence based services analysis as described by Travalini and incorporating generated artificial analysis packages, as taught by Wang.
One of ordinary skill in the art would have been motivated to do this modification in order to better obtain a more balanced collection of data where at least one form/type of data may be less than needed, and thereby use reasonably determined artificial/virtual samples that can better be used for intended service such as diagnosis analysis, and to form balanced classification data for training machine learning models for tuning/updating said models (see p2-4, p25. p7, p9-16, p2, p21, Wang; p128, Travalini).
Regarding claim 10, the combination of Travalini and Wang teaches all the limitations of the base claim as outlined above, and are analyzed as previously discussed with regard to that claim.
Wang further teaches further comprising: determining whether an analysis package is missing a mode (Sufficient data of a desired type/form (i.e. mode) needed form a desired package of data for an analysis can be determined to be missing, see p25, p2, p7, p9-16, p21, Wang); and in response to determining that the analysis package is missing the mode, using a data generator to generate artificial analysis packages and augmenting the analysis package with representative data from the data generator (Virtual data is generated (i.e. a set of artificial analysis packages) with an artificial intelligence generator to augment analysis data package such that the data is representative of a desired form of data, see p7, p9-16, p2, p21, Wang).
It would have been obvious to a person of ordinary skill in the art before the filing date of the claimed invention to modify the building and artificial intelligence based services analysis as described by Travalini and incorporating generated artificial analysis packages for missing mode, as taught by Wang.
One of ordinary skill in the art would have been motivated to do this modification in order to better obtain a more balanced collection of data where at least one form/type of data may be less than needed, and thereby use reasonably determined artificial/virtual samples that can better be used for intended service such as diagnosis analysis, and to form balanced classification data for training machine learning models for tuning/updating said models (see p2-4, p25. p7, p9-16, p2, p21, Wang; p128, Travalini).
• Claim 11 is rejected on the same grounds as claims 1 and 3.
• Claim 12 is rejected on the same grounds as claim 5.
• Claim 13 is rejected on the same grounds as claim 6.
• Claim 14 is rejected on the same grounds as claim 7.
• Claim 15 is rejected on the same grounds as claim 1.
Regarding claim 16, the combination of Travalini and Wang teaches all the limitations of the base claim as outlined above, and are analyzed as previously discussed with regard to that claim.
Travalini further teaches an operations further comprising: receiving a second multi-modal data input characterizing operation of building equipment using multiple modes of data (Additional data to aninitial input data (e.g. text data), such as in the form of images or video (i.e. second multi-modal data input) of a component or component location characterize operation of the component, see p51-52, p151, p61, p65, p79, p42, p34, 146, 26-27, p66, p75, p138, abs, 136, 133, 99, 103, 83, 61, p21, p2, p43, p3, p58, 99, 94, 130-132, 135, 92, 142, Travalini); generating a second analysis package from second the multi-modal data input (Artificial intelligence generated output, can be based on additional data to the initial input data, for example in the form of images or video (i.e. a different mode), see p51-52, p61, p65, p151, p79, p42, 146, p34, 26-27, p66, p138, abs, 136, 133, 99, 103, 83, 61, p21, p2, p43, p3, p58, 99, 94, 130-132, 135, 92, 142, Travalini); using an output model to generate a service relevant multi-modal data output dependent on the second analysis package (An second analysis model such as video or image model can output additional service relevant data in the form of image analysis, context, and diagnosis data based on the additional data, see p51-52, p107-p108, p114, 118, p21, p42, 51, p55-58, p65, p83, 79, Fig. 2A, Fig. 4, p102, p138, p150, p42, p34, 26-27, p66, p138, abs, 136, 133, 99, 103, 83, 61, p21, p2, p43, p3, p58, 99, 94, 130-132, 135, 92, 142, Travalini); and performing an automated action for servicing the building equipment based on the service relevant multi-modal data output (System uses service relevant data output from algorithms and models such as natural language data, image analysis data, etc., to perform automated action for servicing a building component, such as automated user guidance/instructions or automated work order action service for providing maintenance by a technician, see 51-p52, p100, p45, p79, p83, p114, 106-107, 102-103, 100, p32-38, 25, 56, 77, Travalini).
• Claim 17 is rejected on the same grounds as claim 3.
• Claim 18 is rejected on the same grounds as claim 4.
• Claim 19 is rejected on the same grounds as claim 6.
• Claim 20 is rejected on the same grounds as claim 10.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kobren et al., US Patent Publication No. 2023/0401285 teaches augmenting data sets used for training machine learning models by using synthesized data.
Qu et al., US Patent No. 12,518,169 teaches identification of manufacturing defects using training datasets with combined first and second data samples, where the second data is generated from an input.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMILIO J SAAVEDRA whose telephone number is (571)270-5617. The examiner can normally be reached M-F: 9:30am-5:30pm (EST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert E Fennema can be reached at (571) 272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/EMILIO J SAAVEDRA/Primary Patent Examiner, Art Unit 2117
Prosecution Insights