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
This action is responsive to the application filed on 04/11/2024.
Claims 1-20 are pending.
Claims 1-20 are rejected.
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.
Claims 1, 11, and 20 are respectively drawn to a system, method, and non-transitory computer readable storage medium, hence each falls under one of four categories of statutory subject matter (Step 1). Nonetheless, the claims are directed to a judicially recognized exception of an abstract idea without significantly more.
Claims 1, 11, and 20 recite the following, or analogous, limitations “...a plurality of first service requests indicating a plurality of first problems associated with building equipment and a plurality of first actions performed in response to the plurality of first service requests; receiving…a second service request indicating a second problem associated with building equipment; …determining…one or more second actions to perform based on characteristics of the second service request; and…initiating…the one or more second actions to address the second problem associated with the building equipment”. These limitations, as claimed, under its broadest reasonable interpretation, can be evaluated in a human mind, except for the recitation of generic computer components (using artificial intelligence/machine learning, a computer including one or more microprocessors, and a non-transitory computer readable storage medium) (Step 2A). Other than reciting “training…a generative AI [machine learning claim 20] model using training data”, “automatically”, “one or more processors”, and “the generative AI [machine learning claim 20] model”, to perform the exceptions, nothing in the claims preclude the steps from practically being performed in the human mind. For example, a human expert can:
mentally/with the aid of pen and paper …a plurality of first service requests indicating a plurality of first problems associated with building equipment and a plurality of first actions performed in response to the plurality of first service requests (e.g. by thinking of/writing out a first set of remembered issues with an HVAC system and what was done to resolve the issues),
mentally/with the aid of pen and paper receiving…a second service request indicating a second problem associated with building equipment (e.g. by thinking of/writing out a new issue that could occur with the HVAC system),
mentally/with the aid of pen and paper …determining…one or more second actions to perform based on characteristics of the second service request (e.g. by thinking of/writing out a new resolution based on the HVAC issue being a fan not working),
mentally/with the aid of pen and paper …initiating…the one or more second actions to address the second problem associated with the building equipment (e.g. by thinking of/writing out how to resolve the new issue).
Thus, the claims recite a mental process (Step 2A, Prong 1).
Claims 1, 11, and 20 include additional elements, “training…a generative AI [machine learning claim 20] model using training data”, “automatically”, “one or more processors”, and “the generative AI [machine learning claim 20] model”, however the recitations of these elements are at a high level of generality, and amount to adding the words “apply it” (or an equivalent) with the judicial exception (i.e., “training…a generative AI/machine learning model using training data”), or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (i.e., “one or more processors”) (see MPEP 2106.05(f)); and generally link the use of the judicial exception to a particular technological environment or field of use (i.e., “automatically”, “the generative AI/machine learning model”) (see MPEP 2106.05(h)). Hence, each of the additional limitations or in combination do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong 2). The additional elements in the claim do not amount to significantly more than an abstract idea. Furthermore, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using “training…a generative AI [machine learning claim 20] model using training data”, “automatically”, “one or more processors”, and “the generative AI [machine learning claim 20] model” to perform the steps of the independent claims amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. (STEP 2B). As such, claims 1, 11, and 20 are not patent eligible.
Dependent claims 2-10 and 12-19 are also ineligible for the same reasons given with respect to 1, 11, and 20. The dependent claims describe additional mental processes:
mentally/with the aid of pen and paper predicting…a root cause of the second problem corresponding to the second service request based on the characteristics of the second service request and one or more patterns or trends identified from the plurality of first service requests…; wherein...determining the one or more second actions to perform comprises determining an action predicted to resolve the root cause of the second problem…(claims 2 and 12) (e.g. by mentally/writing out the reason the HVAC fan is not working based on remembered run times of the fan and how to fix it)
mentally/with the aid of pen and paper wherein the one or more second actions comprise…creating a service ticket or work order, including parameters of the service ticket or work order, to address the second problem associated with the building equipment (claims 3 and 13) (e.g. by mentally/writing out a service ticket with how to fix the fan)
mentally/with the aid of pen and paper wherein the one or more second actions comprise…initiating a diagnostic test of the building equipment or other building equipment to test a root cause of the second problem predicted…(claims 6 and 16) (e.g. by mentally/writing out fixing the fan will require running an electrical test to find the reason the fan is not working)
mentally/with the aid of pen and paper generating…an indication of the one or more second actions performed to address the second problem associated with the building equipment (claims 7 and 17) (e.g. by mentally/writing out the way to fix the fan issue)
mentally/with the aid of pen and paper wherein the training data indicate one or more types of service activities performed in response to the plurality of first service requests; and…initiating the one or more second actions comprises assigning a technician to handle the second service request…model based on capabilities of one or more technicians with respect to the one or more types of service activities (claims 8 and 18) (e.g. by mentally/writing out a remembered first set of issues with the HVAC system and resolutions, and writing out the best associate for performing the fix on the fan based on their past fixes and time spent on the issues)
mentally/with the aid of pen and paper wherein the training data indicate one or more amounts of time required to perform the plurality of first actions in response to the plurality of first service requests; and…initiating the one or more second actions comprises scheduling a service activity to handle the second service request…based on a predicted amount of time required to perform the service activity to handle the second service request (claims 9 and 19) (e.g. by mentally/writing out an estimate of time for how long resolving the first set of issues required, and writing out an estimate of how much time fixing the fan will take)
mentally/with the aid of pen and paper wherein the training data indicate one or more service vehicles used to service the building equipment responsive to the plurality of first service requests; and…initiating the one or more second actions comprises scheduling a service vehicle to handle the second service request…(claim 10) (e.g. by mentally/writing out the remembered first set of issues and resolutions used a truck, and writing out which truck should be used for fixing the fan)
Again, the dependent claims continued to cover the performance of the limitation in the mind as inherited from the independent claims (Step 2A, Prong 1). The dependent claims 2, 7, 12, and 17 recitation of “by the one or more processors”, dependent claim 7 and 17 recitation of “a user interface comprising” are again recited at a high level and adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)); dependent claims 2, 6, 8-10, 12, 16, and 18-19 recitation of “using the generative AI model”, dependent claims 2-6, 8-10, 12-16, and 18-19 recitation of “automatically”, dependent claims 4-14 recitation of “wherein the one or more second actions comprise…generating one or more control signals and transmitting the one or more control signals to the building equipment to adjust an operation of the building equipment to address the second problem associated with the building equipment”, dependent claims 5-15 recitation of “wherein the one or more second actions comprise…generating one or more control signals and transmitting the one or more control signals to other building equipment, the control signals causing the other building equipment to compensate for the second problem associated with the building equipment”, amount to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)); and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong 2). The additional element in the claims do not amount to significantly more than an abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements to perform the steps of in the dependent claims and perform the steps of the claims amount to no more than mere instructions to apply the exception using generic computer components and adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea; generally link the use of the judicial exception to a particular technological environment or field of use; and these cannot provide an inventive concept. (STEP 2B). As such, dependent claims 2-10 and 12-19 do not amount to significantly more than an abstract idea nor provide any inventive concept, therefore are not patent eligible.
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.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shinde et al (US Pub 20210262689) hereinafter Shinde, in view of DeLuca et al (US Pub 20220198474) hereinafter DeLuca.
Regarding analogous claims 1, 11, and 20, Shinde teaches a method comprising:
training, by one or more processors (paragraphs 0187-0189 teach memory communicatively coupled to a processor executing stored instructions for performing the embodiments of the disclosure), a generative AI [machine learning claim 20] model using training data comprising a plurality of first service requests indicating a plurality of first problems associated with building equipment and a plurality of first actions performed in response to the plurality of first service requests (paragraphs 0128-0129 and 0190-0196 teach “train a model of machine learning models 1120 using historical fault information (service requests) and may update the trained model based on user feedback regarding one or more predicted faults generated by the trained model” and predicted equipment shutdown (first actions) from equipment function data (service requests) and “generate[d] future operational parameters for a BMS system and/or a component thereof (e.g., an HVAC system, etc.)” (first actions));
receiving, by the one or more processors, a second service request indicating a second problem associated with building equipment (paragraphs 190-196 teach “execute the trained model (e.g., using recent operating data (second service request), etc.) to generate predicted operating parameters (e.g., future operating parameters, etc.)” including issuing HVAC (building equipment) control and fault predictions);
automatically determining, by the one or more processors using the generative AI [machine learning claim 20] model, one or more second actions to perform based on characteristics of the second service request (paragraphs 0128-0129 and 0190-0196 teach the model predicting equipment shutdown (second action) from the current equipment operating data (second service requests) and “generate future operational parameters for a BMS system and/or a component thereof (e.g., an HVAC system, etc.)” (second actions)); and
automatically initiating, by the one or more processors, the one or more second actions to address the second problem associated with the building equipment (paragraphs 0128-0129 and 0190-0196 “perform one or more actions related to the predicted fault. For example, control circuit 1150 may automatically generate a work order ticket…[or] disable a HVAC system for a period of time related to the predicted fault to avoid wasting energy”).
Shinde at least implies automatically determining, by the one or more processors using the generative AI [machine learning claim 20] model, one or more second actions to perform based on characteristics of the second service request (see mappings above); however, DeLuca teaches automatically determining, by the one or more processors using the generative AI [machine learning claim 20] model, one or more second actions to perform based on characteristics of the second service request (paragraphs 0011, 0014, and 0033-0037 teach a trained “machine learning model generates a set of service request assignments determined as appropriate for a respective technician of a pool of technicians and includes predicted time to complete, priority, tools required, payout estimate, service site conditions, and technician location”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to implement DeLuca’s teachings of machine learning model assigning service requests based on equipment and technician history data and predicting specific factors of the request into Shinde‘s teaching of machine learning model predicting HVAC equipment faults and steps for operational resolution in order to “provide an improvement in aligning service-providing technician support with the requirements, conditions, and locations of service requests” (DeLuca, paragraphs 0025).
Regarding claims 2 and 12, the combination of Shinde and DeLuca teach all the claim limitations of claims 1 and 11 above; and further teach predicting, by the one or more processors using the generative AI model, a root cause of the second problem corresponding to the second service request based on the characteristics of the second service request and one or more patterns or trends identified from the plurality of first service requests using the generative AI model (Shinde, paragraphs 0128-0129 and 0190-0196 teach the model trained on previous operational data and fault information (identified from the plurality of first service requests) for outlier detection (pattern), wherein “the fault prediction may include a fault type, one or more associated pieces of equipment, a fault category, a fault diagnosis, a root cause” from the current operation data (second service request)); wherein automatically determining the one or more second actions to perform comprises determining an action predicted to resolve the root cause of the second problem using the generative AI model (Shinde, paragraphs 0128-0129 and 0190-0196 teach “the fault prediction may include…a root cause” from the current operation data (second service request), and the model predicting equipment shutdown (action predicted to resolve the root cause) from the current equipment operating data (second service requests) and “generate future operational parameters for a BMS system and/or a component thereof (e.g., an HVAC system, etc.)” (action predicted to resolve the root cause)).
Regarding claims 3 and 13, the combination of Shinde and DeLuca teach all the claim limitations of claims 1 and 11 above; and further teach wherein the one or more second actions comprise automatically creating a service ticket or work order, including parameters of the service ticket or work order, to address the second problem associated with the building equipment (Shinde, paragraphs 0128-0129 and 0190-0196 “perform one or more actions related to the predicted fault. For example, control circuit 1150 may automatically generate a work order ticket…[or] disable a HVAC system for a period of time related to the predicted fault to avoid wasting energy”).
Regarding claims 4 and 14, the combination of Shinde and DeLuca teach all the claim limitations of claims 1 and 11 above; and further teach wherein the one or more second actions comprise automatically generating one or more control signals and transmitting the one or more control signals to the building equipment to adjust an operation of the building equipment to address the second problem associated with the building equipment (Shinde, paragraphs 0128-0129 and 0190-0196 “perform one or more actions related to the predicted fault…For example, control circuit 1150 may disable a HVAC system for a period of time related to the predicted fault to avoid wasting energy”).
Regarding claims 5 and 15, the combination of Shinde and DeLuca teach all the claim limitations of claims 1 and 11 above; and further teach wherein the one or more second actions comprise automatically generating one or more control signals and transmitting the one or more control signals to other building equipment, the control signals causing the other building equipment to compensate for the second problem associated with the building equipment (Shinde, paragraphs 0065-0066 and 0083-0084 teach the model predicting “fault detection for building subsystems 428, building subsystem devices (i.e., building equipment)”, including HVAC subsystem devices, and the “control algorithm configured to attempt to repair the fault or to work-around the fault…[that] may shut-down systems or direct control activities around faulty devices or systems to reduce energy waste, extend equipment life, or assure proper control response” (control…other building equipment to compensate)).
Regarding claims 6 and 16, the combination of Shinde and DeLuca teach all the claim limitations of claims 1 and 11 above; and further teach wherein the one or more second actions comprise automatically initiating a diagnostic test of the building equipment or other building equipment to test a root cause of the second problem predicted by the generative AI model (Shinde, paragraphs 0190-0196 teach “the fault prediction may include…a root cause” from the current operation data, and issue a command to “calculate an amount of energy (e.g., measured in Watts, British-thermal-units (BTUs), etc.) associated with overheating a space (e.g., as in a high zone temperature fault, etc.) (diagnostic test of the building equipment) based on the degree of overheating and/or a length of time the space was overheated (diagnostic test of the building equipment)”).
Regarding claims 7 and 17, the combination of Shinde and DeLuca teach all the claim limitations of claims 1 and 11 above; and further teach generating, by the one or more processors, a user interface comprising an indication of the one or more second actions performed to address the second problem associated with the building equipment (Shinde, paragraphs 0190-0198 teach running a test in view of the predicted fault, including “calculate an amount of energy (e.g., measured in Watts, British-thermal-units (BTUs), etc.) associated with overheating a space (e.g., as in a high zone temperature fault, etc.) (second actions) based on the degree of overheating and/or a length of time the space was overheated (second actions)” and the cost savings from the calculation. Further “transmitting an indication of the cost savings to a user (e.g., displaying the cost savings on a user interface, etc.) (second actions performed).” Paragraph 0201 additionally teaches “a work order ticket may be submitted to a work order ticketing system that instructs maintenance personnel to repair a defective blower fan in a HVAC system to solve a low zone temperature fault (indication…second actions performed)”).
Regarding claims 8 and 18, the combination of Shinde and DeLuca teach all the claim limitations of claims 1 and 11 above; and further teach wherein the training data indicate one or more types of service activities performed in response to the plurality of first service requests (Shinde, paragraphs 0128-0129 and 0190-0196 teach “train a model of machine learning models 1120 using historical fault information (service requests) and may update the trained model based on user feedback regarding one or more predicted faults generated by the trained model” and predicted equipment shutdown (first actions) from equipment function data (service requests) and “generate[d] future operational parameters for a BMS system and/or a component thereof (e.g., an HVAC system, etc.)” (first actions)); and automatically initiating the one or more second actions comprises assigning a technician to handle the second service request using the generative AI model based on capabilities of one or more technicians with respect to the one or more types of service activities (Shinde, paragraphs 0128-0129, 0190-0196, and 0201 teach the model predicting equipment shutdown (second action) or operational parameters for the associated fault from the current equipment operating data (second service requests) and “may automatically generate a work order ticket”; wherein “a work order ticket may be submitted to a work order ticketing system that instructs maintenance personnel to repair a defective blower fan in a HVAC system to solve a low zone temperature fault”).
Regarding claims 9 and 19, the combination of Shinde and DeLuca teach all the claim limitations of claims 1 and 11 above; and further teach wherein the training data indicate one or more amounts of time required to perform the plurality of first actions in response to the plurality of first service requests; and automatically initiating the one or more second actions comprises scheduling a service activity to handle the second service request using the generative AI model based on a predicted amount of time required to perform the service activity to handle the second service request (DeLuca, paragraphs 0011, 0014, and 0033-0037 teach a trained “machine learning model generates a set of service request assignments determined as appropriate for a respective technician of a pool of technicians and includes predicted time to complete, priority, tools required, payout estimate, service site conditions, and technician location”).
Shinde and DeLuca are combinable for the same rationale as set forth above with respect to claims 1, 11, and 20.
Regarding claims 10, the combination of Shinde and DeLuca teach all the claim limitations of claim 1 above; and further teach wherein the training data indicate one or more service vehicles used to service the building equipment responsive to the plurality of first service requests; and automatically initiating the one or more second actions comprises scheduling a service vehicle to handle the second service request using the generative AI model (DeLuca, paragraphs 0011, 0014, and 0033-0037 teach training a machine learning model on technician history and equipment data, for “determin[ing] whether a special tool required for a particular service request is present in the vehicle for a technician designated to perform the service request” and aligning the correct technician for the requested equipment service).
Shinde and DeLuca are combinable for the same rationale as set forth above with respect to claims 1, 11, and 20.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Khalate et al (US Pub 20180373234) teach machine learning HVAC predictions for equipment fault ticket generation for resolving detected issues.
Conclusion
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/C.M./Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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