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 in response to the claimed amendment filed on October 01, 2025, in which claims 1-20 are presented for further examination.
Information Disclosure Statement
The information disclosure statement filed on July 9, 2025 and November 25, 2025 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609. It has been placed in the application file. The information referred to therein has been considered as to the merits.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot in view of a new ground of rejection necessitated by amendment.
Specification
The disclosure is objected to because of the following informalities: specification, pars. [0316], [0334], [0351], [0368], [0380], [0395], [0408] and [0420] recite “the processes 2200-2300 make use of (e.g., train, use, configure, update, etc.) one or more generative AI models to implement and/or execute certain features or steps of the processes”. It is unclear how the information inside the parentheses (e.g., train, use, configure, update, etc.) is correspond with the processes 2200-2300 make use of; and “etc.” renders the sentence indefinite.
Specification, pars [0022] and [0043] recite “Training the generative AI model may include using the feedback in combination with the plurality of first unstructured service reports to configure or update the trained generative AI model” and "training the machine learning model may include using the operational data” It is not clear as to what the applicant meant, and what would the generative AI model and machine learning model be included to use the feedback/operational data. Therefore the clause “may include using” is not understood.
Appropriate correction and amendment are required.
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 and 11 recites “updating, by the one or more processors, a generative AI model comprising a transformer using the plurality of first unstructured service reports”. It is not clear how the generative AI model will be updated using the plurality of first unstructured service reports. The claimed language “updating a generative AI model comprising a transformer using….”is not understood as to what the applicant meant to say.
Claim 17 recites “training, by the one or more processors, a machine learning model comprising a transformer using the plurality of first unstructured service reports”. The claimed language “training a machine learning model comprising a transformer using….”is not understood as to what the applicant meant to say.
However, the specification, par [0043], states “the method further includes receiving, by the one or more processors, feedback indicating a quality of one or more outputs of the generative AI model. Training the generative AI model may include using the feedback in combination with the plurality of first unstructured service reports to configure or update the trained generative AI model”. Such recited portion of the specification does not correspond to “updating, by the one or more processors, a generative AI model comprising a transformer using the plurality of first unstructured service reports”. Appropriate correction and amendment are required.
Claims 2-10 and 12-16 are rejected for incorporating the deficiency of their respective base claims by dependency.
Claim 17 recites “training, by the one or more processors, a machine learning model comprising a transformer using the plurality of first unstructured service reports”. It is unclear how the machine learning model would train that comprising a transformer using the plurality of first unstructured service reports. It is unclear the applicant meant by “a machine learning model comprising a transformer using….”. Claims 18-20 are rejected for incorporating the deficiency of their respective base claims by dependency.
However, the specification, par [0075]-[0080], states “Training the machine learning model may include using the operational data in combination with the plurality of first unstructured service reports to configure the trained machine learning model”. Such recited portion of the specification does not correspond to “training, by the one or more processors, a machine learning model comprising a transformer using the plurality of first unstructured service reports”. Appropriate correction and amendment are required.
Claims 18-20 are rejected for incorporating the deficiency of their respective base claims by dependency.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1 and 11 recites “updating, by the one or more processors, a generative AI model comprising a transformer using the plurality of first unstructured service reports”.
However, the specification, par [0043], states “the method further includes receiving, by the one or more processors, feedback indicating a quality of one or more outputs of the generative AI model. Training the generative AI model may include using the feedback in combination with the plurality of first unstructured service reports to configure or update the trained generative AI model”.
Such recited claimed language is not supported by the original specification as indicated in the paragraph stated above. Appropriate correction and amendment are required.
Claims 2-10 and 12-16 are rejected for incorporating the deficiency of their respective base claims by dependency.
Claim 17 recites “training, by the one or more processors, a machine learning model comprising a transformer using the plurality of first unstructured service reports”.
However, the specification, par [0075]-[0080], states “Training the machine learning model may include using the operational data in combination with the plurality of first unstructured service reports to configure the trained machine learning model”.
Such recited claimed language is not supported by the original specification as indicated in the paragraph stated above. Appropriate correction and amendment are required.
Appropriate correction and amendment are required.
Claims 18-20 are rejected for incorporating the deficiency of their respective base claims by dependency.
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.
Claims 1-20 as best understood by the examiner are rejected under 35 U.S.C. 103 as being unpatentable over Farahat et al., (hereinafter “Farahat”) US 20200258057 in view Vitullo et al., (hereinafter “Vitullo”) US 10,281,363 and Ahmed et al., (hereinafter “Ahmed”) US 20180046173.
As to claim 1, Farahat discloses a method comprising:
receiving, by one or more processors, a plurality of first unstructured service reports corresponding to a plurality of first service requests for servicing building equipment (see [0014], receiving a repair request following an equipment failure)
the plurality of first unstructured service reports comprising unstructured data not conforming to a predetermined format or conforming to a plurality of different predetermined formats the unstructured data comprising outcome data indicating outcomes of one or more service requests of the plurality of first service requests (see [0051], user complaints and error messages may be natural language complaints received from the equipment user as well as any error messages, error codes, fault codes, or the like, received from the equipment or other systems in the environment of the equipment. The user complaints and error messages may be unstructured or semi-structured data that describe the symptoms of the failure that has been requested to be fixed); and
performing, by the one or more processors using the updated generative AI model, one or more actions with respect a second service request subsequent to updating the generative AI model ([0025] and [0050], perform the actions attributed herein to the service computing device, wherein an event data indicates events associated with the equipment before the equipment failed or otherwise was requested to be repaired. Examples of events may be different types of maintenance actions, alarms, or the like and wherein a trained machine learning model(s) is configured to output one or more options for repair actions that may be performed to repair the failure of the equipment along with a probability of success for each output repair action).
Farahat does not disclose the building equipment comprising at least one of HVAC equipment, security equipment, or fire equipment.
On the other hand, Vitullo discloses the building equipment comprising at least one of HVAC equipment, security equipment, or fire equipment (col.16, line 63-col.17, line 10, building equipment include any type of equipment capable of monitoring and/or controlling conditions within a building, like HVAC equipment, lighting equipment, security equipment, fire safety equipment or other types of equipment which can be used in or around a building (e.g., ICT equipment, lifts/escalators, refrigeration equipment, advertising or signage, cooling equipment, vending equipment, etc.)).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Farahat to manage the building equipment comprising at least one of HVAC equipment, security equipment, or fire equipment, in order to optimize performance, and ensure occupant comfort and safety through real-time data.
Neither Farahat nor Vitullo discloses the claimed “updating, by the one or more processors, a generative AI model comprising a transformer using the plurality of first unstructured service reports”.
Meanwhile, Ahmed the claimed “updating, by the one or more processors, a generative AI model comprising a transformer using the plurality of first unstructured service reports (par. [0025], using unsupervised machine learning, data from multiple buildings and/or meta data from the enterprise are used to diagnose building automation or management system operation and/or enterprise function related to building automation” along with paragraph [0034] “a controller implements a coordination control application for overriding, setting, adjusting or altering the operation of another building automation application. Alternatively, the controllers run processes to measure deviation from a set point and control the response. Although Ahmed teaches the step of training, the prior art falls short of teaching the use of a transformer- based neural network. For this facet, the article by Giacaglia is introduced. Giacaglia states. Applicant should duly note that “Transformer” is a type or neural network architecture that have been gaining popularity. ... Transformer was developed to solve the problem of sequence transduction ... that means any task that transforms an input sequence to an output sequence.
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the combined system of Farahat and Vitullo to update a generative AI model comprising a transformer, in order to transform vast amounts of previously untapped, qualitative data into actionable insights and automated processes
As to claims 2-9, the combination of Farahat, Vitullo and Ahmed substantially discloses the limitations of claims 2-9, Farahat discloses the claimed wherein the unstructured data conform to the plurality of different predetermined formats comprising at least two of a text format, a speech format, an audio format, an image format, a video format, or a data file format; the predetermined format is a structured data format comprising one or more predetermined fields or locations and one or more predetermined labels or identifiers characterizing the one or more predetermined fields or locations; the unstructured data comprise freeform data not conforming to the structured data format; and multi-modal data provided by a plurality of different sensory devices comprising at least two of an audio capture device, a video capture device, an image capture device, a text capture device, or a handwriting capture device;
Vitullo discloses the claimed parts data indicating parts usage associated with the building equipment.
Farahat discloses the claimed one or more of the plurality of first service requests corresponding to one or more of the plurality of first unstructured service reports; performing the one or more actions with respect the second service request comprises using the updated generative AI model to identify new correlations and/or patterns between (i) the unstructured data of the plurality of first unstructured service reports and (ii) the additional data from one or more additional data sources.
Vitullo discloses the claimed receiving, by the one or more processors, additional data generated by one or more other models separate from the generative AI model, the one or more other models comprising at least one of: a thermodynamic model configured to predict one or more thermodynamic properties or states of a building space or fluid flow as a result of operation of the building equipment; an energy model configured to predict consumption or generation of one or more energy resources as a result of the operation of the building equipment; a sustainability model configured to predict one or more sustainability metrics as a result of the operation of the building equipment; an occupant comfort model configured to predict occupant comfort as a result of the operation of the building equipment; an infection risk model configured to predict infection risk in one or more building spaces as a result of the operation of the building equipment; or an air quality model configured to predict air quality in one or more building spaces as a result of the operation of the building equipment.
Farahat discloses the claimed wherein training updating the generative AI model comprises using the additional data generated by one or more other models in combination with the unstructured data of the plurality of first unstructured service reports to configure the updated generative AI model.
Farahat discloses the claimed wherein performing the one or more actions comprises using the additional data generated by one or more other models in combination with an output of the updated generative AI model to select an action to perform.
Farahat discloses the claimed providing, by the one or more processors, an output of the updated generative AI model as an input to the one or more other models; wherein the one or more other models generate the additional data based on the output of the updated generative AI model.
As to claims 11-16, the combination of Farahat, Vitullo and Ahmed substantially discloses the limitations of claims 11-16. In addition, Farahat discloses a method comprising: receiving, by one or more processors, a first unstructured service report corresponding to a first service request (see [0014], receiving a repair request following an equipment failure); the first unstructured service report comprising first unstructured data not conforming to a predetermined format or conforming to a plurality of different predetermined formats and wherein the generative AI model is configured using training data comprising a plurality of second unstructured service reports comprising second unstructured data not conforming to the predetermined format or conforming to the plurality of different predetermined formats (see [0051], user complaints and error messages may be natural language complaints received from the equipment user as well as any error messages, error codes, fault codes, or the like, received from the equipment or other systems in the environment of the equipment. The user complaints and error messages may be unstructured or semi-structured data that describe the symptoms of the failure that has been requested to be fixed); and
performing, by the one or more processors, one or more actions with respect to the first service request based on an output of the generative AI model generated from the first unstructured service report ([0025] and [0050], perform the actions attributed herein to the service computing device, wherein an event data indicates events associated with the equipment before the equipment failed or otherwise was requested to be repaired. Examples of events may be different types of maintenance actions, alarms, or the like and wherein a trained machine learning model(s) is configured to output one or more options for repair actions that may be performed to repair the failure of the equipment along with a probability of success for each output repair action).
Farahat does not disclose the building equipment comprising at least one of HVAC equipment, security equipment, or fire equipment and the second unstructured data comprising outcome data indicating outcomes of one or more second service requests associated with the plurality of second unstructured service reports for at least one of HVAC equipment, security equipment, or fire equipment.
On the other hand, Vitullo discloses the claimed “building equipment comprising at least one of HVAC equipment, security equipment, or fire equipment and the second unstructured data comprising outcome data indicating outcomes of one or more second service requests associated with the plurality of second unstructured service reports for at least one of HVAC equipment, security equipment, or fire equipment” (col.16, line 63-col.17, line 10, building equipment include any type of equipment capable of monitoring and/or controlling conditions within a building, like HVAC equipment, lighting equipment, security equipment, fire safety equipment or other types of equipment which can be used in or around a building (e.g., ICT equipment, lifts/escalators, refrigeration equipment, advertising or signage, cooling equipment, vending equipment)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Farahat to manage the building equipment comprising at least one of HVAC equipment, security equipment, or fire equipment, in order to optimize performance, and ensure occupant comfort and safety through real-time data.
Neither Farahat nor Vitullo discloses the claimed “providing, by the one or more processors, the first unstructured service report as an input to a generative AI model comprising a transformer”.
Meanwhile, Ahmed the claimed “providing, by the one or more processors, the first unstructured service report as an input to a generative AI model comprising a transformer
(par. [0025], using unsupervised machine learning, data from multiple buildings and/or meta data from the enterprise are used to diagnose building automation or management system operation and/or enterprise function related to building automation” along with paragraph [0034] “a controller implements a coordination control application for overriding, setting, adjusting or altering the operation of another building automation application. Alternatively, the controllers run processes to measure deviation from a set point and control the response. Although Ahmed teaches the step of training, the prior art falls short of teaching the use of a transformer- based neural network. For this facet, the article by Giacaglia is introduced. Giacaglia states. Applicant should duly note that “Transformer” is a type or neural network architecture that have been gaining popularity. ... Transformer was developed to solve the problem of sequence transduction ... that means any task that transforms an input sequence to an output sequence.
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the combined system of Farahat and Vitullo to update a generative AI model comprising a transformer, in order to transform vast amounts of previously untapped, qualitative data into actionable insights and automated processes.
As to claims 17-20, the combination of Farahat, Vitullo and Ahmed substantially discloses the limitations of claims 17-20. In addition, Farahat discloses a method comprising: receiving, by one or more processors, a plurality of first unstructured service reports corresponding to a plurality of first service requests (see [0014], receiving a repair request following an equipment failure);
the plurality of first unstructured service reports comprising unstructured data not conforming to a predetermined format or conforming to a plurality of different predetermined formats, the unstructured data comprising outcome data indicating outcomes of one or more service requests of the plurality of service requests (see [0051], user complaints and error messages may be natural language complaints received from the equipment user as well as any error messages, error codes, fault codes, or the like, received from the equipment or other systems in the environment of the equipment. The user complaints and error messages may be unstructured or semi-structured data that describe the symptoms of the failure that has been requested to be fixed); and
performing, by the one or more processors using the trained machine learning model, one or more actions with respect a second service request subsequent to training the machine learning model ([0025] and [0050], perform the actions attributed herein to the service computing device, wherein an event data indicates events associated with the equipment before the equipment failed or otherwise was requested to be repaired. Examples of events may be different types of maintenance actions, alarms, or the like and wherein a trained machine learning model(s) is configured to output one or more options for repair actions that may be performed to repair the failure of the equipment along with a probability of success for each output repair action).
Farahat does not disclose servicing building equipment comprising at least one of HVAC equipment, security equipment, or fire equipment.
On the other hand, Vitullo discloses the claimed “servicing building equipment comprising at least one of HVAC equipment, security equipment, or fire equipment” (col.16, line 63-col.17, line 10, building equipment include any type of equipment capable of monitoring and/or controlling conditions within a building, like HVAC equipment, lighting equipment, security equipment, fire safety equipment or other types of equipment which can be used in or around a building (e.g., ICT equipment, lifts/escalators, refrigeration equipment, advertising or signage, cooling equipment, vending equipment)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Farahat to manage the building equipment comprising at least one of HVAC equipment, security equipment, or fire equipment, in order to optimize performance, and ensure occupant comfort and safety through real-time data.
Neither Farahat nor Vitullo discloses the claimed “providing, by the one or more processors, the first unstructured service report as an input to a generative AI model comprising a transformer”.
Meanwhile, Ahmed the claimed “training, by the one or more processors, a machine learning model comprising a transformer using the plurality of first unstructured service report
(par. [0025], using unsupervised machine learning, data from multiple buildings and/or meta data from the enterprise are used to diagnose building automation or management system operation and/or enterprise function related to building automation” along with paragraph [0034] “a controller implements a coordination control application for overriding, setting, adjusting or altering the operation of another building automation application. Alternatively, the controllers run processes to measure deviation from a set point and control the response. Although Ahmed teaches the step of training, the prior art falls short of teaching the use of a transformer- based neural network. For this facet, the article by Giacaglia is introduced. Giacaglia states. Applicant should duly note that “Transformer” is a type or neural network architecture that have been gaining popularity. ... Transformer was developed to solve the problem of sequence transduction ... that means any task that transforms an input sequence to an output sequence.
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the combined system of Farahat and Vitullo to update a generative AI model comprising a transformer, in order to transform vast amounts of previously untapped, qualitative data into actionable insights and automated processes.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
11562008 (involved in detection of entities in unstructured data. Terms are extracted from unstructured data. Entities scores for the terms are calculated using information from a name probability source, a known entity database, and historical context information. The entity scores indicate a probability that the respective terms refer to entities. The presence of detected entities are indicated based on the entity scores.).
11314746 (involved in continuously processing of unstructured data streams are provided. Information may be stored in memory regarding a query, including associated search results and statistics derived at an identified time. After the identified time, unstructured data may be received from a plurality of streams over a communication network. A full-text search may be conducted on the received unstructured data based on the query to yield one or more matches. The stored statistics associated with the query may be retrieved from memory and updated based on the search results of the unstructured data received at the subsequent time. The updated statistics may then be stored in memory for retrieval at a subsequent time)
10235452 (involved in identifying subject matter experts in conjunction with processing of service requests or other communications, for use in an information processing system).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEAN M CORRIELUS whose telephone number is (571)272-4032. The examiner can normally be reached Monday-Friday 6:30a-10p(Midflex).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J Lo can be reached at (571)272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JEAN M CORRIELUS/Primary Examiner, Art Unit 2159 December 11, 2025