DETAILED ACTION
This Final Office Action is in response to Applicant's amendments and arguments filed on June 25, 2026. Applicant has amended claims 1 and 21. Currently, claims 1-14, 21-22 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendments
The 35 U.S.C. 101 rejections of claims 1-14, 21-22 are maintained in light of applicant’s amendments to claims 1 and 21.
Response to Arguments
Applicant’s remarks submitted on 6/25/26 have been considered but are not persuasive. Applicant argues on p. 12 of the remarks that the 101 rejections are improper. Examiner disagrees. Applicant argues on p. 14-15 of the remarks that the claims are not directed to abstract idea. Examiner notes the claims are abstract for the same reasons states in the 5/30/25 board decision and the parts that cannot be performed in the mind or other components are considered additional elements to the abstract idea. Applicant argues on p. 17 of the remarks that the claims integrate the abstract idea into a practical application. Applicant argues that there is an improvement to other technology or technical field. Examiner re-asserts the rational of the 5/30/25 board decision and notes that the claims use technology and the technology provides context or a linked environment to the claims where the abstract idea itself is improved as opposed to another technology or technical field. Applicant on p. 18 provides some scenarios that show an improvement such as scheduling surgery in an OR room. Examiner notes such improvements are not sufficiently tethered to the claims. Applicant further argues the additional elements are significantly more than the abstract idea. Examiner disagrees and notes that such elements are conventional components when managing sensors, which in itself is a conventional component, as well as generic computing components and interfaces as evidenced by para [0002], [0007]-[0014], [0052], [0076]-[0081], [0099]-[0100], [0111], [0126] of applicant's own specification. Examiner further notes these elements function in their conventional and routine way even when analyzed as an ordered combination. Therefore, claims 1-14, 21-22 remain rejected under 101.
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 and 21 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. The limitation “execute the machine learning engine to update the policy, wherein the policy is updated using the determined HVAC system fault and the implemented corrective action as inputs to the machine learning engine and running the machine learning engine with the policy to update the policy” is unclear because the machine learning engine appears to be simultaneously updating the policy and running the policy. Claims 2-14, 22 depend from claims 1 and 21, inherit the same deficiencies and thus rejected for the same reasons.
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 1-14, 21-22 are clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (method, apparatus and non-transitory computer readable medium). Claims 1-14, 21-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1 and 21 recite the abstract idea of generating a policy and train the policy using training data comprising historical data associated with the BMS, wherein the policy is configured to predict corrective actions associated with one or more HVAC system faults and receiving temperature, pressure, and humidity (TPH) data and receiving fault information from a fault detection and diagnostic layer regarding a building subsystem device and determining an HVAC system fault of the building subsystem device based on the TPH data being out of compliance or predicted to be out of compliance with a compliance standard TPH and the fault information and providing the HVAC system fault to the policy and receiving scheduling data including at least one schedule event and executing the policy using the training data and the HVAC system fault, to provide a predicted corrective action to resolve the HVAC system fault wherein the predicted corrective action avoids a conflict with the at least one scheduled event and in response to determining the HVAC system fault, generating a work order and receive a notification indicating the work order has been completed, the notification comprising the determined HVAC system fault and an implemented corrective action. The claims are directed to a type of generating policy related to HVAC system faults and analyzing the data and generating a work order in response to the analysis. Under prong 1 of Step 2A, these claims are considered abstract because the claims are concepts performed in the human mind (including an observation, evaluation, judgment, opinion) as mental processes. The claims are considered concepts performed in the human mind because the claims show training a policy to predict corrective actions associated with one or more HVAC system faults (observation/evaluation/judgment), receive TPH sensor data (observation), receive fault information (observation), determine an HVAC system fault (evaluation/judgment), provide the HVAC fault to the policy (share information), output predicted corrective action (share information), and generate a work order (post-solution activity). Under prong 2 of Step 2A, the judicial exception is not integrated into a practical application because the claims (the judicial exception and any additional elements individually or in combination such as a building management system (BMS) for heating, ventilation, or air conditioning (HVAC) parameters in a building, the BMS comprising: one or more processing circuits comprising one or more memory devices coupled to one or more processors, the one or more memory devices configured to store instructions thereon that, when executed by the one or more processors, cause the one or more processors to perform steps, a machine learning engine and THP sensor data from one or more sensors and responsive to determining the HVAC system fault, automatically generate an instruction to display an interface comprising a work order based-on-the-TPH-sensor-data, to cause the BMS to resolve the HVAC system via the predicted corrective action and populating fields and wherein training the policy includes using the historical fault data as an input to the machine learning engine and running the machine learning engine with the policy to train the policy and executing the policy of the machine learning engine in real-time and automatically generate, in response to an interaction with the interface, a notification for display comprising information for implementing the work order to cause the BMs to resolve the HVAC system fault via the predicted corrective action and execute the machine learning engine to update the policy, wherein the policy is updated using the determined HVAC system fault and the implemented corrective action as inputs to the machine learning engine and running the machine learning engine with the policy to update the policy) are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment such that the claims as a whole is more than a drafting effort designed to monopolize the exception. These limitations at best are merely implementing an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as a building management system (BMS) for heating, ventilation, or air conditioning (HVAC) parameters in a building, the BMS comprising: one or more processing circuits comprising one or more memory devices coupled to one or more processors, the one or more memory devices configured to store instructions thereon that, when executed by the one or more processors, cause the one or more processors to perform steps, a machine learning engine and THP sensor data from one or more sensors and responsive to determining the HVAC system fault, automatically generate an instruction to display an interface comprising a work order based-on-the-TPH-sensor-data, to cause the BMS to resolve the HVAC system via the predicted corrective action and populating fields and wherein training the policy includes using the historical fault data as an input to the machine learning engine and running the machine learning engine with the policy to train the policy and executing the policy of the machine learning engine in real-time and automatically generate, in response to an interaction with the interface, a notification for display comprising information for implementing the work order to cause the BMs to resolve the HVAC system fault via the predicted corrective action and execute the machine learning engine to update the policy, wherein the policy is updated using the determined HVAC system fault and the implemented corrective action as inputs to the machine learning engine and running the machine learning engine with the policy to update the policy (as evidenced by para [0002], [0007]-[0014], [0052], [0076]-[0081], [0099]-[0100], [0111], [0126] of applicant’s own specification) are well understood, routine and conventional in the field. Dependent claims 2-5, 8-9, 11-13, 21-22 also do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements either individually or in combination are merely an extension of the abstract idea itself by further showing generate a first dashboard associated with the first user profile and a second dashboard associated with the second user profile, provide a first subset of information from the work order to the first dashboard, and provide a second subset of information from the work order to the second dashboard and update the second dashboard based on an action entered on the first dashboard and update the work order from either the first dashboard or the second dashboard and assign the work order to the second dashboard from the first dashboard and retrieve a fault causation template, map a plurality of operational parameters relating to an associated HVAC device to the fault causation template, and map the predicted corrective action to the fault causation template, and receive a notification that the work order has been completed, the notification comprising the determined fault and a fault solution, wherein the fault solution is either the predicted corrective action or a different action, and wherein the user is one of a chief compliance officer, a facilities manager, an operating room administrator, a health care professional or a facilities technician and provide the work order to a user interface, receive an indication that the work order has been completed, and provide assistance functionality and receive a request for assistance and provide additional information related to the corrective action and receive scheduling data including a scheduled event, wherein the work order is generated to avoid a conflict with the scheduled event. Claims 6-10, 12, 14 also do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as an application structured to access one of the first user profile or the second user profile and display the associated dashboard on a human machine interface, the associated dashboard displaying at least one of the TPH sensor data or the work order wherein the human machine interface includes a mobile device, a wall mounted panel, a monitor, a tablet, a kiosk, an augmented reality device, a virtual reality device, or a wearable device and provide a populated fault causation template to the user interface and train the policy with the machine learning engine by providing the determined fault and the fault solution to the machine learning engine wherein the machine learning engine includes at least one of a neural network, a reinforcement learning scheme, a model-based control scheme, a linear regression algorithm, a decision tree, a logistic regression algorithm, and a Naive Bayes algorithm and updating the user interface to indicate that the work order has been completed and assistance functionality to the user interface and provide an alert in the building in response to determining the fault, wherein the alert includes at least one of a visual alert, an audible alert, a fault indication, and corrective action indication (as evidenced by para [0002], [0007]-[0014], [0052], [0076]-[0081], [0099]-[0100], [0111], [0126] of applicant’s own specification) are well understood, routine and conventional in the field.
Allowable Subject Matter
Claims 1-14, 21-22 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Campbell et al. (US 2019/0012371 A1), a system for implementing multi-turn dialogs by receiving a series of user utterances, generating a series of responsive system utterances, and labeling the series of responsive system utterances to generate training data for training a dialog management policy
THIS ACTION IS MADE FINAL. 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 extension fee 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 SUJAY KONERU whose telephone number is (571)270-3409. The examiner can normally be reached M-F, 8:30 AM to 5 pm.
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/SUJAY KONERU/
Primary Examiner, Art Unit 3624
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