Prosecution Insights
Last updated: April 19, 2026
Application No. 17/024,404

USER EXPERIENCE SYSTEM FOR IMPROVING COMPLIANCE OF TEMPERATURE, PRESSURE, AND HUMIDITY

Non-Final OA §101
Filed
Sep 17, 2020
Examiner
KONERU, SUJAY
Art Unit
3624
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Johnson Controls Technology Company
OA Round
9 (Non-Final)
58%
Grant Probability
Moderate
9-10
OA Rounds
3y 2m
To Grant
95%
With Interview

Examiner Intelligence

Grants 58% of resolved cases
58%
Career Allow Rate
421 granted / 722 resolved
+6.3% vs TC avg
Strong +37% interview lift
Without
With
+37.0%
Interview Lift
resolved cases with interview
Typical timeline
3y 2m
Avg Prosecution
36 currently pending
Career history
758
Total Applications
across all art units

Statute-Specific Performance

§101
37.9%
-2.1% vs TC avg
§103
50.7%
+10.7% vs TC avg
§102
2.0%
-38.0% vs TC avg
§112
7.4%
-32.6% vs TC avg
Black line = Tech Center average estimate • Based on career data from 722 resolved cases

Office Action

§101
DETAILED ACTION This Non-Final Office Action is in response to Applicant's amendments and arguments and request for continued examination filed on February 26, 2026. Applicant has amended claims 1 and 21. Currently, claims 1-14, 21 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 . Continued Examination Under 37 CFR 1.114 A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/26/26 has been entered. Information Disclosure Statement The information disclosure statement (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Response to Amendments The 35 U.S.C. 101 rejections of claims 1-14, 21 are maintained in light of applicant’s arguments. Response to Arguments Applicant’s arguments submitted on 12/30/25 have been considered but are not persuasive. Applicant argues on p. 12 of the remarks that the claims have limitations that cannot be performed mentally. Examiner notes the abstract idea can be performed mentally and that the claims have additional elements (which are the different components applicant discusses in p. 12-14) that are not part of the abstract idea that are tools for implementing the abstract idea. Applicant argues on p. 15 of the remarks that the claims integrate the abstract idea into practical application. Applicant argues on p. 15 of the remarks that the claims improve another technology or technical field. Applicant argues the claims improve compliance of temperature, pressure and humidity in building management systems. Examiner disagrees and notes the claims improve “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 are generally linking the abstract idea to a specific environment of compliance of temperature, pressure and humidity in building management systems. Examiner further notes the scheduling conflict improves are not sufficiently tethered to the claims. Applicant further argues on p. 17 of the remarks that the claims amount to significantly more than the abstract idea. Examiner disagrees. Examiner notes the combination of elements is not unconventional because the sequence of claims is receiving data, performing analysis and generating results which is the conventional sequence in making assessments and observations on data. Therefore, the claims remain rejected under 101. 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. 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) 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 (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 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 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. Kucera et al. (US 2020/0141653 A1), a flame analytics system that may incorporate a burner, one or more sensors at the burner, a historical database connected to the one or more sensors, a model training module connected to the historical database, and a runtime algorithm module connected to the one or more sensors and the model training module 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. 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, Patricia Munson can be reached on 571- 270-5396. 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. /SUJAY KONERU/ Primary Examiner, Art Unit 3624
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Prosecution Timeline

Sep 17, 2020
Application Filed
Apr 01, 2022
Non-Final Rejection — §101
Jul 06, 2022
Response Filed
Jul 14, 2022
Final Rejection — §101
Sep 06, 2022
Response after Non-Final Action
Sep 13, 2022
Response after Non-Final Action
Oct 20, 2022
Request for Continued Examination
Oct 25, 2022
Response after Non-Final Action
Oct 26, 2022
Non-Final Rejection — §101
Jan 31, 2023
Response Filed
Feb 07, 2023
Applicant Interview (Telephonic)
Feb 07, 2023
Examiner Interview Summary
Feb 13, 2023
Final Rejection — §101
Apr 26, 2023
Examiner Interview Summary
Apr 26, 2023
Applicant Interview (Telephonic)
May 17, 2023
Response after Non-Final Action
Jun 20, 2023
Request for Continued Examination
Jun 27, 2023
Response after Non-Final Action
Jul 21, 2023
Non-Final Rejection — §101
Oct 27, 2023
Response Filed
Oct 30, 2023
Final Rejection — §101
Dec 29, 2023
Response after Non-Final Action
Dec 29, 2023
Notice of Allowance
Jan 10, 2024
Response after Non-Final Action
Feb 29, 2024
Response after Non-Final Action
Mar 10, 2024
Response after Non-Final Action
Mar 21, 2024
Response after Non-Final Action
May 24, 2024
Response after Non-Final Action
May 28, 2024
Response after Non-Final Action
May 29, 2024
Response after Non-Final Action
May 29, 2024
Response after Non-Final Action
May 29, 2025
Response after Non-Final Action
Jul 28, 2025
Request for Continued Examination
Aug 03, 2025
Response after Non-Final Action
Aug 07, 2025
Non-Final Rejection — §101
Oct 28, 2025
Examiner Interview Summary
Oct 28, 2025
Applicant Interview (Telephonic)
Nov 11, 2025
Response Filed
Nov 24, 2025
Final Rejection — §101
Dec 30, 2025
Response after Non-Final Action
Feb 26, 2026
Request for Continued Examination
Mar 13, 2026
Response after Non-Final Action
Mar 23, 2026
Non-Final Rejection — §101 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

9-10
Expected OA Rounds
58%
Grant Probability
95%
With Interview (+37.0%)
3y 2m
Median Time to Grant
High
PTA Risk
Based on 722 resolved cases by this examiner. Grant probability derived from career allow rate.

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