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 applicant’s arguments/remarks and amendments filed on 05/15/2026. Claims 1-3, 7-8, 13-14, and 19-20 have been amended. No Claims have been cancelled. No Claims have been newly added. Accordingly, claims 1-20 are currently pending.
Information Disclosure Statement
The information disclosure statement filed on 05/01/2026 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein has not been considered.
Response to Arguments
Applicant’s arguments, see applicant’s arguments/remarks, filed on 05/15/2026, with respect to the rejection(s) of claim(s) 1-7, 10-13, and 15-20 under 35 U.S.C. 103 as being unpatentable over Zhang, Bier, and Ashgriz, have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Zhang, Ebrahimi, Ashgriz, Androulakis, and Bier as detailed below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim(s) 1-7, 10-13, and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al US 2024/0110717 A1 (hence Zhang) in view of Ebrahimi US 2026/0104291 A1 (hence Ebrahimi), Ashgriz et al US 2017/0153032 A1 (hence Ashgriz), Androulakis US 2023/0103173 A1 (hence Androulakis) and Bier et al US 2023/0228435 A1 (hence Bier).
In re claims 1, and 19-20, Zhang discloses method for controlling building equipment includes providing an occupancy prediction for a building using an occupancy prediction model that uses both historical values and forecast values of an environmental condition as inputs (Abstract) and teaches the following:
determining, by one or more processors, a number of occupants in a space, a location of occupants in the space, and spatial and temporal patterns of traffic of the occupants in the space based on input from one or more sensors and an artificial intelligence classifier (Paragraph 0095 “data indicative of the occupancy of the space”, Paragraph 0102 “facility, building, room, floor, area, etc” reads on location of occupants in the space, Paragraph 0104, and Paragraph 0095 “he occupancy sensors 606 may be access control devices (e.g., RFID readers, door ajar sensors) enabled to count individuals entering and/or leaving a space (e.g., based on detecting RFID tags of such individuals, based on counting opening of a door, etc.)” reads on spatial and temporal patterns of traffic of the occupants in the space);
creating, by the one or more processors, a thermal model of the space (Paragraph 0094 “temperature setpoints” and Paragraph 0107) based on the number of occupants (Paragraph 0095), data about an outside temperature from an outside surface of one or more walls of the space (Paragraph 0096), data about an inside temperature from an inside surface of one or more walls of the space (Paragraph 0037, and 0051-0052);
predicting, by the one or more processors using the thermal model, a temperature at the location of the occupants in the space for a period of time to create a predicted temperature (Fig.9 and Paragraphs 0109 and 0124-0125);
creating, by the one or more processors and for a building management system (BMS), adjustment instructions for the location of the occupants in the space and based on the thermal comfort (Fig.9, #906 and Paragraph 0126 and 0129);
and sending, by the one or more processors, the adjustment instructions to the BMS for adjusting the thermal comfort for the location of the occupants in the space (Fig.9, #908 and Paragraph 0128)
wherein the BMS adjusts an amount of air flow and a level of temperature of the air in the air conditioning system to achieve the adjustment of the thermal comfort for the location of the occupants in the space (Paragraph 0128)
However, Zhang doesn’t explicitly teach the following:
a trajectory of the occupants in the space, moving speed of the occupants in the space, and a temperature of the occupants in the space
infrared sensors
data about wall insulation for one or more walls of the space
predicting, by the one or more processors, a thermal comfort using a predicted mean vote (PMV) model and based on the predicted temperature at the location of the occupants in the space
Nevertheless, Ebrahimi discloses techniques for determining occupancy data in a building include collecting IR imaging data with an infrared (IR) detector configured to collect IR imaging data, the IR detector having a field of view (Abstract) and teaches the following:
a trajectory of the occupants in the space (Claim 12)
infrared sensors (Fig.8A, #801A, and Paragraph 0026)
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Zhang reference to include the infrared sensor to determine a trajectory of an occupant, as taught by Ebrahimi, with a reasonable expectation of success, in order to have an accurate understanding of occupancy density within the building as a function of time and location (Ebrahimi, Paragraph 0023).
Nevertheless, Ashgriz discloses a virtual thermostat to control an air condition of any target zone in an HVAC controlled space (Abstract) and teaches the following:
moving speed of the occupants in the space (Paragraph 0040)
predicting, by the one or more processors, a thermal comfort using a predicted mean vote (PMV) model and based on the predicted temperature (Paragraphs 0008 and 0093)
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Zhang reference to include measuring the human the human thermal comfort using Predicted Mean Vote (PMV), as taught by Ashgriz, with a reasonable expectation of success, in order to utilize a numerical estimation of the air temperature distribution in a space and use that estimation to control an HVAC system, therefore, significantly reducing energy consumption and improving human thermal comfort (Ashgriz, Paragraph 0006).
Nevertheless, Androulakis discloses method of controlling an occupant microclimate system includes determining vehicle environmental conditions, determining occupant personal parameters, predicting a multiple of occupant thermal comfort values based upon at least the environmental conditions, cabin temperature data, and occupant personal parameters (Abstract) and teaches the following:
a temperature of the occupants in the space (Paragraph 0009)
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Zhang reference to include determining occupant personal parameters, as taught by Androulakis, with a reasonable expectation of success, in order to regulate at least one thermal effector based upon the estimated occupant thermal comfort (Androulakis, Abstract).
Nevertheless, Bier discloses a system and a method for determining HVAC set points (Abstract) and teaches the following:
data about wall insulation for one or more walls of the space (Paragraph 0020 “temperature logs” and Paragraph 0066 “the amount of insulation available in the room”)
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Zhang reference to include the amount of insulation available in the room while calculating or determining HVAC setpoints, as taught by Bier, with a reasonable expectation of success, in order to provide a more efficient and less expensive approach for regulating temperature that satisfies the comfort preferences of occupants, while achieving greater energy savings and greater demand reduction, when needed (Bier, Paragraph 0006).
However, Zhang doesn’t explicitly teach the following:
predicting, by the one or more processors, a thermal comfort using a predicted mean vote (PMV) model and based on the predicted temperature;
In re claim 2, Zhang teaches the following:
determining, by one or more processors, a location of the occupants in the space, based on input from the one or more sensors and the artificial intelligence classifier (Paragraph 0095 “the occupancy sensors 606 may be cameras (e.g., thermal camera), for example combined with video and/or image processing adapted to determine a number of people visible in a space”)
In re claim 3, Zhang teaches the following:
wherein the one or more sensors are integrated into physical touch points in the space (Paragraph 0095)
In re claim 4, Zhang teaches the following:
changing, by the one or more processors, the adjustment instructions based on a cost of energy during different time periods (Paragraphs 0066-0068)
In re claim 5, Zhang teaches the following:
wherein the determining the number of occupants in the space includes predicting the number of occupants in the space based on at least one of historical number of occupants during a time period or changes to the number of occupants from the historical number of occupants during a time period (Paragraphs 0005, 0095, and 0104)
In re claim 6, Zhang teaches the following:
wherein the determining the number of occupants in the space includes using a neural network for predicting the number of occupants in the space based on at least one of historical number of occupants during a time period or changes to the number of occupants from the historical number of occupants during a time period (Paragraphs 0005, 0095, and 0104)
In re claim 7, Zhang teaches wherein the creating the thermal model include using a neural network (Paragraph 0124) and Androulakis teaches and the predicting the thermal comfort include using a neural network (Fig.1 and Paragraph 0026, motivation to combine has been provided above)
In re claim 10, Zhang teaches the following:
refining, by the one or more processors, the adjustment instructions to minimize fluctuation in the PMV index out of a comfort range (Paragraphs 0070, 0072, 0110, and 0122)
In re claim 11, Zhang teaches the following:
creating, by the one or more processors, revised adjustment instructions for the BMS to maintain the PMV index in a comfort range (Paragraphs 0070, 0072, 0110, and 0122)
In re claim 12, Zhang teaches the following:
wherein the adjustment instructions include at least one of load balancing while maintaining the PMV index within a percentage of a high end of a comfort range, reducing energy in the space with a lower number of occupants, or activating a first system in the BMS that uses less energy, instead of a second system that uses more energy (Paragraphs 0066-0068)
In re claim 13, Zhang teaches the following:
wherein the one or more sensors are multi-model sensors, wherein the multi-modal sensors include at least one of one or more radar sensors, one or more WiFi based occupancy sensors, or one or more radio frequency (RF) based occupancy sensors (Paragraph 0095)
In re claim 15, Zhang teaches the following:
creating, by the one or more processors, revised adjustment instructions for the BMS based on a profile of the occupant (Paragraphs 0119-0120)
In re claim 16, Zhang teaches the following:
wherein the creating the thermal model in the space is further based on humidity in the space, occupant temperatures of occupants in the space and surface temperatures of surfaces in the space (Paragraphs 0066 and 0086)
In re claim 17, Ashgriz teaches the following:
wherein the creating the thermal model in the space is further based on a mean radiant temperature (MRT) in the space (Paragraphs 0094-0095)
In re claim 18, Zhang teaches the following:
wherein the creating the thermal model in the space is further based on a location of the occupants in the space (Paragraphs 0095, 0099, 0103, and 0105)
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Ebrahimi, Ashgriz, Androulakis, and Bier and further in view of Drees et al US 2021/0207839 A1 (hence Drees).
In re claim 8, the combination of Zhang, Ebrahimi, Ashgriz, Androulakis, and Bier discloses the claimed invention as recited above but doesn’t explicitly teach the following:
wherein the one or more infrared sensors include one or more thermopile sensor
Nevertheless, Drees discloses a system for controlling temperature of a building space (Abstract) and teaches the following:
wherein the one or more infrared sensors include one or more thermopile sensor (Paragraph 0046)
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Zhang reference to include using one or more thermopile sensor, as taught by Drees, with a reasonable expectation of success, in order to detect the presence of a human (Drees, Paragraph 0046).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Ebrahimi, Ashgriz, Androulakis, and Bier and further in view of Berg-Sonne et al US 2012/0150788 A1 (hence Berg-Sonne).
In re claim 9, the combination of Zhang, Ebrahimi, Ashgriz, Androulakis, and Bier discloses the claimed invention as recited above but doesn’t explicitly teach the following:
implementing, by the one or more processors, time slicing to minimize fluctuation in the PMV index out of a comfort range
Nevertheless, Berg-Sonne discloses automated facilities management system has the ability to predict occupant behavior by identifying recurring patterns in the way that people use buildings and comparing them with environmental characteristics (Abstract) and teaches the following:
implementing, by the one or more processors, time slicing to minimize fluctuation in the PMV index out of a comfort range (Fig.5 and Paragraphs 0192-0194)
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Zhang reference to include shorter time slices, as taught by Berg-Sonne, with a reasonable expectation of success, in order to determines the probability of certain behaviors for various time intervals (Berg-Sonne, Paragraph 0194).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Ebrahimi, Ashgriz, Androulakis, and Bier, and further in view of Byoung KR 102368838 B1 (the examiner has provided an English translation and relying upon, hence Byoung).
In re claim 14, the combination of Zhang, Ebrahimi, Ashgriz, Androulakis, and Bier discloses the claimed invention as recited above but doesn’t explicitly teach the following:
wherein the one or more sensors include a plurality of sensors, and a smart mesh system is included between sensors of the plurality of sensors, wherein the smart mesh system is configured to measure the distance between the sensors
Nevertheless, Byoung discloses a method and apparatus for diagnosing and managing a failure of a sensor node in an off-site smart farm (Abstract) and teaches the following:
wherein the one or more sensors include a plurality of sensors, and a smart mesh system is included between sensors of the plurality of sensors, wherein the smart mesh system is configured to measure the distance between the sensors (Abstract)
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Zhang reference to include the sensor mesh structure, as taught by Byoung, with a reasonable expectation of success, in order to estimate a state of an object based on values obtained from a plurality of sensor nodes and position information of each sensor node (Byoung, Abstract).
Conclusion
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 RAMI KHATIB whose telephone number is (571)270-1165. The examiner can normally be reached M-F: 9:00am-5:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin M Piateski can be reached at 571-270 7429. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RAMI KHATIB/Primary Examiner, Art Unit 3669
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