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 application 18646811 filed on April 26, 2024.
Claims 1-20 are pending.
Information Disclosure Statement
As required by M.P.E.P. 609(C), the applicant’s submission of the Information Disclosure Statement dated June 18, 2026 is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609, a copy of the PTOL-1449 initialed and dated by the examiner is attached to the office action.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Venne (US20240044541A1) in further view of Tomastik et al. (US20100299116A1) and Mohos et al. (US20230272934A1).
Regarding claim 1, Venne teaches a method comprising:
receiving, via at least one processor, an occupancy data of one or more zones via a plurality of sensors for a first period of time, wherein the occupancy data comprises at least a number of occupants within each zone for the first period of time (The system and method may be configured to control HVAC systems based on room occupancy ... Room occupancy may be determined ... based on historical data ... HVAC settings can be adjusted to account for number of anticipated occupants ... the database, preferably on a remote server 300 (e.g., on the cloud), aggregates all the data in an historic dataset and keeps a fine granularity of the historic time line for each data point)([0053], [0054], [0058], and [0092]; occupancy data of a zone (e.g., room) for a time period (i.e., historical) is retrieved);
determining, via the at least one processor, one or more … trends for each zone for the first period of time using a trained machine learning (ML) model … (These systems and methods may utilize advanced data processing and/or artificial intelligence … This database become overtime a big data picture of the thermodynamic behavior of the building and is used to extract additional value (e.g., trends) from the data set)([0040] and [0092]; artificial intelligence is used to analyze and detect trends);
predicting, via the at least one processor, occupancy of each of the one or more zones for a second period of time … using the trained ML model … (artificial intelligence and machine learning utilizes historical data to predict impact of current and/or anticipated environmental conditions ... Room occupancy may be ...predicted based on historical data)([0042] and [0054]); and
adjusting, via the at least one processor, the one or more temperature set points for each of the one or more zones … based at least on the prediction (By running analytics on the historic dataset of the building into the program, the program can determine how to control each of the HVAC components 400 to optimize the system ... Occupancy control ... Optimum start/stop heating/cooling ... Space temperature set points and control bands)([0077], [0157], [0159], and [0160]; a HVAC’s parameters (e.g., temperature set points) are controlled based on predicted occupancy).
Venne differs from the claim in that Venne fails to teach the trends are occupancy trends which comprises of occupancy in zones and a number of occupants within each zone for a time period such that a zone’s occupancy is predicted based on a mapping the occupancy trends to fluctuations in occupancy of each of the zones in real-time and to booking status of each of the zones.
However, determining occupancy trends which comprises of occupancy in zones and a number of occupants within each zone for a time period such that a zone’s occupancy is predicted based on a mapping the occupancy trends to fluctuations in occupancy of each of the zones in real-time and to booking status of each of the zones is taught by Tomastik (The application discloses a system and method for estimating occupancy ... occupancy is estimated based on a prediction of occupancy generated by an occupant traffic model ... the occupant traffic model is based on historical or expected traffic patterns of occupants throughout the area or region ... occupancy estimation algorithm 20 generates an occupancy estimate {circumflex over (x)} for each of the five zones ... Occupant traffic model ƒ is a mathematical, computer simulation, or statistical model used to predict expected traffic patterns of occupants ... such model may use a previous estimate of occupancy in the region ... Occupancy estimation algorithm 20 combines the model-based estimate of occupancy provided by the occupant traffic model ƒ with the sensor data z ... the occupancy estimate {circumflex over (x)} can be used to predict occupancy estimates into the near future. Near future occupancy estimates may be useful in controlling applications ... algorithm 20 is an Extended Kalman Filter (EKF) ... The EKF may make use of ... the kinetic motion (KM)-based occupant traffic model ... may be modeled based on statistical occupancy data, simulated occupancy data or stored data regarding the location of occupants ... occupancy data may be based on historical or observed data regarding the likely location of occupants ... In addition, any other stored data such as knowledge regarding scheduled meeting times may be used to initialize the initial occupancy state)([0019], [0021], [0028], [0029], [0032], [0033], [0034], and [0089]; occupancy trends comprising occupancy in zones and a number of occupants within each zone for a time period (i.e., traffic) is determined and correlated to real time data (i.e., current sensor data and scheduled meetings) to predict future occupancy for a zone).
The examiner notes Venne and Tomastik teach determining occupancy. As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Venne to include the determining of Tomastik such that occupancy trends comprising occupancy in zones and a number of occupants within each zone for a time period is determined to predict a zone’s occupancy based on a mapping the occupancy trends to fluctuations in occupancy of each of the zones in real-time and to booking status of each of the zones. One would be motivated to make such a combination to provide the advantage of controlling an application (e.g., HVAC) based on future occupancy ([0032]; Tomastik).
The combination of Venne-Tomastik fails to teach predicting a threshold time to heat or cool a zone at temperature setpoints. However, predicting a threshold time to heat or cool a zone at temperature setpoints is taught by Mohos (A heating profile for a zone is information about how long it takes the room to heat up after the heating has been switched on, and how long it takes the room to cool down after the heating has been switched off. The controller may “learn” the heating profiles over time, using machine learning techniques ... The system's decision-making module can change the status of the room ... The decision-making model can be applied to any zones using several inputs and preferences ... See FIG. 2 … optimization step uses information like the heating/cooling profile of the zones (see also Input 7) in order to determine the amount of time needed for a zone to reach the set temperature determined by the decision-making process … The decision-making process can also change the status of a zone ... through occupancy prediction, see Input 4)([0024], [0034], [0035], [0042], and [0046]; Figure 2 – controlling a HVAC based on predicted (i.e., learned) threshold time to heat or cool a zone at temperature setpoints (i.e., heating profile) is shown).
The examiner notes Venne, Tomastik, and Mohos teach determining occupancy. As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Venne-Tomastik to include the predicting of Mohos such that a threshold time to heat or cool a zone at temperature setpoints is predicted. One would be motivated to make such a combination to provide the advantage of maximizing user comfort while minimalizing energy cost ([0043]; Mohos).
Regarding claim 2, Venne-Tomastik-Mohos teach the method of claim 1, wherein the ML model for each of the one or more zones is trained based at least on the received occupancy data (Venne - predicting HVAC operational requirements based on ... factors including room occupancy ... may utilize advanced data processing and/or artificial intelligence ... to provide predictive HVAC control)([0036] and [0040]; in order to control based room occupancy, artificial intelligence must be trained on occupancy data).
Regarding claim 3, Venne-Tomastik-Mohos teach the method of claim 1, wherein the booking status of the one or more zones corresponds to the one or more zones pre-booked to be occupied by one or more users (Tomastik - stored data such as knowledge regarding scheduled meeting times may be used)([0089]).
Regarding claim 4, Venne-Tomastik-Mohos teach the method of claim 1, wherein the plurality of sensors corresponds to a plurality of zone level occupancy sensors comprising at least one lightning sensors, Wi-Fi Access Points and Bluetooth low energy (BLE) sensors, access readers, or carbon dioxide (CO2) sensors (Venne - HVAC sensors including ... CO2)([0055]).
Regarding claim 5, Venne-Tomastik-Mohos teach the method of claim 1, wherein the one or more zones comprises at least one of a building, a warehouse, a storage unit, or an office space, wherein opening within each zone corresponds to a number of windows and doors present in each zone (Venne - The systems and methods of the invention may be configured for a single building or a network of buildings)([0066]; a building comprises doors and windows as entry/exit points).
Regarding claim 6, Venne-Tomastik-Mohos teach the method of claim 1, wherein the first period of time corresponds to historical time zone and the second period of time corresponds to future time period, wherein the time period comprises at least day, time, season, months, or years (Venne - Room occupancy may be determined and/or predicted based on historical data, schedules (i.e., scheduled room occupancy), day of the week, etc.)([0054]; time period (i.e., historical) data is used to predict for a future time period (e.g., day of the week)).
Regarding claim 7, Venne-Tomastik-Mohos teach the method of claim 1, wherein the one or more temperature set points comprises:
at least one heating set point that initializes a heating cycle to increase temperature of the one or more zones; and at least one cooling set point that initializes a cooling cycle to decrease temperature of the one or more zones (Venne - FIG. 10 is a flowchart illustrating determine optimal start time of heating and cooling systems for each heating and cooling zone ... control HVAC systems based on room occupancy including controlling heating, cooling ... based on actual or predicted occupancy)([0029] and [0053]; heating/cooling set points (e.g., start times) for heating/cooling cycles are controlled), wherein the heating cycle or the cooling cycle is initialized based at least on the threshold time required for each of the one or more zones to heat or cool at the one or more temperature set points (Mohos - This optimization step uses information like the heating/cooling profile of the zones (see also Input 7) in order to determine the amount of time needed for a zone to reach the set temperature determined by the decision-making process)([0042]; start of cycles are controlled based on threshold time to heat or cool a zone at temperature setpoints (i.e., heating profile)).
Regarding claim 8, Venne-Tomastik-Mohos teach the he method of claim 7, wherein the heating cycle and the cooling cycle are identified based at least on the change in the one or more temperature set points, and the heating cycle and the cooling cycle end when the temperature of the one or more zones reaches the one or more temperature set points (Mohos - As each zone's energy demand and response time is different in case of a change in temperature settings, an optimisation algorithm is also used to determine the optimal timing to start increasing, decreasing, turn on and off the heating/cooling devices)([0040]; a temperature setpoint is specific target value at which a control system (i.e., HVAC) ends control of a heating/cooling cycle).
Regarding claim 9, Venne-Tomastik-Mohos teach the method of claim 1, wherein the at least one processor is configured to train the ML model using one or more Artificial Intelligence (AI)/ Machine Learning (ML) techniques (Venne - These systems and methods may utilize advanced data processing and/or artificial intelligence including traditional linear, non-liner regression models, supervised learning, unsupervised learning, deep learning and neural network artificial intelligence technics to provide predictive HVAC control)([0040]; artificial intelligence must be trained (i.e., using learning techniques such as supervised machine learning)).
Regarding system claim 10, the claim generally corresponds to method claim 1, and recites similar features in system form; therefore, the claim is rejected under similar rationale.
Regarding claim 11, Venne-Tomastik-Mohos teach the system of claim 10, wherein the at least one processor is further configured to train the ML model for each of the one or more zones based at least one the received occupancy data using one or more Artificial Intelligence (AI)/ Machine Learning (ML) techniques (Venne - predicting HVAC operational requirements based on ... factors including room occupancy ... These systems and methods may utilize advanced data processing and/or artificial intelligence including traditional linear, non-liner regression models, supervised learning, unsupervised learning, deep learning and neural network artificial intelligence technics to provide predictive HVAC control)([0036] and [0040]; in order to control based room occupancy, artificial intelligence must be trained (i.e., using learning techniques such as supervised machine learning) on occupancy data).
Regarding system claims 12-17, the claim generally corresponds to method claims 3-8, respectively, and recites similar features in system form; therefore, the claims are rejected under similar rationale.
Regarding non-transitory storage medium claims 18-20, the claim generally corresponds to method claims 1, 3, and 6, respectively, and recites similar features in non-transitory storage medium form; therefore, the claims are rejected under similar rationale.
Conclusion
The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider the reference fully when responding to this action. The document cited therein and enumerated below teaches a method and apparatus for occupancy based energy management.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to Yongjia Pan whose telephone number is (571)270-1177. The examiner can normally be reached Monday - Friday, 9:00 AM - 5:00 PM EST.
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/YONGJIA PAN/Primary Examiner, Art Unit 2118
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