DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Remarks
2. Claims 1-20 have been examined and rejected. This Office action is responsive to the amendment filed on April 3, 2026, which has been entered in the above identified application.
Claim Objections
3. The correction to claim 11 has been approved, and the objection to the claim is withdrawn.
Claim Rejections - 35 USC § 102
4. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
5. Claims 1-4, 9-16, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Turner et al (U.S. Patent No. 10,352,884).
5-1. Regarding claims 1, 15, and 20, Turner teaches the claim comprising: identifying an operational constraint for a device in an environment, by disclosing specifying a plurality of constraints in a constraint set including but not limited to temperature constraints, energy efficiency constraints, power consumption constraints, financial costs constraints, and the like constraints 123 [column 45, lines 38-42].
Turner teaches selecting a device setting for the device operating in the environment, by disclosing a thermal control unit 1000 that controls HVAC units, and can regulate the temperature in a zone of a thermal system by supporting active cooling and/or heating based on a temperature set by a user [column 7, lines 1-28]. A thermal control unit is represented by a thermal device 700 in the thermal system [column 6, lines 60-63].
Turner teaches predicting an environmental state of the environment based on the device operating at the device setting and under the operational constraint through application of an environmental state model that maps varying operational constraints and varying device settings to varying environmental states in the environment, by disclosing receiving weather estimations and a plurality of thermal properties observed by the thermal device [column 45, lines 11-22], using such information to determine a current discrete temperature state [column 45, lines 23-25], and constructing a collection of realizable temperature states that can be achieved within a period of time or control duration by controlling a device, for example, an HVAC, and that is not inhibited by an existing constraint [column 45, lines 25-37, 42-58].
Turner teaches predicting an energy state for operating the device in the environment at the device setting and under the operational constraint based on the predicted environmental state through application of an energy consumption model that maps the varying environmental states to varying energy states, by disclosing using a thermal model to predict energy consumption [column 3, lines 15-20; column 8, lines 60-63] based on observing one or more thermal devices 700, one or more further thermal models 600 associated with a site 800, and a weather model 500 [column 9, lines 36-40; column 10, lines 15-23; column 25, line 50 to column 26, line 47]. Use of the thermal model allows for the identification of an optimal path through the collection of temperature states which minimizes a cost function in terms of energy and power [column 38, lines 3-8].
5-2. Regarding claims 2 and 16, Turner teaches all the limitations of claims 1 and 15 respectively, further comprising: predicting different energy states for operating the device at different device settings in the environment and under the operational constraint, by disclosing constructing a collection of realizable temperature states that can be achieved within a period of time or control duration by controlling a device, for example, an HVAC, and that is not inhibited by an existing constraint [column 45, lines 25-37, 42-58].
Turner teaches selecting a device setting of the different device settings for operating the device under the operational constraint based on the different energy states; and facilitating operation of the device in the environment according to the device setting, by disclosing that once an optimal path is determined, producing an action plan in the form of a sequence of commands to be executed by a thermal device, for example a HVAC, to navigate the subset of temperature states comprised in the optimal path during the time specified by the control duration [column 46, lines 4-9].
5-3. Regarding claim 3, Turner teaches all the limitations of claim 1, wherein operation of the device in the environment affects the varying environmental states in the environment, by disclosing that the thermal control unit 1000 controls HVAC units, and can regulate the temperature in a zone of a thermal system by supporting active cooling and/or heating based on a temperature set by a user [column 7, lines 1-28].
5-4. Regarding claim 4, Turner teaches all the limitations of claim 3, wherein the environment is an enclosed space, by disclosing that the zone may be an enclosed environment [column 3, lines 15-20; column 4, lines 36-38; 53-55].
5-5. Regarding claim 9, Turner teaches all the limitations of claim 1, wherein the energy consumption model maps the varying environmental states to varying energy states through multiple regression techniques, by disclosing performing regression analysis [column 23, line 45 to column 26, line 57].
5-6. Regarding claim 10, Turner teaches all the limitations of claim 1, wherein the energy state is identified for the device on one of a device-specific basis, a device-zone specific basis, or a device-group specific basis, by disclosing that the optimal path is for a specific zone and for a device within the zone [column 38, lines 22-28].
5-7. Regarding claim 11, Turner teaches all the limitations of claim 10, further comprising: identifying an operational constraint for another device in the environment, wherein the another device is separate from the device, in a different zone from the device, or in a different group from the device, by disclosing that a site contains more than one zone with each zone containing one or more thermal control units [column 5, lines 51-54] and thus, functions of the system can be performed on other thermal control units in other zones of the site. A plurality of constraints are specified in a constraint set including but not limited to temperature constraints, energy efficiency constraints, power consumption constraints, financial costs constraints, and the like constraints 123 [column 45, lines 38-42].
Turner teaches selecting a device setting for the another device in the environment, by disclosing a thermal control unit 1000 that controls HVAC units, and can regulate the temperature in a zone of a thermal system by supporting active cooling and/or heating based on a temperature set by a user [column 7, lines 1-28]. A thermal control unit is represented by a thermal device 700 in the thermal system [column 6, lines 60-63].
Turner teaches predicting the environmental state of the environment based on the another device operating at the device setting for the another device and under the operational constraint for the another device through application of the environmental state model, by disclosing receiving weather estimations and a plurality of thermal properties observed by the thermal device [column 45, lines 11-22], using such information to determine a current discrete temperature state [column 45, lines 23-25], and constructing a collection of realizable temperature states that can be achieved within a period of time or control duration by controlling a device, for example, an HVAC, and that is not inhibited by an existing constraint [column 45, lines 25-37, 42-58].
Turner teaches predicting an energy state for operating the another device in the environment at the device setting for the another device and under the operational constraint for the another device through application of the energy consumption model independently from applying the energy consumption model to predict the energy state for operating the device in the environment, by disclosing using a thermal model to predict energy consumption [column 3, lines 15-20; column 8, lines 60-63] based on observing one or more thermal devices 700, one or more further thermal models 600 associated with a site 800, and a weather model 500 [column 9, lines 36-40; column 10, lines 15-23; column 25, line 50 to column 26, line 47]. Use of the thermal model allows for the identification of an optimal path through the collection of temperature states which minimizes a cost function in terms of energy and power [column 38, lines 3-8].
5-8. Regarding claim 12, Turner teaches all the limitations of claim 1, further comprising: iteratively updating either or both the environmental state model and the energy consumption model based on new data gathered in the environment, by disclosing update of the various algorithms used to make predictions [column 20, lines 5-39; column 29, lines 10-30; column 31, lines 10-53; column 32, line 60 to column 34, line 5; column 39, line 5 to column 40, line 56].
5-9. Regarding claim 13, Turner teaches all the limitations of claim 12, wherein the new data is generated based on continued operation of the device of a plurality of devices in the environment, by disclosing that the thermal control unit 1000 controls HVAC units, and can regulate the temperature in a zone of a thermal system by supporting active cooling and/or heating based on a set temperature [column 7, lines 1-28]. Changing the set temperature would provide new data for the system.
5-10. Regarding claim 14, Turner teaches all the limitations of claim 12, wherein the new data is generated based on operation of new devices added to the environment, by disclosing that a zone may have one or more thermal control units [column 7, lines 32-35] wherein a thermal control unit defines properties and events associated with a specific zone [column 7, lines 39-52]. Thus, adding a new thermal control unit to the zone would provide new data that the system would use at each update period.
Claim Rejections - 35 USC § 103
6. 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.
7. Claims 5-8 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Turner et al (U.S. Patent No. 10,352,884) in view of Shimokawa et al (U.S. Patent No. 12,410,936).
7-1. Regarding claims 5 and 17, Turner teaches all the limitations of claims 1 and 15 respectively. Turner does not expressly teach wherein the environmental state model is trained across the varying device settings and the varying operational constraints based on historical data related to one or more devices operating in the environment, the method further comprising: defining a specific device state of a plurality of device states for the one or more devices operating in the environment from the historical data according to either or both a specific device setting of the varying device settings and a specific operational constraint of the varying operational constraints using historical data related to previous operations of the specific device in the environment; mapping a specific environmental state of the varying environmental states in the environment to the specific device state based on environmental sensor data included in the historical data; and training the environmental state model based on the mapping of the specific environmental state to the specific device state of the plurality of device states via supervised learning processes. Shimokawa discloses an indoor-temperature estimation apparatus that estimates room temperature [column 6, lines 34-38] based on operation of a temperature control device that affects the temperature of the room [column 6, lines 62-65] and external environment information indicating an outdoor environmental state of the outside of the room [column 7, lines 25-42]. A room temperature model is generated based on room temperature history information and external environment information that indicates the relationship between the outdoor state and room temperature during an unaffected period [column 6, lines 53-61; column 7, lines 51-62; column 8, line 53 to column 9, line 3; column 13, lines 1-10] and a room temperature change model is generated based on the combinations of the room temperature history information, the temperature control information, and the external environment information [column 9, lines 35-41; column 14, lines 42-49]. The models are trained via supervised learning [column 13, lines 1-10; column 14, lines 42-44]. Once generated, the models are used to determine an estimated room temperature in periods unaffected by the temperature control device [column 18, lines 7-12] and periods affected by the temperature control device [column 18, lines 39-47] to generate a final estimated result of the room temperature [column 18, line 63 to column 19, line 9]. This would provide more accurate predictions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to train the models of Turner based on historical data related to previous operations of the specific device in the environment and via supervised learning process, as taught by Shimokawa. This would provide more accurate predictions.
7-2. Regarding claims 6 and 18, Turner-Shimokawa teach all the limitations of claims 5 and 17 respectively, wherein mapping the specific environmental state to the specific device state further comprises: identifying one or more environmental characteristics of the environment that are affected by operation of the one or more devices in the environment at the specific device state from the environmental sensor data included in the historical data; and identifying the specific environmental state corresponds to the specific device state based on the one or more environmental characteristics, by disclosing training the models using historical external environment information [Schimokawa, column 7, lines 51-62].
7-3. Regarding claim 7, Turner-Shimokawa teach all the limitations of claim 6, wherein the one or more environmental characteristics are related to the specific environmental state in a correlation of the varying environmental states across a plurality of environmental characteristics that are affected by the varying operational constraints and the varying device settings, the method further comprising: identifying the specific environmental state based on a relation of the one or more environmental characteristics to the specific environmental state in the correlation of the varying environmental states across the plurality of environmental characteristics including the one or more environmental characteristics, by disclosing training the models using historical external environment information [Schimokawa, column 7, lines 51-62; column 13, lines 30-44].
7-4. Regarding claims 8 and 19, Turner-Shimokawa teach all the limitations of claims 5 and 17 respectively, wherein either or both the environmental state model and the energy consumption model are trained and implemented based on the historical data in an agnostic manner, by disclosing that the models may be trained using data from rooms having similar attributes [Shimokawa, column 15, lines 15-17].
Response to Arguments
8. The Examiner acknowledges the Applicant’s amendments to claims 5, 11, and 17.
Regarding independent claim 1, Applicant alleges that Turner et al (U.S. Patent No. 10,352,884) does not teach identifying an operational constraint for a device in an environment, because Turner neither explicitly associates the constraints with any specific device, nor describes an environment in which such a device associated with the constraints may be located.
Contrary to Applicant’s arguments, based on the limitation in the claim, the “device” in the claim may correspond to any device in Turner whose operation is affected by the constraint. This would include a thermal device [column 6, lines 60-63] that controls HVAC units to regulate the temperature in a zone of the thermal system [column 7, lines 1-28]. Since the thermal device is operated according to an action plan [column 46, lines 4-9], and the action plan is produced based on a determined optimal path that respects established constraints [column 38, lines 3-8], the thermal device is considered as operating under those constraints.
Applicant alleges that the Office changes definition of the device from the Comfort Agent to a thermal control unit when teaching “selecting a device setting for the device operating in the environment.”
Contrary to Applicant’s arguments, as discussed above, the device in the claim corresponds to a thermal device because such a thermal device is operated under constraints according to the action plan. Thus, there is no inconsistency in the rejection.
Applicant alleges that the algorithm of Turner does not teach a model that “maps varying operational constraints and varying device settings to varying environmental states” because Turner constructs a path-optimizing algorithm based on a fixed number of predefined temperature states.
Examiner notes that the term “constraint” in the claim is interpreted as a limitation, restriction, or boundary that confines, controls, or shapes the operation of the device. A model is interpreted as an abstract representation of a real-world system, process, or concept. Contrary to Applicant’s arguments, Turner discloses that a comfort agent component can receive weather estimations and a plurality of thermal properties observed by the thermal device including cool, coefficient, device temperature, energy consumed in active cooling or heating, hold, power capacity, and temperature rate, associated with a specific zone [column 45, lines 11-22]. The cool property indicates active cooling selection for the thermal device [column 7, lines 64-67]. The comfort agent component uses such information to determine a current discrete temperature state [column 45, lines 23-25], and constructs a collection of realizable temperature states that can be achieved within a period of time or control duration by controlling a device, for example, an HVAC, and that is not inhibited by an existing constraint [column 45, lines 25-37]. These realizable temperature states are not predefined, but constructed using a model based on what is achievable within a period of time by controlling a device, and with respect to various constraints. Thus, the realizable temperature states are determined based on a model that defines a relationship between those states, varying constraints, and varying device settings.
Applicant alleges that Turner does not teach a second, distinct model to predict energy states based on predicted environmental states because Turner’s “thermal model” that evaluates energy usage as part of a cost function when choosing among candidate operating paths does not use predicted environmental states as inputs, output predicted energy states, or separate environmental modeling from energy modeling.
Contrary to Applicant’s arguments, Turner discloses using a thermal model to predict energy consumption [column 3, lines 15-20; column 8, lines 60-63] based on observing one or more thermal devices 700, one or more further thermal models 600 associated with a site 800, and a weather model 500 [column 9, lines 36-40; column 10, lines 15-23; column 25, line 50 to column 26, line 47]. The thermal model may be used to estimate energy consumption required to realize a future forecast temperature sequence [column 26, lines 30-33]. Use of the thermal model allows for the identification of an optimal path through the collection of temperature states which minimizes a cost function in terms of energy and power [column 38, lines 3-8]. Thus, in order to determine the optimal path, the thermal model is used to predict energy consumption required to go from a predicted temperature state to a future temperature state.
Applicant alleges that Turner does not teach the steps for operating the control system in the same order as recited in the claims, wherein the order is: (1) Identify constraint, (2) Select device setting, (3) Predict environmental state based on (1) and (2), and (4) Predict energy state based on (3).
Examiner notes that (1) and (2) are not dependent on each other and thus, may be carried out in any order before (3) and (4). Contrary to Applicant’s arguments, Turner discloses a thermal device that controls HVAC units, and can regulate the temperature in a zone of a thermal system by supporting active cooling and/or heating based on a temperature set by a user [column 6, lines 60-63; column 7, lines 1-28]. A collection of realizable temperature states is constructed that can be achieved within a period of time or control duration by controlling a device, for example, an HVAC, and that is not inhibited by an existing constraint [column 45, lines 25-37, 42-58]. Since such realizable temperature states are constructed in consideration of constraints, such constraints must be identified before the construction. A thermal model is used to predict energy consumption [column 3, lines 15-20; column 8, lines 60-63] based on observing one or more thermal devices 700, one or more further thermal models 600 associated with a site 800, and a weather model 500 [column 9, lines 36-40; column 10, lines 15-23; column 25, line 50 to column 26, line 47]. The thermal model may be used to estimate energy consumption required to realize a future forecast temperature sequence [column 26, lines 30-33]. Use of the thermal model allows for the identification of an optimal path through the collection of temperature states which minimizes a cost function in terms of energy and power [column 38, lines 3-8]. Thus, in order to determine the optimal path, a temperature state must first be predicted and used as input into the thermal model to predict energy consumption required to go from the predicted temperature state to a future temperature state.
Similar arguments have been presented for independent claims 15 and 20 and thus, Applicant’s arguments are not persuasive for the same reasons.
Regarding dependent claim 9, Applicant alleges that Turner does not associate the regression technique with a method of mapping the environmental states to energy states because Turner teaches the use of regression techniques for addressing errors in estimates of the thermal coefficient vector, which is a well known technique for addressing indeterminacy in data modeling.
Contrary to Applicant’s arguments, Turner discloses a thermal model that learns a relationship between thermal conditions and resulting energy behavior by solving for a thermal coefficient vector from observed data, which is akin to a multiple linear regression model. The thermal model observes thermal device properties associated with a zone, observes weather model properties, and relates the observed rate of temperature change to the estimated heat transfer [column 10, lines 15-19]. Heat transfer can be estimated as a function of various observations comprising inter-zone temperature differences, energy consumed in active cooling or heating, cloud cover, solar irradiance, and/or the like [column 10, lines 19-22]. These observations are collected into an incident matrix
X
-
i
,
c
,
n
[column 20, lines 43-54]. A reference vector
y
-
i
,
c
,
n
is formed from the observations and represents the observed rate of temperature change [column 20, lines 55-65]. The reference vector
y
-
i
,
c
,
n
is equal to the product of the incident matrix
X
-
i
,
c
,
n
and a thermal coefficient vector
ω
-
i
,
c
,
n
[column 21, lines 22-25]. The thermal coefficient vector
ω
-
i
,
c
,
n
is indirectly solved by minimizing the L2 error in the linear system
y
-
i
,
c
,
n
=
X
-
i
,
c
,
n
ω
-
i
,
c
,
n
[column 21, lines 25-29] using methods such as matrix inversion, Moore-Penrose method, QR decomposition, single value decomposition, or Gaussian elimination [column 21, lines 48-52]. Additionally, rank reduction may be used to increase numerical accuracy prior to solution of the thermal coefficient vector [column 24, lines 12-17]. The resulting coefficients are used to estimate future energy consumption and temperatures [column 25, lines 50-60]. Thus, the thermal model of Turner is considered to map varying environmental states to varying energy states through multiple regression techniques.
Regarding dependent claims 5 and 17, Applicant alleges that Turner and Hanley et al (U.S. Patent No. 10,185,345) describe fundamentally incompatible mathematical modeling frameworks and cannot be combined because Turner discloses an algorithm that relies on a set of predefined states and sets of conditions to direct how the system traverses a trajectory from one state to the next, whereas Hanley teaches use of a generic machine learning algorithm to optimize an energy efficiency model.
Examiner notes that Turner discloses that the system can learn heat transfer characteristics of the thermal systems and the thermal comfort characteristics of the occupants, and control the temperature in a manner that minimizes energy consumption while maintaining occupant comfort [Turner, column 4, lines 38-45]. As discussed above, Turner discloses constructing a collection of realizable temperature states that can be achieved within a period of time or control duration by controlling a device, for example, an HVAC, and that is not inhibited by an existing constraint [Turner, column 45, lines 25-37]. Thus, Turner is not fundamentally incompatible with using a machine learning algorithm for predicting future temperature states. Due to Applicant’s amendments to the claim, Examiner has rejected claim 5 under 35 U.S.C. 103 as being unpatentable over Turner et al (U.S. Patent No. 10,352,884) in view of Shimokawa et al (U.S. Patent No. 12,410,936). Applicant’s arguments have been considered but are moot in view of the new grounds of rejection.
Dependent claims 6, 7, 8, 18, and 19 have also been rejected under 35 U.S.C. 103 as being unpatentable over Turner in view of Shimokawa. Applicant’s arguments have been considered but are moot in view of the new grounds of rejection.
Applicant states that dependent claims 2-14 and 16-19 recite all the limitations of the independent claims, and thus, are allowable in view of the remarks set forth regarding independent claims 1 and 15. However, as discussed above, Turner is considered to teach claims 1 and 15, and consequently, claims 2-14 and 16-19 are rejected.
Conclusion
9. 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.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALVIN H TAN whose telephone number is (571)272-8595. The examiner can normally be reached M-F 10AM-6PM.
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/ALVIN H TAN/Primary Examiner, Art Unit 2118
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