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 .
Response to Amendment
Applicant has submitted amendments to the claims submitted on 3/2/2026 are being examined.
Drawings
There were two drawings submitted on 10/11/2023. One drawing set is of a weapon which is not mentioned in the claims. The second drawing set are directed towards a temperature control system which is what was claimed.
Priority
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
In regards to claim 1, the disclosure of the prior-filed application, provisional application No. 61/624175, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Provisional application 61/624175 does not provide support or enablement for the following limitations in claim 1, “applying the temperature measurements to a bias-correcting model and thereby correcting bias in the temperature measurements and generating corrected temperature measurements and controlling, based at least in part on the corrected temperature measurements, an environment of the enclosure to maintain a thermal profile in the enclosure”. Application No. 17/250586 which claims priority to PCT/US2019/046524 filed Aug. 14, 2019 has been deemed as providing some level of support for the limitations above (0277). Therefore, claim 1 has been determined as having a priority date dating to Aug. 14, 2019. Claim 52 is similar to claim 1 and is similarly determined to have a priority date dating to Aug. 14, 2019.
In regards to claims 23-24 and 27, the disclosure of the prior-filed application, provisional application No. 61/624175, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Provisional application 61/624175 does not provide support or enablement for the following limitations in claim 23-24 and 27, “wherein extrapolating the corrected temperature measurements comprises projecting temperature gradients extending from the locations of the temperature sensors to the grid points”, “wherein the temperature gradients are calculated from a heat transfer rate and the heat transfer rate is characterized by heat conduction”, and “wherein the temperature gradients are modeled based at least in part on a physics simulation”. Examiner was unable to find any adequate support or enablement for these claims. Therefore, the priority will be understood as being to 10/11/2023 which is the filing date of this application.
In regards to claim 126, the disclosure of the prior-filed application, provisional application No. 61/624175, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Provisional application 61/624175 does not provide support or enablement for the following limitations in claim 126, “collecting temperature measurements from temperature sensors mounted at locations in the enclosure; quantifying one or more environmental characteristics biasing the temperature measurements with respect to temperatures of volumes of air at or adjacent to the locations of the sensors; correcting bias in the temperature measurements based at least in part on the one or more environmental characteristics as one or more correction factors to generate corrected temperature measurements; extrapolating the corrected temperature measurements according to a plurality of grid points arranged between the locations of the temperature sensors based at least in part on an estimate of heat transfer in the enclosure to generate grid point temperatures; converting the grid point temperatures to a mapping of a thermal profile; and using the mapping to control the one or more environmental characteristics of the enclosure in order to obtain the target thermal profile”. Examiner was unable to find any adequate support or enablement for this. Therefore, the priority will be understood as being to 10/11/2023 which is the filing date of this application.
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.
Claim(s) 1, 2, 5, 8, 10, 14, 17-18, 44, 45, 52 is/are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al (US PUB. 20150285526, herein Smith) in view of Kamel et al (US PUB. 20120323382, herein Kamel).
Regarding claim 1, Smith teaches A method, comprising:
collecting temperature measurements from a plurality of temperature sensors mounted at locations in an enclosure, wherein one or more of the temperature sensors are subject to temperature biasing by one or more environmental characteristics (0072 “method 200 can be executed for any temperature sensor in communication with the HVAC system controller 105. This can, for example, allow for temperature control from the various sensors. The HVAC system controller 105 can be configured with one or more sensor conditions to determine which of the various sensors is used to provide temperature measurements to the HVAC system controller 105. For example, a sensor in a bedroom can be used for temperature measurements and HVAC system control during nighttime and a sensor in a television room can be used for temperature measurements and HVAC system control during daytime and/or evening time”);
applying the temperature measurements to a bias-correcting model and thereby correcting bias in the temperature measurements and generating corrected temperature measurements (0074 “The temperature measurement can, for example, be determined from the one or more sensors 130A-130C. At 292, the HVAC system controller 105 determines a dynamic correction factor for the temperature measurement. The dynamic correction factor determined at 292 is dependent upon one or more dynamic parameters and by the various information determined from the HVAC system controller 105 identifying a state (e.g., on, off, heating mode, cooling mode, fans only, or the like) of the HVAC system”, 0065 “Generally, the measured temperature will decrease and settle at a max offset. The decreasing portion represents the “on” model 265, and a curve-fitting algorithm can be used to determine a dynamic correction factor that is used to correct temperature when the one or more fans are enabled”);
and controlling, based at least in part on the corrected temperature measurements, an environment of the enclosure to maintain a thermal profile in the enclosure (0015 “HVAC system controller determines a dynamic correction factor based on one or more dynamic parameters and modifies the temperature measurement based on the dynamic correction factor. The method further includes controlling the HVAC system based on the modified temperature measurement”).
The cited prior art do not teach wherein the bias-correcting model is based, at least in part, on a 3D model of an architecture of the enclosure.
Kamel teaches wherein the bias-correcting model is based, at least in part, on a 3D model of an architecture of the enclosure (0083 “the building energy management and control element 314 comprises a graphical user interface and provides visualization to the user of the energy calculations and corrective actions using the two and three dimensional models of the building 104 from the computer aided modeling element”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to have modified the teachings of Smith with the teachings of Kamel since Kamel teaches a means for “actual energy usage exceeds the predicted energy usage, the system transmits an alert to a user and determines corrective measures to reduce energy usage” (abstract).
Regarding claim 2, the cited prior art teach The method of claim 1.
Smith teaches wherein the controlling includes controlling one or more elements of the enclosure include a building system, a climate control element, heating, ventilation, and air conditioning (HVAC) system, a tint level of a tintable window, or a combination thereof (0015 “HVAC system controller determines a dynamic correction factor based on one or more dynamic parameters and modifies the temperature measurement based on the dynamic correction factor. The method further includes controlling the HVAC system based on the modified temperature measurement”).
Regarding claim 5, the cited prior art teach the method of claim 1.
Smith teaches wherein at least one temperature sensor of the one or more temperature sensors that are subject to the temperature biasing by the one or more environmental characteristics is disposed in an envelope of the enclosure, in a framing portion of a window or door, a mullion, and/or a transom, a southern facing or partially southern facing wall of the enclosure (0009 0034 “dynamic parameter” includes, for example, a parameter of a conditioned space that can be dynamically changing. Examples of dynamic parameters include, but are not limited to, airflows; secondary heat sources (such as, but not limited to, fireplaces, space heaters, sunlight, cooking sources (e.g., stoves, ovens, grills, or the like), or the like); energy losses detectable by home automation sensors and capable of being reported to an HVAC system controller (such as, but not limited to, those caused by opening of doors, garage doors, windows, exhaust fans, or the like); losses based on thermal mass of a conditioned space (discussed in additional detail below); properties not related directly to temperature (such as, but not limited to, sources of humidity (e.g., pools, hot tubs, saunas, or the like), clothes dryers, automatic dishwashers, showers, bathrooms, or the like); or other similar parameters that can change over time and can affect environmental control of the conditioned space”).
Regarding claim 8, the cited prior art teach The method of claim 1.
wherein the environmental characteristics include one or more of an outside atmospheric temperature, a solar angle, a solar elevation, a difference between solar azimuth and window azimuth, a solar intensity, solar radiation wavelengths, a solar penetration depth into the enclosure, a tint state of one or more tintable windows of the enclosure, a sensor orientation, one or more properties of a framing portion proximate to a temperature sensor, one or more surface properties external to the enclosure, a proximity of the temperature sensor to an HVAC outlet, a proximity of the temperature sensor to a diffuser, one or more insulation properties of the enclosure, geographic coordinates of the facility and/or orientation of its facades, window elevation, facility elevation, size and/pr placement of facility overhangs, HVAC setpoint, sensor ensemble heat transfer parameters, thermal properties of sensor mounting fixtures, sun-load information, location of vents, airflow speed, air pressure, humidity, window framing heat transmittance, or a combination thereof (0009 0034 “dynamic parameter” includes, for example, a parameter of a conditioned space that can be dynamically changing. Examples of dynamic parameters include, but are not limited to, airflows; secondary heat sources (such as, but not limited to, fireplaces, space heaters, sunlight, cooking sources (e.g., stoves, ovens, grills, or the like), or the like); energy losses detectable by home automation sensors and capable of being reported to an HVAC system controller (such as, but not limited to, those caused by opening of doors, garage doors, windows, exhaust fans, or the like); losses based on thermal mass of a conditioned space (discussed in additional detail below); properties not related directly to temperature (such as, but not limited to, sources of humidity (e.g., pools, hot tubs, saunas, or the like), clothes dryers, automatic dishwashers, showers, bathrooms, or the like); or other similar parameters that can change over time and can affect environmental control of the conditioned space”, surface properties external to the enclosure).
Regarding claim 10, the cited prior art teach The method of claim 1.
further comprising collecting data regarding the environmental characteristics, wherein the bias-correcting model is based, at least in part, on the data (0009 0034 “dynamic parameter” includes, for example, a parameter of a conditioned space that can be dynamically changing. Examples of dynamic parameters include, but are not limited to, airflows; secondary heat sources (such as, but not limited to, fireplaces, space heaters, sunlight, cooking sources (e.g., stoves, ovens, grills, or the like), or the like); energy losses detectable by home automation sensors and capable of being reported to an HVAC system controller (such as, but not limited to, those caused by opening of doors, garage doors, windows, exhaust fans, or the like); losses based on thermal mass of a conditioned space (discussed in additional detail below); properties not related directly to temperature (such as, but not limited to, sources of humidity (e.g., pools, hot tubs, saunas, or the like), clothes dryers, automatic dishwashers, showers, bathrooms, or the like); or other similar parameters that can change over time and can affect environmental control of the conditioned space”).
Regarding claim 14, the cited prior art teach the method of claim 1.
Smith teaches wherein the bias-correcting model comprises associating bias among (i) temperature measurements in empirical data from sensors in standardized sensor installation configurations (0034 “dynamic parameter” includes, for example, a parameter of a conditioned space that can be dynamically changing. Examples of dynamic parameters include, but are not limited to, airflows; secondary heat sources (such as, but not limited to, fireplaces, space heaters, sunlight, cooking sources (e.g., stoves, ovens, grills, or the like), or the like); energy losses detectable by home automation sensors and capable of being reported to an HVAC system controller (such as, but not limited to, those caused by opening of doors, garage doors, windows, exhaust fans, or the like); losses based on thermal mass of a conditioned space (discussed in additional detail below); properties not related directly to temperature (such as, but not limited to, sources of humidity (e.g., pools, hot tubs, saunas, or the like), clothes dryers, automatic dishwashers, showers, bathrooms, or the like); or other similar parameters that can change over time and can affect environmental control of the conditioned space”) and (ii) auxiliary temperature measurements of volumes of air at or adjacent to the standardized sensor installation configurations across a range of one or more environmental characteristics (0124 “determining, by the HVAC system controller, a dynamic correction factor for each of the plurality of sensors based on the temperatures monitored during the fan-enabled and fan-disabled time periods”).
Regarding claim 17, the cited prior art teach The method of claim 1.
Smith teaches wherein the applying is performed at one or more times when biasing of the one or more temperature sensors is estimated and/or predicted (0124 “determining, by the HVAC system controller, a dynamic correction factor for each of the plurality of sensors based on the temperatures monitored during the fan-enabled and fan-disabled time periods”).
Regarding claim 18, the cited prior art teach The method of claim 17.
Smith teaches wherein the one or more times are based, at least in part, on weather conditions, predicted weather conditions, predicted sun irradiation, measured sun irradiation, occupation of the enclosure, a schedule, a timetable of the enclosure, operation of one or more building systems, an outside atmospheric temperature, a solar angle, a solar elevation, a difference between solar azimuth and window azimuth, a solar intensity, solar radiation wavelengths, a solar penetration depth into the enclosure, a tint state of one or more tintable windows of the enclosure, a sensor orientation, one or more properties of a framing portion proximate to a temperature sensor, one or more surface properties external to the enclosure, a proximity of the temperature sensor to an HVAC outlet, a proximity of the temperature sensor to a diffuser, one or more insulation properties of the enclosure, geographic coordinates of the facility and/or orientation of its facades, window elevation, facility elevation, size and/pr placement of facility overhangs, HVAC setpoint, sensor ensemble heat transfer parameters, thermal properties of sensor mounting fixtures, sun-load information, location of vents, airflow speed, air pressure, humidity, window framing heat transmittance, or a combination thereof (0034 “dynamic parameter” includes, for example, a parameter of a conditioned space that can be dynamically changing. Examples of dynamic parameters include, but are not limited to, airflows; secondary heat sources (such as, but not limited to, fireplaces, space heaters, sunlight, cooking sources (e.g., stoves, ovens, grills, or the like), or the like); energy losses detectable by home automation sensors and capable of being reported to an HVAC system controller (such as, but not limited to, those caused by opening of doors, garage doors, windows, exhaust fans, or the like); losses based on thermal mass of a conditioned space (discussed in additional detail below); properties not related directly to temperature (such as, but not limited to, sources of humidity (e.g., pools, hot tubs, saunas, or the like), clothes dryers, automatic dishwashers, showers, bathrooms, or the like); or other similar parameters that can change over time and can affect environmental control of the conditioned space”).
Regarding claim 44, the cited prior art teach The method of claim 1.
Smith teaches further comprising determining that the one or more of the temperature sensors are subject to a temperature bias, wherein the determining is based, at least in part, on the collected temperature measurements (0124 “determining, by the HVAC system controller, a dynamic correction factor for each of the plurality of sensors based on the temperatures monitored during the fan-enabled and fan-disabled time periods”, 0074 “The temperature measurement can, for example, be determined from the one or more sensors 130A-130C. At 292, the HVAC system controller 105 determines a dynamic correction factor for the temperature measurement. The dynamic correction factor determined at 292 is dependent upon one or more dynamic parameters and by the various information determined from the HVAC system controller 105 identifying a state (e.g., on, off, heating mode, cooling mode, fans only, or the like) of the HVAC system”, 0065 “Generally, the measured temperature will decrease and settle at a max offset. The decreasing portion represents the “on” model 265, and a curve-fitting algorithm can be used to determine a dynamic correction factor that is used to correct temperature when the one or more fans are enabled”, 0072).
Regarding claim 45, the cited prior art teach The method of claim 44.
Smith teaches wherein the determining is further based, at least in part, on environmental characteristics that include one or more of an outside atmospheric temperature, a solar angle, a solar elevation, a difference between solar azimuth and window azimuth, a solar intensity, solar radiation wavelengths, a solar penetration depth into the enclosure, a tint state of one or more tintable windows of the enclosure, a sensor orientation, one or more properties of a framing portion proximate to a temperature sensor, one or more surface properties external to the enclosure, a proximity of the temperature sensor to an HVAC outlet, a proximity of the temperature sensor to a diffuser, one or more insulation properties of the enclosure, geographic coordinates of the facility and/or orientation of its facades, window elevation, facility elevation, size and/pr placement of facility overhangs, HVAC setpoint, sensor ensemble heat transfer parameters, thermal properties of sensor mounting fixtures, sun-load information, location of vents, airflow speed, air pressure, humidity, window framing heat transmittance, data collected by facade-level oriented photosensor readings, facade-level oriented electromagnetic sensor readings, facade-level oriented optical sensor readings, external photosensor readings, skyward photosensor readings, skyward electromagnetic sensor readings, skyward optical sensor readings, cloud cover, humidity, air quality index readings, or a combination thereof (0034 “dynamic parameter” includes, for example, a parameter of a conditioned space that can be dynamically changing. Examples of dynamic parameters include, but are not limited to, airflows; secondary heat sources (such as, but not limited to, fireplaces, space heaters, sunlight, cooking sources (e.g., stoves, ovens, grills, or the like), or the like); energy losses detectable by home automation sensors and capable of being reported to an HVAC system controller (such as, but not limited to, those caused by opening of doors, garage doors, windows, exhaust fans, or the like); losses based on thermal mass of a conditioned space (discussed in additional detail below); properties not related directly to temperature (such as, but not limited to, sources of humidity (e.g., pools, hot tubs, saunas, or the like), clothes dryers, automatic dishwashers, showers, bathrooms, or the like); or other similar parameters that can change over time and can affect environmental control of the conditioned space”).
Claim 52 is rejected using similar reasoning as the rejection of claim 1 due to reciting similar limitations but directed towards an apparatus.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al (US PUB. 20150285526, herein Smith) in view of Kamel et al (US PUB. 20120323382, herein Kamel) in view of Binotto et al (US PUB. 20170176958, herein Binotto).
Regarding claim 4, the cited prior art teach The method of claim 1.
Smith teaches wherein:
the plurality of temperature sensors includes a first subset of temperature sensors and a second subset of temperature sensors (0072 “method 200 can be executed for any temperature sensor in communication with the HVAC system controller 105. This can, for example, allow for temperature control from the various sensors. The HVAC system controller 105 can be configured with one or more sensor conditions to determine which of the various sensors is used to provide temperature measurements to the HVAC system controller 105. For example, a sensor in a bedroom can be used for temperature measurements and HVAC system control during nighttime and a sensor in a television room can be used for temperature measurements and HVAC system control during daytime and/or evening time”),
the first subset of temperature sensors includes the one or more temperature sensors that are subject to the temperature biasing by the one or more environmental characteristics (0072 “method 200 can be executed for any temperature sensor in communication with the HVAC system controller 105. This can, for example, allow for temperature control from the various sensors. The HVAC system controller 105 can be configured with one or more sensor conditions to determine which of the various sensors is used to provide temperature measurements to the HVAC system controller 105. For example, a sensor in a bedroom can be used for temperature measurements and HVAC system control during nighttime and a sensor in a television room can be used for temperature measurements and HVAC system control during daytime and/or evening time”, 0015 “HVAC system controller determines a dynamic correction factor based on one or more dynamic parameters and modifies the temperature measurement based on the dynamic correction factor. The method further includes controlling the HVAC system based on the modified temperature measurement”),
and the bias-correcting model is applied to the first subset of temperature sensors (0015 “HVAC system controller determines a dynamic correction factor based on one or more dynamic parameters and modifies the temperature measurement based on the dynamic correction factor. The method further includes controlling the HVAC system based on the modified temperature measurement”) [and not to the second subset of temperature sensors].
The cited prior art do not teach the second subset of temperature sensors does not include the one or more temperature sensors that are subject to the temperature biasing by the one or more environmental characteristics, and not to the second subset of temperature sensors.
Binotto teaches the second subset of temperature sensors does not include the one or more temperature sensors that are subject to the temperature biasing by the one or more environmental characteristics (0028 “Choosing the correct prediction model for the machine's operation based on user goals/presets (and therefore the specific sensors to be used for this prediction), and under certain restrictions that can change rapidly over time”, In some embodiments consistent with FIG. 7 the subset of sensors include at least one of: a visible light, infrared or thermal camera, a pressure sensor, a temperature sensor, a flow sensor, a humidity sensor, a vibration sensor, an chemical sensor (liquid and gas), inductive sensor, capacitive sensor, resistive sensor, fluorescent sensor, wellness sensors, medical sensors, biomedical sensors, and an electric load sensor”)
and the bias-correcting model is applied to the first subset of temperature sensors (taught by Smith) and not to the second subset of temperature sensors (0028 “Choosing the correct prediction model for the machine's operation based on user goals/presets (and therefore the specific sensors to be used for this prediction), and under certain restrictions that can change rapidly over time”, In some embodiments consistent with FIG. 7 the subset of sensors include at least one of: a visible light, infrared or thermal camera, a pressure sensor, a temperature sensor, a flow sensor, a humidity sensor, a vibration sensor, an chemical sensor (liquid and gas), inductive sensor, capacitive sensor, resistive sensor, fluorescent sensor, wellness sensors, medical sensors, biomedical sensors, and an electric load sensor”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to have modified the teachings of Smith and Kamel with the teachings of Binotto since Binotto teaches a means for “minimization of losses via machine failures and/or production inefficiencies, maximizing performance for a given metric such as production volume, maintenance expense or energy consumption” (0028).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al (US PUB. 20150285526, herein Smith) in view of Kamel et al (US PUB. 20120323382, herein Kamel) in view of Gupta et al (US PUB. 20200000386, herein Gupta).
Regarding claim 13, the cited prior art teach The method of claim 1.
The cited prior art do not teach wherein the bias-correcting model comprises a machine learning model.
Gupta teaches wherein the bias-correcting model comprises a machine learning model (0109 “the sensor apparatus employs (i) a training mode of operation, whereby the apparatus (or processing logic associated therewith, whether on-board or off-board on a receiver/processor apparatus or a parent platform apparatus) conducts “machine learning” to model one or more errors (e.g., un-modeled variable system errors) associated with the blood analyte measurement process, and (ii) generation of an operational model (based at least in part on data collected/received in the training mode), which is applied to correct or compensate for the errors during normal operation of the sensor apparatus”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to have modified the teachings of Smith and Kamel with the machine learning means for sensor correction teachings of Gupta since Gupta teaches a means for increased accuracy of the sensor (abstract).
Claim(s) 22-24, 34-35 and 126 is/are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al (US PUB. 20150285526, herein Smith) in view of Kamel et al (US PUB. 20120323382, herein Kamel) in view of Bleyer et al (US PUB. 20210027538, herein Bleyer).
Regarding claim 22, the cited prior art teach The method of claim 1.
The cited prior art do not teach further comprising extrapolating the corrected temperature measurements according to a plurality of grid points arranged between the locations of the temperature sensors based at least in part on an estimate of heat transfer in the enclosure to generate grid point temperatures.
Bleyer teaches further comprising extrapolating the corrected temperature measurements according to a plurality of grid points arranged between the locations of the temperature sensors based at least in part on an estimate of heat transfer in the enclosure to generate grid point temperatures (0063 “Sensor readings map 400 is currently providing a visual depiction of the temperature gradient currently present in room A, as shown by sensor readings 410 (i.e. display of the map results in a visualization of the temperature gradient being presented). For instance, room A is representative of environment 100 from FIG. 1. Here, room A includes the pot 125 that was spewing hot steam. The hot steam is causing a temperature gradient to be present in room A. The temperature gradient is visually illustrated within the sensor readings map 400. Hotter temperatures are provided at locations more proximate to pot 125. As the distance increases away from pot 125, the temperature decreases”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to have modified the teachings of Smith and Kamel with the teachings of Bleyer since Bleyer teaches a means for making it easier to track and monitor where IOT devices are and what conditions they are monitoring (0005).
Regarding claim 23, the cited prior art teach the method of claim 22.
Bleyers teaches wherein extrapolating the corrected temperature measurements comprises projecting temperature gradients extending from the locations of the temperature sensors to the grid points (0063 “Sensor readings map 400 is currently providing a visual depiction of the temperature gradient currently present in room A, as shown by sensor readings 410 (i.e. display of the map results in a visualization of the temperature gradient being presented). For instance, room A is representative of environment 100 from FIG. 1. Here, room A includes the pot 125 that was spewing hot steam. The hot steam is causing a temperature gradient to be present in room A. The temperature gradient is visually illustrated within the sensor readings map 400. Hotter temperatures are provided at locations more proximate to pot 125. As the distance increases away from pot 125, the temperature decreases”).
Regarding claim 24, the cited prior art teach The method of claim 23.
Bleyers teaches wherein the temperature gradients are calculated from a heat transfer rate and the heat transfer rate is characterized by heat conduction (0063 “Sensor readings map 400 is currently providing a visual depiction of the temperature gradient currently present in room A, as shown by sensor readings 410 (i.e. display of the map results in a visualization of the temperature gradient being presented). For instance, room A is representative of environment 100 from FIG. 1. Here, room A includes the pot 125 that was spewing hot steam. The hot steam is causing a temperature gradient to be present in room A. The temperature gradient is visually illustrated within the sensor readings map 400. Hotter temperatures are provided at locations more proximate to pot 125. As the distance increases away from pot 125, the temperature decreases”).
Regarding claim 34, the cited prior art teach The method of claim 22.
Bleyers teaches further comprising converting the grid point temperatures to a mapping of a thermal profile (0063 “Sensor readings map 400 is currently providing a visual depiction of the temperature gradient currently present in room A, as shown by sensor readings 410 (i.e. display of the map results in a visualization of the temperature gradient being presented). For instance, room A is representative of environment 100 from FIG. 1. Here, room A includes the pot 125 that was spewing hot steam. The hot steam is causing a temperature gradient to be present in room A. The temperature gradient is visually illustrated within the sensor readings map 400. Hotter temperatures are provided at locations more proximate to pot 125. As the distance increases away from pot 125, the temperature decreases”).
Regarding claim 35, the cited prior art teach The method of claim 34.
Bleyers teaches wherein the mapping of the target thermal profile is utilized to define an operative temperature at a specified grid point that combines a corresponding grid point temperature and an estimated statistical computation based at least in part on a radiant temperature to generate estimated radiant temperature 0063 “Sensor readings map 400 is currently providing a visual depiction of the temperature gradient currently present in room A, as shown by sensor readings 410 (i.e. display of the map results in a visualization of the temperature gradient being presented). For instance, room A is representative of environment 100 from FIG. 1. Here, room A includes the pot 125 that was spewing hot steam. The hot steam is causing a temperature gradient to be present in room A. The temperature gradient is visually illustrated within the sensor readings map 400. Hotter temperatures are provided at locations more proximate to pot 125. As the distance increases away from pot 125, the temperature decreases”, 0083 0086).
Regarding claim 126, Smith teaches A method for maintaining a target thermal profile in an enclosure, the method comprising:
collecting temperature measurements from temperature sensors mounted at locations in the enclosure (0072 “method 200 can be executed for any temperature sensor in communication with the HVAC system controller 105. This can, for example, allow for temperature control from the various sensors. The HVAC system controller 105 can be configured with one or more sensor conditions to determine which of the various sensors is used to provide temperature measurements to the HVAC system controller 105. For example, a sensor in a bedroom can be used for temperature measurements and HVAC system control during nighttime and a sensor in a television room can be used for temperature measurements and HVAC system control during daytime and/or evening time”);
quantifying one or more environmental characteristics biasing the temperature measurements with respect to temperatures of volumes of air at or adjacent to the locations of the sensors (0015 “HVAC system controller determines a dynamic correction factor based on one or more dynamic parameters and modifies the temperature measurement based on the dynamic correction factor. The method further includes controlling the HVAC system based on the modified temperature measurement”);
correcting bias in the temperature measurements based at least in part on the one or more environmental characteristics as one or more correction factors to generate corrected temperature measurements (0074 “The temperature measurement can, for example, be determined from the one or more sensors 130A-130C. At 292, the HVAC system controller 105 determines a dynamic correction factor for the temperature measurement. The dynamic correction factor determined at 292 is dependent upon one or more dynamic parameters and by the various information determined from the HVAC system controller 105 identifying a state (e.g., on, off, heating mode, cooling mode, fans only, or the like) of the HVAC system”, 0065 “Generally, the measured temperature will decrease and settle at a max offset. The decreasing portion represents the “on” model 265, and a curve-fitting algorithm can be used to determine a dynamic correction factor that is used to correct temperature when the one or more fans are enabled”).
[and using the mapping] to control the one or more environmental characteristics of the enclosure in order to obtain the target thermal profile 0015 “HVAC system controller determines a dynamic correction factor based on one or more dynamic parameters and modifies the temperature measurement based on the dynamic correction factor. The method further includes controlling the HVAC system based on the modified temperature measurement”).
The cited prior art do not teach extrapolating the corrected temperature measurements according to a plurality of grid points arranged between the locations of the temperature sensors based at least in part on an estimate of heat transfer in the enclosure to generate grid point temperatures; converting the grid point temperatures to a mapping of a thermal profile; and using the mapping to control and wherein the bias correcting model is based, at least in part, on a 3D model of an architecture of the enclosure.
Kamel teaches wherein the bias correcting model is based, at least in part, on a 3D model of an architecture of the enclosure (0083).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to have modified the teachings of Smith with the teachings of Kamel since Kamel teaches a means for “actual energy usage exceeds the predicted energy usage, the system transmits an alert to a user and determines corrective measures to reduce energy usage” (abstract).
The cited prior art do not teach extrapolating the corrected temperature measurements according to a plurality of grid points arranged between the locations of the temperature sensors based at least in part on an estimate of heat transfer in the enclosure to generate grid point temperatures; converting the grid point temperatures to a mapping of a thermal profile; and using the mapping to control.
Bleyers teaches extrapolating the corrected temperature measurements according to a plurality of grid points arranged between the locations of the temperature sensors based at least in part on an estimate of heat transfer in the enclosure to generate grid point temperatures; converting the grid point temperatures to a mapping of a thermal profile (0063 “Sensor readings map 400 is currently providing a visual depiction of the temperature gradient currently present in room A, as shown by sensor readings 410 (i.e. display of the map results in a visualization of the temperature gradient being presented). For instance, room A is representative of environment 100 from FIG. 1. Here, room A includes the pot 125 that was spewing hot steam. The hot steam is causing a temperature gradient to be present in room A. The temperature gradient is visually illustrated within the sensor readings map 400. Hotter temperatures are provided at locations more proximate to pot 125. As the distance increases away from pot 125, the temperature decreases”).
and using the mapping to control the one or more environmental characteristics of the enclosure in order to obtain the target thermal profile (0003 “One example of an IOT device is a smart home thermostat used to automatically monitor and control the climate conditions in the home” 0063 “Sensor readings map 400 is currently providing a visual depiction of the temperature gradient currently present in room A, as shown by sensor readings 410 (i.e. display of the map results in a visualization of the temperature gradient being presented). For instance, room A is representative of environment 100 from FIG. 1. Here, room A includes the pot 125 that was spewing hot steam. The hot steam is causing a temperature gradient to be present in room A. The temperature gradient is visually illustrated within the sensor readings map 400. Hotter temperatures are provided at locations more proximate to pot 125. As the distance increases away from pot 125, the temperature decreases”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to have modified the teachings of Smith with the teachings of Bleyer since Bleyer teaches a means for making it easier to track and monitor where IOT devices are and what conditions they are monitoring (0005).
Claim(s) 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al (US PUB. 20150285526, herein Smith) in view of Kamel et al (US PUB. 20120323382, herein Kamel) in view of Bleyer et al (US PUB. 20210027538, herein Bleyer) in further view of VanGlider et al (US PUB. 20190187764, herein VanGlider).
Regarding claim 27, the cited prior art teach The method of claim 23.
The cited prior art do not teach wherein the temperature gradients are modeled based at least in part on a physics simulation.
VanGlider teaches wherein the temperature gradients are modeled based at least in part on a physics simulation (0111 “simulation input subsection 658 may also be used to provide parameters for the simulation, such as the simulation time, and the allowable maximum temperature of data center air (e.g., room, inlet, and return air temperatures), as well as temperature gradient options”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to have modified the teachings of Smith and Kamel and the teachings of Bleyer with the teachings of VanGlider since Vanglider teaches a means for being able to predict temperature events better and allow for notifying operators (0003).
Response to Arguments
Applicant’s arguments, filed 3/2/2026, with respect to the rejection(s) of claim(s) 1 under 35 USC 102 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 Smith et al (US PUB. 20150285526, herein Smith) in view of Kamel et al (US PUB. 20120323382, herein Kamel).
Applicant argues on page 10 that the cited prior art do not teach the amendments to the claims. Examiner agrees. However, as a result of further search and consideration, Kamel has been introduced. Kamel teaches three dimensional model for determining corrective actions for building equipment usage (0083). This corresponds to the broadest reasonable interpretation of the argued limitation.
Therefore, claim 1 is rejected along with its respective dependent claims. Claims 52 and 126 are similar and are similarly rejected along with their respective dependent claims.
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.
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/TAMEEM D SIDDIQUEE/
Primary Examiner
Art Unit 2116
Prosecution Insights