DETAILED ACTION
Claims 1-6, 8-14, 16-18, and 21-23 are pending.
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.
Response to Arguments
Applicant's arguments filed 9/18/2026 have been fully considered but they are not persuasive.
Regarding claims 1-6, 8-14, 16-18, and 21-23, the applicant argues that the cited references do not teach or suggest the claim limitations with respect to independent claim 1 below. Independent claims 9 and 17 are substantially similar to independent claim 1. Dependent claims 2-6, 8, 10-14, 16, 18, and 21-23 depend, directly or indirectly, from independent claims 1, 9, and 17, respectively. The Examiner respectfully disagrees. The cited prior art describe the claim limitations as briefly outlined below and as described in the rejection of claims 1, 9, and 17 below.
processing the audio data to discriminate between active and inactive states of the HVAC system;
Applicant’s arguments are directed to Picardi and Baily not teaching or suggesting “processing the audio data to discriminate between active and inactive states of the HVAC system”. Examiner respectfully disagrees. Picardi describes using audio data to detect and identify HVAC system in an issue state, a healthy state, and transitions therebetween (Picardi: paragraph 0029). As described in Picardi, the issue state can include various broken conditions (e.g., fan is broken) in which the HVAC has failed and the healthy state can include a state with no broken conditions (i.e., HVAC system is properly working) (Picardi: paragraphs 0028, 0033, 0057, 0070, 0077). In other words, Picardi describes an issue state with a broken condition (i.e., inactive state) and a healthy state with no broken conditions (i.e., active state). If Applicant intends for active state and inactive states to have a particular meaning outside of what Picardi teaches, Examiner recommends amending the claims to state the same (e.g., “wherein the active state is when the HVAC system is operating and the inactive state is when the HVAC system is not operating”).
Picardi: “The security system 160 can also train the machine-learning algorithm to detect and identify HVAC systems that have transitioned from an issue status to a healthy status. This indication produced by the machine-learning algorithm can indicate when HVAC service was performed in a monitored property. For example, the security system 160 can use data over a period of time that shows an HVAC system transitioning between an issue status and a healthy status. The data can include thermostat data over a period, such as 5 hours, outdoor temperature data over the period of time, and raw sensor data monitoring the HVAC system 146. The raw sensor data can be media data (e.g., video and photos) from one or more cameras that monitor the HVAC system, audio data from microphones listening to the sounds produced by the HVAC system, thermal imaging data from thermal sensors that monitor the HVAC system, data from the alarm panel 122 that show issues with the HVAC system 146, motion data from motion sensors surrounding the HVAC system 146, and lock sensor data from doors containing the HVAC system 146. Other sensor data can be provided from the monitored property. Additionally, the security system 160 can use data from house products to train the machine-learning algorithm. For example, the security system 160 can use data from outdoor temperature sensors, outdoor barometers, temperatures of various products in the monitored property 102, and data from a client device of the owner of the monitored property 102. The security system can also add the timestamp data to this transitioning training data to predict likelihoods that the system has transitioned from an issue state to a healthy state to predict likelihoods of transitions at particular times of the day. The training can be further based on HVAC systems at other monitored properties that have similar components. The training can include how long it takes for an HVAC system to transition to a health state after an issue has been detected.” Paragraph 0029
Bailey: see the record analysis 520 as illustrated in figure 5 and as described in paragraphs 0128, 0129; “At step 520 the record is analysed to compute a set of parameters relating to the HVAC device (e.g. relating to its operation and/or performance) and/or relating to the premises which the HVAC device controls (e.g. relating to the environmental response of the monitored space), for that operational period.” Paragraph 0128; “Change in value of measured environmental characteristic over length of time for which the HVAC component is on (for example, rate of change of room temperature or other characteristic over activation period, such as rate at which the premises heats up when the boiler is on (e.g. measured in degrees change per hour))” paragraph 0129; “At step 204 the data collected in step 202 is analysed to identify patterns indicative of normal operation of the HVAC component, for example over a series of heating periods it may be possible to model normal operation of the house, such as a pattern showing how long it takes to increase the room temperature by a certain amount.” Paragraph 0092
Accordingly, applicant’s arguments are not persuasive since the cited prior art describe the limitations in these claims.
For at least these reasons, the rejection of the claims is maintained.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 9-13, 17-18, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Patent Application Publication No. 2019/0264936 (Bailey) in view of
U.S. Patent Application Publication No. 2020/0240662 (Picardi).
Claim 1:
The cited prior art describes a method comprising: (Bailey: “The present invention relates, but is not limited, to systems and methods for monitoring environmental control systems. More particularly, the present invention relates to determining faults in heating, ventilation and air conditioning (HVAC) systems.” Paragraph 0002)
monitoring an HVAC system to detect a plurality of sample intervals during which the HVAC system is active, (Bailey: see the collecting data as illustrated in figure 2 and as described in paragraph 0091; “FIG. 2 shows an exemplary method 200 for monitoring an HVAC/environmental control system 100 as described above and shown in FIG. 1. At step 202 the HVAC component is monitored by collecting data relevant to a series of periods of operation of the HVAC component, for example the data may be room temperature values and the HVAC component may be a boiler 140. The temperature values may be values recorded during times when the boiler 140 is turned on. They may also include values recorded before the boiler 140 has been turned on and after the boiler has been turned off.” Paragraph 0091)
Bailey does not explicitly describe audio data as described below. However, Picardi teaches the audio data as described below.
wherein detecting the sample intervals during which the HVAC system is active includes collecting audio data and (Picardi: see the collection of audio data as described in paragraph 0029; “audio data from microphones listening to the sounds produced by the HVAC system” paragraph 0029)
processing the audio data to discriminate between active and inactive states of the HVAC system; (Picardi: “The security system 160 can also train the machine-learning algorithm to detect and identify HVAC systems that have transitioned from an issue status to a healthy status. This indication produced by the machine-learning algorithm can indicate when HVAC service was performed in a monitored property. For example, the security system 160 can use data over a period of time that shows an HVAC system transitioning between an issue status and a healthy status. The data can include thermostat data over a period, such as 5 hours, outdoor temperature data over the period of time, and raw sensor data monitoring the HVAC system 146. The raw sensor data can be media data (e.g., video and photos) from one or more cameras that monitor the HVAC system, audio data from microphones listening to the sounds produced by the HVAC system, thermal imaging data from thermal sensors that monitor the HVAC system, data from the alarm panel 122 that show issues with the HVAC system 146, motion data from motion sensors surrounding the HVAC system 146, and lock sensor data from doors containing the HVAC system 146. Other sensor data can be provided from the monitored property. Additionally, the security system 160 can use data from house products to train the machine-learning algorithm. For example, the security system 160 can use data from outdoor temperature sensors, outdoor barometers, temperatures of various products in the monitored property 102, and data from a client device of the owner of the monitored property 102. The security system can also add the timestamp data to this transitioning training data to predict likelihoods that the system has transitioned from an issue state to a healthy state to predict likelihoods of transitions at particular times of the day. The training can be further based on HVAC systems at other monitored properties that have similar components. The training can include how long it takes for an HVAC system to transition to a health state after an issue has been detected.” Paragraph 0029) (Bailey: see the record analysis 520 as illustrated in figure 5 and as described in paragraphs 0128, 0129; “At step 520 the record is analysed to compute a set of parameters relating to the HVAC device (e.g. relating to its operation and/or performance) and/or relating to the premises which the HVAC device controls (e.g. relating to the environmental response of the monitored space), for that operational period.” Paragraph 0128; “Change in value of measured environmental characteristic over length of time for which the HVAC component is on (for example, rate of change of room temperature or other characteristic over activation period, such as rate at which the premises heats up when the boiler is on (e.g. measured in degrees change per hour))” paragraph 0129; “At step 204 the data collected in step 202 is analysed to identify patterns indicative of normal operation of the HVAC component, for example over a series of heating periods it may be possible to model normal operation of the house, such as a pattern showing how long it takes to increase the room temperature by a certain amount.” Paragraph 0092)
monitoring an environmental parameter during each of the respective sample intervals; and (Bailey: see the monitoring HVAC component 202 as illustrated in figure 2 and as described in paragraph 0091; “FIG. 2 shows an exemplary method 200 for monitoring an HVAC/environmental control system 100 as described above and shown in FIG. 1. At step 202 the HVAC component is monitored by collecting data relevant to a series of periods of operation of the HVAC component, for example the data may be room temperature values and the HVAC component may be a boiler 140. The temperature values may be values recorded during times when the boiler 140 is turned on. They may also include values recorded before the boiler 140 has been turned on and after the boiler has been turned off.” Paragraph 0091)
determining a sample value for each of the sample intervals, each sample value representing a rate of change of the environmental parameter in the respective interval. (Bailey: see the record analysis 520 as illustrated in figure 5 and as described in paragraphs 0128, 0129; “At step 520 the record is analysed to compute a set of parameters relating to the HVAC device (e.g. relating to its operation and/or performance) and/or relating to the premises which the HVAC device controls (e.g. relating to the environmental response of the monitored space), for that operational period.” Paragraph 0128; “Change in value of measured environmental characteristic over length of time for which the HVAC component is on (for example, rate of change of room temperature or other characteristic over activation period, such as rate at which the premises heats up when the boiler is on (e.g. measured in degrees change per hour))” paragraph 0129; “At step 204 the data collected in step 202 is analysed to identify patterns indicative of normal operation of the HVAC component, for example over a series of heating periods it may be possible to model normal operation of the house, such as a pattern showing how long it takes to increase the room temperature by a certain amount.” Paragraph 0092)
One of ordinary skill in the art would have recognized that applying the known technique of Bailey, namely, HVAC performance monitoring system, with the known techniques of Picardi, namely, a HVAC service performance system, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Bailey to analyze data to determine HVAC performance monitoring with the teachings of Picardi to analyze data to monitor a HVAC system would have been recognized by those of ordinary skill in the art as resulting in an improved HVAC monitoring system. In other words, the combination of references provides for analyzing various types of data using various analysis techniques for HVAC performance analysis based on the teachings of analyzing various types of data using various analysis techniques in Bailey and the teachings of analyzing various types of data in Picardi.
Claim 2:
The cited prior art describes the method of claim 1, further comprising detecting a change in HVAC performance based on a comparison between a first plurality of sample values collected in a first time period and a second plurality of sample values collected in a second time period. (Bailey: “Rate of change of an environmental characteristic (e.g. room temperature) at or near the beginning of an operational period;” paragraph 0135; “Rate of change of an environmental characteristic (e.g. room temperature) at or near the end of an operational period;” paragraph 0136; “Summation of the rate of change over the whole set of characteristic (e.g. temperature) measurements, (e.g. a positive sum indicates warming);” paragraph 0137; “If the record does not indicate “expected” behaviour then the heating period is identified as a potential failure of the HVAC system for further investigation. Therefore the method progresses to step 640 in which the one or more parameters relating to the activation period of the HVAC component are analysed further to identify what the suspected fault may be.” Paragraph 0162)
Claim 3:
The cited prior art describes the method of claim 1, wherein the sample value for a sample interval is a ratio between a duration of the respective interval and a total change in the environmental parameter over the course of the respective interval. (Bailey: “Runtime characteristics of the specific system (e.g. periodic oscillations in the environmental characteristic, which correspond to the length of time an HVAC component (e.g. a boiler) will run continuously before shutting itself off for a period of time);” paragraph 0132; “Typical time an environmental characteristic will take to begin to change (e.g. temperature start increasing) after the start of an activation period; [0134] Length of time for which the HVAC component is switched on (or is operating);” paragraph 0133)
Claim 4:
The cited prior art describes the method of claim 1, wherein the sample value for a sample interval is a slope or inverse slop value representing a rate of change of the environmental parameter during at least a portion of the sample interval. (Bailey: see the slope of the line 840 as illustrated in figure 8; see the record analysis 520 as illustrated in figure 5 and as described in paragraphs 0128, 0129; “At step 520 the record is analysed to compute a set of parameters relating to the HVAC device (e.g. relating to its operation and/or performance) and/or relating to the premises which the HVAC device controls (e.g. relating to the environmental response of the monitored space), for that operational period.” Paragraph 0128; “Change in value of measured environmental characteristic over length of time for which the HVAC component is on (for example, rate of change of room temperature or other characteristic over activation period, such as rate at which the premises heats up when the boiler is on (e.g. measured in degrees change per hour))” paragraph 0129; “At step 204 the data collected in step 202 is analysed to identify patterns indicative of normal operation of the HVAC component, for example over a series of heating periods it may be possible to model normal operation of the house, such as a pattern showing how long it takes to increase the room temperature by a certain amount.” Paragraph 0092)
Claim 5:
The cited prior art describes the method of claim 1, wherein the environmental parameter is temperature. (Bailey: “FIG. 2 shows an exemplary method 200 for monitoring an HVAC/environmental control system 100 as described above and shown in FIG. 1. At step 202 the HVAC component is monitored by collecting data relevant to a series of periods of operation of the HVAC component, for example the data may be room temperature values and the HVAC component may be a boiler 140. The temperature values may be values recorded during times when the boiler 140 is turned on. They may also include values recorded before the boiler 140 has been turned on and after the boiler has been turned off.” Paragraph 0091)
Claim 9:
The cited prior art describes a system comprising: (Bailey: “The present invention relates, but is not limited, to systems and methods for monitoring environmental control systems. More particularly, the present invention relates to determining faults in heating, ventilation and air conditioning (HVAC) systems.” Paragraph 0002)
at least one sensor node configured to monitor at least (Bailey: see the HVAC monitoring device 110 as illustrated in figure 1; “The HVAC monitoring device 110 is wirelessly connected to a temperature sensor 112 and a humidity sensor 114 installed within the user's premises. However the sensors 112, 114 may be combined in the HVAC monitoring device 110. Alternatively the sensors 112,114 may communicate with the HVAC monitoring device 110 via wired connections.” Paragraph 0078)
a first parameter and at least (Bailey: see the humidity sensor 114 as illustrated in figure 1; “Environmental sensors 112, 114 (FIG. 1) measure the ambient temperature and humidity and provide the environmental characteristic information to HVAC monitoring device 110 via the wireless transceiver 1412 and the wireless interface 1410.” Paragraph 0234)
a second parameter, the second parameter being an environmental parameter; (Bailey: see the temperature sensor 112 as illustrated in figure 1; “Environmental sensors 112, 114 (FIG. 1) measure the ambient temperature and humidity and provide the environmental characteristic information to HVAC monitoring device 110 via the wireless transceiver 1412 and the wireless interface 1410.” Paragraph 0234)
at least one processor configured to perform at method comprising: (Bailey: see the processor 1500 in the analysis server 180 as illustrated in figure 15B)
detecting, based at least in part on the first parameter, a plurality of sample intervals during which an HVAC system is active,(Bailey: see the collecting data as illustrated in figure 2 and as described in paragraph 0091; “FIG. 2 shows an exemplary method 200 for monitoring an HVAC/environmental control system 100 as described above and shown in FIG. 1. At step 202 the HVAC component is monitored by collecting data relevant to a series of periods of operation of the HVAC component, for example the data may be room temperature values and the HVAC component may be a boiler 140. The temperature values may be values recorded during times when the boiler 140 is turned on. They may also include values recorded before the boiler 140 has been turned on and after the boiler has been turned off.” Paragraph 0091)
Bailey does not explicitly describe audio data as described below. However, Picardi teaches the audio data as described below.
wherein the first parameter comprises an audio parameter, and (Picardi: see the collection of audio data as described in paragraph 0029; “audio data from microphones listening to the sounds produced by the HVAC system” paragraph 0029)
wherein detecting the sample intervals during which the HVAC system is active includes processing the audio data to discriminate between active and inactive states of the HVAC system; (Picardi: “The security system 160 can also train the machine-learning algorithm to detect and identify HVAC systems that have transitioned from an issue status to a healthy status. This indication produced by the machine-learning algorithm can indicate when HVAC service was performed in a monitored property. For example, the security system 160 can use data over a period of time that shows an HVAC system transitioning between an issue status and a healthy status. The data can include thermostat data over a period, such as 5 hours, outdoor temperature data over the period of time, and raw sensor data monitoring the HVAC system 146. The raw sensor data can be media data (e.g., video and photos) from one or more cameras that monitor the HVAC system, audio data from microphones listening to the sounds produced by the HVAC system, thermal imaging data from thermal sensors that monitor the HVAC system, data from the alarm panel 122 that show issues with the HVAC system 146, motion data from motion sensors surrounding the HVAC system 146, and lock sensor data from doors containing the HVAC system 146. Other sensor data can be provided from the monitored property. Additionally, the security system 160 can use data from house products to train the machine-learning algorithm. For example, the security system 160 can use data from outdoor temperature sensors, outdoor barometers, temperatures of various products in the monitored property 102, and data from a client device of the owner of the monitored property 102. The security system can also add the timestamp data to this transitioning training data to predict likelihoods that the system has transitioned from an issue state to a healthy state to predict likelihoods of transitions at particular times of the day. The training can be further based on HVAC systems at other monitored properties that have similar components. The training can include how long it takes for an HVAC system to transition to a health state after an issue has been detected.” Paragraph 0029) (Bailey: see the record analysis 520 as illustrated in figure 5 and as described in paragraphs 0128, 0129; “At step 520 the record is analysed to compute a set of parameters relating to the HVAC device (e.g. relating to its operation and/or performance) and/or relating to the premises which the HVAC device controls (e.g. relating to the environmental response of the monitored space), for that operational period.” Paragraph 0128; “Change in value of measured environmental characteristic over length of time for which the HVAC component is on (for example, rate of change of room temperature or other characteristic over activation period, such as rate at which the premises heats up when the boiler is on (e.g. measured in degrees change per hour))” paragraph 0129; “At step 204 the data collected in step 202 is analysed to identify patterns indicative of normal operation of the HVAC component, for example over a series of heating periods it may be possible to model normal operation of the house, such as a pattern showing how long it takes to increase the room temperature by a certain amount.” Paragraph 0092)
determining a sample value for each of the sample intervals, each sample value representing a rate of change of the environmental parameter in the respective interval. (Bailey: see the record analysis 520 as illustrated in figure 5 and as described in paragraphs 0128, 0129; “At step 520 the record is analysed to compute a set of parameters relating to the HVAC device (e.g. relating to its operation and/or performance) and/or relating to the premises which the HVAC device controls (e.g. relating to the environmental response of the monitored space), for that operational period.” Paragraph 0128; “Change in value of measured environmental characteristic over length of time for which the HVAC component is on (for example, rate of change of room temperature or other characteristic over activation period, such as rate at which the premises heats up when the boiler is on (e.g. measured in degrees change per hour))” paragraph 0129; “At step 204 the data collected in step 202 is analysed to identify patterns indicative of normal operation of the HVAC component, for example over a series of heating periods it may be possible to model normal operation of the house, such as a pattern showing how long it takes to increase the room temperature by a certain amount.” Paragraph 0092)
Bailey and Picardi are combinable for the same rationale as set forth above with respect to claim 1.
Claim 10:
Claim 10 is substantially similar to claim 2 and is rejected based on the same reasons and rationale.
10. (Original) The system of claim 9, wherein the at least one processor is further configured to detect a change in HVAC performance based on a comparison between a first plurality of sample values collected in a first time period and a second plurality of sample values collected in a second time period.
Claim 11:
Claim 11 is substantially similar to claim 3 and is rejected based on the same reasons and rationale.
11. (Original) The system of claim 9, wherein the sample value for a sample interval is a ratio between a duration of the respective interval and a total change in the environmental parameter over the course of the respective interval.
Claim 12:
Claim 12 is substantially similar to claim 4 and is rejected based on the same reasons and rationale.
12. (Original) The system of claim 9, wherein the sample value for a sample interval is a slope or inverse slop value representing a rate of change of the environmental parameter during at least a portion of the sample interval.
Claim 13:
Claim 13 is substantially similar to claim 5 and is rejected based on the same reasons and rationale.
13. (Original) The system of claim 9, wherein the environmental parameter is temperature.
Claim 17:
The cited prior art describes a system comprising: (Bailey: “The present invention relates, but is not limited, to systems and methods for monitoring environmental control systems. More particularly, the present invention relates to determining faults in heating, ventilation and air conditioning (HVAC) systems.” Paragraph 0002)
at least one sensor node configured to (Bailey: see the HVAC monitoring device 110 as illustrated in figure 1; “The HVAC monitoring device 110 is wirelessly connected to a temperature sensor 112 and a humidity sensor 114 installed within the user's premises. However the sensors 112, 114 may be combined in the HVAC monitoring device 110. Alternatively the sensors 112,114 may communicate with the HVAC monitoring device 110 via wired connections.” Paragraph 0078)
monitor temperature and (Bailey: see the temperature sensor 112 as illustrated in figure 1; “Environmental sensors 112, 114 (FIG. 1) measure the ambient temperature and humidity and provide the environmental characteristic information to HVAC monitoring device 110 via the wireless transceiver 1412 and the wireless interface 1410.” Paragraph 0234)
at least one additional parameter; (Bailey: see the humidity sensor 114 as illustrated in figure 1; “Environmental sensors 112, 114 (FIG. 1) measure the ambient temperature and humidity and provide the environmental characteristic information to HVAC monitoring device 110 via the wireless transceiver 1412 and the wireless interface 1410.” Paragraph 0234)
at least one processor configured to perform a method comprising: (Bailey: see the processor 1500 in the analysis server 180 as illustrated in figure 15B)
detecting, based at least in part on the additional parameter, a plurality of sample intervals during which an HVAC system is inactive, (Bailey: see the collecting data as illustrated in figure 2 and as described in paragraph 0091; “FIG. 2 shows an exemplary method 200 for monitoring an HVAC/environmental control system 100 as described above and shown in FIG. 1. At step 202 the HVAC component is monitored by collecting data relevant to a series of periods of operation of the HVAC component, for example the data may be room temperature values and the HVAC component may be a boiler 140. The temperature values may be values recorded during times when the boiler 140 is turned on. They may also include values recorded before the boiler 140 has been turned on and after the boiler has been turned off.” Paragraph 0091)
Bailey does not explicitly describe audio data as described below. However, Picardi teaches the audio data as described below.
wherein the first parameter comprises an audio parameter, and (Picardi: see the collection of audio data as described in paragraph 0029; “audio data from microphones listening to the sounds produced by the HVAC system” paragraph 0029)
wherein detecting the sample intervals during which the HVAC system is active includes processing the audio data to discriminate between active and inactive states of the HVAC system; (Picardi: “The security system 160 can also train the machine-learning algorithm to detect and identify HVAC systems that have transitioned from an issue status to a healthy status. This indication produced by the machine-learning algorithm can indicate when HVAC service was performed in a monitored property. For example, the security system 160 can use data over a period of time that shows an HVAC system transitioning between an issue status and a healthy status. The data can include thermostat data over a period, such as 5 hours, outdoor temperature data over the period of time, and raw sensor data monitoring the HVAC system 146. The raw sensor data can be media data (e.g., video and photos) from one or more cameras that monitor the HVAC system, audio data from microphones listening to the sounds produced by the HVAC system, thermal imaging data from thermal sensors that monitor the HVAC system, data from the alarm panel 122 that show issues with the HVAC system 146, motion data from motion sensors surrounding the HVAC system 146, and lock sensor data from doors containing the HVAC system 146. Other sensor data can be provided from the monitored property. Additionally, the security system 160 can use data from house products to train the machine-learning algorithm. For example, the security system 160 can use data from outdoor temperature sensors, outdoor barometers, temperatures of various products in the monitored property 102, and data from a client device of the owner of the monitored property 102. The security system can also add the timestamp data to this transitioning training data to predict likelihoods that the system has transitioned from an issue state to a healthy state to predict likelihoods of transitions at particular times of the day. The training can be further based on HVAC systems at other monitored properties that have similar components. The training can include how long it takes for an HVAC system to transition to a health state after an issue has been detected.” Paragraph 0029) (Bailey: see the record analysis 520 as illustrated in figure 5 and as described in paragraphs 0128, 0129; “At step 520 the record is analysed to compute a set of parameters relating to the HVAC device (e.g. relating to its operation and/or performance) and/or relating to the premises which the HVAC device controls (e.g. relating to the environmental response of the monitored space), for that operational period.” Paragraph 0128; “Change in value of measured environmental characteristic over length of time for which the HVAC component is on (for example, rate of change of room temperature or other characteristic over activation period, such as rate at which the premises heats up when the boiler is on (e.g. measured in degrees change per hour))” paragraph 0129; “At step 204 the data collected in step 202 is analysed to identify patterns indicative of normal operation of the HVAC component, for example over a series of heating periods it may be possible to model normal operation of the house, such as a pattern showing how long it takes to increase the room temperature by a certain amount.” Paragraph 0092)
determining a sample value for each of the sample intervals, each sample value representing a rate of change of the temperature in the respective interval; and (Bailey: see the record analysis 520 as illustrated in figure 5 and as described in paragraphs 0128, 0129; “At step 520 the record is analysed to compute a set of parameters relating to the HVAC device (e.g. relating to its operation and/or performance) and/or relating to the premises which the HVAC device controls (e.g. relating to the environmental response of the monitored space), for that operational period.” Paragraph 0128; “Change in value of measured environmental characteristic over length of time for which the HVAC component is on (for example, rate of change of room temperature or other characteristic over activation period, such as rate at which the premises heats up when the boiler is on (e.g. measured in degrees change per hour))” paragraph 0129; “At step 204 the data collected in step 202 is analysed to identify patterns indicative of normal operation of the HVAC component, for example over a series of heating periods it may be possible to model normal operation of the house, such as a pattern showing how long it takes to increase the room temperature by a certain amount.” Paragraph 0092)
detecting a change in heat retention or exclusion performance based on a comparison between sample values collected in a first time period and sample values collected in a second time period. (Bailey: see the tracking of data during off calls over time as illustrated in figures 8, 9, 10, 11; “Rate of change of an environmental characteristic (e.g. room temperature) at or near the beginning of an operational period;” paragraph 0135; “Rate of change of an environmental characteristic (e.g. room temperature) at or near the end of an operational period;” paragraph 0136; “Summation of the rate of change over the whole set of characteristic (e.g. temperature) measurements, (e.g. a positive sum indicates warming);” paragraph 0137; “If the record does not indicate “expected” behaviour then the heating period is identified as a potential failure of the HVAC system for further investigation. Therefore the method progresses to step 640 in which the one or more parameters relating to the activation period of the HVAC component are analysed further to identify what the suspected fault may be.” Paragraph 0162; see the record analysis 520 as illustrated in figure 5 and as described in paragraphs 0128, 0129; “At step 520 the record is analysed to compute a set of parameters relating to the HVAC device (e.g. relating to its operation and/or performance) and/or relating to the premises which the HVAC device controls (e.g. relating to the environmental response of the monitored space), for that operational period.” Paragraph 0128; “Change in value of measured environmental characteristic over length of time for which the HVAC component is on (for example, rate of change of room temperature or other characteristic over activation period, such as rate at which the premises heats up when the boiler is on (e.g. measured in degrees change per hour))” paragraph 0129; “At step 204 the data collected in step 202 is analysed to identify patterns indicative of normal operation of the HVAC component, for example over a series of heating periods it may be possible to model normal operation of the house, such as a pattern showing how long it takes to increase the room temperature by a certain amount.” Paragraph 0092)
Bailey and Picardi are combinable for the same rationale as set forth above with respect to claim 1.
Claim 21:
Bailey does not explicitly describe audio data as described below. However, Picardi teaches the audio data as described below.
The cited prior art describes the method of claim 1, wherein the environmental parameter is monitored by a sensor in a sensor node, and the audio data is collected by a microphone in the sensor node. (Picardi: see the sensors 520 as illustrated in figure 5; “The sensors 520 may include a temperature sensor, a humidity sensor, a leaking sensor, or any other type of sensor included in an HVAC system 146.” Paragraph 0091; “audio data from microphones listening to the sounds produced by the HVAC system” paragraph 0029) (Bailey: see the monitoring HVAC component 202 as illustrated in figure 2 and as described in paragraph 0091)
Bailey and Picardi are combinable for the same rationale as set forth above with respect to claim 1.
Claim 22:
Bailey does not explicitly describe audio data as described below. However, Picardi teaches the audio data as described below.
The cited prior art describes the method of claim 1, wherein the processing of the audio data to discriminate between active and inactive states of the HVAC system is performed by a neural network trained to use the audio data to discriminate between active and inactive states of the HVAC system. (Picardi: “The model includes one or more neural networks and is trained using machine learning.” Paragraph 0010; “The security system 160 can also train the machine-learning algorithm to detect and identify HVAC systems that have transitioned from an issue status to a healthy status. This indication produced by the machine-learning algorithm can indicate when HVAC service was performed in a monitored property. For example, the security system 160 can use data over a period of time that shows an HVAC system transitioning between an issue status and a healthy status. The data can include thermostat data over a period, such as 5 hours, outdoor temperature data over the period of time, and raw sensor data monitoring the HVAC system 146. The raw sensor data can be media data (e.g., video and photos) from one or more cameras that monitor the HVAC system, audio data from microphones listening to the sounds produced by the HVAC system, thermal imaging data from thermal sensors that monitor the HVAC system, data from the alarm panel 122 that show issues with the HVAC system 146, motion data from motion sensors surrounding the HVAC system 146, and lock sensor data from doors containing the HVAC system 146. Other sensor data can be provided from the monitored property. Additionally, the security system 160 can use data from house products to train the machine-learning algorithm. For example, the security system 160 can use data from outdoor temperature sensors, outdoor barometers, temperatures of various products in the monitored property 102, and data from a client device of the owner of the monitored property 102. The security system can also add the timestamp data to this transitioning training data to predict likelihoods that the system has transitioned from an issue state to a healthy state to predict likelihoods of transitions at particular times of the day. The training can be further based on HVAC systems at other monitored properties that have similar components. The training can include how long it takes for an HVAC system to transition to a health state after an issue has been detected.” Paragraph 0029; “For example, the security system 160 can use a machine-learning algorithm, such as a deep learning algorithm, an anomaly detecting algorithm, a linear regression algorithm, or a logical regression algorithm, or a combination of various machine-learning algorithms, to name a few examples.” Paragraph 0027) (Bailey: see the record analysis 520 as illustrated in figure 5 and as described in paragraphs 0128, 0129; “At step 520 the record is analysed to compute a set of parameters relating to the HVAC device (e.g. relating to its operation and/or performance) and/or relating to the premises which the HVAC device controls (e.g. relating to the environmental response of the monitored space), for that operational period.” Paragraph 0128; “Change in value of measured environmental characteristic over length of time for which the HVAC component is on (for example, rate of change of room temperature or other characteristic over activation period, such as rate at which the premises heats up when the boiler is on (e.g. measured in degrees change per hour))” paragraph 0129; “At step 204 the data collected in step 202 is analysed to identify patterns indicative of normal operation of the HVAC component, for example over a series of heating periods it may be possible to model normal operation of the house, such as a pattern showing how long it takes to increase the room temperature by a certain amount.” Paragraph 0092)
Bailey and Picardi are combinable for the same rationale as set forth above with respect to claim 1.
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Patent Application Publication No. 2019/0264936 (Bailey) in view of
U.S. Patent Application Publication No. 2020/0240662 (Picardi) and further in view of
U.S. Patent Application Publication No. 2020/0226697 (Han).
Claim 6:
Bailey and Picardi do not explicitly describe a histogram and related processing as described below. However, Han teaches the histogram and related processing as described below.
The cited prior art describes the method of claim 1, further comprising:
for each of a plurality of bins, each bin being associated with a range of sample values, determining a number of the sample values that fall within the associated range to generate histogram data; (Han: see the histogram of energy cost versus temperature as illustrated in figure 4; “The insight preview window 411 may display information identifying the month, the billing amount for that month, and the average temperature for that month. The insight preview window 411 may be provided for display in an overlay at a position that overlaps a location on the energy histogram, or at a position adjacent to the selected data point of the energy histogram.” Paragraph 0071; “In other aspects, the billing advisor engine 250 may determine an energy load curve shape associated with the energy consumption data of an energy user. In this respect, the billing advisor engine 250 may identify respective sections of the energy load curve shape, and map the sections to respective types of loads (e.g., oven element, refrigerator, etc.) for identifying the source of energy consumption. In some examples, the energy load curve shape is a two-dimensional histogram identifying the amount of energy consumption (e.g., kilowatt hour(s)) over time (e.g., seconds, minute(s), hour(s), etc.).” paragraph 0041)
fitting a curve to the histogram data; and (Han: see the curve of temperature data to the energy data as illustrated in figure 4; “In other aspects, the billing advisor engine 250 may determine an energy load curve shape associated with the energy consumption data of an energy user. In this respect, the billing advisor engine 250 may identify respective sections of the energy load curve shape, and map the sections to respective types of loads (e.g., oven element, refrigerator, etc.) for identifying the source of energy consumption. In some examples, the energy load curve shape is a two-dimensional histogram identifying the amount of energy consumption (e.g., kilowatt hour(s)) over time (e.g., seconds, minute(s), hour(s), etc.).” paragraph 0041)
detecting a change in HVAC performance based on a change over time in at least one parameter of the curve. (Han: see the home diagnosis including heating and energy saving tips as illustrated in figure 5; “generating an interface comprising a plurality of insights that correspond to the one or more energy consumption factors, the plurality of insights identifying one or more explanations as to why the billing amount of the utility bill exceeded a cost expectation of the second user; mapping at least a portion of the utility bill to the interface; and providing the interface for display, the plurality of insights being displayed based on one or more interactions between the first user and the interface.” Paragraph 0004; “For example, the processor 236, using the disaggregation engine 248, performs disaggregation of HVAC energy usage based on heating coefficients and cooling coefficients.” Paragraph 0056; “The energy consumption analytics system 660 also may be in communication with a third party weather service, such as the National Weather Service (not shown). For example, the energy consumption analytics system 660 may receive corresponding outdoor temperatures from the third party weather service via the network 650 (e.g., e-mails, downloaded FTP files, and XML feeds). In this respect, the energy consumption analytics system 660 may use weather data from the third party weather service to determine a projected billing amount for a particular billing period, and identify any changes in the projected billing amount relative to a previous billing cycle due to weather patterns identified in the weather data. For example, forecasted weather conditions (e.g., the temperature, the humidity, the barometric pressure, precipitation, etc.) may indicate that an energy user's HVAC system is likely to be in greater use, thereby likely to cause an increase in the energy user's energy load curve. The energy consumption analytics system 660 may notify the energy user of insight information relating to a billing amount of an inquired utility bill through a follow-up communication (or an energy usage alert notification).” Paragraph 0080)
One of ordinary skill in the art would have recognized that applying the known technique of Bailey, namely, HVAC performance monitoring system, and the known techniques of Picardi, namely, a HVAC service performance system, with the known techniques of Han, namely, energy usage analytics system for energy consumers, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Bailey to analyze data to determine HVAC performance monitoring and the teachings of Picardi to analyze data to monitor a HVAC system with the teachings of Han to analyze data to determine device energy usage would have been recognized by those of ordinary skill in the art as resulting in an improved HVAC monitoring system. In other words, the combination of references provides for analyzing various types of data using various analysis techniques for HVAC performance analysis based on the teachings of analyzing various types of data using various analysis techniques in Bailey, the teachings of analyzing various types of data in Picardi, and the teachings of using histograms to analyze data for energy monitoring in Han.
Claim 14:
Claim 14 is substantially similar to claim 6 and is rejected based on the same reasons and rationale.
14. (Original) The system of claim 9, wherein the at least one processor is further configured to perform:
for each of a plurality of bins, each bin being associated with a range of sample values, determining a number of the sample values that fall within the associated range to generate histogram data;
fitting a curve to the histogram data; and
detecting a change in HVAC performance based on a change over time in at least one parameter of the curve.
Claims 8, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Patent Application Publication No. 2019/0264936 (Bailey) in view of
U.S. Patent Application Publication No. 2020/0240662 (Picardi) and further in view of
U.S. Patent Application Publication No. 2010/0010679 (Kassel).
Claim 8:
Bailey and Picardi do not explicitly describe electrical transient data as described below. However, Kassel teaches the electrical transient data as described below.
The cited prior art describes the method of claim 1,
wherein monitoring the HVAC system comprises collecting electrical transient data, and (Kassel: see the current flow as illustrated in figure 3 and as described in paragraphs 0008, 0124; see the data collected including current flow events, power events, current, and voltage as described in paragraphs 0008-0021)
wherein the detection of a sample interval during which the HVAC system is active is based at least in part on the electrical transient data. (Kassel: “Order, Ranking and Sequence Logic--as the previous blocks inputs change state, they are filtered for specific characteristics such as passing a threshold level, meeting a combinational-logic condition occurring in a particular sequence or exceeding some time duration previously established through settings made during initial configuration. Based on the configuration choices, values or options turned on or off sets up the criteria for which specific input(s) will be used to trigger an output event. Further, a weighting factor may be assigned to any selected inputs that will further influence the amount of change necessary to promulgate an output change. So based on such factors as repetition rate, duration or interval of said input's change, an outputs activity such as start-time, on duration, tie to another output or even ignoring action can be tailored. Ranking of which inputs will be the primary influence and which the tertiary, is orchestrated through a dynamic status ranking calculation using input activity placed through a programmable highpass fast averaging filter.” Paragraph 0125) (Bailey: see the record analysis 520 as illustrated in figure 5 and as described in paragraphs 0128, 0129; “At step 520 the record is analysed to compute a set of parameters relating to the HVAC device (e.g. relating to its operation and/or performance) and/or relating to the premises which the HVAC device controls (e.g. relating to the environmental response of the monitored space), for that operational period.” Paragraph 0128; “Change in value of measured environmental characteristic over length of time for which the HVAC component is on (for example, rate of change of room temperature or other characteristic over activation period, such as rate at which the premises heats up when the boiler is on (e.g. measured in degrees change per hour))” paragraph 0129; “At step 204 the data collected in step 202 is analysed to identify patterns indicative of normal operation of the HVAC component, for example over a series of heating periods it may be possible to model normal operation of the house, such as a pattern showing how long it takes to increase the room temperature by a certain amount.” Paragraph 0092)
One of ordinary skill in the art would have recognized that applying the known technique of Bailey, namely, HVAC performance monitoring system, and the known techniques of Picardi, namely, a HVAC service performance system, with the known techniques of Kassel, namely, a HVAC monitoring system, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Bailey to analyze data to determine HVAC performance monitoring and the teachings of Picardi to analyze data to monitor a HVAC system with the teachings of Kassel to analyze data to monitor a HVAC system would have been recognized by those of ordinary skill in the art as resulting in an improved HVAC monitoring system. In other words, the combination of references provides for analyzing various types of data using various analysis techniques for HVAC performance analysis based on the teachings of analyzing various types of data using various analysis techniques in Bailey, the teachings of analyzing various types of data in Picardi, and the teachings of analyzing various types of data to monitor a HVAC system in Kassel.
Claim 16:
Claim 16 is substantially similar to claim 8 and is rejected based on the same reasons and rationale.
16. (Original) The system of claim 9,
wherein the first parameter is electrical transient data, and
wherein the detection of a sample interval during which the HVAC system is active is based at least in part on the electrical transient data.
Claim 18:
Claim 18 is substantially similar to claim 8 and is rejected based on the same reasons and rationale.
18. (Original) The system of claim 17, wherein the at least one additional parameter further comprises voltage transient data.
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Patent Application Publication No. 2019/0264936 (Bailey) in view of
U.S. Patent Application Publication No. 2020/0240662 (Picardi) and further in view of
Sikora, Jan, et al. "Influence of environmental noise on quality control of hvac devices based on convolutional neural network." Applied Sciences 11.16 (2021): 7484 (Sikora).
Claim 23:
Bailey and Picardi do not explicitly describe a CNN as described below. However, Sikora teaches the CNN as described below.
The cited prior art describes the method of claim 22, wherein the neural network is a convolutional neural network. (Sikora: see the training and use of the CNN to classify automotive HVAC noise as illustrated in figure 9 and as described in section 2.5)
One of ordinary skill in the art would have recognized that applying the known technique of Bailey, namely, HVAC performance monitoring system, and the known techniques of Picardi, namely, a HVAC service performance system, with the known techniques of Sikora, namely, a HVAC quality analysis system, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Bailey to analyze data to determine HVAC performance monitoring with the teachings of Picardi to analyze data to monitor a HVAC system and the teaching of Sikora to analyze data using various techniques to monitor a HVAC system would have been recognized by those of ordinary skill in the art as resulting in an improved HVAC monitoring system. In other words, the combination of references provides for analyzing various types of data using various analysis techniques for HVAC performance analysis based on the teachings of analyzing various types of data using various analysis techniques in Bailey and the teachings of analyzing various types of data in Picardi and the analyzing of data using various techniques in Sikora.
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication No. 2021/0302043 describes a HVAC performance tracking system.
U.S. Patent Application Publication No. 2017/0276571 describes a fault detection system for building equipment.
U.S. Patent Application Publication No. 2018/0087798 describes analyzing data for HVAC for HVAC efficiency.
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/Christopher E. Everett/Primary Examiner, Art Unit 2117
