DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
1. The information disclosure statement (IDS) submitted on 9/03/2024 and is in compliance with the provisions of 37 CFR 1.97. According, the information disclosure statement is being considered by the Examiner.
Examiner Notes
2. Examiner cites particular paragraphs, columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Claim Rejections - 35 USC § 102
3. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
4. Claims 1-4, 7-14, 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Davies et al. (US. Pub. 2021/0288450: hereinafter “Davies”).
Regarding claim 1, Davies discloses a method for identifying anomalous behavior in an operation of an appliance (the smart power plug is able to read the voltage and current being consumed by the appliance, analyze from the reading and notify the user to warn of a potential fault developing within the appliance being monitored, see paragraph [0042]) that is plugged into a socket receptacle of a smart socket (an appliance is plugged into a socket 2 of a smart power plug as shown in Figs. 1 and 2AB, see [0053]), wherein the smart socket includes a measurement unit that (current sensors and voltage sensors 4 in Fig. 1) is configured to sample a current and a voltage delivered by the smart socket to the appliance (“The processor 9 is configured to determine power consumption data from data relating to the current and voltage measurements made by the current and voltage sensors, and to monitor the performance of the electrical appliance using the power consumption data”, in [0063]), the method comprising: during a learning time, identifying a baseline appliance profile for the appliance that is plugged into the socket receptacle of the smart socket, the baseline appliance profile is based at least in part on the current and the voltage, sampled by the measurement unit of the smart socket at each of a plurality of sample times during the learning time, and delivered by the smart socket to the appliance (“A power plug attached to a particular appliance type, the processor is pre-programmed or configured to extract a particular type of data, i.e. one or more particular features from the power consumption data. The plug may also comprise stored information specific to the particular appliance or appliance type used for comparison” in [0090]. “The benchmark value is learned from previous cycles, there are multiple benchmark values (equivalent to a baseline profile for a particular appliance type) depending on the program being used by the washing machine. The particular washing machine program is identified as a pattern of switch-on and switch-off cycles in the power consumption data, and if a new program is used then a new benchmark is learned, i.e. the value for this program is stored. The processor uses the benchmark value to compare with the present operation value” in [0111-112]); during an operational time subsequent to the learning time: identifying an operational profile for the appliance, the operational profile is based at least in part on the current and the voltage, sampled by the measurement unit of the smart socket at each of a plurality of sample times during the operational time, and delivered by the smart socket to the appliance (“the power plug is able to read the voltage and current, being consumed by the appliance, at high frequency to store, analyse and communicate the performance of the appliance. The plug may be able to analyse and store one or more feature sets of the appliance . The feature sets (equivalent to baseline sets) may then be subsequently compared to the current performance of the appliance, where a substantial deviation from the historical feature sets is able to trigger a communication, for example with the user, to warn of a potential fault developing within the appliance being monitored, in [0042]); comparing the operational profile of the appliance to the baseline appliance profile (see [0042]); detecting and/or predicting an anomalous behavior in the operation of the appliance based at least in part on the comparison of the operational profile of the appliance to the baseline appliance profile (“The feature sets may then be subsequently compared to the current performance of the appliance, where a substantial deviation from the historical feature sets is able to trigger a communication, for example with the user, to warn of a potential fault developing within the appliance being monitored” in [0042]); and taking action in response to detecting and/or predicting the anomalous behavior in the operation of an appliance (notify to the user about a potential fault developing within the appliance being monitored. See [0042, 93, 103]).
Regarding claim 2, Davies discloses the method of claim 1, comprising: determining a baseline energy consumption profile of the appliance that is plugged into the socket receptacle of the smart socket based at least in part on the current and the voltage, as sampled by the measurement unit of the smart socket at one or more of the plurality of sample times during the learning time, wherein the baseline appliance profile includes the baseline energy consumption profile of the appliance (see at least in [0024, 42, 65, 92, 111-112]); determining an operational energy consumption profile of the appliance based at least in part on the current and the voltage, as sampled by the measurement unit of the smart socket at one or more of the plurality of sample times during the operational time (see at least in [0024, 42, 65, 92, 111-112]); and wherein detecting and/or predicting the anomalous behavior in the operation of the appliance is based at least in part on the comparison of the operational energy consumption profile of the appliance to the baseline energy consumption profile of the appliance (see [0042, 93, 124]).
Regarding claim 3, Davies discloses the method of claim 1, comprising: determining a baseline power consumption profile of the appliance that is plugged into the socket receptacle of the smart socket based at least in part on the current and the voltage, as sampled by the measurement unit of the smart socket at one or more of the plurality of sample times during the learning time, wherein the baseline appliance profile includes the baseline power consumption profile of the appliance (see at least in [0024, 42, 65, 92, 111-112]); determining an operational power consumption profile of the appliance based at least in part on the current and the voltage, as sampled by the measurement unit of the smart socket at one or more of the plurality of sample times during the operational time (see at least in [0024, 42, 65, 92, 111-112]); and wherein detecting and/or predicting the anomalous behavior in the operation of the appliance is based at least in part on the comparison of the operational power consumption profile of the appliance to the baseline power consumption profile of the appliance (see [0042, 93, 124]).
Regarding claim 4, Davies discloses the method of claim 1, comprising: determining a baseline power factor profile of the appliance that is plugged into the socket receptacle of the smart socket based at least in part on the current and the voltage, as sampled by the measurement unit of the smart socket at one or more of the plurality of sample times during the learning time, wherein the baseline appliance profile includes the baseline power factor profile of the appliance (see at least in [0024, 42, 65, 92, 111-112]); determining an operational power factor profile of the appliance based at least in part on the current and the voltage, as sampled by the measurement unit of the smart socket at one or more of the plurality of sample times during the operational time (see at least in [0024, 42, 65, 92, 111-112]); and wherein detecting and/or predicting the anomalous behavior in the operation of the appliance is based at least in part on the comparison of the operational power factor profile of the appliance to the baseline power factor profile of the appliance (see [0042, 93, 124]).
Regarding claim 7, Davies discloses the method of claim 1, comprising: classifying the appliance that is plugged into the socket receptacle of the smart socket into one of a plurality of predetermined appliance types, wherein classifying the appliance includes comparing the current and/or the voltage sampled by the measurement unit of the smart socket at one or more of the plurality of sample times during the learning time, and/or one or more measures derived at least in part from the current and/or the voltage sampled by the measurement unit of the smart socket at one or more of the plurality of sample times during the learning time, with each of a plurality of predetermined appliance profiles each associated with one of the plurality of predetermined appliance types to identifying a matching one of the plurality of predetermined appliance profiles; and using the matching one of the plurality of predetermined appliance profiles as the baseline appliance profile for the appliance that is plugged into the socket receptacle of the smart socket (see [0090, 94, 97, 106, 125]).
Regarding claim 8, Davies discloses the method of claim 7, wherein each of the plurality of predetermined appliance profiles is learned using machine learning (see [00112]).
Regarding claim 9, Davies discloses the method of claim 1, wherein the baseline appliance profile includes a power consumption signature of the appliance that is plugged into the socket receptacle of the smart socket (see [0092, 95, 103, 112, 124]).
Regarding claim 10, Davies discloses the method of claim 1, wherein the baseline appliance profile includes an energy consumption signature of the appliance that is plugged into the socket receptacle of the smart socket (see [0092, 95, 103, 112, 124]).
Regarding claim 11, Davies discloses the method of claim 1, wherein the baseline appliance profile includes a power factor signature of the appliance that is plugged into the socket receptacle of the smart socket (see [0092, 95, 103, 112, 124]).
Regarding claim 12, Davies discloses the method of claim 1, wherein the measurement unit of the smart socket is configured to report one or more measures derived at least in part from the current and/or the voltage sampled by the measurement unit of the smart socket at one or more of the plurality of sample times during the learning time, and one or more measures derived at least in part from the current and/or the voltage sampled by the measurement unit of the smart socket at one or more of the plurality of sample times during the operational time (see [0063, 128, 146]).
Regarding claim 13, Davies discloses the method of claim 1, comprising transmitting an alert to notify a user of the detected and/or predicted anomalous behavior in the operation of the appliance (see [0042]).
Regarding claim 14, Davies discloses the method of claim 1, in response to the detected and/or predicted anomalous behavior in the operation of the appliance, turning off power to the socket receptacle of the smart socket and thus turning power off to the appliance that is plugged into the socket receptacle of the smart socket (see [0038-39, 42, 66, 104]).
Regarding claim 18, Davies discloses a system (a system in Fig. 1) for identifying anomalous behavior in an operation of an appliance (the smart power plug is able to read the voltage and current being consumed by the appliance, analyze from the reading and notify the user to warn of a potential fault developing within the appliance being monitored, see paragraph [0042]) that is plugged into a socket receptacle of a smart socket (an appliance is plugged into a socket 2 of a smart power plug as shown in Figs. 1 and 2AB, see [0053]), wherein the smart socket includes a measurement unit that is configured identify an energy use of the appliance delivered by the smart socket to the appliance (“The processor 9 is configured to determine power consumption data from data relating to the current and voltage measurements made by the current and voltage sensors, and to monitor the performance of the electrical appliance using the power consumption data”, in [0063]), the system comprising: a memory (11 in Fig. 1) for storing the identified energy use of the appliance delivered by the smart socket to the appliance (“the power plug could be attached to a washing machine or dishwasher to analyse and store the power consumption information of the appliance over time”, in [0103]. Also see [0042]); a controller (9 in Fig. 1) operatively coupled to the memory (11), the controller configured to: detect an energy use pattern of the appliance based on the stored energy use identified by the measurement unit of the smart socket (“The processor 9 is configured to determine power consumption data from data relating to the current and voltage measurements made by the current and voltage sensors, and to monitor the performance of the electrical appliance using the power consumption data. The storage 11 may store further information used for processing the power consumption data, for example a set of benchmark features and/or one or more threshold values for example” in [0063, 65]. Also see [0078, 92, 112, 128]); compare the energy use pattern to an expected energy use pattern for the appliance (“Pattern changes” may refer to changes in a collected feature set before and after the occurrence of a fault or a developing fault. In turn, a benchmark feature set may comprise an aggregation of the historic performance of the appliance. The performance data represents an understanding of the appliance power consumption at various instances within its operational cycle. The immediate power consumption cycle is then compared to historic benchmark data to infer performance degradation” in [0124]. Also see at least in [0111-112]); when the energy use pattern deviates from the expected energy use pattern for the appliance in accordance with one or more predetermined deviation criteria, detect and/or predict an anomalous behavior in an operation of an appliance (“the plug may be able to infer the performance of the appliance over time in order to determine whether the appliance is operating efficiently. By way of example only, the plug may be able to analyse and store one or more feature sets of the appliance. The feature sets may then be subsequently compared to the current performance of the appliance, where a substantial deviation from the historical feature sets is able to trigger a communication, for example with the user, to warn of a potential fault developing within the appliance being monitored” in [0042]); and take action in response to detecting and/or predicting the anomalous behavior in the operation of an appliance (a substantial deviation from the historical feature sets is able to trigger a communication, for example with the user, to warn of a potential fault developing within the appliance being monitored, in [0042]. Also see [0092, 103]).
Regarding claim 19, Davies discloses the system of claim 18, wherein the controller is configured to: classify the appliance into a selected one of a plurality of appliance types based at least in part on the energy use pattern of the appliance, wherein each of the plurality of appliance types has a corresponding expected energy use pattern; and compare the energy use pattern of the appliance to the corresponding expected energy use pattern for the selected one of the plurality of appliance types (see [0090, 94, 97, 106, 122, 125, and 130]).
Regarding claim 20, Davies discloses the system of claim 19, wherein the plurality of appliance types include one or more of a clothes dryer, a clothes washer, a dishwasher, a light, a television, a freezer, a refrigerator, a garage door opener, a computer, a modem, and a printer (see [00103-105]).
5. Claim 18 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jeon (US. Pub. 2016/0274161: hereinafter “Jeon”).
Regarding claim 18, Joen discloses a system (a system in Fig. 1) for identifying anomalous behavior in an operation of an appliance (a diagnostic system for diagnosing an abnormal state of a home appliance 100, see abstract and Figs. 1, 3) that is plugged into a socket receptacle of a smart socket (a home appliance 100 is plugged to an electrical socket of a smart plug 200 in Fig. 1, see [0022]), wherein the smart socket (200) includes a measurement unit that is configured identify an energy use of the appliance delivered by the smart socket to the appliance (“the home appliance 100 is operated, the smart plug 200 in which the plug of the home appliance 100 is inserted may collect information about the power consumption of the home appliance 100 through the power measuring part 230”, see [0033]), the system comprising: a memory (320 in Fig. 2) for storing the identified energy use of the appliance delivered by the smart socket to the appliance (“electric power information and a reference power value, like an energy rating, of the home appliance 100, a specific user's power using pattern, a power using pattern according to malfunction of the home appliance 100, or the like, may be stored in the memory 320”, see [0027]); a controller (210 in fig. 2) operatively coupled to the memory (320), the controller configured to: detect an energy use pattern of the appliance based on the stored energy use identified by the measurement unit of the smart socket (The external server 300 may receive the consumed electric power value of the home appliance 100 which is measured by the smart plug 200 (S11). The external server 300 may analyze an electric power consumption pattern by comparing the electric power value of the home appliance 100 with the received consumed electric power value (S12), and determine whether there is an abnormal state, see at least in [0030]); compare the energy use pattern to an expected energy use pattern for the appliance (“The external server 300 may analyze an electric power consumption pattern by comparing the electric power value of the home appliance 100 with the received consumed electric power value”, in [0030]); when the energy use pattern deviates from the expected energy use pattern for the appliance in accordance with one or more predetermined deviation criteria, detect and/or predict an anomalous behavior in an operation of an appliance (“electric power information and a reference power value, like an energy rating, of the home appliance 100, a specific user's power using pattern, a power using pattern according to malfunction of the home appliance 100, or the like, may be stored in the memory 320. The abnormal operation detecting part 310 may determine the operation state of the home appliance 100 by comparing the power consumption of the home appliance 100 received from the smart plug 200 with a value stored in the memory 320”, in [0027]); and take action in response to detecting and/or predicting the anomalous behavior in the operation of an appliance (hen the abnormal operation of the home appliance 100 is detected, a signal for notifying the abnormal operation may be transmitted to the terminal 400 and the home appliance 100, and information about the abnormal operation and a user manual corresponding to the abnormal operation may be provided to the user, in [0035].).
Claim Rejections - 35 USC § 103
6. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
7. Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Davies in view of Parfitt et al. (US. Pub. 2025/0210920; hereinafter “Parfitt”).
Regarding claim 5, Davies discloses the method of claim 1, except for explicitly specify wherein the smart socket includes an in-built temperature sensor for sampling a temperature inside the smart socket, wherein the baseline appliance profile includes a baseline temperature profile that is based at least in part on the temperature inside the smart socket at each of a plurality of temperature sample times during the learning time correlated with the current and the voltage sampled by the measurement unit of the smart socket at one or more of the plurality of sample times during the learning time.
Parfitt discloses a smart socket device (100 in Figs. 1A-1C) includes an in-built temperature sensor (111) for sampling a temperature inside the smart socket (100, see Figs. 1A-1C), wherein the baseline appliance profile includes a baseline temperature profile that is based at least in part on the temperature inside the smart socket at each of a plurality of temperature sample times during the learning time correlated with the current and the voltage sampled by the measurement unit of the smart socket at one or more of the plurality of sample times during the learning time (see [0008, 10, 88-89]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to employ the monitoring system of Davies by having smart socket includes an in-built temperature sensor for sampling a temperature inside the smart socket, wherein the baseline appliance profile includes a baseline temperature profile that is based at least in part on the temperature inside the smart socket at each of a plurality of temperature sample times during the learning time correlated with the current and the voltage sampled by the measurement unit of the smart socket at one or more of the plurality of sample times during the learning time, as taught by Parfitt for purpose of providing a smart socket device provides a more accurate means of identifying the temperature and avoids the need for a sensor to be accurately positioned to encounter the conductor of the PCB. The instruction from a remote device may originate from a smart user device of an occupant or an emergency responder, and thus improving the surface temperature detection accuracy and consistency (see the summary).
8. Claim 15-17 is rejected under 35 U.S.C. 103 as being unpatentable over Davies in view of Payne et al. (US. Pub. 2025/0044329; hereinafter “Payne”).
Regarding claim 15, Davies discloses a method for identifying anomalous behavior in an operation of an appliance (the smart power plug is able to read the voltage and current being consumed by the appliance, analyze from the reading and notify the user to warn of a potential fault developing within the appliance being monitored, see paragraph [0042]) that is plugged into a socket receptacle of a smart socket (an appliance is plugged into a socket 2 of a smart power plug as shown in Figs. 1 and 2AB, see [0053]), wherein the smart socket includes a measurement unit (current sensors and voltage sensors 4 in Fig. 1) that is configured to sample one or more of voltage, current, power, and energy delivered by the smart socket to the appliance (“The processor 9 is configured to determine power consumption data from data relating to the current and voltage measurements made by the current and voltage sensors, and to monitor the performance of the electrical appliance using the power consumption data”, in [0063]), the method comprising: during a learning time: monitoring one or more of voltage, current, power, and energy that is sampled by the measurement unit of the smart socket and delivered by the smart socket to the appliance that is plugged into the socket receptacle of the smart socket during at least part of the learning time, resulting in a monitored electrical behavior of the appliance (“A power plug attached to a particular appliance type, the processor is pre-programmed or configured to extract a particular type of data, i.e. one or more particular features from the power consumption data. The plug may also comprise stored information specific to the particular appliance or appliance type used for comparison” in [0090]. “The benchmark value is learned from previous cycles, there are multiple benchmark values (equivalent to a baseline profile for a particular appliance type) depending on the program being used by the washing machine. The particular washing machine program is identified as a pattern of switch-on and switch-off cycles in the power consumption data, and if a new program is used then a new benchmark is learned, i.e. the value for this program is stored. The processor uses the benchmark value to compare with the present operation value” in [0111-112]); during an operation time: monitoring one or more of voltage, current, power, and energy that is sampled by the measurement unit of the smart socket and delivered by the smart socket to the appliance that is plugged into the socket receptacle of the smart socket during at least part of the operational time, resulting in a monitored operational behavior (“the power plug is able to read the voltage and current, being consumed by the appliance, at high frequency to store, analyse and communicate the performance of the appliance. The plug may be able to analyse and store one or more feature sets of the appliance . The feature sets may then be subsequently compared to the current performance of the appliance, where a substantial deviation from the historical feature sets is able to trigger a communication, for example with the user, to warn of a potential fault developing within the appliance being monitored, in [0042]); comparing the monitored operational behavior of the appliance with the predefined baseline appliance profile that corresponds to the predetermined appliance type into which the appliance has been classified (The feature sets (equivalent to baseline sets) may then be subsequently compared to the current performance of the appliance, where a substantial deviation from the historical feature sets is able to trigger a communication, for example with the user, to warn of a potential fault developing within the appliance being monitored, in [0042]); detecting and/or predicting an anomalous behavior in the appliance based at least in part on the comparison of the monitored operational behavior of the appliance with the predefined baseline appliance profile that corresponds to the predetermined appliance type into which the appliance has been classified (The feature sets (equivalent to baseline sets) may then be subsequently compared to the current performance of the appliance, where a substantial deviation from the historical feature sets is able to trigger a communication, for example with the user, to warn of a potential fault developing within the appliance being monitored, in [0042]); and taking action in response to detecting and/or predicting the anomalous behavior in the operation of an appliance (notify to the user about a potential fault developing within the appliance being monitored. See [0042, 93, 103]).
Davies does not explicitly specify that based on the monitored electrical behavior of the appliance, classifying the appliance into one of a plurality of predetermined appliance types, wherein each of the plurality of predetermined appliance types has a corresponding predefined baseline appliance profile. Payne discloses, in Figs. 2-5, a method for monitoring and analyzing of home appliances energy use, comprising based on the monitored electrical behavior of the appliance, classifying the appliance into one of a plurality of predetermined appliance types, wherein each of the plurality of predetermined appliance types has a corresponding predefined baseline appliance profile (determining the plurality of electricity usage levels may include applying a trained machine learning model to the real-time electricity characteristics for the time interval to determine energy usage by a plurality of distinct electrical loads at the structure, generating the energy use profile may include applying a trained machine learning model to the plurality of electricity usage levels for the time interval to determine the one or more energy use scores, determining the appliance usage levels may include identifying the individual electrical appliances based upon the real-time electricity characteristics, such as by applying a trained machine learning model to detect the electrical appliances from unique electrical signatures of the appliances, wherein the energy use profile may detail specific types, classes, or specifications of electrical appliances that behave differently or consume a different amount of electricity compared to other electrical devices within the structure 202. The processor may obtain training data from which to train an machine learning model for use in generating energy use data within an energy use profile, the training data may include voltage or other electricity characteristics associated with individually identifiable loads, e.g., electrical appliances, in order to train the machine learning model to identify such electrical devices or conditions of such electrical devices. See at least in [0010, 49, 53, 58-60])
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to employ the monitoring system of Davies by monitoring and analyzing of home appliances energy use, comprising based on the monitored electrical behavior of the appliance, classifying the appliance into one of a plurality of predetermined appliance types, wherein each of the plurality of predetermined appliance types has a corresponding predefined baseline appliance profile, as taught by Payne for purpose of providing a smart learning method for monitoring and analyzing electrical energy use of electrical appliances in a structure i.e. home, offices and a business.
Regarding claim 16, Davies and Payne disclose the method of claim 15, Davies further teaches wherein the plurality of predetermined appliance types include one or more of a clothes dryer, a clothes washer, a dishwasher, a light, a television, a freezer, a refrigerator, a garage door opener, a computer, a modem, and a printer (see [0120]).
Regarding claim 17, Davies and Payne disclose the method of claim 15, Payne further teaches wherein at least part of the predefined baseline appliance profile for each of the plurality of predetermined appliance types is learned using machine learning that is trained using one or more of voltage, current, power, and energy sampled from a plurality of training appliances of the corresponding appliance type ([0049, 57-59] of Payne).
Allowable Subject Matter
9. Claims 6 are objected to as being dependent upon a rejected base claim, but would be allowable if corrected to overcome the claim objections set forth above in this Office action and to include all of the limitations of the base claim, any intervening claims.
Regarding claim 6, the cited references, alone or in combination, do not disclose nor fairly suggest:
“ … the operational profile is based at least in part on the current and the voltage, sampled by the measurement unit of the smart socket at each of a plurality of sample times during the operational time, and delivered by the smart socket to the appliance, and an operational temperature profile that is based at least in part on the temperature inside the smart socket at each of a plurality of temperature sample times during the operational time and correlated with the current and the voltage sampled by the measurement unit of the smart socket at one or more of the plurality of sample times during the operational time; and wherein detecting and/or predicting the anomalous behavior in the operation of the appliance includes comparing the operational temperature profile, correlated with the current and the voltage sampled by the measurement unit of the smart socket at one or more of the plurality of sample times during the operational time, to the baseline temperature profile of the baseline appliance profile.” in combination with all other elements as claimed in claims 1 and 5.
Conclusion
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to THANG LE whose telephone number is (571)272-9349. The examiner can normally be reached on Monday thru Friday 7:30AM-5:00PM EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Huy Phan can be reached on (571) 272-7924. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THANG X LE/Primary Examiner, Art Unit 2858
1/29/2026