DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-21 are rejected under 35 U.S.C. 103 as being unpatentable over by Singla et al. IN 202311084884 A (“Singla”) in view of Williams et al. US 2022/0139190 (“Williams”).
Singla discloses:
1. A computer system for monitoring levels of gases in a home, the computer system comprising: at least one memory device, at least one processor in communication with the at least one memory device (e.g., Fig. 1 #102: “Multiprocessor/CPU”), at least one smart sensor for the home (e.g., Fig. 1 #101: “LP Gas Sensors”), and at least one gas shutoff valve of the home (e.g., pg. 8: “gas valves”), the at least one processor configured to:
receive sensor data from the at least one smart sensor (e.g., Fig. 1: “Collect sensor reading”, pg. 8: “Highly sensitive sensor are positioned strategically throughout the system to detect a wide range of gases”);
compare the sensor data to one or more parameters generated using an artificial intelligence model to determine an abnormal level of gas is present in the home (e.g., pg. 8: “Sophisticated artificial intelligence algorithms interpret real-time data acquired by these sensors, allowing the system to distinguish between possible gas leaks and regular environmental fluctuations. By constantly analyzing past data, the machine learning algorithms in use improve their capacity to recognize patterns linked to gas concentrations suggestive of leaks”), wherein the artificial intelligence model is trained based upon historical sensor data (e.g., pg. 9: “Artificial Intelligence models (104) are trained and created using a dataset of senor readings”) Thus, Singla’s disclosure that the AI model is trained based upon historical sensor data from “a home” also implicitly reads on training the AI model based upon historical sensor data from a plurality of “homes”); and
in response to determining that an abnormal level of gas is present in the home, control the at least one gas shutoff valve to close to prevent further gas leaks at the home (e.g., Fig. 1: “Real time monitoring and decision making”, pg. 8: “The system has automated features for quick response in addition to alerting users, including turning off gas valves”, pg. 9: “These models serve as the foundation for the system’s autonomous response to changing gas concentrations through real-time monitoring and decision-making capabilities”).
2. The computer system of Claim 1, wherein the at least one processor is further configured to transmit content data to a user device associated with the home that, when received by the user device, causes the user device to generate a user interface including an indicator that an abnormal level of gas is present in the home (e.g., Fig. 1: “Alert generate on phone”, pg. 8: “Enhancing accessibility, a user-friendly interface shows the status of the system, historical patterns, and real-time data”, pg. 9: “An interface for monitoring is created, offering historical data, alert notifications, and visualizations of gas concentrations”).
3. The computer system of Claim 1, wherein the at least one processor is further configured to identify, using the one or more parameters, a location of a gas leak within the home (e.g., pg. 8: “Sophisticated artificial intelligence algorithms interpret real-time data acquired by these sensors, allowing the system to distinguish between possible gas leaks and regular environmental fluctuations. By constantly analyzing past data, the machine learning algorithms in use improve their capacity to recognize patterns linked to gas concentrations suggestive of leaks”).
4. The computer system of Claim 3, wherein the at least one processor is in communication with a plurality of gas shutoff valves of the home, and wherein the at least one processor is further configured to select at least one of the plurality of gas shutoff valves to close based upon the location of the gas leak (e.g., Fig. 1: “Real time monitoring and decision making”, pg. 8: “The system has automated features for quick response in addition to alerting users, including turning off gas valves”, pg. 9: “These models serve as the foundation for the system’s autonomous response to changing gas concentrations through real-time monitoring and decision-making capabilities”).
5. The computer system of Claim 3, wherein the at least one processor is further configured to transmit content data to a user device associated with the home that, when received by the user device, causes the user device to generate a user interface including the location of the gas leak (e.g., Fig. 1: “Alert generate on phone”, pg. 8: “Enhancing accessibility, a user-friendly interface shows the status of the system, historical patterns, and real-time data”, pg. 9: “An interface for monitoring is created, offering historical data, alert notifications, and visualizations of gas concentrations”).
6. The computer system of Claim 1, wherein the at least one processor is further configured to generate the artificial intelligence model based upon the historical sensor data (e.g., pg. 9: “Artificial Intelligence models (104) are trained and created using a dataset of senor readings”) Thus, Singla’s disclosure that the AI model is trained based upon historical sensor data from “a home” also implicitly reads on training the AI model based upon historical sensor data from a plurality of “homes”).
7. The computer system of Claim 6, wherein the at least one processor is further configured to:
receive further sensor data relating to the plurality of homes;
update the artificial intelligence model based upon the further sensor data; and
update the one or more parameters using the updated artificial intelligence model (e.g., pg. 9: “Artificial Intelligence models (104) are trained and created using a dataset of senor readings”).
8. The computer system of Claim 1, further comprising a home controller disposed in the home and including the at least one processor (e.g., Fig. 1 #102: “Multiprocessor/CPU”), wherein the at least one processor is further configured to receive the one or more parameters from a server computing device (e.g., Fig. 1 #103: “Azure Cloud platform”).
9. The computer system of Claim 1, wherein the parameters include one or more baseline norms, and wherein the at least one processor is configured to compare the sensor data to the one or more baseline norms to determine an abnormal level of gas is present in the home (e.g., pg. 8: “Sophisticated artificial intelligence algorithms interpret real-time data acquired by these sensors, allowing the system to distinguish between possible gas leaks and regular environmental fluctuations. By constantly analyzing past data, the machine learning algorithms in use improve their capacity to recognize patterns linked to gas concentrations suggestive of leaks”).
10. The computer system of Claim 1, wherein the parameters include one or more digital profiles including a plurality of data values corresponding to different types of sensor data (e.g., Fig. 1: “Kinds of gas”), and wherein the at least one processor is configured to compare the sensor data to the plurality of data values of the digital profiles to determine an abnormal level of gas is present in the home (e.g., pg. 8: “Sophisticated artificial intelligence algorithms interpret real-time data acquired by these sensors, allowing the system to distinguish between possible gas leaks and regular environmental fluctuations. By constantly analyzing past data, the machine learning algorithms in use improve their capacity to recognize patterns linked to gas concentrations suggestive of leaks”).
11. A computer-implemented method for gas monitoring in a home, the computer-implemented method performed by a computer system including at least one memory device and at least one processor in communication with the at least one memory device (e.g., Fig. 1 #102: “Multiprocessor/CPU”), at least one smart sensor for a home (e.g., Fig. 1 #101: “LP Gas Sensors”), and at least one gas shutoff valve of the home (e.g., pg. 8: “gas valves”), the computer-implemented method comprising:
receiving sensor data from the at least one smart sensor (e.g., Fig. 1: “Collect sensor reading”, pg. 8: “Highly sensitive sensor are positioned strategically throughout the system to detect a wide range of gases”);
comparing the sensor data to one or more parameters generated using an artificial intelligence model to determine an abnormal level of gas is present in the home (e.g., pg. 8: “Sophisticated artificial intelligence algorithms interpret real-time data acquired by these sensors, allowing the system to distinguish between possible gas leaks and regular environmental fluctuations. By constantly analyzing past data, the machine learning algorithms in use improve their capacity to recognize patterns linked to gas concentrations suggestive of leaks”), wherein the artificial intelligence model is trained based upon historical sensor data (e.g., pg. 9: “Artificial Intelligence models (104) are trained and created using a dataset of senor readings”) Thus, Singla’s disclosure that the AI model is trained based upon historical sensor data from “a home” also implicitly reads on training the AI model based upon historical sensor data from a plurality of “homes”); and
in response to determining that an abnormal level of gas is present in the home, controlling the at least one gas shutoff valve to close to prevent further gas leaks at the home.
12. The computer-implemented method of Claim 11, further comprising transmitting content data to a user device associated with the home that, when received by the user device, causes the user device to generate a user interface including an indicator that an abnormal level of gas is present in the home (e.g., Fig. 1: “Alert generate on phone”, pg. 8: “Enhancing accessibility, a user-friendly interface shows the status of the system, historical patterns, and real-time data”, pg. 9: “An interface for monitoring is created, offering historical data, alert notifications, and visualizations of gas concentrations”).
13. The computer-implemented method of Claim 11, further comprising identifying, using the one or more parameters, a location of a gas leak within the home (e.g., pg. 8: “Sophisticated artificial intelligence algorithms interpret real-time data acquired by these sensors, allowing the system to distinguish between possible gas leaks and regular environmental fluctuations. By constantly analyzing past data, the machine learning algorithms in use improve their capacity to recognize patterns linked to gas concentrations suggestive of leaks”).
14. The computer-implemented method of Claim 13, wherein the at least one processor is in communication with a plurality of gas shutoff valves of the home, and the computer-implemented method further comprises selecting at least one of the plurality of gas shutoff valves to close based upon the location of the gas leak (e.g., Fig. 1: “Real time monitoring and decision making”, pg. 8: “The system has automated features for quick response in addition to alerting users, including turning off gas valves”, pg. 9: “These models serve as the foundation for the system’s autonomous response to changing gas concentrations through real-time monitoring and decision-making capabilities”).
15. The computer-implemented method of Claim 13, further comprising transmitting content data to a user device associated with the home that, when received by the user device, causes the user device to generate a user interface including the location of the gas leak (e.g., Fig. 1: “Alert generate on phone”, pg. 8: “Enhancing accessibility, a user-friendly interface shows the status of the system, historical patterns, and real-time data”, pg. 9: “An interface for monitoring is created, offering historical data, alert notifications, and visualizations of gas concentrations”).
16. The computer-implemented method of Claim 11, further comprising generating the artificial intelligence model based upon the historical sensor data (e.g., pg. 9: “Artificial Intelligence models (104) are trained and created using a dataset of senor readings”) Thus, Singla’s disclosure that the AI model is trained based upon historical sensor data from “a home” also implicitly reads on training the AI model based upon historical sensor data from a plurality of “homes”).
17. The computer-implemented method of Claim 16, further comprising:
receiving further sensor data relating to the plurality of homes;
updating the artificial intelligence model based upon the further sensor data; and
updating the one or more parameters using the updated artificial intelligence model (e.g., pg. 9: “Artificial Intelligence models (104) are trained and created using a dataset of senor readings”).
18. The computer-implemented method of Claim 11, wherein the computer system further includes a home controller disposed in the home and including the at least one processor (e.g., Fig. 1 #102: “Multiprocessor/CPU”), and wherein the computer-implemented method further comprises receiving by the home controller the one or more parameters from a server computing device (e.g., Fig. 1 #103: “Azure Cloud platform”).
19. The computer-implemented method of Claim 11, wherein the parameters include one or more baseline norms, and wherein the computer-implemented method further comprises comparing the sensor data to the one or more baseline norms to determine an abnormal level of gas is present in the home (e.g., pg. 8: “Sophisticated artificial intelligence algorithms interpret real-time data acquired by these sensors, allowing the system to distinguish between possible gas leaks and regular environmental fluctuations. By constantly analyzing past data, the machine learning algorithms in use improve their capacity to recognize patterns linked to gas concentrations suggestive of leaks”).
20. The computer-implemented method of Claim 11, wherein the parameters include one or more digital profiles including a plurality of data values corresponding to different types of sensor data (e.g., Fig. 1: “Kinds of gas”), and wherein the computer-implemented method further comprises comparing the sensor data to the plurality of data values of the digital profiles to determine an abnormal level of gas is present in the home (e.g., pg. 8: “Sophisticated artificial intelligence algorithms interpret real-time data acquired by these sensors, allowing the system to distinguish between possible gas leaks and regular environmental fluctuations. By constantly analyzing past data, the machine learning algorithms in use improve their capacity to recognize patterns linked to gas concentrations suggestive of leaks”).
21. At least one non-transitory computer-readable media having computer-executable instructions embodied thereon, wherein when executed by a computer system including at least one memory device and at least one processor in communication with the at least one memory device (e.g., Fig. 1 #102: “Multiprocessor/CPU”), at least one smart sensor for a home (e.g., Fig. 1 #101: “LP Gas Sensors”), and at least one gas shutoff valve of the home (e.g., pg. 8: “gas valves”), the computer-executable instructions cause the at least one processor to:
receive sensor data from the at least one smart sensor (e.g., Fig. 1: “Collect sensor reading”, pg. 8: “Highly sensitive sensor are positioned strategically throughout the system to detect a wide range of gases”);
compare the sensor data to one or more parameters generated using an artificial intelligence model to determine an abnormal level of gas is present in the home (e.g., pg. 8: “Sophisticated artificial intelligence algorithms interpret real-time data acquired by these sensors, allowing the system to distinguish between possible gas leaks and regular environmental fluctuations. By constantly analyzing past data, the machine learning algorithms in use improve their capacity to recognize patterns linked to gas concentrations suggestive of leaks”), wherein the artificial intelligence model is trained based upon historical sensor data (e.g., pg. 9: “Artificial Intelligence models (104) are trained and created using a dataset of senor readings”) Thus, Singla’s disclosure that the AI model is trained based upon historical sensor data from “a home” also implicitly reads on training the AI model based upon historical sensor data from a plurality of “homes”); and
in response to determining that an abnormal level of gas is present in the home, control the at least one gas shutoff valve to close to prevent further gas leaks at the home (e.g., Fig. 1: “Real time monitoring and decision making”, pg. 8: “The system has automated features for quick response in addition to alerting users, including turning off gas valves”, pg. 9: “These models serve as the foundation for the system’s autonomous response to changing gas concentrations through real-time monitoring and decision-making capabilities”).
Singla discloses that the artificial intelligence model is trained based upon historical sensor data relating to a home (e.g., pg. 9: “Artificial Intelligence models (104) are trained and created using a dataset of senor readings”), but not explicitly “a plurality of homes” as recited in claims 1, 6, 11, 16, and 21.
Williams discloses a “trained machine learning model being trained using historical sensor data collected at a plurality of home environments” (e.g., Fig. 5, claim 27).
Singla and Williams are analogous art since both pertain to the use of artificial intelligence models in home environments to detect abnormalities in sensed conditions, including leaks.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Singla with Williams in order to increase the accuracy and reliability of the trained model of Singla by training it with sensed data from multiple homes, as taught by Williams.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Simpkins et al. US 2025/0110022A1 discloses an apparatus and a method for monitoring a dwelling or set of dwellings using at least one sensor node. In some embodiments, a sensor node collects both audio data and non-audio data and detects at least one event or condition based at least in part on the audio data and the non-audio data. In other embodiments, audio data or non-audio data is used on its own or in other combinations. Processing may be performed to detect the presence of events, or changes in the frequency or duration of events, that may indicate a need for corrective action or preventative maintenance. Monitoring may apply to systems such as HVAC, electrical, structural, and plumbing, among others. Maintenance recommendations and monitoring parameters may by communicated to a user (e.g. to a resident, a property manager, a customer, or a designated service or maintenance person) over a networked service.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN A JARRETT whose telephone number is (571)272-3742. The examiner can normally be reached M-F 9:00-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth Lo can be reached at 571-272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RYAN A JARRETT/Primary Examiner, Art Unit 2116
10/14/26
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