DETAILED ACTION
Claims 1-23 are pending.
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 .
Claim Rejections - 35 USC § 102
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 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 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.
Claim(s) 1-23 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hutz,
US Patent No. 10,868,712 (hereinafter Hutz).
Regarding claims 1-23, Hutz discloses all the claimed limitations, as outlined below:
Claim 1. A computer system for home monitoring, the computer system comprising at least one memory device, at least one processor in communication with the at least one memory device, and at least one smart sensor for a home, the at least one processor configured to: receive sensor data from the at least one smart sensor; compare the sensor data to one or more parameters generated using an artificial intelligence model to identify at least one alert condition for the home, wherein the artificial intelligence model is trained based upon historical sensor data relating to a plurality of homes; and 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 identified at least one alert condition (C12 L4-27 - - receiving sensor/image data from cameras located at smart homes. These images are compared using machine learning techniques. Via the cooperative monitoring network and historic data of movements of other persons having previously traveled through the same region alerts/alarms are generated. Alarms are provided to residents.)
Claim 2. 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 relating to the plurality of homes (C12 L4-27 - - receiving sensor/image data from cameras located at smart homes. These images are compared using machine learning techniques. Via the cooperative monitoring network and historic data of movements of other persons having previously traveled through the same region alerts/alarms are generated. Alarms are provided to residents.)
Claim 3. The computer system of Claim 2, 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 (C12 L4-27 - - receiving sensor/image data from cameras located at smart homes. These images are compared using machine learning techniques. Via the cooperative monitoring network and historic data of movements of other persons having previously traveled through the same region alerts/alarms are generated. Alarms are provided to residents.)
Claim 4. The computer system of Claim 1, further comprising a home controller disposed in the home and including the at least one processor, wherein the at least one processor is further configured to receive the one or more parameters from a server computing device (C12 L4-27 - - receiving sensor/image data from cameras located at smart homes. These images are compared using machine learning techniques. Via the cooperative monitoring network and historic data of movements of other persons having previously traveled through the same region alerts/alarms are generated. Alarms are provided to residents.)
Claim 5. 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 identify the at least one alert condition (C12 L4-27 - - receiving sensor/image data from cameras located at smart homes. These images are compared using machine learning techniques. Via the cooperative monitoring network and historic data of movements of other persons having previously traveled through the same region alerts/alarms are generated. Alarms are provided to residents.)
Claim 6. 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, 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 identify the at least one alert condition (C12 L4-27 - - receiving sensor/image data from cameras located at smart homes. These images are compared using machine learning techniques. Via the cooperative monitoring network and historic data of movements of other persons having previously traveled through the same region alerts/alarms are generated. Alarms are provided to residents.)
Claim 7. The computer system of Claim 1, wherein that at least one processor is further configured to: generate at least one recommendation based upon the identified at least one alert condition; and cause the user interface to display the generated at least one recommendation (C12 L4-27 - - receiving sensor/image data from cameras located at smart homes. These images are compared using machine learning techniques. Via the cooperative monitoring network and historic data of movements of other persons having previously traveled through the same region alerts/alarms are generated. Alarms are provided to residents.)
Claim 8. The computer system of Claim 1, wherein the at least one processor is further configured to: receive home data relating to the home; generate at least one recommendation for placing smart sensors within the home; and cause the user interface to display the generated at least one recommendation (C12 L4-27 - - receiving sensor/image data from cameras located at smart homes. These images are compared using machine learning techniques. Via the cooperative monitoring network and historic data of movements of other persons having previously traveled through the same region alerts/alarms are generated. Alarms are provided to residents.)
Claim 9. The computer system of Claim 1, wherein the at least one processor is further configured to cause the user interface to display one or more of numbers, indicators, or graphics representing at least some of the received sensor data (C12 L4-27 - - receiving sensor/image data from cameras located at smart homes. These images are compared using machine learning techniques. Via the cooperative monitoring network and historic data of movements of other persons having previously traveled through the same region alerts/alarms are generated. Alarms are provided to residents.)
Claim 10. The computer system of Claim 1, wherein the at least one smart sensor comprises one or more of airflow sensors, air quality sensors, chemical sensors, humidity sensors, moisture sensors, odor sensors, particulate matter sensors, pressure sensors, or temperature sensors (C3 L39-67 and C4 L1-8).
Claim 11. The computer system of Claim 1, wherein the at least one alert condition includes one or more of moisture, mold, animal pests, carbon monoxide, low airflow, duct blockage, high temperature, low temperature, or poor air quality (C3 L39-67 and C4 L1-8).
Claim 12. A computer-implemented method for home monitoring 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 and at least one smart sensor of a home, the computer-implemented method comprising: receiving sensor data from the at least one smart sensor; comparing the sensor data to one or more parameters generated using an artificial intelligence model to identify at least one alert condition for the home, wherein the artificial intelligence model is trained based upon historical sensor data relating to a plurality of homes; and 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 identified at least one alert condition (C12 L4-27 - - receiving sensor/image data from cameras located at smart homes. These images are compared using machine learning techniques. Via the cooperative monitoring network and historic data of movements of other persons having previously traveled through the same region alerts/alarms are generated. Alarms are provided to residents.)
Claim 13. The computer-implemented method of Claim 12, further comprising generating the artificial intelligence model based upon the historical sensor data relating to the plurality of homes (C12 L4-27 - - receiving sensor/image data from cameras located at smart homes. These images are compared using machine learning techniques. Via the cooperative monitoring network and historic data of movements of other persons having previously traveled through the same region alerts/alarms are generated. Alarms are provided to residents.)
Claim 14. The computer-implemented method of Claim 13, 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 (C12 L4-27 - - receiving sensor/image data from cameras located at smart homes. These images are compared using machine learning techniques. Via the cooperative monitoring network and historic data of movements of other persons having previously traveled through the same region alerts/alarms are generated. Alarms are provided to residents.)
Claim 15. The computer-implemented method of Claim 12, wherein the computer system further includes a home controller disposed in the home and including the at least one processor, and wherein the computer-implemented method further comprises receiving the one or more parameters from a server computing device (C12 L4-27 - - receiving sensor/image data from cameras located at smart homes. These images are compared using machine learning techniques. Via the cooperative monitoring network and historic data of movements of other persons having previously traveled through the same region alerts/alarms are generated. Alarms are provided to residents.)
Claim 16. The computer-implemented method of Claim 12, 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 identify the at least one alert condition (C12 L4-27 - - receiving sensor/image data from cameras located at smart homes. These images are compared using machine learning techniques. Via the cooperative monitoring network and historic data of movements of other persons having previously traveled through the same region alerts/alarms are generated. Alarms are provided to residents.)
Claim 17. The computer-implemented method of Claim 12, wherein the parameters include one or more digital profiles including a plurality of data values corresponding to different types of sensor data, and wherein the computer-implemented method further comprises comparing the sensor data to the plurality of data values of the digital profiles to identify the at least one alert condition (C12 L4-27 - - receiving sensor/image data from cameras located at smart homes. These images are compared using machine learning techniques. Via the cooperative monitoring network and historic data of movements of other persons having previously traveled through the same region alerts/alarms are generated. Alarms are provided to residents.)
Claim 18. The computer-implemented method of Claim 12, further comprising generate at least one recommendation based upon the identified at least one alert condition; and cause the user interface to display the generated at least one recommendation (C12 L4-27 - - receiving sensor/image data from cameras located at smart homes. These images are compared using machine learning techniques. Via the cooperative monitoring network and historic data of movements of other persons having previously traveled through the same region alerts/alarms are generated. Alarms are provided to residents.)
Claim 19. The computer-implemented method of Claim 12, further comprising: receive home data relating to the home; generate at least one recommendation for placing smart sensors within the home; and cause the user interface to display the generated at least one recommendation (C12 L4-27 - - receiving sensor/image data from cameras located at smart homes. These images are compared using machine learning techniques. Via the cooperative monitoring network and historic data of movements of other persons having previously traveled through the same region alerts/alarms are generated. Alarms are provided to residents.)
Claim 20. The computer-implemented method of Claim 12, further comprising causing the user interface to display one or more of numbers, indicators, or graphics representing at least some of the received sensor data (C12 L4-27 - - receiving sensor/image data from cameras located at smart homes. These images are compared using machine learning techniques. Via the cooperative monitoring network and historic data of movements of other persons having previously traveled through the same region alerts/alarms are generated. Alarms are provided to residents.)
Claim 21. The computer-implemented method of Claim 12, wherein the at least one smart sensor includes one or more of airflow sensors, air quality sensors, chemical sensors, humidity sensors, moisture sensors, odor sensors, particulate matter sensors, pressure sensors, or temperature sensors (C3 L39-67 and C4 L1-8).
Claim 22. The computer-implemented method of Claim 12, wherein the at least one alert condition includes one or more of moisture, mold, animal pests, carbon monoxide, low airflow, duct blockage, high temperature, low temperature, or poor air quality (C3 L39-67 and C4 L1-8).
Claim 23. 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 and at least one smart sensor of a home, the computer-executable instructions cause the at least one processor to: receive sensor data from the at least one smart sensor; compare the sensor data to one or more parameters generated using an artificial intelligence model to identify at least one alert condition for the home, wherein the artificial intelligence model is trained based upon historical sensor data relating to a plurality of homes; and 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 identified at least one alert condition (C12 L4-27 - - receiving sensor/image data from cameras located at smart homes. These images are compared using machine learning techniques. Via the cooperative monitoring network and historic data of movements of other persons having previously traveled through the same region alerts/alarms are generated. Alarms are provided to residents.)
Citation of Pertinent Prior Art
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Dahmen, Jessamyn, et al. "Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats." Journal of reliable intelligent environments 3.2 (2017): 83-98.
Calacci, Dan, Jeffrey J. Shen, and Alex Pentland. "The cop in your neighbor's doorbell: Amazon ring and the spread of participatory mass surveillance." Proceedings of the ACM on Human-Computer Interaction 6.CSCW2 (2022): 1-47.
Gandapur, Maryam Qasim, and Elena Verdú. "ConvGRU-CNN: Spatiotemporal deep learning for real-world anomaly detection in video surveillance system." IJIMAI 8.4 (2023): 88-95.
Yang, Bin, et al. "Computer vision technology for monitoring of indoor and outdoor environments and HVAC equipment: a review." Sensors 23.13 (2023): 6186.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARLOS R ORTIZ RODRIGUEZ whose telephone number is (571)272-3766. The examiner can normally be reached on Mon-Fri 10:00 am- 6:30 pm.
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/CARLOS R ORTIZ RODRIGUEZ/ Primary Examiner, Art Unit 2119
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