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 .
Claim Status
Claims 1-26 are pending.
Claim Rejections - 35 USC § 102
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)(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.
Claim(s) 1, 2, 4-16, 19, 20 and 23-26 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ramesh (US 2025/0111768).
Regarding claim 1,
Ramesh teaches a system comprising:
a server([0076] teaches a monitoring and alerting service 350 may be implemented by one or more devices (e.g., one or more servers));
a plurality of sensors located at one or more dwellings, the plurality of sensors configured to generate sensor data and communicate the sensor data to the server ([0002] teaches performing operations including detecting that a first event has occurred based at least on first sensor data collected from a first subset of the set of IoT devices; modifying a set of conditions used to detect whether a second event has occurred based at least on second sensor data collected from a second subset of the set of IoT devices; or modifying a manner in which the second sensor data is generated by or collected from the second subset of the set of IoT devices; [0025] teaches that some smart doorbells utilize an integrated camera to monitor for motion on a doorstep; [0055] teaches IoT devices 306, 308, 310 and 312); and
monitoring software configured to run on the server(130 in fig. 1 )
wherein the monitoring software is configured to process the sensor data and generate at least one user interface([0044] teaches audio command processing module 130 may operate to process and analyze the received audio data to recognize user 132's verbal command; [0045] teaches the audio data may be alternatively or additionally processed and analyzed by an audio command processing module 216 in media device 106 (see FIG.2); [0084] teaches that monitoring and alerts manager 352 may enable a user to select an event for monitoring from among a set of predefined events, wherein monitoring and alerts manager 352 may present a set of predefined events to a user via monitoring and alerts manager UI 316);
wherein the at least one user interface is accessible through a remote compute device ([0069] teaches in FIG. 3, monitoring and alerting client 314 may include a monitoring and alerts manager user interface (UI) 316) ; and
wherein the at least one user interface is configured to display at least one of the sensor data and one or more alerts (fig. 3 teaches a user device with an alert UI 320; [0072] teaches that alert UI 320 may be configured to generate an alert responsive to a communication from monitoring and alerting service 350. Such communication may be generated by monitoring and alerting service 350).
Regarding claim 2,
Ramesh teaches a machine-learning model configured to run on the server; wherein the machine-learning model is configured to automatically generate the one or more alerts based upon the sensor data ([0083] teaches a trained machine learning (ML) model that processes audio captured by an IoT device to determine if the sound of a smoke alarm is detected, or a trained ML model that processes video or images captured by an IoT device).
Regarding claim 4,
Ramesh teaches that the machine-learning model generates the one or more alerts in response to the sensor data deviating from a set of threshold values ([0003] teaches detecting whether an event has occurred may comprise modifying a threshold associated with a condition in the set of conditions; [0002] teaches that alerts may be modified.)
Regarding claim 5,
Ramesh teaches that the machine-learning model is configured to automatically adjust the set of threshold values ([0042] teaches that crowdsource server(s) 128 may operate to be automatically turned on and/or off).
Regarding claim 6,
Ramesh teaches that the system further comprises a database; and the server is further configured to store the sensor data and the set of predetermined threshold values in the database ([0038] teaches that each content server 120 may store content 122 and metadata 124.)
Regarding claim 7,
Ramesh teaches that the set of threshold values includes a set of combination threshold values, the set of combination threshold values being determined from threshold values for two or more individual sensor types from the plurality of sensors ([0055] teaches collecting data from various sensor types; [0079] teaches a set of combination thresholds drawn to an existence or non-existence of a phenomenon (e.g., temperature is above or below a certain threshold, light is or is not coming through a window, smoke is detected or not detected, carbon dioxide is detected or not detected, a certain sound is audible or inaudible.)
Regarding claim 8,
Ramesh teaches that the one or more alerts include a security alert, a fire alert, a mold alert, an occupancy alert, a carbon monoxide alert, a water leak alert, an electrical alert, a sensor status alert, or a combination thereof ([0055], [0080], [0131]).
Regarding claim 9,
Ramesh teaches that the plurality of sensors includes at least one of a water flow sensor, a water leak sensor, a temperature sensor, a motion sensor, a license plate recognition sensor, a smoke detector, a carbon monoxide sensor, a thermal imaging sensor, a barometric sensor, a power line sensor, a current sensor, and combinations thereof ([0058]).
Regarding claim 10,
Ramesh teaches that the plurality of sensors are connected through the internet of things (IoT) ([0001]).
Regarding claim 11,
Ramesh teaches that the sensor data comprises multimodal telemetry data ([0055] teaches collection of data from various sensor types for monitoring and alert production related thereto).
Regarding claim 12,
Ramesh teaches that the at least one user interface comprises one or more of a property owner interface, a property manager interface, and a tenant interface ([0080] teaches detecting condition(s) 402 such as person in house while homeowner is on vacation, etc. as is understood in the art, this data would be utilized by any one of a property owner interface, tenant interface or property manager interface; [0114] teaches parties to whom alerts may be directed).
Regarding claim 13,
Ramesh teaches that the property owner interface displays the sensor data and the one or more alerts for at least one property owned by a property owner, the at least one property including at least one of the one or more dwellings; the property manager interface displays the sensor data and the one or more alerts for at least one of the one or more dwellings managed by a property manager; and the tenant interface displays the sensor data and the one or more alerts for at least one of the one or more dwellings assigned to a tenant ([0070] teaches monitoring and alerts manager 352 may present a set of predefined events to a user via monitoring and alerts manager UI 316; [0072] teaches alert UI 320 may be configured to generate an alert responsive to a communication from monitoring and alerting service 350).
Regarding claim 14,
Ramesh teaches that the monitoring software provides automatic communication between two or more of the property owner (320 in fig. 3), the property manager (350 in fig. 3), and the tenant (302 in fig. 3).
Regarding claim 15,
Ramesh teaches that the system is configured to automatically update the status of the one or more alerts based upon updated sensor data, input from the tenant, input from the property manager, input from the property owner, communication with the authorities, or a combination thereof ([0091] teaches that event monitoring service 354 may operate to monitor for an occurrence of an event for which monitoring has been activated by monitoring and alerting service 350 (e.g., either automatically or based on user input as described above); [0114] teaches that alerts may be directed to users associated with a premises (e.g., homeowners, home occupants, business owners, office occupants)).
Regarding claim 16,
Ramesh teaches that the remote compute device for at least one of the user interfaces comprises a mobile device; and at least one of the user interfaces comprises a mobile device application ([0036] teaches a remote control 110. Remote control 110 can be any component, part, apparatus and/or method for controlling media device 106 and/or display device 108, such as a remote control, a tablet, laptop computer, smartphone, wearable, on-screen controls, integrated control buttons, audio controls, or any combination thereof).
Regarding claim 19,
Ramesh teaches a method of monitoring a multi-unit dwelling, the method comprising:
providing the system according to claim 2;
receiving, at the server, the sensor data from the plurality of sensors ([0002] teaches performing operations including detecting that a first event has occurred based at least on first sensor data collected from a first subset of the set of IoT devices; modifying a set of conditions used to detect whether a second event has occurred based at least on second sensor data collected from a second subset of the set of IoT devices; or modifying a manner in which the second sensor data is generated by or collected from the second subset of the set of IoT devices; [0025] teaches that some smart doorbells utilize an integrated camera to monitor for motion on a doorstep; [0055] teaches IoT devices 306, 308, 310 and 312);
executing the machine-learning model on the sensor data, the machine-learning model generating the one or more alerts([0083] teaches a trained machine learning (ML) model that processes audio captured by an IoT device to determine if the sound of a smoke alarm is detected, or a trained ML model that processes video or images captured by an IoT device);
generating the at least one user interface accessible through the remote compute device([0036] teaches a remote control 110. Remote control 110 can be any component, part, apparatus and/or method for controlling media device 106 and/or display device 108, such as a remote control, a tablet, laptop computer, smartphone, wearable, on-screen controls, integrated control buttons, audio controls, or any combination thereof)..
Regarding claim 20,
Ramesh further teaches automatically sending a signal to at least one of emergency services, a property owner, a property manager, and a tenant for at least one of the one or more alerts ([0114] teaches alerts may be directed to users associated with a premises (e.g., homeowners, home occupants, business owners, office occupants) and/or to other entities such as emergency services (e.g., 911 services), home security companies, police departments, fire departments, or the like).
Regarding claim 23,
Ramesh teaches that the sensors specific to the tenant include the sensors associated with a tenant dwelling assigned to the tenant ([0114]), the sensor data associated with a vehicle ([0053]) of the tenant, the sensor data relating to location of the tenant, or a combination thereof.
Regarding claim 24,
Ramesh teaches that the training the machine-learning model wherein, the training includes: providing a training set including at least one of tenant data, property data, and significant events related thereto; and training the machine-learning model to generate the one or more alerts with the training set ([0088] teaches that algorithm may advantageously be used to detect novel object classes (e.g., object classes specified by a user via natural language) that generalize beyond a limited number of base classes labeled during the training phase of the algorithm; [0091] teaches event monitoring service 354 may operate to monitor for an occurrence of an event for which monitoring has been activated by monitoring and alerting service 350 (e.g., either automatically or based on user input as described above). [0114] teaches that alerts may be directed to users associated with a premises (e.g., homeowners, home occupants, business owners, office occupants).
Regarding claim 25,
Ramesh teaches that the training set includes at least one of tenant data and property compared to significant events related thereto ([0134 teaches speech analysis to be used in a machine learning model, interpreted as corresponding to “tenant data” as broadly recited).
Regarding claim 26,
Ramesh teaches a non-transitory, processor-readable medium storing instructions that, when executed by a processor, cause the processor to implement the method according to claim 19 (see the rejection of claim 19, above; [0176]).
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.
Claim(s) 3, 17, 18, 21 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ramesh (US 2025/0111768) in view of Du (U.S. Pub. 2022/0311779).
Regarding claim 3,
Ramesh teaches the system of claim 2, but fails to further teach that the machine-learning model is an unsupervised machine-learning model, a supervised machine-learning model, a deep learning model, or a convolutional neural network (CNN).
DU teaches a system wherein the machine-learning model is an unsupervised machine-learning model, a supervised machine-learning model, a deep learning model, or a convolutional neural network (CNN) ([0059] teaches that machine learning analytics engine 406 can aggregate events of IoT devices in operation using common factor aggregation machine learning. Common factor aggregation creates various aggregations and transformations from the incoming data events leveraging on supervised classification, unsupervised clustering-based machine learning, and multi-layer deep learning to model various behavior patterns of IoT devices so the IoT devices can be grouped/labelled based on their behaviors).
Before the effective filing date of the invention it would have been obvious to modify the system of Ramesh per the teachings of DU and utilize any of the specifically recited ML models disclosed by Du, for the purpose of improving accuracy, adaptability and efficiency of processing sensor data from multiple sensors/dwellings so that meaningful information and alerts may be generated therefrom.
Regarding claims 17 and 21,
Du teaches teach that the system is further configured to generate a behavior score for a tenant based upon the sensor data from one or more sensors of the plurality of sensors that are specific to the tenant ([0037] teaches generation of a progressive risk score).
Regarding claims 18 and 22,
Du teaches teach that the system is further configured to dynamically allocate a reward for the tenant based on the behavior score ([0037] teaches calculation of reward using a model).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIONNE PENDLETON whose telephone number is (571)272-7497. The examiner can normally be reached M-F 9a-5pm.
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/DIONNE PENDLETON/Primary Examiner, Art Unit 2689
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