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 .
Claims 1-20 are pending.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Turon (US 20170003736) in view of Wang et al. (US 12218950) and Williams et al. (US 20240063658).
Regarding claim 1, Turon teaches
A processor-implemented method for managing power draw reduction of one or more IoT devices, the method comprising:
determining, continuously, a micro-location of an individual to dynamically pinpoint a location of the individual in an indoor location, based on detecting a wearable IoT device as the individual moves around the indoor location, and wherein the determined micro-location includes latitude and longitude; ([0067], “a low-power wireless communication chip (e.g., ZigBee chip) that regularly sends out messages regarding the occupancy of the room and the amount of light in the room, including instantaneous messages coincident with the occupancy sensor detecting the presence of a person in the room.” and [0071], “For example, the low-powered and spokesman nodes detect the person's movement through the smart-home environment and communicate corresponding messages through the mesh network.”)
determining if the individual has entered a new area of the indoor location; ([0067], “a low-power wireless communication chip (e.g., ZigBee chip) that regularly sends out messages regarding the occupancy of the room and the amount of light in the room, including instantaneous messages coincident with the occupancy sensor detecting the presence of a person in the room” and [0071], “For example, the low-powered and spokesman nodes detect the person's movement through the smart-home environment and communicate corresponding messages through the mesh network.”)
upon determining that the individual has entered the new area of the indoor location, analyzing, by a trained neural network, a current micro-location of the individual, device interaction history, and device exception list; ([0071], “the mesh network can be used to automatically turn on and off lights as a person transitions from room to room. For example, the low-powered and spokesman nodes detect the person's movement through the smart-home environment and communicate corresponding messages through the mesh network. Using the messages that indicate which rooms are occupied, the central server or cloud-computing system 64 or some other device activates and deactivates the smart wall switches 54 to automatically provide light as the person moves from room to room in the smart-home environment 30. … users may provide pre-configuration information that indicates which smart wall plugs 56 provide power to lamps and other light sources, such as the smart night-light 65.”, [0059], “the smart thermostat 46 and other smart devices “learn” by observing occupant behavior. For example, the smart thermostat learns occupants' preferred temperature set-points for mornings and evenings, and it learns when the occupants are asleep or awake, as well as when the occupants are typically away or at home, for example.”, [0063], “the cloud-computing system 64 may receive data from each of the devices within the smart-home environment 30, such that the data regarding the smart-home environment 60 may be stored remotely, analyzed”, And [0092], “For example, use statistics, use statistics relative to use of other devices, use patterns, and/or statistics summarizing sensor readings can be generated by the processing engine 86 and transmitted. The results or statistics can be provided via the Internet 62. In this manner, the processing engine 86 can be configured and programmed to derive a variety of useful information from the home data 82” where the pre-configuration information is interpreted as the device exception list and where learning by observing occupant behavior is interpreted as a device interaction history.)
providing one or more commands to one or more IoT devices in one or more non-individually present areas to manage electricity flow to the one or more IoT devices; and ([0071], “ the mesh network can be used to automatically turn on and off lights as a person transitions from room to room. For example, the low-powered and spokesman nodes detect the person's movement through the smart-home environment and communicate corresponding messages through the mesh network. Using the messages that indicate which rooms are occupied, the central server or cloud-computing system 64 or some other device activates and deactivates the smart wall switches 54 to automatically provide light as the person moves from room to room in the smart-home environment 30. Further, users may provide pre-configuration information that indicates which smart wall plugs 56 provide power to lamps and other light sources, such as the smart night-light 65.”)
providing one or more commands to one or more IoT devices in the newly entered area of the indoor location to manage electricity flow to the one or more IoT devices. ([0071], “the mesh network can be used to automatically turn on and off lights as a person transitions from room to room. For example, the low-powered and spokesman nodes detect the person's movement through the smart-home environment and communicate corresponding messages through the mesh network. Using the messages that indicate which rooms are occupied, the central server or cloud-computing system 64 or some other device activates and deactivates the smart wall switches 54 to automatically provide light as the person moves from room to room in the smart-home environment 30. Further, users may provide pre-configuration information that indicates which smart wall plugs 56 provide power to lamps and other light sources, such as the smart night-light 65.”)
Turon teaches determining an individual’s micro-location but does not specifically teach using wearable devices.
Wang teaches
determining, continuously, a micro-location of an individual … , based on detecting a wearable IoT device as the individual moves (col. 54, lines 13-10, “When device data 1108 is utilized, the device data 1108 may indicate the presence of a personal device associated with a user, such as a mobile phone and/or a wearable device. These devices may send out beacons that are received at a given primary device, indicating that the devices are proximate to the primary device and also indicating that the user associated with such devices are present.”)
Turon and Wang are analogous art. Wang is cited to teach a similar concept of detecting presence of an individual in a smart home and suggesting activating or deactivating a device based on the presence in a location in the house. Turon is capable of determining an individual’s location based on sensors but does not specifically teach that the location of the person within a building can be determined by a using cell phone/wearable device. Wang teaches that a cell phone/wearable device can be used in determining where a person in the house. Based on Wang, it would have been obvious before the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Turon to use a cell phone/wearable device to determine the person’s location in a house. Furthermore, being able to use a cell phone/wearable device to determine the person’s location in a house improves on Turon by being able to improve detection of presence states. To one of ordinary skill in the art before the effective filing data of the invention it would have been advantageous to make this modification because “This feedback data may be queried by the activity models 129 and/or the guardrail database 214 to improve detection of presence states and/or to improve the rules utilized in association with the guardrail database 214.”, col. 24, lines 50-58
Turon and Wang teach a device exception list but does not teach a device exception list includes a define condition, including always having power, having power for a defined amount of time, and having a threshold interaction history.
Turon and Wang do not teach but Williams at teaches
upon determining that the individual has entered the new area of the indoor location, analyzing, by a trained machine learning model, a current micro-location of the individual, device interaction history, and device exception list; ([0362], “In some embodiments, the energy usage classification comprises a wasted energy classification based on one or more of: a location of a user of the device being more than a predefined distance from the power outlet from which the device is drawing power; a determination that user input has not been detected by the device in a period of time; and an operational state of the device.”, [0363], “In some embodiments, the power monitored data is captured using a machine-learning model running on the microcontroller configured to receive monitored data from the power monitor, the monitored data representing the monitored operational state of each power outlet and one or more characteristics of power drawn from each power outlet, and the microcontroller is further configured to determine, using the machine-learning model if the monitored data includes data indicated wasted energy.”, [0313], “In some embodiments, however, such as that shown in FIG. 5, the remote energy monitoring system first checks if any exception rules may apply, for example exceptions for to that type of device, to that power socket and/or that outlet, or to a user associated with that power socket, so that although an indication that a wasted energy usage classification has been assigned, no control signal to cause the power outlet to turn off is sent.”, And [0303], “ the lower half of FIG. 5. ML model 44a is trained to identity a device type for an energy consuming device 36 based on the power drawn characteristics provided in the data uploaded to the remote energy monitoring system 40 by the power socket 10 of that power outlet 12. In some embodiments however, once trained, the ML model 44b is implemented on the edge of a distributed energy monitoring system 40 by a power socket according to any of the disclosed embodiments. The trained ML model 44b accordingly runs on the microcontroller of the power socket in some embodiments, in which case the output 508 of the ML model 44b comprises the power data comprising the power drawn characteristics and/or the operational state of a device along with the device type and time-stamp information.”)
wherein the device exception list are devices associated with a defined condition, thereby determining the IoT devices defined as always having power, having power for a defined amount of time, and having a threshold interaction history; ([0376], “This example may trigger remote control of a power socket(s) 10 which are registered to a user in the remote monitoring system 40. A power outlet 12 or power socket 10 is disabled for power provision if the remote monitoring system 40 user is known to be away from the location of the power socket 10 and hasn't requested an exception to automated control. This rule only starts where the user has been determined by the remote monitoring system to have been ‘away’ for more than a predetermined amount of time, for example, 30 minutes.” [0073 and 75], “the power socket is configured to respond to a received power request signal to meter power drawn from at least one output. … the metered power is provided for a predetermined amount of time” Where the user being away for a predetermined amount of time is interpreted as a list associated with a defined condition of having a threshold interaction history)
Turon, Wang, and Williams are analogous art. Williams is cited to teach a similar concept of power management in a building. Turon is capable of determining an individual’s location based on sensors but does not specifically teach using a trained machine learning model and using a list to set rules for specific devices (exception list). Williams teaches that a trained machine learning model can be used for analyzing device power wastage based on individual location, device interactions and exception rules for the device. Based on Williams, it would have been obvious before the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Turon and Wang to use a trained machine learning model can be used for analyzing device power wastage based on individual location, device interactions and exception rules for the device.. Furthermore, being able to use a trained machine learning model can be used for analyzing device power wastage based on individual location, device interactions and exception rules for the device on Turon and Wang by being able to prevent wasting power based on the location of the user. To one of ordinary skill in the art before the effective filing data of the invention it would have been advantageous to make this modification because “an improved energy management system which makes it easier to track when a power socket outlet is still providing power to a device despite not being actively used by a user, and especially to an energy management system that enables power management for groups of power outlets such as can be found in buildings etc.”, [0003]
Regarding claim 2, Turon teaches wherein providing the one or more commands to the one or more IoT devices to manage the electricity flow to the one or more IoT devices comprises sending an auto shut-off command and/or a turn-on command via one or more cloud-based services, based on whether the individual is leaving or entering a micro-location. ([0071], “the mesh network can be used to automatically turn on and off lights as a person transitions from room to room. For example, the low-powered and spokesman nodes detect the person's movement through the smart-home environment and communicate corresponding messages through the mesh network.” And [0065], “the mesh network enables the central server or cloud-computing system 64 to regularly receive data from all of the smart devices in the home, make inferences based on the data, and send commands back to one of the smart devices to accomplish some of the smart-home objectives ” where a person transitions from room to room is interpreted as an individual entering or leaving a micro-location)
Regarding claim 3, Wang teaches wherein analyzing the individual's current micro-location, device interaction history, and device exception list comprises entering an intelligent power management mode upon a flipping of a Boolean expression. (col. 2, lines (col. 2, lines 55-61, “a system may include a trigger component that may be configured to detect one or more trigger events for providing a device control suggestion to a user device. For example, the trigger component may detect changes in user presence states associated with a given environment. To illustrate, a user may move from outside an environment, such as a home for example, to within the environment.” Where a trigger event is interpreted as flipping a Boolean expression)
Regarding claim 4, Turon teaches wherein the one or more commands to the one or more IoT devices in the one or more non-individually present areas and the one or more IoT devices in the newly entered area of the indoor location are based upon the analyzed individual's current micro-location, device interaction history, and device exception list.( [0071], “the mesh network can be used to automatically turn on and off lights as a person transitions from room to room. For example, the low-powered and spokesman nodes detect the person's movement through the smart-home environment and communicate corresponding messages through the mesh network. Using the messages that indicate which rooms are occupied, the central server or cloud-computing system 64 or some other device activates and deactivates the smart wall switches 54 to automatically provide light as the person moves from room to room in the smart-home environment 30. … users may provide pre-configuration information that indicates which smart wall plugs 56 provide power to lamps and other light sources, such as the smart night-light 65.”, [0059], “the smart thermostat 46 and other smart devices “learn” by observing occupant behavior. For example, the smart thermostat learns occupants' preferred temperature set-points for mornings and evenings, and it learns when the occupants are asleep or awake, as well as when the occupants are typically away or at home, for example.”)
Regarding claim 5, Turon teaches further comprising: providing one or more commands to the one or more IoT devices in the indoor location, to manage the electricity flow of the one or more IoT devices, is based on one or more individuals' current micro-location, one or more individuals' device interaction history, and one or more individuals' device exception list. ([0071], “the mesh network can be used to automatically turn on and off lights as a person transitions from room to room. For example, the low-powered and spokesman nodes detect the person's movement through the smart-home environment and communicate corresponding messages through the mesh network. Using the messages that indicate which rooms are occupied, the central server or cloud-computing system 64 or some other device activates and deactivates the smart wall switches 54 to automatically provide light as the person moves from room to room in the smart-home environment 30. … users may provide pre-configuration information that indicates which smart wall plugs 56 provide power to lamps and other light sources, such as the smart night-light 65.”, [0059], “the smart thermostat 46 and other smart devices “learn” by observing occupant behavior. For example, the smart thermostat learns occupants' preferred temperature set-points for mornings and evenings, and it learns when the occupants are asleep or awake, as well as when the occupants are typically away or at home, for example.”, [0063], “the cloud-computing system 64 may receive data from each of the devices within the smart-home environment 30, such that the data regarding the smart-home environment 60 may be stored remotely, analyzed”, [0092], “For example, use statistics, use statistics relative to use of other devices, use patterns, and/or statistics summarizing sensor readings can be generated by the processing engine 86 and transmitted. The results or statistics can be provided via the Internet 62. In this manner, the processing engine 86 can be configured and programmed to derive a variety of useful information from the home data 82” and [0053], “ the smart-home environment “learns” who is an occupant and permits the devices 66 associated with those individuals to control the smart devices of the home”)
Regarding claim 6, Wang teaches wherein analyzing the individual's current micro-location, the device interaction history, and the device exception list, comprises training and using a recurrent neural network. (col. 20, lines 45-48, “the machine learning models may be configured to be trained utilizing a training dataset associated with the presence detections and target device usage data. The models may be trained for multiple user accounts” and col. 43, lines 64-65, “ system may be built on deep neural network (DNN)/recursive neural network (RNN) structures” and col. 58, line 65, col. 59, line 5, “the current-activity model 1314 may be utilized to determine that at a current time a given environment is associated with an active state based at least in part on event data associated with the current time. In examples, the current-activity model 1314 may be trained based at least in part on the output from the neural network model and/or from the output of the historical-activity model 1312.”)
Regarding claim 7, Turon teaches wherein the managing of electricity flow to the one or more IoT devices comprises connecting to one or more intermediary devices. ([0066], “ a user can use the portable electronic device (e.g., a smartphone) 66 to send commands over the Internet 62 to the central server or cloud-computing system 64, which then relays the commands to the spokesman nodes in the smart-home environment 30”.
As to claims 8 and 15, Turon, Wang, and Williams teach these claims according to the reasoning provided in claim 1.
As to claims 9 and 16, Turon, Wang, and Williams teach these claims according to the reasoning provided in claim 2.
As to claims 10 and 17, Turon, Wang, and Williams teach these claims according to the reasoning provided in claim 3.
As to claims 11 and 18, Turon, Wang, and Williams teach these claims according to the reasoning provided in claim 4.
As to claims 12 and 19, Turon, Wang, and Williams teach these claims according to the reasoning provided in claim 5.
As to claims 13 and 20, Turon, Wang, and Williams teach these claims according to the reasoning provided in claim 6.
As to claim 14, Turon, Wang, and Williams teach this claim according to the reasoning provided in claim 7.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHERI L. HARRINGTON whose telephone number is (571)270-0468. The examiner can normally be reached Generally, M-F, 7:30a-4p.
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/CHERI L HARRINGTON/Examiner, Art Unit 2176 April 18, 2026
/JAWEED A ABBASZADEH/Supervisory Patent Examiner, Art Unit 2176
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