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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. 202341005904, filed on 01/30/2023.
Information Disclosure Statement
The Information Disclosure Statement (IDS) submitted on 04/23/2025 and 12/17/2025 have been considered by the Examiner. The submission is in compliance with the provisions of 37 CFR 1.97.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-8 and 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Mallinson et al. WO. 2018/063539 A1, (hereinafter Mallinson) in view of Sakura et al. U.S. Pub. No. 2018/0262394 A1, (hereinafter Sakura).
Regarding claim 1.
Mallinson teaches a control method of an electronic device for handling determined sensory perception changes of a user in an Internet of Things (IoT) environment (Mallinson teaches in Fig. 1A element 100 and Para. [0034] the client device (i.e., electronic device) collection of data input into the discomfort classification modeler and further, Mallinson teaches in Para. [0041] discomfort and/or sickness caused by certain patterns of VR content 115 (e.g., aerobatic air plane simulation) by the test user 105 may induce a change in the galvanic skin resistance, possibly due to an increase in sweating), the control method comprising:
measuring at least one first sensory parameter of at least one first IoT device in the IoT environment, wherein the at least one first sensory parameter indicates a determined sensory perception change of the user based on the user using the at least one first IoT device over a first period of time (Mallinson teaches in Fig. 1A element 100A (i.e., first IoT device) and Para. [0031] the system 100A can be implemented in a testing environment in order to learn under what conditions users will experience discomfort and/or sickness when viewing and/or interacting with VR content (i.e., the test indicates the first sensor measurement parameter) and further, Mallinson teaches in Para. [0041] it is possible that discomfort and/or sickness caused by certain patterns of VR content 115 (e.g., aerobatic air plane simulation) by the test user 105 may induce a change in the galvanic skin resistance, possibly due to an increase in sweating. Further, brain activity may be measured and recorded over time, to determine if there is some correlation between the measured brain activity and discomfort and/or sickness caused by certain patterns of VR content 115…, and further, a time stamp (i.e., a first period of time) may be collected in association with both the collected test data associated with the VR content 115 and the physiological data of test user 10…); and
identifying at least one second IoT device in the IoT environment used by the user after the at least one first IoT device, wherein the at least one second IoT device is operating in a first configuration (Mallinson teaches in in Para. [0063] the testers interact with corresponding VR content that is executing on client device 100 using HMD 102 (i.e., one second IoT device). That is, in one embodiment, one application may be used by one or more testers to generate data used for building a discomfort and/or sickness recognition model…, one or more testers to generate the data used to build a discomfort and/or sickness recognition model that is configured in part to identify VR content…).
Mallinson does not explicitly teach controlling the at least one second IoT device to switch from the first configuration to a second configuration based on the at least one first sensory parameter.
However, Sakura teaches controlling the at least one second IoT device to switch from the first configuration to a second configuration based on the at least one first sensory parameter (Sakura teaches in Para. [0141] the device state-based operation configuration model can be updated to include a mapping of the second configuration instructions to causing a state change in a device from the first configuration state to the second configuration state).
Therefore, Mallison and Sakura are analogues arts and they are in the same field of endeavor as they both are directed to impact level and discomfort level, often using an ML model and predict the IoT device using sensor-detected movement and/or user intent and changing a configuration state in devices.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the
claimed invention to modify the teachings of the device state-based operation configuration to include a mapping of the second configuration instructions to causing a state change in a device from the first configuration state to the second configuration state ([0141]) as taught, by Sakura into the teachings of Mallison invention. One would have been motivated to do so in order to the received desired configuration instructions can be generated based on the current configuration state and received user input using device state-based operation configuration model. The local state aware device manager is adapted to use stream processing of streams of data transmitted to and from infrastructure network device as portion of the infrastructure network device providing IoT devices network service access, to maintain current configuration state of the infrastructure network device.
Regarding claim 2.
Mallinson in view of Sakura teaches based on the user using the at least one second IoT device, identifying whether a second period of time is reached indicating the determined sensory perception change of the user has reverted to a prior state (Mallinson teaches in Para. [0042] the test data collected in association with the VR content 115 and the physiological data from the test user 105 can be correlated…, a time stamp may be collected in association with both the collected test data associated with the VR content 115 and the physiological data of test user 105, wherein data with corresponding and possibly lagging time stamps may be correlated with each other…, and further, Sakura teaches in Para. [0049] the state aware device configuration agnostic mediator 114 can determine operation changes that need to be made to the device operating in its current configuration state in order to achieve a desired configuration state at the device); and
controlling, based on the second period of time being reached, the at least one second IoT device to switch from the second configuration to the first configuration (Mallinson teaches in Para. [0106] wherein the predicted outputs are compared against true or known outputs in order to modify parameters in the deep learning engine so that over time (i.e., time being reached), the predicted outputs closely align with the true or known outputs and Sakura teaches in Para. [0051] the state aware device configuration agnostic mediator 114 functions to remotely maintain current configuration state data indicating current configuration states of devices. For example, the state aware device configuration agnostic mediator 114 can maintain current configuration state data indicating a current configuration state of either or both an IoT device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of remotely maintain the current configuration ([0051]) as taught, by Sakura into the teachings of Mallison invention. One would have been motivated to do so in order to determine a current configuration state of the IoT device in an efficient manner. (Sakura. [0051]).
Regarding claim 3.
Mallinson in view of Sakura teaches wherein the controlling the at least one second IoT device to switch from the first configuration to the second configuration comprises: determining an impact level of the first period of time on the determined sensory perception of the user (Mallinson teaches in Fig. 1A element 100 and Para. [0034] the client device (i.e., electronic device) collection of data input into the discomfort classification modeler and in Para. [0031] the system 100A can be implemented in a testing environment in order to learn under what conditions users will experience discomfort and/or sickness when viewing and/or interacting with VR content (i.e., the test indicates the first sensor measurement parameter) and further, Sakura teaches in Para. [0051] the state aware device configuration agnostic mediator 114 functions to remotely maintain current configuration state data indicating current configuration states of devices. For example, the state aware device configuration agnostic mediator 114 can maintain current configuration state data indicating a current configuration state of either or both an IoT device);
determining a discomfort level for operating the at least one second IoT device based on a first relationship between at least one second sensory parameter of the at least one second IoT device and the impact level (Mallinson teaches in Claim 2, determining a level of discomfort for the first VR content based on the model, wherein the model predicts a level of discomfort experienced by a prospective user when interacting with the first VR content);
determining an impact duration for the impact level based on the first period of time (Mallinson teaches in Claim 2. Determining…., assigning a discomfort classification for the first VR content based on the determined level of discomfort);
determining at least one third sensory parameter of the at least one second IoT device affecting the determined sensory perception of the user while using the at least one second IoT device (Mallison teaches in Para. [0057] results can be compared to predetermined and true discomfort and/or sickness reactions in order to refine and/or modify the parameters (i.e., note that here the term “parameters” includes the claimed “at least one third sensory parameter”) used by the deep learning engine 190 to iteratively determine which VR content or patterns of VR content will induce discomfort and/or sickness in users…);
determining, based on the at least one third sensory parameter, at least one counteracting sensory parameter of the at least one second IoT device for offsetting the impact level (Mallison teaches in Para. [0057] results can be compared to predetermined and true discomfort and/or sickness reactions in order to refine and/or modify the parameters (i.e., note that here the term “parameters” includes the claimed “at least one third sensory parameter”) used by the deep learning engine 190 to iteratively determine which VR content or patterns of VR content will induce discomfort and/or sickness in users…, and further, Mallison teaches in Para. [0089] s well as tested discomfort and/or sickness information obtained from user 290, the user classification model is configured to classify user 290 giving an indication how user 290 is affected by VR content.);
determining the second configuration based on the impact duration and the at least one counteracting sensory parameter such that the discomfort level is reduced (Mallison teaches in Claim 8, wherein the discomfort classification is taken from a group of discomfort classifications including a first classification level indicating high discomfort and a second classification level indicating low (i.e., level of discomfort is reduced) or no discomfort); and
controlling the at least one second IoT device to switch from the first configuration to the second configuration (Mallinson teaches in Para. [0106] wherein the predicted outputs are compared against true or known outputs in order to modify parameters in the deep learning engine so that over time (i.e., time being reached), the predicted outputs closely align with the true or known outputs and Sakura teaches in Para. [0051] the state aware device configuration agnostic mediator 114 functions to remotely maintain current configuration state data indicating current configuration states of devices. For example, the state aware device configuration agnostic mediator 114 can maintain current configuration state data indicating a current configuration state of either or both an IoT device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of remotely maintain the current configuration ([0051]) as taught, by Sakura into the teachings of Mallison invention. One would have been motivated to do so in order to determine a current configuration state of the IoT device in an efficient manner. (Sakura. [0051]).
Regarding claim 4.
Mallinson further teaches wherein determining the impact level comprises: determining a user profile (Mallinson teaches in Para. [0087] active or passive feedback 250 is also collected from user 290. For example, physiological data associated with user 290 may be collected while user 290 is interacting with VR content 201);
determining, based on the user profile, a second relationship between the at least one first sensory parameter and the first period of time (Mallinson teaches in Para. [0041] It is possible that discomfort and/or sickness caused by certain patterns of VR content 115 (e.g., aerobatic air plane simulation) by the test user 105 may induce a change in the galvanic skin resistance, possibly due to an increase in sweating. Further, brain activity may be measured and recorded over time, to determine if there is some correlation between the measured brain activity and discomfort and/or sickness caused by certain patterns of VR content 115…, and further, a time stamp (i.e., a first period of time) may be collected in association with both the collected test data associated with the VR content 115 and the physiological data of test user 10…); and
determining the impact level based on the second relationship (Mallinson teaches in Para. [0061] the training dataset for the neural network 190 is from a same data domain. For instance, the neural network 190 is trained for learning which VR content…,the neural network 190 is trained for gaming applications…).
Regarding claim 5.
Mallinson teaches wherein the impact level is determined based on at least one Machine Learning (ML) model (Mallinson teaches in Fig. 3 and Para. [0021] the model level of discomfort determined by a discomfort recognition model that is built by a deep learning engine (i.e., ML)…).
Regarding claim 6.
Mallinson teaches wherein the measuring the at least one first sensory parameter comprises: identifying a first plurality of sensory parameters of the at least one first IoT device based on the user using the at least one first IoT device (Mallinson teaches in Fig. 1A element 100A (i.e., first IoT device) and Para. [0031] the system 100A can be implemented in a testing environment in order to learn under what conditions users will experience discomfort and/or sickness when viewing and/or interacting with VR content (i.e., the test indicates the first sensor measurement parameter) and further, Mallinson teaches in Para. [0041] It is possible that discomfort and/or sickness caused by certain patterns of VR content 115 (e.g., aerobatic air plane simulation) by the test user 105 may induce a change in the galvanic skin resistance, possibly due to an increase in sweating);
determining one or more sensory parameters from among the first plurality of sensory parameters corresponding to the first period of time, wherein the one or more sensory parameters comprise the at least one first sensory parameter (Mallinson teaches in Para. [0041] it is possible that discomfort and/or sickness caused by certain patterns of VR content 115 (e.g., aerobatic air plane simulation) by the test user 105 may induce a change in the galvanic skin resistance, possibly due to an increase in sweating. Further, brain activity may be measured and recorded over time, to determine if there is some correlation between the measured brain activity and discomfort and/or sickness caused by certain patterns of VR content 115…, and further, a time stamp (i.e., a first period of time) may be collected in association with both the collected test data associated with the VR content 115 and the physiological data of test user 10…); and
measuring the at least one first sensory parameter (Mallinson teaches in Para. [0041] sensors 125 passively monitor the physiological data while the test user 105 is interacting with VR content 115…, For example, electro gastrographic and galvanic skin resistance may measure the differences in electrical conductivity or characteristics of the skin and muscles…).
Regarding claim 7.
Mallison teaches wherein the identifying the at least one second IoT device comprises: identifying, based on receiving information from at least one sensor of an IoT device in the IoT environment, a movement of the user toward the at least one second IoT device (Mallison teaches in Para.[ 0003] more complex VR systems may be integrated with movement sensors that allow a user to make moves in a real world that may then be translated in some form to the world of VR. For instance, hand gestures/movements may be used to interact with VR objects, and moving through the real world (e.g., walking in the physical environment) may be translated to similar movement in the VR environment (e.g., walking or running in the VR environment));
determining a user intent based on at least one of past user interaction data for the at least one second IoT device and current context data of the electronic device or received from the at least one first IoT device (Mallison teaches in Para. [0010] the method includes accessing a model that identifies a plurality of learned patterns associated with the generation of corresponding baseline VR content that is likely to cause discomfort. The method includes processing a first application to generate data associated with simulated user interactions with first VR content of the first application. Also, see Para. [0037]-[0038]); and
predicting the at least one second IoT device based on at least one of the movement or the user intent (Mallison teaches in Para. [0001] systems for training a deep learning engine to build a model configured for classifying VR content according to predicted levels of discomfort associated with a typical or predetermined type of user interacting with the VR content).
Regarding claim 8.
Mallison in view of Sakura teaches wherein controlling the at least one second IoT device to switch from the second configuration to the first configuration comprises: determining, based on at least one Machine Learning (ML) model, at least one correction value of at least one counteracting sensory parameter of the at least one second IoT device (Mallinson teaches in Fig. 3 and Para. [0021] the model level of discomfort determined by a discomfort recognition model that is built by a deep learning engine (i.e., ML)…);
determining the second period of time based on the at least one correction value (Mallinson teaches in Para. [0106] wherein the predicted outputs are compared against true or known outputs in order to modify parameters in the deep learning engine so that over time (i.e., time being reached), the predicted outputs closely align with the true or known outputs and Sakura teaches in Para. [0051] the state aware device configuration agnostic mediator 114 functions to remotely maintain current configuration state data indicating current configuration states of devices. For example, the state aware device configuration agnostic mediator 114 can maintain current configuration state data indicating a current configuration state of either or both an IoT device); and
based on the second period of time being reached, controlling the at least one second IoT device to switch to the first configuration from the second configuration, wherein at least one second sensory parameter of the at least one second IoT device does not indicate the determined sensory perception change (Mallinson teaches in Para. [0042] the test data collected in association with the VR content 115 and the physiological data from the test user 105 can be correlated…, a time stamp may be collected in association with both the collected test data associated with the VR content 115 and the physiological data of test user 105, wherein data with corresponding and possibly lagging time stamps may be correlated with each other…, and further, Sakura teaches in Para. [0049] the state aware device configuration agnostic mediator 114 can determine operation changes that need to be made to the device operating in its current configuration state in order to achieve a desired configuration state at the device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of determining the operation changes that need to be made to the device operating in its current configuration state ([0049]) as taught, by Sakura into the teachings of Mallison invention. One would have been motivated to do so in order to increase the threshold by fifty to achieve a desired threshold of one hundred maximum served clients and further in order to achieve a desired configuration state at the device (Sakura. [0049]).
Regarding claim 11.
Mallison in view of Sakura teaches wherein the control method further comprises controlling the at least one second IoT device to switch from the first configuration to the second configuration such that an increase in a discomfort level caused by a sudden change between the at least one first sensory parameter and at least one second sensory parameter of the at least one second IoT device is reduced (Mallinson teaches in Para. [0106] wherein the predicted outputs are compared against true or known outputs in order to modify parameters in the deep learning engine so that over time (i.e., time being reached), the predicted outputs closely align with the true or known outputs and Sakura teaches in Para. [0051] the state aware device configuration agnostic mediator 114 functions to remotely maintain current configuration state data indicating current configuration states of devices. For example, the state aware device configuration agnostic mediator 114 can maintain current configuration state data indicating a current configuration state of either or both an IoT device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of remotely maintain the current configuration ([0051]) as taught, by Sakura into the teachings of Mallison invention. One would have been motivated to do so in order to determine a current configuration state of the IoT device in an efficient manner. (Sakura. [0051]).
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Mallinson in view of Sakura further in view of Chan U.S. Pub. No. 2018/0131215 A1, (hereinafter Chan).
Regarding claim 9. Mallinson in view of Sakura teaches the control method as claimed in claim 1.
Mallinson in view of Sakura does not explicitly teach wherein the first period of time indicates a prolonged usage of the at least one first IoT device by the user in the IoT environment, and wherein the prolonged usage is determined based on a real-time usage duration of the at least one first IoT device, a reference usage duration of the at least one first IoT device, a user profile, and current contextual parameter.
However, Chan teaches wherein the first period of time indicates a prolonged usage of the at least one first IoT device by the user in the IoT environment, and wherein the prolonged usage is determined based on a real-time usage duration of the at least one first IoT device, a reference usage duration of the at least one first IoT device, a user profile, and current contextual parameter (Chan teaches in Para. [0008] the duration that a user can operate the device on a single battery charge is increased as well as prolonging the battery lifespan to maintain a maximum charge. That is, the battery, from a power and charging perspective, is more sparingly utilized by the power controller in terms of providing output power and for more prolonged device usage the power buffer is depleted before the battery is required to deliver any output power).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of prolonging the battery lifespan to maintain a maximum charge ([0008]) as taught, by Chan into the teachings of Mallison in view of Sakura invention. One would have been motivated to do so in order to the controller provides duration that a user can operate a device on a single battery charge to be increased and prolonged a battery lifespan to maintain a maximum charge. The controller utilizes a communications bus that facilitates a coupling and communication between various components of a compute in efficient manner.
Regarding claim 10.
Chan further teaches wherein the real-time usage duration is determined based on real-time sensor data received from the at least one first IoT device, and the reference usage duration is determined based on pre-defined global usage data (Chan teaches in Para. [0020] the power controller 110 intelligently determines that the input power source is a continuous and sustained power source and the previous charging history indicates a very late evening (e.g., midnight) timeframe the power controller 110 can control and direct charging of battery 130 when power consuming device 170 is plugged into such electrical outlet at that designated time.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of using the power controller 110 intelligently determines that the input power source is a continuous and sustained power source and the more glances compared to sustained power needed by a power consuming body ([0020] and [0026]) as taught, by Chan into the teachings of Mallison invention. One would have been motivated to do so in order to prolong overall usage on a single battery charge where glances are continuously powered by a replenished power buffering unit via various power sources in an efficient manner. (Chan. [0020] and [0026]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BERHANU SHITAYEWOLDETSADIK whose telephone number is (571)270-7142. The examiner can normally be reached M-F.
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/BERHANU SHITAYEWOLDETSADIK/Examiner, Art Unit 2455
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