DETAILED ACTION
Claims 1, 3-15, and 17-22 filed July 20th, 2026, are pending in the current 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 .
Response to Arguments
Applicants’ arguments with respect to claim(s) 1, 3-15, and 17-22 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.
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, 3-15, and 17-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over de Zambotti et al. (US2014/0316191) in view of Khare et al. (US2021/0225505) in view of De Donato et al. (US2024/0104861) (the Examiner will be citing from the provisional application No. 63/470,921 filed June 4th, 2023)
Consider claim 1, where de Zambotti teaches a method for providing feedback to a user in an extended reality environment, comprising: establishing a first connection between a wearable device and an extended reality device, a second connection between a user device communicatively coupled with the wearable device and the extended reality device, or both; (See de Zambotti ¶31-32 where the system 100 presents an initial immersive virtual environment 116 and enters "feedback mode," in which the sensor(s) 232 periodically detect the physiological responses of the user to the presented immersive virtual environment 116, and provide the sleep assistant 218 with physiological signals that can be used by the sleep assistant 218 to determine the user's state of relaxation as it changes over time. An example of an external physiological sensor 232 is "textile electrodes," which are formed by knitting or weaving conductive fibers into apparel or garments. Textile electrodes can pick up signals from the heart and other muscles. The physiological activity sensed by the textile electrodes are transmitted through the conductive fibers that are woven into the garment to a processing unit, which then passes the received signals to the mobile or wearable computing device 210, generally through a wireless data connection.) determining one or more biometric data types to acquire based on an extended reality experience initiated via the extended reality device (See de Zambotti Fig. 3 and ¶16 where the mobile/wearable computing device 210 and/or the virtual reality device 240 analyze one or more physiological parameters that are obtained or derived from sensor signals. As used herein, "physiological parameters" may refer to, among other things, breathing rate (respiration rate) (e.g., breaths per minute), heart rate (e.g., beats per minute), brain activity (e.g. electroencephalographic signals), body movements, muscle activity; or any other type of measurable human physiological activity, or any combination of the foregoing) receiving, from the extended reality device, a request specifying the one or more biometric data types to acquire, wherein the request is received via the first connection, the second connection, or both; (See de Zambotti Fig. 3 and ¶31-32, 37-40 where the biofeedback VR sleep assistant 218 located in the virtual reality device 240 may receive sensor signals 328 from the physiological sensors 232 and the user customization data 344 from the UI subsystem 224 located in the mobile computing device 210) acquiring a first set of biometric data of the one or more biometric data types via one or more sensors of the wearable device; transmitting, to the extended reality device, the first set of biometric data; (See de Zambotti Fig. 3 and ¶31-32, 38 where once the user's baseline condition or "physiological status" is established, the system 100 presents an initial immersive virtual environment 116 and enters "feedback mode," in which the sensor(s) 232 periodically detect the physiological responses of the user to the presented immersive virtual environment 116, and provide the sleep assistant 218 with physiological signals that can be used by the sleep assistant 218 to determine the user's state of relaxation as it changes over time. The physiological signal acquisition module 312 receives sensor signals 328 from the physiological sensor(s) 232, 262 from time to time during operation of the computing device 210 at a specified sampling rate, which may correspond to a sampling rate performed by the computing device 210) and providing feedback to the user via the extended reality device, wherein the feedback is based at least in part on the first set of biometric data. (See de Zambotti ¶49-51 where in the illustrated embodiments, the system 100 increases the degree of virtual immersion in response to reductions in the user's respiration rate. Once the user's respiration has decreased, the system 100 can make adjustments to the immersive virtual environment 116 based on other criteria, such as the previously presented stages of the immersive virtual environment 116 (e.g., adjust the quantity or speed of visual features based on the quantity or speed of the visual features presented in the previous stage).
In TABLE 2 below, an illustrative example of a mapping function relating to the use of muscle activity as primary feedback parameter is shown. As discussed above, system 100 can adjust the immersive virtual environment 116 (and/or an aspect of user's physical environment) in response to the detection of the user's muscle activity. For example, two electromyogram (EMG) sensors can be incorporated in a "sleep mask" to detect the muscle activity of corrugator supercilii muscle (by detecting the electrical potential generated by muscle bundles))
De Zambotti teaches receiving biometric data, however De Zambotti does not explicitly teach a request and provide biometric data in response to the request, however, in an analogous field of endeavor Khare teaches a request and providing biometric data in response to the request. (See Khare ¶45 where a type of sensor might send individual measurements in response to request commands such that the system must send requests commands at the desired frequency.) Therefore, it would have been obvious to one of ordinary skill in the art that the received physiological signals of de Zambotti would be provided in response to a request as taught by Khare. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using known methods of receiving data to yield the desired result of receiving the data.
De Zambotti teaches acquiring biometric data, however De Zambotti does not explicitly teach wherein acquiring the first set of biometric data comprises reducing a frequency of, or suspending, one or more measurement operations for one or more other biometric data types to conserve power at the wearable device. However, in an analogous field of endeavor De Donato teaches wherein acquiring the first set of biometric data comprises reducing a frequency of, or suspending, one or more measurement operations for one or more other biometric data types to conserve power at the wearable device. (See De Donato ¶419, 652-654 where the computer system 101 reduces a frequency of sensor measurements and/or sensor measurement processing (e.g., sensors for gaze tracking, and sensors for world tracking) to a greater degree during first (e.g., sleep, or low power) state 12002 compared to second (e.g., lower power, standby, or hibernation) state 12004.) Therefore, it would have been obvious for one of ordinary skill in the art to modify the biometric data acquisition of De Zambotti by adding the operational state of the biometric data acquisition of De Donato. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of applying the appropriate context to the detected situation in order to help conserve battery power. (See De Donato ¶654)
Consider claim 3, where de Zambotti in view of Khare in view of De Donato teaches the method of claim 1, wherein the request comprises an indication of a duration, a frequency, or both, of transmission or acquisition of the first set of biometric data, wherein the first set of biometric data is transmitted or acquired in accordance with the duration, the frequency, or both. (See Khare ¶45 where for example, one type of sensor might begin sending measurements once every 20 milliseconds in response to a single command to begin streaming data. Another type of sensor might require that the measurement frequency be specified in the “begin streaming” command. Yet another type of sensor might send individual measurements in response to request commands such that the system must send requests commands at the desired frequency.) Therefore, it would have been obvious to one of ordinary skill in the art that the received physiological signals of de Zambotti would be provided in response to a request as taught by Khare. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using known methods of receiving data to yield the desired result of receiving the data.
Consider claim 4, where de Zambotti in view of Khare in view of De Donato teaches the method of claim 1, further comprising: suspending acquisition of the one or more other of biometric data types while acquiring the first set of biometric data based at least in part on the request, wherein the one or more other of biometric data types are different from the one or more biometric data types. (See de Zambotti ¶50-53, 56-57 where In the illustrated embodiments, the system 100 increases the degree of virtual immersion in response to reductions in the user's respiration rate. Once the user's respiration has decreased, the system 100 can make adjustments to the immersive virtual environment 116 based on other criteria. In TABLE 2, an illustrative example of a mapping function relating to the use of muscle activity as primary feedback parameter is shown. As discussed above, system 100 can adjust the immersive virtual environment 116 (and/or an aspect of user's physical environment) in response to the detection of the user's muscle activity. )
Consider claim 5, where de Zambotti in view of Khare in view of De Donato teaches the method of claim 1, further comprising: acquiring a second set of biometric data of the one or more biometric data types via the one or more sensors of the wearable device in accordance with a first measurement frequency during a first time interval that occurs prior to the request; and increasing the first measurement frequency to a second measurement frequency in response to the request, wherein the first set of biometric data is acquired in accordance with the second measurement frequency. (See Khare ¶38 where at 58, the connection application is capable of sending one or more commands to the one or more sensors to set one or more sensor parameters and start streaming measurements. More specifically, the connection application is configured (programmed) to transmit one or more commands to a remote-controlled device and/or to the one or more wireless communication-enabled data sensors to change, adjust, and/or modify one or more sensor settings. A change, adjustment, or modification of a sensor setting includes any control and/or configuring of a sensor and/or its one or more settings. One or more settings may include items like mode of operation, sampling rate, data range, gain, power mode, voltages, etc. It may also include an ability to provide updates to the sensor (e.g., firmware), or command the sensor to take an action. Thus, Khare teaches configuring the sensor to increase a sampling rate) Therefore, it would have been obvious to one of ordinary skill in the art that the received physiological signals of de Zambotti would be provided in response to a request as taught by Khare. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using known methods of configuring known parameters in the art to yield the desired result.
Consider claim 6, where de Zambotti in view of Khare in view of De Donato teaches the method of claim 1, further comprising: acquiring a second set of biometric data of the one or more other biometric data types via the one or more sensors of the wearable device in accordance with a first measurement frequency during a first time interval that occurs prior to the request, the one or more other biometric data types being different from the one or more first types of biometric data types. (See Khare ¶38 where at 58, the connection application is capable of sending one or more commands to the one or more sensors to set one or more sensor parameters and start streaming measurements. More specifically, the connection application is configured (programmed) to transmit one or more commands to a remote-controlled device and/or to the one or more wireless communication-enabled data sensors to change, adjust, and/or modify one or more sensor settings. A change, adjustment, or modification of a sensor setting includes any control and/or configuring of a sensor and/or its one or more settings. One or more settings may include items like mode of operation, sampling rate, data range, gain, power mode, voltages, etc. It may also include an ability to provide updates to the sensor (e.g., firmware), or command the sensor to take an action. Thus, Khare teaches configuring the sensor to decrease a sampling rate) Therefore, it would have been obvious to one of ordinary skill in the art that the received physiological signals of de Zambotti would be provided in response to a request as taught by Khare. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using known methods of configuring known parameters in the art to yield the desired result.
Consider claim 7, where de Zambotti in view of Khare in view of De Donato teaches the method of claim 1, wherein receiving the request comprises: receiving, at the user device via the second connection, the request; and forwarding the request to the wearable device via a third connection between the wearable device and the user device. (See Khare ¶31 where in another refinement, the one or more antennas or transmission subsystems include an on or in-body transceiver (“on-body transceiver”) that optionally acts as another sensor or is integrated within a biological sensor. The on-body transceiver is operable to communicate with the one or more wireless communication-enabled sensors on a targeted subject's body or across one or more target subjects, and may itself track one or more types of biological data (e.g., positional data). Therefore, it would have been obvious for one of ordinary skill in the art to modify the data collection of de Zambotti to be an aggregated in a data hub as taught by Khare. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of aggregating different types of data to formulate a more complete picture of the user state.
Consider claim 8, where de Zambotti in view of Khare in view of De Donato teaches the method of claim 1, wherein receiving the request comprises: receiving, at the wearable device via the first connection, the request. (See Khare ¶45 where for example, one type of sensor might begin sending measurements once every 20 milliseconds in response to a single command to begin streaming data. Another type of sensor might require that the measurement frequency be specified in the “begin streaming” command. Yet another type of sensor might send individual measurements in response to request commands such that the system must send requests commands at the desired frequency.) Therefore, it would have been obvious to one of ordinary skill in the art that the received physiological signals of de Zambotti would be provided in response to a request as taught by Khare. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using known methods of receiving data to yield the desired result of receiving the data.
Consider claim 9, where de Zambotti in view of Khare in view of De Donato teaches the method of claim 1, wherein transmitting the first set of biometric data comprises: transmitting, from the wearable device, the first set of biometric data to the user device via a third connection between the wearable device and the user device; and forwarding, from the user device, the first set of biometric data to the extended reality device via the second connection. (See Khare ¶31 where in another refinement, the one or more antennas or transmission subsystems include an on or in-body transceiver (“on-body transceiver”) that optionally acts as another sensor or is integrated within a biological sensor. The on-body transceiver is operable to communicate with the one or more wireless communication-enabled sensors on a targeted subject's body or across one or more target subjects, and may itself track one or more types of biological data (e.g., positional data). Therefore, it would have been obvious for one of ordinary skill in the art to modify the data collection of de Zambotti to be an aggregated in a data hub as taught by Khare. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of aggregating different types of data to formulate a more complete picture of the user state.
Consider claim 10, where de Zambotti in view of Khare in view of De Donato teaches the method of claim 1, wherein receiving the request comprises: transmitting, from the wearable device, the first set of biometric data to the extended reality device via the first connection. (See Khare ¶51 where The data may be provided directly to the gaming system via the web browser or accessed by the gaming system once the data is provided to a web browser-based system. For example, if the user has a high-stress level or has an elevated heart rate (comparative to other users who are also playing a similar game or relative to their baseline heart rate), the one or more subjects within the game may also experience similar data-related responses (e.g., high stress level, elevated heart rate) which may provide an advantage, disadvantage or other indication to the user and/or the one or more subjects in the game.) Therefore, it would have been obvious for one of ordinary skill in the art to modify the data collection of de Zambotti to be a direct connection as taught by Khare. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of accounting for latency in more real-time applications.
Consider claim 11, where de Zambotti in view of Khare in view of De Donato teaches the method of claim 1, wherein the feedback comprises one or more of visual feedback, audio feedback, haptic feedback, or any combination thereof. (See de Zambotti ¶16 where the illustrative immersive virtual environment 116 includes a combination of visual 118 and audio 130 stimuli, but other embodiments may include other types of sensory stimuli, such as tactile, temperature, taste, smell, and others, alternatively or in addition to the visual 118 and audio 130 stimuli.)
Consider claim 12, where de Zambotti in view of Khare in view of De Donato teaches the method of claim 1, wherein the extended reality device comprises a headset, glasses, or goggles. (See de Zambotti Fig. 1 and ¶22 where The illustrative visual display system 112 is embodied as commercially available virtual reality eyewear.)
Consider claim 13, where de Zambotti in view of Khare in view of De Donato teaches the method of claim 1, wherein the wearable device comprises a finger ring wearable device or a wrist wearable device. (See de Zambotti ¶20 where he illustrative wearable smart-sensor and mobile computing device 210 includes a computing device 110 (e.g., a smartphone, a tablet computer, an attachable/detachable electronic device such as a clip-on device, a smart watch, smart glasses, a smart wristband, smart jewelry, and/or smart apparel))
Consider claim 14, where de Zambotti in view of Khare in view of De Donato teaches the method of claim 1, wherein the first set of biometric data comprises heart rate data, temperature data, respiration rate data, stress data, blood oxygen data, blood glucose data, or a combination thereof. (See de Zambotti ¶16 where s used herein, "physiological parameters" may refer to, among other things, breathing rate (respiration rate) (e.g., breaths per minute), heart rate (e.g., beats per minute), brain activity (e.g. electroencephalographic signals), body movements, muscle activity; or any other type of measurable human physiological activity, or any combination of the foregoing.)
Consider claim 15, where de Zambotti teaches a system for providing feedback to a user in an extended reality environment, comprising: a wearable device comprising one or more sensors configured to measure biometric data from a user; a user device communicatively coupled with the wearable device; (See de Zambotti ¶31-32 where an example of an external physiological sensor 232 is "textile electrodes," which are formed by knitting or weaving conductive fibers into apparel or garments. Textile electrodes can pick up signals from the heart and other muscles. The physiological activity sensed by the textile electrodes are transmitted through the conductive fibers that are woven into the garment to a processing unit, which then passes the received signals to the mobile or wearable computing device 210, generally through a wireless data connection.) an extended reality device communicatively coupled with the wearable device, the user device, or both, and one or more processors communicatively coupled with the user device, the wearable device, or both, (See de Zambotti Fig. 3 and ¶31-32, 37-40 where the biofeedback VR sleep assistant 218 located in the virtual reality device 240 may receive sensor signals 328 from the physiological sensors 232 and the user customization data 344 from the UI subsystem 224 located in the mobile computing device 210) wherein the one or more processors are configured to: establish a first connection between a wearable device and an extended reality device, a second connection between a user device communicatively coupled with the wearable device and the extended reality device, or both; (See de Zambotti ¶31-32 where the system 100 presents an initial immersive virtual environment 116 and enters "feedback mode," in which the sensor(s) 232 periodically detect the physiological responses of the user to the presented immersive virtual environment 116, and provide the sleep assistant 218 with physiological signals that can be used by the sleep assistant 218 to determine the user's state of relaxation as it changes over time. An example of an external physiological sensor 232 is "textile electrodes," which are formed by knitting or weaving conductive fibers into apparel or garments. Textile electrodes can pick up signals from the heart and other muscles. The physiological activity sensed by the textile electrodes are transmitted through the conductive fibers that are woven into the garment to a processing unit, which then passes the received signals to the mobile or wearable computing device 210, generally through a wireless data connection.) determining one or more biometric data types to acquire based on an extended reality experience initiated via the extended reality device (See de Zambotti Fig. 3 and ¶16 where the mobile/wearable computing device 210 and/or the virtual reality device 240 analyze one or more physiological parameters that are obtained or derived from sensor signals. As used herein, "physiological parameters" may refer to, among other things, breathing rate (respiration rate) (e.g., breaths per minute), heart rate (e.g., beats per minute), brain activity (e.g. electroencephalographic signals), body movements, muscle activity; or any other type of measurable human physiological activity, or any combination of the foregoing) receive, from the extended reality device, a biometric data types to acquire, wherein the request is received via the first connection, the second connection, or both; (See de Zambotti Fig. 3 and ¶31-32, 37-40 where the biofeedback VR sleep assistant 218 located in the virtual reality device 240 may receive sensor signals 328 from the physiological sensors 232 and the user customization data 344 from the UI subsystem 224 located in the mobile computing device 210) acquire a first set of biometric data of the one or more biometric data types via the one or more sensors of the wearable device; transmit, to the extended reality device, the first set of biometric data; (See de Zambotti Fig. 3 and ¶31-32, 38 where once the user's baseline condition or "physiological status" is established, the system 100 presents an initial immersive virtual environment 116 and enters "feedback mode," in which the sensor(s) 232 periodically detect the physiological responses of the user to the presented immersive virtual environment 116, and provide the sleep assistant 218 with physiological signals that can be used by the sleep assistant 218 to determine the user's state of relaxation as it changes over time. The physiological signal acquisition module 312 receives sensor signals 328 from the physiological sensor(s) 232, 262 from time to time during operation of the computing device 210 at a specified sampling rate, which may correspond to a sampling rate performed by the computing device 210) and provide feedback to the user via the extended reality device, wherein the feedback is based at least in part on the first set of biometric data. (See de Zambotti ¶49-50 where in the example of TABLE 1, a single physiological parameter (respiration rate) is mapped to both visual and audio elements of an immersive virtual environment. Each value of the physiological parameter corresponds to a different stage of the immersive virtual environment, and each stage of the immersive virtual environment relates to audio and visual features that have different values. The illustrative audio feature is gain (e.g., volume) and the illustrative visual features are the number of primary foreground elements (e.g., fish in the example of FIG. 1), the speed of object movement (e.g., the speed at which the fish travel across the display), and the densities of secondary foreground elements (e.g., the density of the bubbles of FIG. 1).
De Zambotti teaches receiving biometric data, however De Zambotti does not explicitly teach a request and providing biometric data in response to the request, however, in an analogous field of endeavor Khare teaches a request specifying the one or more and providing biometric data types to acquire in response to the request. (See Khare ¶45 where a type of sensor might send individual measurements in response to request commands such that the system must send requests commands at the desired frequency.) Therefore, it would have been obvious to one of ordinary skill in the art that the received physiological signals of de Zambotti would be provided in response to a request as taught by Khare. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using known methods of receiving data to yield the desired result of receiving the data.
De Zambotti teaches acquiring biometric data, however De Zambotti does not explicitly teach wherein acquiring the first set of biometric data comprises reducing a frequency of, or suspending, one or more measurement operations for one or more other biometric data types to conserve power at the wearable device. However, in an analogous field of endeavor De Donato teaches wherein acquiring the first set of biometric data comprises reducing a frequency of, or suspending, one or more measurement operations for one or more other biometric data types to conserve power at the wearable device. (See De Donato ¶419, 652-654 where the computer system 101 reduces a frequency of sensor measurements and/or sensor measurement processing (e.g., sensors for gaze tracking, and sensors for world tracking) to a greater degree during first (e.g., sleep, or low power) state 12002 compared to second (e.g., lower power, standby, or hibernation) state 12004.) Therefore, it would have been obvious for one of ordinary skill in the art to modify the biometric data acquisition of De Zambotti by adding the operational state of the biometric data acquisition of De Donato. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of applying the appropriate context to the detected situation in order to help conserve battery power. (See De Donato ¶654)
Consider claim 17, where de Zambotti in view of Khare in view of De Donato teaches the system of claim 15, wherein the request comprises an indication of a duration, a frequency, or both, of transmission or acquisition of the first set of biometric data, wherein the first set of biometric data is transmitted or acquired in accordance with the duration, the frequency, or both. (See Khare ¶45 where for example, one type of sensor might begin sending measurements once every 20 milliseconds in response to a single command to begin streaming data. Another type of sensor might require that the measurement frequency be specified in the “begin streaming” command. Yet another type of sensor might send individual measurements in response to request commands such that the system must send requests commands at the desired frequency.) Therefore, it would have been obvious to one of ordinary skill in the art that the received physiological signals of de Zambotti would be provided in response to a request as taught by Khare. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using known methods of receiving data to yield the desired result of receiving the data.
Consider claim 18, where de Zambotti in view of Khare in view of De Donato teaches the system of claim 15, wherein the one or more processors are further configured to: suspend acquisition of the one or more other biometric data types while acquiring the first set of biometric data based at least in part on the request, wherein the one or more other biometric data types are different from the one or more biometric data types. (See de Zambotti ¶50-53, 56-57 where In the illustrated embodiments, the system 100 increases the degree of virtual immersion in response to reductions in the user's respiration rate. Once the user's respiration has decreased, the system 100 can make adjustments to the immersive virtual environment 116 based on other criteria. In TABLE 2, an illustrative example of a mapping function relating to the use of muscle activity as primary feedback parameter is shown. As discussed above, system 100 can adjust the immersive virtual environment 116 (and/or an aspect of user's physical environment) in response to the detection of the user's muscle activity. )
Consider claim 19, where de Zambotti in view of Khare in view of De Donato teaches the system of claim 15, wherein the one or more processors are further configured to: acquire a second set of biometric data of the one or more biometric data types via the one or more sensors of the wearable device in accordance with a first measurement frequency during a first time interval that occurs prior to the request; and increase the first measurement frequency to a second measurement frequency in response to the request, wherein the first set of biometric data is acquired in accordance with the second measurement frequency. (See Khare ¶38 where at 58, the connection application is capable of sending one or more commands to the one or more sensors to set one or more sensor parameters and start streaming measurements. More specifically, the connection application is configured (programmed) to transmit one or more commands to a remote-controlled device and/or to the one or more wireless communication-enabled data sensors to change, adjust, and/or modify one or more sensor settings. A change, adjustment, or modification of a sensor setting includes any control and/or configuring of a sensor and/or its one or more settings. One or more settings may include items like mode of operation, sampling rate, data range, gain, power mode, voltages, etc. It may also include an ability to provide updates to the sensor (e.g., firmware), or command the sensor to take an action. Thus, Khare teaches configuring the sensor to increase a sampling rate) Therefore, it would have been obvious to one of ordinary skill in the art that the received physiological signals of de Zambotti would be provided in response to a request as taught by Khare. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using known methods of configuring known parameters in the art to yield the desired result.
Consider claim 20, where de Zambotti in view of Khare in view of De Donato teaches the system of claim 15, wherein the one or more processors are further configured to: acquire a second set of biometric data of the one or more other biometric data types via the one or more sensors of the wearable device in accordance with a first measurement frequency during a first time interval that occurs prior to the request, the one or more other biometric data types, different from the one or more biometric data types; and decrease the first measurement frequency to a second measurement frequency in response to the request. (See de Zambotti ¶50-53, 56-57 where In the illustrated embodiments, the system 100 increases the degree of virtual immersion in response to reductions in the user's respiration rate. Once the user's respiration has decreased, the system 100 can make adjustments to the immersive virtual environment 116 based on other criteria. In TABLE 2, an illustrative example of a mapping function relating to the use of muscle activity as primary feedback parameter is shown. As discussed above, system 100 can adjust the immersive virtual environment 116 (and/or an aspect of user's physical environment) in response to the detection of the user's muscle activity. )
Consider claim 21 where de Zambotti in view of Khare in view of De Donato teaches the method of claim 1, wherein the request further specifies movement data to acquire, and the method further comprising: acquiring the movement data via the one or more sensors of the wearable device in response to the request. (See de Zambotti ¶16 where the mobile/wearable computing device 210 and/or the virtual reality device 240 analyze one or more physiological parameters that are obtained or derived from sensor signals. As used herein, "physiological parameters" may refer to, among other things, breathing rate (respiration rate) (e.g., breaths per minute), heart rate (e.g., beats per minute), brain activity (e.g. electroencephalographic signals), body movements, muscle activity; or any other type of measurable human physiological activity, or any combination of the foregoing.)
Consider claim 22 where de Zambotti in view of Khare in view of De Donato teaches the method of claim 1, wherein acquiring the first set of biometric data further comprises reducing a frequency of, or suspending, one or more additional measurement operations for movement data to conserve the power at the wearable device. (See De Donato ¶419, 652-654 where the computer system 101 reduces a frequency of sensor measurements and/or sensor measurement processing (e.g., sensors for gaze tracking, and sensors for world tracking) to a greater degree during first (e.g., sleep, or low power) state 12002 compared to second (e.g., lower power, standby, or hibernation) state 12004.) Therefore, it would have been obvious for one of ordinary skill in the art to modify the biometric data acquisition of De Zambotti by adding the operational state of the biometric data acquisition of De Donato. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of applying the appropriate context to the detected situation in order to help conserve battery power. (See De Donato ¶654)
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.
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WILLIAM LU
Primary Examiner
Art Unit 2624
/WILLIAM LU/Primary Examiner, Art Unit 2624
