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 New Zealand Application No. NZ800482, filed on May 30, 2023.
Acknowledgment is made of applicant’s claim for international priority under 35 U.S.C. 371. The current application is a national stage entry of international application PCT/NZ2024/050061 filed on May 30, 2024
Information Disclosure Statement
The information disclosure statement (IDS) submitted on December 1, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
As a result of the Preliminary Amendment filed on December 1, 2025, claims 1-3, 6-11, 16-22 and 40-43 are pending. Claims 3, 6-11, 16, 18-20 and 22 are amended. Claims 4-5, 12-15, 23-39 and 44-46 are canceled.
Also as a result of the Preliminary Amendment filed on December 1, 2025, the Specification has been amended to make reference to prior filed applications.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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) 1-3, 6-11, 16, 17, 19-22 and 40-43 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sun et al., United States Patent Application Publication No. US 2024/0169681 A1 (priority to provisional applications US 63,384,944, filed on November 23, 2022; US 63,497,176, filed on April 19, 2023).
Regarding claim 1, Sun discloses a wearable device (Figs. 1-10, generally, Summary), comprising:
a body configured to receive a part of the anatomy of a user (Figs. 10, assembly, #1062; Detailed Description, [0210-0218]);
one or more sensors operatively connected to the body that are configured to provide data
relating to the location, orientation and/or configuration of the body in use (Figs. 3-6, Detailed Description, [0057-0065], “The finger position sensor 475 senses a position of the finger (e.g., when a user activates the one of the buttons) and this information is processed by the controller 460…. The camera 110, includes at least one sensor for sensing light emitted by the light sources 485 and a controller for processing the light images received for the light sources 485 to detect positions of the controller over time (e.g., as was described above in conjunction with the illumination sources of the controllers 300 shown in FIGS. 1A-2E”);
a processor configured to process the data to determine anatomical position information (Fig. 3, controller, #460; Detailed Description, [0053-0056]); and
a communications module operatively connected to the processor, and configured to
communicate with at least one remote device (Figs. 3-6, antenna; Detailed Description, [0054]; See also antenna, #538 and Detailed Description, [0058]),
wherein the processor is configured to compare the anatomical position information against a
pre-configured set of anatomical positions linked to one or more events, to determine whether
the anatomical position information corresponds to one of the pre-configured anatomical
positions (Figs. 5-6, Detailed Description, [0058-0070], “At step 530, the computing system generates a vision-based 6DoF pose estimation for the handheld device by processing the cropped image, metadata associated with the image, and first sensor data from one or more sensors associated with the handheld device using a second machine-learning model. At step 540, the computing system generates a motion-sensor-based 6DoF pose estimation for the handheld device by integrating second sensor data from the one or more sensors associated with the handheld device”; See also Detailed Description, [0084-0093], “For example, the user 602 can perform one or more hand gestures that are detected by the wrist-wearable device 700 (e.g., using one or more EMG sensors and/or IMUs, described below in reference to FIGS. 7A-7B) and/or AR device 800 (e.g., using one or more image sensors or cameras, described below in reference to FIGS. 8A-8B) to provide a user input.”),
wherein the processor receives context information from any one or more of the remote device,
the events sent to the remote device; and/or from existing context information and anatomical
position data (Detailed Description, [0065-0070], “he constrained 6DoF pose estimation may be inferred using heuristics based on the IMU data, human motion models, and context information associated with an application the handheld device is used for. As an example, one or more motion models may be used to infer a constrained 6DoF pose estimation. In some embodiments, the one or more motion models comprise a context-information-based motion model. An application the user is currently engaged with may be associated with a particular set of movements of the use”), and wherein the processor uses the context information together with the anatomical position information to determine whether the processor should send an event of the linked one or more events to the remote device via the communications module (See Figs. 5-6, Detailed Description, [0058-0070], “In some embodiments, the computing system generates a final 6DoF pose estimation for the handheld device based on the vision-based 6DoF pose estimation and the motion-sensor-based 6DoF pose estimation. The computing system generates the final 6DoF pose estimation using an EKF. As an example, the pose fusion unit may generate a final 6DoF pose estimation for the handheld device based on the vision-based 6DoF pose estimation and the motion-sensor-based 6DoF pose estimation. The pose fusion unit may comprise an EKF.”; See also Detailed Description, [0084-0093][0116])
Regarding claim 2, Sun discloses wherein the processor is configured to use a first algorithm or model with a first piece of context information, and a second algorithm or model to process the data with a second piece of context information (Detailed Description, [0058-0070], “. A first machine-learning model may receive images at a pre-determined interval from one or more cameras. The first machine learning model may be a detection network. In some embodiments, the one or more cameras may take pictures of a hand of a user or a handheld device at a pre-determined interval and provide the images to the first machine-learning model. For example, the one or more cameras may provide images to the first machine-learning model 30 times per second… As an example, the first machine-learning model may process the received image along with additional information to generate a cropped image. The cropped image may comprise a hand of a user holding the handheld device and/or a handheld device. The cropped image may be provided to a second machine-learning model. The second machine-learning model may be a direct pose regression network….In some embodiments, the one or more motion models comprise a context-information-based motion model”).
Regarding claim 3, Sun discloses wherein the first algorithm or model is an AI model trained to detect a first range of anatomical positions and wherein the second algorithm or model is an AI model trained to detect a second range of anatomical positions (Detailed Description, [0058-0070], “. A first machine-learning model may receive images at a pre-determined interval from one or more cameras. The first machine learning model may be a detection network. In some embodiments, the one or more cameras may take pictures of a hand of a user or a handheld device at a pre-determined interval and provide the images to the first machine-learning model. For example, the one or more cameras may provide images to the first machine-learning model 30 times per second… As an example, the first machine-learning model may process the received image along with additional information to generate a cropped image. The cropped image may comprise a hand of a user holding the handheld device and/or a handheld device. The cropped image may be provided to a second machine-learning model. The second machine-learning model may be a direct pose regression network….In some embodiments, the one or more motion models comprise a context-information-based motion model”).
Regarding claim 6, Sun discloses wherein the data provided by the one or more sensors are capacitance readings relating to a configuration of the wearable device as a result of deformation in the body of the wearable device due to the user's anatomical positioning within the wearable device (Fig. 3, Detailed Description, [0052-0058], “The capacitive touch controller 440 is coupled to multiple sensors such that the input board 402 receives sensed signals from capacitive sensors resulting from a user's touch. For example, the capacitive sensors include a thumbstick sensor 410, an “A” button sensor 415, and/or a “B” button sensor 420. For example, the thumbstick sensor 410 senses a signal resulting from the user touching the thumbstick 410. Further, the button sensors 415 and 420 sense signals resulting from the user touching the buttons 415 and 420. Other capacitive sensors may be included for other user-input keys (e.g., a directional pad).”).
Regarding claim 7, Sun discloses wherein the anatomical position information is hand position information and wherein the hand position information comprises information on the relative positioning of each of the fingers of the user with respect to the palm of the hand of the user (Detailed Description, [0053-0056], “ The finger position sensor 475 senses a position of the finger (e.g., when a user activates the one of the buttons) and this information is processed by the controller 460.”; See also Detailed Description, [0116][0210-0220], “ Further, the haptic controller 1076 can provide one or more signals to cause the pressure-changing device 1067 to inflate one or more bladders 1064 in a first portion of a smart textile-based garment 1000 (e.g., a first finger), while one or more bladders 1064 in a second portion of the smart textile-based garment 1000 (e.g., a second finger) remain unchanged. Additionally, the haptic controller 1076 can provide one or more signals to cause the pressure-changing device 1067 to inflate one or more bladders 1064 in a first smart textile-based garment 1000 to a first pressure and inflate one or more other bladders 1064 in the first smart textile-based garment 1000 to a second pressure different from the first pressure”).
Regarding claim 8, Sun discloses wherein the anatomical position information is hand position information and wherein the hand position information includes information of the amount of bend in any one or more of the scapho-trapezium/trapezoid. the carpometacarpal, the metacarpophalangeal, the proximal Interphalangeal, and distal interphalangeal joints (Detailed Description, [0116], “The muscular activations performed by the user can include static gestures, such as placing the user's hand palm down on a table; dynamic gestures, such as grasping a physical or virtual object; and covert gestures that are imperceptible to another person, such as slightly tensing a joint by co-contracting opposing muscles or using sub-muscular activations. The muscular activations performed by the user can include symbolic gestures (e.g., gestures mapped to other gestures, interactions, or commands, for example, based on a gesture vocabulary that specifies the mapping of gestures to commands).”’ See also Detailed Description, [0210-0212]).
Regarding claim 9, Sun discloses wherein the anatomical position information includes location and/or orientation information provided by a sensor in the form of an inertial motion unit (See also Detailed Description, [0076]), accelerometer, gyroscope (see Detailed Description, [0061], “. In some embodiments, the first sensor data comprises a gravity vector estimate generated from a gyroscope.”) or magnetometer (Detailed Description, [0187]).
Regarding claim 10, wherein the processor is configured to send the event via the communications module to the remote device (Detailed Description, [0084-0090], “For example, the user 602 can perform one or more hand gestures that are detected by the wrist-wearable device 700 (e.g., using one or more EMG sensors and/or IMUs, described below in reference to FIGS. 7A-7B) and/or AR device 800 (e.g., using one or more image sensors or cameras, described below in reference to FIGS. 8A-8B) to provide a user input… The wrist-wearable device 700, the AR device 800, and/or the HIPD 900 can operate alone or in conjunction to allow the user 602 to interact with the AR environment. In some embodiments, the HIPD 900 is configured to operate as a central hub or control center for the wrist-wearable device 700, the AR device 800, and/or another communicatively coupled device…. The user 602's gestures performed on the HIPD 900 can be provided and/or displayed on another device”).
Regarding claim 11, Sun discloses wherein the processor is configured to determine whether the anatomical position information corresponds to a pre-configured anatomical position using a machine learning model (Detailed Description, [0058-0070]);
wherein the machine learning model is configured to determine whether the anatomical position information corresponds to a pre-configured anatomical position using a neural network (Detailed Description, [0058-0070] on machine learning; See next Detailed Description, [0076][0106-0116] on specifically neural based signals being used);
wherein the neural network provides a confidence score indicating the likely match to any one of the pre-configured anatomical positions (Detailed Description, [0058-0070], “The metadata and the first sensor data may be optional input to the second machine-learning model. The second machine-learning model may generate a vision-based 6DoF pose estimation 316 and a vision-based-estimation confidence score corresponding to the generated vision-based 6DoF pose estimation by processing the cropped image. In particular embodiments, the second machine-learning model also processes the metadata and the first sensor data to generate the vision-based 6DoF pose estimation and the vision-based-estimation confidence score.”);
wherein the processor is configured to determine whether the anatomical position information
corresponds to one of the pre-configured set of anatomical positions when the confidence score
exceeds a predetermined threshold (Detailed Description, [0058-0070], “In some embodiments, the EKF takes a constrained 6DoF pose estimation as input when a combined confidence score calculated based on the vision-based-estimation confidence score and the motion-sensor-based-estimation confidence score is lower than a pre-determined threshold”);
wherein the processor is configured to send the event or update the context information when the confidence score for one of the pre-configured anatomical positions is higher than the confidence
score for any other of the pre-configured anatomical positions (Detailed Description, [0058-0008], “The computing system also generates a motion-sensor-based-estimation confidence score corresponding to the motion-sensor-based 6DoF pose estimation. As an example, the handheld device tracking component may receive second sensor data from each of the one or more handheld devices. The second sensor data may be captured by the one or more IMU sensors associated with the handheld device at a pre-determined interval. For example, the handheld device may send the second sensor data 500 times per second to the handheld device tracking component. An IMU integrator module in the motion-sensor-based pose estimation unit may access the second sensor data. The IMU integrator module may integrate N recently received second sensor data to generate a motion-sensor-based 6DoF pose estimation for the handheld device. The IMU integrator module may also generate a motion-sensor-based-estimation confidence score corresponding to the generated motion-sensor-based 6DoF pose estimation….In some embodiments, the computing system generates a final 6DoF pose estimation for the handheld device based on the vision-based 6DoF pose estimation and the motion-sensor-based 6DoF pose estimation. The computing system generates the final 6DoF pose estimation using an EKF. As an example, the pose fusion unit may generate a final 6DoF pose estimation for the handheld device based on the vision-based 6DoF pose estimation and the motion-sensor-based 6DoF pose estimation. The pose fusion unit may comprise an EKF.”).
Regarding claim 16, Sun discloses wherein the processor is further configured to switch between a first mode in which anatomical position data is communicated with the remote device (Detailed Description, [0023-0024], “FIG. 1A is an illustration showing a scenario in which a user of an artificial-reality system 100 is holding a controller that includes a desirable arrangement of illumination sources to assist with positional determinations for the controller in accordance with some embodiments. The virtual-reality system 100 includes cameras 110A and 110B of a virtual-reality headset 650, a handheld controller 300 with desirably-positioned illumination sources (e.g., depicted as LEDs in this example and represented with the three lines extending from each LED to represent transmission of light), and positional determination logic 120 (which also includes LED-based positional determination logic 122A and/or IMU-based positional determination logic 122B). “), and a second mode, wherein the events are communicated with the remote device (Detailed Description, [0084-0090], describing hand gestures which provide an input; See also Detailed Description, [0137] on haptic events).
Regarding claim 17, Sun discloses wherein the first mode and second mode are active simultaneously (Detailed Description, [0023-0024][0084-0090][0137]).
Regarding claim 19, Sun discloses wherein the processor is configured to send the event or update the context information if the pre-configured anatomical position is detected for a predetermined period of time (Detailed Description, [0057], “The camera 110, includes at least one sensor for sensing light emitted by the light sources 485 and a controller for processing the light images received for the light sources 485 to detect positions of the controller over time (e.g., as was described above in conjunction with the illumination sources of the controllers 300 shown in FIGS. 1A-2E).”; See also Detailed Description, [0076])
Regarding claim 20, Sun discloses wherein the event comprises one or more of: a keypress (Sun claim 16; See also Detailed Description,[0036-0050] [0233-0240] on buttons); a multi-media command (Detailed Description, [0073-0080]); an animation event (Detailed Description, [0071]); an augmented/virtual/mixed reality event (Detailed Description, [0152]); displacement on one more axes (Detailed Description, [0196]); anatomic position and/or orientation data (Detailed Description, [0108]); a biometric identification event (Detailed Description, [0076-0080]); an authorization event (Detailed Description, [0106]); or a user-defined event (Detailed Description, [0186]).
Regarding claim 21, this is met by the rejection to claim 9.
Regarding claim 22, Sun discloses wherein the processor is configured to operate in:
a first mode in which the anatomical position information is provided to the communications
module for communication with the remote device (Detailed Description, [0023-0024], “FIG. 1A is an illustration showing a scenario in which a user of an artificial-reality system 100 is holding a controller that includes a desirable arrangement of illumination sources to assist with positional determinations for the controller in accordance with some embodiments. The virtual-reality system 100 includes cameras 110A and 110B of a virtual-reality headset 650, a handheld controller 300 with desirably-positioned illumination sources (e.g., depicted as LEDs in this example and represented with the three lines extending from each LED to represent transmission of light), and positional determination logic 120 (which also includes LED-based positional determination logic 122A and/or IMU-based positional determination logic 122B).”; See also Figs. 3-6, antenna; Detailed Description, [0054]; See also antenna, #538 and Detailed Description, [0058]), and
a second mode in which the processor compares the anatomical position information against a
pre-configured set of anatomical positions, wherein when the anatomical position information is
determined to correspond to a pre-configured anatomical position, the processor is configured to
send an event to the communications module for communicating the event to the remote device
or to update the context information (Detailed Description, [0084-0090], describing hand gestures which provide an input See also Detailed Description, [0137] on haptic events).
Regarding claim 40, Sun discloses a processor-implemented method of controlling a remote device using at least one wearable device (Figs. 1-10, generally) which comprises, a body configured to receive a part of the anatomy of a user (Figs. 10, assembly, #1062; Detailed Description, [0210-0218]); one or more sensors operatively connected to the body that are configured to provide data relating to the location, orientation and/or configuration of the body in use (Figs. 3-6, Detailed Description, [0057-0065], “The finger position sensor 475 senses a position of the finger (e.g., when a user activates the one of the buttons) and this information is processed by the controller 460…. The camera 110, includes at least one sensor for sensing light emitted by the light sources 485 and a controller for processing the light images received for the light sources 485 to detect positions of the controller over time (e.g., as was described above in conjunction with the illumination sources of the controllers 300 shown in FIGS. 1A-2E”); a processor configured to process the data to determine anatomical position information (Fig. 3, controller, #460; Detailed Description, [0053-0056]); and a communications module operatively connected to the processor (Figs. 3-6, antenna; Detailed Description, [0054]; See also antenna, #538 and Detailed Description, [0058]), comprising steps of:
in a first mode of operation:
A) processing the data from the one or more sensors to provide anatomical position
information (See Detailed Description, [0023-0024])
B) comparing the anatomical position information to a pre-configured set of anatomical positions linked to one or more events (Figs. 5-6, Detailed Description, [0058-0070], “At step 530, the computing system generates a vision-based 6DoF pose estimation for the handheld device by processing the cropped image, metadata associated with the image, and first sensor data from one or more sensors associated with the handheld device using a second machine-learning model. At step 540, the computing system generates a motion-sensor-based 6DoF pose estimation for the handheld device by integrating second sensor data from the one or more sensors associated with the handheld device”; See also Detailed Description, [0084-0093], “For example, the user 602 can perform one or more hand gestures that are detected by the wrist-wearable device 700 (e.g., using one or more EMG sensors and/or IMUs, described below in reference to FIGS. 7A-7B) and/or AR device 800 (e.g., using one or more image sensors or cameras, described below in reference to FIGS. 8A-8B) to provide a user input.”);
C) determining when the anatomical position information corresponds to a pre-configured
anatomical position (Figs. 5-6, Detailed Description, [0058-0070][0084-0093]);
D) communicating the corresponding events to the remote device using the communications
module to thereby control the remote device (Detailed Description, [0116-0121]; See also Detailed Description, [0216-0222], discussing controlling pressure and haptic feedback);
and in a second mode of operation:
E) processing the data from the one or more sensors to provide anatomical position information (See Detailed Description, [0023-0024], “ The virtual-reality system 100 includes cameras 110A and 110B of a virtual-reality headset 650, a handheld controller 300 with desirably-positioned illumination sources (e.g., depicted as LEDs in this example and represented with the three lines extending from each LED to represent transmission of light), and positional determination logic 120 (which also includes LED-based positional determination logic 122A and/or IMU-based positional determination logic 122B). In some examples, an algorithm/method is utilized to combine the IMU or motion-based positional determinations with the LED or vision-based positional determinations and an example of this is shown and described in conjunction with FIG. 5)….The positional determination logic 120 can include an LED-based positional determination logic 122A and/or an IMU-based positional determination logic 122B. Positional determination logic 120 can be just one aspect and other modules can be associated with the virtual-reality headset 650, and the other modules can include, for example, camera controller logic, display rendering logic, audio processing logic, and logic for processing other signals received directly from the handheld controller 300 (e.g., signals generated by operation of buttons 435 (shown in FIG. 1B) or the thumbstick 430 (shown in FIG. 1B) of the handheld controller).”);
and
F) communicating the anatomical position information to the remote device using the
communications module to thereby control the remote device (Detailed Description, [0116-0121]; See also Detailed Description, [0216-0222], discussing controlling pressure and haptic feedback).
Regarding claim 41, Sun discloses wherein one or more of the pre-configured set of anatomical positions provides the processor with context information about the intended behaviour of the user (Detailed Description, [0065-0070], “The constrained 6DoF pose estimation may be inferred using heuristics based on the IMU data, human motion models, and context information associated with an application the handheld device is used for. As an example, one or more motion models may be used to infer a constrained 6DoF pose estimation. In some embodiments, the one or more motion models comprise a context-information-based motion model. “).
Regarding claim 42, Sun discloses wherein with a first context the processor is configured to use a first algorithm or model to process the data and with a second context, the processor is configured to use a second algorithm or model to process the data (Detailed Description, [0058-0070], “. A first machine-learning model may receive images at a pre-determined interval from one or more cameras. The first machine learning model may be a detection network. In some embodiments, the one or more cameras may take pictures of a hand of a user or a handheld device at a pre-determined interval and provide the images to the first machine-learning model. For example, the one or more cameras may provide images to the first machine-learning model 30 times per second… As an example, the first machine-learning model may process the received image along with additional information to generate a cropped image. The cropped image may comprise a hand of a user holding the handheld device and/or a handheld device. The cropped image may be provided to a second machine-learning model. The second machine-learning model may be a direct pose regression network….In some embodiments, the one or more motion models comprise a context-information-based motion model”).
Regarding claim 43, Sun discloses a wearable device (Figs. 1-10, generally, Summary), comprising:
a body configured to receive a part of the anatomy of a user (Figs. 10, assembly, #1062; Detailed Description, [0210-0218]);
one or more sensors operatively connected to the body that are configured to provide data
relating to the location, orientation and/or configuration of the body in use (Figs. 3-6, Detailed Description, [0057-0065], “The finger position sensor 475 senses a position of the finger (e.g., when a user activates the one of the buttons) and this information is processed by the controller 460…. The camera 110, includes at least one sensor for sensing light emitted by the light sources 485 and a controller for processing the light images received for the light sources 485 to detect positions of the controller over time (e.g., as was described above in conjunction with the illumination sources of the controllers 300 shown in FIGS. 1A-2E”);
a processor configured to process the data to determine anatomical position information (Fig. 3, controller, #460; Detailed Description, [0053-0056]); and
a communications module operatively connected to the processor, and configured to
communicate with at least one remote device (Figs. 3-6, antenna; Detailed Description, [0054]; See also antenna, #538 and Detailed Description, [0058]),
wherein the processor is configured to receive context information from the remote device (Detailed Description, [0065-0070], “The constrained 6DoF pose estimation may be inferred using heuristics based on the IMU data, human motion models, and context information associated with an application the handheld device is used for. As an example, one or more motion models may be used to infer a constrained 6DoF pose estimation. In some embodiments, the one or more motion models comprise a context-information-based motion model. An application the user is currently engaged with may be associated with a particular set of movements of the use”), and
based on the context information enable or disable one or more of the events (Examiner’s note—based on disjunctive, enable is read upon; Detailed Description, [0065-0070][0084-0093]), such that after
receiving a first piece of context information, the processor is configured to select from a first list of events corresponding to the predetermined anatomical positions, and after receiving a second piece of context information the processor is configured to select from a second list of events corresponding to the predetermined anatomical positions (See Figs. 5-6, Detailed Description, [0058-0070], “In some embodiments, the computing system generates a final 6DoF pose estimation for the handheld device based on the vision-based 6DoF pose estimation and the motion-sensor-based 6DoF pose estimation. The computing system generates the final 6DoF pose estimation using an EKF. As an example, the pose fusion unit may generate a final 6DoF pose estimation for the handheld device based on the vision-based 6DoF pose estimation and the motion-sensor-based 6DoF pose estimation. The pose fusion unit may comprise an EKF.”; See also Detailed Description, [0084-0093][0116]).
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.
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.
Claim(s) 18 is rejected under 35 U.S.C. 103 as being unpatentable over Sun in view of Whitmire et al., United States Patent Application Publication No. US 2024/0019938 A1.
Regarding claim 18, Sun discloses every element of claim 16 but does not explicitly disclose wherein the processor is configured to switch between, enable or disable either of the first mode and the second mode when a sequence of anatomical positions are detected which correspond to a pre-configured sequence of anatomical positions associated with a change of operating mode.
Whitmire, in a similar field of endeavor, discloses a wearable device (Figs. 1-5, generally) wherein the processor is configured to switch between, enable or disable either of the first mode and the second mode when a sequence of anatomical positions are detected which correspond to a pre-configured sequence of anatomical positions associated with a change of operating mode (Detailed Decryption, [0083], “For example, the time-of-flight sensors 258 are disabled (in a low-power state) while the wrist-wearable device 202 is beyond the surface edge 212. In some embodiments, additional sensors can be used to detect various aspects of actions, including gestures, performed by the user 201, and other aspects of the user's physical surroundings. For example, IMU sensors 260 can be used to detect a specific force, angular rate, and/or orientation of a body part of the user 201 and/or their surroundings at any of the wrist-wearable device 102, the head-wearable device 104, or another electronic device”; See also Detailed Description, [0187]).
It would have been obvious to one of ordinary skill in the art to have further modified the processor Sun to include the specific disabling features of Whitmire, to provide wherein the processor is configured to switch between, enable or disable either of the first mode and the second mode when a sequence of anatomical positions are detected which correspond to a pre-configured sequence of anatomical positions associated with a change of operating mode. The motivation to combine these arts is for power reduction purposes in accordance with particular positional determinations (See Whitmire Detailed Description, [0187]). The fact that Whitmire and Sun disclose very similar forms of wearable devices with positional tracking makes this combination more easily implemented.
Other References
The following references are also cited as pertinent but may not be specifically relied upon within this Action: Keller et al. (US 2019/0212821 A1); Edelson et al. (US 2024/0302899 A1).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KWIN XIE whose telephone number is (571)272-7812. The examiner can normally be reached 9:00 AM - 5:00 PM.
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/KWIN XIE/Primary Examiner, Art Unit 2626
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