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 .
Election/Restrictions
Claims 2-5, 14, 15 and 20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected species, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 5/5/26.
Applicant's election with traverse of species in the reply filed on 5/5/26 is acknowledged. The traversal is on the ground(s) that examiner did not provide appropriate explanation of separate classification or separate status in the art, or a different field of search. This is not found persuasive because as noted on requirement of restriction/election mailed on 3/18/26, claims 2-5, 14, 15 and 20 which is directed toward specific of method of model to track controller of headset (such as tracking controller using light pattern or inertial sensor) are divergent subject matter from claims 6-12, 16-18, which is directed toward specific of determining spatial relationship between hand and controller and/or determining whether user is holding controller and requires different field of search.
The requirement is still deemed proper and is therefore made FINAL.
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.
Claims 13, 16, 19, 1, 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over LeBeau et al., US 20220086205 A1 (hereinafter “LeBeau”), in further view of Sarwar et al., US 12481894 B1 (hereinafter “Sarwar”) and Ye et al., US 20230398434 A1 (hereinafter “Ye”).
Regarding claim 13, LeBeau discloses a headset device (fig. 2A-2C headset with controller, paragraph 39) comprising:
one or more processors; and a memory coupled to the one or more processors, the memory storing instructions that cause the one or more processors to perform (fig. 1, processors 110 and memory 150, paragraph 44: The processors 110 can have access to a memory 150 … Memory 150 can include program memory 160 that stores programs and software, such as an operating system 162, XR work system 164, and other application programs 166):
capturing images using one or more cameras of the headset device (paragraphs 41, 42, 46, 51, “One or more cameras (not shown) integrated with the HMD 200 can detect the light points”); tracking a controller operable to control the headset device using a first method based on the images (paragraph 52, “FIG. 2C illustrates controllers 270, which, in some implementations, a user can hold in one or both hands to interact with an artificial reality environment presented by the HMD 200 and/or HMD 250. The controllers 270 can be in communication with the HMDs, either directly or via an external device (e.g., core processing component 254). The controllers can have their own IMU units, position sensors, and/or can emit further light points. The HMD 200 or 250, external sensors, or sensors in the controllers can track these controller light points to determine the controller positions and/or orientations (e.g., to track the controllers in 3 DoF or 6 DoF). The compute units 230 in the HMD 200 or the core processing component 254 can use this tracking, in combination with IMU and position output, to monitor hand positions and motions of the user. The controllers can also include various buttons (e.g., buttons 272A-F) and/or joysticks (e.g., joysticks 274A-B), which a user can actuate to provide input and interact with objects. As discussed below, controllers 270 can also have tips 276A and 276B, which, when in scribe controller mode, can be used as the tip of a writing implement in the artificial reality working environment.”);
tracking one or more hands of a user of the headset device using a second method based on the images (paragraphs 46, 51, “In various implementations, the IMU 215, position sensors 220, and locators 225 can track movement and location of the HMD 200 in the real-world and in a virtual environment in three degrees of freedom (3 DoF) or six degrees of freedom (6 DoF). For example, the locators 225 can emit infrared light beams which create light points on real objects around the HMD 200. As another example, the IMU 215 can include e.g., one or more accelerometers, gyroscopes, magnetometers, other non-camera-based position, force, or orientation sensors, or combinations thereof. One or more cameras (not shown) integrated with the HMD 200 can detect the light points. Compute units 230 in the HMD 200 can use the detected light points to extrapolate position and movement of the HMD 200 as well as to identify the shape and position of the real objects surrounding the HMD 200”, “Similarly to the HMD 200, the HMD system 250 can also include motion and position tracking units, cameras, light sources, etc., which allow the HMD system 250 to, e.g., track itself in 3 DoF or 6 DoF, track portions of the user (e.g., hands, feet, head, or other body parts), map virtual objects to appear as stationary as the HMD 252 moves, and have virtual objects react to gestures and other real-world objects”, paragraph 53, “instead of or in addition to controllers, one or more cameras included in the HMD 200 or 250, or from external cameras, can monitor the positions and poses of the user's hands to determine gestures and other hand and body motions);
determining a spatial relationship between the one or more hands and the controller; determining the user is holding the controller based on the spatial relationship; and responsive to determining the user is holding the controller, switching an input mode of the headset device from a hand mode to a controller mode such that the controller is operable to control the headset device
(paragraph 69: “The keyboard passthrough hand state can be enabled when the hand state controller 438 detects that the user's hands are within a threshold distance of an MR keyboard, and this mode can include determining the contours of the user's hands and enabling passthrough so the user can see the real-world version of just their hands as they are over the MR keyboard. The ghost interactive hand state can be enabled when the hand state controller 438 detects that the user's hands are within a threshold distance of an interactive object, and this mode can include showing the user's hands as only partially opaque and can include casting a ray from the user's hand”, paragraph 70: “Scribing controller 440 can detect whether a user is holding a controller normally or as a scribe tool. When scribing controller 440 detects that the user is holding a controller as a scribe tool, and that the tip of the controller is against a (real or virtual) surface, the scribing controller 440 can cause writing to be implemented according to the movement of the tip of the controller”, paragraphs 32, “The normal controller mode can be enabled when the user is holding the controller normally (e.g., with her hand wrapped around the controller with fingers over buttons). The scribe controller mode can be enabled when the user is holding a controller as a writing implement (e.g., when an end of the controller opposite primary controller buttons is being held between thumb and one or more fingers)”).
LeBeau does not specifically disclose that the first method of tracking controller and second method of tracking hand are a first model and a second model.
In similar field of endeavor, the concept of using machine learning model to track user’s hand and controller of HMD device, however, are well known in the art.
Sarwar discloses the concept of using a machine learning model to track user hand movement in headset artificial reality system (col. 7, ln. 48-63: “The model store 430 stores parameters of machine learning models. Examples of machine learning models that may be executed by the smart sensor 320 of an artificial reality headset include machine learning models used for eye tracking and hand tracking. The model execution module 415 executes the machine learning models stored in the model store 430. For example, the model execution module 415 may receive sensor data from eye tracking sensors and execute the machine learning model to perform eye tracking. As another example, the model execution module 415 may receive sensor data from hand tracking sensors and execute the machine learning model to perform hand tracking”).
Ye discloses the concept of using a machine learning model to track controller movement and position relative to headset (fig. 2. 3, paragraphs 43-48, tracking controller by headset using light source and camera, paragraph 65: “A trained machine learning model 805 may be applied to the processed events. The model 805 may include information about the configuration of the light sources such as the size of the light sources and their relative locations with respect to the controller body. The machine learning model may be trained with training event data having corresponding masked positions and orientations of a controller as will be discussed in a later section. The trained machine learning model is applied to the processed event data to determine a correspondence 806 between the detected pulses 804 and a pose 808. The trained machine learning model may fit a pose 808, e.g., position and orientation, of the controller to the one or more processed events, e.g., detected LED pulses 804. Alternatively, a fitting algorithm may be applied to the processed events instead of the trained model 805. The fitting algorithm may use a hand developed model of the light sources to fit a position and orientation of the controller to the processed events. Alternatively, the fitting algorithm may be a hypothesis and test type algorithm which tries all the possible permutations of light correspondences, and finds the best fitting use redundant light sources. After that a tracking/prediction algorithm can be applied to keep tracking the light sources. Additionally, the predicted current pose may be used to predict the next pose 809. Inertial data from the IMU 807 may be fused 810 with the predicted pose 808 to generate the final predicted position and orientation of the controller. The fusion may be performed by a trained machine learning algorithm, trained to refine controller position and orientation using inertial data. Alternatively, the fusion may be performed by for example and without limitation a Kalman filter, or nonlinear optimization”).
It would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the concept of tracking user’s hand using machine learning model such as disclosed by Sarwar, and the concept of tracking controller of headset using machine learning model such as disclosed by Ye, into the headset system of LeBeau, such that the headset additional use a first machine learning model to track user’s hand and second machine learning model to track controller, to constitute wherein the first method of tracking controller and second method of tracking hand are a first model and a second model, such is incorporation of a known concept into known device to yield predictable result, the result would have been predictable and would provide the benefit of increase tracking accuracy and robustness and improve user experience.
Regarding claim 1, this is a method claim counterpart of device claim 13, both reciting substantially similar subject matter. Accordingly, claim 1 is rejected for the same reasons as claim 13.
Regarding claim 19, this is a Beauregard claim (i.e., "non-transitory machine-readable medium") counterpart of device claim 13, both reciting substantially similar subject matter. Accordingly, claim 19 is rejected for the same reasons as claim 13.
Regarding claim 16, LeBeau in view of Sarwar and Ye discloses the headset device of claim 13, wherein determining the user is holding the controller based on the spatial relationship comprises performing a proximity threshold test comprising a threshold distance between the controller and the one or more hands (see LeBeau, paragraphs 69, 94-100 “The keyboard passthrough hand state can be enabled when the hand state controller 438 detects that the user's hands are within a threshold distance of an MR keyboard, and this mode can include determining the contours of the user's hands and enabling passthrough so the user can see the real-world version of just their hands as they are over the MR keyboard. The ghost interactive hand state can be enabled when the hand state controller 438 detects that the user's hands are within a threshold distance of an interactive object”).
Regarding claim 8, this is a method claim counterpart of device claim 16, both reciting substantially similar subject matter. Accordingly, claim 8 is rejected for the same reasons as claim 16.
Regarding claim 9, LeBeau in view of Sarwar and Ye discloses the method of claim 8, wherein performing the proximity threshold test comprises checking shape of certain parts of the one or more hands (see LeBeau, paragraph 95, “process 1000 can identify contours of the user's hands. For example, using machine vision techniques, a virtual model of the user's hands can be continually determined to identify, for example, the hand outlines, shape, position, etc. At block 1006, process 1000 can enable passthrough for the area inside (and in some cases, a set amount around) the outline of the hand. This allows the user to see the real-world version of just their hands while in the artificial reality working environment, which provides more precise movements in relation to the MR keyboard, allowing the user a natural and seamless way to use their keyboard while remaining in the artificial reality working environment”, see also LeBeau, fig. 14, 15C, paragraphs 102, 109, activating scribing mode when contour of user’s hand is holding controller as a scribe tool).
Claims 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over LeBeau in view of Sarwar and Ye, as applied in claims 1, 13 above, and further view of Yan et al., US 20200372702 A1 (hereinafter “Yan”).
Regarding claim 17, LeBeau in view of Sarwar and Ye discloses the headset device of claim 13, wherein determining the spatial relationship uses the first model and the second model (see combination as made in rejection of independent claim 1 / 13 / 29, tracking user’s hand and control using first and second model).
LeBeau in view of Sarwar and Ye only does not specifically outline wherein the spatial relationship comprises distance, moving directions, and speed between the controller and the one or more hands.
In similar field of endeavor tracking handheld controller of artificial reality headset device (see fig. 2, abstract, headset with handheld controller), Yan discloses the concept of tracking handheld controller using velocity, as well as motion model that account for movement direction / velocity of controller to help improve tracking of handheld controller (paragraph 7, “the controller tracking sub-system can have two components, a FOV tracking component (also referred to as a constellation tracking component) and a non-FOV tracking component (also referred to as a “corner-case” tracking component) that applies specialized motion models when one or more of the controllers are not readily trackable within the field of view of sensors and cameras of an AR system. In particular, under typical operating conditions, the FOV tracking component receives HMD state data and controller measurement data (velocity, acceleration etc.) to compute image-based controller state data for a hand-held controller. If the hand-held controller is trackable (e.g., within the field of view, not occluded, and not at rest), the image-based controller state data is used to determine the controller pose and the non-FOV tracking component is bypassed. If the hand-held controller is not trackable within the field of view and the hand-held controller measurement data meets activation conditions for one or more tracking corner cases, the non-FOV tracking component applies one or more activated specialized motion models to compute model-based controller state data for one or more of the hand-held controllers. The model-based controller state data is then used to determine the controller pose.”, see paragraphs 63-85 on detail of motion model including tracking speed / acceleration and orientation of controller, “the controller measurements can include linear and angular acceleration, linear and angular velocity, and other motion related data received from controller 114 or derived from data received from controller 114. Input controller measurements 510 may also include non-image-based controller measurements generated by HMD 112, such as distance measurements obtained using radar tracking or near-field communication distance tracking”).
It would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the concept of tracking handheld controller of virtual/mixed reality headset using motion model, such as disclosed by Yan, into the device of LeBeau in view of Sarwar and Ye which track spatial relationship between hand and controller, to further improve tracking models of headset device, to constitute wherein the spatial relationship comprises distance, moving directions, and speed between the controller and the one or more hands, such is incorporation of a known concept into known device to yield predictable result, the result would have been predictable and would provide the benefit of increase tracking accuracy and robustness and improve user experience.
Regarding claim 6, this is a method claim counterpart of device claim 17, both reciting substantially similar subject matter. Accordingly, claim 6 is rejected for the same reasons as claim 17.
Claims 7 is rejected under 35 U.S.C. 103 as being unpatentable over LeBeau in view of Sarwar and Ye, as applied in claims 1 above, and further view of Nietfeld et al., US 20190138107 A1 (hereinafter “Nietfeld”).
Regarding claim 7, LeBeau in view of Sarwar and Ye discloses the method of claim 1.
LeBeau in view of Sarwar and Ye does not disclose in particular wherein determining the spatial relationship uses one or more proximity sensors, wherein the spatial relationship comprises changes in characteristics of the proximity sensors.
In similar field of endeavor of tracking virtual / mixed reality headset controller, Nietfeld disclose headset controllers may include proximity sensors to detect touch / force or lack therefor of user’s hand holding controller (paragraphs 30, 33, 107, “The controller may also include an array of proximity sensors that are spatially distributed along a length of the handle and that are responsive to a proximity of the user's fingers. The proximity sensors may include any suitable technology, such as capacitive sensors, for sensing a touch input and/or a proximity of the hand of the user relative to the controller. The array of proximity sensors may generate touch data that indicates a location of finger(s) grasping the controller or when the user is not grasping the controller, a distance disposed between the handle and the fingers of the user (e.g., through measuring capacitance). In some instances, the proximity sensors may also detect a hand size of the user grasping the controller, which may configure the controller according to different settings. For instance, depending on the hand size, the controller may adjust to make force-based input easier for users with smaller hands”, “the array of proximity sensors may detect touch input, or a lack of touch input, at the controller. The touch data may indicate the locations of the fingers of the user relative to the controller, for instance, through measuring capacitance. The capacitance may vary with the distance disposed between the finger and the controller. In doing so, the controller may detect when the user grips the controller with one finger, two fingers, three fingers, and so forth. With the capacitance, the controller may also detect the relative placement of the fingers with respect to the controller, such as when the fingers of the user are not touching the controller”, “FIG. 10B illustrates the user 1000 holding the controller 1002 with all four fingers and the thumb. Here, the touch data 124 generated by the array of proximity sensors of the controller 1002 may indicate the grasp of the user 1000. The force data 126 generated by the FSR (e.g., the FSR 900) may indicate the force in which the user 1000 grasps the controller 1002. The controller 1002 may transmit the touch data 124 and/or the force data 126 to the remote computing resource(s) 112 where the remote computing resource(s) 112 may select the model(s) 120 corresponding to the touch data 124 and/or the force data 126. The animation 128 corresponding to the model(s) 120 may generate a hand gesture that represents a closed first gesture, a grabbing gesture, and so forth”).
It would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the concept of determining hand grasping status of controller using proximity sensor, such as disclosed by Nietfeld, into the device of LeBeau in view of Sarwar and Ye which track spatial relationship between hand and controller, to further improve tracking models of headset device, to constitute wherein determining the spatial relationship uses one or more proximity sensors, wherein the spatial relationship comprises changes in characteristics of the proximity sensors, such is incorporation of a known concept into known device to yield predictable result, the result would have been predictable and would provide the benefit of increase tracking accuracy and robustness and improve user experience.
Claims 18 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over LeBeau in view of Sarwar and Ye, as applied in claims 1, 13 above, and further view of Ishikawa et al., US 20240382829 A1 (hereinafter “Ishikawa”).
Regarding claim 18, LeBeau in view of Sarwar and Ye discloses the headset device of claim 13.
LeBeau in view of Sarwar and Ye does not specifically disclose wherein the controller is a right-hand controller or a left-hand controller, and wherein determining the user is holding the controller comprises: determining a right hand of the user is holding the right-hand controller and a left hand of the user is holding the left-hand controller.
In similar field of endeavor, Ishikawa discloses the concept of distinguishing left-hand and right-hand controller and whether user is correctly holding left/right handed controller using left/right hand (paragraphs 16, 18, 21, 33, 79, 83 98, 99, “at least one first sensor 21 that outputs a value corresponding to a spatial position displacement of each finger of the user and detects whether or not each finger of the user is coming close to a surface of the grip section 11 is disposed in a position, on the grip section 11, with which the bases of the middle finger, the ring finger, and the little finger of the user are in contact when the user is holding the controller main body 10”, “According to this example, when the user holds the controller apparatus 1 mistakenly with the opposite hand (that is, when the controller apparatus 1 for the right hand is held with the left hand, for example), it is possible to issue an alarm upon detection of a user finger touch through the second sensors 22 prior to detection of a user finger touch through the first sensors 21”).
It would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the concept of distinguishing left-hand and right-hand controller and whether user is correctly holding left/right handed controller using left/right hand, such as disclosed by Ishikawa, into the device of LeBeau in view of Sarwar and Ye which track spatial relationship between hand and controller, to further improve tracking models of headset device, to constitute disclose wherein the controller is a right-hand controller or a left-hand controller, and wherein determining the user is holding the controller comprises: determining a right hand of the user is holding the right-hand controller and a left hand of the user is holding the left-hand controller, such is incorporation of a known concept into known device to yield predictable result, the result would have been predictable and would provide the benefit of increase tracking accuracy and robustness and improve user experience.
Regarding claim 10, this is a method claim counterpart of device claim 18, both reciting substantially similar subject matter. Accordingly, claim 10 is rejected for the same reasons as claim 18.
Claims 11 is rejected under 35 U.S.C. 103 as being unpatentable over LeBeau in view of Sarwar and Ye, as applied in claims 1 above, and further view of Huang et al., CN 115577332 A (hereinafter “Huang”),
Regarding claim 11, LeBeau in view of Sarwar and Ye discloses the method of claim 1.
LeBeau in view of Sarwar and Ye does not specifically disclose wherein determining the user is holding the controller comprises: distinguishing whether a hand holding the controller is the a user's hand or a non-user's hand by analyzing movement history of the hand holding the controller.
In similar field of endeavor, Huang discloses the concept of recognizing user identify of controller based on acquired information of user fingers holding controller (fig. 2, 4, controller with sensors 202 to detect user finger contact points, fig. 1, 9, paragraphs 51-57, Step 101: Obtain the first information collected by the collection unit of the electronic device; Step 101: Obtain the first information collected by the collection unit of the electronic device; Here, the first information at least includes a relative positional relationship between a plurality of first contact points; the contact points are generated by the user's fingers touching the collection unit when holding the electronic device; Step 102: matching the first information with the second information in the first database to obtain a first matching result; Here, the first database includes a plurality of user identifiers and second information corresponding to each user identifier; each second information includes at least a relative positional relationship between a plurality of second contact points; Step 103: Based on the first matching result, identify the user currently holding the electronic device).
It would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the concept of recognizing user identify of controller based on acquired information of user fingers holding controller, such as disclosed by Huang, into the device of LeBeau in view of Sarwar and Ye which track spatial relationship between hand and controller, to further improve tracking models of headset device, to constitute disclose wherein determining the user is holding the controller comprises: distinguishing whether a hand holding the controller is the a user's hand or a non-user's hand by analyzing movement history of the hand holding the controller, such is incorporation of a known concept into known device to yield predictable result, the result would have been predictable and would provide the benefit of increase tracking accuracy and robustness and improve user experience.
Claims 12 is rejected under 35 U.S.C. 103 as being unpatentable over LeBeau in view of Sarwar and Ye, as applied in claims 1 above, and further view of Wu, US 20220365589 A1 (hereinafter “Wu”).
Regarding claim 12, LeBeau in view of Sarwar and Ye discloses the method of claim 1.
LeBeau in view of Sarwar and Ye does not disclose in particular wherein switching the input mode of the headset device from the hand mode to the controller mode further comprises reducing a frequency of tracking the one or more hands using the second model.
In similar field of endeavor, Wu identifies problem of system resource constraint (paragraphs 3-5, “mainstream Virtual Reality (VR)/Augmented Reality (AR)/Mixed Reality (MR) head-mounted integrated devices can simultaneously support interaction of a gamepad tracking controller and gesture recognition. However, switching over usage of two current interactive modes needs to be manually set. Generally, switching options of the two interactive modes, i.e., an interactive mode based on gamepad tracking controller and an interactive mode based on gesture recognition are set in a User Interface (UI), through which settings are manually switched by a user. … In a traditional processing mechanism, a system receives, through an application layer, a control command instructing which module to process currently. However, in the field of VR/AR/MR, based on usage data of multiple users, it can be concluded that the usage frequency of the gamepad tracking controller is usually higher than that of gesture recognition. The usage frequency of gesture recognition is seriously affected by tedious manual switching over usage of the two interactive modes, and user experience is reduced)”, and discloses the concept of dynamically switching tracking mode / frequency of tracking between hand tracking mode and controller tracking mode, such that hand / gesture tracking is disabled when controller tracking condition is met and controller tracking is activated (fig. 1, paragraphs 36-41, “As shown in FIG. 1, in the switching method of interactive modes of a head-mounted device according to the embodiment of the present disclosure, the interactive modes of the head-mounted device include a gamepad tracking interactive mode and a bare hand tracking interactive mode which are able to be switched over each other. The switching method includes the following operations S110 to S160. In S110, 6Dof tracking data and IMU data of a gamepad are acquired, the 6Dof tracking data including position data and attitude data of the gamepad. In S120, a standard deviation of the position data, a standard deviation of the attitude data and a standard deviation of accelerometer data in the IMU data are acquired, respectively. In S130, whether the standard deviation of the position data, the standard deviation of the attitude data and the standard deviation of the accelerometer data within a current first preset duration meet a first preset condition is determined. In S140, in cases where the standard deviation of the position data, the standard deviation of the attitude data and the standard deviation of the accelerometer data meet the first preset condition, it is determined that the gamepad tracking interactive mode is not started, and the bare hand tracking interactive mode is started”).
It would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the concept of dynamically switching tracking mode / frequency of tracking between hand tracking mode and controller tracking mode, such that hand / gesture tracking is disabled when controller tracking condition is met and controller tracking is activated, such as disclosed by Wu, into the device of LeBeau in view of Sarwar and Ye which track spatial relationship between hand and controller, to constitute wherein switching the input mode of the headset device from the hand mode to the controller mode further comprises reducing a frequency of tracking the one or more hands using the second model, such is incorporation of a known concept into known device to yield predictable result, the result would have been predictable and would provide the benefit of improved system efficiency and user experience of hand/controller tracking headset.
Conclusion
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/PEIJIE SHEN/Examiner, Art Unit 2622
/PATRICK N EDOUARD/Supervisory Patent Examiner, Art Unit 2622
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