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 .
Response to Amendment
The Amendment filed June 4th, 2026 have been entered. Claims 1-7 remain pending in the application. Claims 1-7 have been amended. Claims 8-20 have been canceled. The Examiner acknowledges that the cancelation of Claims 14-20 renders the duplicate claims objection to Claims 1-7 and the 35 U.S.C. 103 rejection of Claims 8-20 moot. The amendments to the Claims have overcome each and every 112(a) and 112(b) rejection previously set forth in the Non-Final Office Action mailed March 4th, 2026.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The 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 1-5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Segura (Improved virtual reality perception with calibrated stereo and variable focus for industrial use, 2017), in view of Ye (US 9734419 B1), and Yoon (US 10277893 B1).
Regarding Claim 1, Segura teaches a calibration system for virtual reality head mounted displays (VR HMDS) having a left eye display and a right eye display (Fig. 3 demonstrates left HMD and right HMD),
with an adjustable head mounted display interpupillary distance (HMD IPD) between them, (Section 2.1, pg. 97: “This calibration step is performed only once for our CCs and is used later on for the calibration of any HMD”), comprising:
a measurement device, wherein the measurement device comprises a left camera and a right camera (Fig. 3 demonstrates an HMD measurement device with a left and right camera),
wherein the left camera as well as the right camera are intrinsically and extrinsically calibrated (Section 2.1: “The camera pair is previously calibrated, intrinsically (each camera) and extrinsically (the right camera with respect to the left)”);
wherein the left camera as well as the right camera are mounted within the system (Fig. 3 demonstrates a left and right camera mounted on an apparatus);
creating a virtual target object at a specific chosen position in a space in the field of view of the measurement device, (Fig. 3: “HMD stereo projections calibration. A pair of rigidly attached calibration cameras (CC) substitute the eyes. The HMD displays’ misalignment is exaggerated in the illustration”, where Figure 3 also demonstrates a target point X as perceived on the left and right displays of the HMD in relation to the measurement device)
wherein the specific target position has a chosen distance from the measurement device (Fig. 3 Clearly demonstrates that x, y, z axis is centered with respect to the camera. Therefore, distance to a point X in 3D space is implicit);
generating and transmitting the images of said virtual target object to the displays of the VR HMD (Fig. 3, Fig. 4 clearly demonstrate virtual targets which are implicitly calculated and transmitted since they have been rendered and displayed on the HMD; Section 2.1, pg. 98: “Our calibration process is based on the projection of an arbitrary point in 3D space onto two different image planes, each with its own coordinate system: the HMD displays coordinate systems and the calibration cameras coordinate systems. In the equations in this section xcam will denote points projected on a CC image, and xdisp will denote points projected on an HMD display. Both are in homogeneous coordinates, as they are used in projective geometry equations”);
representing the virtual target object on the displays of the VR HMD (Fig. 3, Fig. 4 clearly demonstrate a virtual target object on the displays of the VR HMD);
taking the images of the displays with the left camera as well as the right camera (Fig. 4 clearly demonstrates an image of the displays; Section 2, pg. 97: “The camera captures a pattern presented in the HMD display. From analysis of the captured pattern, they compute a mapping between the camera image and the HMD display coordinates”);
detecting the virtual target object within the images of the displays (Section 2.1, d, pg. 99: “We know the display coordinates of the points, as we have rendered them, and the user marks their corresponding camera image coordinates”);
Segura does not teach a validation system per se, nor a VR HMD that is attachable to it in the same way as the VR HMD would be removably attached to the head of a user; It does not teach the mounting of the left and right camera to adjust the camera IPD between the left and the right camera; it does not explicitly teach a control unit that connects to the left as well as the right camera, wherein the control unit is configured to deliver control signals to the VR HMD, when the VR HMD is removably attached to the measurement device and when the HMD IPD of the VR HMD is manually or automatically adjusted to the prechosen camera IPD, wherein the control unit is also configured to perform a validation measurement method. It does not explicitly teach determining the distance from the left camera as well as from the right camera via a triangulation, and does not compare the determined distance with the chosen distance, and make a subsequent validation of the chosen distance, where the difference or relative error between the determined distance and the chosen distance is inside a validation interval, or denying the distance validation, if said difference or relative error is outside said interval.
However, while Segura does not explicitly teach that its validation system is attachable to the HMD, the broadest reasonable interpretation of removably attached to the head of a user would just mean anything that fits inside the HMD, and can be secured via a fixation element such as a strap; this is implicit in Segura, considering the device is placed close to/within the HMD for viewing the left and right displays.
Furthermore, Ye teaches a validation system with a control unit configured to perform a validation method (Drawing of pg. 1 demonstrates a system with a computer which is obvious in the art as a controller),
where the validation system determines the distance from the left camera as well as from the right camera via a triangulation, compares it to the target distance to determine a distance error or relative error, and passes a subsequent distance validation test for the VR HMD of the target distance, when the difference or relative error between the determined distance and the target distance is inside a validation interval, or failing the distance validation test, if said difference or relative error is outside said interval. (Column 5, Lines 58-67: “By way of further illustration, and as provided in the Tsai publication, FIG. 1A illustrates the basic geometry of the camera model. (X.sub.w,Y.sub.w,Z.sub.w), defining the 3D coordinates of the object point P in the 3D world coordinate system. (X,Y,Z) define the 3D coordinates 185 of the object point P in the 3D camera coordinate system 188, which is centered at point O, the optical center, with the z axis the same as the optical axis (see also FIG. 1). (x,y) is the image coordinate system centered at O.sub.i (intersection of the optical axis z and the front image plane 186) and parallel to x and y axes”; Fig. 1A also illustrates the point in relation to the camera in terms of its optical center and orientation with regards to the xyz axis. Column 9, Lines 22-51: “The accuracy of the calibration of an embodiment with two or more cameras with a common viewing area can be validated by acquiring a single image of the calibration object that is substantially similar to the one used for calibration (e.g. calibration object 170). The features of the calibration object are extracted from each image, using in an embodiment the above-described checkerboard feature extractor vision system software tool. Once the features are extracted, the correspondence between the features is established. In the case of the checkerboard calibration plate, a fiducial on the plate helps identify correspondence between features in the two images. Using the triangulation procedure described above (referencing FIG. 1B), the positions of these features in the 3D world coordinate system are computed. Given n corresponding points all the images, after triangulation, there will be n points computed in the world coordinate system, denoted by X.sub.i.sup.extracted, 1≦i≦n. Ideally, if the cameras are perfectly calibrated, in the absence of noise the rays obtained during triangulation would intersect at one point (see FIG. 1B). However, in practice the rays are not guaranteed to intersect. The triangulation discrepancy or residual is computed as the sum of shortest distance between the triangulated point P and the ray (R.sub.1 and R.sub.2) from each camera that was used to compute the triangulated point. In this embodiment, the root mean square (RMS) value of this parameter for all the feature points is computed using the images acquired during validation. This value is compared to the value obtained for the calibration images. If it is above a certain acceptance threshold, then the user is asked to repeat the calibration” Notes: Ye teaches a calibration system similar in structure to that of Segura, with 2 cameras focused on calibration target that is represented in 3D space in relation to cameras. Considering the xyz system is oriented with respect to the camera, triangulating the position of a target point P within the 3D space is in relation to the cameras, and therefore, distance is also inherent with the triangulation of point P, since the origin of the system of coordinates is at the camera(s). With regards to the validation process, the triangulated point P is compared to the intersect of the two rays, such that if the distance between the two points is greater than a threshold, the calibration is repeated. The threshold can be represented as a threshold range instead, where the direction of the distance is relevant. A validation system is interpreted to be a system that performs validation).
Segura and Ye are considered analogous in the art with regards to calibration of twin camera systems using target calibration points. Validation of results is a well-known motivation within the art, as ensuring proper calibration via validation of a test value (in which a point and distance to a point are inherent to each other in triangulation as demonstrated above) enables the efficient function of the system via verification of known variables. Furthermore, control units for systems that perform triangulation are obvious in the art; a motivation for using a control unit for triangulation is to automate the task and make it more efficient.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the calibration unit of Segura with the validation system with a control unit and process of Ye; Doing so would yield the predictable result of verifying proper calibration of the system, as well as double checking known variables, and automating the triangulation process.
Furthermore, Yoon teaches a measurement device comprising of a left camera and a right camera, wherein the left camera as well as the right camera can move to adjust the camera IPD between the left camera and the right camera (Col 9, Line 61-67, Col 10, Line 1-16: “In one embodiment, the controller 450 generate first imaging instructions for the characterization camera 430 and second instructions for the characterization camera 440. The first imaging instruction include positions for the characterization camera 430, and the second imaging instructions include corresponding positions of the second camera characterization 440. The characterization cameras 430 and 440 move to each position in the first and second imaging instructions, resulting in a different IPD value. The first and second imaging instructions further provide a number of exposures for each position of the characterization cameras 430 and 440. Sometimes, one of the two characterization cameras 430 and 440 moves and the other one does not move. For example, a position of the characterization camera 430 corresponds to multiple positions of the characterization camera 440 (or the other way), resulting in multiple IPD values. The characterization camera 430 may capture multiple images at the position through multiple exposures so that the characterization camera 430 captures at least one image for each IPD value. The characterization cameras 430 and 440 can each be the characterization camera described in conjunction with FIG. 3”; Fig. 4 illustrates the movement capacity of the cameras)
with a controller that is attached to both cameras, as well as the VR HMD (Drawing on pg 1 clearly illustrates a controller attached to the HMD and camera assembly),
wherein the control unit is configured to deliver control signals to the VR HMD, when the VR HMD is attached to the measurement device and when the HMD IPD of the VR HMD is manually or automatically adjusted to the prechosen camera IPD (Col 9, Lines 33-41: “The controller 450 provides presenting instructions that cause the HMD under test 410 to present test patterns and imaging instructions that cause the camera assembly 420 to captures images of the test patterns. In some embodiments, the presenting instructions and imaging instructions are received from a user of the HMD under test 410. Alternatively, the presenting instructions and imaging instructions are generated by the controller 450, e.g., based on input parameters received by the controller 450”. Notes: attached in its broadest reasonable interpretation is any connection, which is implicit in the controller being connected to the VR HMD for sending instructions. Additionally, HMDs are well established in the art as being adjustable to IPD).
Segura and Yoon are considered analogous in the art with regards to the utilization of a left and right camera representing the eyes of a user in conjunction with HMDs for measurement purposes. A motivation for implementing adjustable left and right cameras would be to better represent the position of the eyes of a specific person. Additionally, control units for systems that are attached to both an HMD with adjustable IPD and cameras are obvious in the art with regards to being able to control the HMD with adjustable IPD. A motivation for doing so would be to consolidate control of the system to a single control unit, as well as automate the tasks being performed.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the calibration system of Segura with the validation system with a control unit and validation method of Yoon; Doing so would yield the predictable result of allowing the system of Segura to better model the IPD of a specific person, as well as consolidate the operation and control of the system to a single control unit.
Regarding Claim 2, the system of Claim 1 is rejected over Segura as modified.
Segura as modified teaches a validation measurement (Ye, Column 5, Lines 58-67: “By way of further illustration, and as provided in the Tsai publication, FIG. 1A illustrates the basic geometry of the camera model. (X.sub.w,Y.sub.w,Z.sub.w), defining the 3D coordinates of the object point P in the 3D world coordinate system. (X,Y,Z) define the 3D coordinates 185 of the object point P in the 3D camera coordinate system 188, which is centered at point O, the optical center, with the z axis the same as the optical axis (see also FIG. 1). (x,y) is the image coordinate system centered at O.sub.i (intersection of the optical axis z and the front image plane 186) and parallel to x and y axes”; Fig. 1A also illustrates the point in relation to the camera in terms of its optical center and orientation with regards to the xyz axis. Ye, Column 9, Lines 22-51: “The accuracy of the calibration of an embodiment with two or more cameras with a common viewing area can be validated by acquiring a single image of the calibration object that is substantially similar to the one used for calibration (e.g. calibration object 170). The features of the calibration object are extracted from each image, using in an embodiment the above-described checkerboard feature extractor vision system software tool. Once the features are extracted, the correspondence between the features is established. In the case of the checkerboard calibration plate, a fiducial on the plate helps identify correspondence between features in the two images. Using the triangulation procedure described above (referencing FIG. 1B), the positions of these features in the 3D world coordinate system are computed. Given n corresponding points all the images, after triangulation, there will be n points computed in the world coordinate system, denoted by X.sub.i.sup.extracted, 1≦i≦n. Ideally, if the cameras are perfectly calibrated, in the absence of noise the rays obtained during triangulation would intersect at one point (see FIG. 1B). However, in practice the rays are not guaranteed to intersect. The triangulation discrepancy or residual is computed as the sum of shortest distance between the triangulated point P and the ray (R.sub.1 and R.sub.2) from each camera that was used to compute the triangulated point. In this embodiment, the root mean square (RMS) value of this parameter for all the feature points is computed using the images acquired during validation. This value is compared to the value obtained for the calibration images. If it is above a certain acceptance threshold, then the user is asked to repeat the calibration”; Notes: Ye teaches a calibration system similar in structure to that of Segura, with 2 cameras focused on calibration target that is represented in 3D space in relation to cameras. Considering the xyz system is oriented with respect to the camera, triangulating the position of a target point P within the 3D space is in relation to the cameras, and therefore, distance is also inherent with the triangulation of point P, since the origin of the system of coordinates is at the camera(s). With regards to the validation process, the triangulated point P is compared to the intersect of the two rays, such that if the distance between the two points is greater than a threshold, the calibration is repeated. The threshold can be represented as a threshold range instead, where the direction of the distance is relevant. A validation system is interpreted to be a system that performs validation)
conducted for a plurality of positions of a virtual target object having different predetermined virtual distances from the validation system and optionally different positions in the field of view of the VR HMD (Segura, Section 2.1, c, 98-99: “For each of the left and right sides, any virtual 3D point X is projected onto a point xdisp in the display coordinate system as expressed in Eq. 3. The same 3D point is projected onto a point xcam in the camera image coordinate system, as expressed in Eq. 1. These two projected points have to be equivalent but they are in different coordinate systems so their equations cannot be combined. A mapping between these two coordinate systems is needed to solve the problem. In a low distortion environment this mapping can be approximated by a 3 × 3 transform matrix M in homogenous coordinates (i.e. a homography) as shown in (Eq. 4)”; Segura, Section 2.1, e, 99: where xi for i = 1...N are the projections of a set of arbitrary virtual 3D points Xi… [the] set of N random points in the space in front of the viewer and visible by both eyes and projects them with the current value of the projection parameters)
and an IPD validation is only passed if the distance validation of all or of a predetermined percentage of distance validations are passed (Ye, Column 9, Lines 22-51: “The accuracy of the calibration of an embodiment with two or more cameras with a common viewing area can be validated by acquiring a single image of the calibration object that is substantially similar to the one used for calibration (e.g. calibration object 170). The features of the calibration object are extracted from each image, using in an embodiment the above-described checkerboard feature extractor vision system software tool. Once the features are extracted, the correspondence between the features is established. In the case of the checkerboard calibration plate, a fiducial on the plate helps identify correspondence between features in the two images. Using the triangulation procedure described above (referencing FIG. 1B), the positions of these features in the 3D world coordinate system are computed. Given n corresponding points all the images, after triangulation, there will be n points computed in the world coordinate system, denoted by X.sub.i.sup.extracted, 1≦i≦n. Ideally, if the cameras are perfectly calibrated, in the absence of noise the rays obtained during triangulation would intersect at one point (see FIG. 1B). However, in practice the rays are not guaranteed to intersect. The triangulation discrepancy or residual is computed as the sum of shortest distance between the triangulated point P and the ray (R.sub.1 and R.sub.2) from each camera that was used to compute the triangulated point. In this embodiment, the root mean square (RMS) value of this parameter for all the feature points is computed using the images acquired during validation. This value is compared to the value obtained for the calibration images. If it is above a certain acceptance threshold, then the user is asked to repeat the calibration”; Notes: Given that a method of validation is available for one point, it would obvious to a person having ordinary skill in the art to base validation of a plurality of points off of some threshold for the successful validation of said plurality of points).
Regarding Claim 3, the system according to Claim 2 is rejected over Segura as modified.
Segura as modified teaches a control unit configured to conduct the validation measurement (Ye, Drawing of pg. 1 demonstrates a system with a computer which is obvious in the art as a controller; Ye, Column 5, Lines 58-67: “By way of further illustration, and as provided in the Tsai publication, FIG. 1A illustrates the basic geometry of the camera model. (X.sub.w,Y.sub.w,Z.sub.w), defining the 3D coordinates of the object point P in the 3D world coordinate system. (X,Y,Z) define the 3D coordinates 185 of the object point P in the 3D camera coordinate system 188, which is centered at point O, the optical center, with the z axis the same as the optical axis (see also FIG. 1). (x,y) is the image coordinate system centered at O.sub.i (intersection of the optical axis z and the front image plane 186) and parallel to x and y axes”; Fig. 1A also illustrates the point in relation to the camera in terms of its optical center and orientation with regards to the xyz axis. Ye, Column 9, Lines 22-51: “The accuracy of the calibration of an embodiment with two or more cameras with a common viewing area can be validated by acquiring a single image of the calibration object that is substantially similar to the one used for calibration (e.g. calibration object 170). The features of the calibration object are extracted from each image, using in an embodiment the above-described checkerboard feature extractor vision system software tool. Once the features are extracted, the correspondence between the features is established. In the case of the checkerboard calibration plate, a fiducial on the plate helps identify correspondence between features in the two images. Using the triangulation procedure described above (referencing FIG. 1B), the positions of these features in the 3D world coordinate system are computed. Given n corresponding points all the images, after triangulation, there will be n points computed in the world coordinate system, denoted by X.sub.i.sup.extracted, 1≦i≦n. Ideally, if the cameras are perfectly calibrated, in the absence of noise the rays obtained during triangulation would intersect at one point (see FIG. 1B). However, in practice the rays are not guaranteed to intersect. The triangulation discrepancy or residual is computed as the sum of shortest distance between the triangulated point P and the ray (R.sub.1 and R.sub.2) from each camera that was used to compute the triangulated point. In this embodiment, the root mean square (RMS) value of this parameter for all the feature points is computed using the images acquired during validation. This value is compared to the value obtained for the calibration images. If it is above a certain acceptance threshold, then the user is asked to repeat the calibration”; Notes: Ye teaches a calibration system similar in structure to that of Segura, with 2 cameras focused on calibration target that is represented in 3D space in relation to cameras. Considering the xyz system is oriented with respect to the camera, triangulating the position of a target point P within the 3D space is in relation to the cameras, and therefore, distance is also inherent with the triangulation of point P, since the origin of the system of coordinates is at the camera(s). With regards to the validation process, the triangulated point P is compared to the intersect of the two rays, such that if the distance between the two points is greater than a threshold, the calibration is repeated. The threshold can be represented as a threshold range instead, where the direction of the distance is relevant. A validation system is interpreted to be a system that performs validation))
for at least two of the plurality of virtual target objects in one pass (Segura, Section 2.1, c, 98-99: “For each of the left and right sides, any virtual 3D point X is projected onto a point xdisp in the display coordinate system as expressed in Eq. 3. The same 3D point is projected onto a point xcam in the camera image coordinate system, as expressed in Eq. 1. These two projected points have to be equivalent but they are in different coordinate systems so their equations cannot be combined. A mapping between these two coordinate systems is needed to solve the problem. In a low distortion environment this mapping can be approximated by a 3 × 3 transform matrix M in homogenous coordinates (i.e. a homography) as shown in (Eq. 4)”; Segura, Section 2.1, e, 99: where xi for i = 1...N are the projections of a set of arbitrary virtual 3D points Xi… [the] set of N random points in the space in front of the viewer and visible by both eyes and projects them with the current value of the projection parameters; Segura, Section 2.1, e, 99: “In each iteration the algorithm uses a set of N random points in the space” Notes: Given that a method of validation is available for one virtual point, it would obvious to a person having ordinary skill in the art to validate additional virtual points).
Regarding Claim 4, the system of Claim 1 is rejected over Segura as modified.
Segura as modified teaches a validation measurement (Ye, Column 5, Lines 58-67: “By way of further illustration, and as provided in the Tsai publication, FIG. 1A illustrates the basic geometry of the camera model. (X.sub.w,Y.sub.w,Z.sub.w), defining the 3D coordinates of the object point P in the 3D world coordinate system. (X,Y,Z) define the 3D coordinates 185 of the object point P in the 3D camera coordinate system 188, which is centered at point O, the optical center, with the z axis the same as the optical axis (see also FIG. 1). (x,y) is the image coordinate system centered at O.sub.i (intersection of the optical axis z and the front image plane 186) and parallel to x and y axes”; Fig. 1A also illustrates the point in relation to the camera in terms of its optical center and orientation with regards to the xyz axis. Ye, Column 9, Lines 22-51: “The accuracy of the calibration of an embodiment with two or more cameras with a common viewing area can be validated by acquiring a single image of the calibration object that is substantially similar to the one used for calibration (e.g. calibration object 170). The features of the calibration object are extracted from each image, using in an embodiment the above-described checkerboard feature extractor vision system software tool. Once the features are extracted, the correspondence between the features is established. In the case of the checkerboard calibration plate, a fiducial on the plate helps identify correspondence between features in the two images. Using the triangulation procedure described above (referencing FIG. 1B), the positions of these features in the 3D world coordinate system are computed. Given n corresponding points all the images, after triangulation, there will be n points computed in the world coordinate system, denoted by X.sub.i.sup.extracted, 1≦i≦n. Ideally, if the cameras are perfectly calibrated, in the absence of noise the rays obtained during triangulation would intersect at one point (see FIG. 1B). However, in practice the rays are not guaranteed to intersect. The triangulation discrepancy or residual is computed as the sum of shortest distance between the triangulated point P and the ray (R.sub.1 and R.sub.2) from each camera that was used to compute the triangulated point. In this embodiment, the root mean square (RMS) value of this parameter for all the feature points is computed using the images acquired during validation. This value is compared to the value obtained for the calibration images. If it is above a certain acceptance threshold, then the user is asked to repeat the calibration”; Notes: Ye teaches a calibration system similar in structure to that of Segura, with 2 cameras focused on calibration target that is represented in 3D space in relation to cameras. Considering the xyz system is oriented with respect to the camera, triangulating the position of a target point P within the 3D space is in relation to the cameras, and therefore, distance is also inherent with the triangulation of point P, since the origin of the system of coordinates is at the camera(s). With regards to the validation process, the triangulated point P is compared to the intersect of the two rays, such that if the distance between the two points is greater than a threshold, the calibration is repeated. The threshold can be represented as a threshold range instead, where the direction of the distance is relevant. A validation system is interpreted to be a system that performs validation))
comprising the distance validation of distances for a plurality of different HMD IPDs (Yoon, Col 9, Line 61-67, Col 10, Line 1-16: “In one embodiment, the controller 450 generate first imaging instructions for the characterization camera 430 and second instructions for the characterization camera 440. The first imaging instruction include positions for the characterization camera 430, and the second imaging instructions include corresponding positions of the second camera characterization 440. The characterization cameras 430 and 440 move to each position in the first and second imaging instructions, resulting in a different IPD value. The first and second imaging instructions further provide a number of exposures for each position of the characterization cameras 430 and 440. Sometimes, one of the two characterization cameras 430 and 440 moves and the other one does not move. For example, a position of the characterization camera 430 corresponds to multiple positions of the characterization camera 440 (or the other way), resulting in multiple IPD values. The characterization camera 430 may capture multiple images at the position through multiple exposures so that the characterization camera 430 captures at least one image for each IPD value. The characterization cameras 430 and 440 can each be the characterization camera described in conjunction with FIG. 3”. Notes: considering Segura as modified teaches a left and right camera that can adjust IPD of the measurement device, the measurement device is clearly capable of performing the distance validation for any of the different IPDs as adjusted by the measurement device).
wherein the distance validation is performed with a reduction of the target HMD IPD, starting from the highest possible IPD of the VR HMD to the lowest possible IPD of the VR HMD providing a IPD range validation for each IPD value of the VR HMD, or vice versa (Yoon, Col 4, Lines 49-67, Col 5, Lines 1-9: “The camera assembly 350 includes one or more characterization cameras that capture images of test patterns presented by the HMD under test 310 (i.e., images presented by an electronic display through one or more lenses) in accordance with imaging instructions. A characterization camera is a camera configured to mimic a human eye that is used to characterize lenses of a HMD under test. A characterization camera is configured to mimic movement of the human eye, optical qualities of a human eye, physical dimensions of a human eye, or some combination thereof. For example, the characterization camera may have multiple degrees of freedom of movement in order to, e.g., change orientation about a center of rotation in the same manner as a human eye changes orientation. And different positions (e.g., orientations) of the characterization camera could correspond to different gaze angles of a human eye. Additionally, in some embodiments where there are two characterization cameras to mimic the left and right eyes of a user, the two characterization cameras are able to translate relative to each other to, e.g., measure effects of inter-pupillary distance (IPD) on the device under test. For example, an IPD between the two characterization cameras may be adjusted over some range of values. In alternate embodiments, the IPD may be fixed at a particular distance (e.g., 63.5 mm). In some embodiments, a characterization camera may translate away from or closer to the device under test. This would, e.g., measure effects of different eye relief on the images presented by the device under test”. Notes: Segura as modified teaches adjusting IPD over a range of values; it would be obvious to a person having ordinary skill in the art that adjusting the IPD over a range between the minimum and maximum IPD values is included in the idea of adjusting the IPD over a range of values).
wherein the device validation is only passed if the IPD range validation of all or of a predetermined percentage of IPD interval validations are passed (Ye, Column 9, Lines 22-51: “The accuracy of the calibration of an embodiment with two or more cameras with a common viewing area can be validated by acquiring a single image of the calibration object that is substantially similar to the one used for calibration (e.g. calibration object 170). The features of the calibration object are extracted from each image, using in an embodiment the above-described checkerboard feature extractor vision system software tool. Once the features are extracted, the correspondence between the features is established. In the case of the checkerboard calibration plate, a fiducial on the plate helps identify correspondence between features in the two images. Using the triangulation procedure described above (referencing FIG. 1B), the positions of these features in the 3D world coordinate system are computed. Given n corresponding points all the images, after triangulation, there will be n points computed in the world coordinate system, denoted by X.sub.i.sup.extracted, 1≦i≦n. Ideally, if the cameras are perfectly calibrated, in the absence of noise the rays obtained during triangulation would intersect at one point (see FIG. 1B). However, in practice the rays are not guaranteed to intersect. The triangulation discrepancy or residual is computed as the sum of shortest distance between the triangulated point P and the ray (R.sub.1 and R.sub.2) from each camera that was used to compute the triangulated point. In this embodiment, the root mean square (RMS) value of this parameter for all the feature points is computed using the images acquired during validation. This value is compared to the value obtained for the calibration images. If it is above a certain acceptance threshold, then the user is asked to repeat the calibration”; Notes: Given that a method of validation is available for one IPD, it would be obvious to a person having ordinary skill in the art to base validation of multiple IPDs in a device capable of adjusting the IPD across a range (which Segura as modified is capable of) off of a threshold of acceptance).
Regarding Claim 5, the system of Claim 4 is rejected over Segura as modified.
Segura as modified teaches a validation measurement (Ye, Column 5, Lines 58-67: “By way of further illustration, and as provided in the Tsai publication, FIG. 1A illustrates the basic geometry of the camera model. (X.sub.w,Y.sub.w,Z.sub.w), defining the 3D coordinates of the object point P in the 3D world coordinate system. (X,Y,Z) define the 3D coordinates 185 of the object point P in the 3D camera coordinate system 188, which is centered at point O, the optical center, with the z axis the same as the optical axis (see also FIG. 1). (x,y) is the image coordinate system centered at O.sub.i (intersection of the optical axis z and the front image plane 186) and parallel to x and y axes”; Fig. 1A also illustrates the point in relation to the camera in terms of its optical center and orientation with regards to the xyz axis. Ye, Column 9, Lines 22-51: “The accuracy of the calibration of an embodiment with two or more cameras with a common viewing area can be validated by acquiring a single image of the calibration object that is substantially similar to the one used for calibration (e.g. calibration object 170). The features of the calibration object are extracted from each image, using in an embodiment the above-described checkerboard feature extractor vision system software tool. Once the features are extracted, the correspondence between the features is established. In the case of the checkerboard calibration plate, a fiducial on the plate helps identify correspondence between features in the two images. Using the triangulation procedure described above (referencing FIG. 1B), the positions of these features in the 3D world coordinate system are computed. Given n corresponding points all the images, after triangulation, there will be n points computed in the world coordinate system, denoted by X.sub.i.sup.extracted, 1≦i≦n. Ideally, if the cameras are perfectly calibrated, in the absence of noise the rays obtained during triangulation would intersect at one point (see FIG. 1B). However, in practice the rays are not guaranteed to intersect. The triangulation discrepancy or residual is computed as the sum of shortest distance between the triangulated point P and the ray (R.sub.1 and R.sub.2) from each camera that was used to compute the triangulated point. In this embodiment, the root mean square (RMS) value of this parameter for all the feature points is computed using the images acquired during validation. This value is compared to the value obtained for the calibration images. If it is above a certain acceptance threshold, then the user is asked to repeat the calibration”; Notes: Ye teaches a calibration system similar in structure to that of Segura, with 2 cameras focused on calibration target that is represented in 3D space in relation to cameras. Considering the xyz system is oriented with respect to the camera, triangulating the position of a target point P within the 3D space is in relation to the cameras, and therefore, distance is also inherent with the triangulation of point P, since the origin of the system of coordinates is at the camera(s). With regards to the validation process, the triangulated point P is compared to the intersect of the two rays, such that if the distance between the two points is greater than a threshold, the calibration is repeated. The threshold can be represented as a threshold range instead, where the direction of the distance is relevant. A validation system is interpreted to be a system that performs validation)
that further comprises the validation of distances for the same plurality of different HMD IPDs a second time (Yoon, Col 9, Line 61-67, Col 10, Line 1-16: “In one embodiment, the controller 450 generate first imaging instructions for the characterization camera 430 and second instructions for the characterization camera 440. The first imaging instruction include positions for the characterization camera 430, and the second imaging instructions include corresponding positions of the second camera characterization 440. The characterization cameras 430 and 440 move to each position in the first and second imaging instructions, resulting in a different IPD value. The first and second imaging instructions further provide a number of exposures for each position of the characterization cameras 430 and 440. Sometimes, one of the two characterization cameras 430 and 440 moves and the other one does not move. For example, a position of the characterization camera 430 corresponds to multiple positions of the characterization camera 440 (or the other way), resulting in multiple IPD values. The characterization camera 430 may capture multiple images at the position through multiple exposures so that the characterization camera 430 captures at least one image for each IPD value. The characterization cameras 430 and 440 can each be the characterization camera described in conjunction with FIG. 3”. Notes: considering Segura as modified teaches a left and right camera that can adjust IPD of the measurement device, the measurement device is clearly capable of performing the distance validation for any of the different IPDs as adjusted by the measurement device. Furthermore, it would be obvious to a person having ordinary skill in the art that performing the validations on a range of values can be performed any number of times, and doing so would be akin to “double checking” or “triple checking” results, which is obvious in the art),
wherein the validation is performed with an increase of the target HMD IPD, starting from the lowest possible IPD of the VR HMD to the highest possible IPD of the VR HMD, or vice versa (Yoon, Col 4, Lines 49-67, Col 5, Lines 1-9: “The camera assembly 350 includes one or more characterization cameras that capture images of test patterns presented by the HMD under test 310 (i.e., images presented by an electronic display through one or more lenses) in accordance with imaging instructions. A characterization camera is a camera configured to mimic a human eye that is used to characterize lenses of a HMD under test. A characterization camera is configured to mimic movement of the human eye, optical qualities of a human eye, physical dimensions of a human eye, or some combination thereof. For example, the characterization camera may have multiple degrees of freedom of movement in order to, e.g., change orientation about a center of rotation in the same manner as a human eye changes orientation. And different positions (e.g., orientations) of the characterization camera could correspond to different gaze angles of a human eye. Additionally, in some embodiments where there are two characterization cameras to mimic the left and right eyes of a user, the two characterization cameras are able to translate relative to each other to, e.g., measure effects of inter-pupillary distance (IPD) on the device under test. For example, an IPD between the two characterization cameras may be adjusted over some range of values. In alternate embodiments, the IPD may be fixed at a particular distance (e.g., 63.5 mm). In some embodiments, a characterization camera may translate away from or closer to the device under test. This would, e.g., measure effects of different eye relief on the images presented by the device under test”. Notes: Segura as modified teaches adjusting IPD over a range of values; it would be obvious to a person having ordinary skill in the art that adjusting the IPD over a range between the minimum and maximum IPD values is included in the idea of adjusting the IPD over a range of values), and
the hysteresis validation is only passed if the difference or relative error between the IPD range validation value of the upward adjusted HMD IPD and the IPD range validation value of the downward adjusted HMD IPD is inside a hysteresis validation interval (Ye, Column 9, Lines 22-51: “The accuracy of the calibration of an embodiment with two or more cameras with a common viewing area can be validated by acquiring a single image of the calibration object that is substantially similar to the one used for calibration (e.g. calibration object 170). The features of the calibration object are extracted from each image, using in an embodiment the above-described checkerboard feature extractor vision system software tool. Once the features are extracted, the correspondence between the features is established. In the case of the checkerboard calibration plate, a fiducial on the plate helps identify correspondence between features in the two images. Using the triangulation procedure described above (referencing FIG. 1B), the positions of these features in the 3D world coordinate system are computed. Given n corresponding points all the images, after triangulation, there will be n points computed in the world coordinate system, denoted by X.sub.i.sup.extracted, 1≦i≦n. Ideally, if the cameras are perfectly calibrated, in the absence of noise the rays obtained during triangulation would intersect at one point (see FIG. 1B). However, in practice the rays are not guaranteed to intersect. The triangulation discrepancy or residual is computed as the sum of shortest distance between the triangulated point P and the ray (R.sub.1 and R.sub.2) from each camera that was used to compute the triangulated point. In this embodiment, the root mean square (RMS) value of this parameter for all the feature points is computed using the images acquired during validation. This value is compared to the value obtained for the calibration images. If it is above a certain acceptance threshold, then the user is asked to repeat the calibration”; Notes: Given that a method of validation is available for one IPD, it would be obvious to a person having ordinary skill in the art to base validation of multiple IPDs in a device capable of adjusting the IPD across a range (which Segura as modified is capable of) off of a threshold of acceptance. The broadest reasonable interpretation of a hysteresis validation is checking whether the cumulative error of distance validations of the IPDs from high to low or vice versa of two validations are within some interval of one another. Segura as modified teaches validation with regards to a threshold interval, which can similarly be applied as a hysteresis validation interval on the cumulative errors from the two validations across a range of IPD values).
Regarding Claim 7, the system of Claim 1 is rejected over Segura as modified.
Segura as modified teaches a left camera as well as a right camera are mounted within the validation system (Segura, Fig. 3 demonstrates a left and right camera mounted on an apparatus)
for a movement of the left camera as well as of the right camera are to adjust the relief distance of the left camera as well as of the right camera from the VR HMD when removably attached, creating camera-eye HMD display distance deviating validation results (Yoon, Col 5, lines 5-9: “In some embodiments, a characterization camera may translate away from or closer to the device under test. This would, e.g., measure effects of different eye relief on the images presented by the device under test” Notes: the broadest reasonable interpretation of removably attached to the head of a user would just mean anything that fits inside the HMD, and can be secured via a fixation element such as a strap; this is implicit in Segura, considering the device is placed close to/within the HMD for viewing the left and right displays).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Segura (Improved virtual reality perception with calibrated stereo and variable focus for industrial use, 2017) in view of Ye (US 9734419 B1) and Yoon (US 10277893 B1), in further view of Fang (US 10735710 B2).
Regarding Claim 6, the system of Claim 1 is rejected over Segura as modified.
Segura as modified teaches a left camera as well as a right camera are mounted within the validation system for a movement of the left camera as well as the right camera, when the validation system is removably attached (Segura, Fig. 3 demonstrates a left and right camera mounted on an apparatus; Yoon, Col 9, Line 61-67, Col 10, Line 1-16: “In one embodiment, the controller 450 generate first imaging instructions for the characterization camera 430 and second instructions for the characterization camera 440. The first imaging instruction include positions for the characterization camera 430, and the second imaging instructions include corresponding positions of the second camera characterization 440. The characterization cameras 430 and 440 move to each position in the first and second imaging instructions, resulting in a different IPD value. The first and second imaging instructions further provide a number of exposures for each position of the characterization cameras 430 and 440. Sometimes, one of the two characterization cameras 430 and 440 moves and the other one does not move. For example, a position of the characterization camera 430 corresponds to multiple positions of the characterization camera 440 (or the other way), resulting in multiple IPD values. The characterization camera 430 may capture multiple images at the position through multiple exposures so that the characterization camera 430 captures at least one image for each IPD value. The characterization cameras 430 and 440 can each be the characterization camera described in conjunction with FIG. 3”; Yoon, Fig. 4 illustrates the movement capacity of the cameras; Notes: the broadest reasonable interpretation of removably attached to the head of a user would just mean anything that fits inside the HMD, and can be secured via a fixation element such as a strap; this is implicit in Segura, considering the device is placed close to/within the HMD for viewing the left and right displays).
Segura as modified does not teach adjusting the height position of the left camera as well as the right camera vis-à-vis the height of the horizontal centreline of the VR HMD, creating height deviating from the Design Eye Point validation results.
However, Fang teaches adjusting the height position of the left camera as well as the right camera (Col 3 lines 39 – 46: “As the first camera 12 and the second camera 14 are arranged along the second direction D2, which is not shown in figures, and arrangement of the first camera 12 and the second camera 14 along the first direction D1 is slightly deviated relative to the original production design, the operational processor 16 can execute the stereo vision image calibration procedure of the present invention to repair and acquire the correct stereo vision image computation result”; Figure 2 also illustrates the vertical adjustment of the cameras)
Segura as modified and Fang are considered analogous in the art with regards to the calibration of cameras. A common motivation within the art is to adjust the positioning of cameras during the calibration to account for how the cameras perceive the calibration target; this is evident in Segura as modified as well, where adjustments to the camera positioning have been made horizontally.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the validation system of Segura as modified with the ability to adjust the height position of the left camera as well as the right camera of Fang; Doing so would yield the predictable result of a validation system that is more effectively calibrated due to increased camera position variance.
While Segura as modified with Fang does not explicitly teach adjusting the position of the left and right cameras vis-à-vis the horizontal centreline of the VR HMD creating height deviating from the Design Eye Point validation results, It should be noted that a validation system with a left and right camera capable of vertical adjustment would inherently have the ability to adjust the left and right camera vis-à-vis the horizontal centreline of the VR HMD, and doing so would inherently create height deviating from the Design Eye Point validation results; Design Eye Point validation results, in their broadest reasonable interpretation, is the ideal position to view the VR HMD.
Response to Arguments
Applicant's arguments filed June 4th, 2026 have been fully considered but they are not persuasive.
Regarding Claim 1, the Applicant contends that none of the cited references disclose “a measurement device configured in a way that an HMD to be calibrated or validated is attachable with a fixation element to it in a way as such an HMD would be removably attached to the head of a user”, and more specifically, that Segura does not teach configuring its “rigidly attached calibration cameras” to be removably attached to the head of a user; Additionally, the Applicant contends that none of the cited references disclose any of the limitations related to the virtual target object having a distance and measuring a distance to the virtual target object (e.g., “wherein this specific chosen position has a target distance (d_intended) from the measurement device (10),” as recited by limitation (a), and also limitation (d)-(f)).
As noted in the rejection of Claim 1, while Segura does not explicitly teach that the measurement device is configured in a way that an HMD to be calibrated or validated is attachable with a fixation element to it in a way as such an HMD would be removably attached to the head of a user, the broadest reasonable interpretation of attachable with a fixation element to it in a way as such an HMD would be removably attached to the head of a user would be anything that fits inside the HMD, and can be secured via a fixation element such as a strap; this is implicit in Segura, considering the device is necessarily placed within the HMD for viewing the left and right displays, where the left and right camera substitute the eyes (Figure 3: “HMD stereo projections calibration. A pair of rigidly attached calibration cameras (CC) substitute the eyes”; Results: “A compact stereo camera is placed in the HMD where a user’s eyes are normally located… This is the calibration camera pair (CC). The camera captures the two HMD displays”), it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention for the measurement device to be configured such that an HMD to be calibrated or validated is attachable with a fixation element, such as a strap, in the same way that an HMD would be removably attached to the head of a user.
Regarding Segura allegedly not teach configuring its “rigidly attached calibration cameras” to be removably attached to the head of a user, the Examiner notes that the recited limitation of Claim 1 in question states “a measurement device configured in a way that an HMD to be calibrated or validated is attachable with a fixation element to it in a way as such an HMD would be removably attached to the head of a user”; The limitation states configuring a measurement device such that the measurement device is attached to an HMD with a fixation element in the same way that the HMD would be attached to the head of a user, therefore whether or not the measurement device is removably attachable to the head of a user is irrelevant.
As noted in the rejection of Claim 1, Segura in combination with Ye teaches a virtual target object (wherein a virtual target object, in its broadest reasonable interpretation, is essentially defined by at least a virtual point), wherein this specific chosen position has a target distance from the measurement device (Segura, Fig. 3: “HMD stereo projections calibration. A pair of rigidly attached calibration cameras (CC) substitute the eyes. The HMD displays’ misalignment is exaggerated in the illustration”, where Figure 3 also demonstrates a target point X as perceived on the left and right displays of the HMD in relation to the measurement device; Segura, Fig. 3, Fig. 4 clearly demonstrate a virtual target object on the displays of the VR HMD; Segura, Section 2.1, d, pg. 99: “We know the display coordinates of the points, as we have rendered them, and the user marks their corresponding camera image coordinates”; Segura, Section 2.1, pg 98: “projection of an arbitrary point in 3D space onto two different image planes, each with its own coordinate system: the HMD displays coordinate systems and the calibration cameras coordinate systems. In the equations in this section xcam will denote points projected on a CC image, and xdisp will denote points projected on an HMD display. Both are in homogeneous coordinates, as they are used in projective geometry equations”), where measuring the distance can be explicitly taught by Ye (Ye, Column 5, Lines 58-67: “By way of further illustration, and as provided in the Tsai publication, FIG. 1A illustrates the basic geometry of the camera model. (X.sub.w,Y.sub.w,Z.sub.w), defining the 3D coordinates of the object point P in the 3D world coordinate system. (X,Y,Z) define the 3D coordinates 185 of the object point P in the 3D camera coordinate system 188, which is centered at point O, the optical center, with the z axis the same as the optical axis (see also FIG. 1). (x,y) is the image coordinate system centered at O.sub.i (intersection of the optical axis z and the front image plane 186) and parallel to x and y axes”; Ye, Fig. 1A also illustrates the point in relation to the camera in terms of its optical center and orientation with regards to the xyz axis. Ye, Column 9, Lines 22-51: “The accuracy of the calibration of an embodiment with two or more cameras with a common viewing area can be validated by acquiring a single image of the calibration object that is substantially similar to the one used for calibration (e.g. calibration object 170). The features of the calibration object are extracted from each image, using in an embodiment the above-described checkerboard feature extractor vision system software tool. Once the features are extracted, the correspondence between the features is established. In the case of the checkerboard calibration plate, a fiducial on the plate helps identify correspondence between features in the two images. Using the triangulation procedure described above (referencing FIG. 1B), the positions of these features in the 3D world coordinate system are computed. Given n corresponding points all the images, after triangulation, there will be n points computed in the world coordinate system, denoted by X.sub.i.sup.extracted, 1≦i≦n. Ideally, if the cameras are perfectly calibrated, in the absence of noise the rays obtained during triangulation would intersect at one point (see FIG. 1B). However, in practice the rays are not guaranteed to intersect. The triangulation discrepancy or residual is computed as the sum of shortest distance between the triangulated point P and the ray (R.sub.1 and R.sub.2) from each camera that was used to compute the triangulated point. In this embodiment, the root mean square (RMS) value of this parameter for all the feature points is computed using the images acquired during validation. This value is compared to the value obtained for the calibration images. If it is above a certain acceptance threshold, then the user is asked to repeat the calibration” Notes: Ye teaches a calibration system similar in structure to that of Segura, with 2 cameras focused on calibration target that is represented in 3D space in relation to cameras. Considering the xyz system is oriented with respect to the camera, triangulating the position of a target point P within the 3D space is in relation to the cameras, and therefore, distance is also inherent with the triangulation of point P, since the origin of the system of coordinates is at the camera(s)). A person having ordinary skill in the art before the effective filing date of the claimed invention would have found the combination of Segura and Ye obvious; for more detail, refer to the rejection of Claim 1.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., the validation of a projection is different from the validation as described in the original specification) are not recited in the rejected claim. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
The applicant further refers to Segura’s conclusion, penultimate Conclusion paragraph, which reads: “On the other hand we will make a proof of concept for our focus control proposal. We will use variable optical power lenses and moving displays for this purpose. Anyway, the strategy for controlling focus based on the distance to the main object is limiting. Users looking at objects at different depths will be unable to correctly focus as they would in the real world and in many applications there is no main object. A more realistic effect would involve the use of an eye tracking system to sense the user’s 3D gaze point and control focus based on the distance to the point the user is looking at. The use of virtual reality in advanced manufacturing scenarios, including collaborative engineering and training can be enhanced by the techniques presented in this work. They should provide a more correct perception of virtual space and shapes, as well as higher visual comfort”, as suggesting that Segura does not teach simulating a virtual object with a distance or determining the distance to the simulated object. The Examiner notes that Segura states “Anyway, the strategy for controlling focus based on the distance to the main object is limiting. Users looking at objects at different depths will be unable to correctly focus as they would in the real world and in many applications there is no main object”, which is in reference to Segura’s own work determining a distance to a virtual main object for focus purposes (Section 3, pg 101: “The application should be able to select a focus distance at any time and apply it on the hardware, or continuously vary it frame after frame if the main object moves. When doing so, the display field of view may change. So, the rendering engine needs to adapt to the new FOV. The control flow should be as follows. The application requests a focus distance. The optical power controller computes a system optical power, obtains the corresponding focus command (i.e. a display position or a lens current, depending on the hardware) using the calibration curves and applies it. Then the controller computes the new FOV corresponding to the applied optical power, from the calibrated FOV curve and passes it to the graphics rendering engine”), where the focus function is dependent on the validation of a distance to a virtual arbitrary point (virtual object) as described in Section 2. Segura is describing that their method of controlling focus of an HMD based on the distance to a simulated object has limitations, rather than describing that controlling the focus of an HMD based on the distance to a simulated object is not yet addressed; this is supported by the fact that Segura suggests that a better method may be to control focus based on an eye tracking system to sense the user’s 3D gaze point (Conclusion: “A more realistic effect would involve the use of an eye tracking system to sense the user’s 3D gaze point and control focus based on the distance to the point the user is looking at”). Therefore, in the broadest reasonable interpretation of validating the measured distance to the virtual target object is taught by Segura in view of Ye and Yoon (refer to the rejection of Claim 1), as well as by the fact that Segura’s validation works with any HMD (Section 2.1, pg 7: “This calibration step is performed only once for our CCs and is used later on for the calibration of any HMD”), including immersive HMDs, which necessitate simulating an environment, and hence simulating a virtual object or a virtual point (Section 2, pg 7: “They are thus not usable in immersive HMDs (i.e. non-see-through) in which there is no view of the real world. In immersive HMDs the lack of real references makes projection errors less noticeable but still a correct geometric perception requires correct projection parameters”; Section 2.1, pg 98: “projection of an arbitrary point in 3D space onto two different image planes, each with its own coordinate system: the HMD displays coordinate systems and the calibration cameras coordinate systems. In the equations in this section xcam will denote points projected on a CC image, and xdisp will denote points projected on an HMD display. Both are in homogeneous coordinates, as they are used in projective geometry equations”).
Claims 2-7, being dependent on Claim 1, are accordingly not patentable for at least the same reasons as Claim 1, and are rejected.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAYMOND CHUN LAM LI whose telephone number is (571)272-5124. The examiner can normally be reached M-F 8:30-5.
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/RAYMOND CHUN LAM LI/Examiner, Art Unit 2614
/KENT W CHANG/Supervisory Patent Examiner, Art Unit 2614
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