CTNF 18/712,292 CTNF 101783 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. 07-30-06 This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a transmission module,” “an acquisition module,” “a determination module,” and “an update module,” in claim 9. See “the processor 102” regarding S1-S4 on pages 23-24 of the specification. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejection – 35 USC § 103 07-21-aia AIA Claim (s) 1, 2, 6, 8-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin (US11189037B2), hereinafter referenced as Lin, in view of Iyer (US10482677B1), hereinafter referenced as Iyer, and Jurgenson (US10997783B2), hereinafter referenced as Jurgenson Regarding claim 1, Lin teaches A method for relocating a target object, comprising: (“a repositioning method and apparatus in a camera pose tracking process” [¶ 6, Lin] “a device adds a figure of a virtual human to an image photographed by the camera. As the camera moves in the real world, the image photographed by the camera changes, and a photographing position of the virtual human also changes” [¶ 45, Lin]) The virtual human is the target object. Lin teaches of a method for repositioning in relations to virtual humans. acquiring a first camera pose matrix and a first image during relocating of the target object in a virtual space (“obtaining a current image acquired by the camera after an ith anchor image in the plurality of anchor images, i being an integer greater than 1; obtaining an initial feature point and an initial pose parameter in the first anchor image in the plurality of anchor images in a case that the current image satisfies a repositioning condition, the initial pose parameter being used to indicate a camera pose of the camera during acquisition of the first anchor image” [¶ 7-8, Lin] “As the camera moves in the real world, the image photographed by the camera changes, and a photographing position of the virtual human also changes, thereby simulating an effect that the virtual human is still in the image and the camera photographs the image and the virtual human while the position and pose are changing, so as to present a realistic three-dimensional picture to a user.” [¶ 45, Lin]) Lin teaches of obtaining an initial pose parameter of the camera at the time of the first anchor image (reads on acquiring a first camera pose matrix and a first image). As the camera moves around in the real world, the virtual human also changes in the simulation to simulating an effect that the virtual human is changing to present a realistic three-dimensional picture to a user (reads on image during relocating of the target object in a virtual space). the first image is an image shot by the camera in a real space after the last locating, and the target object is positioned in the real space; (“During an ith tracking process corresponding to the ith anchor image, the camera acquires a current image. The current image is a frame of image acquired after the ith anchor image, i being an integer greater than 1.” [¶ 62, Lin]. “AR is a technology that as a camera acquires an image, a camera pose parameter of the camera in the real world (or referred to as the three-dimensional world or the actual world) is calculated in real time” [¶ 44, Lin] “a device adds a figure of a virtual human to an image photographed by the camera. As the camera moves in the real world, the image photographed by the camera changes, and a photographing position of the virtual human also changes” [¶ 45, Lin]) Lin teaches the current image captured by the camera in the real world after the i-th anchor image (reads on the first image shot by the camera in real space after the last locating), and a figure of a virtual human to an image photographed by the camera (the target object is positioned in the real space). calculating a second camera pose matrix according to the first camera pose matrix and the first image (“Step 704 : Calculate a target pose parameter of the camera during acquisition of the current image according to a positioning result of the first repositioning and a positioning result of the second repositioning. It is assumed that the positioning result of the first repositioning includes Rmf and Tmf . Rmf is a rotation matrix of the change of the camera from an initial pose parameter to a keyframe pose parameter, Tmf is the displacement vector of the change of the camera from the initial pose parameter to the keyframe pose parameter, and the target pose parameter of the camera during acquisition of the current image is calculated by using the following formula: [ R T 0 1 ] = [ Rcm Smf * Tcm 0 ] * [ Rmf Tmf 0 1 ] R and T being the target pose parameter, and Smf being a scale of a target keyframe” [¶ 108, Lin] “the device calculates a homography matrix between two frames of image according to the keyframe feature point and the target feature point; and decomposes the homography matrix to obtain the pose change amount including Rcm and Tcm of the change of the camera from the keyframe pose parameter to the target pose parameter” [¶ 106, Lin]) Lin teaches of a target pose parameter, rotation matrix R and translation vector T, for the camera at the time of the current image (reads on second camera pose matrix). This is derived from the initial pose parameter (reads on first camera pose matrix) and feature point tracking result from the current image (reads on first image). determining a first target pose matrix according to the second camera pose matrix (“a device adds a figure of a virtual human to an image photographed by the camera. As the camera moves in the real world, the image photographed by the camera changes, and a photographing position of the virtual human also changes, thereby simulating an effect that the virtual human is still in the image and the camera photographs the image and the virtual human while the position and pose are changing, so as to present a realistic three-dimensional picture to a user.” [¶ 45, Lin]) Lin teaches that photographing the position of the virtual human (reads on the first target pose matrix) is derived from and updates with the camera’s pose, and establishing a dependency of the object pose on the camera pose reads on (second camera pose matrix). updating second pose information of the target object in the virtual space according to the first target pose matrix, wherein the second pose information is pose information, acquired during the last locating, of the target object in the virtual space. (“Feature point tracking is then performed on the image 5 relative to the image 1. The image 4 is determined as the second anchor image in a case that the effect of feature point tracking is poorer than a preset condition (for example, there is a relatively small quantity of matching feature points). Feature point tracking is performed on the image 5 relative to the image 4, and a displacement change amount of the camera during the photographing of the image 4 and the photographing of the image 5 is calculated. A displacement change amount of the camera between the photographing of the image 4 and the photographing of the image 1 and the initial pose parameter are then combined to calculate a pose parameter of the camera during the photographing of the image 5.” [¶ 51, Lin] “a device adds a figure of a virtual human to an image photographed by the camera. As the camera moves in the real world, the image photographed by the camera changes, and a photographing position of the virtual human also changes, thereby simulating an effect that the virtual human is still in the image and the camera photographs the image and the virtual human while the position and pose are changing, so as to present a realistic three-dimensional picture to a user.” [¶ 45, Lin]) Lin teaches a computed camera pose parameter derived from the prior anchor’s pose and the current image is used to update ongoing pose tracking. This overwrites the prior locating result with the new calculated results. This anchor-switching mechanism updates the running pose estimate using newly computed pose parameters (reads on updating second pose information of the target object in the virtual space according to the first target pose matrix). Lin additionally the initial pose parameter is what is carried forwarded and combined with subsequent displacement changes to compute every new pose. It is retrieved from the original locating moment used and as the fixed reference (reads on second pose information is pose information, acquired during the last locating). The virtual object’s position in the virtual space is maintained relative to this stored reference to make the initial pose parameter the prior virtual-space pose of the target object in that new calculation overwrite. Lin fails to teach the following: wherein the first camera pose matrix is configured to denote pose information of a camera on a mobile terminal in the virtual space during last locating of the target object in the virtual space; wherein the first target pose matrix pose matrix is configured to denote first pose information of the target object in the point cloud space; wherein the second camera pose matrix is configured to denote pose information corresponding to the camera in a point cloud space when the camera shoots the first image; However, Iyer the following: wherein the first camera pose matrix is configured to denote pose information of a camera on a mobile terminal in the virtual space during last locating of the target object in the virtual space, (“receive first SLAM data obtained by a first Head-Mounted Device (HMD) worn by a first user during execution of an xR application” [¶ 5, Iyer] “a first SLAM data may include landmark data found in a Region of Interest (ROI) within an infrared (IR) or near-IR (NIR) frame captured via a first camera mounted on the first HMD” [¶ 6, Iyer]) First SLAM, Simultaneous Localization and Mapping, (reads on the first camera pose matrix) includes landmark data found in a region of interest (reads on target object in the virtual space) captured by a camera on the HMD (reads on a mobile terminal). The first SLAM data is during execution of an xR application, implying the landmark data found in a region of interest in the first SLAM is the last locating of the target object in the virtual space. wherein the first target pose matrix pose matrix is configured to denote first pose information of the target object in the point cloud space (“The system state in an EKF for SLAM may be a 1×(6+3N) vector, where N is the number of landmarks. In that case, there may be 3 coordinates (e.g., x, y, z) for each landmark, and 6 coordinates (e.g., x, y, z, pitch, roll, yaw) for the user.” [¶ 58, Iyer]) SLAM is a 1×(6+3N) vector (reads on pose matrix) with information N landmarks (reads on pose information of the target object) in 3 coordinates (e.g., x, y, z) for each landmark, and 6 coordinates (e.g., x, y, z, pitch, roll, yaw) for the user (reads on point cloud space). Iyer BASE is analogous art with respect to Lin because they are from the same field of endeavor, namely simultaneous localization and mapping. Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Lin with the feature of Iyer to incorporate a first SLAM data is during execution of an xR application, implying the landmark data found in a region of interest in the first SLAM is the last locating of the target object in the virtual space, and a 1×(6+3N) vector with information N landmarks in 3 coordinates (e.g., x, y, z) for each landmark, and 6 coordinates (e.g., x, y, z, pitch, roll, yaw) for the user. A person of ordinary skill in the art would do such in order to improve users’ immersion in virtual environments. Lin in view of Iyer fail to teach the following: wherein the second camera pose matrix is configured to denote pose information corresponding to the camera in a point cloud space when the camera shoots the first image; Jurgenson does. Jurgenson teaches: wherein the second camera pose matrix is configured to denote pose information corresponding to the camera in a point cloud space when the camera shoots the first image; (“a 3D cloud model may include key points that extend significantly beyond the perspective of the image, with only the portion of the 3D cloud model relevant to the environment and the initial rough location estimate used in the matching with a device camera image.” [¶ 59, Jurgenson]) Jurgenson teaches a 3D cloud model (reads on the second camera pose matrix) includes key points relevant to the environment and the initial rough location estimate used in the matching with a device camera image (reads on denote pose information corresponding to the camera in a point cloud space when the camera shoots the first image). Jurgenson BASE is analogous art with respect to Lin in view of Iyer because they are from the same field of endeavor, namely simultaneous localization and mapping. Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Lin in view of Iyer with the feature of Jurgenson to incorporate a 3D cloud model which includes key points relevant to the environment and the initial rough location estimate used in the matching with a device camera image. A person of ordinary skill in the art would do such in order to improve the augmented reality experience. Regarding claim 2, Lin in view of Iyer and Jurgenson teaches the method for relocating a target object according to claim 1, wherein the updating second pose information of the target object in the virtual space according to the first target pose matrix , and additionally teaches the following. Lin teaches updating the second pose information of the target object in the virtual space to third pose information according to the first target pose matrix as follows: determining a second target pose matrix according to the first camera pose matrix and the first target pose matrix, (“performs calculation according to a position change of the feature point between the current image and the anchor image to obtain a pose change of the camera in the real world.” [¶ 5, Lin] “Step 704 : Calculate a target pose parameter of the camera during acquisition of the current image according to a positioning result of the first repositioning and a positioning result of the second repositioning. It is assumed that the positioning result of the first repositioning includes Rmf and Tmf . Rmf is a rotation matrix of the change of the camera from an initial pose parameter to a keyframe pose parameter, Tmf is the displacement vector of the change of the camera from the initial pose parameter to the keyframe pose parameter, and the target pose parameter of the camera during acquisition of the current image is calculated by using the following formula: [ R T 0 1] = [R-cm Smf * Tcm 0] * [Rmf Tmf 0 1] R and T being the target pose parameter, and Smf being a scale of a target keyframe.” [¶ 108-111, Lin]) Lin teaches of the first repositioning result Rmf/Tmf (reads on the first camera pose matrix) which represents the camera’s pose change from the initial pose to the keyframe pose. The second repositioning result Tcm (reads on the first target pose matrix) is the pose relationship used in the update. There inputs are combined in Step 704 in which the first repositioning pose [Rmf Tmf 0 1] multiplied with the second repositioning pose [R-cm Smf * Tcm 0] to produce a new pose [ R T 0 1] (reads on the second target pose matrix). The resulting matrix is the updated third pose information in the virtual space. wherein the second target pose matrix is configured to denote the third pose information of the target object in the virtual space after the last locating; and updating the second pose information to the third pose information in a case that an error value between the second pose information and the third pose information is greater than a first preset value. (“The repositioning condition is used to indicate that a tracking process of the current image relative to the ith anchor image fails, or, the repositioning condition is used to indicate that an accumulated error in historical tracking processes is already greater than the preset condition.” [¶ 64, Lin]) Lin teaches of an accumulated error greater than the preset condition (reads on that an error value between the second pose information and the third pose information is greater than a first preset value). The accumulated error is measured across the historical tracking chain over time implying a pose-to-pose comparison. Lin teaches an update is only applied when an error condition exceeds a present threshold. Regarding claim 6, Lin in view of Iyer and Jurgenson teaches the method for relocating a target object according to claim 1 , and additionally teaches the following. Lin teaches triggering relocating of the target object in the virtual space in a case that a preset condition is satisfied; wherein the preset condition comprises at least one of the following: image data of an image shot by the camera in the real space are changed; (“The device determines whether the current image satisfies the repositioning condition. The repositioning condition is used to indicate that a tracking process of the current image relative to the ith anchor image fails, or, the repositioning condition is used to indicate that an accumulated error in historical tracking processes is already greater than the preset condition. In an optional embodiment, the device tracks the current image relative to the ith anchor image, and determines that a tracking process of the current image relative to the ith anchor image fails, and the current image satisfies the repositioning condition in a case that a feature point matching the ith anchor image does not exist in the current image or a quantity of feature points in the current image that match the ith anchor image is less than a first quantity.” [¶ 90-91, Lin]) Lin teaches of changing image data as a triggering condition in which when tracking camera or cannot find the original reference point it and the tracking failed. Once the device decides a repositioning condition has been met, it drops the current tracking path and initiates a relocation process. (reads on image data of an image shot by the camera in the real space are changed) Regarding claim 8, Lin in view of Iyer and Jurgenson teaches the method for relocating a target object according to claim 1 , and additionally teaches the following. Lin teaches a second camera pose matrix denoting points (“the device calculates a homography matrix between two frames of image according to the initial feature point and the target feature point; and decomposes the homography matrix to obtain the pose change amount including Rrelocalize and Trelocalize of the change of the camera from the initial pose parameter to the target pose parameter.” [¶ 74, Lin]) Lin teaches of a homography matrix (reads on second camera pose matrix) which detail the points (the rotation and translation) of the camera at the moment the first image was captures. wherein in the point cloud space, a position of the mobile terminal is taken as an origin, a forward direction of the mobile terminal is taken as a Z-axis direction, and an up direction of the mobile terminal is taken as a Y-axis direction. (“The X axis is defined by a vector product Y*Z, and a direction tangential to the ground at a current position of the device on the X axis points to the east. 2. A direction tangential to the ground at the current position of the device on the Y axis points to the north pole of the geomagnetic field. 3. The Z axis points to the sky and is perpendicular to the ground. During camera pose tracking in the field of AR, for example, in a scenario of using a mobile phone to photograph a desktop to play an AR game, due to a special use scenario of AR, a fixed plane (for example, a desktop or a wall surface) in the real world is usually continuously photographed, the effect of directly using an SLAM repositioning method in the related art is relatively poor, and it is still necessary to provide a repositioning solution applicable to the field of AR.” [¶ 48-49, Lin]) Lin teaches a device centered 3D coordinate frame in which the device’s current position is the origin and the three orthogonal axes are defined according to the device orientation. In the specific AR use scenarios (photographing a desktop or wall) places the camera in a forward and up direction along the Z and Y axes, depending on the camera is held (reads on mobile terminal is taken as a Z-axis direction, and an up direction of the mobile terminal is taken as a Y-axis direction). And Jurgenson teaches the following: calculating a spatial point position of each pixel in image data of the first image, and constructing the point cloud space according to the spatial point position, (“A blind match of point cloud 305 to environment 301 which starts only with the points of the point cloud 305 and the image used for AR image 300 is highly processor intensive, requiring significant amoungs of resources to check every possible perspective, elevation, azimuth, distance, and relative coordinate position of the image against the 3D point cloud. However, by using an initial rough location determination based on a global positioning system (GPS) signal, network assisted location services, or other systems or sensors for generating a rough location, the possible matches with the 3D point cloud for the image are significantly limited.” [¶ 58, Jurgenson]) Jurgenson teaches of determining the point cloud to environment by using the point cloud, the image used for AR image, and an initial rough location GPS signal (reads on calculating a spatial point position of each pixel in image data of the first image). Such points are within a 3D point cloud environment (read on constructing the point cloud space according to the spatial point position). wherein the spatial point position of each pixel is acquired according to scanner; and (“point cloud data of an environment may be captured using a 3D scanner to generate a point cloud of an environment.” [¶ 33, Jurgenson]) Jurgenson teaches of point cloud data (reads on spatial point position of each pixel) is acquired according to 3D scanner. displaying the point cloud space in a visual form in the mobile terminal; (“a device display area 1090 may present augmented reality images as described herein” [¶ 88, Jurgenson]) Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Lin in view of Iyer and Jurgenson with the feature of Jurgenson to incorporate spatial point positions for a point cloud space generation and displaying visuals in a mobile terminal. A person of ordinary skill in the art would do such in order to improve the augmented reality experience. Claim 9 is rejected using the same rationale or bases as applied to claim 1 and the mentioned structure. Additionally, claim 9 recites the following structure: an apparatus (“Embodiments of this application provide a repositioning method and apparatus in a camera pose tracking process, a device, and a storage medium, so that a problem that the effect of directly using an SLAM repositioning method in the related art in an AR use scenario is relatively poor can be resolved.” [¶ 6, Lin]) a transmission module (“The image obtaining module 1620” [¶ 207, Lin]) an acquisition module (“The Hash search module 1640” [¶ 208, Lin]) a determination module (“The repositioning module 1660” [¶ 209, Lin]) an update module (“The parameter calculation module 1680” [¶ 210, Lin]) Claim 10 is rejected using the same rationale or bases as applied to claim 1 and the mentioned structure. Additionally, claim 10 recites the following structure: computer-readable storage medium, storing a computer program, wherein the computer program is configured to, when executed by a processor, cause the processor to implement the method (“a non-transitory computer-readable storage medium is provided, the storage medium storing at least one instruction, the at least one instruction being loaded and executed by a processor to implement the foregoing repositioning method.” [¶ 20, Lin]) Claim 11 is rejected using the same rationale or bases as applied to claim 1 and the mentioned structure. Additionally, claim 11 recites the following structure: an electronic apparatus, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the method (“FIG. 4 is a structural block diagram of an electronic device according to an exemplary embodiment of this application. The device includes a processor 420, a memory 440, a camera 460, and an IMU 480.” [¶ 53, Lin] “The memory 440 stores one or more instructions, codes, code segments and/or programs. The instruction, code, code segment and/or program is executed by the processor 420 to implement an SLAM repositioning method provided in the following embodiments.” [¶ 55, Lin)) 07-21-aia AIA Claim (s) 3, 4, and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Iyer, Jurgenson, and Lovberg (US9779502B1), hereinafter referenced as Lovberg Regarding claim 3, Lin in view of Iyer and Jurgenson teaches the method for relocating a target object according to claim 2 , however fail to teach compensating the third pose information with the error value; and alternatively, acquiring a compensation parameter corresponding to the error value, and compensating the third pose information with a quotient value obtained by dividing the error value by the compensation parameter. But Lovberg does. Lovberg teaches the following: compensating the third pose information with the error value; and (“The system can utilize an iteration procedure whereby an approximate first-order solution is proposed and tested against the identified precise target point projections on the cameras to determine residual errors which are then divided by the local derivatives with respect to each component of rotation and translation, to determine an iterative correction” [¶ 5, Lovberg]) Lovberg teaches of using the derived positional error to correct the pose estimate. Moreover, a pose tracking procedure in which an initial estimated pose solution is evaluated against observed target point positions. The gap between the estimated and actual position is the residual error. The estimated pose target pose (read son the third pose information) is compensated using the residual error (reads on error value). alternatively, acquiring a compensation parameter corresponding to the error value, and compensating the third pose information with a quotient value obtained by dividing the error value by the compensation parameter. (“The residual error is divided by the local derivatives with respect to each component of rotation and translation, to determine an iterative correction.” [¶ 55, Lovberg]) Lovberg teaches of acquiring a local derivative with respect to each component of rotation and translation which relates to the position and rotational error (reads on acquiring a compensation parameter corresponding to the error value), and computing a correction by dividing the residual error by that local derivative (reads on compensating the third pose information with a quotient value obtained by dividing the error value by the compensation parameter). Lovberg performs division of the error by the derivative to produce the correction (reads on quotient value obtained by dividing the error value by the compensation parameter). Lovberg BASE is analogous art with respect to Lin in view of Iyer and Jurgenson because they are from the same field of endeavor, namely simultaneous localization and mapping. Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Lin in view of Iyer and Jurgenson with the feature of Lovberg to incorporate an estimated pose target pose compensated using the residual error, and alternatively acquiring a local derivative with respect to each component of rotation and translation which relates to the position and rotational error to compute a correction by dividing the residual error by that local derivative. A person of ordinary skill in the art would do such in order to improve the augmented reality experience. Regarding claim 4, Lin in view of Iyer, Jurgenson, and Lovberg in view of Iyer and Jurgenson teaches the method for relocating a target object according to claim 3 , and additionally teaches the following. Lovberg teaches wherein the compensating the third pose information with the error value comprises: determining a coordinate compensation value corresponding to the error value; and (“a computer processor is programmed to determine the target movement in Cartesian coordinates of x, y and z and pitch, roll and yaw utilizing an algorithm adapted to identify a set of precise target points on the precision optical target and the x, y and z displacement and the pitch, roll and yaw rotation of the precise target points based on optical images collected by the at least two cameras” [¶ 5, Lovberg] “The residual error is divided by the local derivatives with respect to each component of rotation and translation, to determine an iterative correction.” [¶ 55, Lovberg]) Lovberg teaches the residual errors are computed separately for each component of translation x, y and z and that dividing each by the corresponding local derivative yields an iterative correction for that translation component. Dividing the translational residual error by the local translational derivative yields the correction value for each spatial coordinate, y and z (reads on determining a coordinate compensation value corresponding to the error value). adjusting spatial coordinates in the third pose information according to the coordinate compensation value. (“The system can utilize an iteration procedure whereby an approximate first-order solution is proposed and tested against the identified precise target point projections on the cameras to determine residual errors which are then divided by the local derivatives with respect to each component of rotation and translation, to determine an iterative correction. The system can be configured to repeat the above actions until residual error becomes smaller than desired accuracy.” [¶ 5, Lovberg] “The offset of the precision optical target from this pivot point position is Δy=0, Δx−4.5″, Δz=5.5″. The precision of these offsets is not critical since all motions of interest are relative motions. The six measurements are x, y, and z distances and roll, pitch and yaw angles.” [¶ 7, Lovberg]) Lovberg teaches x, y, and z spatial coordinates are updated each iteration using correction value until the residual falls within the desired accuracy. The iterative procedure of updating the spatial coordinates (x, y, z) of the tracked pose using the computed transitional at each step reads on adjusting spatial coordinates in the third pose information according to the coordinate compensation value. Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Lin in view of Iyer, Jurgenson, and Lovberg with the feature of Lovberg to incorporate dividing the translational residual error by the local translational derivative to yield the correction value for each spatial coordinate, x, y and z and updating each coordinate in which each iteration using correction value until the residual falls within the desired accuracy. A person of ordinary skill in the art would do such in order to improve the augmented reality experience. Regarding claim 5, Lin in view of Iyer, Jurgenson, and Lovberg teaches the method for relocating a target object according to claim 3 , and additionally teaches the following. Lovberg teaches wherein the compensating the third pose information with the error value comprises: determining an angle compensation value corresponding to the error value, (“The system can utilize an iteration procedure whereby an approximate first-order solution is proposed and tested against the identified precise target point projections on the cameras to determine residual errors which are then divided by the local derivatives with respect to each component of rotation and translation, to determine an iterative correction.” [¶ 5, Lovberg]) Lovberg teaches of each rotational component (pitch, roll, yaw). The residual error attributable to rotation is divided by the local derivative with respect to the rotational component to produce rotational correction. The rotational correction is the angle compensation value in which the angular error is used to adjust the angular component of the pose. The angular residual error divided by the local rotational derivative yields the iterative correlation for each angle component (reads on determining an angle compensation value corresponding to the error value). wherein the angle compensation value is configured to denote an angle difference between a spatial angle of the camera during the last locating and a spatial angle after the last locating; and (“With repetition rates in the range of 100 times per second, the full 6-DOF movement determination can be performed for each repetition. In these embodiments the results of each movement determination is used for the initial first order solution during the next iteration.” [¶ 5, Lovberg] “The six measurements are x, y, and z distances and roll, pitch and yaw angles. In some embodiments, the measurements are up-dated at a rate of about 100 solutions per second with a latency of about 10 milliseconds.” [¶ 7, Lovberg]) Lovberg teaches of chained successive pose determinations with each resulting determination feed forwarded as the starting solution for the next. The angular correction at each step is inherently represented by the angular difference (reads on angle compensation value) between the pose at the prior locating event and the pose at the current locating event (reads on an angle difference between a spatial angle of the camera during the last locating and a spatial angle after the last locating). adjusting a spatial angle in the third pose information according to the angle compensation value. (“a computer processor is programmed to determine the target movement in Cartesian coordinates of x, y and z and pitch, roll and yaw utilizing an algorithm adapted to identify a set of precise target points on the precision optical target and the x, y and z displacement and the pitch, roll and yaw rotation of the precise target points based on optical images collected by the at least two cameras. The system can utilize an iteration procedure whereby an approximate first-order solution is proposed and tested against the identified precise target point projections on the cameras to determine residual errors which are then divided by the local derivatives with respect to each component of rotation and translation, to determine an iterative correction. The system can be configured to repeat the above actions until residual error becomes smaller than desired accuracy.” [¶ 5, Lovberg]) Lovberg teaches of an iterative correction of pitch, roll, and yaw in which each iteration applies the computed angular correction to update the pose’s angular value until the desired accuracy. This adjustment of the spatial angles reads on adjusting a spatial angle in the third pose information according to the angle compensation value. Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Lin in view of Iyer, Jurgenson, and Lovberg with the feature of Lovberg to incorporate an angular residual error divided by the local rotational derivative yields the iterative correlation for each angle component in which the angular correction at each step is inherently represented by the angular difference between the pose at the prior locating event and the pose at the current locating event. This iterative correction of pitch, roll, and yaw in which each iteration applies the computed angular correction to update the pose’s angular value until the desired accuracy. A person of ordinary skill in the art would do such in order to improve the augmented reality experience . 07-21-aia AIA Claim (s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Iyer, Jurgenson, and Bhuvaneswaran (An efficient estimation and removal of noise parameters from current read out digital image sensor using variance transforms), hereinafter referenced as Bhuvaneswaran . Regarding claim 7, Lin in view of Iyer and Jurgenson teaches the method for relocating a target object according to claim 1 , and additionally teaches the following. Lin teaches wherein an acquisition method for the first image comprises one of the following: selecting, in a case that the camera on the mobile terminal shoots a plurality of images in the real space, poorer or equal to a present condition a sequence of images. (“the device determines the first frame of image in the image sequence (or one frame of image satisfying a predetermined condition in several frames of image in the front) as the first anchor image, performs feature point tracking on a subsequently acquired image relative to the first anchor image, and calculates a camera pose parameter of the camera according to a feature point tracking result. In a case that the effect of feature point tracking of a current frame of image is poorer than a preset condition, a previous frame of image of the current frame of image is determined as the second anchor image, feature point tracking is performed on a subsequently acquired image relative to the second anchor image, and the camera pose parameter of the camera is calculated according to a feature point tracking result. The rest is deduced by analogy. The device may sequentially perform camera pose tracking on a plurality of consecutive anchor images.” [¶ 61, Lin]) Lin teaches selecting of a sequence of images acquired by the camera (reads on the camera on the mobile terminal shoots a plurality of images in the real space) if an image quality satisfies or not a predetermined condition (selecting an image from the plurality of images as the first image). Lin in view of Iyer and Jurgenson fail to teach an image having an image noise number less than or equal to a fourth preset value from the plurality of images as the first image. However, Bhuvaneswaran teaches: an image having an image noise number less than or equal to a fourth preset value from the plurality of images as the first image wherein an image has noise values and the significant features (“To estimation poisson noise this is de-noised using successive approximation and filtering techniques. First the noise is removed by median filter; and then removed by wiener filter. Second noisy image is denoised with the help of wavelet based techniques using thresholding. Third thresholding is applied on the result of first and second simultaneously for image denoising and fourth PSNR (Peak Signal To Noise Ratio), MSE (Mean Square Error) calculated and results are compared in all cases.” [Page 2 Section 3, Bhuvaneswaran] “Different method has been used to denoise an image in wavelet domain. They either based on estimation techniques or thresholding. In the thresholding method, the coefficients that are less than a given threshold are eliminated. There are two general types of thresholding techniques: Soft and Hard Thresholding.” [Page 5 Section 7D, Bhuvaneswaran]). Bhuvaneswaran teaches of removing noise based on a threshold value in which the coefficients that are less than a given threshold (reads on fourth preset value from the plurality of images as the first image) is eliminated. Bhuvaneswaran is analogous art with respect to Lin because they are from the same field of endeavor, namely image processing. Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Lin in view of Iyer and Jurgenson with the feature of Bhuvaneswaran to incorporate a of removing noise based on a threshold. A person of ordinary skill in the art would do such in order to improve image quality. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUNE NGUYEN whose telephone number is (571)272-8919. The examiner can normally be reached M-TH 7:00AM - 5:00PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Devona E Faulk can be reached at (571) 272-7515. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DUNE NGOC NGUYEN/Examiner, Art Unit 2618 /DEVONA E FAULK/Supervisory Patent Examiner, Art Unit 2618 Application/Control Number: 18/712,292 Page 2 Art Unit: 2618 Application/Control Number: 18/712,292 Page 3 Art Unit: 2618 Application/Control Number: 18/712,292 Page 4 Art Unit: 2618 Application/Control Number: 18/712,292 Page 5 Art Unit: 2618 Application/Control Number: 18/712,292 Page 6 Art Unit: 2618 Application/Control Number: 18/712,292 Page 7 Art Unit: 2618 Application/Control Number: 18/712,292 Page 8 Art Unit: 2618 Application/Control Number: 18/712,292 Page 9 Art Unit: 2618 Application/Control Number: 18/712,292 Page 10 Art Unit: 2618 Application/Control Number: 18/712,292 Page 11 Art Unit: 2618 Application/Control Number: 18/712,292 Page 12 Art Unit: 2618 Application/Control Number: 18/712,292 Page 13 Art Unit: 2618 Application/Control Number: 18/712,292 Page 14 Art Unit: 2618 Application/Control Number: 18/712,292 Page 15 Art Unit: 2618 Application/Control Number: 18/712,292 Page 16 Art Unit: 2618 Application/Control Number: 18/712,292 Page 17 Art Unit: 2618 Application/Control Number: 18/712,292 Page 18 Art Unit: 2618 Application/Control Number: 18/712,292 Page 19 Art Unit: 2618 Application/Control Number: 18/712,292 Page 20 Art Unit: 2618 Application/Control Number: 18/712,292 Page 21 Art Unit: 2618 Application/Control Number: 18/712,292 Page 22 Art Unit: 2618 Application/Control Number: 18/712,292 Page 23 Art Unit: 2618 Application/Control Number: 18/712,292 Page 24 Art Unit: 2618 Application/Control Number: 18/712,292 Page 25 Art Unit: 2618 Application/Control Number: 18/712,292 Page 26 Art Unit: 2618 Application/Control Number: 18/712,292 Page 27 Art Unit: 2618 Application/Control Number: 18/712,292 Page 28 Art Unit: 2618 Application/Control Number: 18/712,292 Page 29 Art Unit: 2618 Application/Control Number: 18/712,292 Page 30 Art Unit: 2618
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