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 .
Status of Claims
Applicant's amendments filed on 24 June 2026 have been entered. Claims 8 and 17 have been amended. No claims have been canceled. No claims have been added. Claims 1-18 are still pending in this application, with claims 1 and 10 being independent.
Response to Arguments
Rejections under 35 USC § 112/Double Patenting
Examiner notes that all outstanding 112 and DP issues have been resolved. The corresponding rejections are accordingly withdrawn.
Rejections under 35 USC § 103
Applicant's arguments filed 24 June 2026 have been fully considered but they are not persuasive.
Applicant argues, with respect to independent claims 1 and 10, that “Office Action, at pages 11 and 12, alleges that the animation of some object based on previously tracked user motion in Chen teaches identifying individual actions, and obtaining virtual content information based on the actions, but this is unsupported by the cited sections of Chen. Chen instead merely describes deforming a mesh that represents some animated object based on the previously tracked motion of a human subject. Different types of actions are not determined at all, but instead the human subject's motion is merely tracked. The information defining the animated object made up of the mesh to be deformed is not obtained based on differentiated actions. It is static, and the mesh is merely deformed to match the previously tracked motion. At playback, no consideration of a type of action that was made by the human subject is contemplated in Chen. Instead, the animated object is simply presented with the mesh deformations that match the previously tracked user motion. As such, Chen fails to teach or suggest the claim features reproduced above. The cited sections of Yasutake do not address these deficiencies of Chen. Therefore, the proposed combination of references does not teach or suggest the claim features reproduced above.”
Examiner respectfully disagrees with Applicant’s interpretation of the Chen reference. Specifically, Examiner points to the cited portions of Chen, for example in cited Paragraph [0057], which discloses: “which nodes in the graph are linked to each joint (e.g. through the point constraints described above) will depend on the position of the user when the tracked skeleton is obtained. As the user has freedom to adopt different body positions, the user can influence or directly control which parts of their body are mapped to particular parts of the object being animated. In order to assist the user in this, a graphical user interface (GUI) may be provided which provides visual feedback to the user, for example by displaying both the tracked skeleton and the object (or more particularly, the mesh of the object). Various presentation techniques may be used to make this GUI clearer or more intuitive, for example, by making the object partially transparent and/or showing the tracked skeleton in a bright color and/or by displaying skeleton overlaid over the object (or vice-versa). In some examples, the GUI is arranged such that the user appears to walk up to the object within the GUI. In some examples, lines or links 602 may be shown in the GUI 600 between a joint 604 on the skeleton 605 and the k-nearest nodes in the graph representing the object mesh 606 as shown in FIG. 6. The links 602 may be displayed dynamically as the user moves towards the object 606 within the scene displayed in the GUI 600. As the user moves, and their skeleton is tracked, the links may be updated (as the k-nearest nodes change) to enable the user to control the mapping between joints and nodes.” Examiner asserts that this clearly reads on the claimed “determine individual actions of the non-stationary living entities as indicated by an arrangement of the multiple linkage points.” Thus, Examiner maintains that the cited portions of Chen read on the limitations, as currently claimed.
In response to applicant's argument that Chen is nonanalogous art, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, Chen teaches tracking and animation of virtual objects, clearly in the same field of endeavor as the instant application.
Allowable Subject Matter
Claims 2, 8, 11, and 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is an examiner’s statement of reasons for allowability:
Claims 2 and 11 are allowable over the prior art of record since the cited references taken individually or in combination fails to particularly disclose or suggest a system or method wherein detecting the multiple linkage points is based on the transponders and determining the images of the virtual content is based on the transponders of the non-stationary living entities indicating one or more of the actions, as presented in the environment of the remaining limitations of claim 2 (and substantially similar limitations in claim 11). It is noted that the closest prior art, Chen, shows the system of claim 1, wherein the one or more processors are further configured to: receive signals from transponders of the non-stationary living entities within the field of view such that the transponders are in motion in accordance with the non-stationary living entities, wherein the signals from the transponders indicate multiple locations of the transponders, wherein the signals from the transponders define multiple positions of the multiple linkage points with respect to the non-stationary living entities that correlate to specific portions of the virtual content items. However, Chen fails to disclose or suggest wherein detecting the multiple linkage points is based on the transponders and determining the images of the virtual content is based on the transponders of the non-stationary living entities indicating one or more of the actions.
Claims 8 and 17 are allowable over the prior art of record since the cited references taken individually or in combination fails to particularly disclose or suggest a system or method , wherein the one or more processors are further configured to: obtain audio information indicating the presence of the non-stationary living entities within a proximity of the user to facilitate detecting the non-stationary living entities or receiving the signals of the transponders, wherein the audio information comprises a sound that is associated with the non-stationary living entities, as presented in the environment of the remaining limitations of claim 8 (and substantially similar limitations in claim 17). It is noted that the closest prior art, Chen, shows the system of claim 1. However, Chen fails to disclose or suggest , wherein the one or more processors are further configured to: obtain audio information indicating the presence of the non-stationary living entities within a proximity of the user to facilitate detecting the non-stationary living entities or receiving the signals of the transponders, wherein the audio information comprises a sound that is associated with the non-stationary living entities.
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.
Claims 1, 3-7, 10, and 12-16 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US Pub. 2014/0035901), hereinafter Chen, in view of Yasutake (US Pub. 2015/0371447).
Regarding claim 1, Chen discloses a system configured to utilize non-stationary living entities as markers for virtual content viewed in an augmented reality environment, the system comprising: a display device configured to superimpose images of virtual content over a real-world view of a user to create a visual effect of the augmented reality environment being present in a real world, wherein the real-world view of the user is an outlook of the real world from the point of view of the user (Fig. 3, item 320; Paragraph [0023]: A visual display may be provided to the user at this attachment stage so that they can position their body such that it approximates the shape of the object to be animated (e.g. such that their tracked skeleton approximately aligns with the object) and so the attachment of the skeleton to the graph is done in a more intuitive manner. Image 104 shows the overlapping of the skeleton 107 and the chair 108; Paragraph [0025]: Once the transformations have been computed on the deformation graph (in block 118), these transformations are applied to the input mesh and the corresponding motion (i.e. the animation) of the object is rendered for display to the user (block 120). Images 105 and 106 show two example images from an animation of the chair; Paragraph [0045]: generates the 3D geometry from the real-world 3D object and outputs the 3D geometry to both the Embed stage 202 and the Warp stage; Paragraph [0056]: When attaching the tracked skeleton to the deformation graph (in block 116), this may be performed automatically by adding point constraints in space. For example, each joint in the tracked skeleton (or each point in the body tracking data) may be made a constraint. In such an example, at attachment time, the position of each joint pl (or other point, where joints are not used) is stored along with Euclidean distances to the k-nearest nodes in the deformation graph. As the user moves, each joint (or point) moves to a new position ql and the graph is transformed such that the k-nearest nodes; Paragraph [0057]: presentation techniques may be used to make this GUI clearer or more intuitive, for example, by making the object partially transparent and/or showing the tracked skeleton in a bright color and/or by displaying skeleton overlaid over the object (or vice-versa). In some examples, the GUI is arranged such that the user appears to walk up to the object within the GUI); electronic storage that stores information related to the virtual content (Fig. 3; Paragraph [0031]: computer executable instructions may be provided using any computer-readable media that is accessible by computing based device 300); one or more physical computer processors configured by computer-readable instructions (Fig. 3) to: receive user input, via a user interface, that includes virtual content information that define individual virtual content items, wherein the virtual content information defines one or more colors, a shape, one or more sounds, a size, and/or a position for the individual virtual content items (Fig. 3; Paragraph [0022]: the animation method uses the body of the user as an input and body tracking data for the user is acquired using a sensor and received from the sensor (in block 114). Any suitable sensor may be used, including but not limited to, non-contact sensors such as camera-based systems (e.g. Kinect™, Wii™) and marker-based tracking systems (e.g. using Vicon™ markers) and contact-based sensors such as a multi-touch device. The body tracking data defines positions of one or more points on a body and any type of body tracking data may be used which enables correspondences between the sensor data and nodes in the deformation graph to be determined; Paragraph [0057]: which nodes in the graph are linked to each joint (e.g. through the point constraints described above) will depend on the position of the user when the tracked skeleton is obtained. As the user has freedom to adopt different body positions, the user can influence or directly control which parts of their body are mapped to particular parts of the object being animated. In order to assist the user in this, a graphical user interface (GUI) may be provided which provides visual feedback to the user, for example by displaying both the tracked skeleton and the object (or more particularly, the mesh of the object). Various presentation techniques may be used to make this GUI clearer or more intuitive, for example, by making the object partially transparent and/or showing the tracked skeleton in a bright color and/or by displaying skeleton overlaid over the object (or vice-versa). In some examples, the GUI is arranged such that the user appears to walk up to the object within the GUI; Paragraph [0056]: When attaching the tracked skeleton to the deformation graph (in block 116), this may be performed automatically by adding point constraints in space. For example, each joint in the tracked skeleton (or each point in the body tracking data) may be made a constraint. In such an example, at attachment time, the position of each joint pl (or other point, where joints are not used) is stored along with Euclidean distances to the k-nearest nodes in the deformation graph. As the user moves, each joint (or point) moves to a new position ql and the graph is transformed such that the k-nearest nodes); store the virtual content information in the electronic storage (Fig. 3; Paragraph [0056]: When attaching the tracked skeleton to the deformation graph (in block 116), this may be performed automatically by adding point constraints in space. For example, each joint in the tracked skeleton (or each point in the body tracking data) may be made a constraint. In such an example, at attachment time, the position of each joint pl (or other point, where joints are not used) is stored along with Euclidean distances to the k-nearest nodes in the deformation graph. As the user moves, each joint (or point) moves to a new position ql and the graph is transformed such that the k-nearest nodes); obtain an image of a field of view of the real-world view of the user visible via the display device (Paragraph [0023]: enable the body of the user to be used as an input, this tracked skeleton (as defined by the body tracking data received in block 114) is attached to the deformation graph (block 116). As the deformation graph is a representation of the input mesh, the tracked skeleton may also be considered to be attached to the mesh. The attachment of the skeleton to the graph may be performed automatically without user input or in response to voice commands from the user, as is described in more detail below. A visual display may be provided to the user at this attachment stage so that they can position their body such that it approximates the shape of the object to be animated (e.g. such that their tracked skeleton approximately aligns with the object) and so the attachment of the skeleton to the graph is done in a more intuitive manner. Image 104 shows the overlapping of the skeleton 107 and the chair; Paragraph [0025]: Once the transformations have been computed on the deformation graph (in block 118), these transformations are applied to the input mesh and the corresponding motion (i.e. the animation) of the object is rendered for display to the user (block 120). Images 105 and 106 show two example images from an animation of the chair. The first image 105 shows the chair walking and the second image 106 shows the chair jumping. These images are generated, using the method described above, when the user walks and jumps respectively); detect multiple linkage points for the non-stationary living entities (Paragraph [0022]: the animation method uses the body of the user as an input and body tracking data for the user is acquired using a sensor and received from the sensor (in block 114). Any suitable sensor may be used, including but not limited to, non-contact sensors such as camera-based systems (e.g. Kinect™, Wii™) and marker-based tracking systems (e.g. using Vicon™ markers) and contact-based sensors such as a multi-touch device. The body tracking data defines positions of one or more points on a body and any type of body tracking data may be used which enables correspondences between the sensor data and nodes in the deformation graph to be determined); determine individual actions of the non-stationary living entities as indicated by an arrangement of the multiple linkage points (Fig. 5; Paragraph [0022]: body tracking data defines positions of one or more points on a body and any type of body tracking data may be used which enables correspondences between the sensor data and nodes in the deformation graph to be determined; Paragraph [0057]: shown in FIG. 5, the node (or vertex) positions within the deformation graph are defined initially using sampling (block 502). These node positions may defined by traversing the vertices of the input mesh or by distributing nodes over the surface of the object and then using Poisson Disk sampling (which may also be referred to as Poisson Disk pattern generation). Where nodes are defined by traversing the vertices of the input mesh, the method involves selecting a region of surface (e.g. selecting a triangle and then a point inside the triangle), picking a radius based on the total area of surface and using dart throwing until a desired number of samples is reached); obtain the virtual content information from the electronic storage based on the actions (Paragraph [0079]: the tracked skeleton may be used to animate more than one object (e.g. a user's legs may be attached to a deformation graph of a chair and the same user's arms may be attached to a deformation graph of a desk lamp). Alternatively, only part of a user's body may be mapped to the deformation graph of an object (e.g. only a user's arms or only their fingers). Similarly, an entire skeleton may be used to animate only part of an object. Consequently, the attachment stage described above may be used to attach all or part of one or more skeletons to all or part of one or more deformation graphs. In some examples, the attachment process may occur in multiple phases, for example a first phase may be used to attach a first part of a skeleton to a first part of a deformation graph and a second phase may be used to attach a second part of a skeleton to a second part of a deformation graph and the user may be able to move in between phases; Paragraph [0080]: objects being animated are non-humanoid objects where such objects may be inanimate objects (such as items of furniture or other household items) or animate objects (such as animals). In some examples, however, the objects being animated may be humans, such that, for example, one user can possess the avatar for another user. Furthermore, whilst the skeletons which are tracked and attached to the deformation graph are, in most examples, human skeletons, (i.e. the skeleton(s) of the user(s)), the methods are also applicable to scenarios where the tracked skeleton belongs to an animal and it is the animal skeleton that is attached to the deformation graph generated from the input mesh); determine images of the virtual content items to be displayed in the augmented reality environment for the non-stationary living entities based on at least the virtual content information and the field of view (Paragraph [0023]: enable the body of the user to be used as an input, this tracked skeleton (as defined by the body tracking data received in block 114) is attached to the deformation graph (block 116). As the deformation graph is a representation of the input mesh, the tracked skeleton may also be considered to be attached to the mesh. The attachment of the skeleton to the graph may be performed automatically without user input or in response to voice commands from the user, as is described in more detail below. A visual display may be provided to the user at this attachment stage so that they can position their body such that it approximates the shape of the object to be animated (e.g. such that their tracked skeleton approximately aligns with the object) and so the attachment of the skeleton to the graph is done in a more intuitive manner. Image 104 shows the overlapping of the skeleton 107 and the chair; Paragraph [0025]: Once the transformations have been computed on the deformation graph (in block 118), these transformations are applied to the input mesh and the corresponding motion (i.e. the animation) of the object is rendered for display to the user (block 120). Images 105 and 106 show two example images from an animation of the chair. The first image 105 shows the chair walking and the second image 106 shows the chair jumping. These images are generated, using the method described above, when the user walks and jumps respectively).
Chen does not explicitly disclose wherein the field of view is the area that comprises the real-world view of the user; and cause the images of the virtual content items to be displayed in the augmented reality environment so that the images of the virtual content items are superimposed over the non-stationary living entities in the real-world view of the user such that the user views the images of the virtual content items in motion in accordance with the actions of the non-stationary living entities in the augmented reality environment via the display device.
However, Yasutake teaches an augmented reality system including superimposing animated objects on living entities (Abstract), further comprising wherein the field of view is the area that comprises the real-world view of the user (Paragraph [0048]: an AR based environment for a first group of users who are physically located at the substantially same or similar location, where the AR based environment is a real world environment including interactive virtual objects (e.g., 3D AR objects); and (ii) an AV based environment for a second group of users who are physically located at different locations from the first group of users, where the AR based environment is a virtual reality environment including interactive virtual objects. In some embodiments, the AR based environment includes the first group of users as real persons, as well as virtual objects related to (e.g., representing, controlled by, manipulated by, etc.) the second group of users. In some embodiments, the AV based environment includes virtual objects related to (e.g., representing) the first group of users, as well as those virtual objects related to the second group of users); and cause the images of the virtual content items to be displayed in the augmented reality environment so that the images of the virtual content items are superimposed over the non-stationary living entities in the real-world view of the user such that the user views the images of the virtual content items in motion in accordance with the actions of the non-stationary living entities in the augmented reality environment via the display device (Fig. 4D; Paragraph [0049]: Each user of the hybrid reality environment can interact with each other user through the one or more server devices. In some embodiments, each user from the first group of users can, within the AR based environment, interact with each other user from the first group of users in a face-to-face manner (i.e., the two users are physically at the same location in the real world and interacting with each other); and interact with each virtual object related to the second group of users. On the other hand, each user from the second group of users can control or manipulate a corresponding virtual object related to that user to, within the AV based environment, interact with each virtual object related to the first group of users and the virtual object related to each other user from the second group of users (i.e., the two virtual objects are virtually at the same location in the virtual world and interacting with each other, controlled or manipulated by the corresponding users). In such a way, each user from the first group of users or the second group of users can physically or virtually interact with each other user from the first group of users or the second group of users; Paragraph [0080]: FIG. 4D is a block diagram illustrating functions performed by an AR application in connection with the schematic illustrations of FIGS. 4A-4C. Instructions for such an AR application can be stored in a memory of a computer device (e.g., a mobile device, a smart phone, etc.) of a user, and performed by a processor of that computer device. As shown in FIG. 4D, a 3D video camera installed at the computer device (e.g., at a rear side of a mobile device) can be used to capture the light from the subject (i.e., the real person), and convert, in a real-time manner, collected raw data into 3D location data in accordance with the coordinate system of set at the computer device. The AR application can also overlay the 3D AR creature (i.e., the AR tiger) in a scene of the AR based environment). Yasutake teaches that this will allow for hybrid reality environments and interactions in real time between users (Abstract; Paragraph [0060]). Therefore, it would have been obvious to one of ordinary skill in the art to have modified Chen with the features of above as taught by Yasutake so as to allow for hybrid reality environments and interactions in real time between users as presented by Yasutake.
Regarding claim 3, Chen, in view of Yasutake teaches the system of claim 1, Chen discloses wherein the virtual content items comprise an animation associated with the actions (Paragraph [0025]: transformations are applied to the input mesh and the corresponding motion (i.e. the animation) of the object is rendered for display to the user (block 120). Images 105 and 106 show two example images from an animation of the chair. The first image 105 shows the chair walking and the second image 106 shows the chair jumping. These images are generated, using the method described above, when the user walks and jumps respectively).
Regarding claim 4, Chen, in view of Yasutake teaches the system of claim 1, Chen discloses wherein the multiple positions of each of the multiple linkage points in the real world defines the reference frame of the virtual content items with respect to the real world and the non-stationary living entities (Fig. 5; Paragraph [0022]: body tracking data defines positions of one or more points on a body and any type of body tracking data may be used which enables correspondences between the sensor data and nodes in the deformation graph to be determined; Paragraph [0041]: shown in the first flow diagram 400 in FIG. 4, this example method of generating an input mesh, comprises three stages (blocks 401-403). The 3D reconstruction stage (block 401) estimates the 6-DoF (Degree of Freedom) pose of the moving Kinect™ camera while the user scans the object with the camera, and fuses depth data continuously into a regular 3D voxel grid data structure which may be stored on a GPU. Surface data is encoded implicitly into voxels as signed distances, truncated to a predefined region around the surface, with new values integrated using a weighted running average. The global pose of the moving depth camera is predicted using point-plane ICP (Iterative Closest Point), and drift is mitigated by aligning the current raw depth map with the accumulated model (instead of the previous raw frame). The system produces a 3D volumetric reconstruction of the scene accumulated from the moving depth camera; Paragraphs [0044]-[0048]: meshing stage (block 403) automatically extracts and triangulates the desired foreground isosurface stored implicitly in the voxel grid. A geometric isosurface is extracted from the foreground labeled volumetric dataset using a GPU-based marching cubes algorithm. For each voxel, the signed distance value at its eight corners is computed. The algorithm uses these computed signed distances as a lookup to produce the correct polygon at the specific voxel…distance metric which is used in performing the Poisson Disk sampling may be a Euclidean distance metric; however, this can miss areas of the surface with high curvature and result in artifacts where semantically unrelated parts are linked. Consequently, an alternative distance metric may be used in sampling: a 5D orientation-aware distance metric. Where this metric is used, the sampling may be referred to as `orientation-aware sampling).
Regarding claim 5, Chen, in view of Yasutake teaches the system of claim 1, Chen discloses wherein the images of the virtual content items are generated based further on multiple positions of the display device in the real world and the multiple positions of each of the multiple linkage points in the real world (Fig. 4; Paragraph [0041]: shown in the first flow diagram 400 in FIG. 4, this example method of generating an input mesh, comprises three stages (blocks 401-403). The 3D reconstruction stage (block 401) estimates the 6-DoF (Degree of Freedom) pose of the moving Kinect™ camera while the user scans the object with the camera, and fuses depth data continuously into a regular 3D voxel grid data structure which may be stored on a GPU. Surface data is encoded implicitly into voxels as signed distances, truncated to a predefined region around the surface, with new values integrated using a weighted running average. The global pose of the moving depth camera is predicted using point-plane ICP (Iterative Closest Point), and drift is mitigated by aligning the current raw depth map with the accumulated model (instead of the previous raw frame). The system produces a 3D volumetric reconstruction of the scene accumulated from the moving depth camera; Paragraphs [0044]-[0048]: meshing stage (block 403) automatically extracts and triangulates the desired foreground isosurface stored implicitly in the voxel grid. A geometric isosurface is extracted from the foreground labeled volumetric dataset using a GPU-based marching cubes algorithm. For each voxel, the signed distance value at its eight corners is computed. The algorithm uses these computed signed distances as a lookup to produce the correct polygon at the specific voxel…distance metric which is used in performing the Poisson Disk sampling may be a Euclidean distance metric; however, this can miss areas of the surface with high curvature and result in artifacts where semantically unrelated parts are linked. Consequently, an alternative distance metric may be used in sampling: a 5D orientation-aware distance metric. Where this metric is used, the sampling may be referred to as `orientation-aware sampling).
Regarding claim 6, Chen, in view of Yasutake teaches the system of claim 1, Chen discloses wherein the images of the virtual content items are generated based further on a size of the arrangement of the multiple linkage points within the field of view of the user (Paragraph [0039]: the input mesh may be downloaded from the internet or may be generated by scanning the object (e.g. using the same sensor 314 which is also used in tracking the user's motion). The methods described herein can use arbitrary meshes, and these meshes need not be complete and may be polygon soups (including incomplete polygon soups), triangle meshes, point clouds, volumetric data, watertight 3D models, etc. In various examples, the input mesh may be generated from a real-world non-human (e.g. inanimate) object of reasonable physical size and surface reflectance).
Regarding claim 7, Chen, in view of Yasutake teaches the system of claim 1, Chen discloses wherein the field of view is defined based on location information and orientation information, the location information indicating at least a current location associated with the display device, and the orientation information indicating at least a pitch angle, a roll angle, and a yaw angle associated with the display device (Fig. 4; Paragraph [0041]: shown in the first flow diagram 400 in FIG. 4, this example method of generating an input mesh, comprises three stages (blocks 401-403). The 3D reconstruction stage (block 401) estimates the 6-DoF (Degree of Freedom) pose of the moving Kinect™ camera while the user scans the object with the camera, and fuses depth data continuously into a regular 3D voxel grid data structure which may be stored on a GPU).
Regarding claim 10, the limitations of this claim substantially correspond to the limitations of claim 1; thus they are rejected on similar grounds.
Regarding claim 12, the limitations of this claim substantially correspond to the limitations of claim 3; thus they are rejected on similar grounds.
Regarding claim 13, the limitations of this claim substantially correspond to the limitations of claim 4; thus they are rejected on similar grounds.
Regarding claim 14, the limitations of this claim substantially correspond to the limitations of claim 5; thus they are rejected on similar grounds.
Regarding claim 15, the limitations of this claim substantially correspond to the limitations of claim 6; thus they are rejected on similar grounds.
Regarding claim 16, the limitations of this claim substantially correspond to the limitations of claim 7; thus they are rejected on similar grounds.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chen, in view of Yasutake, and further in view of Haitani et al. (US Patent 10664903), hereinafter Haitani.
Regarding claim 9, Chen, in view of Yasutake teaches the system of claim 1, wherein determining the images of the virtual content items to be displayed in the augmented reality environment for the non-stationary living entities.
Chen, in view of Yasutake does not explicitly disclose employing machine learning on the actions of the non-stationary living entities.
However, Haitani teaches an augmented reality system including superimposing animated objects on living entities (Abstract; Column 6), further comprising employing machine learning on the actions of the non-stationary living entities (Column 10, lines 1-31: objects or portions thereof expressed within imaging data may be associated with a label or labels according to one or more machine-learning classifiers, algorithms or techniques, including but not limited to nearest neighbor methods or analyses, artificial neural networks, factorization methods or techniques, K-means clustering analyses or techniques, similarity measures such as log likelihood similarities or cosine similarities, latent Dirichlet allocations or other topic models, or latent semantic analyses). Haitani teaches that this will provide an enhanced user interface (Column 10). Therefore, it would have been obvious to one of ordinary skill in the art to have modified Chen, in view of Yasutake with the features of above as taught by Haitani so as to allow for enhanced user interface as presented by Haitani.
Regarding claim 18, the limitations of this claim substantially correspond to the limitations of claim 9; thus they are rejected on similar grounds.
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 MATTHEW D SALVUCCI whose telephone number is (571)270-5748. The examiner can normally be reached M-F: 7:30-4:00PT.
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/MATTHEW SALVUCCI/Primary Examiner, Art Unit 2613
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