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 Arguments
Applicant’s arguments, see pg. 8 of remarks, filed 02/03/2026, with respect to the rejection(s) of claim(s) 1-7, and 9-13 under 103 have been fully considered. Regarding the original portions of the claim, the original grounds of rejection is maintained. Pertaining to the amended claim limitations: “acquire a plurality of shape data based on the acquired attribute information, wherein each shape data of the plurality of shape data representing represents a respective object of the one or more objects” and “and wherein the data set includes a plurality of object data, wherein each object data of the plurality of object data includes a respective combination of the one or more objects represented by the plurality of shape data and the one or more objects in the respective combination are arranged in a respective pattern of positional relationships of the different patterns of positional relationships.” the arguments are found to be persuasive. Therefore, the original rejection is withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Joshi.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, and 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Yokoyama (U.S. Patent no. 20210046545 A1) in view of Joshi (Pub No. US 20130120383 A1) and further in view of Aghamohammadi (U.S. Patent No. 20170157769 A1) and further in view of Bartoli (U.S. Patent No. 20250061585 A1).
As per claim 1, Yokoyama teaches the claimed:
an information processing apparatus ([0057]) comprising: one or more memories storing instructions ([0033]); and one or more processors ([0028]), wherein the one or more processors execute the instructions to: acquire region information representing a three-dimensional space ([0036]);
Yokoyama alone does not explicitly teach the remaining claim limitations.
However, Yokoyama in combination with Joshi teaches the claimed:
acquire a plurality of shape data based on the acquired attribute information, wherein each shape data of the plurality of shape data representing represents a respective object of the one or more objects; (Joshi [0056]: “In contrast to conventional methods that use an implicit surface representation to model surfaces, embodiments may use a polygon (e.g., triangle) mesh to inflate the given curves. Embodiments may use the inflation metaphor, but without using the chordal axis. Embodiments may allow both smooth and sharp internal boundaries drawn directly on the inflated surface to modify the surface. In contrast to conventional methods, embodiments implement a linear system and work around its deficiencies, instead of using a slower, iterative non-linear solver that is not guaranteed to converge. In addition, embodiments may provide a greater range of modeling operations than conventional methods. While this approach may not allow the solution of the whole mesh as a unified system, embodiments provide an alternative patch-based approach which may be more intuitive to users, as the global solve in conventional methods may result in surface edits tending to have frustrating global effects. While embodiments are generally described as using a triangle mesh, other polygon meshes may be used.” These are the plurality of shapes that represent objects.).
Yokoyama alone does not explicitly teach the remaining claim limitations.
However, Yokoyama in combination with Aghamohammadi teaches the claimed:
and generate a data set including a plurality of shape data and a plurality of arrangement data of the plurality of shape data so that a spatial coverage ratio of the plurality of shape data in the three-dimensional space is a predetermined ratio or more based on the acquired region information, (Aghamohammadi [0029]: “In one configuration, to determine a map, the map may be partitioned into voxels (e.g., cells). Each voxel may have a state of being occupied (e.g., full), partially occupied, or empty. When generating a map using the incremental approach (e.g., incremental data), conventional techniques may calculate inconsistent maps, may not account for the uncertainty in a determined occupancy level of a voxel, and/or may not determine the occupancy level (e.g., full, partially full, or empty) of voxels. … In the present disclosure, occupancy level may refer to the ratio of an occupancy over a space. Furthermore, occupancy level may also be referred to as occupancy and/or density”) Aghamohammadi describes the ratio of an occupancy of voxels over a space. The occupancy data is the shape data of the spatial coverage ratio. Additionally, voxels imply a 3-D object. In the case of Aghamohammadi, the coverage data is based on the map, which is the region information acquired by the robot.)
Yokoyama alone does not explicitly teach the remaining claim limitations.
However, Yokoyama in combination with Bartoli teaches the claimed
wherein the plurality of arrangement data have different patterns of positional relationships among the plurality of shape data. (Bartoli [0007]-[0008]: “[0007] The placement of the camera, i.e. its position and its orientation in a given coordinate system, is calculated in real time with respect to a 3D model of the environment in which the camera is moving, so as to be able to display, in the image captured by the camera, augmented elements from a precalculated and preoperative augmented reality model. This pre-operative augmented reality model typically comes from preoperative or peroperative imaging by CT, MRI, US or other modality used in radiology. These preoperative or peroperative images, whether they are 2D or 3D, are assumed to be registered on the 3D model in advance.
[0008] In particular, the placement of the camera is calculated from a keyframe base and a base containing the placement of each keyframe in the repository of the 3D model of the peroperative environment which acts by comparing the current image with the keyframes of this keyframe base.” Bartoli teaches the camera’s position and orientation based on the arrangement of the 3D model being analyzed. This implies that there are a variety of arrangements and positional relationships between the shapes in the scene. These different shape arrangements could be converted into voxels as taught by Yokoyama.).
Yokoyama alone does not explicitly teach the remaining claim limitations.
However, Yokoyama in combination with Joshi teaches the claimed:
and wherein the data set includes a plurality of object data, wherein each object data of the plurality of object data includes a respective combination of the one or more objects represented by the plurality of shape data and the one or more objects in the respective combination are arranged in a respective pattern of positional relationships of the different patterns of positional relationships. (The positional relationships between objects concern the way they are facing in relation to each other. This would concern their surface normal, as shown in figs. 9a-9c of the applicant’s drawings. Joshi teaches the relative positions of the objects using surface normals, which are constrained in how they can face each other. This is the plurality of patterns. Joshi [0024]: “FIGS. 14a and 14b show an example surface generated with a smooth position constraint and with a concave angle at the boundary as specified using surface normal constraints, according to one embodiment.”.
[0025] FIGS. 15a and 15b show an example surface generated with a smooth position constraint and with a flat angle at the boundary as specified using surface normal constraints, according to one embodiment.
[0026] FIGS. 16a and 16b show an example surface generated with a smooth position constraint and with a convex angle at the boundary as specified using surface normal constraints, according to one embodiment.” Since they are constrained in their normal, as well as boundary conditions. These are positional relationships among the plurality of relationships. Joshi [0051]: “Various embodiments may use mean curvature constraints, surface normal constraints, or a combination of mean curvature constraints and surface normal constraints, as boundary conditions to control the inflation. The mean curvature of a surface is an extrinsic measure of curvature that locally describes the curvature of the surface. Thus, a mean curvature constraint is a specified value for the mean curvature at a particular boundary location, i.e. at a particular point or vertex on an external or external boundary, or for a particular segment of an external or internal boundary. A surface normal, or simply normal, to a flat surface is a vector perpendicular to that surface. Similarly, a surface normal to a non-flat surface is a vector perpendicular to the tangent plane at a point on the surface. Thus, a surface normal constraint specifies that, at this point on the surface (i.e., at a point on an external or internal boundary of the surface), the surface normal is to point in the specified direction. As an example, a user may want the surface normal at a point on a boundary to be facing 45 degrees out of plane to generate a 45 degree bevel, and thus may set the surface normal constraint to 45 degrees at the point. Surface normal constraint values may be specified at a particular boundary location, i.e. at a particular point or vertex on an external or internal boundary, or for a particular segment of an external or internal boundary.” The constraints on the surface normal are the preset patterns, and the arrangement of objects is a combination of positional relationships between objects that are constrained by which way they are facing. Joshi [0178]: “3D modeling tool 300 may provide a user interface 302 that provides one or more textual and/or graphical user interface elements, modes or techniques via which a user may enter, modify, indicate or select images, or regions of images, to be inflated (represented by input image 310), enter, modify, or select position constraints, select a gravity option or similar arbitrary directional flow option, input or draw strokes into shapes, images, or regions of digital images, specify or modify smooth or sharp position and boundary constraints including mean curvature constraints and surface normal constraints, specify pixel-position constraints, deactivate and reactivate constraints before, during, or after inflation, select internal constraints for which separate patches are to be generated, selectively discard a patch surface, draw constraint curves on geometric shape primitives, change the angle parameter and vary the surface only along one side of a boundary, cut-and-paste a constraint from one location on a surface to another location to thus make a copy of the constraint at the new location, and in general provide input to and/or control various aspects of surface inflation and 3D modeling as described herein using embodiments of a 3D modeling tool 300 as described herein.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the determination of positional relationships between objects and construction based on these combinations as taught by Joshi with the system of Yokoyama in order to determine directions certain 3D objects face based on their surface normal and construct larger scenes based on these relationships.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine 3-D spatial region populated with object taught by Yokoyama voxel occupancy data and relationship to space, taught by Aghamohammadi. This would give a user the ability to analyze the relationship between the objects in the region and determine the space they occupy. A device that tracked 3-D spatial data would be motivated to determine its presence in different parts of the associated region to identify, modify, or avoid the object represented by that data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine 3-D spatial region populated with the camera parameters modified in relationship to different arrangement of 3-D object data in an environment as taught by Bartoli with the object voxel data generation of a scene as taught by Yokoyama to use a camera to capture a 3-D object to convert into voxels and adapt the camera to differences in the objects the user might seek to analyze.
As per claims 10 and 11, these claims are similar in scope to limitations recited in claim 1, and thus are rejected under the same rationale.
Regarding claim 2, Yokoyama alone does not explicitly teach the remaining claim limitations.
However, Yokoyama in combination with Aghamohammadi teaches the claimed:
information processing apparatus according to claim 1, wherein the region information includes information indicating a range of the three-dimensional space and information indicating a size of a voxel, which is a unit volume element forming a region corresponding to the three-dimensional space (Aghamohammadi [0040]: “As shown in FIG. 3B, according to an aspect of the present disclosure, the robot 300 may be placed in an environment to be mapped 306. The environment to be mapped 306 may include multiple voxels 308. As shown in FIG. 3B, based on the measurements by the sensor, the sensor may determine an occupancy level of each voxel 308 within the measurement cone 302. It should be noted that the voxels 308 of FIG. 3B are for illustrative purposes, the voxels of the present disclosure are not limited to the size or number of voxels shown in FIG. 3B.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine 3-D spatial region populated with voxels as representations of those objects as taught by Aghamohammadi with the system of Yokoyama in order to define the primitives representing the objects in a standardized way.
Regarding claim 3, Yokoyama does not disclose the claimed limitations:
However, Yokoyama in combination with Aghamohammadi discloses the claimed:
3. The information processing apparatus according to claim 2, wherein the one or more processors further execute the instructions to calculate the spatial coverage ratio based on a respective number of voxels included within each shape data of the plurality of shape data. (Aghamohammadi [0029]: “In one configuration, to determine a map, the map may be partitioned into voxels (e.g., cells). Each voxel may have a state of being occupied (e.g., full), partially occupied, or empty. When generating a map using the incremental approach (e.g., incremental data), conventional techniques may calculate inconsistent maps, may not account for the uncertainty in a determined occupancy level of a voxel, and/or may not determine the occupancy level (e.g., full, partially full, or empty) of voxel” Aghamohammadi describes determining the number of voxels based on the occupancy level. Aghamohammadi [0057]: “In EQUATION 1, z.sub.0:k are the predicted measurements that will be collected by the sensor from time step 0 to time step k. That is, EQUATION 1 recursively determines the probability of the occupancy level (d) at time step k given the sensor measurements from time step 0 to time step k (z.sub.0:k). The occupancy level (d) is for the entire map. That is, d is the collection of all voxels in the map d.sup.l to d.sup.g, where g is the number of voxels in the map. In EQUATION 1, p(z.sub.k|d) is the likelihood of obtaining a measurement (z) at time step k given the occupancy level (d) of all voxels in the map.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the voxel occupancy data taught by Aghamohammadi with the system of Yokoyama to analyze the objects being generated and show that they fill the amount of space desired by the user.
As per claim 12, Yokoyama alone does not explicitly teach the claimed limitations.
However, Yokoyama in combination with Aghamohammadi teaches the claimed:
12. The information processing apparatus according to the information processing apparatus according to wherein the plurality of shape data are arranged not to overlap in the plurality of arrangement data. (Aghamohammadi figures 6a-6g show a robot mapping an area in a space and shows objects in the space that do not overlap.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the non-overlapping shape data as taught by Aghamohammadi with the system of Yokoyama in order to analyze and process the spatial occupancy of separate shapes that do not overlap, as this will give different results for spatial occupancy than a scene with objects that do overlap.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Yokoyama in view of Joshi and further in view of Aghamohammadi and further in view of Bartoli and further in view of Litvin (U.S. Patent No. 8787669 B2).
As per claim 4, Yokoyama alone does not explicitly teach the remaining claim limitations. However, Yokoyama in combination with Aghamohammadi and Litvin teaches the claimed: 4. The information processing apparatus according to claim 2, wherein the one or more processors further execute the instructions to calculate a spatial coverage ratio of each object, of the one or more objects, in the three- dimensional space based on a number of voxels intersecting with a surface of object as represented by the shape data of the object. (Litvin teaches the information processing apparatus according to claim 2, wherein the one or more processors further execute instructions to calculate a ratio of the object in the spatial region based on a number of voxels intersecting with a surface of the shape of the object (49-50). Litvin teaches the information processing apparatus according to claim 2, wherein the one or more processors further execute the instructions to calculate a ratio of the object in the spatial region based on a number of voxels intersecting with a surface of the shape of the object. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to analyze the voxels in the spatial region of Yokoyama by breaking a shape into layers. Finding the surface voxels of an object indicates its volume and the space in the region it occupies, as well as its interaction or overlap with other objects. The voxels in the layers are three-dimensional.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the determination of intersecting voxel surfaces to detect the spatial coverage ratio as taught by Litvin with the system of Yokoyama modified by Aghamohammadi in order to account for intersecting objects in the calculation of spatial coverage.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Yokoyama in view of Joshi and further in view of Aghamohammadi and further in view of Bartoli and further in view of Bai (U.S. Patent No. 20220270387 A1).
Regarding claim 5, Yokoyama does not explicitly teach the claimed. However, Yokoyama in combination with Bai teaches the claimed:
5. The information processing apparatus according to claim 1, wherein each object of the one or more objects is a person, and the attribute information includes at least information about a number of persons, a body height, and a body width. (Bai [0029], [0030]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine Bai with Yokoyama in view of Aghamohammadi analyze the attributes of height, width, and number of instances for human objects, as those are some of a human’s most notable attributes when analyzing their presence in a special region. Furthermore, one of ordinary skill in the art at the time of filing would be motivated to use the spatial region and object data analyses disclosed by claim 1 and taught by Yokoyama to analyze the positions and dimensions of human subjects, which are central to many computer graphics applications.
As per claim 13, Yokoyama alone does not explicitly teach the claimed limitations.
However, Yokoyama in combination with Bai teaches the claimed:
13. The information processing apparatus according to wherein the attribute information includes respective attribute information regarding each object the one or more objects, and wherein, for each object of the one or more objects, the respective attribute information regarding the object is different from the respective attribute information reqardinq all other objects of the plurality of objects. (Bai teaches the structure being analyzed being a model of a human body. That model is divided into sets of reference control points. These points are used to control attributes like height and width. These different control points can set those different attributes of the body structure individually. Bai [0075]: “Both the reference three-dimensional human body model and the customized three-dimensional human body model below are obtained by changing the positions of the bone points and body surface feature points in the standard three-dimensional human body model. In order to facilitate the distinction, the bone points and body surface feature points of the standard three-dimensional human body model are marked as standard bone points A1 and standard body surface feature points B1, respectively; the bone points and body surface feature points of the reference three-dimensional human body model are marked as reference bone points A2 and reference body surface feature points B2, respectively; the bone points and body surface feature points of the customized three-dimensional human body model are marked as customized bone points A4 and customized body surface feature points B4, respectively.” The surface feature points of the different body parts are different from each other since they concern other body parts.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the control points of a 3-D model of a human body as taught by Bai with the system of Yokoyama because a human body is a common subject to be analyzed by converting it to a 3-D structure of voxels. It would be beneficial to be able to modify the control points of a human body to accurately control and analyze the voxels used to represent it.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Yokoyama and further in view of Joshi and further in view of Aghamohammadi further in view of Bartoli and further in view of Fuchikami (U.S. Patent No. 9832436 B1).
Regarding claim 6, Yokoyama does not explicitly teach the claimed limitations. However, Yokoyama in combination with Fuchikami teaches the claimed. The information processing apparatus according to claim 1, wherein the one or more processors further execute the instructions to: determine of the different patterns of positional arrangements data based on the region information. (Fuchikami claim 1: “An image projection system for projecting a content image toward a projection target, the system comprising: a non-visible light projection apparatus that projects a pattern image for shape measurement toward the projection target by using non-visible light; an imaging device that captures the pattern image projected to the projection target; a measurement control device that acquires three-dimensional shape information of the projection target based on the captured pattern image; a projected image processing device that converts a previously prepared content image into a projection content image corresponding to the projection target based on the shape information;” Fuchikami discloses measuring the distances of the objects related to different positions and shapes. These are the plurality of patterns of arrangement. Fuchikami col. Lines 10-20: “In the measurement processing, the first infrared ray projection apparatus 3L projects the pattern image (ST201), and imaging device 5 captures the pattern image (ST202) in the same manner as the case of the above first exemplary embodiment. Next, calculation device 6 associates each pixel of the infrared ray image of infrared ray projection apparatus 3 with each pixel of the captured image of imaging device 5 based on the captured image acquired by imaging device 5 and measures distances (positions and shapes) related to each pixel (ST303).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the shape data pattern acquisition of Fuchikami with the system of Yokoyama to capture the objects in the scene’s shape data to analyze and determine the pattern the make based on the desired characteristics of the scene, as understanding the pattern would help give clarity nature of the objects.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Yokoyama in view of Joshi and further in view of Aghamohammadi and further in view of Bartoli and further in view of Zhang (U. S. Patent No. 20220301220-A1).
Regarding claim 7, Yokoyama alone does not explicitly teach the remaining claim limitations.
However, Yokoyama in combination with Zhang teaches the claimed: The information processing apparatus according to claim 1, wherein the one or more processors further execute the instructions to give priority to a shape data of the plurality of shape data based on a dispersion of positions of arranged shape data. (Zhang [0071]- [0073]: “[0071] In a possible implementation, the object distribution images may include: an image of a distribution range of the at least one to-be-analyzed object in the target object.
[0072] In a possible implementation, the area determination part is configured to: obtain, from the object distribution images, a reference image corresponding to the anchor point; and determine, according to a serial number or ranking position of the reference image among the object distribution images, the range of area where the current to-be-analyzed object corresponding to the anchor point is located in the target object.
[0073] In a possible implementation, the device may further include a feedback part. The feedback part is configured to: display the reference image corresponding to the anchor point in a display mode different from a mode of displaying a non-reference image among the object distribution images, to distinguish the reference image from the non-reference image; and feed an obtained display result back to a user in real time.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the object data in the spatial region and evaluate them based on the variance in position and relationship with other objects as taught by Zhang with the system of Yokoyama in order to help with ray tracing and luminosity algorithms, spatial subdivisions, or finding the topography of a scene.
Claims 9 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Yokoyama and further in view of Joshi and further in view of Aghamohammadi and further in view of Bartoli and further in view of Itakura (US Patent no. 20200126290 A1).
Regarding claim 9, Yokoyama alone does not explicitly teach the remaining claim limitations.
However, Yokoyama in combination with Bartoli teaches the claimed:
the information processing apparatus according to claim 1 wherein the one or more processors further execute the instructions to: set camera parameters of an imaqe-capturinq system to be used to capture an image of an object in the three-dimensional space; (Bartoli [0007]-[0008]).
generate an imaging simulation image using the plurality of shape data, the camera parameters, and the data set; (Bartoli [0007]-[0008] teaches basing the parameters on the data. Imaging simulation. Bartoli [0007]: “The placement of the camera, i.e. its position and its orientation in a given coordinate system, is calculated in real time with respect to a 3D model of the environment in which the camera is moving, so as to be able to display, in the image captured by the camera, augmented elements from a precalculated and preoperative augmented reality model. This pre-operative augmented reality model typically comes from preoperative or peroperative imaging by CT, MRI, US or other modality used in radiology. These preoperative or peroperative images, whether they are 2D or 3D, are assumed to be registered on the 3D model in advance.
[0008] In particular, the placement of the camera is calculated from a keyframe base and a base containing the placement of each keyframe in the repository of the 3D model of the peroperative environment which acts by comparing the current image with the keyframes of this keyframe base.” The position and orientation of the camera are the parameters.).
Yokoyama alone does not explicitly teach the remaining claim limitations.
However, Yokoyama in combination with Itakura teaches the claimed:
estimate a three-dimensional shape of the object in the three-dimensional space using the imaging simulation image, and evaluate a shape estimation accuracy based on a shape indicated by the shape data and the estimated three-dimensional shape of the object in the three- dimensional space (Itakura [0060|: “The allowable angle θ.sub.thr indicates the angle between virtual viewpoints in which it is possible to determine that a possibility of having captured the same region of the object at the virtual viewpoint and the image capturing viewpoint is strong and the allowable angle θ.sub.thr is set by taking into consideration the error of the shape estimation and the error of the image capturing viewpoint information. In a case where the beam angle data of an image capturing apparatus is less than or equal to the allowable angle θ.sub.thr, the effectiveness degree of the image capturing apparatus is set to 1, which is the maximum value. As described previously, as the angle between viewpoints becomes larger, the influence of the error of the shape estimation becomes greater. Because of this, the effectiveness degree of the image capturing apparatus is found in accordance with equation (6) so that as the beam angle data increases, the effectiveness degree decreases. However, the calculation method of the effectiveness degree is not limited to the above and it may also be possible for the effectiveness degree to decrease nonlinearly as the beam angle data increases. Further, the setting of the allowable angle is not indispensable and it may be possible to use a variety of methods, such as a method of finding the effectiveness degree so that the effectiveness degree decreases as the beam angle data increases by setting the effectiveness degree in a case where the beam angle data is 0 to 1, which is the maximum value. The camera effectiveness degree determination unit 252 outputs the found effectiveness degree for each image capturing apparatus to the camera priority level determination unit 254 and a rendering weight determination unit 255” The image capturing device estimates the shape of the object from the virtual viewpoint. The error of the shape estimation is shown through the accuracy of the shape estimation. Itakura describes this being done with three-dimensional objects. Itakura [0029]: “In the rendering processing in the present embodiment, by using position information on a virtual viewpoint 401 located at an arbitrary viewpoint position and three-dimensional shape data of an object, a region 403 of the object corresponding to a pixel 402 on a virtual viewpoint image, which is a rendering target, is specified. After that, from multi-viewpoint image data, image data (406, 407) including pixels (404, 405) having captured the specified region”).
One of ordinary skill in the art at the time of filing would be motivated to set camera parameters taught by Bartoli to observe a scene in Joshi spatial region from a specific angle and distance to analyze the data visually. Furthermore, one of ordinary skill would be motivated to estimate the shape of an object generated by Yokoyama’s method 3-D object and compare it to a known value. Image analysis often involved determining the dimensions of a 3-D object from a 2-D image perspective. 3-D images are evaluated in 2-D space. Object properties like volume, relation to light source, or other physical characteristics must be determined from a 2-D image. Likewise, often measurements of observed or generated data are compared to known values to evaluate a method’s fidelity, as is done with Itakura. These can include image generation and image detection methods whose accuracy needs to be verified.
As per claim 14, this claim is similar in scope to limitations recited in claim 9, and thus is
rejected under the same rationale.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/THOMAS JOHN FOSTER/Examiner, Art Unit 2616
/HAI TAO SUN/Primary Examiner, Art Unit 2616
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