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 amendment filed 07/30/2025 has been entered and made of record. Claims 1-8 and 13-20 are amended. No New Claim was added. No Claims were cancelled. Claims 1-20 are pending.
The applicant argues on page 13, that the amendments to claim 20 would overcome the claim rejection under 35 U.S.C. §101. The Examiner agrees that these amendments would overcome previous rejection under 35 U.S.C. §101 and the rejection is now withdrawn.
The applicant argues on page 13, the amendments to the claims 1-18 and 20 would avoid any invocation of 35 U.S.C §112(f) and would overcome the rejections under 35 U.S.C. §112(a) and 35 U.S.C. §112(b). The Examiner agrees and these claims will no longer be interpreted under 35 U.S.C. §112(f) and the rejections under 35 U.S.C. §112(a) and 35 U.S.C. §112(b) are withdrawn.
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection set forth below.
The applicant argues on page 15 of the remarks filed that the cited prior art of (Metzler et al. US PG-Pub(US 20180158200 A1) would not disclose the newly amended limitations of wherein the first measurement data is based on a luminance of reflected light and wherein the second measurement data is obtained using a simultaneous localization and mapping technique. The Examiner agrees as Metzler does not appear to teach this limitation. However, after further search and consideration the newly discovered art of Yamamoto et al. US PG-Pub(US 20210064893 A1) would disclose wherein the first measurement data is based on a luminance of reflected light in ¶[0259]-¶[0260] and wherein the second measurement data is obtained using a simultaneous localization and mapping technique in ¶[0201]-¶[0202]. Please see updated rejection claim rejections under 35 USC § 103.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 14-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 14 recites the limitation "the candidate position calculation unit" in the end of claim 14. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 12-16 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Metzler et al. US PG-Pub(US 20180158200 A1) in view of Yamamoto et al. US PG-Pub(US 20210064893 A1).
Regarding Claim 1, Metzler teaches an information processing apparatus comprising: circuitry(¶[0068] discloses a handheld device or computer to perform tasks related to image processing.) configured to obtain multiple candidate positions based on first measurement data regardining a three-dimensional position and is obtained by a sensor ([0065] “With a surveying instrument, a first 3D point cloud of a first setting at a first position is obtained. Setting is to be understood as the scene which the surveying instrument is capturing, in particular through laser scanning. The surveying instrument is placed at said first position by a user and while standing at the first position, the surveying instrument carries out a scan to record a point cloud of the setting.[0066] A first image based Simultaneous Localisation and Mapping (SLAM) process is initiated by capturing first initial image data at the first position with a camera unit comprised by the surveying instrument, wherein said camera unit comprises one or more cameras, and wherein the first initial image data and the first 3D point cloud share a first overlap. The first image data may be one or more images. The first image data, i.e. at least one of the one or more images, cover the first setting at least in part (overlap).”, as disclosed in ¶[0065]-¶[0066], the prior art uses a surveying instrument to generate multiple point cloud data of the scene and as shown in figure 1 shows the various positions acquired by the sensor.); and determine, based on the candidate positions and second measurement data regarding the three-dimensional position and is obtained by the sensor, determine any one of the candidate positions to be a determined position. ([0077] “The method according the invention may also comprise: merging the first 3D point cloud, the first SLAM point cloud and the second 3D point cloud to a total point cloud within a single coordinate system.[0078] Optionally, with the corresponding features, the second 3D point cloud is spatially linked with the first final image data based on a second overlap, which second overlap the first final image data and the second 3D point cloud share. Additionally, scale is provided by the highly precise laser scan (second 3D point cloud), this time at the “end” of the first SLAM process, i.e. for the final image data.”, as disclosed in ¶[0077]-¶[0078], the prior art uses a SLAM process to determine the 3-d position of the sensor by merging point cloud data for a final image.)
Metzler does not explicitly teach wherein the first measurement data is based on a luminance of reflected light and wherein the second measurement data is obtained using a simultaneous localization and mapping technique.
Yamamoto teaches wherein the first measurement data is based on a luminance of reflected light([0259] “a light-receiving unit that takes an image of an object with a reflectance higher than a predetermined reflectance by receiving reflected light of the light projected by the light projection unit and reflected by the object; and [0260] an orientation estimation unit that estimates own orientation on the basis of the image taken by the light-receiving unit.”, as disclosed in ¶[0259]-¶[0260], the prior art receiving measurement data of reflected light data of an object from the light projection unit.), and wherein the second measurement data is obtained using a simultaneous localization and mapping technique. ([0201] “In step S39, the feature point extraction unit 225 extracts feature points including the positions of the centers of gravity of the reflector regions from the information of the reflector regions and outputs the feature points as feature point information to the orientation estimation unit 226.”
[0202] “In step S40, the orientation estimation unit 226 uses, for example, SLAM or the like to estimate the self-orientation on the basis of the feature point information including the positions of the centers of gravity of the reflector regions and outputs the estimation result as a reflector usage orientation estimation result to the estimation result integration unit 205.”, ¶[0201]-¶[0202] disclose the idea of receiving measurement data of feature points of an object and using SLAM (Simultaneous Localization And Mapping) to determine the position of the object received.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Metzler with Yamamoto in order to for the first measurement data to pertain to reflected light and second measurement data is obtained using SLAM. One skilled in the art would have been motivated to modify Metzler in this manner in order to have a signal processing method, a program, and a moving body that can highly accurately estimate a self-position. (Yamamoto, ¶[0001])
Regarding Claim 2, the combination of Metzler and Yamamoto teach the information processing apparatus according to claim 1, where Metzler further teaches wherein the circuitry is further configured to, estimate, based on the candidate positions and the second measurement data, a position and orientation of the sensor and obtain position and orientation estimation information, and determine, based on the position and orientation estimation information, the determined position from the candidate positions. ([0032], “While being moved from the first scan station to the next scan station, with means of its components, the surveying instrument is acquiring a set of images and angular rate data, estimating the angular displacement from angular rate data, extracting and tracking 2D features locations in the image data, and using at least the 2D feature locations and angular displacements in a stochastic filtering (e.g. Kalman filter) or in a non-linear optimisation in order to reconstruct the sparse 3D point cloud and to estimate the pose of the scanner.”, as disclosed in ¶[0032], the prior art is able to estimate the position and orientation of the scanner by using the point cloud data acquired.)
Regarding Claim 3, the combination of Metzler and Yamamoto teach the information processing apparatus according to claim 2, where Metzler further teaches wherein the second measurement data includes one or multiple measurement positions(¶[0027], “laser scanning from different surveying instrument positions and recording a plurality of images or a video during the movement of the surveying instrument between these positions”, as disclosed in ¶[0027], the surveying instrument is able to acquire data from different positions when moved.), and wherein circuitry is further configured to perform a selection process for selecting a predetermined number of positions from the candidate positions and the measurement positions, and generate the position and orientation estimation information based on the predetermined number of positions.( [0099] “According to the invention, the estimation is initialized based on a laser scan, in particular based on the initial laser scan. The estimation includes current and past sensor modalities, but limited to a certain time or space window. The selection is preferably done in a probabilistic manner. [0100] High accuracy is achieved by at least: combining inertial and visual sensor modalities for pose estimation, initializing the pose estimation using the 3D point cloud at a scan station, and using a stochastic filtering or non-linear optimisation framework, at least partially, to do pose estimation.”, ¶[0099]-¶[0100] disclose the idea of selecting based on the highest degree of probability an estimated position and orientation of the device.)
Regarding Claim 12, the combination of Metzler and Yamamoto teach the information processing apparatus according to claim 2, where Metzler further teaches wherein the determined position is used to re-estimate a position and orientation of the sensor. ([0100] High accuracy is achieved by at least: combining inertial and visual sensor modalities for pose estimation, initializing the pose estimation using the 3D point cloud at a scan station, and using a stochastic filtering or non-linear optimisation framework, at least partially, to do pose estimation. ¶[0100] discloses using inertial and visual data to determine the position and orientation of the device.)
Regarding Claim 13, the combination of Metzler and Yamamoto teach the information processing apparatus according to claim 1, where Metzler further teaches wherein circuitry is further configured to obtain, as first position and orientation information, a position and orientation of the sensor at a first time and obtain, as second position and orientation information, a position and orientation of the sensor at a second time different from the first time([0121] “In a first step, a user performs a laser scan at a first station, and then moves the scanner to a second station (images are recorded during this movement) and another scan is performed at the second station” ¶[0121] disclose obtained data from two different time points and positions.), and determine the determined position from the candidate positions based on the candidate positions, the first position and orientation information, the second measurement data obtained at the second time, and the second position and orientation information, the candidate positions being obtained based on the first measurement data obtained at the first time. ([0127] “4) At this point the following correspondences or matches between image data and point clouds are available: [0128] 4a. 2D-2D match is a pair of points on two different images from the internal camera unit corresponding to the same 3D point in the object space. For each 2D-2D match, a “visual” 3D point is computed using forward intersection of rays defined by 2D image points. [0129] 4b. 2D-3D match is represented by an image point from the internal camera unit and a corresponding 3D point from a laser scan on the given scanning station. These matches are identified automatically from images and point clouds recorded at the same scanning station (e.g. for stations 1 and 2). This is possible because images and point clouds from the same station are recorded in the same coordinate frame, therefore 3D laser points could be directly projected onto the images. Matches are identified independently for station 1 and station 2, and no image tracks are required. [0130] 4c. 3D-3D match is represented by two 3D points from different scans corresponding to the overlapping area in object space. These matches are computed as neighbouring points between two scans.”, as disclosed in this section of the prior art, the first data acquired at a first time point and second data acquired at a different time is used to determine the final position of the object.)
Regarding Claim 14, the combination of Metzler and Yamamoto teach the information processing apparatus according to claim 13, where Metzler further teaches wherein the candidate positions include multiple first candidate positions, and wherein the circuitry is further configured to calculate a projection position with respect to a two-dimensional image corresponding to the second position and orientation information for a corresponding one of the first candidate positions, and determine the determined position based on the first candidate position and multiple second candidate positions that are based on the second measurement data at the projection position and are obtained by the candidate position calculation unit. ([0077] The method according the invention may also comprise: merging the first 3D point cloud, the first SLAM point cloud and the second 3D point cloud to a total point cloud within a single coordinate system.[0078] Optionally, with the corresponding features, the second 3D point cloud is spatially linked with the first final image data based on a second overlap, which second overlap the first final image data and the second 3D point cloud share. Additionally, scale is provided by the highly precise laser scan (second 3D point cloud), this time at the “end” of the first SLAM process, i.e. for the final image data.), ¶[0077] discloses using 3d point cloud projection data to determine a final position of the device.)
Regarding Claim 15, the combination of Metzler and Yamamoto teach the information processing apparatus according to claim 14, where Metzler further teaches wherein the circuitry calculates, for each of the first candidate positions, a distance between the first candidate position and each of the multiple second candidate positions, and determines the determined position based on the distance. ([0131]”5) In a last step, one global optimisation procedure (e.g. bundle adjustment) is performed in an iterative manner. The following errors are minimized together in the same optimisation procedure: [0132] 5a. Distances between image points and projections of corresponding “visual” 3D points in the image space (based on 2D-2D matches); [0133] 5b. Distances between image points and projections of corresponding 3D points from laser scans and the image space (based on 2D-3D matches). Station 1 is considered a reference station, and therefore all 3D points from this station might be directly used in optimisation. 3D points from the second station are converted to the coordinate frame of the station 1 using position and orientation of station 2 which are available by the current iteration of the optimization”, as disclosed in this section the prior art determines a distance between the image points acquired and uses those points to determine the position of the instrument.)
Regarding Claim 16, the combination of Metzler and Yamamoto teach the information processing apparatus according to claim 15, where Metzler further teaches wherein the circuitry determines the first candidate position for which the distance is shortest to be the determined position. ([0082] “3D-to-3D distances can be computed from any 3D points correspondences, whereas the correspondences can be defined by the closest distance, the normal projection of a point to a plane, the closest distance between lines or any other meaningful geometric relation, but can be established by feature detection and matching as well.”, as disclosed in this section of the prior art the closest distance between the images is used to determine the position of the object.)
Regarding Claim 19, Metzler teaches an information processing method comprising: obtaining by a processor([0068] The first 3D point cloud is spatially linked with the first initial image data based on the first overlap. This may be performed with a computing unit comprised by the surveying instrument, but also mobile computing (e.g. handheld device, tablet, or notebook computer) or cloud computing is an option if the surveying instrument is equipped with according communicative technology.), multiple candidate positions based on first measurement data that is for a three-dimensional position and is obtained by a sensor([0065] “With a surveying instrument, a first 3D point cloud of a first setting at a first position is obtained. Setting is to be understood as the scene which the surveying instrument is capturing, in particular through laser scanning. The surveying instrument is placed at said first position by a user and while standing at the first position, the surveying instrument carries out a scan to record a point cloud of the setting.[0066] A first image based Simultaneous Localisation and Mapping (SLAM) process is initiated by capturing first initial image data at the first position with a camera unit comprised by the surveying instrument, wherein said camera unit comprises one or more cameras, and wherein the first initial image data and the first 3D point cloud share a first overlap. The first image data may be one or more images. The first image data, i.e. at least one of the one or more images, cover the first setting at least in part (overlap).”, as disclosed in ¶[0065]-¶[0066], the prior art uses a surveying instrument to generate multiple point cloud data of the scene and as shown in figure 1 shows the various positions acquired by the sensor.); and determining, based on the candidate positions and second measurement data regarding the three- dimensional position and is obtained by the sensor, that any one of the candidate positions to be a determined position. ([0077] “The method according the invention may also comprise: merging the first 3D point cloud, the first SLAM point cloud and the second 3D point cloud to a total point cloud within a single coordinate system.[0078] Optionally, with the corresponding features, the second 3D point cloud is spatially linked with the first final image data based on a second overlap, which second overlap the first final image data and the second 3D point cloud share. Additionally, scale is provided by the highly precise laser scan (second 3D point cloud), this time at the “end” of the first SLAM process, i.e. for the final image data.”, as disclosed in ¶[0077]-¶[0078], the prior art uses a SLAM process to determine the 3-d position of the sensor by merging point cloud data for a final image.)
Metzler does not explicitly teach wherein the first measurement data is based on a luminance of reflected light and wherein the second measurement data is obtained using a simultaneous localization and mapping technique.
Yamamoto teaches wherein the first measurement data is based on a luminance of reflected light([0259] “a light-receiving unit that takes an image of an object with a reflectance higher than a predetermined reflectance by receiving reflected light of the light projected by the light projection unit and reflected by the object; and [0260] an orientation estimation unit that estimates own orientation on the basis of the image taken by the light-receiving unit.”, as disclosed in ¶[0259]-¶[0260], the prior art receiving measurement data of reflected light data of an object from the light projection unit.), and wherein the second measurement data is obtained using a simultaneous localization and mapping technique. ([0201] “In step S39, the feature point extraction unit 225 extracts feature points including the positions of the centers of gravity of the reflector regions from the information of the reflector regions and outputs the feature points as feature point information to the orientation estimation unit 226.”
[0202] “In step S40, the orientation estimation unit 226 uses, for example, SLAM or the like to estimate the self-orientation on the basis of the feature point information including the positions of the centers of gravity of the reflector regions and outputs the estimation result as a reflector usage orientation estimation result to the estimation result integration unit 205.”, ¶[0201]-¶[0202] disclose the idea of receiving measurement data of feature points of an object and using SLAM (Simultaneous Localization And Mapping) to determine the position of the object received.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Metzler with Yamamoto in order to for the first measurement data to pertain to reflected light and second measurement data is obtained using SLAM. One skilled in the art would have been motivated to modify Metzler in this manner in order to have a signal processing method, a program, and a moving body that can highly accurately estimate a self-position. (Yamamoto, ¶[0001])
Regarding Claim 20, Metzler teaches a non-transitory computer-readable storage medium having embodied thereon a program for causing a computer to execute a method, the method comprising: ([0090] Some embodiments of the invention also relates to a Computer Programme Product comprising programme code which is stored on a machine-readable medium, or being embodied by an electromagnetic wave comprising a programme code segment, and having computer-executable instructions for performing the steps of a method described herein, in particular when run on a surveying instrument described herein.) obtaining multiple candidate positions based on first measurement data regarding a three-dimensional position and is obtained by a sensor([0065] “With a surveying instrument, a first 3D point cloud of a first setting at a first position is obtained. Setting is to be understood as the scene which the surveying instrument is capturing, in particular through laser scanning. The surveying instrument is placed at said first position by a user and while standing at the first position, the surveying instrument carries out a scan to record a point cloud of the setting.[0066] A first image based Simultaneous Localisation and Mapping (SLAM) process is initiated by capturing first initial image data at the first position with a camera unit comprised by the surveying instrument, wherein said camera unit comprises one or more cameras, and wherein the first initial image data and the first 3D point cloud share a first overlap. The first image data may be one or more images. The first image data, i.e. at least one of the one or more images, cover the first setting at least in part (overlap).”, as disclosed in ¶[0065]-¶[0066], the prior art uses a surveying instrument to generate multiple point cloud data of the scene and as shown in figure 1 shows the various positions acquired by the sensor.); and determining, based on the candidate positions and second measurement data regarding the three-dimensional position obtained by the sensor, any one of the candidate positions to be a determined position. ([0077] “The method according the invention may also comprise: merging the first 3D point cloud, the first SLAM point cloud and the second 3D point cloud to a total point cloud within a single coordinate system.[0078] Optionally, with the corresponding features, the second 3D point cloud is spatially linked with the first final image data based on a second overlap, which second overlap the first final image data and the second 3D point cloud share. Additionally, scale is provided by the highly precise laser scan (second 3D point cloud), this time at the “end” of the first SLAM process, i.e. for the final image data.”, as disclosed in ¶[0077]-¶[0078], the prior art uses a SLAM process to determine the 3-d position of the sensor by merging point cloud data for a final image.)
Metzler does not explicitly teach wherein the first measurement data is based on a luminance of reflected light and wherein the second measurement data is obtained using a simultaneous localization and mapping technique.
Yamamoto teaches wherein the first measurement data is based on a luminance of reflected light([0259] “a light-receiving unit that takes an image of an object with a reflectance higher than a predetermined reflectance by receiving reflected light of the light projected by the light projection unit and reflected by the object; and [0260] an orientation estimation unit that estimates own orientation on the basis of the image taken by the light-receiving unit.”, as disclosed in ¶[0259]-¶[0260], the prior art receiving measurement data of reflected light data of an object from the light projection unit.), and wherein the second measurement data is obtained using a simultaneous localization and mapping technique. ([0201] “In step S39, the feature point extraction unit 225 extracts feature points including the positions of the centers of gravity of the reflector regions from the information of the reflector regions and outputs the feature points as feature point information to the orientation estimation unit 226.”
[0202] “In step S40, the orientation estimation unit 226 uses, for example, SLAM or the like to estimate the self-orientation on the basis of the feature point information including the positions of the centers of gravity of the reflector regions and outputs the estimation result as a reflector usage orientation estimation result to the estimation result integration unit 205.”, ¶[0201]-¶[0202] disclose the idea of receiving measurement data of feature points of an object and using SLAM (Simultaneous Localization And Mapping) to determine the position of the object received.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Metzler with Yamamoto in order to for the first measurement data to pertain to reflected light and second measurement data is obtained using SLAM. One skilled in the art would have been motivated to modify Metzler in this manner in order to have a signal processing method, a program, and a moving body that can highly accurately estimate a self-position. (Yamamoto, ¶[0001])
Claims 4-10 are rejected under 35 U.S.C. 103 as being unpatentable over Metzler et al. US PG-Pub(US 20180158200 A1) in view of Yamamoto et al. US PG-Pub(US 20210064893 A1) in view of Pham et al. US PG-Pub(US 20150254527 A1).
Regarding Claim 4, while the combination of Metzler and Yamamoto teach the information processing apparatus according to claim 3, Metzler does not explicitly teach wherein the circuitry, by executing the selection process and a generation process for generating position and orientation generation information based on the predetermined number of positions multiple times, generates multiple items of position and orientation generation information, and selects the position and orientation estimation information from the multiple items of position and orientation generation information.
Pham teaches wherein the circuitry, by executing the selection process and a generation process for generating position and orientation generation information based on the predetermined number of positions multiple times, generates multiple items of position and orientation generation information, and selects the position and orientation estimation information from the multiple items of position and orientation generation information. ([0031] In an embodiment, a method for object recognition is provided, the method comprising: [0032] receiving a plurality of votes, wherein each vote corresponds to a prediction of an objects pose and position; [0033] for each vote, assigning 3D ball representations to features of the object, wherein the radius of each ball represents the scale of the feature in the with respect to the frame of the object, the position of each ball representing the translation the feature in the frame of the object, [0034] determining the vote that provides the best match by comparing the features as represented by the 3D ball representations for each vote with a database of 3D representations of features for a plurality of objects and poses, wherein comparing the features comprises comparing the scale and translation as represented by the 3D balls; and [0035] selecting the vote with the greatest number of features that match an object and pose in said database. As disclosed in ¶[0032]-¶[0035], a voting process is used to estimate the position and pose of the object based on the images received.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Metzler and Yamamoto with Pham in order to use votes to select the orientation and position of the object. One skilled in the art would have been motivated to modify Metzler and Yamamoto in this manner in order to determine similarity between objects and their poses. (Pham, ¶[0018])
Regarding Claim 5, the combination of Metzler, Yamamoto and Pham teach the information processing apparatus according to claim 3, where Pham further teaches wherein circuitry is further configured not to select two or more of the candidate positions as the predetermined number of positions in each selection process. ([0057] “At test time, features extracted from the scene are matched with previously extracted features from training data by comparing their descriptions and generating an initial set of votes in step S105. The votes are hypotheses predicting the object identity along with its pose, consisting of a position and an orientation and additionally a scale if scales are unknown. The best vote is then selected and returned as final prediction in step S109.”, as disclosed in ¶[0057], the best vote is selected as the final prediction for the position and orientation which means the rest of the votes are not selected.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Metzler and Yamamoto with Pham in order to use votes to select the orientation and position of the object. One skilled in the art would have been motivated to modify Metzler and Yamamoto in this manner in order to determine similarity between objects and their poses. (Pham, ¶[0018])
Regarding Claim 6, the combination of Metzler, Yamamoto and Pham teach the information processing apparatus according to claim 4, where Pham further teaches wherein, for each item of the position and orientation generation information, the circuitry is further configured to calculate a distance between, for each of the candidate position and the measurement position, an observation position that appears in a two-dimensional image obtained by the sensor and a projection position with respect to a two-dimensional image corresponding to the position and orientation generation information, and select selects the position and orientation estimation information based on the distance between the observation position and the projection position for each item of the position and orientation generation information. ([0117] In step S509, the search tree is used to find the nearest neighbour for each of the scene features within a vote. The search is performed as shown in FIG. 13. Here, the scene feature is represented by "A". Each internal tree node i has a feature B.sub.i and a threshold C.sub.i. Each leaf node i has an item D.sub.i. To find a nearest neighbour for a given feature A is done by comparing the distance between A and B, using either of equations (18) or (19) above. Eventually, a leaf node D.sub.i will be selected as the nearest neighbour.
[0118] In step S511, the distance between the scene feature and the selected nearest neighbour is compared with a threshold. If the distance is greater than the threshold then the nearest neighbour is not considered to be a match. If the distance is less than a threshold then a match is determined. The number of matches for each vote with an object are determined and the vote with the largest number of matches is determined to be the correct vote.as disclosed in ¶[0117]-¶[0118], the prior art calculates a distance between the two positions and compares them to a threshold in order to determine if there is a match)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Metzler and Yamamoto with Pham in order to calculate distances between the 2d points of the images. One skilled in the art would have been motivated to modify Metzler and Yamamoto in this manner in order to determine similarity between objects and their poses. (Pham, ¶[0018])
Regarding Claim 7, the combination of Metzler, Yamamoto and Pham teach the information processing apparatus according to claim 6, where Pham further teaches wherein the circuitry is further configured to cast predetermined votes for position and orientation generation information for which the distance between the observation position and the projection position is less than a threshold and select selects, as the position and orientation estimation information, the position and orientation generation information having a greatest number of the predetermined votes. ([0117] In step S509, the search tree is used to find the nearest neighbour for each of the scene features within a vote. The search is performed as shown in FIG. 13. Here, the scene feature is represented by "A". Each internal tree node i has a feature B.sub.i and a threshold C.sub.i. Each leaf node i has an item D.sub.i. To find a nearest neighbour for a given feature A is done by comparing the distance between A and B, using either of equations (18) or (19) above. Eventually, a leaf node D.sub.i will be selected as the nearest neighbour.
[0118] In step S511, the distance between the scene feature and the selected nearest neighbour is compared with a threshold. If the distance is greater than the threshold then the nearest neighbour is not considered to be a match. If the distance is less than a threshold then a match is determined. The number of matches for each vote with an object are determined and the vote with the largest number of matches is determined to be the correct vote. [0117]-¶[0118] disclose the idea of calculating distance between the scene feature and closest neighbor and comparing it to a threshold to determine a vote with the highest number of matches.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Metzler and Yamamoto with Pham in order to use votes to select the orientation and position of the object. One skilled in the art would have been motivated to modify Metzler and Yamamoto in this manner in order to determine similarity between objects and their poses. (Pham, ¶[0018])
Regarding Claim 8, the combination of Metzler, Yamamoto and Pham teach the information processing apparatus according to claim 7, where Pham further teaches wherein the circuitry determines a candidate position satisfying a predetermined condition from among the multiple candidate positions to be the determined position. ([0078] The number of matches of features for a particular vote are calculated. Then the process determines if there are any further votes available in step S211. If further votes are available, the next vote is selected in step S213 and the process is repeated from step S205. Once all votes have been analysed, the vote with the highest number of matching features is selected in step S215 as the predicted pose and object. ¶[0078] discloses determining a position if the vote has the highest number of matching features.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Metzler and Yamamoto with Pham in order to use votes to select the orientation and position of the object. One skilled in the art would have been motivated to modify Metzler and Yamamoto in this manner in order to determine similarity between objects and their poses. (Pham, ¶[0018])
Regarding Claim 9, the combination of Metzler, Yamamoto and Pham teach the information processing apparatus according to claim 8, where Pham further teaches wherein the predetermined condition includes a first condition of being included in the predetermined number of positions used to generate the position and orientation estimation information. ([0078] The number of matches of features for a particular vote are calculated. Then the process determines if there are any further votes available in step S211. If further votes are available, the next vote is selected in step S213 and the process is repeated from step S205. Once all votes have been analysed, the vote with the highest number of matching features is selected in step S215 as the predicted pose and object. ¶[0078] discloses determining a position if the vote has the highest number of matching features.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Metzler and Yamamoto with Pham in order to use votes to select the orientation and position of the object. One skilled in the art would have been motivated to modify Metzler and Yamamoto in this manner in order to determine similarity between objects and their poses. (Pham, ¶[0018])
Regarding Claim 10, the combination of Metzler, Yamamoto and Pham teach the information processing apparatus according to claim 8, where Pham further teaches wherein the predetermined condition includes a second condition of having cast the predetermined votes for the position and orientation estimation information. ([0078] discloses casting votes to determine a final position and orientation for the object.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Metzler and Yamamoto with Pham in order to use votes to select the orientation and position of the object. One skilled in the art would have been motivated to modify Metzler and Yamamoto in this manner in order to determine similarity between objects and their poses. (Pham, ¶[0018])
Claims 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Metzler et al. US PG-Pub(US 20180158200 A1) in view of Yamamoto et al. US PG-Pub(US 20210064893 A1) in view Ishigaki et al. US PG-Pub(US 20230243643 A1).
Regarding Claim 17, while the combination of Metzler and Yamamoto teach the information processing apparatus according to claim 1, they do not explicitly teach wherein the sensor measures the first measurement data regarding the three-dimensional position in relation to an object surface based on a phase shift between irradiation light and light reflected on the object surface by the irradiation light.
Ishigaki teaches wherein the sensor measures the first measurement data regarding the three-dimensional position in relation to an object surface based on a phase shift between irradiation light and light reflected on the object surface by the irradiation light. ([0100] “This configuration enables the reference light component and the object light component of the light transmitted through each of the polarizers 75 of the polarizer array 72 to interfere with each other in four different phase differences. Accordingly, this configuration generates four different interfering lights that have phase differences between the reference light and the object light differing by 90 degrees each.[0101] “Concrete settings are designed to give a phase shift amount of “0 degree” with regard to the reference light component of the light transmitted through the first polarizers 75a, a phase shift amount of “90 degrees” with regard to the reference light component of the light transmitted through the second polarizers 75b, a phase shift amount of “180 degrees” with regard to the reference light component of the light transmitted through the third polarizers 75c and a phase shift amount of “270 degrees” with regard to the reference light component of the light transmitted through the fourth polarizers 75d.”, as disclosed in ¶[0100]-¶[0101] the prior art calculates phase shift between the light radiated and reflected.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Metzler and Yamamoto with Ishigaki in order to calculate the phase shift between the light radiated and reflected. One skilled in the art would have been motivated to modify Metzler and Yamamoto in this manner in order to measure the shape of an object to be measured or a measurement object. (Ishigaki, ¶[0001])
Regarding Claim 18, the combination of Metzler, Yamamoto and Ishigaki teach the information processing apparatus according to claim 17, where Ishigaki further teaches wherein the circuitry obtains the multiple candidate positions based on a modulation frequency for the irradiation light and the first measurement data (¶[0028], “the respective light-receiving elements (for example, four different types of polarizers having set angles of “0 degree”, “45 degrees”, “90 degrees”, and “135 degrees” of transmission axes); an angle data storage unit (i.e., a storage device) configured to store transmission axis absolute angle data obtained by a previous actual measurement of an absolute angle of a transmission axis of the polarizer with regard to each light-receiving element in the imaging element; and an image processing unit (i.e., a control device) configured to calculate a phase difference between the reference light and the object light with regard to a predetermined measurement position of the measurement object by a phase shift method”, as disclosed in ¶[0028], the prior art sets different angles in order to calculate different phase differences.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Metzler and Yamamoto with Ishigaki in order to calculate the phase shift between the light radiated and reflected. One skilled in the art would have been motivated to modify Metzler and Yamamoto in this manner in order to measure the shape of an object to be measured or a measurement object. (Ishigaki, ¶[0001])
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Metzler et al. US PG-Pub(US 20180158200 A1) in view of Yamamoto et al. US PG-Pub(US 20210064893 A1) in view of Pham et al. US PG-Pub(US 20150254527 A1) in view of Mukai US PG-Pub(US 20150170354 A1).
Regarding Claim 11, while the combination of Metzler, Yamamoto and Pham teach the information processing apparatus according to claim 8, they do not explicitly teach wherein the predetermined condition includes a third condition of having a shortest distance between the observation position and the projection position in the position and orientation estimation information.
Mukai teaches wherein the predetermined condition includes a third condition of having a shortest distance between the observation position and the projection position in the position and orientation estimation information. (¶[0058] (4) If the smallest value is smaller than a threshold value that is set in advance, the same ID as the ID assigned to the human position that is from the last time and is at the shortest distance is assigned to the current human position. The assignment process ends at this point, and any further steps are not carried out. (5) If the smallest value is larger than the preset threshold value, a new number that has not been assigned (or has not been used) is assigned as the ID to the current human position. ¶[0058] discloses determining the shortest distance between two different positions.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Metzler, Yamamoto and Pham with Mukai in order to determine the shortest distance between the two positions. One skilled in the art would have been motivated to modify Metzler, Yamamoto and Pham in this manner in order to display the existing position of an object caught by a camera on a map. (Mukai, ¶[0001])
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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