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 Remarks page 8, filed 12/16/2025, with respect to the Interpretations of Claims 1-12 and 14-15 under 35 U.S.C. 112(f) have been fully considered and are persuasive. The Interpretations of Claims 1-12 and 14-15 under 35 U.S.C. 112(f) have been withdrawn.
Applicant’s arguments, see Remarks page 8, filed 12/16/2025, with respect to the Rejections of Claims 5-6 and 9-11 under 35 U.S.C. 112(b) have been fully considered and are persuasive. The Rejections of Claims 5-6 and 9-11 have been withdrawn.
Applicant’s arguments, see Remarks page 9, filed 12/16/2025, with respect to the Rejection of Claim 15 under 35 U.S.C. 101, as the claimed invention being directed to non-statutory subject matter, have been fully considered and are persuasive. The Rejection of Claim 15 has been withdrawn.
Applicant's arguments, see Remarks pages 9-11, filed 12/16/2025, with respect to the Rejections of Claims 1-15 under 35 U.S.C. 101, the claimed invention being directed to an abstract idea without significantly more, have been fully considered but they are not persuasive.
On Pages 9-10 of Remarks, Applicant argues:
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Examiner respectfully disagrees.
MPEP 2106.04(II)(A)(1) discloses:
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Upon examination of the limitations present in amended claim 1, it is found that the claimed invention is directed towards an abstract idea, a mental process of recognizing a follow-up target. The limitations, which Examiner asserts are directed towards the abstract idea are:
“recognize the follow-up target”
“change a recognition algorithm for recognizing the follow-up target based on distance to the follow-up target”
“determine a trajectory of the follow-up target”
“in a case where it is determined that recognition of the follow-up target will be obstructed by an obstacle”
Wherein these limitations are directed towards the mental process of recognizing a “follow-up” target, mentally determining the target’s trajectory, and mentally determining whether the target will be obstructed by an obstacle. Wherein, the process, or algorithm, for recognizing the target is changed based on a distance from the target, such as the recognition of a target person based on either their facial features or clothing colors.
Thus, the limitations of claim 1 are directed to a judicial exception, and an evaluation must be made as to whether the claim recite additional elements that integrate the judicial exception into a practical application.
On Pages 10-11 of Remarks, Applicant argues:
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Examiner respectfully disagrees.
If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. See MPEP § 2106.05(a).
Applicant claims the limitations directed towards the “change a recognition algorithm for recognizing the follow-up target based on a distance to the follow-up target,” “determine a trajectory of the follow-up target,” and “in a case where it is determined that recognition of the follow-up target will be obstructed by an obstacle, control the movable device based on a position of the follow-up target so that the distance to the follow-up target becomes shorter,“ integrate the claimed invention into a practical application and disclose improvements to the technological field.
However, the limitations fail to disclose a technical explanation of how the circuitry and movable device are implemented, such that they allow for a novel improvement to the technological field of autonomous robot target following. For example, beyond a recitation of generically recited circuitry and movable device, the claims fail to disclose technical explanations towards the process of changing a recognition algorithm based on a distance, the process of determining a trajectory, or the process of determining the obstruction of the target and controlling the moveable device. Rather, the limitations amount to nothing more than instructions to apply the abstract ideas using a generic computer, which does not provide a meaningful limitation because the claim merely states that the abstract idea should be applied to achieve a desired result. Thus, the claim does not integrate the abstract ideas into a practical application or add significantly more.
Therefore, the rejection of claim 1 under 35 U.S.C. 101 is maintained.
As per claim(s) 14-15, arguments made in rejecting claim(s) 1 are analogous.
Applicant’s arguments, see Remarks pages 11-14, filed 12/16/2025, with respect to the Rejections of amended claim(s) 1 and 14-15 under 35 U.S.C. 103 have been fully considered and are moot in view of the new grounds of rejection (detailed in the rejections below) necessitated by Applicant’s amendment to the claim(s).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9, 12-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process of recognizing a follow-up target) without significantly more.
Claim(s) 1 recite(s):
“…recognize the follow-up target…change a recognition algorithm for recognizing the follow-up target based on a distance to the follow-up target”; Which can be reasonably be interpreted as a human observer mentally recognizing a target in different manners based upon the target’s distance, such as recognizing a target person by their facial features or clothing color.
“…determine a trajectory of the follow-up target”; Which can be reasonably be interpreted as a human observer mentally determining the trajectory of the follow-up target.
“…in a case where it is determined that recognition of the follow-up target will be obstructed by an obstacle”; Which can be reasonably be interpreted as a human observer mentally determining whether the follow-up target will be obstructed.
This judicial exception is not integrated into a practical application because of additional elements:
“…circuitry”; are generically recited computer elements that do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and pertain to a generically recited circuitry.
“…drive a movable device for following a follow-up target,” and “control the movable device based on a position of the follow-up target so that the distance to the follow-up target becomes shorter”; are generically recited computer methods that do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and pertain to a generically recited controlling/driving of a generically recited movable device.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of additional elements:
“…circuitry”; are well-understood, routine, and conventional computer elements that do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and pertain to a well-understood, routine, and conventional circuitry.
“…drive a movable device for following a follow-up target,” and “control the movable device based on a position of the follow-up target so that the distance to the follow-up target becomes shorter”; are well-understood, routine, and conventional computer methods that do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and pertain to a well-understood, routine, and conventional controlling/driving of a well-understood, routine, and conventional movable device.
Depending claims 2-9, and 12-13 do not remedy these deficiencies.
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of setting the recognition algorithm based on the distance from the target being within a threshold, without significantly more. A person can mentally recognize a target based on distinguishable features. This claim is not patent eligible.
Claim 3-4 and 7-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of detecting a target based on regions extracted from an image, without significantly more. A person can view the images and recognize a target based on features mentally extracted. The claims are not patent eligible.
Claim 5-6 and 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of detecting a target based on data from a ranging sensor, without significantly more. A person can view the produced range data and recognize a target based on features mentally extracted. The claims are not patent eligible.
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of detecting a target based on a normal distance, wherein the normal distance is the distance where the entire target can be recognized. This is a mental process. The claims are not patent eligible.
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of detecting a target, wherein the target is a person. This is a mental process. The claims are not patent eligible.
As per claim(s) 14-15, arguments made in rejecting claim(s) 1 are analogous, respectively. Note that claim 15 recites the additional elements: “A non-transitory computer-readable storage medium storing computer-readable instructions” and “a computer,” which are generically recited and well-understood, routine, and conventional.
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.
Claim(s) 1-4, 8, and 12-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (Robust Human Following by Deep Bayesian Trajectory Prediction for Home Service Robots) hereinafter referenced as Lee, in view of Amano et al. (JP2017151535A) hereinafter referenced as Amano, and Nishimura (US2018259971A1).
Regarding claim 1, Lee discloses: An information processing device comprising: circuitry configured to drive a movable device for following a follow-up target (Lee: Figure 4; SECTION IV. A. Infrastructure Setting: “The used laptop was the Asus EeePC 1215N laptop (Intel AtomTM D525 Dual Core Processor) to execute the Turtlebot2.”; Wherein the robots are controlled using the laptop processor),
recognize the follow-up target (Lee: Figure 4; SECTION III. A. Detecting and Learning to Follow a Person: Perception and Learning: “To detect people in a real-time manner, we employed the YOLOv2 [13] algorithm.”; SECTION IV. A. Infrastructure Setting: “For deep learning modules, we prepared a GPU server, Ubuntu 14.04 (ROS Indigo) based 12GB memory PASCAL GPU slotted computer.”),
and determine a trajectory of the follow-up target (Lee: Abstract: “The variational Bayesian techniques robustly predict the trajectory of the target by empowering the following ability of the robot when target is lost. We experimentally demonstrate the capability of the deep Bayesian trajectory prediction method on real-time usage”).
Lee does not disclose expressly: change a recognition algorithm for recognizing the follow-up target based on a distance to the follow-up target.
Amano discloses: change a recognition algorithm for recognizing the follow-up target based on a distance to the follow-up target (Amano: 0197: “in the tracking process of the object recognition device 1 according to this embodiment, the matching process method is switched depending on the distance to the object. That is, when the object is close, the number of pixels in the detection area is large, so rough matching processing is performed to perform template matching using a thinned prediction area and a thinned template in which pixels are significantly thinned out. Furthermore, when the object is far away, a part matching process is performed in which template matching is performed using a thinned prediction region and a thinned template in the same manner as in the rough matching process to identify the rough position of the object, and template matching is performed using a part template.”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to substitute the person reidentification algorithm disclosed by Lee with the distance based image matching algorithms taught by Amano. The suggestion/motivation for doing so would have been “in the case of short distances, rough matching processing is adopted, which increases the detection accuracy of the detection area by frame correction processing… in the case of long distances…part matching processing is adopted, which improves the detection accuracy of the detection area by correction processing using template matching that uses part templates” (Amano: 0197). Further, one skilled in the art could have substituted the elements as described above by known methods with no change in their respective functions, and the substitution would have yielded nothing more than predictable results.
Lee in view of Amano does not disclose expressly: in a case where it is determined that recognition of the follow-up target will be obstructed by an obstacle, control the movable device based on a position of the follow-up target so that the distance to the follow-up target becomes shorter.
Nishimura discloses: the movement of the robot to a distance closer to the target when it is predicted that the target moving along the predicted trajectory will be obstructed (Nishimura: 0042: “If it is determined that the target is occluded by the obstacle after the elapse of a certain period of time, the movement control unit 18 performs processing for finding out measures to avoid the occlusion…if determining that the target is occluded by the obstacle after the elapse of a certain period of time, the movement control unit 18 determines whether or not there is a path along which the target is able to be acquired continuously on the basis of the motion vector of the target and the size and traveling distance of the obstacle”;
0068: “ If the area of the face part occluded by the obstacle 71 is 50% or more, the movement control unit 18 calculates a motion vector Vt of the target 51 , a motion vector Vo1 of the obstacle 71 , and an orthogonal projection vector Voh . The target movement prediction unit 14 then refers to the orthogonal projection vector Voh and determines whether or not the robot 10 is able to avoid a collision with the obstacle 71 (the predicted destination of the robot 10 does not overlap with the predicted destination of the obstacle 71 ) if the robot 10 moves in the same direction as the direction indicated by the motion vector Vt of the target 51 (step S 9 ).”; Wherein the robot, in front of the target, moves towards them if it predicts the obstacle, which will block the person, can be avoided.).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement the surrounding object movement prediction unit disclosed by Nishimura for avoiding the obstacles disclosed by Lee in view of Amano. The suggestion/motivation for doing so would have been “it is an object of the present invention to provide an autonomous mobile robot capable of preventing the robot from losing sight of a target in the case of encountering an obstacle.” (Nishimura: 0014-0015). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Lee in view of Amano with Nishimura to obtain the invention as specified in claim 1.
Regarding claim 2, Lee in view of Amano and Nishimura discloses: The information processing device according to claim 1, wherein the processing circuitry is further configured to set, in a case where the distance to the follow-up target is a distance equal to or longer than a predetermined distance threshold, the recognition algorithm to a recognition algorithm for normal distance, and set, in a case where the distance to the follow-up target is shorter than the distance threshold, the recognition algorithm to a recognition algorithm for short distance (Amano: 0197: “in the tracking process of the object recognition device 1 according to this embodiment, the matching process method is switched depending on the distance to the object. That is, when the object is close…so rough matching processing is performed to perform template matching using a thinned prediction area and a thinned template in which pixels are significantly thinned out. Furthermore, when the object is far away, a part matching process is performed in which template matching is performed using a thinned prediction region and a thinned template in the same manner as in the rough matching process to identify the rough position of the object, and template matching is performed using a part template.”).
Regarding claim 3, Lee in view of Amano and Nishimura discloses: The information processing device according to claim 2, wherein the processing circuitry is further configured to detect a region matching an entire feature of the follow-up target from an image imaged by a camera as the recognition algorithm for normal distance (Amano: 0192: “The third template matching unit 617 of the matching unit 610 performs template matching based on the two part templates updated by the third updating unit 636 for the previous frame, within the detection area 860 detected by template matching by the second template matching unit 616 in the current frame…That is, the third template matching unit 617 detects images within the detection area 860 that match or can be considered to match (hereinafter simply referred to as "matching") the part templates 870 and 871 (or part templates 870a and 871a)...The third template matching unit 617 calculates the SAD based on each of the part templates 870 and 871 (or part templates 870a and 871a) while raster scanning the detection area 860, and finds the position of the image where the SAD is smallest.”; Wherein the matching of part template images constitutes the detection of a region matching an entire feature.).
Regarding claim 4, Lee in view of Amano and Nishimura discloses: The information processing device according to claim 2, wherein the processing circuitry is further configured to detect a region of a mobile body as a region of the follow-up target from an image imaged by a camera as the recognition algorithm for normal distance (Amano: 0192: “The third template matching unit 617 of the matching unit 610 performs template matching based on the two part templates updated by the third updating unit 636 for the previous frame, within the detection area 860 detected by template matching by the second template matching unit 616 in the current frame…That is, the third template matching unit 617 detects images within the detection area 860 that match or can be considered to match (hereinafter simply referred to as "matching") the part templates 870 and 871 (or part templates 870a and 871a)...The third template matching unit 617 calculates the SAD based on each of the part templates 870 and 871 (or part templates 870a and 871a) while raster scanning the detection area 860, and finds the position of the image where the SAD is smallest.”).
Regarding claim 8, Lee in view of Amano and Nishimura discloses: The information processing device according to claim 2, wherein the processing circuitry is further configured to detect a region matching a feature amount corresponding to the follow-up target from an image imaged by a camera as the recognition algorithm for short distance (Amano: 0160: “The first template matching unit 613 of the matching unit 610 performs template matching based on the thinning template updated by the first updating unit 632 for the previous frame within the thinning prediction region 801 (802) that has been thinned by the first thinning processing unit 612 in the current frame…That is, the first template matching unit 613 detects an image that matches or can be considered to match the thinned template 811 (812) within the thinned prediction region 801 (802)…The first template matching unit 613 calculates the SAD based on the thinning template 811 (812) while raster scanning the thinning prediction region 801 (802), and determines the position of the image with the smallest SAD.”; Wherein the object recognition based on the calculation of image similarity constitutes the detection of a region matching a feature amount.).
Regarding claim 11, Lee in view of Amano and Nishimura discloses: The information processing device according to claim 2, wherein the processing circuitry is further configured to predict a trajectory of the follow-up target (Lee: Abstract: “The variational Bayesian techniques robustly predict the trajectory of the target by empowering the following ability of the robot when target is lost. We experimentally demonstrate the capability of the deep Bayesian trajectory prediction method on real-time usage”),
and in a case where it is predicted that the follow-up target that moves along the predicted trajectory deviates from a capturing range in which the follow-up target is recognized, control the movable device so that a distance to the follow-up target becomes shorter than the distance threshold (Nishimura: 0042: “If it is determined that the target is occluded by the obstacle after the elapse of a certain period of time, the movement control unit 18 performs processing for finding out measures to avoid the occlusion…if determining that the target is occluded by the obstacle after the elapse of a certain period of time, the movement control unit 18 determines whether or not there is a path along which the target is able to be acquired continuously on the basis of the motion vector of the target and the size and traveling distance of the obstacle”;
0068: “ If the area of the face part occluded by the obstacle 71 is 50% or more, the movement control unit 18 calculates a motion vector Vt of the target 51 , a motion vector Vo1 of the obstacle 71 , and an orthogonal projection vector Voh. The target movement prediction unit 14 then refers to the orthogonal projection vector Voh and determines whether or not the robot 10 is able to avoid a collision with the obstacle 71 (the predicted destination of the robot 10 does not overlap with the predicted destination of the obstacle 71 ) if the robot 10 moves in the same direction as the direction indicated by the motion vector Vt of the target 51 (step S 9 ).”; Wherein the robot, in front of the target, moves towards them if it predicts the obstacle, which will block the person, can be avoided. Also, wherein the person being occluded, or blocked, constitutes a deviation from the capturing range.).
Regarding claim 12, Lee in view of Amano and Nishimura discloses: The information processing device according to claim 2, wherein the normal distance is a distance at which an entire follow-up target is recognized (Amano: 0141-0144: “and if the estimated distance is a long distance equal to or greater than a predetermined distance (step S121: long distance), the process proceeds to step S123…<Step S123> The third thinning unit 615, second template matching unit 616, and third template matching unit 617 of the matching unit 610 perform part matching processing using a template based on the detection region detected in the previous frame…Through the processes of steps S121 to S123 described above, the matching process (matching process of tracking process) is performed by the matching unit 610 of the tracking processing unit 520.”; Wherein the matching, and thus tracking of an object, constitutes the entire follow-up target being recognized).
Regarding claim 13, Lee in view of Amano and Nishimura discloses: The information processing device according to claim 1, wherein the follow-up target is a person (Lee: Abstract).
As per claim(s) 14, arguments made in rejecting claim(s) 1 are analogous.
As per claim(s) 15, arguments made in rejecting claim(s) 1 are analogous. In addition, the laptop disclosed in Section: IV. A. Infrastructure Setting of Lee implies the presence of “A non-transitory computer-readable storage medium storing computer-readable instructions.”
Claim(s) 5-7, and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Amano and Nishimura, and further in view of Basso et al. (Fast and Robust Multi-people Tracking from RGB-D Data for a Mobile Robot) hereinafter referenced as Basso.
Regarding claim 5, Lee in view of Amano and Nishimura discloses: The information processing device according to claim 2.
Lee in view of Amano and Nishimura does not disclose expressly: wherein the processing circuitry is further configured to detect a region of a mobile body as a region of the follow-up target from a measurement range measured by a ranging sensor as the recognition algorithm for normal distance.
Basso discloses: the detection of a target person as a mobile body using a ranging sensor for detection (Basso: 1 Introduction and Related Work: “The track initialization procedure, which relies on a HOG people detector, allows to minimize the number of false positives and the online learning person classifier is used every time a person is lost, in order to recover the correct person ID even after a full occlusion.”; 2 System Overview: “As reported in Fig. 2, the RGB-D data are processed by three main software blocks: filtering, detection and tracking. The filtering block consists in a smart down-sampling of the 3D point cloud…The detection block performs the necessary operations for clustering the remaining points and selecting the clusters of points containing people, that are subsequently passed to the tracking module.”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to supplement the person detection and tracking methods for normal and short distances disclosed by Lee in view of Amano and Nishimura with the methods for point cloud clustering and people detection and tracking as taught by Basso. The suggestion/motivation for doing so would have been “With the advent of reliable and affordable RGB-D sensors a rapid boosting of robots capabilities can be envisioned…it constitutes a very rich source of information that can be simply used on a mobile platform” (Basso: 1 Introduction and Related Work; Wherein the addition of point cloud data increases the accuracy of the methods.). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Lee in view of Amano and Nishimura with Basso to obtain the invention as specified in claim 5.
Regarding claim 6, Lee in view of Amano and Nishimura discloses: The information processing device according to claim 2.
Lee in view of Amano and Nishimura does not disclose expressly: wherein the processing circuitry is further configured to detect a region matching a shape of the follow-up target from a measurement range measured by a ranging sensor as the recognition algorithm for normal distance.
Thus, Lee in view of Amano and Nishimura does not disclose expressly the detection of the target person by matching the shape of the target with a region of radar sensor data for detection at the normal distance.
Basso discloses: the detection of a target person by matching the shape of the target with a region of radar sensor data (Basso: 4 Detection: “In order to divide the scene into different clusters, our algorithm remove all 3D points belonging to the floor from the output of the previous software block…the different clusters are no longer connected through the floor, so they can be easily calculated by labeling neighboring 3D points on the basis of their Euclidean distances…For each cluster, we estimate: the height from the ground plane, the centroid, the distance from the sensor and the corresponding blob in the RGB image. Clusters with height out of a plausible range for an adult person are discarded before computing the subsequent features and do not pass to the tracking phase.”; Wherein the extraction of cluster points based on human features constitutes the matching of a target’s shape.)
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to supplement the person detection and tracking methods for normal and short distances disclosed by Lee in view of Amano and Nishimura with the methods for point cloud clustering and people detection and tracking as taught by Basso. The suggestion/motivation for doing so would have been “With the advent of reliable and affordable RGB-D sensors a rapid boosting of robots capabilities can be envisioned…it constitutes a very rich source of information that can be simply used on a mobile platform” (Basso: 1 Introduction and Related Work; Wherein the addition of point cloud data increases the accuracy of the methods.). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Lee in view of Amano and Nishimura with Basso to obtain the invention as specified in claim 6.
Regarding claim 7, Lee in view of Amano and Nishimura discloses: The information processing device according to claim 2.
Lee in view of Amano and Nishimura does not disclose expressly: wherein the processing circuitry is further configured to detect a region matching a representative color of the follow-up target from an image imaged by a camera as the recognition algorithm for short distance.
Thus, Lee in view of Amano and Nishimura does not disclose expressly the detection of the target person by detecting a region matching a representative color of the target for detection at the short distance.
Basso discloses: the detection of the target person by detecting a region matching a representative color of the target (Basso: 5.2 Online Classifier: “we maintain for each track an online classifier based on Adaboost...we select the image pixels belonging to the person by exploiting the blob mask given by the detector; 2. we compute the 3D color histogram of these points on the three RGB channels; 3. we select a set of randomized parallelepipeds (one for each weak classifier) inside the histogram. The feature value is given by the sum of histogram elements that fall inside a given parallelepiped.
For the training phase, since the approach introduced by [10] gave us poor performances, we use as positive sample the color histogram of the target, while as negative samples we consider the histograms calculated on the detections not associated to the current track.”; Wherein a target is detected based on a model trained using the target’s corresponding colors).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the person reidentification algorithm for normal and short distances disclosed by Lee in view of Amano and Nishimura with the inclusion of the trained online classifier disclosed Basso. The suggestion/motivation for doing so would have been “This approach has the advantage to select only the colors that really characterize the target and distinguish it from all the others.” (Basso: 5.2 Online Classifier). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Lee in view of Amano and Nishimura with Basso to obtain the invention as specified in claim 7.
Regarding claim 9, Lee in view of Amano and Nishimura discloses: The information processing device according to claim 2.
Lee in view of Amano and Nishimura does not disclose expressly: wherein the processing circuitry is further configured to detect a region of a mobile body as a region of the follow-up target from a measurement range measured by a ranging sensor as the recognition algorithm for short distance.
Thus, Lee in view of Amano and Nishimura does not disclose expressly the detection of the target person as a mobile body using a ranging sensor for detection at the short distance.
Basso discloses: the detection of a target person as a mobile body using a ranging sensor for detection (Basso: 1 Introduction and Related Work: “The track initialization procedure, which relies on a HOG people detector, allows to minimize the number of false positives and the online learning person classifier is used every time a person is lost, in order to recover the correct person ID even after a full occlusion.”; 2 System Overview: “As reported in Fig. 2, the RGB-D data are processed by three main software blocks: filtering, detection and tracking. The filtering block consists in a smart down-sampling of the 3D point cloud…The detection block performs the necessary operations for clustering the remaining points and selecting the clusters of points containing people, that are subsequently passed to the tracking module.”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to supplement the person detection and tracking methods for normal and short distances disclosed by Lee in view of Amano and Nishimura with the methods for point cloud clustering and people detection and tracking as taught by Basso. The suggestion/motivation for doing so would have been “With the advent of reliable and affordable RGB-D sensors a rapid boosting of robots capabilities can be envisioned…it constitutes a very rich source of information that can be simply used on a mobile platform” (Basso: 1 Introduction and Related Work; Wherein the addition of point cloud data increases the accuracy of the methods.). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Lee in view of Amano and Nishimura with Basso to obtain the invention as specified in claim 9.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANTHONY J RODRIGUEZ whose telephone number is (703)756-5821. The examiner can normally be reached Monday-Friday 10am-7pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sumati Lefkowitz can be reached at (571) 272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ANTHONY J RODRIGUEZ/Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
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