DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The Amendment filed 20 February, 2026 (hereinafter “the Amendment’) has been entered and considered. Claims 1-2, 14, and 17-18 have been amended. Claim 19 has been added. Claims 1-19 are rejected. All modifications to the grounds of rejection set forth in the present action were necessitated by Applicants’ claim amendments.
Response to Amendment
§112 Rejections
2. The §112 Rejections from the associated Final Rejection dated 12/29/2025 are withdrawn.
Prior Art Rejections
3. On pages 14-15 of the Amendment the Applicant contends that Yoshida fails to disclose the newly added features of Amended claim 1. The Applicant further contends that Yoshida does not disclose or suggest that among the plurality of items of tracking information, tracking information that has already been associated with the person information is excluded from a target used in the collation processing. The Examiner respectfully disagrees and repeats from Yoshida, P[0007]: where the collation range is set and the argument that all detections beyond the set collation range are not collated is upheld. The tracking information that has already been associated with each person that is detected is collected at all ranges, however, only the tracking information within the collation range is collated, therefore the tracking information that has been detected beyond the collation range is excluded/omitted. The current features present in the claim do not preclude this interpretation. Fig. 14 of Yishida shows that first the tracking target is detected, then identified, then next the tracking information is generated based on the identified target. After the tracking information is generated is when the collation range is set for a plurality of frames. Only tracking information that was associated with the target individual that is within the collation range is actually collated, reducing the volume of data that needs to be processed, and this process repeats upon each verification frame acquired. Therefore, Yoshida does indeed teach the newly added features of the independent claims.
The rejection of claim 2 has been modified, which was made necessary by the Applicants amendment to the claim.
All new grounds of rejection set forth by the Examiner were necessitated by the Applicant’s amendment to the claims.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
4. Claims 1, 3-11, 13-15, and 17-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20230206468 A1: Noboru Yoshida et al., (herein after “Yoshida”).
Regarding claim 1, An information processing apparatus comprising:
one or more memories storing instructions (Yoshida, P[0137]: “The main storage device 92 may be a volatile memory such as a dynamic random access memory (DRAM). A nonvolatile memory such as a magnetoresistive random access memory (MRAM) may be configured and added as the main storage device 92.”); and
one or more processors executing the instructions to (Yoshida, P[0135]: “the computer 90 includes a processor 91”:
determine, from among a plurality of items of tracking information corresponding to a plurality of persons detected from a video image frame, tracking information that is a target to be used in a collation processing for performing association of the tracking information with person information of a person, based on a change of a feature amount over time of each of the plurality of persons (Yoshida discloses acquiring a plurality of tracking information data corresponding to a plurality of people in Fig. 7-8 and determining tracking information based on a change of feature amount over time in P[0048]: “For example, the tracking unit 13 may detect the tracking target from the verification frame by a detection technique using a feature amount such as a motion vector. The tracking target of the tracking unit 13 is a person or an object that moves (also referred to as a moving object). For example, in a case where the tracking target is a person, the tracking unit 13 detects the tracking target from the verification frame using a face detection technique or the like.”), and
execute the collation processing in which the person information to be associated with the determined tracking information is identified, based on a similarity between the feature amount of the person corresponding to the determined tracking information and a plurality of feature amounts stored in association with a plurality of items of person information in a storage device, where Yoshida discloses collation processing in P[0008]: “the method executed by a computer includes acquiring a designation range designated for each of a plurality of image frames constituting video data, extracting the image frame to be verified from the video data, detecting a tracking target for each of the extracted image frames, setting a collation range for the detected tracking target, adjusting the collation range based on the designation range set for each of the image frames, and generating, for each of the image frames, a tracking image in which the collation range is associated with the tracking target.”, where the tracking target among the plurality of targets, as shown in Fig. 7-8, utilizes a verification frame that contains a plurality of feature and location information which is stored in advance, as disclosed in P[0048]: “For example, the tracking unit 13 may detect the tracking target from the verification frame by a detection technique using a feature amount such as a motion vector. The tracking target of the tracking unit 13 is a person or an object that moves (also referred to as a moving object). For example, in a case where the tracking target is a person, the tracking unit 13 detects the tracking target from the verification frame using a face detection technique or the like.”. Where the present Specification of the Application describes feature amount information in P[0020]: “Hereinafter, the information regarding the detection regions in which the items of tracking information are connected is referred to as a “feature amount”. The feature amount is the coordinates of the detection region and image features.”
wherein, among the plurality of items of tracking information, tracking information that has already been associated with the person information is excluded from a target used in the collation processing is disclosed by Yoshida, P[0007]: “ a tracking unit that extracts the image frame to be verified from the video data, detect a tracking target for each of the extracted image frames, set a collation range for the detected tracking target, and adjust the collation range based on the designation range set for each of the image frames, and a display information generation unit that generates, for each of the image frames, a tracking image in which the collation range is associated with the tracking target.”, where the collation range is set and the argument that all detections beyond the set collation range are not collated is upheld. The tracking information that has already been associated with each person that is detected is collected at all ranges, however, only the tracking information within the collation range is collated, therefore the tracking information that has been detected beyond the collation range is excluded/omitted. The current features present in the claim do not preclude this interpretation. Fig. 14 of Yishida shows that first the tracking target is detected, then identified, then next the tracking information is generated based on the identified target. After the tracking information is generated is when the collation range is set for a plurality of frames. Only tracking information that was associated with the target individual that is within the collation range is actually collated, reducing the volume of data that needs to be processed, and this process repeats upon each verification frame acquired.
Regarding claim 3, wherein the one or more processors determine tracking information to be a target to be used in the collation processing based on a difference between a first image feature of each of the plurality of persons in a first video image frame and a second image feature of each of the plurality of persons in a second video image frame photographed later than the first video image frame is disclosed by Yoshida, where the features of the plurality of people are compared to one another across frames to single out the target image, as shown in Fig. 7-8, and in P[0048]: “For example, the tracking unit 13 may detect the tracking target from the verification frame by a detection technique using a feature amount such as a motion vector. The tracking target of the tracking unit 13 is a person or an object that moves (also referred to as a moving object). For example, in a case where the tracking target is a person, the tracking unit 13 detects the tracking target from the verification frame using a face detection technique or the like.”.
Regarding claim 4, wherein, from among the plurality of persons, the one or more processors determine tracking information corresponding to a person, in which a difference between a first image feature in the first video image frame and a second image feature in the second video image frame photographed later than the first video image frame is greater than or equal to a threshold is determined to be a target to be used in the collation processing, and Yoshida discloses utilizing a plurality of thresholds for space, time, and range for collation processing, as shown in P[0044]: “The tracking unit 13 stores a spatial threshold value and a time threshold value. The spatial threshold value is a spatial threshold value set in association with the tracking target detected from the target image frame to be verified (also referred to as a verification frame). The time threshold value is a reference for extracting an image frame to be collated with a verification frame to be verified from video data constituted by a plurality of image frames. The spatial threshold value and the time threshold value are preset values. The spatial threshold value and the time threshold value may be changeable according to a user’s operation. For example, the spatial threshold value and the time threshold value may be commonly set for all image frames constituting the video data, or may be set for each image frame.”, and P[0045]: “The spatial threshold value is a value related to the collation range of the tracking target. For example, when the collation range is a circle, the spatial threshold value is set as the diameter or the radius of the collation range.” Where the data needs to be greater than or equal to the time threshold for the process to acquire the appropriate frame.
Regarding claim 5, wherein the one or more processors do not set tracking information of a person, from among the plurality of persons, in which the difference between the first image feature in the first video image frame and the second image feature in the second video image frame is less than the threshold, to a target to be used in the collation processing is disclosed by Yoshida in P[0048]: “For example, the tracking unit 13 may detect the tracking target from the verification frame by a detection technique using a feature amount such as a motion vector. The tracking target of the tracking unit 13 is a person or an object that moves (also referred to as a moving object). For example, in a case where the tracking target is a person, the tracking unit 13 detects the tracking target from the verification frame using a face detection technique or the like.”, where the face detection technique relies on thresholds to determine whether or not the detected person is or is not the target person. Detections that do not result in a positive determination of a target person are not collated.
Regarding claim 6, wherein if person information to be associated with a tracking information that is a target to be used in the collating processing is identified by the collating processing, the one or more processors associates the identified person information and the determined tracking information describes two mutually inclusive properties of targeted collation. For collation to occur, the target information must match the person information and are therefore associated. Yoshida, P[0048]: “For example, the tracking unit 13 may detect the tracking target from the verification frame by a detection technique using a feature amount such as a motion vector. The tracking target of the tracking unit 13 is a person or an object that moves (also referred to as a moving object). For example, in a case where the tracking target is a person, the tracking unit 13 detects the tracking target from the verification frame using a face detection technique or the like.”
Regarding claim 7, wherein if a first person information is identified by the collation processing to be person information to be associated with tracking information of a first person that has been detected in the first video image frame, the one or more processors associate the first person information and a first tracking information corresponding to the first person, and do not perform the collation processing for the first tracking information in the second video image frame photographed later than the first video image frame is disclosed by Yoshida, where Yoshida teaches that detections outside of a specific range are omitted from collation, furthermore Yoshida teaches that a same individual my be tracked across a progressive set of frames, therefore when a target person moves out of range, the already detected individual with already detected tracking information are not collated in the later frame which is commensurate with the description of claim 7 found in the Remarks dated 09/18/2025 pertaining to the response to the prior §112(b) rejection to claim 7.
Regarding claim 8, wherein the one or more processors determine tracking information to be a target to be used in the collation processing based on a similarity of feature amounts between the plurality of persons is disclosed by Yoshida in P[0048]: “For example, the tracking unit 13 may detect the tracking target from the verification frame by a detection technique using a feature amount such as a motion vector. The tracking target of the tracking unit 13 is a person or an object that moves (also referred to as a moving object). For example, in a case where the tracking target is a person, the tracking unit 13 detects the tracking target from the verification frame using a face detection technique or the like.”.
Regarding claim 9, wherein the one or more processors perform the collation processing based on a similarity between the feature amount of a person corresponding to the determined tracking information and the feature amount associated with the person information is disclosed by Yoshida in P[0048]: “For example, the tracking unit 13 may detect the tracking target from the verification frame by a detection technique using a feature amount such as a motion vector. The tracking target of the tracking unit 13 is a person or an object that moves (also referred to as a moving object). For example, in a case where the tracking target is a person, the tracking unit 13 detects the tracking target from the verification frame using a face detection technique or the like.”.
Regarding claim 10, wherein the one or more processors narrow down a candidate of person information to be used in the collation processing of the person in a second video image frame photographed later than a first video image frame based on a comparison between the feature amount of the person corresponding to the determined tracking information in the first video image frame and the plurality of feature amounts stored in the storage device in association with a plurality of items of person information is disclosed by Yoshida in P[0048]: “For example, the tracking unit 13 may detect the tracking target from the verification frame by a detection technique using a feature amount such as a motion vector. The tracking target of the tracking unit 13 is a person or an object that moves (also referred to as a moving object). For example, in a case where the tracking target is a person, the tracking unit 13 detects the tracking target from the verification frame using a face detection technique or the like.”, and Fig. 7-8 shows a plurality of candidates.
Regarding claim 11, wherein the one or more processors determine a feature amount to be used for the collation processing for the tracking information that has been detected from the second video image frame based on a result of the narrowing describes mutually inclusive aspects of target determination and narrowing. The process of the selection of the target person narrows the results over successive frames and therefore affect which data is to be used for the collation processing. See Yoshida in P[0048]: “For example, the tracking unit 13 may detect the tracking target from the verification frame by a detection technique using a feature amount such as a motion vector. The tracking target of the tracking unit 13 is a person or an object that moves (also referred to as a moving object). For example, in a case where the tracking target is a person, the tracking unit 13 detects the tracking target from the verification frame using a face detection technique or the like.”, and Fig. 7-8 shows a plurality of candidates.
Regarding claim 13, wherein the one or more processors determine a combination of tracking information to be a target of the collation processing and a feature amount to be a comparison target to a feature amount of the tracking information from among the plurality of feature amounts stored in the storage device, and determine tracking information and person information not included in the combination as another combination for the collation processing to be executed. Yoshida discloses using features to distinguish similarity between frames in P[0048] by utilizing a reference image and utilize a time and space threshold to limit the amount of features acquired for collation processing, where the feature information outside of the thresholds is omitted and forms their own group of data.
Regarding claim 14, wherein if the one or more processors determine that tracking information of a first person is to be associated with a first person information after performing the collation processing based on feature amounts of each of the first person and a second person that have been detected from a video image frame and a feature amount associated with the first person information and second person information stored in the storage device, the one or more processors execute the collation processing based on a feature amount associated with the second person information stored in the storage device and a feature amount of the second person that have been detected from a video image frame without executing the collation processing based on a feature amount associated with the second person information stored in the storage device and a feature amount of the first person that have been detected from a video image frame. Claim 14 relates to collation processing of a second person without using all of the data collected on the first person which is disclosed by Yoshida. Yoshida discloses a plurality of tracking targets in P[0066] and Fig. 7, where the reference image only relates to one person, and therefore omits data about the other tracking targets, and Fig. 14 discloses that this process is iterative for a plurality of frames and targets when a new verification frame is provided. So, the second person information relates to the second target which is separate from the first target.
Regarding claim 15, wherein the one or more processors cause a display unit to display at least one of the tracking information and the person information together with the video image frame is disclosed by Yoshida in Fig. 16, Element 220: “Terminal Device”, and Fig. 22, Element S217.
Claims 17 and 18 recite features nearly identical to those recited in claim 1. Claims 17 and 18 are rejected for reasons analogous to those discussed above in conjunction with claim 1.
Regarding claim 19, wherein the feature amount is corresponding to a detection region of a person in a video image frame is disclosed by Yoshida in Fig. 7-8 and P[0048]: “For example, the tracking unit 13 may detect the tracking target from the verification frame by a detection technique using a feature amount such as a motion vector. The tracking target of the tracking unit 13 is a person or an object that moves (also referred to as a moving object). For example, in a case where the tracking target is a person, the tracking unit 13 detects the tracking target from the verification frame using a face detection technique or the like.”, where the feature amount provided from detection corresponds to the detection region which is disclosed by Yoshida as the image display region in P[0065]: “Around the tracking target T in the image frame, a circle centered on the tracking target T and having a spatial threshold value as a diameter is set as a collation range. In the image display region 150, a circle indicating the collation range set around the tracking target T is displayed in a shape related to the viewpoint, the angle of view, and the like of the monitoring camera 110.”
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.
5. Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Yoshida in view of 20130050502 A1: Hiroo Saido et al., (herein after “Saito(2)”).
Regarding claim 2, wherein, among the plurality of items of tracking information, tracking information corresponding to a person, from among the plurality of persons, in which a difference between a first feature amount in a first video image frame and a second feature amount in a second video image frame photographed later than the first video image frame is less than or equal to a threshold is excluded from a target used in the collation processing, where a small change in feature amounts detected across frames for the same target yields no collation processing due to the change being small/equal to or below a threshold. Yoshida does not explicitly teach this feature.
However, Saito(2) discloses this feature explicitly in P[0183]: “In order to exclude erroneous detection results, the scene selecting unit 127 excludes a detection result in which the size for the detected position has a variation equal to or less than a predetermined threshold, excludes a detection result having a movement equal to or less than a predetermined movement, or excludes a detection result using character recognition information obtained by character recognition processing of the image of the surrounding. Thus, the scene selecting unit 127 can exclude erroneous detections attributed to posters or characters.”, where the present Specification of the Application describes feature amount information in P[0020]: “Hereinafter, the information regarding the detection regions in which the items of tracking information are connected is referred to as a “feature amount”. The feature amount is the coordinates of the detection region and image features.”, where movement is dictated based upon the coordinates, and when the change is smaller than a threshold, it is considered erroneous data and not collated.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yoshida to exclude erroneous data by not collating when the change in feature amount is small, as taught by Saito(2), to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. It is predictable that the proposed modification would have provided the benefit of increased computational speed through reducing computational load by reducing the volume of the data by excluding erroneous data.
Regarding claim 12, Yoshida discloses using person information corresponding to tracking information in association with the crossing of the person with a structure, for example see the obstructing columns in Fig. 9-12, instead of crossing with another person. That is, Yoshida does not explicitly disclose wherein if a crossing of the person in the video image is detected, the one or more processors execute the collation processing for tracking information in association with the crossing after the crossing occurs, by using the person information corresponding to tracking information in association with the crossing before the crossing occurs.
However, Saito(2) explicitly discloses tracking where one individual crosses in front of another individual for collating frames (Saito(2), P[0039]: “The person tracking system shown in FIG. 1 may collate (face collation) a tracking target face image”) of the tracked individual in Fig. 4, and furthermore in P[0063-0064]: “there may be complicated movements such as crossing of the persons, so that the face tracking unit 27 obtains more than one tracking result. The face tracking unit 27 can select a tracking result to be output, on the basis of the reliability. The reliability is determined in consideration of information such as the number of obtained frames and the number of detected faces. For example, the face tracking unit 27 can set a numerical value of reliability on the basis of the number of frames in which tracking is successful.”, where frames are taken over time, so frames from before and after the crossing are used. Therefore, Saito collates all best shot images of the tracked individual, and Saito(2) includes collating images depicting crossings, where Saito collates the best images at the end of the process, which includes images before and after the images depicting crossings provided by Saito(2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yoshida to collate obstructed images, as taught by Saito(2), to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. It is predictable that the proposed modification would have provided the benefit of continuously tracking faces regardless of obstruction (Saito(2), P[0139]).
6. Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Yoshida in view of 20200159908 A1: Takuro Okuyama, (herein after “Okuyama”).
Regarding claim 16, wherein the one or more processors cause at least one of a first indicator indicating information on the person (Yoshida, Fig. 24, Display Information Generation Unit, and Fig. 18 for frames of the person with frame and ID information present.), a second indicator indicating a reason why the collation processing for the person has not been completed, and a frame surrounding the person to be displayed on the display unit. Yoshida does not explicitly disclose “a second indicator indicating a reason why the collation processing for the person has not been completed”. That is, Yoshida does not explicitly disclose providing a character/alert indication a reason why the collation processing has not been completed.
However, Okuyama discloses providing notification when collation fails in Fig. 6, Element S19, and furthermore in P[0053]: “in a case where the acquired gait data and face data are not associated or stored in the authentication data storage part 16, the gate control part 14 judges that authentication of the person has failed. After that, the gate control part 14 causes the display part included by the second camera C2 to display that the authentication has failed”, where the indicated reason is “the acquired gait data and face data are not associated or stored in the authentication data storage”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yoshida to provide an alert for failed collation, as taught by Okuyama, to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. It is predictable that the proposed modification would have provided the benefit of automatically informing the user of the reason why the collation failed, reducing the time spent by the user determining why the process failed.
Conclusion
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/TY MITCHELL BEATTY/Examiner, Art Unit 2663
/GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698
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