DETAILED ACTION
This Action is in response to Applicant’s response filed on 06/10/2026. Claims 1-10 and newly adding claim 11 are still pending in the present application. This Action is made FINAL.
Response to Arguments
With respect to 35 U.S.C. 101 Rejection: Applicant argues that the amended claims 1, 9 and 10 to include patent-eligible subject matter which include additional elements that integrate the judicial exception into a practical application. After reviewing the amendments and argument filed on 06/10/2026 , the Examiner has withdrawn the previous 101 rejection for the following reason: The claims recites steps and features that an additional element (or combination of elements) may have integrated the exception into a practical application include: Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claims.
With respect to 35 U.S.C. 102 Rejection: Applicant's arguments filed on 06/10/2026 have been fully considered but are moot in view of the new ground(s) rejection in view of Miyano (U.S. 20160042243 A1).
Claim Status
Claim(s) 1 and 9-11 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Miyano (U.S. 20160042243 A1).
Claim(s) 2 and 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miyano (U.S. 20160042243 A1), in view of Ito et al (U.S. 20100034464 A1; Ito).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miyano (U.S. 20160042243 A1), in view of Iwamoto et al (U.S. 20110135203 A1; Iwamoto).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miyano (U.S. 20160042243 A1), in view of Oami Ryoma (WO-2020217368 A1).
Claim(s) 5 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miyano (U.S. 20160042243 A1), in view of Ito et al (U.S. 20100034464 A1; Ito), and in further view of Oami Ryoma (WO-2020217368 A1).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miyano (U.S. 20160042243 A1), in view of Ito et al (U.S. 20100034464 A1; Ito), and in further view of Oami Ryoma (WO-2020217368 A1), and further view of Iwamoto et al (U.S. 20110135203 A1; Iwamoto).
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.
Claim(s) 1 and 9-11 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Miyano (U.S. 20160042243 A1).
Regarding claim 1, Miyano discloses a feature extraction apparatus (Fig.1 and Paragraph 24: “FIG. 1 is a block diagram depicting an exemplary embodiment of an object monitoring system”) comprising: at least one memory configured to store instructions; and at least one processor configured to execute, according to the instructions, a process (Paragraph 61: “the program may be stored in a storage unit (not depicted) in an information terminal for realizing the object monitoring system, with the CPU reading the program and, according to the program, “) comprising:
receiving an object image and extracting at least one feature of an object included in the received object image; (Fig.1 and Paragraphs 29-31: “The monitoring target extraction camera 13 is a camera installed to capture a monitoring target appearing in a predetermined range. The monitoring target is determined according to the type of object that is presumed to be the cause of the specific state. … The monitoring target image captured by the monitoring target extraction camera 13 is used by the below-mentioned monitoring target feature extraction means 18 to extract a feature that can be used for specifying the monitoring target.”) and
determining a value of M (specifying the monitoring target) (the value of M is an integer equal to or larger than one but equal to or smaller than N), which is the number of object images whose features will be extracted (Paragraphs 46-47: “ The monitoring target feature extraction means 18 extracts a feature used for specifying the monitoring target, from the monitoring target image. Here, the monitoring target feature extraction means 18 uses the monitoring target image in a period (hereafter referred to as “predicted period”) determined based on the predicted time.” … the feature extracted from the monitoring target image, a feature with which the monitoring target can be specified is selected beforehand according to the monitoring target. As an example, in the case where the monitoring target is a person, the monitoring target feature extraction means 18 may extract a face feature from the monitoring target image as the feature with which the person can be specified.”) of N (monitoring targets) (the value of N is an integer equal to or larger than two) object images with which a first tracking ID, which is an identifier allocated to one object, (Paragraph 41: “The number of monitoring targets analyzed by the monitoring target analysis means 16 may be one or more. For example, in the case where the monitoring target is a person, the monitoring target analysis means 16 may determine that every person captured at the time of detection of the specific state is suspicious, and analyze every person as the monitoring target.”) is associated, in accordance with a usage status of processing resources of the feature extraction apparatus prior to extracting features for the M object images, wherein the usage status of processing resources is based on a maximum number of permitted feature extraction operations. (Paragraph 57: “The object specification means 20 compares, for each type of specific state, features of monitoring targets extracted from a plurality of monitoring target images by the monitoring target feature extraction means 18, and specifies a monitoring target that appears with a high frequency as the object. … and specify, as the object, a monitoring target for which a feature estimated to be identical is extracted at least a predetermined number of times.”; Paragraph 88: “the object specification means may specify the monitoring target for which a feature estimated to be identical is extracted at least a predetermined number of times, as the object. Moreover, the monitoring target feature extraction means 84 may calculate a likelihood indicating a degree of probability of the monitoring target, together with the feature of the monitoring target, wherein the object specification means specifies the monitoring target using the likelihood.”)
Regarding claim 9, Miyano discloses A method executed by a feature extraction apparatus, (Fig.1 and Paragraph 24: “FIG. 1 is a block diagram depicting an exemplary embodiment of an object monitoring system”) the method comprising:
extracting at least one feature of an object included in an object image; (Fig.1 and Paragraphs 29-31: “The monitoring target extraction camera 13 is a camera installed to capture a monitoring target appearing in a predetermined range. The monitoring target is determined according to the type of object that is presumed to be the cause of the specific state. … The monitoring target image captured by the monitoring target extraction camera 13 is used by the below-mentioned monitoring target feature extraction means 18 to extract a feature that can be used for specifying the monitoring target.”) and
determining a value of M (specifying the monitoring target) (the value of M is an integer equal to or larger than one but equal to or smaller than N), which is the number of object images whose features will be extracted (Paragraphs 46-47: “ The monitoring target feature extraction means 18 extracts a feature used for specifying the monitoring target, from the monitoring target image. Here, the monitoring target feature extraction means 18 uses the monitoring target image in a period (hereafter referred to as “predicted period”) determined based on the predicted time.” … the feature extracted from the monitoring target image, a feature with which the monitoring target can be specified is selected beforehand according to the monitoring target. As an example, in the case where the monitoring target is a person, the monitoring target feature extraction means 18 may extract a face feature from the monitoring target image as the feature with which the person can be specified.”) of N (monitoring targets) (the value of N is an integer equal to or larger than two) object images with which a first tracking ID, which is an identifier allocated to one object, (Paragraph 41: “The number of monitoring targets analyzed by the monitoring target analysis means 16 may be one or more. For example, in the case where the monitoring target is a person, the monitoring target analysis means 16 may determine that every person captured at the time of detection of the specific state is suspicious, and analyze every person as the monitoring target.”) is associated, in accordance with a usage status of processing resources of the feature extraction apparatus prior to extracting features for the M object images, wherein the usage status of processing resources is based on a maximum number of permitted feature extraction operations. (Paragraph 57: “The object specification means 20 compares, for each type of specific state, features of monitoring targets extracted from a plurality of monitoring target images by the monitoring target feature extraction means 18, and specifies a monitoring target that appears with a high frequency as the object. … and specify, as the object, a monitoring target for which a feature estimated to be identical is extracted at least a predetermined number of times.”; Paragraph 88: “the object specification means may specify the monitoring target for which a feature estimated to be identical is extracted at least a predetermined number of times, as the object. Moreover, the monitoring target feature extraction means 84 may calculate a likelihood indicating a degree of probability of the monitoring target, together with the feature of the monitoring target, wherein the object specification means specifies the monitoring target using the likelihood.”).
Regarding claim 10, Miyano discloses A non-transitory computer readable medium storing a program for causing a feature extraction apparatus to execute processing(Fig.1 and Paragraph 61: “the program may be stored in a storage unit (not depicted) in an information terminal for realizing the object monitoring system, with the CPU reading the program and, according to the program, “) comprising:
extracting at least one feature of an object included in an object image; (Fig.1 and Paragraphs 29-31: “The monitoring target extraction camera 13 is a camera installed to capture a monitoring target appearing in a predetermined range. The monitoring target is determined according to the type of object that is presumed to be the cause of the specific state. … The monitoring target image captured by the monitoring target extraction camera 13 is used by the below-mentioned monitoring target feature extraction means 18 to extract a feature that can be used for specifying the monitoring target.”) and
determining a value of M (specifying the monitoring target) (the value of M is an integer equal to or larger than one but equal to or smaller than N), which is the number of object images whose features will be extracted (Paragraphs 46-47: “ The monitoring target feature extraction means 18 extracts a feature used for specifying the monitoring target, from the monitoring target image. Here, the monitoring target feature extraction means 18 uses the monitoring target image in a period (hereafter referred to as “predicted period”) determined based on the predicted time.” … the feature extracted from the monitoring target image, a feature with which the monitoring target can be specified is selected beforehand according to the monitoring target. As an example, in the case where the monitoring target is a person, the monitoring target feature extraction means 18 may extract a face feature from the monitoring target image as the feature with which the person can be specified.”) of N (monitoring targets) (the value of N is an integer equal to or larger than two) object images with which a first tracking ID, which is an identifier allocated to one object, (Paragraph 41: “The number of monitoring targets analyzed by the monitoring target analysis means 16 may be one or more. For example, in the case where the monitoring target is a person, the monitoring target analysis means 16 may determine that every person captured at the time of detection of the specific state is suspicious, and analyze every person as the monitoring target.”) is associated, in accordance with a usage status of processing resources of the feature extraction apparatus prior to extracting features for the M object images, wherein the usage status of processing resources is based on a maximum number of permitted feature extraction operations. (Paragraph 57: “The object specification means 20 compares, for each type of specific state, features of monitoring targets extracted from a plurality of monitoring target images by the monitoring target feature extraction means 18, and specifies a monitoring target that appears with a high frequency as the object. … and specify, as the object, a monitoring target for which a feature estimated to be identical is extracted at least a predetermined number of times.”; Paragraph 88: “the object specification means may specify the monitoring target for which a feature estimated to be identical is extracted at least a predetermined number of times, as the object. Moreover, the monitoring target feature extraction means 84 may calculate a likelihood indicating a degree of probability of the monitoring target, together with the feature of the monitoring target, wherein the object specification means specifies the monitoring target using the likelihood.”).
Regarding claim 11, Miyano discloses the maximum number of permitted feature extraction operations is based on a difference between a maximum number of times the feature extraction operation can be performed in a unit period and a number of times the feature extraction operation was performed in a most recent unit period. (Paragraphs 52-57: “he monitoring target feature extraction means 18 may calculate a likelihood indicating the degree of probability of the monitoring target, together with the feature of the monitoring target. For instance, the monitoring target feature extraction means 18 may assign the highest likelihood to the monitoring target extracted from the monitoring target image captured at the time close to the predicted time, and the lower likelihood to the monitoring target extracted from the monitoring target image captured at the time farther from the predicted time. … he object specification means 20 compares, for each type of specific state, features of monitoring targets extracted from a plurality of monitoring target images by the monitoring target feature extraction means 18, and specifies a monitoring target that appears with a high frequency as the object. the object specification means 20 may compare features of monitoring targets corresponding to a common type of specific state from among the features of the monitoring targets stored in the gray list storage means 19, and specify, as the object, a monitoring target for which a feature estimated to be identical is extracted at least a predetermined number of times.”)
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.
Claim(s) 2 and 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miyano (U.S. 20160042243 A1), in view of Ito et al (U.S. 20100034464 A1; Ito).
Regarding claim 2, Miyano discloses outputting object information comprising received information; and receiving the N object images and outputting, of the N object images that have been received, the M object images whose features will be extracted to the process of the extracting (Paragraph 41: “The number of monitoring targets analyzed by the monitoring target analysis means 16 may be one or more. …. As the feature extracted from the monitoring target image, a feature with which the monitoring target can be specified is selected beforehand according to the monitoring target. As an example, in the case where the monitoring target is a person, the monitoring target feature extraction means 18 may extract a face feature from the monitoring target image as the feature with which the person can be specified. …. the monitoring target feature extraction means 18 may extract other features as the feature with which the monitoring target can be specified.”)
However, Miyano does not disclose outputting, of the N object images, (N-M) object images whose features will not be extracted other than the M object images whose features will be extracted, to the process of the outputting of object information.
Ito discloses the process further comprises: outputting object information comprising received information; and receiving the N object images and outputting, of the N object images that have been received, (Paragraphs 19-20: “In step S330, the object detection unit 120 detects object using N features extracted by the N feature extraction units 151 (g.sub.1, g.sub.2, . . . , g.sub.N) stored in the storage unit 150 … Function f.sub.D is, for example, a classifier which separates pre-learned object for generating N feature extraction units from background thereof.”) the M object images whose features will be extracted to the process of the extracting, and outputting, of the N object images, (N-M) object images whose features will not be extracted other than the M object images whose features will be extracted, to the process of the outputting of object information. (Paragraph 30: “In step S350, the feature selection unit 130 selects M feature extraction units from N feature extraction units such that degree in separation of the confidence value c.sub.D which represents object-likelihood between the object and background thereof becomes larger, in order to adapt to change of appearances of the object and background thereof. The output of the unselected N-M feature extraction units is treated as 0 in the calculation of c.sub.”).
Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Miyano by including feature selecting unit that is taught by Ito, to make the invention that an apparatus and a method for tracking an image; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving robustness and high speed tracking of an object of an image processing . (Ito: Paragraph 5)
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention.
Regarding claim 4, Miyano discloses the outputting of object information comprises outputting the object information comprising an object image ID of each of the M object images and extracted information indicating that features of each of the M object images whose features will be extracted have been extracted ((Paragraph 41: “The number of monitoring targets analyzed by the monitoring target analysis means 16 may be one or more. …. As the feature extracted from the monitoring target image, a feature with which the monitoring target can be specified is selected beforehand according to the monitoring target. As an example, in the case where the monitoring target is a person, the monitoring target feature extraction means 18 may extract a face feature from the monitoring target image as the feature with which the person can be specified. …. the monitoring target feature extraction means 18 may extract other features as the feature with which the monitoring target can be specified.”)
However, Miyano does not disclose outputting, of the N object images, (N-M) object images whose features will not be extracted other than the M object images whose features will be extracted, to the process of the outputting of object information.
Ito discloses the outputting of object information comprises outputting the object information comprising an object image ID of each of the M object images and extracted information indicating that features of each of the M object images whose features will be extracted have been extracted (Paragraph 23: “In step S340, the object tracking unit 140 tracks object using M features extracted by M feature extraction units selected by the feature selection unit 130.”) and an object image ID of the (N- M) object images and unextracted information indicating features of each of the (N-M) object images whose features will not be extracted have not been extracted. (Paragraph 30: “In step S350, the feature selection unit 130 selects M feature extraction units from N feature extraction units such that degree in separation of the confidence value c.sub.D which represents object-likelihood between the object and background thereof becomes larger, in order to adapt to change of appearances of the object and background thereof. The output of the unselected N-M feature extraction units is treated as 0 in the calculation of c.sub.”)
Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Miyano by including feature selecting unit that is taught by Ito, to make the invention that an apparatus and a method for tracking an image; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving robustness and high speed tracking of an object of an image processing . (Ito: Paragraph 5)
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miyano (U.S. 20160042243 A1), in view of Iwamoto et al (U.S. 20110135203 A1; Iwamoto).
Regarding claim 3, Miyano discloses all the claims invention except wherein the process further comprises selecting, from P (the value of P is an integer equal to or larger than N) object images with which the first tracking ID is associated, the N object images based on P priorities associated with the respective P object images and outputting the N selected object images to the process of the sorting, and outputting (P-N) object images other than the N selected object images to the process of the outputting of object information.
Iwamoto discloses the process further comprises selecting, from P (the value of P is an integer equal to or larger than N) object images with which the first tracking ID is associated, (Paragraph 93-94: “ Next, the feature extraction parameter generation unit 12 generates, for each of the M types of features, a feature extraction parameter which is a parameter for extracting a feature from the image, and stores it in the feature extraction parameter storing unit 23 (S102).Then, the feature extraction section 131 of the feature extraction unit 13 extracts the M types of features from each of the original images in the original image storing unit 21 in accordance with the extraction parameters for the M types of features, and stores them in the feature storing unit 24 (S103).”) the N object images based on P priorities associated with the respective P object images and outputting the N selected object images to the process of the sorting, (Paragraph 95: “ the feature selection unit 14 receives the M types of features of the original images and the altered images stored in the feature storing unit 24, … and selects N types of features from the M types of features, with the discrimination capability which is a degree of discriminating different images and the robustness which is a degree that the value of a feature does not vary due to an alteration process applied to an image being evaluation criteria. and outputs them (S105) “) and outputting (P-N) object images other than the N selected object images to the process of the outputting of object information. (Paragraph 98: “Then, the feature selection unit 14 judges whether or not the N types of features are determined (S108), and if the N types of features have not been determined, the feature selection unit 14 returns to step S107 and continue to determine the remaining types of features. On the other hand, if the N types of features have been determined, the feature selection unit 14 outputs the determined N types of features to a storing unit”)
Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Miyano by including selecting the features for feature extraction that is taught by Iwamoto, to make the invention that selecting features suitable for image signatures for discriminating images; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving the accuracy of determining identity of images. (Iwamoto: Paragraph 25)
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miyano (U.S. 20160042243 A1), in view of Oami Ryoma (WO-2020217368 A1).
Regarding claim 7, Miyano discloses the process further comprises: detecting, in each of a plurality of captured images, an object region that corresponds to an object, identifying positions of the respective object regions in the plurality of captured images, (Paragraphs 26-28“The state detection camera 11 is preferably installed to have a large depression angle, in order to capture an image from which a state occurring in a predetermined range can be detected more appropriately. … the range captured by the state detection camera 11 is referred to as “first range”, to distinguish it from the range captured by the below-mentioned monitoring target extraction camera 13. The first range is determined according to the installation position, installation angle, set zoom, and the like of the state detection camera 11. The state image storage means 12 may store the time and position at which the state image is captured, information for identifying the camera capturing the state image, and the like, together with the state image. ”; )
However, Miyano does not disclose attaching object IDs to the respective object regions, and outputting a plurality of object images, each of the object images comprising a captured image where the object region is detected, image identification information indicating the captured image, information regarding the identified positions, and the object ID; and attaching one tracking ID to all object images of one object using the plurality of object images received from the process of the detecting and outputting, to the process of the extracting, the plurality of object images to which the tracking ID is attached.
Ryoma discloses the process further comprises detecting, in each of a plurality of captured images, an object region that corresponds to an object, (Paragraphs 39-40: “The detection unit 202 detects an object from the video output by the video acquisition unit 201, and outputs the detection result as detection result information. When the object is a person, the detection unit 202 detects a person area by using a detector that has learned the image features of the person”) identifying positions of the respective object regions in the plurality of captured images, attaching object IDs to the respective object regions, and outputting a plurality of object images, each of the object images comprising a captured image where the object region is detected, image identification information indicating the captured image, information regarding the identified positions, and the object ID; (Paragraph 41: “The detection unit 202 generates detection result information from the information of the detected object. The detection result information includes information for identifying a frame such as frame time information or frame number and information on the detected object. The object information includes the detection position and size of the object. … When a plurality of objects are detected, the detection result information includes the information of the plurality of detected objects in the generated detection result information, and includes an identifier that distinguishes the detected objects within the same frame. The identifier is ID information assigned to distinguish a plurality of objects detected in the same frame,”) attaching one tracking ID to all object images of one object using the plurality of object images received from the process of the detecting and outputting, to the process of the extracting, the plurality of object images to which the tracking ID is attached. (Paragraphs 43-46: “ The tracking unit 203 performs a tracking process called Tracking by Detection based on the detection result information. That is, the tracking unit 203 is included in the tracking result information of the objects up to the previous time, and which detection object included in the detection result information of the current time corresponds to each object to be tracked. And update the tracking results. The tracking unit 203 may predict the position of the object to be tracked by the Kalman filter or the particle filter and associate it with the detected object at the current time … The tracking result information includes the position and size of the object on the image, the identifier assigned to each tracking object, and the associated detection object identifier.”)
Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Miyano by including the information processing device that is taught by Ryoma, to make the invention that the information processing device selects an object for feature quantity extraction even in a situation where a large number of objects are displayed on the screen; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving the variation (diversity) of the acquired features as well as reducing the calculation cost required for extracting the feature amount. (Ryoma: Paragraph 180)
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention.
Claim(s) 5 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miyano (U.S. 20160042243 A1), in view of Ito et al (U.S. 20100034464 A1; Ito), and in further view of Oami Ryoma (WO-2020217368 A1).
Regarding claim 5, Miyano, as modified by Ito discloses all the claims invention. Ito further discloses the determining comprises determining a value of L (the value of L is an integer equal to or larger than one but equal to or smaller than K), which is the number of object images whose features will be extracted of K (the value of K is an integer equal to or larger than two) object images (Figs. 1-3; Paragraph 11: “The feature selection unit 130 selects M (M is a positive integer smaller than N) feature extraction units from N feature extraction units”) Paragraph 30: “In step S350, the feature selection unit 130 selects M feature extraction units from N feature extraction units such that degree in separation of the confidence value c.sub.D which represents object-likelihood between the object and background thereof becomes larger, in order to adapt to change of appearances of the object and background thereof.”) with which a second tracking ID different from the first tracking ID is associated, (Paragraph 46: “step 331, the control unit 160 proceeds to step S320 and processes the next image when the control unit 160 determines that detection of the object is unsuccessful ("No" in step S331). The control unit 160 proceeds to step S350 when the control unit 160 determine that detection of the object is successful ("Yes" in step S331).”, the person ordinary skill in the art would know that the process tracking object of the next image is interpreted as “second tracking ID is associated are object images detected in K second image frames”) in accordance with a situation of processing resources of the feature extraction apparatus; . (Paragraph 23: “ In step S340, the object tracking unit 140 tracks object using M features extracted by M feature extraction units selected by the feature selection unit 130.”; Paragraph 11: “The object tracking unit 140 tracks the object using the M features extracted from the selected M (M is a positive integer smaller than N) feature extraction units.”) the sorting comprises receiving the K object images and outputting, of the K received object images, (Paragraph 19: “In step S310, the control unit 160 stores image sequence acquired by the acquisition unit 110 in the storage unit 150. … In step S330, the object detection unit 120 detects object using N features extracted by the N feature extraction units 151 (g.sub.1, g.sub.2, . . . , g.sub.N) stored in the storage unit 150.”) the L object images whose features will be extracted to the process of the extracting, and outputting, of the K object images, (K-L) object images whose features will not be extracted other than the L object images whose features will be extracted to the process of the outputting of object information, (Paragraph 30: “In step S350, the feature selection unit 130 selects M feature extraction units from N feature extraction units such that degree in separation of the confidence value c.sub.D which represents object-likelihood between the object and background thereof becomes larger, in order to adapt to change of appearances of the object and background thereof. The output of the unselected N-M feature extraction units is treated as 0 in the calculation of c.sub.”) the N object images with which the first tracking ID is associated are object images detected in N first image frames captured by a first camera, (Fig.1: acquisition unit 110) and the K object images with which the second tracking ID is associated are object images detected in K second image frames (Paragraphs 44-46: “the control unit 160 determines that the present mode is the tracking mode in a case where detection and tracking of the object in the previous image are successful and feature selection is performed for at least one object in step S350. … in step S330, the object detection unit 120 detects objects using N features extracted by the N feature extraction units g.sub.1, g.sub.2, . . . , g.sub.N stored in the storage unit 150. … n step 331, the control unit 160 proceeds to step S320 and processes the next image when the control unit 160 determines that detection of the object is unsuccessful ("No" in step S331). The control unit 160 proceeds to step S350 when the control unit 160 determine that detection of the object is successful ("Yes" in step S331).”, the person ordinary skill in the art would know that the process tracking object of the next image is interpreted as “second tracking ID is associated are object images detected in K second image frames”)
However, Miyano, as modified by Ito, does not disclose the K object images with which the second tracking ID is associated are object images detected in K second image frames captured by a second camera different from the first camera.
Ryoma discloses the N object images with which the first tracking ID is associated are object images detected in N first image frames (Paragraphs 26-27 : “In step S104, the selection unit 102 selects only the objects whose feature quantity predicted by the prediction unit 101 in step S102 satisfies a predetermined condition among the plurality of objects. In step S106, the feature amount extraction unit 103 extracts the feature amount from the object selected by the selection unit 102 in step S104.”) captured by a first camera, the K object images with which the second tracking ID is associated are object images detected in K second image frames (Paragraphs 26-27 : “In step S104, the selection unit 102 selects only the objects whose feature quantity predicted by the prediction unit 101 in step S102 satisfies a predetermined condition among the plurality of objects. In step S106, the feature amount extraction unit 103 extracts the feature amount from the object selected by the selection unit 102 in step S104.”) captured by a second camera different from the first camera. ( Paragraph 7: “Features are used to collate objects detected between different cameras and to search for the same or similar objects in previously captured and stored footage.”; Fig.2 and Paragraph 37 : “The image acquisition unit 201 acquires images captured by one or more imaging devices such as cameras (not shown). The photographing device captures an image of an area or an object to be monitored.”; it shows that the plurality of images captured by plurality or two cameras read as “ first image capture by first camera” and “second image capture by second camera” ).
Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Miyano and Ito by including the information processing device that is taught by Ryoma, to make the invention that the information processing device selects an object for feature quantity extraction even in a situation where a large number of objects are displayed on the screen; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving the variation (diversity) of the acquired features. (Ryoma: Paragraph 114)
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention.
Regarding claim 8, Miyano, as modified by Ito and Ryoma, discloses all the claims invention. Ryoma further discloses the process further comprises: detecting, in each of a plurality of first image frames captured by the first camera, an object region that corresponds to an object, (Paragraphs 38-40: “The video acquisition unit 201 outputs the acquired video as a moving image sequence to the detection unit 202, the tracking unit 203, and the feature amount extraction unit 208. … The detection unit 202 detects an object from the video output by the video acquisition unit 201, and outputs the detection result as detection result information. When the object is a person, the detection unit 202 detects a person area by using a detector that has learned the image features of the person”) identifying positions of the respective object regions in the first image frames, attaching object IDs to the respective object regions, and outputting a plurality of first object images, each of the plurality of first object images comprising a first image frame where the object region is detected, image identification information indicating the first image frame, information regarding the identified positions, and the object ID; (Paragraph 41: “The detection unit 202 generates detection result information from the information of the detected object. The detection result information includes information for identifying a frame such as frame time information or frame number and information on the detected object. The object information includes the detection position and size of the object. … When a plurality of objects are detected, the detection result information includes the information of the plurality of detected objects in the generated detection result information, and includes an identifier that distinguishes the detected objects within the same frame. The identifier is ID information assigned to distinguish a plurality of objects detected in the same frame,”) attaching one tracking ID to all first object images of one object using the plurality of first object images received from the process of the detecting, and outputting, to the feature extraction apparatus, the plurality of first object images to which the tracking ID is attached; (Paragraphs 43-46: “ The tracking unit 203 performs a tracking process called Tracking by Detection based on the detection result information. That is, the tracking unit 203 is included in the tracking result information of the objects up to the previous time, and which detection object included in the detection result information of the current time corresponds to each object to be tracked. And update the tracking results. The tracking unit 203 may predict the position of the object to be tracked by the Kalman filter or the particle filter and associate it with the detected object at the current time … The tracking result information includes the position and size of the object on the image, the identifier assigned to each tracking object, and the associated detection object identifier.”) detecting, in each of a plurality of second image frames captured by the second camera, an object region that corresponds to an object, (Paragraphs 38-40: “The video acquisition unit 201 outputs the acquired video as a moving image sequence to the detection unit 202, the tracking unit 203, and the feature amount extraction unit 208. … The detection unit 202 detects an object from the video output by the video acquisition unit 201, and outputs the detection result as detection result information. When the object is a person, the detection unit 202 detects a person area by using a detector that has learned the image features of the person”) identifying positions of the respective object regions in the second image frames, attaching object IDs to the respective object regions, and outputting a plurality of second object images, each of the plurality of second object images comprising a second image frame where the object region is detected, image identification information indicating the second image frame, information regarding the identified positions, and the object ID; (Paragraph 41: “The detection unit 202 generates detection result information from the information of the detected object. The detection result information includes information for identifying a frame such as frame time information or frame number and information on the detected object. The object information includes the detection position and size of the object. … When a plurality of objects are detected, the detection result information includes the information of the plurality of detected objects in the generated detection result information, and includes an identifier that distinguishes the detected objects within the same frame. The identifier is ID information assigned to distinguish a plurality of objects detected in the same frame,”) and attaching one tracking ID to all second object images of one object using the plurality of second object images received from the process of the detecting, and outputting the plurality of second object images to which the tracking ID is attached to the process of the extracting. (Paragraphs 43-46: “ The tracking unit 203 performs a tracking process called Tracking by Detection based on the detection result information. That is, the tracking unit 203 is included in the tracking result information of the objects up to the previous time, and which detection object included in the detection result information of the current time corresponds to each object to be tracked. And update the tracking results. The tracking unit 203 may predict the position of the object to be tracked by the Kalman filter or the particle filter and associate it with the detected object at the current time … The tracking result information includes the position and size of the object on the image, the identifier assigned to each tracking object, and the associated detection object identifier.”)
Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Ito by including the information processing device that is taught by Ryoma, to make the invention that the information processing device selects an object for feature quantity extraction even in a situation where a large number of objects are displayed on the screen; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving the variation (diversity) of the acquired features as well as reducing the calculation cost required for extracting the feature amount. (Ryoma: Paragraph 180)
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miyano (U.S. 20160042243 A1), in view of Ito et al (U.S. 20100034464 A1; Ito), and in further view of Oami Ryoma (WO-2020217368 A1), and further view of Iwamoto et al (U.S. 20110135203 A1; Iwamoto).
Regarding claim 6, Miyamo, as modified by Ito and Ryoma, discloses all the claims invention except wherein the process comprises: selecting, from P (the value of P is an integer equal to or larger than N) object images with which the first tracking ID is associated, the N object images based on P priorities associated with the respective P object images and outputting the N selected object images to the process of the sorting, and outputting (P-N) object images other than the N selected object images to the process of the outputting of object information, and selecting, from Q (the value of Q is an integer equal to or larger than K) object images with which the second tracking ID is associated, the K object images based on Q priorities associated with the Q respective object images and outputting the K selected object images to the process of the sorting, and outputting (Q-K) object images other than the K selected object images to the process of the outputting of object information.
Iwamoto discloses selecting, from P (the value of P is an integer equal to or larger than N) object images with which the first tracking ID is associated, (Paragraph 93-94: “ Next, the feature extraction parameter generation unit 12 generates, for each of the M types of features, a feature extraction parameter which is a parameter for extracting a feature from the image, and stores it in the feature extraction parameter storing unit 23 (S102).Then, the feature extraction section 131 of the feature extraction unit 13 extracts the M types of features from each of the original images in the original image storing unit 21 in accordance with the extraction parameters for the M types of features, and stores them in the feature storing unit 24 (S103).”) the N object images based on P priorities associated with the respective P object images and outputting the N selected object images to the process of the sorting, (Paragraph 95: “ the feature selection unit 14 receives the M types of features of the original images and the altered images stored in the feature storing unit 24, … and selects N types of features from the M types of features, with the discrimination capability which is a degree of discriminating different images and the robustness which is a degree that the value of a feature does not vary due to an alteration process applied to an image being evaluation criteria. and outputs them (S105) “) and outputting (P-N) object images other than the N selected object images to the process of the outputting of object information. (Paragraph 98: “Then, the feature selection unit 14 judges whether or not the N types of features are determined (S108), and if the N types of features have not been determined, the feature selection unit 14 returns to step S107 and continue to determine the remaining types of features. On the other hand, if the N types of features have been determined, the feature selection unit 14 outputs the determined N types of features to a storing unit”), and selecting, from Q (the value of Q is an integer equal to or larger than K) object images with which the second tracking ID is associated, ( Paragraphs 27-28: “The original image storing unit 21 is an image database which stores a plurality of original images in association with image IDs such as numbers for uniquely identifying the respective original images. … … the group of original images stored in the original image storing unit 21 is used for selecting features suitable for image signatures, it is desirable to include as many original images as possible (for example, not less than ten thousand images).”, it show the selecting features suitable is perform on plurality of images IDs read as “second tracking ID”; Paragraph 93-94: “ Next, the feature extraction parameter generation unit 12 generates, for each of the M types of features, a feature extraction parameter which is a parameter for extracting a feature from the image, and stores it in the feature extraction parameter storing unit 23 (S102).Then, the feature extraction section 131 of the feature extraction unit 13 extracts the M types of features from each of the original images in the original image storing unit 21 in accordance with the extraction parameters for the M types of features, and stores them in the feature storing unit 24 (S103).”) the K object images based on Q priorities associated with the Q respective object images and outputting the K selected object images to the process of the sorting, (Paragraph 95: “ the feature selection unit 14 receives the M types of features of the original images and the altered images stored in the feature storing unit 24, … and selects N types of features from the M types of features, with the discrimination capability which is a degree of discriminating different images and the robustness which is a degree that the value of a feature does not vary due to an alteration process applied to an image being evaluation criteria. and outputs them (S105) “) and outputting (Q-K) object images other than the K selected object images to the process of the outputting of object information. (Paragraph 98: “Then, the feature selection unit 14 judges whether or not the N types of features are determined (S108), and if the N types of features have not been determined, the feature selection unit 14 returns to step S107 and continue to determine the remaining types of features. On the other hand, if the N types of features have been determined, the feature selection unit 14 outputs the determined N types of features to a storing unit”).
Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Miyano, Ito and Ryoma by including selecting the features for feature extraction that is taught by Iwamoto, to make the invention that selecting features suitable for image signatures for discriminating images; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving the accuracy of determining identity of images. (Iwamoto: Paragraph 25)
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention.
Relevant Prior Art Directed to State of Art
Kuzuka et al (U.S. 20170220894 A1), “Image Processing Device, Image Processing Method and Program”, teaches about an image processing device that detects an object on a moving image, the device including: a plurality of object feature amount calculation units configured to calculate an object feature amount, which is related to an object on an image; a feature amount selection unit configured to select an object feature amount to be used from the plurality of object feature amounts; an object information calculation unit configured to calculate object information related to an object on the current frame by using the selected feature amount and object information related to the object on the previous frame; and an object attribute information calculation unit configured to calculate object attribute information related to the object on the current frame on the basis of the calculated object information related to the object on the current frame.
Wang et al (U.S. 20190205694 A1), “Multi-Resolution Feature Description For Object Recognition”, teaches about techniques and systems are provided for determining features for one or more objects in one or more video frames. A size of the object can be determined based on the image, for example based on inter-eye distance of a face. Based on the size, either a high-resolution set of features or a low-resolution set of features is selected to compare to the features of the object. The object can be identified by matching the features of the object to matching features from the selected set of features.
Takada (U.S. 20180137632 A1), “ System and Method of Hybrid Tracking for Match Moving”, teaches about a system and method that maximizes tracking speed of an object in a sequence of images by selecting a technique for tracking the object independently for each frame in a video. The system includes an object feature detector that detects object features in a reference frame of the video and a feature comparator that determines a number of object features in each frame in the sequence of images that match the detected object features in the reference frame. Moreover, a tracking pattern selector selects the type of object tracking to track the object in the current frame based on the determined matched object features between the reference frame and the current frame of the video.
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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/DUY TRAN/Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
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