2Notice 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on May 26, 2026 has been entered.
Response to Arguments
Applicant's arguments filed May 26, 2026 have been fully considered but they are not persuasive.
Regarding claim 1,
applicant states that “Kim does not teach that appearance data may be retrieved from a feature map. Kim teaches that appearance data may be retrieved from either a face recognition unit or an object detection unit. Neither the face recognition unit nor the object detection unit depend on a feature map to gather appearance data. Kim then teaches that location data may be retrieved from a feature map generated in a movement path analysis unit, the movement path analysis unit being separate from the face recognition unit and the object detection unit. Therefore, Kim does not teach both location detection data and appearance detection data being gathered on the basis of a single feature map.”. Examiner disagrees with this statement. Kim teaches wherein the detection model includes a detection model for generating the location detection data (The object detecting unit 210 may include at least one of a motion detecting unit, a head-and-shoulder (HS) detecting unit, a tracking unit, and a face detecting unit, as described later. Page 3 8th paragraph) and the appearance detection data (2 or 4, the customer attribute recognition unit 230 may extract the customer attribute information from the object information input from the object detection unit 210. [ Customer attribute means a characteristic that can be classified by type such as sex, history, facial expression, and the like. Page 7 6th paragraph) together on the basis of a single feature map (In addition, in the present invention, instead of using a separate face detection unit for recognizing the customer attribute, the face detection unit 440 of the object detection unit 210 described with reference to FIG. By reusing or sharing detection results, the entire process can be speeded up. Page 7 11th paragraph. In FIG. 4, the object detection unit 210 receives a forward camera image and performs an operation of performing pre-processing (i.e., object detection) for acquiring customer information therefrom. An image (or a video stream) input from a forward camera may be sequentially (or cascaded) processed by the motion detecting unit 410, the HS detecting unit 420, the tracking unit 430, and the face detecting unit 440. Accordingly, the customer analysis system can operate at high speed or in real time by reducing the calculation load. Page 4 7th paragraph.
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applicant states that “Kim teaches that a coordinate value is assigned within a coordinate determination unit as part of a head and shoulder detection module. The coordinate determination unit and the shoulder detection module are separate from the movement path analysis unit which, as previously described, creates the feature map. As such, neither coordinate determination unit nor the shoulder detection module assign coordinate points based on the feature map.”. Examiner disagrees with this statement. In Kim’s teaching, the object detection unit 210 (which includes a motion detecting unit, a head-and-shoulder (HS) detecting unit, a tracking unit, and a face detecting unit) uses the same coordinate values assigned by the HS detecting unit 420 (The detection determination unit 425 may assign unique identification information (e.g., ID) to each HS object detected in the two-dimensional image. This is to track HS objects until they disappear from the camera view. The detection determination unit 425 can transmit the unique identification information and the coordinate value of the HS object to the tracking unit 430. Page 6 4th paragraph.
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Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Kim (Korean Patent Publication No.: KR 20170006356 A), hereinafter Kim, in view of Ko (Korean Patent Publication No.: KR 101448392 B1), hereinafter Ko.
Regarding claim 1, Kim teaches a method for analyzing a visitor on the basis of a video (METHOD FOR CUSTOMER ANALYSIS BASED ON TWO-DIMENSION VIDEO AND APPARATUS FOR THE SAME. Title; see also Abstract) in an edge computing environment (The image analysis apparatus 120 may be installed in a store (i.e., in an edge computing environment rather than a server) or installed in a separate PC or server at a separate place. Page 3 5th paragraph), the method comprising the steps of: extracting feature data (The feature extraction unit 422 may divide each of the pyramid images into small blocks and extract a local feature for each block. Page 5 6th paragraph) from a captured video (The video information from each camera may be provided to the video stream composition and transmission unit 330 through various interfaces. Page 4 4th paragraph) of an offline space (In the following description, the customer information acquisition technology and the customer information analysis technology according to the present invention are applied to the off-line store, but the scope of the present invention is not limited thereto. Page 3 2nd paragraph); generating location detection data on a location (The heat map can display the distribution of a plurality of objects in the camera view and the frequency of staying at a specific location. Page 9 8th paragraph) and appearance detection data on an appearance of the object from the feature data (2 or 4, the customer attribute recognition unit 230 may extract the customer attribute information from the object information input from the object detection unit 210. [ Customer attribute means a characteristic that can be classified by type such as sex, history, facial expression, and the like. Page 7 6th paragraph) using an artificial neural network-based detection model (The map learning method is a type of machine learning for deriving a function from training data. The training data typically includes the attributes for the input object in vector form, indicating what the desired result is for each vector. Page 5 last paragraph), wherein the detection model includes a detection model for generating the location detection data (The object detecting unit 210 may include at least one of a motion detecting unit, a head-and-shoulder (HS) detecting unit, a tracking unit, and a face detecting unit, as described later. Page 3 8th paragraph) and the appearance detection data (2 or 4, the customer attribute recognition unit 230 may extract the customer attribute information from the object information input from the object detection unit 210. [ Customer attribute means a characteristic that can be classified by type such as sex, history, facial expression, and the like. Page 7 6th paragraph) together on the basis of a single feature map (In addition, in the present invention, instead of using a separate face detection unit for recognizing the customer attribute, the face detection unit 440 of the object detection unit 210 described with reference to FIG. By reusing or sharing detection results, the entire process can be speeded up. Page 7 11th paragraph. In FIG. 4, the object detection unit 210 receives a forward camera image and performs an operation of performing pre-processing (i.e., object detection) for acquiring customer information therefrom. An image (or a video stream) input from a forward camera may be sequentially (or cascaded) processed by the motion detecting unit 410, the HS detecting unit 420, the tracking unit 430, and the face detecting unit 440. Accordingly, the customer analysis system can operate at high speed or in real time by reducing the calculation load. Page 4 7th paragraph.
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); integrating the location detection data the appearance detection data [[of]] with respect to a target object (As described above, the present invention can provide at least one of outbound counting, customer attribute recognition, face recognition (i.e., customer identification), and moving line analysis of a customer visiting a specific place using a two-dimensional image obtained by a camera have. Page 9 9th paragraph. In addition, in the present invention, instead of using a separate face detection unit for recognizing the customer attribute, the face detection unit 440 of the object detection unit 210 described with reference to FIG. By reusing or sharing detection results, the entire process can be speeded up. Page 7 11th paragraph) by assigning, via at least one coordinate point (The detection determination unit 425 may assign unique identification information (e.g., ID) to each HS object detected in the two-dimensional image. This is to track HS objects until they disappear from the camera view. The detection determination unit 425 can transmit the unique identification information and the coordinate value of the HS object to the tracking unit 430. Page 6 4th paragraph.
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) on the single feature map (In addition, in the present invention, instead of using a separate face detection unit for recognizing the customer attribute, the face detection unit 440 of the object detection unit 210 described with reference to FIG. By reusing or sharing detection results, the entire process can be speeded up. Page 7 11th paragraph. In FIG. 4, the object detection unit 210 receives a forward camera image and performs an operation of performing pre-processing (i.e., object detection) for acquiring customer information therefrom. An image (or a video stream) input from a forward camera may be sequentially (or cascaded)processed by the motion detecting unit 410, the HS detecting unit 420, the tracking unit 430, and the face detecting unit 440. Accordingly, the customer analysis system can operate at high speed or in real time by reducing the calculation load. Page 4 7th paragraph.
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), at least a part of the location detection data (The object detecting unit 210 may include at least one of a motion detecting unit, a head-and-shoulder (HS) detecting unit, a tracking unit, and a face detecting unit, as described later. Page 3 8th paragraph) and at least a part of the appearance detection data (2 or 4, the customer attribute recognition unit 230 may extract the customer attribute information from the object information input from the object detection unit 210. [ Customer attribute means a characteristic that can be classified by type such as sex, history, facial expression, and the like. Page 7 6th paragraph) corresponding to the at least one coordinate point to the target object (When one or more of the front view or the back view (i.e., the HS object) is detected in the frontal view detection unit 423 and the backward view detection unit 424, the coordinate determination unit 425 can output the coordinate value of the corresponding area. Page 6 3rd paragraph. The detection determination unit 425 may assign unique identification information (e.g., ID) to each HS object detected in the two-dimensional image. This is to track HS objects until they disappear from the camera view. The detection determination unit 425 can transmit the unique identification information and the coordinate value of the HS object to the tracking unit 430. Page 6 4th paragraph); tracking the target object in the captured video with reference to the integrated detection data (The object detecting unit 210 may include at least one of a motion detecting unit, a head-and-shoulder (HS) detecting unit, a tracking unit, and a face detecting unit, as described later. Page 3 8th paragraph).
Kim does not teach the following limitations as further recited, but Ko further teaches determining entry or exit of the target object (The number of visitors and the number of passengers are counted using Line Drawing method for the tracked objects. That is, two virtual reference lines A (inside) and B (outside) are generated at a position of 30 pixels from the upper and lower sides of the image, with the resolution of the upper camera image being 640 x 480 pixels (Pixel) The number of entries (IN) is counted if the first pass of B passes through A, and the number of outlets (OUT) passed first through A and passed through B are respectively counted by the counting unit 540 shown in FIG. 8. Page 6 9th paragraph) by determining whether the target object passes a predetermined detection line with reference to information on the tracking of the target object (More specifically, when an object of a human head is traced and the tracked object passes the virtual reference line shown in FIG. 20, it is determined whether or not to count the access. Page 10 9th paragraph)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kim to incorporate the teachings of Ko to determine entry or exit of the target object by determining whether the target object passes a predetermined detection line with reference to information on the tracking of the target object in order to accurately count the number of passengers who are moving.
Regarding claim 3, Kim in the combination teaches the method of Claim 1, wherein the location detection data (The heat map can display the distribution of a plurality of objects in the camera view and the frequency of staying at a specific location. Page 9 8th paragraph), and wherein the appearance detection data (Also, the present invention proposes a method for providing a more accurate customer analysis using a two-dimensional image-based customer information acquisition technology. For example, people counting outgoing customers, recognition of specific customers (e.g., VIP or blacklist customers) using customer face recognition technology, customer attributes using image analysis techniques (e.g., gender Or age of the customer), and the customer's copper line analysis can be applied to the two-dimensional image-based customer information acquisition technique proposed in the present invention. Page 2 7th paragraph).
Ko in the combination further teaches the location detection data (Seven face feature ratio values and four wrinkle density values are used as an input vector. Page 10 4th paragraph), width, height (The labeling can define information such as width, height, area, and density indicating the number of pixels compared with area by defining connected pixels as one object, i.e., a label. Page 4 8th paragraph), and center offset of a bounding box corresponding to the object (The blob assigned to the object has a center point and a rectangular coordinate, which is used to check whether a virtual baseline has passed. Page 10 11th paragraph. FIGS. 9A and 9B show tag numbers given to the objects extracted from the first object extracting unit 300 and the second object extracting unit 400, respectively. The color of the outline of the rectangular area of the designated blob (i.e., a bounding box) is set differently. Page 6 5th paragraph
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Claim 10 is drawn to a non-transitory computer-readable storage medium having executable instructions stored for carrying out the method of claim 1. Therefore, claim 10 corresponds to method claim 1, and is rejected for the same reasons of obviousness as used above.
Apparatus claim 11 is drawn to the apparatus corresponding to the method of using same as claimed in claim 1. Therefore apparatus claim 11 corresponds to method claim 1, and is rejected for the same reasons of obviousness as used above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEI ZHAO whose telephone number is (703)756-1922. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, VU LE can be reached at (571)272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LEI ZHAO/Examiner, Art Unit 2668
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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