DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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.
Claim Rejections - 35 USC § 103
3. 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 set forth in Graham v. John Deere Co., 383 U.S. 1,148 USPQ 459 (1966), that are applied 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.
4. Claims 20-23, 25, 27-31, 33, and 35-37 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Kwon et al. (US Publication 2014/0355823, hereinafter Kwon) in view of Cheng et al. (English Translation of Chinese Publication CN 106372606 02-2017).
Regarding claim 20, Kwon discloses a video search device comprising:
a processor; and a memory for storing program instructions to be executed by the processor (Kwon, fig. 1, para’s 0033-0038, video search apparatus), wherein the processor, when executing the instructions, is configured to:
receive a first image from a first channel camera (Kwon, para’s 0034-0035, receiving a first image captured by a first video capture unit of a plurality of video capture units);
receive a second image from a second channel camera independent of the first channel camera (Kwon, para’s 0034-0035, receiving a second image captured by a second video capture unit of the plurality of video capture units);
extract a first object having a plurality of attribute items from the first image (Kwon, para’s 0078-0079, the search unit 150 may search the data captured by the first video capture unit, and receive metadata about a first person/object);
extract a second object having a plurality of attribute items from the second image (Kwon, para’s 0078-0079, the search unit 150 may search the data captured by the second video capture unit, and receive metadata about a second person/object),
wherein the attribute items of the first object and the second object represent features that are distinguishable between objects through visual observation (Kwon, fig. 8, para’s 0079-0080, the provision unit 160 may provide the people included in the metadata found by the search unit 150 to the user through the video screen unit 121. When there is no captured image (e.g., in the storage server 20) in which all of the people included in the found metadata appear simultaneously, the provision unit 160 may provide information edited to include all of the people on the screen provided to the user. For example, the provision unit 160 may provide found people 81 through 84 on one screen as shown in FIG. 8. The attribute items such as gender, shape and height of the first person and the second person represent features that are clearly distinguishable between objects through visual observation. Alternatively, the provision unit 160 may provide an image (or video) of the people 81 through 84 only without a background image such as a vehicle at the K apartment. The user may select one or more suspects from the people 81 through 84 provided by the provision unit 160 by, e.g., touching them. When the user selects a person, the object input unit 127 may receive the selected person (object) as object setting information, and the object query generation unit 140 may generate an object query using the object setting information such that data including the same or similar person to the person selected by the user is searched for. Then, the search unit 150 may search for metadata including the same or similar person to the person selected by the user based on the setting of the object query generated by the object query generation unit 140).
Kwon does not explicitly disclose but Cheng discloses assign identifiers to the first object and the second object based on a similarity between the attribute items of the first object and the attribute items of the second object, wherein assigning the identifier comprises: assigning the same identifier to the first object and the second object when the similarity is greater than a reference value; and assigning different identifiers to the first object and the second object when the similarity is less than the reference value (Cheng, para’s 0138-0144, first, in step 501, multiple data-based feature information of the target object is compared in parallel with the corresponding data-based feature information of comparison objects in the database. For example, in step 510, the first data-based feature information of the target object is compared with the first data-based feature information of all comparison objects in the database; in step 512, the second data-based feature information of the target object is compared with the second data-based feature information of all comparison objects in the database; ... in step 51n, the nth data-based feature information of the target object is compared with the nth data-based feature information of all comparison objects in the database. In step 502, it is determined whether there are identical or similar data-related features in the database for each data-related feature of the target object. For example, in step 520, it is determined whether the first data-based feature information of the comparison object in the database is the same as or close to the first data-based feature information of the target object; in step 521, it is determined whether the second data-based feature information of the comparison object in the database is the same as or close to the second data-based feature information of the target object; in step 52n, it is determined whether the nth data-based feature information of the comparison object in the database is the same as or close to the nth data-based feature information of the target object. In step 503, for each data feature, all close comparison objects are extracted. For example, in step 530, all comparison objects that are the same as or similar to the first data-based feature information of the target object are extracted; in step 531, all comparison objects that are the same as or similar to the second data-based feature information of the target object are extracted; in step 53n, all comparison objects that are the same as or similar to the data-based feature information of the target object are extracted. In step 504, it is determined whether there are comparison objects with the same or similar data feature information in greater numbers than a predetermined value. For example, it can be determined whether there are comparison objects with the same or similar data feature information in greater than a predetermined value by judging whether there are comparison objects that appear more often than a set number in steps 530 to 530n. When there are more than a predetermined number of comparison objects with the same or similar data feature information, the process proceeds to step S204 to retrieve the identity information of the comparison object, such as name, gender, age, height, place of origin, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Cheng’s features into Kwon’s invention for saving resource and time of search operation by effectively recognizing potential target objects that have been previously identified.
Regarding claim 21, Kwon-Cheng discloses the device of claim 20, wherein the attribute items of the first object and the attribute items of the second object differ in number (Cheng, para’s 0138-0144, first, in step 501, multiple data-based feature information of the target object is compared in parallel with the corresponding data-based feature information of comparison objects in the database. For example, in step 510, the first data-based feature information of the target object is compared with the first data-based feature information of all comparison objects in the database; in step 512, the second data-based feature information of the target object is compared with the second data-based feature information of all comparison objects in the database; ... in step 51n, the nth data-based feature information of the target object is compared with the nth data-based feature information of all comparison objects in the database. In step 502, it is determined whether there are identical or similar data-related features in the database for each data-related feature of the target object. This implies that the attribute items of the target object and the attribute items of the comparison object may differ in number. In step 503, for each data feature, all close comparison objects are extracted. For example, in step 530, all comparison objects that are the same as or similar to the first data-based feature information of the target object are extracted; in step 531, all comparison objects that are the same as or similar to the second data-based feature information of the target object are extracted; in step 53n, all comparison objects that are the same as or similar to the data-based feature information of the target object are extracted. In step 504, it is determined whether there are comparison objects with the same or similar data feature information in greater numbers than a predetermined value. For example, it can be determined whether there are comparison objects with the same or similar data feature information in greater than a predetermined value by judging whether there are comparison objects that appear more often than a set number in steps 530 to 530n. When there are more than a predetermined number of comparison objects with the same or similar data feature information, the process proceeds to step S204 to retrieve the identity information of the comparison object, such as name, gender, age, height, place of origin, etc.).
The motivation and obviousness arguments are the same as claim 20.
Regarding claim 22, Kwon-Cheng discloses the claim 20, wh0erein the processor, when executing the instructions, is further configured to: receive a search command from a user; and search at least some of the objects based on the assigned identifier (Kwon, fig, 10 step S1040, fig. 11, Step 1130, search command; Cheng, para. 0132, step S405, search command; para. 0143, when there are more than a predetermined number of comparison objects with the same or similar data feature information, the process proceeds to step S204 to retrieve the identity information of the comparison object, such as name, gender, age, height, place of origin, etc.; obviously search can be performed based on object name/identifier).
The motivation and obviousness arguments are the same as claim 20.
Regarding claim 23, Kwon-Cheng discloses the device of claim 22, wherein a search result that satisfies a search criterion entered by the user is stored in a storage (Kwon, fig’s 10 and 11, step S1040 and S1050, storing search result and providing it to users).
Regarding claim 25, Kwon-Cheng discloses the device of claim 24, wherein the storage is further configured to store the attribute items of the first object and the attribute items of the second object (Kwon, fig. 10, step S1010, storing metadata of objects in video content).
Regarding claim 27, Kwon-Cheng discloses the device of claim 20, wherein the similarity increases as a number of matching attribute items between the first objects and the second objects increases (Cheng, para’s 0138-0144, first, in step 501, multiple data-based feature information of the target object is compared in parallel with the corresponding data-based feature information of comparison objects in the database. For example, in step 510, the first data-based feature information of the target object is compared with the first data-based feature information of all comparison objects in the database; in step 512, the second data-based feature information of the target object is compared with the second data-based feature information of all comparison objects in the database; ... in step 51n, the nth data-based feature information of the target object is compared with the nth data-based feature information of all comparison objects in the database. In step 502, it is determined whether there are identical or similar data-related features in the database for each data-related feature of the target object. For example, in step 520, it is determined whether the first data-based feature information of the comparison object in the database is the same as or close to the first data-based feature information of the target object; in step 521, it is determined whether the second data-based feature information of the comparison object in the database is the same as or close to the second data-based feature information of the target object; in step 52n, it is determined whether the nth data-based feature information of the comparison object in the database is the same as or close to the nth data-based feature information of the target object. In step 503, for each data feature, all close comparison objects are extracted. For example, in step 530, all comparison objects that are the same as or similar to the first data-based feature information of the target object are extracted; in step 531, all comparison objects that are the same as or similar to the second data-based feature information of the target object are extracted; in step 53n, all comparison objects that are the same as or similar to the data-based feature information of the target object are extracted. In step 504, it is determined whether there are comparison objects with the same or similar data feature information in greater numbers than a predetermined value. For example, it can be determined whether there are comparison objects with the same or similar data feature information in greater than a predetermined value by judging whether there are comparison objects that appear more often than a set number in steps 530 to 530n. When there are more than a predetermined number of comparison objects with the same or similar data feature information, the process proceeds to step S204 to retrieve the identity information of the comparison object, such as name, gender, age, height, place of origin, etc.; the disclosure above implies wherein the similarity increases as a number of matching attribute items between the first objects and the second objects increases).
The motivation and obviousness arguments are the same as claim 20.
Claims 28-31, 33, and 35 are rejected for the same reasons as claims 20-23, 25, and 27.
Claim 36 is rejected for the same reasons as claims 20. Kwon-Cheng further discloses:
storing the assigned identifiers in a storage (Kwon, fig’s 10 and 11, step S1040 and S1050, storing search result and providing it to users; Cheng, para. 0143, the process proceeds to step S204 to retrieve the identity information of the comparison object, such as name, gender, age, height, place of origin); and
monitoring at least one object based on the stored identifier (Kwon, para. 0115, object tracking; Cheng, para’s 0102 and 0178, the remote monitoring device generates formatted data of the target object and sends it to the server (or cloud) through the communication unit); and wherein the similarity is determined based on a number of attribute items common to the first object and the second object, the attribute items representing features that are distinguishable between the objects through visual observation (Cheng, para’s 0138-0144, first, in step 501, multiple data-based feature information of the target object is compared in parallel with the corresponding data-based feature information of comparison objects in the database. For example, in step 510, the first data-based feature information of the target object is compared with the first data-based feature information of all comparison objects in the database; in step 512, the second data-based feature information of the target object is compared with the second data-based feature information of all comparison objects in the database; ... in step 51n, the nth data-based feature information of the target object is compared with the nth data-based feature information of all comparison objects in the database. In step 502, it is determined whether there are identical or similar data-related features in the database for each data-related feature of the target object. For example, in step 520, it is determined whether the first data-based feature information of the comparison object in the database is the same as or close to the first data-based feature information of the target object; in step 521, it is determined whether the second data-based feature information of the comparison object in the database is the same as or close to the second data-based feature information of the target object; in step 52n, it is determined whether the nth data-based feature information of the comparison object in the database is the same as or close to the nth data-based feature information of the target object. In step 503, for each data feature, all close comparison objects are extracted. For example, in step 530, all comparison objects that are the same as or similar to the first data-based feature information of the target object are extracted; in step 531, all comparison objects that are the same as or similar to the second data-based feature information of the target object are extracted; in step 53n, all comparison objects that are the same as or similar to the data-based feature information of the target object are extracted. In step 504, it is determined whether there are comparison objects with the same or similar data feature information in greater numbers than a predetermined value. For example, it can be determined whether there are comparison objects with the same or similar data feature information in greater than a predetermined value by judging whether there are comparison objects that appear more often than a set number in steps 530 to 530n. When there are more than a predetermined number of comparison objects with the same or similar data feature information, the process proceeds to step S204 to retrieve the identity information of the comparison object, such as name, gender, age, height, place of origin, etc.).
Regarding claim 37, Kwon-Cheng discloses the method of claim 36, wherein the assigning the identifiers and the storing the assigned identifiers are performed by a first processor and the monitoring the at least one object is performed by a second processor that communicates with the first processor via a network channel (Kwon, fig’s 10 and 11, step S1040 and S1050, storing search result and providing it to users; para. 0115, object tracking; Cheng, para. 0143, the process proceeds to step S204 to retrieve the identity information of the comparison object, such as name, gender, age, height, place of origin; para’s 0102 and 0178, the remote monitoring device generates formatted data of the target object and sends it to the server (or cloud) through the communication unit; off-loading and distributing workload to multiple processor is well known in the art to better manage system resources, reduce bottleneck and save processing time).
The motivation and obviousness arguments are the same as claim 36.
5. Claims 24, 26, 32, 34, 38 and 39 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Kwon-Cheng, as applied to claims 23, 31, and 36 above, in view of Spence et al. (US Publication 2020/0066305).
Regarding claim 24, Kwon-Cheng discloses the device of claim 23.
Kwon-Cheng does not explicitly disclose but Spence discloses wherein the storage is configured to store a representative image for a video (Spence, para. 0188, means for displaying a thumbnail image representative of the digital media file in a display window on a display device of the computing device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Spence’s features into Kwon-Cheng’s invention for enhancing user’s search experience by visually displaying a representative content of the video.
Regarding claim 26, Kwon-Cheng-Spence discloses the device of claim 24, further comprising: a display configured to display a video obtained as the search result when the processor searches for the data, wherein the display is configured to display the representative image associated with the video as thumbnails (Spence, para. 0188, means for displaying a thumbnail image representative of the digital media file in a display window on a display device of the computing device).
The motivation and obviousness arguments are the same as claim 24.
Claims 32 and 34 are rejected for the same reasons as claims 24 and 26.
Regarding claim 38, Kwon-Cheng discloses the method of claim 36, further comprising: storing the assigned identifiers (Kwon, fig’s 10 and 11, step S1040 and S1050, storing search result and providing it to users.; Cheng, para. 0143, retrieve the identity information of the comparison object, such as name, gender, age, height, place of origin).
Kwon-Cheng does not explicitly disclose but Spence discloses storing a representative image for a video (Spence, para. 0188, means for displaying a thumbnail image representative of the digital media file in a display window on a display device of the computing device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Spence’s features into Kwon-Cheng’s invention for enhancing user’s search experience by storing a representative content of the video with an identifier.
Regarding claim 39, Kwon-Cheng-Spence discloses the method of claim 38, further comprising: displaying the representative image associated with the video as thumbnails (Spence, para. 0188, means for displaying a thumbnail image representative of the digital media file in a display window on a display device of the computing device).
The motivation and obviousness arguments are the same as claim 38.
Conclusion
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to LOI H TRAN whose telephone number is (571)270-5645. The examiner can normally be reached 8:00AM-5:00PM PST FIRST FRIDAY OF BIWEEK OFF.
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/LOI H TRAN/ Primary Examiner, Art Unit 2484
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