DETAILED ACTION
1. This communication is responsive to the Amendment filed 9/19/2025.
Claims 1-12 have been amended. Claims 13-18 have been added. Claims 1-18 are pending in this application. This action is made Final.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
3. 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.
4. 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
5. 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.
6. Claims 1-13, 15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over NAGAYOSHI (US 2016/0078314) in view of Chen et al. (US 2002/0178149) hereinafter Chen.
In claim 1, NAGAYOSHI discloses “An image processing apparatus comprising:
at least one memory configured to store one or more instructions; and
at least one processor configured to execute the one or more instructions to:
acquire a first query still image and at least one second query still image ([0030] The image feature value is typically represented as a numeric vector. The respective elements of the numeric vector include numeric values representing colors of the image, values of the gradient strength and direction of image, and values of frequency distribution calculated based on those values. The closer the image feature values are, the more similar respective images are to each other [0031] variations in facial images of each individual is schematically shown with feature value distributions 101 each having an oval shape that is defined by the image feature value 1 and the image feature value 2. The respective feature value distributions 101 of the image feature values that are located at different locations in the vector space of the image feature value indicate variations in faces of different individuals. The directions with a greater degree of variations (major axis of the ellipse in this example) differ among individuals as each person has different facial features);
compute, for each of a plurality of feature types, a first feature value from the first query still image ([0031] variations in facial images of each individual is schematically shown with feature value distributions 101 each having an oval shape that is defined by the image feature value 1 and the image feature value 2. The respective feature value distributions 101 of the image feature values that are located at different locations in the vector space of the image feature value indicate variations in faces of different individuals. The directions with a greater degree of variations (major axis of the ellipse in this example) differ among individuals as each person has different facial features [0034] images that have similar image feature values are located close to each other. In the case of facial image, for example, it is known that if the feature values obtained from two-dimensional facial images are similar to each other, generally, the three-dimensional structures of respective faces are also similar to each other [0037] The data ID is identification information that specifies data. Data is an image, for example. Each image, i.e., data, is stored in the memory device 302. The image feature value is a numeric vector that indicates the features of an image, i.e., data. Thus, the image feature values within the same cluster are similar to each other);
compute, for each of the plurality of feature types, a second feature value from the at least one second query still image ([0031] variations in facial images of each individual is schematically shown with feature value distributions 101 each having an oval shape that is defined by the image feature value 1 and the image feature value 2. The respective feature value distributions 101 of the image feature values that are located at different locations in the vector space of the image feature value indicate variations in faces of different individuals. The directions with a greater degree of variations (major axis of the ellipse in this example) differ among individuals as each person has different facial features [0034] images that have similar image feature values are located close to each other. In the case of facial image, for example, it is known that if the feature values obtained from two-dimensional facial images are similar to each other, generally, the three-dimensional structures of respective faces are also similar to each other [0037] The data ID is identification information that specifies data. Data is an image, for example. Each image, i.e., data, is stored in the memory device 302. The image feature value is a numeric vector that indicates the features of an image, i.e., data. Thus, the image feature values within the same cluster are similar to each other);
compute, for each of the plurality of feature types, an arithmetic average value or a weighted average value of the first feature value and the second feature value ([0045] the intra-class variance-covariance matrix ΣW, i is obtained based on the average of the plurality of image feature values obtained from the plurality of facial images of person “i” and image feature values of a group of images of person “j” that are within a prescribed distance from the average of the plurality of the image feature values. Each person makes up one class)”.
NAGAYOSHI does not appear to explicitly disclose however, Chen discloses “generate, as an integrated query, a set of the arithmetic average value or the weighted average value of the plurality of feature types ([0017] During the first phase, common features of multimedia content in a multimedia database are extracted by feature extraction units 12 and 14 and stored in feature databases 16 and 18. In the second phase (i.e., similarity retrieval), a user selects an initial query image either through an on-line editing interface 20 or from one or more sample images stored in the database 10. If a new image is created from the on-line interface 20, features in the new image must be extracted by both feature extraction units 12 and 14 before searching in the feature databases 16 and 18. If one of the sample images stored in the database 10 were used as the initial query image, then only an identification code will be sent to the feature databases 16 and 18, which sends associated feature of the initial query image to a UQM 22 (universal query mechanism) such that both query units 24 and 26 in the UQM can generate a representative feature vector for retrieving a set of sample query images, claim 20 (“one or more universal query units for generating adjusted weight factors based on said salient and common feature vectors, generating new feature vectors based on said adjusted weight factors, and generating new query images based on said new feature vectors”))”.
Hence, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine NAGAYOSHI and Chen, the suggestion/motivation for doing so would have been to use a universal query mechanism (UQM) to locate statistically silent common features among sample query images from different feature sets and adjust the weight factor for each feature to meet a user's query demand (Abstract).
In claim 2, Chen teaches
The image processing apparatus according to claim 1, wherein the at least one processor is further configured to execute the one or more instructions to search for a still image by using the integrated query as a key ([0021] Through multi-instance or relevance feedback, the UQM as shown in FIG. 2 seeks to find common features among sample query images. Weight factors are adjusted such that statistically common features dominate in the representative feature vector for next sample query images).
In claim 3, Chen teaches
The image processing apparatus according to claim 2, wherein the at least one processor is further configured to execute the one or more instructions to search for the still image by using a weighting value being set for each of a plurality of types of feature values ([0021] Through multi-instance or relevance feedback, the UQM as shown in FIG. 2 seeks to find common features among sample query images. Weight factors are adjusted such that statistically common features dominate in the representative feature vector for next sample query images).
In claim 4, Chen teaches
The image processing apparatus according to claim 3, wherein the at least one processor is further configured to execute the one or more instructions to search for the still image by using the integrated query and the weighting value for each of the feature values being set, based on a degree of similarity between each of a plurality of types of feature values computed from the first query still image and each of a plurality of types of feature values computed from the at least one second query still image ([0017] During the first phase, common features of multimedia content in a multimedia database are extracted by feature extraction units 12 and 14 and stored in feature databases 16 and 18. In the second phase (i.e., similarity retrieval), a user selects an initial query image either through an on-line editing interface 20 or from one or more sample images stored in the database 10. If a new image is created from the on-line interface 20, features in the new image must be extracted by both feature extraction units 12 and 14 before searching in the feature databases 16 and 18. If one of the sample images stored in the database 10 were used as the initial query image, then only an identification code will be sent to the feature databases 16 and 18, which sends associated feature of the initial query image to a UQM 22 (universal query mechanism) such that both query units 24 and 26 in the UQM can generate a representative feature vector for retrieving a set of sample query images, [0021] Through multi-instance or relevance feedback, the UQM as shown in FIG. 2 seeks to find common features among sample query images. Weight factors are adjusted such that statistically common features dominate in the representative feature vector for next sample query images, claim 20 (“one or more universal query units for generating adjusted weight factors based on said salient and common feature vectors, generating new feature vectors based on said adjusted weight factors, and generating new query images based on said new feature vectors”)).
In claim 13, Chen teaches
The image processing apparatus according to claim 1, wherein the at least one processor is further configured to execute the one or more instructions to:
compute, for each of the plurality of feature types, a degree of similarity between the first feature value and the second feature value, set, for each of the plurality of feature types, a weighting value based on the degree of similarity between the first feature value and the second feature value, and search for a still image by using the integrated query and the weighting values being set for each of the plurality of feature types ([0009] a content-based retrieval method and apparatus which finds the most common features among each set of sample query images from multi-instance or relevance feedback. In particular, each set of sample query images is constructed by finding similar images shapes in the database. The resulting sample query images are statistically similar to query input, i.e. relative instead of absolute similarity. A probability distribution model for the feature vectors is used to dynamically adjust weights such that most common ones among sample query images dominates feedback query. Whenever new feature sets are devised, they could be acquired by the query system. The query unit then searches from all feature sets such that the statistically common features become the new query vector. Accordingly, the UQM accommodates new feature sets easily and adjusts weights for various features dynamically according to a user's query and statistics of the database [0010] the indexing system provides relevance feedback to learn what user's intention is and generating a new feature vector for next query. The UQM finds statistically salient common features among sample query images with different feature sets).
7. Claims 14, 16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over NAGAYOSHI (US 2016/0078314) in view of Chen et al. (US 2002/0178149) hereinafter Chen, and further in view of HU et al. (US 2021/0097101) hereinafter HU.
In claim 14, per rejections in claim 1
NAGAYOSHI and Chen do not appear to explicitly disclose however, HU discloses “The image processing apparatus according to claim 1, wherein the first query still image and the at least one second query still image each include a different person, with both persons showing a same pose ([0011] determining whether the candidate person has the pose similar to the reference person may include calculating a pose similarity between the candidate person and the reference person based on the reference keypoint data and the candidate keypoint data, determining, in response to the pose similarity being greater than a predetermined threshold, that the candidate person has the pose similar to the reference person [0055] the target images may be arranged in a descending order of the pose similarity. If the target image includes a plurality of candidate persons that have the pose similar to the reference person, the greatest pose similarity may be used for the target images in the arrangement. Then, the predetermined number of target images with the high pose similarity may be determined as the final target images)”.
Hence, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine NAGAYOSHI, Chen and HU, the suggestion/motivation for doing so would have been to provide a method for image search that search the feature points of the image and the pose of the person in the image ([0034][0035]).
Claims 5-8 and 15-16 are essentially same as claims 1-4 and 13-14 except that they recite claimed invention as a method and are rejected for the same reasons as applied hereinabove.
Claims 9-12 and 17-18 are essentially same as claims 1-4 and 13-14 except that they recite claimed invention as a non-transitory storage medium and are rejected for the same reasons as applied hereinabove.
Response to Arguments
8. With respect to claims 1-18, Applicant has amended the claims 1-12 underlined above and added new claims 13-18; however, upon further search and consideration, a new ground(s) of rejections is made in view of newly found prior art listed above.
Conclusion
9. 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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUAWEN A PENG whose telephone number is (571)270-5215. The examiner can normally be reached Mon thru Fri 9 am to 5 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sherief Badawi can be reached at 571-272-9782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HUAWEN A PENG/Primary Examiner, Art Unit 2169
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