DETAILED ACTION
Notice 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 .
Response to Arguments
Applicant's arguments filed 12/12/25 have been fully considered but they are not persuasive.
Regarding 35 USC 101, While this tasks can be considered mental or mathematical in nature, they have no practical application in a process that, were it not to be performed in a computing device, would be applied by an unassisted human in sorting images and determining similarity between images. These are tasks that in general do not require any determination or measurement of skeletal structures of a person in the images, and comparisons in pose do not require numerical analysis, and even if some sort of objective measure was required, humans not requiring any sort of determination of any underlying skeletal structure. The intermediate step of skeletal analysis is common in computer image analysis algorithms and necessary to transform what is provided in pixel information into a structure that computers can compare, a tasks computers require but humans do not. Therefore, the 35 USC 101 rejection is withdrawn.
Regarding claim 1, Applicant states that the list of similar image in Watanabe ‘158 (fig 8) allows a user to determine the quantity and that the claimed feature actively providing information indicating a quantity on a display and that Watanabe ‘158 requires a user to actually count (Applicants Remarks pages 2-3). Examiner disagrees with Applicant. The claim limitations states, “displaying, on the display, the plurality of similar images together with information indicating, at least as a number, a quantity of the query images in which the similar image satisfies the reference” The claim limitation does not explicitly recite a “quantity” but “at least as a number” of the quantity of query images. Watanabe ‘158 displays After all queries are grouped, an importance level of the group is calculated and the groups are rearranged according to the importance level (paragraph 0056 and fig 6). This would show, by at least as a number, a quantity of query images. The grouped images that are arranged by importance or similarity would, thereby satisfy, the “at least a number, a quantity of query images.
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.
Claim(s) 1, 3, 5-8, 11, 14, 17 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe et al 20160217158 in view of Watanabe et al US 2019/0147292 (Watanabe 292).
Regarding claim 1, Watanabe et al 20160217158 teaches an image selection apparatus (image search device 105(paragraph 0032), comprising:
at least one memory storing instructions (memory (paragraph 0139); and
at least one processor configured to execute the instructions to perform operations (a processor (paragraph 0139) comprising:
acquiring, based on computing a feature value of a detected skeleton structure of a person in a query image stored in a database, a plurality of pieces of query information that are information generated for each of the plurality of query images, from the query image and indicate a feature of the query image (When the user 1200 inputs the pose information and the retrieval condition to the computer 1201 (S1221), the computer 1201 generates a query by converting the input pose information and image into features (S1222), and obtains similar images from the image database 108 (Fig 12 and paragraph 0098);
searching for, from a plurality if images, a plurality of similar images (A user first provides a set of search queries to the system. The method of providing queries includes a method of specifying a tag and a method of directly providing an image file. When a tag is specified, the user inputs a tag (reference) such as a person's name in the tag input area 1001. When the image reading button 1002 is clicked, images having the specified tag are read (selected) from images registered in the image database and are displayed in the query image display area 1004 (paragraph 0088) fig. 8 El. 803 shows “display in order of similarity of all search results”);
displaying the plurality of similar images on a display (images registered in the image database and are displayed in the query image display area 1004 (paragraph 0088), and also setting a display position or a display order of each of the plurality of similar images on the display unit by using a number of the query images in which the similar image satisfies the reference (When the image reading button 1002 is clicked, images having the specified tag are read from images registered in the image database and are displayed in the query image display area 1004 (fig 8 (803) and paragraph 0088).
displaying, on the display, the plurality of similar images together with information indicating, at least as a number, a quantity of the query images in which the similar image satisfies the reference (After all queries are grouped, an importance level of the group is calculated and the groups are rearranged according to the importance level (paragraph 0056). This would show, by at least as a number, a quantity of query images. The grouped images that are arranged by importance or similarity would, thereby satisfy, the “at least a number, a quantity of query images, see fig 6 also)
Watanabe et al ‘158 fails to teach of acquiring, based on computing a feature value of a detected skeleton structure of a person in a query image stored in a database, a plurality of pieces of query information that are information generated for each of the plurality of query images, from the query image and indicate a feature of the query image and indicate a feature of the query image,;
outputting a display illustrating a plurality of candidate images clustered respectively into ones of a first cluster and one or more second clusters, the first cluster being determined as representing a first pose, and the one or more second clusters being determined as representing one or more second poses;
identifying a plurality of query images based on a user input that is to the display and is a selection of the cluster;
a plurality of similar images whose degree of similarity to at least one of the query images satisfies a reference by computing a degree of similarity between a first feature value of a first skeleton structure included in the one or more of the plurality of pieces of query information of the first cluster of the selection by the user input, and a second feature value of a second skeleton structure included in the plurality of images;
Watanabe et al 292 acquiring, based on computing a feature value of a detected skeleton structure of a person in a query image stored in a database, a plurality of pieces of query information that are information generated for each of the plurality of query images, from the query image and indicate a feature of the query image (When the user 1200 inputs the pose information (feature value of a skeleton) and the retrieval condition to the computer 1201 (S1221), the computer 1201 generates (computes) a query by converting the input pose information and image into features (skeleton), and obtains similar images from the image database 108 (Fig 12 and paragraph 0098);
outputting a display illustrating a plurality of candidate images clustered respectively into ones of a first cluster and one or more second clusters, the first cluster being determined as representing a first pose, and the one or more second clusters being determined as representing one or more second poses (retrieval result is converted into a screen including appropriate information by the retrieval result displaying unit 112 and displayed in the retrieval result display region 1004 (paragraph 0093) Note: see fig 10, retrieval result display region 1004 displays multiple group pose images that display different poses (first cluster and second cluster), these images read on candidate images as they are candidate images of the pose input region 1001. This would read on outputting a display illustrating a plurality of candidate images clustered respectively into ones of a first cluster and one or more second clusters, the first cluster being determined as representing a first pose, and the one or more second clusters being determined as representing one or more second poses);
identifying a plurality of query images based on a user input that is to the display and is a selection of the cluster (image retrieving apparatus 104 can retrieve images including a person in a similar pose information by using the pose information input by the user as a query. Pieces of image data 806, 807, and 808 are respectively original images from which the personal data 803, 804, and 805 (paragraph 0079). image retrieving unit 111 retrieves similar images from the image database 108 according to the pose features obtained in step S902 and the retrieval condition obtained in step S903 (S904) (paragraph 0083));
a plurality of similar images whose degree of similarity to at least one of the query images satisfies a reference by computing a degree of similarity between a first feature value of a first skeleton structure included in the one or more of the plurality of pieces of query information of the first cluster of the selection by the user input, and a second feature value of a second skeleton structure included in the plurality of images (the pose retriever 130 may calculate a pose likelihood value of a candidate pose, may calculate a pose naturalness measurement value of the candidate pose based on the database 930, and may collectively determine the calculated pose likelihood value and the calculated pose naturalness measurement value, to retrieve the most likely pose (paragraph 0082) An image retrieving apparatus 104 executes steps S1406 to S1409 with respect to each person obtained in step S1402 (S1405). The image retrieving unit 111 calculates the degree of similarity between the pose features for filter obtained in step S1402 and pose features of a target person (S1406) (paragraph 0105) Note: the person (query image) is used as a reference and feature (first skeleton). The pose features (second skeleton) that are obtained from the filter is compared to the pose feature (first skeleton) of the target person are used to calculate (compute) a degree of similarity
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Watanabe et al to include: acquiring, based on computing a feature value of a detected skeleton structure of a person in a query image stored in a database, a plurality of pieces of query information that are information generated for each of the plurality of query images, from the query image and indicate a feature of the query image; outputting a display illustrating a plurality of candidate images clustered respectively into ones of a first cluster and one or more second clusters, the first cluster being determined as representing a first pose, and the one or more second clusters being determined as representing one or more second poses; identifying a plurality of query images based on a user input that is to the display and is a selection of the cluster; a plurality of similar images whose degree of similarity to at least one of the query images satisfies a reference by computing a degree of similarity between a first feature value of a first skeleton structure included in the one or more of the plurality of pieces of query information of the first cluster of the selection by the user input, and a second feature value of a second skeleton structure included in the plurality of images.
The reason of doing so would be to select the desired image according to specific features.
Regarding claim 3, Watanabe et al teaches an image selection apparatus (image search device 105(paragraph 0032), comprising:
acquiring a plurality of pieces of query information that are information generated for each of a plurality of query images and indicate a feature of the query image (plural query images 601 are input, image feature values are calculated from each image and similar image search results 602 are obtained from the image database 108. search result 602 includes similar images, their similarity and an identification number of the closest cluster selected in a search using clustering (paragraph 0054);
selecting, by using the plurality of pieces of query information, a plurality of similar images whose degree of similarity to at least one of the query images satisfies a reference (A user first provides a set of search queries to the system. The method of providing queries includes a method of specifying a tag and a method of directly providing an image file. When a tag is specified, the user inputs a tag (reference) such as a person's name in the tag input area 1001. When the image reading button 1002 is clicked, images having the specified tag are read (selected) from images registered in the image database and are displayed in the query image display area 1004 (paragraph 0088); and
displaying on a display, the plurality of similar images together with information that can determine a number of the query images in which the similar image satisfies the reference or indicating correspondence between the similar image and the query images (fig 10 displays all of the similar images of the tag “Alice” by level of importance. The tag is the reference and the images are ranked by how similar that are to the tag. Also see paragraphs 0090-0093)
Regarding claim 5, Watanabe et al teaches wherein the operations comprise displaying, on the display unit, display, each of the plurality of similar images together with the query image in which the similar image satisfies the reference (similar images are extracted from a search result related to the group, for example, rearranged in the order of higher similarity, and output as the search result for each group (fig 6 and paragraph 0057) Since similar images having closer features can be confirmed for each group together by the abovementioned processing, compared with a case where similar images for plural queries are collectively displayed, the quality of the search result is enhanced. (FIG 8 and paragraph 0058).
Regarding claim 6, Watanabe et al teaches wherein the operations comprise:
displaying the plurality of query images on the display (a query image display area 1004 (fig 10), and
displaying when at least one of the similar images is selected, the query image in which the at least one of the similar images satisfies the reference on the display in an identifiable state from another of the query image (When a tag is specified, the user inputs a tag such as a person's name in the tag input area 1001. When the image reading button 1002 is clicked, images having the specified tag are read from images registered in the image database and are displayed in the query image display area 1004 (paragraph 0088). a similar image search is executed using a specified query group and similar images for each group are displayed in the search result display area 1006 (paragraph 0090)
Regarding claim 7, Watanabe et al teaches wherein the operations comprise:
displaying the plurality of query images on the display (a query image display area 1004 and search result display area 1006 (fig 10), and
displaying, when at least one of the query images is selected, the similar image whose degree of similarity to the selected query image satisfies a reference on the display unit in an identifiable state from another of the similar image (When a tag is specified, the user inputs a tag such as a person's name in the tag input area 1001. When the image reading button 1002 is clicked, images having the specified tag (reference) are read from images registered in the image database and are displayed in the query image display area 1004 (paragraph 0088). a similar image search is executed using a specified query group and similar images for each group are displayed in the search result display area 1006. The groups are rearranged and displayed according to an importance level. To enable the user to utilize for material of judgment for the following operation, numeric values of importance levels may also be displayed. (paragraph 0090)Note: the images displayed by importance level are displayed in an identifiable state from other similar images by percent value
Regarding claim 8, Watanabe et al teaches wherein the plurality of pieces of query information are similar to each other (a similar image search is executed using a specified query group and similar images for each group are displayed in the search result display area 1006 (paragraph 0090 and fig 10).
Regarding claim 11, Watanabe et al ‘158 teaches an image selection method, comprising,
by a computer (image search device 105(paragraph 0032):
acquiring a plurality of pieces of query information that are information generated for each of a plurality of query images and indicate a feature of the query image (plural query images 601 are input, image feature values are calculated from each image and similar image search results 602 are obtained (acquired) from the image database 108. search result 602 includes similar images, their similarity and an identification number of the closest cluster selected in a search using clustering (paragraph 0054);
selecting, by using the plurality of pieces of query information, a plurality of similar images whose degree of similarity to at least one of the query images satisfies a reference (A user first provides a set of search queries to the system. The method of providing queries includes a method of specifying a tag and a method of directly providing an image file. When a tag is specified, the user inputs a tag (reference) such as a person's name in the tag input area 1001. When the image reading button 1002 is clicked, images having the specified tag are read (selected) from images registered in the image database and are displayed in the query image display area 1004 (paragraph 0088); and
displaying the plurality of similar images on a display (images registered in the image database and are displayed in the query image display area 1004 (paragraph 0088), and also setting a display position or a display order of each of the plurality of similar images on the display unit by using a number of the query images in which the similar image satisfies the reference (When the image reading button 1002 is clicked, images having the specified tag are read from images registered in the image database and are displayed in the query image display area 1004 (fig 8 (803) and paragraph 0088)..
displaying, on the display, the plurality of similar images together with information indicating, at least as a number, a quantity of the query images in which the similar image satisfies the reference (After all queries are grouped, an importance level of the group is calculated and the groups are rearranged according to the importance level (paragraph 0056). This would show, by at least as a number, a quantity of query images. The grouped images that are arranged by importance or similarity would, thereby satisfy, the “at least a number, a quantity of query images (see fig 6 also)
Watanabe et al ‘158 fails to teach of acquiring, based on computing a feature value of a detected skeleton structure of a person in a query image stored in a database, a plurality of pieces of query information that are information generated for each of the plurality of query images, from the query image and indicate a feature of the query image and acquiring the plurality of pieces of query information comprises storing the feature value and the plurality of pieces of query information in the database;
outputting a display illustrating a plurality of candidate images clustered respectively into ones of a first cluster and one or more second clusters, the first cluster being determined as representing a first pose, and the one or more second clusters being determined as representing one or more second poses;
identifying a plurality of query images based on a user input that is to the display and is a selection of the cluster;
searching for, from a plurality of images a plurality of similar images whose degree of similarity to at least one of the query images satisfies a reference by computing a degree of similarity between a first feature value of a first skeleton structure included in the one or more of the plurality of pieces of query information of the first cluster of the selection by the user input, and a second feature value of a second skeleton structure included in the plurality of images;
Watanabe et al 292 acquiring, based on computing a feature value of a detected skeleton structure of a person in a query image stored in a database, a plurality of pieces of query information that are information generated for each of the plurality of query images, from the query image and indicate a feature of the query image and acquiring the plurality of pieces of query information comprises storing the feature value and the plurality of pieces of query information in the database; (When the user 1200 inputs the pose information (feature value of a skeleton) and the retrieval condition to the computer 1201 (S1221), the computer 1201 generates (computes) a query by converting the input pose information and image into features (skeleton), and obtains similar images from the image database 108 (Fig 12 and paragraph 0098);
outputting a display illustrating a plurality of candidate images clustered respectively into ones of a first cluster and one or more second clusters, the first cluster being determined as representing a first pose, and the one or more second clusters being determined as representing one or more second poses (retrieval result is converted into a screen including appropriate information by the retrieval result displaying unit 112 and displayed in the retrieval result display region 1004 (paragraph 0093) Note: see fig 10, retrieval result display region 1004 displays multiple group pose images that display different poses (first cluster and second cluster), these images read on candidate images as they are candidate images of the pose input region 1001. This would read on outputting a display illustrating a plurality of candidate images clustered respectively into ones of a first cluster and one or more second clusters, the first cluster being determined as representing a first pose, and the one or more second clusters being determined as representing one or more second poses);
identifying a plurality of query images based on a user input that is to the display and is a selection of the cluster (image retrieving apparatus 104 can retrieve images including a person in a similar pose information by using the pose information input by the user as a query. Pieces of image data 806, 807, and 808 are respectively original images from which the personal data 803, 804, and 805 (paragraph 0079). image retrieving unit 111 retrieves similar images from the image database 108 according to the pose features obtained in step S902 and the retrieval condition obtained in step S903 (S904) (paragraph 0083));
searching for, from a plurality of images a plurality of similar images whose degree of similarity to at least one of the query images satisfies a reference by computing a degree of similarity between a first feature value of a first skeleton structure included in the one or more of the plurality of pieces of query information of the first cluster of the selection by the user input, and a second feature value of a second skeleton structure included in the plurality of images (the pose retriever 130 may calculate a pose likelihood value of a candidate pose, may calculate a pose naturalness measurement value of the candidate pose based on the database 930, and may collectively determine the calculated pose likelihood value and the calculated pose naturalness measurement value, to retrieve the most likely pose (paragraph 0082) An image retrieving apparatus 104 executes steps S1406 to S1409 with respect to each person obtained in step S1402 (S1405). The image retrieving unit 111 calculates the degree of similarity between the pose features for filter obtained in step S1402 and pose features of a target person (S1406) (paragraph 0105) Note: the person (query image) is used as a reference and feature (first skeleton). The pose features (second skeleton) that are obtained from the filter is compared to the pose feature (first skeleton) of the target person are used to calculate (compute) a degree of similarity
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Watanabe et al to include: acquiring, based on computing a feature value of a detected skeleton structure of a person in a query image stored in a database, a plurality of pieces of query information that are information generated for each of the plurality of query images, from the query image and indicate a feature of the query image and acquiring the plurality of pieces of query information comprises storing the feature value and the plurality of pieces of query information in the database; outputting a display illustrating a plurality of candidate images clustered respectively into ones of a first cluster and one or more second clusters, the first cluster being determined as representing a first pose, and the one or more second clusters being determined as representing one or more second poses; identifying a plurality of query images based on a user input that is to the display and is a selection of the cluster; searching for, from a plurality of images a plurality of similar images whose degree of similarity to at least one of the query images satisfies a reference by computing a degree of similarity between a first feature value of a first skeleton structure included in the one or more of the plurality of pieces of query information of the first cluster of the selection by the user input, and a second feature value of a second skeleton structure included in the plurality of images.
The reason of doing so would be to select the desired image according to specific features.
Regarding claim 14, Watanabe et al teaches a non-transitory computer-readable medium storing a program causing a computer to perform the image selection method according to claim11 (A program for realizing each function and information such as a table and a file can be stored in a recording device such as a memory, a hard disk and an SSD (Solid State Drive) and a record medium such as an IC card, an SD card and DVD (paragraph 0139).
Regarding claim 17, Watanabe teaches of a camera (a camera may also be provided so as to enable directly inputting a photographed image (paragraph 0029)
wherein searching for the plurality of similar images comprises obtaining the plurality of similar images as a result of an image search query input of the first cluster of the selection by the user input (A user first provides a set of search queries to the system. The method of providing queries includes a method of specifying a tag and a method of directly providing an image file. When a tag is specified, the user inputs a tag (reference) such as a person's name in the tag input area 1001. When the image reading button 1002 is clicked, images (first cluster) having the specified tag are read (selected) from images registered in the image database and are displayed in the query image display area 1004 (paragraph 0088) fig. 8 El. 803 shows “display in order of similarity of all search results”)
Watanabe fails to teach the image selection apparatus according to wherein acquiring the plurality of pieces of query information comprises acquiring the query image from a camera and acquiring the plurality of pieces of query information based on the plurality of query images being identified based on the query image from the camera, and wherein searching for the plurality of similar images comprises obtaining the plurality of similar images as a result of an image search query input of the first cluster of the selection by the user input.
Watanabe 292 teaches the image selection apparatus according to wherein acquiring the plurality of pieces of query information comprises acquiring the query image and acquiring the plurality of pieces of query information based on the plurality of query images being identified based on the query image (When the user 1200 inputs the pose information (feature value of a skeleton) and the retrieval condition to the computer 1201 (S1221), the computer 1201 generates (computes) a query by converting the input pose information and image into features (skeleton), and obtains similar images from the image database 108 (Fig 12 and paragraph 0098), and
wherein searching for the plurality of similar images comprises obtaining the plurality of similar images as a result of an image search query input of the first cluster of the selection by the user input (A user first provides a set of search queries to the system. The method of providing queries includes a method of specifying a tag and a method of directly providing an image file. When a tag is specified, the user inputs a tag (reference) such as a person's name in the tag input area 1001. When the image reading button 1002 is clicked, images having the specified tag are read (selected) from images registered in the image database and are displayed in the query image display area 1004 (paragraph 0088) fig. 8 El. 803 shows “display in order of similarity of all search results”).
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Watanabe et al in view of Watanabe 292 to include: the image selection apparatus according to wherein acquiring the plurality of pieces of query information comprises acquiring the query image from a camera and acquiring the plurality of pieces of query information based on the plurality of query images being identified based on the query image from the camera, and wherein searching for the plurality of similar images comprises obtaining the plurality of similar images as a result of an image search query input of the first cluster of the selection by the user input.
The reason of doing so would be to select the desired image according to specific features.
Regarding claim 18, Watanabe et al fails to teach the image selection apparatus according to wherein the first cluster is obtained based on determining that the one or more of the plurality of pieces of query information and ones of the plurality of images of the first cluster share the first pose, wherein the one or more second clusters is obtained based on determining that the others the plurality of pieces of query information and others of the plurality of images of the one or more second clusters share the one or more second poses
Watanabe et al 292 teaches the image selection apparatus according to wherein the first cluster is obtained based on determining that the one or more of the plurality of pieces of query information and ones of the plurality of images of the first cluster share the first pose, wherein the one or more second clusters is obtained based on determining that the others the plurality of pieces of query information and others of the plurality of images of the one or more second clusters share the one or more second poses (image retrieving apparatus 104 can retrieve images (first cluster) including a person in a similar pose information (first pose) by using the pose information input by the user as a query. Pieces of image data 806, 807, and 808 are respectively original images from which the personal data 803, 804, and 805 (paragraph 0079). image retrieving unit 111 retrieves similar images (second cluster) from the image database 108 according to the pose features (second pose) obtained in step S902 and the retrieval condition obtained in step S903 (S904) (paragraph 0083)
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Watanabe et al in view of Watanabe 292 to include: the image selection apparatus according to wherein the first cluster is obtained based on determining that the one or more of the plurality of pieces of query information and ones of the plurality of images of the first cluster share the first pose, wherein the one or more second clusters is obtained based on determining that the others the plurality of pieces of query information and others of the plurality of images of the one or more second clusters share the one or more second poses.
The reason of doing so would be to select the desired image according to specific features.
Claim(s) 9, 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe et al 20160217158 in view of Watanabe et al US 2019/0147292 (Watanabe 292) further in view of Yoo et al US 2013/0028517.
Regarding claim 9, Watanabe et al in view of Watanabe et al 292 teaches all of the limitations of claim1,
Watanabe et al in view of Watanabe 292 fails to teach wherein a type of a feature indicated by first query information is a type different from that of second query information.
Yoo et al teaches wherein a type of a feature indicated by first query information is a type different from that of second query information (object pose detection apparatus include a key joint detector 110, and a pose retriever 130, for example. The key joint detector 110 may detect key joint data of the object 101 from the depth image 102. (paragraph 0024) Note: the object pose detection apparatus detects a key joint (query information) of an object (query image) and pose (query information) of an object (query image), see paragraph 0026-0028)
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Watanabe et al in view of Watanabe 292 to include: wherein a type of a feature indicated by first query information is a type different from that of second query information.
The reason of doing so would be to select the desired image according to specific features.
Regarding claim 10, Watanabe et al in view of Watanabe 292 fails to teach wherein the query information indicates a pose of a person included in the query image, and the first query information indicates a pose of a whole body of the person, and the second query information indicates a state of a specific portion of the body of the person.
Yoo et al teaches wherein the query information indicates a pose of a person included in the query image, and the first query information indicates a pose of a whole body of the person, and the second query information indicates a state of a specific portion of the body of the person (object pose detection apparatus include a key joint detector 110, and a pose retriever 130. The key joint detector 110 may detect key joint data of the object 101 from the depth image 102. (paragraph 0024). the pose retriever 130 may identify the predefined object pose that has the highest determined similarity according to various schemes (paragraph 0027). To generate, e.g., identify, the at least one candidate pose for the object 101, the pose retriever 130 may use key joint data, and extracting at least one candidate pose for the object 101 from the database 120 based on the key joint data (paragraph 0028) Note: the key joint (query information) of an object (query image) and pose (query information) of an object (query image) is used to identify an object 101 in database 120, therefore, the key joint (specific portion of the body) and pose of object (pose of body) is used to identify object 101, see paragraph 0026-0028)
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Watanabe et al in view of Watanabe 292 to include: wherein the query information indicates a pose of a person included in the query image, and the first query information indicates a pose of a whole body of the person, and the second query information indicates a state of a specific portion of the body of the person.
The reason of doing so would be to select the desired image according to specific features.
Conclusion
Any inquiry concerning this communication should be directed to Michael Burleson whose telephone number is (571) 272-7460 and fax number is (571) 273-7460. The examiner can normally be reached Monday thru Friday from 8:00 a.m. – 4:30p.m. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Akwasi Sarpong can be reached at (571) 270- 3438.
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Michael Burleson
Patent Examiner
Art Unit 2681
/AKWASI M SARPONG/SPE, Art Unit 2681 01/01/2025
Michael Burleson
December 27, 2025
/MICHAEL BURLESON/