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 amendment filed 10/15/2025 has been entered and made of record. Claims 1, 6, 11, 16 and 21 are amended. New Claims 31-34 were added. Claims 2, 9, 12 and 19 cancelled. Claims 1, 3-8, 10-11, 13-17, 20-21 and 31-34 are pending.
Applicant’s remarks in view of the newly presented amendments have been considered but are not found to be persuasive for at least the following reasons:
The applicant argues that the prior art of Watanabe et al. US PG-Pub(US 20190147292 A1) and previously cited prior art do not explicitly teach acquiring query information indicating a pose of a person, by using the query information and reference pose information indicating a reference pose, setting at least one of a threshold value for selecting at least one target image from a plurality of selection target images and a threshold value for classifying the plurality of selection target images. The Examiner respectfully disagrees as Watanabe would disclose these limitations. Watanabe discloses acquiring query information indicating a pose of a person in [0061] “FIG. 4 is a diagram for explaining the result of the image recognizing processing executed by the image retrieving apparatus 104. When an input image 401 in which a plurality of people appears is input, a region and pose for each person are recognized.”, in this section of the prior art it shows that an input image is input into the apparatus and a region alongside the pose for each person is recognized from the input query of the user. Watanabe further teaches by using the query information and reference pose information indicating a reference pose([0060] “The image retrieving apparatus 104 according to the present embodiment extracts the pose information of the object from the input image so that the user can retrieve the images having similar poses in addition to the appearance of the image.”, ¶[0060] discloses extracting pose information from an image querying an image database to see if a similar pose matches the reference pose extracted.), setting at least one of a threshold value for selecting at least one target image from a plurality of selection target images and a threshold value for classifying the plurality of selection target images in ¶[0036], “The pose information is a set of one or more feature points, and each feature point is expressed by coordinates in the image and a value of the reliability. The reliability of the feature point is indicated by a real number equal to or more than zero and equal to or less than one, and as the reliability gets closer to one, a probability that the feature point is indicated by the correct coordinates is higher”, as disclosed in ¶[0036], the prior art sets a value of reliability to classify the pose information extracted in order to classify the pose in the image and as the number gets closer to one that means the probability of the pose being determine is high. ¶0072] also discloses using the feature points extracted to determine a degree of similarity between the pose obtained and the reference. Figure 10 shows the output of how accurate the similarity is between the reference pose and pose stored in the database. The Applicant argues that the word reliability is not the same as the word threshold in the claim however, in the broadest reasonable interpretation of the claim language the term “threshold” is just an arbitrary number used to perform a classification of the image and ¶[0036] discloses that a reliability value is used to determine a pose extracted from the image. Thus the applicant’s arguments are not persuasive.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-5, 10-11, 13-15 and 20-21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Watanabe et al. US PG-Pub(US 20190147292 A1).
Regarding Claim 1, Watanabe teaches an image selection apparatus([0008] FIG. 1 is a block diagram of a configuration of an image retrieving system) comprising: at least one memory configured to store instructions([0033], The image storing apparatus 101 is a storage medium for storing still image data or moving image data and includes a hard disk drive incorporated in a computer or a storage system connected by a network such as a Network Attached Storage (NAS) or a Storage Area Network (SAN).); at least one processor configured to execute the instructions to perform operations, the operations([0051] FIG. 2 is a block diagram of an exemplary hardware configuration of the image retrieving system 100 according to the present embodiment. In FIG. 2, the image retrieving apparatus 104 includes a processor 201 and a storage apparatus 202 which are mutually connected. The storage apparatus 202 may include an arbitrary kind of storage medium) comprising:
acquiring query information indicating a pose of a person ([0060] “The image retrieving apparatus 104 according to the present embodiment extracts the pose information of the object from the input image so that the user can retrieve the images having similar poses in addition to the appearance of the image. [0061] FIG. 4 is a diagram for explaining the result of the image recognizing processing executed by the image retrieving apparatus 104. When an input image 401 in which a plurality of people appears is input, a region and pose for each person are recognized.”, ¶[0060] discloses extracting pose information from an image to determine if a similar pose is in the image database.)by using the query information and reference pose information indicating a reference pose([0060] “The image retrieving apparatus 104 according to the present embodiment extracts the pose information of the object from the input image so that the user can retrieve the images having similar poses in addition to the appearance of the image.”, ¶[0060] discloses extracting pose information from an image to determine if a similar pose is in the image database.),
setting at least one of a threshold value for selecting at least one target image from a plurality of selection target images and a threshold value for classifying the plurality of selection target images (¶[0036], “The pose information is a set of one or more feature points, and each feature point is expressed by coordinates in the image and a value of the reliability. The reliability of the feature point is indicated by a real number equal to or more than zero and equal to or less than one, and as the reliability gets closer to one, a probability that the feature point is indicated by the correct coordinates is higher. In the registering processing, features which are obtained by quantifying a feature of appearance of the image and information on an attribute identified by the image recognizing processing are extracted, and the extracted information is registered in the image database 108 in association with the pose information.”, ¶[0036] discloses the idea of using a reliability value to determine if the feature points extracted match a pose in the database.),and by using the threshold value and the query information(¶[0036] discloses the idea of using a threshold/reliability value to determine if the feature points extracted match a pose in the database.), selecting the at least one target image from the plurality of selection target images or classifying the plurality of selection target images(¶[0037] “The image retrieving apparatus 104 executes retrieving processing to retrieve an image which matches the retrieval condition from the image database 108 by using the retrieval condition specified by a user from the input apparatus 102 and present the information on the display apparatus 103. In the retrieving processing, the user specifies the pose information as the retrieval condition”, ¶[0037] discloses selecting an image from the database that matches the retrieval condition specified by the user.).
Regarding Claim 3, Watanabe teaches teach the image selection apparatus according to claim 1, wherein the operations comprise acquiring the reference pose information by using an input from a user. ([0037] “The image retrieving apparatus 104 executes retrieving processing to retrieve an image which matches the retrieval condition from the image database 108 by using the retrieval condition specified by a user from the input apparatus 102 and present the information on the display apparatus 103. In the retrieving processing, the user specifies the pose information as the retrieval condition. For example, the user determines the pose information to be used for retrieval by moving the feature points displayed on the display apparatus 103. Details will be described later with reference to FIG. 10. If the pose information to be used can be specified, the pose information may be input by the dedicated device or by sentences or voice. The user can obtain an image including an object which matches the specified pose information through the image retrieval using the pose information. That is, when the user retrieves a desired image, the user can find an image including a similar pose by inputting the pose information of the object in addition to metadata such as a place and a time and the features of the appearance of the image”, ¶[0037] discloses a user is able to specify a pose to retrieve from the image database.)
Regarding Claim 4, Watanabe teaches the image selection apparatus according to claim 1, wherein the operations comprise generating the reference pose information by statistically processing the plurality of selection target images. (¶[0041], “The features extracting unit 107 extracts features used for retrieving an image from the pose information. The features can be extracted by an arbitrary method as long as the features indicate the pose information. In the following description, features calculated from the pose information are indicated as “pose features”, and features indicating an appearance of the image other than the pose features are indicated as “image features”, and these features are distinguished from each other. That is, the image features are value, which can be compared between images, a color, of the features such as a shape, and the like of the image. Both features are values with which similarity between the images can be compared, and, for example, expressed by fixed-length vectors. For example, the pose features may be the coordinates of the respective feature points included in the pose information which are arranged. In a case where coordinates are used as the feature point, by executing normalizing processing by using the size and the center coordinates of the object, the similar pose features regarding the objects of which apparent sizes are different or objects respectively existing at different coordinates can be obtained”, as disclosed in ¶[0041], the prior art extracts features from the image and determines coordinates of the feature points to determine the pose in the image.)
Regarding Claim 5, Watanabe teaches the image selection apparatus according to claim 4, wherein the operations comprise acquiring selection information for selecting part of the plurality of selection target images and generating the reference pose information by statistically processing the selection target image indicated by the selection information. ([0042] “In the present embodiment, the pose features are used for retrieval. However, it is possible to collect features of a typical pose and makes a pose identifier learn the collected features by machine learning. The features extracting unit 107 identifies the pose by using the pose identifier which has learned the features and may register the pose in the image database 108 in association with personal information.”, as disclosed in ¶[0042] the features extracted in the image are used to identify the pose and then registration is performed in the database to determine similar poses from the database.)
Regarding Claim 10, Watanabe teaches the image selection apparatus according to claim 1, wherein the reference pose information includes relative positions of a plurality keypoints indicating parts of a human body different from each other. ([0030] “The term “pose” here indicates a set of feature points which commonly exists in a target object. For example, in a case of a person, the “pose” can be defined by a set of feature points such as {head, neck, right shoulder, right elbow, right wrist, left shoulder, left elbow, left wrist, right waist, right knee, right ankle, left waist, left knee, and left ankle}. The feature points are detected by image recognizing processing and have information on coordinates and reliability in an image. The “reliability” here is a value indicating a probability that the feature point exists at the detected coordinates, and is calculated based on statistical information.” as disclosed in this section of the prior art the set of feature points extracted pertain the parts of the human body used to determine the pose of the human.)
Regarding Claim 11, Watanabe teaches an image selection method comprising, by a computer([0051] FIG. 2 is a block diagram of an exemplary hardware configuration of the image retrieving system 100 according to the present embodiment. In FIG. 2, the image retrieving apparatus 104 includes a processor 201 and a storage apparatus 202 which are mutually connected. The storage apparatus 202 may include an arbitrary kind of storage medium): threshold value setting processing comprising, acquiring query information indicating a pose of a person ([0060] “The image retrieving apparatus 104 according to the present embodiment extracts the pose information of the object from the input image so that the user can retrieve the images having similar poses in addition to the appearance of the image.”, ¶[0060] discloses extracting pose information from an image to determine if a similar pose is in the image database.),by using the query information and reference pose information indicating a reference pose ([0060] “The image retrieving apparatus 104 according to the present embodiment extracts the pose information of the object from the input image so that the user can retrieve the images having similar poses in addition to the appearance of the image.”, ¶[0060] discloses extracting pose information from an image to determine if a similar pose is in the image database.),
and image selection processing comprising by using the threshold value and the query information(¶[0036] discloses the idea of using a threshold/reliability value to determine if the feature points extracted match a pose in the database.), selecting the at least one target image from the plurality of selection target images or classifying the plurality of selection target images(¶[0037] “The image retrieving apparatus 104 executes retrieving processing to retrieve an image which matches the retrieval condition from the image database 108 by using the retrieval condition specified by a user from the input apparatus 102 and present the information on the display apparatus 103. In the retrieving processing, the user specifies the pose information as the retrieval condition”, ¶[0037] discloses selecting an image from the database that matches the retrieval condition specified by the user.).
Regarding Claim 13, Watanabe teaches the image selection method according to claim 11, further comprising, by the computer, in the threshold value setting processing, acquiring the reference pose information by using an input from a user. ([0037] “The image retrieving apparatus 104 executes retrieving processing to retrieve an image which matches the retrieval condition from the image database 108 by using the retrieval condition specified by a user from the input apparatus 102 and present the information on the display apparatus 103. In the retrieving processing, the user specifies the pose information as the retrieval condition. For example, the user determines the pose information to be used for retrieval by moving the feature points displayed on the display apparatus 103. Details will be described later with reference to FIG. 10. If the pose information to be used can be specified, the pose information may be input by the dedicated device or by sentences or voice. The user can obtain an image including an object which matches the specified pose information through the image retrieval using the pose information. That is, when the user retrieves a desired image, the user can find an image including a similar pose by inputting the pose information of the object in addition to metadata such as a place and a time and the features of the appearance of the image”, ¶[0037] discloses a user is able to specify a pose to retrieve from the image database.)
Regarding Claim 14, Watanabe teaches the image selection method according to claim 11, further comprising, by the computer, in the threshold value setting processing, generating the reference pose information by statistically processing the plurality of selection target images. (¶[0041], “The features extracting unit 107 extracts features used for retrieving an image from the pose information. The features can be extracted by an arbitrary method as long as the features indicate the pose information. In the following description, features calculated from the pose information are indicated as “pose features”, and features indicating an appearance of the image other than the pose features are indicated as “image features”, and these features are distinguished from each other. That is, the image features are value, which can be compared between images, a color, of the features such as a shape, and the like of the image. Both features are values with which similarity between the images can be compared, and, for example, expressed by fixed-length vectors. For example, the pose features may be the coordinates of the respective feature points included in the pose information which are arranged. In a case where coordinates are used as the feature point, by executing normalizing processing by using the size and the center coordinates of the object, the similar pose features regarding the objects of which apparent sizes are different or objects respectively existing at different coordinates can be obtained”, as disclosed in ¶[0041], the prior art extracts features from the image and determines coordinates of the feature points to determine the pose in the image.)
Regarding Claim 15, Watanabe teaches the image selection method according to claim 14, where Watanabe further teaches further comprising, by the computer, in the threshold value setting processing, acquiring selection information for selecting part of the plurality of selection target images and generating the reference pose information by statistically processing the selection target image indicated by the selection information. ([0042] “In the present embodiment, the pose features are used for retrieval. However, it is possible to collect features of a typical pose and makes a pose identifier learn the collected features by machine learning. The features extracting unit 107 identifies the pose by using the pose identifier which has learned the features and may register the pose in the image database 108 in association with personal information.”, as disclosed in ¶[0042] the features extracted in the image are used to identify the pose and then registration is performed in the database to determine similar poses from the database.)
Regarding Claim 20, Watanabe teaches the image selection method according to claim 11, wherein the reference pose information includes relative positions of a plurality of keypoints indicating parts of a human body different from each other. ([0030] “The term “pose” here indicates a set of feature points which commonly exists in a target object. For example, in a case of a person, the “pose” can be defined by a set of feature points such as {head, neck, right shoulder, right elbow, right wrist, left shoulder, left elbow, left wrist, right waist, right knee, right ankle, left waist, left knee, and left ankle}. The feature points are detected by image recognizing processing and have information on coordinates and reliability in an image. The “reliability” here is a value indicating a probability that the feature point exists at the detected coordinates, and is calculated based on statistical information.” as disclosed in this section of the prior art the set of feature points extracted pertain the parts of the human body used to determine the pose of the human.)
Regarding Claim 21, Watanabe teaches a non-transitory computer-readable medium storing a program for causing a computer to execute perform operations([0051] FIG. 2 is a block diagram of an exemplary hardware configuration of the image retrieving system 100 according to the present embodiment. In FIG. 2, the image retrieving apparatus 104 includes a processor 201 and a storage apparatus 202 which are mutually connected. The storage apparatus 202 may include an arbitrary kind of storage medium):, the operations comprising: acquiring query information indicating a pose of a person ([0060] “The image retrieving apparatus 104 according to the present embodiment extracts the pose information of the object from the input image so that the user can retrieve the images having similar poses in addition to the appearance of the image.”, ¶[0060] discloses extracting pose information from an image to determine if a similar pose is in the image database.)by using the query information and reference pose information indicating a reference pose([0060] “The image retrieving apparatus 104 according to the present embodiment extracts the pose information of the object from the input image so that the user can retrieve the images having similar poses in addition to the appearance of the image.”, ¶[0060] discloses extracting pose information from an image to determine if a similar pose is in the image database.),
setting at least one of a threshold value for selecting at least one target image from a plurality of selection target images and a threshold value for classifying the plurality of selection target images (¶[0036], “The pose information is a set of one or more feature points, and each feature point is expressed by coordinates in the image and a value of the reliability. The reliability of the feature point is indicated by a real number equal to or more than zero and equal to or less than one, and as the reliability gets closer to one, a probability that the feature point is indicated by the correct coordinates is higher. In the registering processing, features which are obtained by quantifying a feature of appearance of the image and information on an attribute identified by the image recognizing processing are extracted, and the extracted information is registered in the image database 108 in association with the pose information.”, ¶[0036] discloses the idea of using a reliability value to determine if the feature points extracted match a pose in the database.),and by using the threshold value and the query information(¶[0036] discloses the idea of using a threshold/reliability value to determine if the feature points extracted match a pose in the database.), selecting the at least one target image from the plurality of selection target images or classifying the plurality of selection target images(¶[0037] “The image retrieving apparatus 104 executes retrieving processing to retrieve an image which matches the retrieval condition from the image database 108 by using the retrieval condition specified by a user from the input apparatus 102 and present the information on the display apparatus 103. In the retrieving processing, the user specifies the pose information as the retrieval condition”, ¶[0037] discloses selecting an image from the database that matches the retrieval condition specified by the user.).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 6-7 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe et al. US PG-Pub(US 20190147292 A1) in view of Kim et al. US Patent(US 6557010 B1).
Regarding Claim 6, while Watanabe teaches the image selection apparatus according to claim 1, Watanabe does not explicitly teach wherein the operations comprise setting the threshold value by multiplying a value indicating a difference between the query information and the reference pose information by a constant.
Kim teaches wherein the operations comprise setting the threshold value by multiplying a value indicating a difference between the query information and the reference pose information by a constant. (Col 6, Lines 1-14, “comparing the similarity between the query posture and the posture descriptors in the posture database, the difference values of, the posture descriptors can be calculated by directly summing the difference values, or by obtaining the differences of the relation information among the head, body, arm, leg, and other parts of the body and differently determining their weighted values. Specifically, the similarity (difference) can be obtained by multiplying head information difference, body information difference, arm information difference, leg information difference, and relation information difference by a head information weighted value, body information weighted value, arm information weighted value, leg information weighted value, and relation information weighted value determined according to need, respectively, and summing the resultant multiplied values.”, as disclosed in Col 6, Lines 1-14, the prior art multiplies the difference values between a pose and a query of a body part to determine a similarity value in order to determine which pose matches the one in the data base as further disclosed in Col 6, Lines 61-65.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Watanabe with Kim in order to multiply the difference between the query and reference pose. One skilled in the art would have been motivated to modify Watanabe in this manner in order to rapidly and accurately search a human 3D posture database using the posture descriptors. (Kim, Col 1, Lines 15-16)
Regarding Claim 7, the combination of Watanabe and Kim teach the image selection apparatus according to claim 6, where Kim further teaches wherein the operations comprise setting the threshold value by further using a result of statistical processing of the plurality of selection target images. (Col 7, Lines 11-19, “the posture descriptor extracting sections 10 and 10' may be a device for extracting position information of the minimum number of joints by applying the BAPs to the standard body model, or a device for extracting position information of the minimum number of joints by applying the BAPs to the standard body model and extracting and using as the posture descriptors the relation information of respective parts of the human body and shape information of joints of an arm and leg”, the prior art discloses in this section a minimum number of joints is set and extracted in the image when processing the posture of the target.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Watanabe with Kim in order to set the threshold based on calculating the posture of the target. One skilled in the art would have been motivated to modify Watanabe in this manner in order to rapidly and accurately search a human 3D posture database using the posture descriptors. (Kim, Col 1, Lines 15-16)
Regarding Claim 16, while Watanabe teaches the image selection method according to claim 11, Watanabe does not explicitly teach further comprising, by the computer, in the threshold value setting processing, setting the threshold value by multiplying a value indicating a difference between the query information and the reference pose information by a constant.
Kim teaches further comprising, by the computer, in the threshold value setting processing, setting the threshold value by multiplying a value indicating a difference between the query information and the reference pose information by a constant. (Col 6, Lines 1-14, “comparing the similarity between the query posture and the posture descriptors in the posture database, the difference values of, the posture descriptors can be calculated by directly summing the difference values, or by obtaining the differences of the relation information among the head, body, arm, leg, and other parts of the body and differently determining their weighted values. Specifically, the similarity (difference) can be obtained by multiplying head information difference, body information difference, arm information difference, leg information difference, and relation information difference by a head information weighted value, body information weighted value, arm information weighted value, leg information weighted value, and relation information weighted value determined according to need, respectively, and summing the resultant multiplied values.”, as disclosed in Col 6, Lines 1-14, the prior art multiplies the difference values between a pose and a query of a body part to determine a similarity value in order to determine which pose matches the one in the data base as further disclosed in Col 6, Lines 61-65.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Watanabe with Kim in order to multiply the difference between the query and reference pose. One skilled in the art would have been motivated to modify Watanabe in this manner in order to rapidly and accurately search a human 3D posture database using the posture descriptors. (Kim, Col 1, Lines 15-16)
Regarding Claim 17, the combination of Watanabe and Kim teach the image selection method according to claim 16, where Kim further teaches further comprising, by the computer, in the threshold value setting processing, setting the threshold value by using a result of statistical processing of the plurality of selection target images. (Col 7, Lines 11-19, “the posture descriptor extracting sections 10 and 10' may be a device for extracting position information of the minimum number of joints by applying the BAPs to the standard body model, or a device for extracting position information of the minimum number of joints by applying the BAPs to the standard body model and extracting and using as the posture descriptors the relation information of respective parts of the human body and shape information of joints of an arm and leg”, the prior art discloses in this section a minimum number of joints is set and extracted in the image when processing the posture of the target.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Watanabe with Kim in order to set the threshold based on calculating the posture of the target. One skilled in the art would have been motivated to modify Watanabe in this manner in order to rapidly and accurately search a human 3D posture database using the posture descriptors. (Kim, Col 1, Lines 15-16)
Claims 8 and 34 are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe et al. US PG-Pub(US 20190147292 A1) in view of Yoo et al. US PG-Pub(US 20130028517 A1).
Regarding Claim 8, while Watanabe teaches the image selection apparatus according to claim 1, Watanabe does not explicitly teach wherein the operations comprise causing a terminal to display, in a multidimensional space including each of a plurality of feature values characterizing a pose as an axis, a position of the target image and a position of at least one of the selection target images different from the target image.
Yoo teaches wherein the operations comprise causing a terminal to display, in a multidimensional space including each of a plurality of feature values characterizing a pose as an axis, a position of the target image and a position of at least one of the selection target images different from the target image. (¶[0052] “Thereafter, key joint data of the object 601 from the database 120 is obtained based on a searched for pose having a pose class ID corresponding to the classified pose. As only an example, the key joint detector 110 of FIG. 1 may search the database 120 for the pose, from among a plurality of poses stored in the database 120, whose pose class ID matches the pose class ID of the classified pose, and may detect key joint data from the found pose. Depending on embodiment, the key joint detector 110 may detect key joint data by searching another database, other than the database 120, which may be accessed independently from the database 120, and obtaining the key joint data from a found matching pose in the other database. [0053] In operation 640, a 3D position of the classified pose may then be calculated, e.g., to detect the key joint data of the object 601. Accordingly, as only an example, the key joint detector 110 may detect the key joint data of the object 601”, as disclosed in ¶[0052]-¶[0053] the prior art extracts a 3d position and pose from the object in order to generate a skeleton model and ¶[0086] discloses displaying the skeleton model when searching for the pose.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Watanabe with Yoo in order to display a 3d position and pose of the object. One skilled in the art would have been motivated to modify Watanabe in this manner in order to detect an object pose. (Yoo, Abstract)
Regarding Claim 34, while Watanabe teaches the image selection method according to claim 11, Watanabe does not explicitly teach further comprising, by the computer, in the image selection processing, causing a terminal to display, in a multidimensional space including each of a plurality of feature values characterizing a pose as an axis, a position of the target image and a position of at least one of the selection target images different from the target image.
Yoo teaches further comprising, by the computer, in the image selection processing, causing a terminal to display, in a multidimensional space including each of a plurality of feature values characterizing a pose as an axis, a position of the target image and a position of at least one of the selection target images different from the target image. (¶[0052] “Thereafter, key joint data of the object 601 from the database 120 is obtained based on a searched for pose having a pose class ID corresponding to the classified pose. As only an example, the key joint detector 110 of FIG. 1 may search the database 120 for the pose, from among a plurality of poses stored in the database 120, whose pose class ID matches the pose class ID of the classified pose, and may detect key joint data from the found pose. Depending on embodiment, the key joint detector 110 may detect key joint data by searching another database, other than the database 120, which may be accessed independently from the database 120, and obtaining the key joint data from a found matching pose in the other database. [0053] In operation 640, a 3D position of the classified pose may then be calculated, e.g., to detect the key joint data of the object 601. Accordingly, as only an example, the key joint detector 110 may detect the key joint data of the object 601”, as disclosed in ¶[0052]-¶[0053] the prior art extracts a 3d position and pose from the object in order to generate a skeleton model and ¶[0086] discloses displaying the skeleton model when searching for the pose.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Watanabe with Yoo in order to display a 3d position and pose of the object. One skilled in the art would have been motivated to modify Watanabe in this manner in order to detect an object pose. (Yoo, Abstract)
Claims 31-33 are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe et al. US PG-Pub(US 20190147292 A1) in view of Sugita et al. US PG-Pub(US 20160371542 A1).
Regarding Claim 31, while Watanabe teaches the image selection apparatus according to claim 1, Watanabe does not explicitly teach wherein the operations comprise: determining a difference between the pose of the query information and the reference pose of the reference pose information; and adjusting the threshold value based on the difference.
Sugita teaches wherein the operations comprise: determining a difference between the pose of the query information and the reference pose of the reference pose information(¶[0049], “The pose difference evaluation module 105 evaluates (detects) a difference between the shapes indicated by the subject three-dimensional model (indicating the estimated body shape and pose of the subject P) and the target three-dimensional model. (Namely, the pose difference evaluation module 105 evaluates a spatial difference between the poses specified in these three-dimensional models.)”, ¶[0049] discloses determine the differences between the obtained pose of the subject and a reference pose of the human in the database.); and adjusting the threshold value based on the difference. (¶[0120], “Also, the embodiment is constructed such that the evaluation value is calculated portion by portion, and it is determined whether each of the evaluation values calculated in respective portions is equal to or less than a threshold, thereby evaluating (detecting) portion by portion whether there is a pose difference. In addition, by setting thresholds in respective portions, only the pose difference of a portion to be notified to the subject P can be detected, for example.”, ¶[0120] discloses adjusting the threshold based on the differences of the pose when an evaluation value is below a threshold.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Watanabe with Sugita in order to determine the differences between two poses and set a threshold based on the difference. One skilled in the art would have been motivated to modify Watanabe in this manner in order to calculate an evaluation value indicating the amount of a difference in shape (a pose difference) between the subject three-dimensional model and the target three-dimensional model. (Sugita, ¶[0074])
Regarding Claim 32, while Watanabe teaches the image selection method according to claim 11, Watanabe does not explicitly teach further comprising: determining a difference between the pose of the query information and the reference pose of the reference pose information; and adjusting the threshold value based on the difference.
Sugita teaches further comprising: determining a difference between the pose of the query information and the reference pose of the reference pose information (¶[0049], “The pose difference evaluation module 105 evaluates (detects) a difference between the shapes indicated by the subject three-dimensional model (indicating the estimated body shape and pose of the subject P) and the target three-dimensional model. (Namely, the pose difference evaluation module 105 evaluates a spatial difference between the poses specified in these three-dimensional models.)”, ¶[0049] discloses determine the differences between the obtained pose of the subject and a reference pose of the human in the database.); and adjusting the threshold value based on the difference. (¶[0120], “Also, the embodiment is constructed such that the evaluation value is calculated portion by portion, and it is determined whether each of the evaluation values calculated in respective portions is equal to or less than a threshold, thereby evaluating (detecting) portion by portion whether there is a pose difference. In addition, by setting thresholds in respective portions, only the pose difference of a portion to be notified to the subject P can be detected, for example.”, ¶[0120] discloses adjusting the threshold based on the differences of the pose when an evaluation value is below a threshold.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Watanabe with Sugita in order to determine the differences between two poses and set a threshold based on the difference. One skilled in the art would have been motivated to modify Watanabe in this manner in order to calculate an evaluation value indicating the amount of a difference in shape (a pose difference) between the subject three-dimensional model and the target three-dimensional model. (Sugita, ¶[0074])
Regarding Claim 33, while Watanabe teaches the non-transitory computer-readable medium according to claim 21, Watanabe does not explicitly teach wherein the operations comprise: determining a difference between the pose of the query information and the reference pose of the reference pose information; and adjusting the threshold value based on the difference.
Sugita teaches wherein the operations comprise: determining a difference between the pose of the query information and the reference pose of the reference pose information(¶[0049], “The pose difference evaluation module 105 evaluates (detects) a difference between the shapes indicated by the subject three-dimensional model (indicating the estimated body shape and pose of the subject P) and the target three-dimensional model. (Namely, the pose difference evaluation module 105 evaluates a spatial difference between the poses specified in these three-dimensional models.)”, ¶[0049] discloses determine the differences between the obtained pose of the subject and a reference pose of the human in the database.); and adjusting the threshold value based on the difference. (¶[0120], “Also, the embodiment is constructed such that the evaluation value is calculated portion by portion, and it is determined whether each of the evaluation values calculated in respective portions is equal to or less than a threshold, thereby evaluating (detecting) portion by portion whether there is a pose difference. In addition, by setting thresholds in respective portions, only the pose difference of a portion to be notified to the subject P can be detected, for example.”, ¶[0120] discloses adjusting the threshold based on the differences of the pose when an evaluation value is below a threshold.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Watanabe with Sugita in order to determine the differences between two poses and set a threshold based on the difference. One skilled in the art would have been motivated to modify Watanabe in this manner in order to calculate an evaluation value indicating the amount of a difference in shape (a pose difference) between the subject three-dimensional model and the target three-dimensional model. (Sugita, ¶[0074])
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/HAN HOANG/Examiner, Art Unit 2661
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