DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This final office action is in response to the amendment filed 8 August 2025.
Claims 1-4 and 6-12 are pending. Claim 5 is cancelled. Claims 1 and 11-12 are independent claims.
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.
Claims 1-4 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Matsuzaki (JP 2012174222, published 10 September 2019, translation provided by WIPO) and further in view of Liang et al. (US 2017/0024423, published 26 January 2017, hereafter Liang) and further in view of Kawai (US 2023/0012026, filed 24 December 2019).
As per independent claim 1, Matsuzaki discloses a feature learning system comprising:
at least one memory storing instructions (paragraph 0059: Here, the image recognition program is implemented in a computer having a recording medium, such as a memory card or CD-ROM, and a recording device storing data)
at least one processor configured to execute the instructions to perform operations (paragraph 0059: Here, the image recognition program is implemented in a computer via a CPU), comprising:
defining a degree of similarity (paragraphs 0039 and 0045-0046: Here, a similarity score is calculated for matching objects to a class) between two classes related to two feature vectors, respectively (paragraphs 0039-0042: Here, a plurality of classes are identified within an image based upon one or more feature vectors. In this instance, the image objects are identified and processed. Each of these image objects act as feature vectors for creating a composite classification for the image)
acquiring the degree of similarity based on a combination of classes to which a plurality of feature vectors acquired as processing targets belong, respectively (paragraphs 0022-0030 and 0039-0042: Here, in addition to the automatic classification, the user is presented with the automatic classification. The user either confirms/denies the classification to improve the accuracy of the classifier. The user feedback provides a degree of similarity for each classification)
generating learning data including the plurality of feature vectors and the degree of similarity (paragraph 0022: Here, the user confirmed/denied data is used for further training of the classifier)
performing machine learning using the learning data (paragraph 0022)
Matsuzaki fails to specifically disclose wherein the degree of similarity is computed based on an angle formed by eigenvectors.
However, Liang, which is analogous to the claimed invention because it is directed toward determining similarity based upon eigenvectors, discloses wherein the degree of similarity is computed based on an angle formed by eigenvectors (paragraph 0041: Here, the similarity is calculated based upon the angle between the two eigenvectors). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Liang with Matsuzaki, with a reasonable expectation of success, as it would have allowed a user to compare different classification to identify their similarity (Liang: paragraphs 0038-0041).
Matsuzaki fails to specifically disclose:
a similarity database and a feature database including a plurality of feature vectors, each feature vector associated with a class indicating a type of action to which the feature vector belongs
storing, in the similarity database, the degree of similarity corresponding to a combination of the two classes to which the two feature vectors belong, respectively
acquiring as processing targets, from the feature database, the two feature vectors related the two classes, among the plurality of feature vectors
However, Kawai, which is analogous to the claimed invention because it is directed toward using eigenvectors for a degree of similarity, discloses:
a similarity database (Figure 1, item 112) and a feature database (Figure 1, item 111) including a plurality of feature vectors, each feature vector associated with a class indicating a type of action to which the feature vector belongs (paragraph 0044: Here, the feature database stores a plurality of action features along with class information associated with each action feature)
storing, in the similarity database, the degree of similarity corresponding to a combination of the two classes to which the two feature vectors belong, respectively (paragraphs 0048-0049: Here, a eigenvector similarity degree is calculated and stored)
acquiring as processing targets, from the feature database, the two feature vectors related the two classes, among the plurality of feature vectors (paragraph 0048: Here, the features, stored in a feature database, are retrieved from the feature database and the eigenvector is calculated)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Kawai with Matsuzaki-Liang, with a reasonable expectation of success, as it would have allowed for storage of contents within a database to facilitate efficient data storage and retrieval of similarities represented by eigenvectors (Kawai: paragraph 0048).
As per dependent claim 2, Matsuzaki discloses wherein the operations comprise:
defining a mathematical equation for determining a degree of similarity between the two classes based on the two feature vectors (paragraph 0041-0042: Here, the degree of similarity between the two classes is used to calculate a weight independently of the user evaluation. Further, the user evaluation is combined with this calculated weight to define a mathematical equation for determining a composite degree of similarity between the two classes based on the two feature vectors)
acquiring the mathematical equation for determining a degree of similarity related to a combination of classes to which the plurality of feature vectors acquired as the processing targets belong, respectively (paragraph 0042)
computing a degree of similarity by substituting the plurality of feature vectors into the mathematical equation (paragraphs 0042-0043)
As per dependent claim 3, Matsuzaki discloses wherein the degree of similarity is computed based on a norm of a difference between the feature vectors (paragraph 0041: Here, the values for the similarity between the feature vectors and the classes are normalized) or between vectors acquired by performing dimensionality reduction on the feature vectors, or an angle formed by the vectors.
As per dependent claim 4, Matsuzaki discloses wherein the operation comprises using metric learning (paragraphs 0022 and 0041-0042: Here, the machine learning method is implemented using the similarity data of the feature vectors).
With respect to independent claim 11, the applicant discloses the limitations substantially similar to those in claim 1. Claim 11 is similarly rejected.
With respect to independent claim 12, the applicant discloses the limitations substantially similar to those in claim 1. Claim 12 is similarly rejected.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Matsuzaki, Liang, and Kawai, and further in view of Kim et al. (US 2017/0132408, published 11 May 2017, hereafter Kim).
As per dependent claim 6, Matsuzaki, Liang, and Kawai disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Matsuzaki discloses determining a similarity to a class by using a feature vector (paragraphs 0041-0042).
Matsuzaki fails to specifically disclose wherein the degree of similarity is computed based on a false recognition rate. However, Kim discloses wherein the degree of similarity is computed based on a false recognition rate (paragraph 0110: Here, a threshold value for a false recognition (acceptance) rate is maintained). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Kim with Matsuzaki, with a reasonable expectation of success, as it would have allowed for replacing a feature vector if the false recognition rate for a feature is above a threshold (Kim: paragraph 0116). This would have improved classification of data by removing high error rate feature vectors from consideration for classification.
Claims 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Matsuzaki, Liang, and Kawai and further in view of Marsden et al. (US 11854308, filed 14 February 2017, hereafter Marsden).
As per dependent claim 7, Matsuzaki, Liang, and Kawai disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Matsuzaki discloses wherein:
the feature vector is a feature of a human action
a class to which the feature vector belongs is a type of action to which the feature of the human action belongs
However, Marsden, which is analogous to the claimed invention because it is directed toward using machine learning to recognize hand gestures, discloses:
the feature vector is a feature of a human action (column 6, line 51- column 7, line 9)
a class to which the feature vector belongs is a type of action to which the feature of the human action belongs (column 6, line 51- column 7, line 9: Here, an input of a hand image is provided to the neural network. The feature vectors associated with the hand image are used to classify the image into various hand poses, such as open-hand poses, fist poses, grab poses, V-shaped poses, or pinch poses)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Marsden with Matsuzaki, with a reasonable expectation of success, as it would have allowed for using these trained classification for identifying new hand poses (Marsden: column 7, lines 7-9). This would have further allowed for classification of image data including human actions.
As per dependent claim 8, Matsuzaki and Marsden disclose the limitations similar to those in claim 7, and the same rejection is incorporated herein. Matsuzaki discloses wherein the feature of the human action includes sensor information of one or more of a visible light camera (paragraph 0057), an infrared camera, and a depth sensor.
As per dependent claim 9, Matsuzaki and Marsden disclose the limitations similar to those in claim 7, and the same rejection is incorporated herein. Marsden discloses wherein:
the feature of the human action includes human skeletal information (column 12, line 49- column 13, line 15: Here, skeletal information, such as the location of bones, metacarpals, and joints are tracked in space to define the position of the hand in space)
the human skeletal information at least includes positional information for one or more of a head, a neck, a left elbow, a right elbow, a left hand (column 6, line 51- column 7, line 9), a right hand (column 6, line 51- column 7, line 9: Here, it is noted that Matsuzaki fails to specifically disclose whether a hand is a left hand or a right hand. However, it does disclose a “hand.” As humans only have two hands, a left and a right hand, the examiner interprets the “hand” of Matsuzaki as being one of a right hand or a left hand, thereby satisfying the claim limitation. Additionally, Figure 10 shows a plurality of “right hand” images), a hip, a left knee, a right knee, a left foot, and a right foot
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Marsden with Matsuzaki, with a reasonable expectation of success, as it would have allowed for using these trained classification for identifying new hand poses (Marsden: column 7, lines 7-9). This would have further allowed for classification of image data including human actions.
As per dependent claim 10, Matsuzaki and Marsden disclose the limitations similar to those in claim 9, and the same rejection is incorporated herein. Marsden discloses wherein the degree of similarity is computed based on a distance between related parts in the human skeletal information or an angle formed by segments connected parts in the human skeletal information (column 12, line 49- column 13, line 15: Here, the joint angle is measured for use in hand gesture recognition).
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Marsden with Matsuzaki, with a reasonable expectation of success, as it would have allowed for using these trained classification for identifying new hand poses (Marsden: column 7, lines 7-9). This would have further allowed for classification of image data including human actions.
Response to Arguments
Applicant’s arguments filed 8 August 2025, with respect to the rejection of claims under 35 USC 101 have been fully considered and are persuasive. The rejection has been withdrawn.
Applicant’s arguments with respect to the rejection of claims under 35 USC 102 and 35 USC 103have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Matsuzaki, Liang, and Kawai.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Sukegawa et al. (US 2014/0079299): Discloses calculating the similarity between classes based on the angle formed by eigenvectors related to the principal (paragraph 0038)
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE R STORK whose telephone number is (571)272-4130. The examiner can normally be reached 8am - 2pm; 4pm - 6pm.
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/KYLE R STORK/Primary Examiner, Art Unit 2128