DETAILED ACTION
Notice of Pre-AIA or AIA Status
1.The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
2. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
3.Claims 1-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
4. In claim 1, line.7 recites the limitation; “a distribution parameter indicating a distribution of the attribute feature”; The step for “distributing the attribute feature” is not clearly written in the claims. Also, it is not clear how and where the attribute features are being distributed. Appropriate clarification is required.
5. In claim 1,lines. 8-9 recite: “training biometric information which is the biometric information for training”. It is not clear what is meant by this limitation. Appropriate clarification is required.
6. In claim 1, lines.10-11 recite: “a machine learning model configured to output information indicating a feature of a living body when the biometric information is input, It is not clear what is meant by the underlined limitation, especially the term “a feature of a living body”. Furthermore, if the biometric information is a fingerprint, what is the feature of living body that is indicated. Appropriate clarification is required.
7. In claim 1, lines.12-15 recite: “authentication accuracy of biometric authentication based on the biometric information, and has a low correlation with an identification feature extracted from the biometric information based on a predetermined condition”. It is not clear what is meant by the underlined limitation, especially the term “a low correlation”. Appropriate clarification is required.
8. In claim1, lines.16-17 recite: “the identification feature is a feature which is extracted from the biometric information and used for collation in the biometric authentication”. It is not clear what is meant by the underlined limitation, especially the term “collation in the biometric authentication”. Appropriate clarification is required.
9. The other independent claims 10 and 11 also recite the same limitation as claim 1, therefore they are also rejected under 35 USC 112 (b).
10. Claims 2-9 don’t cure the deficiency of claim 1 and are rejected under 35 USC 112 (b) for their dependency upon claim 1.
Examiner Note: A rejection over prior art is not feasible at this time for claims 1, 10 and 11. The claims 1,10 and 11 are replete with indefiniteness such that it cannot be ascertained as to what the scope of the claims are with respect to applying prior art.
11. The closest prior art of the record are:
Semba et.al. (US Pub.No.2021/0216617) discloses a biometric authentication device includes: a first memory; and a processor configured to: acquire an image that includes a face of one or more persons; specify a region that a face of a head person of a waiting line in which the one or more persons line up appears from the acquired image; extract feature of the face of the head person included in the specified region; select, by a first collation process of collating the extracted feature with feature indicating facial characteristics of persons and biometric information of each person, a group of persons similar to the head person among the persons; acquire the biometric information of the head person; and authenticate the head person by a second collation process of collating the biometric information of each person included in the group among the biometric information with the acquired biometric information of the head person.
Nada et.al. (US Pub.No.2013/0251213) discloses a biometric information processing apparatus includes a biometric sensor configured to acquire biometric information of a first instance, a second instance and a third instance; a processor configured to execute a procedure, the procedure comprising: extracting an authentication feature used for matching from the biometric information of each of the second instance and the third instance; normalizing the relative positions of authentication features of the second instance and the third instance by using the biometric information of the first instance; and extracting a relative feature indicating a relative positional relationship between the authentication features of the second instance and the third instance normalized in the normalizing procedure.
Wang et.al. (US Pub.No.2024/0338868) discloses the systems and techniques may use one or more trained machine-learning models to generate the modified image. For example, the systems and techniques may use a first machine-learning model (e.g., a multi-level attribute encoder) to encode the source image into a number of source features (e.g., feature vectors). The source features may include attribute features (e.g., implicitly representative of one or more attributes of a face represented by the image), a pose feature (e.g., implicitly representative of a source head pose of the face), and a gaze feature (e.g., implicitly representative of a source gaze of the face). One or more of the source features may be based on a neural network of the multi-level attribute encoder. For example, the source features may be outputs of one or more layers of the neural network.
Sly et.al. (US Pub.No.2021/0326433) discloses a non-deterministic biometric input, such as voice and face images, can be used to generate a characteristic identity vector, against which authentication input is measured. Biometric input is variable in the sense that a voice sounds somewhat different from day to day and a face is at a different angle, with different hair appearance, or in different light from photo to photo. Multiple biometric samples are collected to represent a range of sounds and appearances. There are a variety of ways to generate features of and to average these samples, in order to create a representative biometric identity indicator. In one example, a convolutional neural network (CNN) is applied to samples to extract features from each sample, after some number of layers. For instance, an ImageNet CNN or ArcFace model can be applied to extract image features. These features can be used to embed an image into a multi-dimensional space and normalized to position the image on a surface, one unit from the center of the multi-dimensional space. Then, an average of multiple samples on the surface can be selected to represent the user.
Nada et.al. (US Pub.No.2012/0250954) discloses a biometric information processing device includes, a biometric sensor configured to acquire a plurality of different biometric information elements; an authentication feature extracting unit configured to extract an authentication feature for use in authentication for each of the plurality of different biometric information elements acquired by the biometric sensor; a supplemental feature extracting unit configured to extract a supplemental feature relating to the authentication feature for each of the plurality of different biometric information elements; and a combined feature extracting unit configured to extract a combined feature in which a plurality of the supplemental features extracted by the supplemental feature extracting unit are reflected.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEREENA T CATTUNGAL whose telephone number is (571)270-0506. The examiner can normally be reached Mon-Fri : 7:30 AM-5 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynn Feild can be reached at 571-272-2092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DEREENA T CATTUNGAL/Primary Examiner, Art Unit 2431
Prosecution Insights