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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1 and 11 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 15 of U.S. Patent No. 12,099,929. Although the claims at issue are not identical, they are not patentably distinct from each other because at least one examined application claim is either anticipated by, or would have been obvious over, the reference claim(s) as shown in the following comparison.
Examined Application Claim(s)
Reference Patent Claim(s)
1. A method of selecting a name based on a face image, comprising:
receiving a face image;
generating a face vector corresponding to the face image;
selecting a plurality of name vectors from a dataset that maps names to name vectors in a vector space, wherein each name vector comprises representations associated with a plurality of words associated with a name corresponding to the name vector, wherein the plurality of name vectors are position vectors;
selecting a name vector from the plurality of name vectors based on a plurality of similarity scores calculated for the plurality of name vectors, wherein for each name vector, a similarity score is calculated based on the face vector and each name vector; and
outputting a name based on the selected name vector.
1. A method of selecting a face image based on a name, comprising:
receiving a name;
selecting, based on the name, a name vector from a plurality of name vectors in a dataset that maps a plurality of names to a plurality of corresponding name vectors in a vector space, wherein each name vector comprises representations associated with a plurality of words associated with each name, wherein the plurality of name vectors are position vectors;
receiving a plurality of face vectors corresponding to a plurality of face images; selecting a face vector from the plurality of face vectors based on a plurality of similarity scores calculated for the plurality of corresponding face vectors, wherein for each name vector, a similarity score is calculated based on the name vector and each face vector; and
outputting a face image based on the selected face vector.
11. A system of selecting a name based on a face image, comprising:
one or more processors and memory storing one or more programs that when executed by the one or more processors cause the one or more processors to:
receive a face image; generate a face vector corresponding to the face image;
select a plurality of name vectors from a dataset that maps names to name vectors in a vector space, wherein each name vector comprises representations associated with a plurality of words associated with a name corresponding to the name vector, wherein the plurality of name vectors are position vectors;
select a name vector from the plurality of name vectors based on a plurality of similarity scores calculated for the plurality of name vectors, wherein for each name vector, a similarity score is calculated based on the face vector and each name vector; and
output a name based on the selected name vector.
15. A system for selecting a face image based on a name, comprising:
one or more processors and memory storing one or more programs that when executed by the one or more processors cause the one or more processors to:
receive a name;
select, based on the name, a name vector from a plurality of name vectors in a dataset that maps a plurality of names to a plurality of corresponding name vectors in a vector space, wherein each name vector comprises representations associated with a plurality of words associated with each name, wherein the plurality of name vectors are position vectors;
receive a plurality of face vectors corresponding to a plurality of face images; select a face vector from the plurality of face vectors based on a plurality of similarity scores calculated for the plurality of corresponding face vectors, wherein for each name vector, a similarity score is calculated based on the name vector and each face vector; and
output a face image based on the selected face vector.
Correction is required.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-21 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hu et al. (Pub. No. US 2018/0005022).
Regarding claim 1. Hu teaches a method of selecting a name based on a face image (Hu, the Abstract), comprising:
receiving a face image (Hu, Fig. 4, pp [85]: acquiring a human face picture);
generating a face vector corresponding to the face image (Hu, Fig. 4, pp [86]-[88], [92]: extracting face features, processing data from a human face model, building face high dimensional vector);
selecting a plurality of name vectors from a dataset that maps names to name vectors in a vector space, wherein each name vector comprises representations associated with a plurality of words associated with a name corresponding to the name vector, wherein the plurality of name vectors are position vectors (Hu, Fig. 4, pp [89]-[92], [101], [164]: building face and name vector, calculating their Euclidean distance vectors for face features in a database, calculating weight of names for the inputted face pictures based on a certain threshold to match the face picture to the right name);
selecting a name vector from the plurality of name vectors based on a plurality of similarity scores calculated for the plurality of name vectors, wherein for each name vector, a similarity score is calculated based on the face vector and each name vector (Hu, Fig. 4, pp [89]-[92]: building face and name vector, calculating their Euclidean distance vectors for face features in a database, calculating weight of names for the inputted face pictures based on a certain threshold to match the face picture to the right name); and
outputting a name based on the selected name vector (Hu, Fig. 4, pp [89]-[92]: building face and name vector, calculating their Euclidean distance vectors for face features in a database, calculating weight of names for the inputted face pictures based on a certain threshold to match the face picture to the right name).
Regarding claim 11. Hu teaches a system of selecting a name based on a face image (Hu, the Abstract), comprising:
one or more processors and memory (Hu, pp [274]), storing one or more programs that when executed by the one or more processors cause the one or more processors to:
receive a face image (Hu, Fig. 4, pp [85]: acquiring a human face picture);
generate a face vector corresponding to the face image (Hu, Fig. 4, pp [86]-[88], [92]: extracting face features, processing data from a human face model, building face high dimensional vector);
select a plurality of name vectors from a dataset that maps names to name vectors in a vector space, wherein each name vector comprises representations associated with a plurality of words associated with a name corresponding to the name vector, wherein the plurality of name vectors are position vectors (Hu, Fig. 4, pp [89]-[92], [101], [164]: building face and name vector, calculating their Euclidean distance vectors for face features in a database, calculating weight of names for the inputted face pictures based on a certain threshold to match the face picture to the right name);
select a name vector from the plurality of name vectors based on a plurality of similarity scores calculated for the plurality of name vectors, wherein for each name vector, a similarity score is calculated based on the face vector and each name vector (Hu, Fig. 4, pp [89]-[92]: building face and name vector, calculating their Euclidean distance vectors for face features in a database, calculating weight of names for the inputted face pictures based on a certain threshold to match the face picture to the right name); and
output a name based on the selected name vector (Hu, Fig. 4, pp [89]-[92]: building face and name vector, calculating their Euclidean distance vectors for face features in a database, calculating weight of names for the inputted face pictures based on a certain threshold to match the face picture to the right name).
Regarding claim 21. Hu teaches a non-transitory computer-readable storage medium storing instructions for selecting a name based on a face image (Hu, the Abstract, pp [10]-[11]), wherein the instructions, when executed by one or more processors of a system cause the system to:
receive a face image (Hu, Fig. 4, pp [85]: acquiring a human face picture);
generate a face vector corresponding to the face image (Hu, Fig. 4, pp [86]-[88], [92]: extracting face features, processing data from a human face model, building face high dimensional vector);
select a plurality of name vectors from a dataset that maps names to name vectors in a vector space, wherein each name vector comprises representations associated with a plurality of words associated with a name corresponding to the name vector, wherein the plurality of name vectors are position vectors (Hu, Fig. 4, pp [89]-[92], [101], [164]: building face and name vector, calculating their Euclidean distance vectors for face features in a database, calculating weight of names for the inputted face pictures based on a certain threshold to match the face picture to the right name);
select a name vector from the plurality of name vectors based on a plurality of similarity scores calculated for the plurality of name vectors, wherein for each name vector, a similarity score is calculated based on the face vector and each name vector (Hu, Fig. 4, pp [89]-[92]: building face and name vector, calculating their Euclidean distance vectors for face features in a database, calculating weight of names for the inputted face pictures based on a certain threshold to match the face picture to the right name); and
output a name based on the selected name vector (Hu, Fig. 4, pp [89]-[92]: building face and name vector, calculating their Euclidean distance vectors for face features in a database, calculating weight of names for the inputted face pictures based on a certain threshold to match the face picture to the right name).
Regarding claim 2. Hu teaches the method of claim 1, wherein the face vector comprises a predefined number of elements (Hu, Fig. 4, pp [92]; Fig. 9, pp [154]).
Regarding claim 3. Hu teaches the method of claim 1, comprising:
relating the plurality of name vectors to a plurality of corresponding transformed name vectors, wherein each of the plurality of corresponding transformed name vectors comprises the predefined number of elements (Hu, Fig. 4, pp [89]-[92]; Fig. 9, pp [151]-[154]); and
calculating a similarity score between the face image and each name vector based on the face vector and a transformed name vector corresponding to each name vector (Hu, Fig. 4, pp [89]-[92]; Fig. 9, pp [151]-[154]).
Regarding claim 4. Hu teaches the method of claim 3, wherein relating the plurality of name vectors to a plurality of corresponding transformed name vectors comprises:
using an affine map to generate the plurality of transformed name vectors, each based on a name vector of the plurality of name vectors (Hu, pp [89]-[92], [151]-[154]).
Regarding claim 5. Hu teaches the method of claim 3, wherein relating plurality of name vectors to the plurality of corresponding transformed name vectors comprises:
using a neural network comprising at least two layers to generate the plurality of transformed name vectors based on the plurality of name vectors (Hu, pp [86], [148]).
Regarding claim 6. Hu teaches the method of claim 3, wherein calculating the similarity score between the face image and each name vector comprises:
calculating a Euclidean distance between the face vector and the transformed name vector corresponding to each name vector (Hu, pp [92], [154]).
Regarding claim 7. Hu teaches the method of claim 1, wherein generating the face vector corresponding to the face image comprises:
applying a plurality of face-vectorization algorithms to generate a plurality of corresponding face sub-vectors (Hu, pp [89]-[92], [151]-[154]).
Regarding claim 8. Hu teaches the method of claim 7, wherein generating the face vector corresponding to the face image comprises:
concatenating the plurality of face sub-vectors to generate the face vector (Hu, pp [89]-[92], [151]-[154]).
Regarding claim 9. Hu teaches the method of claim 1, wherein the representations are generated by word embedding the plurality of words associated with the name corresponding to the name vector (Hu, pp [101], [164]).
Regarding claim 10. Hu teaches the method of claim 1, wherein selecting a name vector from the plurality of name vectors based on a plurality of similarity scores calculated for the plurality of corresponding name vectors comprises selecting the name vector associated with the highest similarity score (Hu, pp [90]-[92], [152]-[154]).
Regarding claim 12. Hu teaches the system of claim 11, wherein the face vector comprises a predefined number of elements (Hu, Fig. 4, pp [92]; Fig. 9, pp [154]).
Regarding claim 13. Hu teaches the system of claim 11, the one or more programs, when executed by the one or more processors, cause the one or more processors to:
relate the plurality of name vectors to a plurality of corresponding transformed name vectors, wherein each of the plurality of corresponding transformed name vectors comprises the predefined number of elements (Hu, Fig. 4, pp [89]-[92]; Fig. 9, pp [151]-[154]); and
calculate a similarity score between the face image and each name vector based on the face vector and a transformed name vector corresponding to each name vector (Hu, Fig. 4, pp [89]-[92]; Fig. 9, pp [151]-[154]).
Regarding claim 14. Hu teaches the system of claim 13, wherein relating the plurality of name vectors to a plurality of corresponding transformed name vectors comprises:
using an affine map to generate the plurality of transformed name vectors, each based on a name vector of the plurality of name vectors (Hu, pp [89]-[92], [151]-[154]).
Regarding claim 15. Hu teaches the system of claim 13, wherein relating plurality of name vectors to the plurality of corresponding transformed name vectors comprises:
using a neural network comprising at least two layers to generate the plurality of transformed name vectors based on the plurality of name vectors (Hu, pp [86], [148]).
Regarding claim 16. Hu teaches the system of claim 13, wherein calculating the similarity score between the face image and each name vector comprises:
calculating a Euclidean distance between the face vector and the transformed name vector corresponding to each name vector (Hu, pp [92], [154]).
Regarding claim 17. Hu teaches the system of claim 11, wherein generating the face vector corresponding to the face image comprises:
applying a plurality of face-vectorization algorithms to generate a plurality of corresponding face sub-vectors (Hu, pp [89]-[92], [151]-[154]).
Regarding claim 18. Hu teaches the system of claim 17, wherein generating the face vector corresponding to the face image comprises concatenating the plurality of face sub-vectors to generate the face vector (Hu, pp [89]-[92], [151]-[154]).
Regarding claim 19. Hu teaches the system of claim 11, wherein the representations are generated by word embedding plurality of words associated with the name corresponding to the name vector (Hu, pp [101], [164]).
Regarding claim 20. Hu teaches the system of claim 11, wherein selecting the name vector from the plurality of name vectors based on a plurality of similarity scores calculated for the plurality of corresponding name vectors comprises selecting the name vector associated with the highest similarity score (Hu, pp [90]-[92], [152]-[[154]).
Relevant reference(s) to the claims but not used in the rejection above
Ross et al. (Pub. No. US 2011/0116690), teaches methods and systems for automated identification of celebrity face images are provided that generate a name list of prominent celebrities, obtain a set of images and corresponding feature vectors for each name, detect faces within the set of images, and remove non-face images. An analysis of the images is performed using an intra-model analysis, an inter-model analysis, and a spectral analysis to return highly accurate biometric models for each of the individuals present in the name list. Recognition is then performed based on precision and recall to identify the face images as belonging to a celebrity or indicate that the face is unknown.
Zhang (Pub. No. US 2012/0114197), teaches method and system for identifying names of entities, such as people, in an image automatically. Visually similar images of entities are retrieved, including text proximate to the visually similar images. The collected text is mined for names of entities, and the detected names are analyzed. A name may be associated with the entity in the image, based on the analysis. The method further automatically identifies a name of a person in an image. The identifying includes detecting visual features from a received image and collecting visually similar images to the received image along with text that is proximate or surrounding the visually similar images. A name, and/or other additional information, is determined from the text and output to a user. In one embodiment, an output of the applied techniques is a database of images of people, such as celebrities, including pertinent information associated with the people in the images such as: a name of each person, a birth date, a gender, an occupation of each person, and the like.
Conclusion
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/HUY C HO/Primary Examiner, Art Unit 2644
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