DETAILED ACTION
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements submitted on 02/18/2025 have been considered by the Examiner and made of record in the application file.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-10 and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin (US 2011/0305404 A1) in view of Alrasheed (US 2020/0034604 A1).
Regarding claims 1 and 14-15, Lin discloses an information processing apparatus/method [claim 15: computer-readable non-transitory storage medium: (paragraph 34)] comprising a control unit that (Lin discloses a face hallucination system 700 implemented on a computer system 800 having memory 810 and processor 820. Processor 820 includes the projection, matching, basis decomposition and face hallucination modules, see paragraphs 31 and 34)
acquires unique feature information unique to a face of a target person from a low-quality captured face image including the face of the target person, (Lin receives LR face image 312 and projects it into a manifold domain as yL. Lin explains that the manifold domain is a subspace where the face difference may be expressed and that the projection matrix projects the target LR face into that same manifold space, see paragraphs 21 and 23-24)
extracts a plurality of (The target face and training faces are projected into the same manifold where facial differences are represented. Lin then selects the k most similar face images to target representation yL, see paragraphs 24-26 and 29.) from a learning database (Lin prepares training database 310, which collects training face images and later states that training database 310 collects and stores a plurality of training images 712, see paragraphs 23 and 31-32.) based on the unique feature information, and (Selection is based on yL, the projected representation of target LR face image 312. Lin uses k-NN to select the training images most similar to yL in the manifold domain, see paragraphs 24-26 and 32.)
outputs a learning data set (The selected matching face images constitute training set 316 or training set 732. Matching module 730 selects training set 732 and provides it for subsequent basis decomposition. Thus, Lin produces a target specific training set from the selected images, see paragraphs 23, 25-26 and 32.) for quality enhancement processing of improving quality of the low-quality captured face image (Lin uses the selected training set to obtain high resolution prototype faces and reconstruct high resolution face image 322 from LR face image 312. The experimental results state that the method may effectively improve the face reconstruction quality, see paragraphs 23, 32-35 and 37.) based on the plurality of third person images. (The selected matching training images are basis decomposed to obtain high resolution prototype faces; those prototype faces are then used to reconstruct the HR version of the target LR face, see paragraphs 25-28 and 32-34.)
Lin fails to specifically disclose extracting a plurality of third person images different from the target person.
In related art, Alrasheed discloses extracting a plurality of third person images different from the target person. (Alrasheed identifies matching image sources of one or more other persons similar in appearance to the subject based on determined facial characteristics, see paragraphs 78-82 and 92-98.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Alrasheed into the teachings of Lin to effectively facilitate a search for people with similar faces.
Regarding claim 2, Lin, as modified by Alrasheed, discloses the claimed invention wherein the unique feature information includes attribute information of the target person. (Alrasheed constructs a person model containing a digital representation of the subject’s face together with personal information, see paragraphs 123-125 and 133-134.)
Regarding claim 3, Lin, as modified by Alrasheed, discloses the claimed invention wherein the attribute information includes information regarding at least one of nationality, age, gender, race, and language of the target person. (Alrasheed discloses gender and age, see paragraphs 124, 139, 147 and 175.)
Regarding claim 4, Lin, as modified by Alrasheed, discloses the claimed invention wherein the unique feature information includes face part information regarding a part of the face of the target person. (Alrasheed states that the system analyzes each part or a visible portion of a picture or video into its own model, see paragraphs 149 and 157-168.)
Regarding claim 5, Lin, as modified by Alrasheed, discloses the claimed invention wherein the face part information includes information regarding any one of a position of the part in the face, a shape of the part, and a color of the part. (Alrasheed discusses nose shape, see paragraphs 157 and 163-168.)
Regarding claim 6, Lin, as modified by Alrasheed, discloses the claimed invention wherein the unique feature information includes image unique information that is information unique to the face of the target person in the captured face image. (Alrasheed analyzes image or video sources to determine characteristics that vary with the particular captured image, particularly the subject’s facial expression, see paragraphs 13, 32-36, 127-137 and 141-146.)
Regarding claim 7, Lin, as modified by Alrasheed, discloses the claimed invention wherein the image unique information includes information regarding at least one of an emotion, an utterance, and a tone of a voice of the target person. (Alrasheed states that an expression can convey emotion such as disgust, anger, fear, sadness, happiness, surprise and contempt, see paragraphs 32-36.)
Regarding claim 8, Lin, as modified by Alrasheed, discloses the claimed invention wherein the learning database stores the third person image having a higher quality than the captured face image and including a face of a third person in association with the unique feature information unique to the face of the third person. (Lin stores training face images in training database 310, see paragraphs 21-32, and its experiment uses 64x64 high resolution training faces versus 16x16 LR test images, see paragraph 35. Alrasheed discloses database image sources having facial characteristics associated with them, see paragraphs 47-60, and stores detected information alongside the original media using a common key, see paragraph 152, and compares the target against facial representations of other users stored in database 363, see paragraph 175.
Regarding claim 9, Lin, as modified by Alrasheed, discloses the claimed invention wherein the control unit extracts the plurality of third person images based on a distance between the captured face image and the third person image in a high-dimensional feature amount space in which the captured face image and the third person image are plotted. (Lin projects the target LR face and database training faces into the same manifold feature domain, with the projection specifically making differences among faces readily expressible. Lin then uses k-NN to select the k most similar training faces to project target representation YL and selects hundreds of faces based on matching to the target, see paragraphs 24-26, 29 and 32.)
Regarding claim 10, Lin, as modified by Alrasheed, discloses the claimed invention wherein the control unit outputs the learning data set including the plurality of third person images as teacher images. (Lin selects k matching face images and forms them into a training set used to learn the prototype basis faces for reconstruction, see paragraphs 23, 25-26 and 32.)
Claim 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Alrasheed and in further view of Shinmei (US 2007/0165112 A1).
Regarding claim 11, Lin, as modified by Alrasheed, discloses the claimed invention except for wherein the plurality of third person images is used to generate a student image based on the captured face image.
In related art, Shinmei discloses the plurality of third person images is used to generate a student image based on the captured face image. (Shinmei teaches generating a student image from a teacher image based on degradation information obtained from a captured image, specifically by extracting noise from the captured image and adding that noise to the teacher image to generate a student image corresponding to the captured image, see paragraphs 23 and 173-181)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Shinmei into the teachings of Lin and Alrasheed to effectively remove noise included in images taken by the image sensor more accurately.
Claim 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Alrasheed and in further view of Bertan (US 2019/0205617 A1.)
Regarding claim 12, Lin, as modified by Alrasheed, discloses the claimed invention except for wherein the control unit acquires the unique feature information based on text information extracted from a captured image including the target person.
In related art, Bertan discloses the control unit acquires the unique feature information based on text information extracted from a captured image including the target person. (Bertan discloses an OCR module that processes an identification document image containing both text and an image of the user and outputs the person’s image together with the identified text, see paragraphs 29-30. The extracted text is used to determine facial or person attributes, for example determining that the user is male from OCR recognized text “sex: male” and determining the user’s age from an OCR extracted birthdate, see paragraphs 31-32. The extracted demographic and biometric information is further used for facial detection and image acquisition, see paragraphs 35-36.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Bertan into the teachings of Lin and Alrasheed to effectively identify information to facilitate image acquisition.
Claim 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Alrasheed and in further view of Welbourne (US 2019/0313014 A1).
Regarding claim 13, Lin, as modified by Alrasheed, discloses the claimed invention except for wherein the control unit acquires the unique feature information based on voice information generated from sound data corresponding to a moving image including the target person.
In related art, Welbourne discloses acquiring the unique feature information based on voice information generated from sound data corresponding to a moving image including the target person. (Welbourne discloses capturing video data of a user and corresponding audio data, detecting speech in the audio, and performing speaker recognition to generate identity information associated with the speaking user, see paragraphs 25-27. The speaker recognition information generated from the audio is provided to a facial recognition module and used together with the corresponding video data to identify the user’s face in the video, see paragraphs 28 and 32.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Welbourne into the teachings of Lin and Alrasheed to effectively improve facial recognition models.
Conclusion
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/BOBBAK SAFAIPOUR/Primary Examiner, Art Unit 2665
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