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 .
DETAILED ACTION
Priority
Acknowledgment is made of applicant’s foreign priority claim, for U.S. Application No. 19/050,868, based on a foreign application filed on 08/13/2022.
Status of Claims
Claims 1–15 are pending in the application.Claims 1-6, 8-15 are rejected.
Claims 7 is objected to.
Allowable Subject Matter
Claims 7 is objected to as being dependent upon a rejected base claim(s), but would be allowable if rewritten in independent form including all of the limitations of the base claim(s) and any intervening claim(s).
Overview of Grounds of Rejection
Ground of Rejection
Claim(s)
Statute(s)
Reference(s)
Ground 1
1–4, 8, 11, 12, 14, 15
§ 103
Gokturk et al. (US20140369626A1), Nechyba et al. (US20140016837A1), and Ioffe et al. (US20120070042A1)
Ground 2
5, 6
§ 103
Gokturk et al. (US20140369626A1), Nechyba et al. (US20140016837A1), Ioffe et al. (US20120070042A1), and Shaburov et al. (US20170019633A1)
Ground 3
9, 13
§ 103
Gokturk et al. (US20140369626A1), Nechyba et al. (US20140016837A1), Ioffe et al. (US20120070042A1), and Ton-That (US20210042527A1)
Ground 4
10
§ 103
Gokturk et al. (US20140369626A1), Nechyba et al. (US20140016837A1), Ioffe et al. (US20120070042A1), and Kurtz et al. (US20090185723A1)
Claim Rejections - 35 USC § 103
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 of this title, 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.
(Please see the cited paragraphs, sections, pages, or surrounding text in the references for the paraphrased content.)
Ground of Rejection 1
Claims 1, 2, 3, 4, 8, 11, 12, 14, 15 are rejected under 35 U.S.C. § 103 as being unpatentable over Gokturk et al. (US20140369626A1) in view of Nechyba et al. (US20140016837A1), and further in view of Ioffe et al. (US20120070042A1).
As per Claim 1, Gokturk teaches the following portion of Claim 1, which recites:“1. A method for displaying a particular user and being performed by an electronic device, the method comprising:”
Gokturk et al. teaches that “a system such as described may be implemented on a local computer or terminal, in whole or in part.” Gokturk et al., ¶ [0187]. Gokturk et al. further teaches that “The objectified image renderings are images that are displayed with individually detected objects being separately selectable” and that “Objectified image renderings correspond to images that contain recognized objects.” Gokturk et al., ¶¶ [0059], [0276]. Gokturk et al. also teaches that “the image file may be rendered in objectified form” and that, when viewed, regions having recognition information are made active. Gokturk et al., ¶ [0281].
Accordingly, Gokturk et al. teaches a computer-implemented method performed by an electronic device for displaying a particular user as a recognized object or active region within an image.
Gokturk alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Nechyba, they collectively teach some of the limitation(s).
Gokturk and Nechyba teach the following portion of Claim 1, which recites:“capturing, using a camera of the electronic device, at least one input image frame including at least one user;”
Gokturk et al. does not directly teach the claimed camera-capture operation. Nechyba et al. teaches that “the computing device may capture an image of the user's face for authentication purposes.” Nechyba et al., ¶ [0025]. Nechyba et al. further teaches that a camera lens may be part of a front-facing or rear-facing camera of the computing device and that “One or both of the front-facing and rear-facing cameras may be capable of capturing still images, video, or both.” Nechyba et al., ¶ [0034].
Thus, Nechyba et al. teaches capturing, using a camera of the electronic device, at least one input image frame including at least one user.
Gokturk teaches the following portion of Claim 1, which recites:“determining, from the at least one input image frame, a plurality of pixels associated with the at least one user;”
Gokturk et al. teaches that “the input image is traversed through discrete image elements across at least a relevant portion of the image.” For a digital image, “this step may be performed by pixel-by-pixel traversal across an image file.” Gokturk et al. further teaches that “At each pixel, a variable size window around the pixel is tested to be face or non-face.” Gokturk et al., ¶ [0068].
Gokturk et al. further teaches computing a detection confidence by taking a weighted average of “all pixels in the detected face region.” Gokturk et al., ¶ [0070].
Accordingly, Gokturk et al. identifies a detected face region and determines the pixels belonging to that region, thereby determining a plurality of pixels associated with the at least one user.
Gokturk teaches the following portion of Claim 1, which recites:“extracting a plurality of features of the at least one user based on the plurality of pixels associated with the at least one user;”
Gokturk et al. teaches that “Given a face, the system can extract features from the face that describe the given face.” Gokturk et al., ¶ [0300].
Gokturk et al. further teaches that a face feature vector may contain information derived by applying principal component analysis to “several regions of the detected face,” including “the whole face, the left eye, and the right eye.” Gokturk et al., ¶ [0302]. Gokturk et al. also teaches computing color histograms for “the hair region and the skin region of a person being recognized.” Gokturk et al., ¶ [0303]. Additional features may include information concerning sex, ethnicity, and hairstyle. Gokturk et al., ¶ [0304].
Thus, Gokturk et al. teaches extracting a plurality of features of the at least one user based on the plurality of pixels associated with the at least one user, including face, eye, skin, hair, sex, ethnicity, and hairstyle features.
Gokturk teaches the following portion of Claim 1, which recites:“weighting each of the plurality of features based on an amount of information corresponding each of the plurality of features;”
Gokturk et al. teaches that “The different parts of the feature vector (PCA face and eye regions; skin and hair color histograms; and sex, ethnicity, and hair classification) are weighted by their importance.” Gokturk et al., ¶ [0305].
The importance assigned to each feature represents its relative informational or discriminative contribution to recognizing the person. Thus, weighting the features according to their importance teaches weighting each of the plurality of features based on an amount of information corresponding each of the plurality of features.
Gokturk teaches the following portion of Claim 1, which recites:“generating identity information corresponding to the at least one user based on the weighted plurality of features;”
Gokturk et al. teaches that the differently weighted portions of the feature vector are “combined into a single face feature vector.” Gokturk et al., ¶ [0305].
Gokturk et al. further teaches that “the identifier of the person generated from the person analysis component 1222 is a recognition signature 1253,” wherein the identifier “substantially uniquely identifies the person from other persons.” Gokturk et al., ¶ [0190].
Therefore, Gokturk et al. generates a face feature vector or recognition signature from the weighted features. The resulting recognition signature constitutes identity information corresponding to the at least one user based on the weighted plurality of features.
Gokturk and Nechyba teach the following portion of Claim 1, which recites:“determining whether the generated identity information matches at least one identity information stored in a database, wherein the at least one identity information comprises a plurality of identities associated with a plurality of authorized users;”
Gokturk et al. does not directly characterize the stored identities as identities associated with authorized users. Nechyba et al. teaches that “a computing device may store images of the faces of one or more authorized users (or ‘enrollment images’).” Nechyba et al. further teaches that the computing device may “compare the captured facial image to the enrollment images associated with authorized users.” If the facial-recognition programs determine “an acceptable level of match between the captured facial image and at least one enrollment image,” the device authenticates the user. Nechyba et al., ¶ [0025].
The stored enrollment images, or the facial representations derived from those images, constitute stored identity information. The disclosure of enrollment images associated with one or more authorized users includes an embodiment having a plurality of stored identities associated with a plurality of authorized users.
Accordingly, Nechyba et al. teaches determining whether the generated identity information matches at least one identity information stored in a database, wherein the at least one identity information comprises a plurality of identities associated with a plurality of authorized users.
Gokturk and Nechyba alone do not explicitly teach all the limitation(s) of the claim. However, when combined with Ioffe, they collectively teach all of the limitation(s).
Nechyba and Ioffe teach the following portion of Claim 1, which recites:“and displaying the plurality of pixels associated with the at least one user based on determining that the generated identity information matches the at least one identity information in the database.”
Nechyba et al. teaches the preceding determination that the facial information of the captured user matches an enrollment identity associated with an authorized user. Nechyba et al., ¶ [0025].
For the display-result portion of the limitation, Ioffe et al. teaches that identity masking is performed before an image is viewed by others. Ioffe et al., ¶ [0020]. Ioffe et al. further teaches that a face detector may “search an image database to verify if a detected region matches such an image.” Significantly, “If the detected region has a match in the database, it is unmarked and is not processed for identity masking.” Ioffe et al., ¶ [0028].
Ioffe et al. also teaches that processed images are stored in a processed-image database accessible by an image server, and that “Image server 130 can be accessed by image viewers.” Ioffe et al., ¶ [0022]. Once all face regions have been processed, “the processed image will be output in stage 470 to processed image database 120.” Ioffe et al., ¶ [0041].
Accordingly, in the proposed combination, Nechyba et al.’s authorized-user identity match supplies the match condition. Ioffe et al.’s matching-region treatment causes the corresponding face region to remain unmarked and unmasked. The resulting image is then output and made accessible to image viewers. Thus, the original pixels associated with the matching authorized user remain visible and are displayed, teaching displaying the plurality of pixels associated with the at least one user based on determining that the generated identity information matches the at least one identity information in the database.
Before the effective filing date of the claimed invention, a person of ordinary skill in the art would have been motivated to modify Gokturk et al.’s feature-based person-recognition and objectified-image rendering method with Nechyba et al.’s camera-based capture and authorized-user enrollment matching, and further with Ioffe et al.’s database-controlled identity-masking technique. The modification would improve security and privacy by permitting the device to recognize authorized users and preserve the visibility of their corresponding image regions while allowing nonmatching or unauthorized users to be masked. The references employ compatible and well-known image-capture, face-detection, feature-extraction, feature-weighting, database-matching, and image-rendering operations. Combining those operations according to their established functions would have produced the predictable result of displaying the pixels of a recognized authorized user while selectively obscuring other detected users.
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As per Claim 2, Gokturk alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Ioffe, they collectively teach all of the limitation(s).
Ioffe teaches Claim 2, which recites:“The method as claimed in claim 1, further comprising performing a function corresponding to at least one of masking, filtering, and blurring the plurality of pixels associated with the at least one user based on determining that the generated identity information does not match the at least one identity information in the database.”
Ioffe et al. teaches searching an image database to determine whether a detected face region matches an image in the database and states that “If the detected region has a match in the database, it is unmarked and is not processed for identity masking.” Ioffe et al., ¶ [0028]. Conversely, the remaining detected face regions are processed using an identity-masking algorithm to obscure their identities, including blurring the face regions. Ioffe et al., ¶¶ [0029]-[0030], [0039]. Ioffe et al. further teaches that “Each pixel in the selected face region is blurred” according to the applied blur algorithm. Ioffe et al., ¶ [0040].
Accordingly, Ioffe et al. teaches performing masking or blurring on the plurality of pixels associated with a user when the detected user region does not match an image stored in the database. Because Claim 2 requires at least one of masking, filtering, or blurring, Ioffe et al.’s masking and blurring disclosures satisfy the claimed alternatives.
The rationale and motivation to combine the references as set forth for claim 1 are incorporated herein by reference for the present claim.
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As per Claim 3, Gokturk teaches Claim 3, which recites:“The method as claimed in claim 1, wherein the plurality of features comprises at least one of information, indicating facial cues associated with the at least one user, and information indicating non-facial cues associated with the at least one user.”
Gokturk et al. teaches generating a recognition signature using “two or more of the following characteristics: facial features (e.g. eye or eye region including eye brow, nose, mouth, lips and ears), clothing and/or apparel, hair (including color, length and style) and gender.” Gokturk et al., ¶ [0039]. Gokturk et al. also teaches that person recognition may be based on “facial features, clothing, apparel” and other person-recognition information. Gokturk et al., ¶ [0189].
Accordingly, the facial features constitute information indicating facial cues associated with the at least one user, while clothing, apparel, hair, and gender constitute information indicating non-facial cues associated with the at least one user.
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As per Claim 4, Gokturk teaches Claim 4, which recites: “The method as claimed in claim 3, wherein the at least one of the information, indicating facial cues associated with the at least one user, and the information, indicating non-facial cues associated with the at least one user, comprises at least one of clothing, color, texture, style, body size, hair, face, pose, position, and viewpoint.”
Gokturk et al. teaches generating a recognition signature using “facial features,” “clothing and/or apparel,” and “hair (including color, length and style).” Gokturk et al., ¶ [0039]. Gokturk et al. also identifies marker features including “clothing, apparel, hair style, shape or color, and body shape.” Gokturk et al., ¶ [0066].
Accordingly, Gokturk et al. directly teaches that the facial and non-facial cue information includes at least clothing, color, style, hair, and face, thereby satisfying the claimed alternative.
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As per Claim 8, Gokturk alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Nechyba and Ioffe, they collectively teach all of the limitation(s).
Nechyba teaches the following portion of Claim 8, which recites: “The method as claimed in claim 1, further comprising: determining that the at least one user is authorized to appear in a media associated with the at least one input image frame based on the generated identity information of the at least one user matching with the at least one identity information in the database;”
Nechyba et al. teaches comparing a captured facial image with stored enrollment images “associated with authorized users” and authenticating the user when there is “an acceptable level of match” with an enrollment image. Nechyba et al., ¶ [0025].
Ioffe teaches the following portion of Claim 8, which recites: “and displaying the plurality of pixels associated with the at least one user in the media on determining that the user is authorized to appear in the media.”
Ioffe et al. teaches searching an image database to determine whether a detected face region matches an image not subject to privacy concerns and states that, “If the detected region has a match in the database, it is unmarked and is not processed for identity masking.” Ioffe et al., ¶ [0028]. The resulting processed image is output and made accessible to image viewers. Ioffe et al., ¶¶ [0022], [0041].
Accordingly, the combined teachings determine that a matching authorized user may remain visible in the media and display the original pixels associated with that user without masking.
The rationale and motivation to combine the references as set forth for claim 1 are incorporated herein by reference for the present claim.
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Claim 11 does not include any additional limitations that would significantly distinguish it from claim 1. Therefore, it is likewise rejected under 35 U.S.C. § 103 in view of the same references and for the same reasons set forth above.
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Claim 12 does not include any additional limitations that would significantly distinguish it from claims 1 and 2. Therefore, it is likewise rejected under 35 U.S.C. § 103 in view of the same references and for the same reasons set forth above.
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Claim 14 does not include any additional limitations that would significantly distinguish it from claims 1, 2, and 3. Therefore, it is likewise rejected under 35 U.S.C. § 103 in view of the same references and for the same reasons set forth above.
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Claim 15 does not include any additional limitations that would significantly distinguish it from claims 1, 2, 3, and 4. Therefore, it is likewise rejected under 35 U.S.C. § 103 in view of the same references and for the same reasons set forth above.
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Ground of Rejection 2
Claims 5, 6 are rejected under 35 U.S.C. § 103 as being unpatentable over Gokturk et al. (US20140369626A1) in view of Nechyba et al. (US20140016837A1), further in view of Ioffe et al. (US20120070042A1), and still further in view of Shaburov et al. (US20170019633A1).
As per Claim 5, Gokturk alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Shaburov, they collectively teach all of the limitation(s).
Shaburov teaches the following portion of Claim 5, which recites:“The method as claimed in claim 1, wherein displaying the plurality of pixels associated with the at least one user, comprises: determining at least one output image frame for displaying the plurality of pixels associated with the at least one user;”
Shaburov et al. teaches receiving a video containing “a sequence of video images (also known as video frames)” and identifying an object of interest, such as the face or body of a user, within the video images. Shaburov et al., ¶¶ [0101], [0103].
Shaburov teaches the following portion of Claim 5, which recites: “determining at least one visual effect to be applied to the at least one output image frame;”
Shaburov et al. teaches receiving a request to blur or otherwise modify the background and selecting modifications including “Gaussian smoothing or a lens blurring algorithm,” changes in resolution, colors, posterization, or pixelization. Shaburov et al., ¶¶ [0102], [0111].
Shaburov teaches the following portion of Claim 5, which recites:“determining at least one background frame using the at least one visual effect;”
Shaburov et al. teaches identifying the background in each video image by separating the user from the image or selecting the image portion outside the user mesh, and then modifying the identified background in each video image using the selected visual effect. Shaburov et al., ¶¶ [0107]-[0111].
Shaburov teaches the following portion of Claim 5, which recites: “determining at least one modified output image frame by merging the at least one output image frame and the at least one background frame;”
Shaburov et al. teaches that “the computing device generates a modified video by combining the modified background with the image of the object of interest.” Shaburov et al., ¶ [0114].
Shaburov teaches the following portion of Claim 5, which recites: “and displaying the at least one modified output image frame.”
Shaburov et al. teaches receiving and decoding the modified video and “display[ing] the video with the background blurred” on the receiving participant’s display. Shaburov et al., ¶ [0068].
Accordingly, Shaburov et al. teaches identifying an output video frame containing the user, selecting and applying a visual effect to its background, combining the modified background with the user image, and displaying the resulting modified output frame.
Before the effective filing date of the claimed invention, a POSITA would have been motivated to incorporate Shaburov et al.’s background-modification and compositing technique into the combined system of Claim 1 to enhance video presentation by applying visual effects to the background while preserving and displaying the recognized user, yielding the predictable result of a modified output frame combining the user image with an effects-processed background.
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As per Claim 6, Gokturk alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Shaburov, they collectively teach all of the limitation(s).
Shaburov teaches the following portion of Claim 6, which recites:“The method as claimed in claim 1, wherein determining the plurality of pixels associated with the at least one user, comprises: segmenting the plurality of pixels associated with the at least one user from the at least one input image frame;”
Shaburov et al. teaches identifying a background by “separating the at least one object of interest from each image based on the mesh” and selecting an image portion that excludes pixels associated with the user mesh. Shaburov et al., ¶ [0107].
Shaburov teaches the following portion of Claim 6, which recites: “and generating at least one pixel map including the segmented plurality of pixels associated with the at least one user.”
Shaburov et al. teaches “forming a binary mask associated with the at least one object of interest” and determining “object pixels associated with the object of interest.” The binary mask is then aligned to the user mesh for each image. Shaburov et al., ¶¶ [0108]-[0110].
Accordingly, Shaburov et al. segments the user-associated pixels from each input frame and generates a binary pixel map identifying those segmented pixels.
The rationale and motivation to combine the references as set forth for claim 5 are incorporated herein by reference for the present claim.
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Ground of Rejection 3
Claims 9, 13 are rejected under 35 U.S.C. § 103 as being unpatentable over Gokturk et al. (US20140369626A1) in view of Nechyba et al. (US20140016837A1), further in view of Ioffe et al. (US20120070042A1), and still further in view of Ton-That (US20210042527A1)
As per Claim 9, Gokturk alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Ton-That, they collectively teach all of the limitation(s).
Ton-That teaches Claim 9, which recites:“The method as claimed in claim 1, wherein the identity information corresponding to the at least one user is generated using at least one deep neural network (DNN) model.”
Ton-That teaches “transforming the facial image data to facial recognition data” and performing facial recognition “based on a neural network algorithm (e.g., deep convolutional neural network (CNN)).” Ton-That, ¶¶ [0044]-[0045]. Ton-That further teaches a facial-embedding process “using the neural network to convert facial images to vectors,” where the resulting vector represents the captured user’s face. Ton-That, ¶ [0069]. The employed machine-learning module “comprises a deep convolutional neural network (CNN).” Ton-That, ¶ [0070].
Accordingly, Ton-That teaches generating identity information corresponding to the at least one user using at least one DNN model.
Before the effective filing date of the claimed invention, a POSITA would have been motivated to incorporate Ton-That’s DNN-based facial-embedding technique into the combined system of Claim 1 to improve the accuracy and robustness of generating user identity information from facial images, yielding the predictable result of a discriminative facial vector suitable for comparison with stored identities.
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As per Claim 13, Gokturk alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Ton-That, they collectively teach all of the limitation(s).
Ton-That teaches Claim 13, which recites:“The electronic device as claimed in claim 12, wherein the identity information is a feature vector.”
Ton-That teaches that “the facial recognition data include a vector representation of the captured facial image of the subject” and that the vector representation may comprise “a 512 point vector.” Ton-That, ¶ [0069]. Ton-That further teaches using a neural network “to convert facial images to vectors.” Ton-That, ¶ [0069].
Accordingly, Ton-That teaches that the generated facial identity information is a feature vector.
The findings and rationale set forth above for claims 11 and 12 are incorporated herein. Before the effective filing date, a POSITA would have been motivated to represent the identity information in the combined electronic device as Ton-That’s feature vector to facilitate efficient and accurate comparison with stored user identities, yielding predictable results.
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Ground of Rejection 4
Claim 10 is rejected under 35 U.S.C. § 103 as being unpatentable over Gokturk et al. (US20140369626A1) in view of Nechyba et al. (US20140016837A1), further in view of Ioffe et al. (US20120070042A1), and still further in view of Kurtz et al. (US20090185723A1).
As per Claim 10, Gokturk alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Kurtz et al., they collectively teach all of the limitation(s).
Kurtz teaches Claim 10, which recites:“The method as claimed in claim 1, wherein the amount of information associated with the corresponding feature of the plurality of features comprises at least one of a face direction, a color of texture, a distance from camera, a focus towards camera, and a presence of obstacles in the face.”
Kurtz et al. teaches estimating facial pose and extracting up to 82 facial feature points, and explains that “person recognition tasks are easiest with frontal poses, as the greatest number of face points (1-82) is accessible.” Kurtz et al., ¶ [0041].
Accordingly, the amount of facial-feature information available for recognition depends on the user’s face direction, with a frontal direction providing a greater amount of accessible feature information. This satisfies at least the claimed face direction alternative.
Before the effective filing date, a POSITA would have been motivated to use Kurtz et al.’s face-direction information to weight facial features according to their recognition value, thereby improving identification accuracy with predictable results.
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Conclusion
The prior art made of record and relied upon in this action is as follows:
Patent Literature:
Gokturk et al. (US20140369626A1) — “System and Method for Providing Objectified Image Renderings Using Recognition Information from Images”
Ioffe et al. (US20120070042A1) — “Automatic Face Detection and Identity Masking in Images, and Applications Thereof”
Kurtz et al. (US20090185723A1) — “Enabling Persistent Recognition of Individuals in Images”
Nechyba et al. (US20140016837A1) — “Facial Recognition”
Shaburov et al. (US20170019633A1) — “Background Modification in Video Conferencing”
Ton-That (US20210042527A1) — “Methods for Providing Information About a Person Based on Facial Recognition”
Non-Patent Literature (NPL):
(none)
Note: A PDF copy of each NPL reference is attached with this Office Action. URLs are included for applicant convenience. If a link becomes unavailable in the future, the citation information may be used to locate the reference or access archived versions via the Wayback Machine.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is listed as follows:
Patent Literature:
(none)
Non-Patent Literature (NPL):
(none)
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/ADEEL BASHIR/
Examiner, Art Unit 2616
/DANIEL F HAJNIK/Supervisory Patent Examiner, Art Unit 2616
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