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 .
Claim Status
Claims 1-15 are currently pending in the application filed 9/23/2024
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 9/23/2024 has been considered by the Examiner.
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, 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 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.
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.
Claims 1, 2, 4, 6, 8-13, 15, 17, and 19-22 are rejected under 35 U.S.C. 103 as being unpatentable by Sensharma et al. (US 9,510,196 B2) in view of Zheng (US 10,783,369 B2)
Regarding claim 1, Sensharma teaches:
A method for integrated liveness detection and identity verification of a user using a device having at least one front camera and at least one rear camera, the method comprising:
(Sensharma, [col. 2 Line 31]; “the image capture mechanisms may include a front-facing camera 454-1 on the mobile device as well as a rear-facing camera 454-2 on the mobile device, although additional cameras may also be utilized.”)
Examiner Note: The applicant's specification at the Field of the Invention describes "methods and systems for verifying liveness of a subject and authenticity of an identification document via a device having cameras." Sensharma discloses a mobile device performing user authentication by liveness verification and facial recognition using both a front-facing camera 454-1 and a rear-facing camera 454-2, which reads on the claimed device "having at least one front camera and at least one rear camera" used for "integrated liveness detection and identity verification." The integration of the identification document into this verification is supplied by Zheng, which performs the same on-site identity verification using a rear camera for the identity document and a front camera for the face of a document-holding person.
initiating a verification process via an application stored in the device;
(Sensharma, [col. 4, Line 23]; “Memory 205 may also store user authentication engine 240 and one or more modules of the user authentication engine 240 (i.e., pulse rate estimator 230, pulse correlator 232, facial recognition engine 234, and authentication decision engine 236) to implement embodiments described herein.”)
(Sensharma, [col. 4. Line 63]; “User authentication engine 240 processes the request and activates front-facing camera 227-1 and rear-facing camera 227-2.”)
Examiner Note: The applicant's specification describes an application that is pre-installed on the device and starts the verification process. Sensharma's user authentication engine 240 is stored in memory 205 of the mobile device, and it processes the request to start authentication and turn on the cameras. That engine equates to "application stored in the device."
activating both at least one front camera from the at least one front camera and at least one rear camera from the at least one rear camera of the device by the application;
(Sensharma, [col. 5, Line 2]; “User authentication engine 240 may also activate both cameras 227-1 and 227-2 when the liveness verification or facial recognition processes are performed in parallel. The activation of the cameras 227-1 and 227-2 causes mobile device 210 to capture digital video data with each camera.”)
simultaneously with a video stream of a face of the user from the activated at least one front camera
(Sensharma, [col. 2, Line 59]; “As illustrated in FIG. 4, the front facing camera 454-1 captures a first digital video data 456-1 of a user that includes the user’s face, while the rear facing camera 454-2 captures a second digital video data 456-2 of another part of the user, such as the user’s hand as illustrated in FIG. 4.”)
Examiner Note: The applicant's specification describes recording the two video streams at the same time, stating that "the rear camera of the device is able to record a video stream of an identification document while the front camera of the device is able simultaneously to record a video stream of the face of the user," so what is recorded simultaneously is the rear-camera stream and the front-camera stream of the face, captured together. Sensharma records the streams in the same way, because its front camera captures the user's face "while" its rear camera captures a second video stream, and the word "while" shows the two streams are taken at the same time. Sensharma's "while" therefore reads on recording the rear stream simultaneously with the front-camera stream of the face, as the applicant uses that term.
Sensharma fails to teach:
recording a video stream of an identification document from the activated at least one rear camera
Zheng teaches:
recording a video stream of an identification document from the activated at least one rear camera
(Zheng, [Col 13 Line 6]; “while or after using a rear camera for the ‘on-site collection of an image of an identity document,’ using a front camera to collect ‘a facial image of a document-holding person.’”)
Examiner Note: Sensharma supplies the recording of two simultaneous video streams, the face stream from the front camera, and the temporal pairing of the front and rear streams. Sensharma’s rear camera, however, images “another part of the user, such as the user’s hand” (col. 2, ll. 61-65) rather than an identification document. Zheng supplies the missing subject matter by capturing the identification document with the rear camera at the same time the front camera captures the facial image of the document-holding person. The applicant’s specification defines the identification document as “a passport, a driver’s license, a military identification, other governmental identification, or other verified identification documents,” which is consistent with Zheng’s on-site collection of an image of an identity document and is what is read upon here.
Sensharma teaches:
synchronizing video frames from the both video streams;
(Sensharma, [col. 5, Line 36]; "In Eulerian Video Magnification (EVM), pulse rate estimator 230 applies spatial decomposition to the video data, applies temporal filtering to video frames, and amplifies the results to visualize the flow of blood in a user's skin over a period of time (e.g., the face captured in the video data 456-1 by front-facing camera 454-1 or 227-1, as well as the hand, arm, etc. captured in the video data 456-2 by rear-facing camera 456-2 or 227-2).")
(Sensharma, [col. 5, Line 54]; "In one embodiment, pulse correlator 232 performs statistical analysis on the pulse rates to calculate the cross-correlation between the two pulse signals corresponding to the front-facing and rear facing camera respectively to determine whether the estimated pulse rates correspond (i.e., belong to the same user).")
Examiner Note: The specification does not tie synchronization to one method. In its description of the synchronization algorithms (Motion matching module 100), the specification states that "Synchronization algorithms ensure a precise combination of data from both cameras," and that besides comparing the jitter of base points, "it is also possible to use an artificial convolutional neural network, which is trained to accept two video streams as input, returning a decision on stream synchronization." This means that synchronizing the video frames is combining the two streams and reaching a decision that they are in sync, by any method. Sensharma does this by applying temporal filtering to the video frames of both streams and then cross-correlating those streams to decide that they belong to the same user, which reads on synchronizing video frames from the both video streams.
analyzing said synchronized video frames and detecting liveness of the user
(Sensharma, col. 3, Line 2]; "When the pulse rates can be correlated by processing logic, then processing logic can conclude that the image captured by the front-facing camera 454-1 and the rear-facing camera 454-2 are capturing live images of the same person.")
Sensharma fails to teach:
and liveness of the identification document,
wherein the video stream from the activated at least one rear camera is analyzed to capture and to extract a face image from the identification document and to recognize the identification document,
Zheng teaches:
and liveness of the identification document,
(Zheng, claim 1; "performing first verification on the image to determine a probability at which the image is from a screen print; performing second verification on the image to determine a probability at which the image is from a copy; determining a probability at which the image is from a physical document based on the probability at which the image is from a screen print and the probability at which the image is from a copy; verifying a source of the image based on the probability at which the image is from a physical document")
Examiner Note: Sensharma analyzes the two synchronized streams and detects liveness of the user. When the pulse rates from the two streams correlate, Sensharma concludes the front and rear cameras are capturing live images of the same live person. This is the user-liveness check, which lines up with the passive facial liveness the specification performs on the user. Sensharma has no identification document, so it does not detect liveness of a document. Zheng fills that in. It checks whether the document image is a screen print or a copy versus a real physical document, which is the document-liveness check and lines up with the passive document liveness the specification performs on the document. The claim calls for detecting liveness of both the user and the identification document, so Sensharma covers the user side and Zheng covers the document side
wherein the video stream from the activated at least one rear camera is analyzed to capture and to extract a face image from the identification document and to recognize the identification document,
(Zheng, [col 16 Line 33]; “online check may be performed on text information and a documental facial image in the identity information”)
Examiner Note: Zheng analyzes the rear-camera document capture to recognize the document by extracting its text information and to obtain the “documental facial image,” which reads on extracting a face image from the identification document and recognizing the identification document. Sensharma does not capture a document and therefore does not teach this; it is supplied by Zheng.
Sensharma teaches:
and the video stream from the activated at least one front camera is analyzed to capture a live image of the face of the user;
(Sensharma, [col. 5, Line 10 ]; “Facial recognition engine 234 analyzes the digital video data from front-facing camera 227-1 to locate a face within the digital video data. Once facial recognition engine 234 has located a face, facial recognition engine 234 performs a facial recognition process on the face depicted in the image data.”)
Examiner Note: Sensharma analyzes the front-facing camera’s video to locate and process the user’s face, and the liveness verification confirms the recognized face corresponds to a live person currently seeking authentication (col. 6), which reads on capturing “a live image of the face of the user.”
Sensharma fails to teach:
comparing said live image of the face of the user with the face image extracted from the identification document; and
Zheng teaches:
comparing said live image of the face of the user with the face image extracted from the identification document; and
(Zheng, [col 16 Line 65];“the second verifying unit 54 is configured to perform cross-verification on the documental facial image in the identity information, the facial image of the document-holding person and the facial image obtained from the online check.”)
Examiner Note: Zheng cross-verifies the documental facial image (the face extracted from the identification document) against the facial image of the document-holding person collected on site (the live image of the user’s face), which reads on the claimed comparing step. Sensharma compares the captured face against a stored database (col. 2) rather than against a document-extracted face, so this is supplied by Zheng.
Sensharma teaches:
confirming verification or not confirming verification of an identity of the user in a result of the comparing step and/or liveness detection.
(Sensharma, [col. 3, Line 11]; “Processing logic then determines whether the liveness verification and facial recognition processes were both successfully completed (processing block 108). When either process fails, the authentication process fails, and the user is denied access to the mobile device. However, when both the liveness verification and facial recognition are successful, processing logic authenticates the user and grants the authenticated user access to the mobile device (processing block 110).”)
Examiner Note: Sensharma confirms or denies based on the results of the facial recognition and liveness verification, which reads on the claimed confirming or not confirming verification of the identity of the user in a result of the comparing step and/or liveness detection.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have implemented the identification-document capture, document liveness verification, document recognition and facial-image extraction, and the comparison of the live face against the document face as taught by Zheng into the dual-camera, simultaneous-video, synchronized-liveness authentication method of Sensharma. The reason to combine is as follows: (1) Sensharma performs liveness detection and facial recognition with synchronized front- and rear-camera video streams but verifies only the co-presence of two parts of a live user and does not verify any identification document; (2) Zheng, in the same field of remote identity verification, adds rear-camera capture and verification of the identification document, recognition and facial-image extraction from the document, and matching of the live face to the document face; and (3) combining Zheng’s document handling with Sensharma’s synchronized dual-stream liveness allows the live user and a genuine identification document to be verified together in a single synchronized capture, improving resistance to replay and presentation attacks, which is the stated goal of both references. The combination applies Zheng’s known document-verification technique to Sensharma’s known dual-camera device ready for the improvement, with a reasonable expectation of success and without changing the manner in which Sensharma captures and correlates the two video streams.
Regarding claim 2, the combination of Sensharma and Zheng teaches:
wherein synchronization of said video frames from the video streams and analysis of said video frames provide a measure for liveness detection.
(Sensharma, [col. 6, Line 12]; “In one embodiment, pulse correlator 232 determines the statistical confidence of a pulse match, and compares this confidence of a pulse match to a pulse match threshold value.”)
Examiner Note: Sensharma reaches its liveness decision from a measure it computes by analyzing the two synchronized streams together. The pulse correlator looks at both streams and produces a statistical confidence value for how well they match, then compares that value against a threshold to decide liveness. That confidence value is the measure for liveness detection. The pulse is only what Sensharma reads out of the video frames, while the measure itself is the confidence that the two synchronized streams correspond, which reads on the synchronization and analysis of the video frames providing a measure for liveness detection.
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Sensharma and Zheng. The motivation for the combination is to provide a measure for liveness detection from the synchronization and analysis of the two video streams. (Sensharma, [col. 6, Line 2]; "When the estimated and adjusted pulse rates correspond, pulse correlator 232 determines that the image data captured by front-facing camera 227-1 and rear-facing camera 227-2 are capturing live image data of the same user.")
Regarding claim 4, The combination of Sensharma and Zheng teaches:
wherein the method further includes synchronization with at least one motion sensor of the device, wherein such synchronization further verifies the liveness of the video streams.
(Zheng, [Col 16 Line 20]; “comparing the identity document image, the first background image, and the second background image based on position information of the gyroscope when collecting the identity document image, the first background image, and the second background image, to determine the first verification result”)
Examiner Note: Zheng correlates the captured images with position information from the gyroscope, a motion sensor of the device, to determine that the captures are from the same photographing scene, which reads on synchronization with at least one motion sensor that further verifies liveness. It would have been obvious to combine this with Sensharma for the reasons stated for claim 1, namely to strengthen the anti-spoofing co-presence check of the synchronized capture.
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Sensharma and Zheng. The motivation for the combination is to further verify the liveness of the video streams using a motion sensor of the device. (Zheng, [Col 13, Line 66]; "The first verification result may be used to represent a probability at which the identity document image, the first background image, and the second background image are from the same photographing scene.")
Regarding claim 6, the combination of Sensharma and Zheng teaches:
wherein said at least one motion sensor is at least one gyroscope and/or at least one accelerometer.
(Zheng, [Col 13 Line 63; “based on position information of the gyroscope when collecting the identity document image, the first background image, and the second background image”)
Examiner Note: Zheng’s gyroscope reads on the claimed “at least one gyroscope and/or at least one accelerometer.”
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Sensharma and Zheng. The motivation for the combination is to use a gyroscope as the motion sensor that verifies the captures are from the same real-time scene. (Zheng, [Col 13, Line 66]; "The first verification result may be used to represent a probability at which the identity document image, the first background image, and the second background image are from the same photographing scene.")
Regarding claim 8, the combination of Sensharma and Zheng teaches:
further comprising, when analyzing the video stream of the at least one rear camera, extracting personal data from the identification document using advanced optical character recognition (OCR) and pattern recognition algorithms.
(Zheng, [col. 11 Line 11]; "The text information may include information such as a document ID, a user's name, and the like, which may be obtained through Optical Character Recognition (OCR), and the documental facial images may include head images recognized via image recognition techniques.")
Examiner Note: Zheng obtains the text information of the identity document, including the document ID and the user's name, through Optical Character Recognition (OCR). The document ID and the user's name are the personal data extracted from the identification document, and Zheng obtains them by OCR, which reads on extracting personal data from the identification document using optical character recognition. Zheng also recognizes the documental facial images using image recognition techniques, which reads on the claimed pattern recognition algorithms.
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Sensharma and Zheng. The motivation for the combination is to extract the personal data from the identification document for verification of the identity information. (Zheng, [Col. 4, Line 31]; "the embodiments of the specification verify the authenticity of images of the identity documents and the authenticity of identity information in the images of the identity documents, and therefore, can effectively solve the problems of document content forgery, document re-make, and the like")
Regarding claim 9, the combination of Sensharma and Zheng teaches:
wherein the steps of the method are used in remote onboarding and Know Your Customer (KYC) procedures.
(Zheng, [Col 1 Line 48]; “Embodiments of the specification provide document verification and identity verification methods and devices to solve the problem that identity verification solutions according to the current technologies cannot meet the level of security required by online handling of business.”)
(Zheng, [col. 5, Line 28]; "The business may be various business that can be handled online, such as remote account opening.")
Examiner Note: The applicant's specification uses remote onboarding and KYC only as the setting for the remote identity verification. The specification describes the invention as "specifically tailored for remote onboarding and Know Your Customer (KYC) processes," where the user is authenticated remotely to gain access to a required resource. This limitation therefore asks only that the method be used in a remote procedure for onboarding or checking a customer. Zheng's remote account opening is one of those procedures, because opening an account remotely requires verifying the customer's identity from an identity document, and that is what Know Your Customer onboarding is. Zheng's remote account opening therefore reads on the claimed remote onboarding and KYC procedures.
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Sensharma and Zheng. The motivation for the combination is to use the steps of the method in remote onboarding and Know Your Customer (KYC) procedures, such as remote account opening. (Zheng, [Col 7, Line 1]; "The implementation of the step 240 will be described through examples below by taking 'remote account opening' as an example.")
Regarding claim 10, the combination of Sensharma and Zheng:
wherein, during recording of the video stream from the at least one rear camera, the identification document is held by the user.
(Zheng, [Col 13 Line 8]; “using a front camera to collect ‘a facial image of a document-holding person.’”)
Examiner Note: Zheng’s “document-holding person” establishes that the identification document is held by the user during the rear-camera capture, which reads on this.
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Sensharma and Zheng. The motivation for the combination is to have the identification document held by the user during the rear-camera recording. (Zheng, [Col 14, Line 13]; "which can obviate the problem of fake identity caused by inconsistency between the identity of the document-holding person and the identity proved by the identity document, thereby further increasing the identity verification capability.")
Regarding claim 11, the combination of Sensharma and Zheng:
further comprising, during recording of the video stream of the identification document from the at least one rear camera, recording a part of a body of the user in the video stream from the at least one rear camera together with the identification document.
(Sensharma, [Col. 2, Line 61]; “the rear facing camera 454-2 captures a second digital video data 456-2 of another part of the user, such as the user’s hand as illustrated in FIG. 4.”)
Examiner Note: Sensharma records a part of the user’s body, the user’s hand, in the rear-camera video stream. Combined with Zheng’s document-holding person, the rear-camera stream records a part of the user’s body together with the identification document, which reads on this.
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Sensharma and Zheng. The motivation for the combination is to record a part of the body of the user together with the identification document in the rear-camera stream. (Sensharma, [col. 3, Line 2]; "When the pulse rates can be correlated by processing logic, then processing logic can conclude that the image captured by the front-facing camera 454-1 and the rear-facing camera 454-2 are capturing live images of the same person.")
Regarding claim 12 (drawn to a system):
The rejection and proposed combination of Sensharma and Zheng explained in the rejection of method claim 1 render obvious the corresponding elements of the system of claim 12, because the same operations occur in the operation of the proposed combination as discussed above. Thus, the analysis presented above for claim 1 is equally applicable to claim 12.
Sensharma teaches:
A system:
(Sensharma, [col. 2 Line 31]; "the image capture mechanisms.")
A storage device:
(Sensharma, col. 4, ll. 24-28; “Memory 205 may also store user authentication engine 240 and one or more modules of the user authentication engine 240 ... to implement embodiments described herein.”)
(Sensharma, col. 4, ll. 62-64; “User authentication engine 240 processes the request and activates front-facing camera 227-1 and rear-facing camera 227-2.”)
Examiner Note: Sensharma's memory 205 storing the user authentication engine 240 provides the claimed storage device that stores the application, and that engine initiates the authentication and activates both cameras, which is the claimed application configured to initiate the verification process and to activate the cameras.
Regarding claims 12, 13, 15, 17, and 19-22, the rationale in the rejection of claims 1, 2, 4, 6, and 8-11 is provided herein. In addition, the method of claims 1, 2, 4, 6, and 8-11 corresponds to the system of claims 12, 13, 15, 17, and 19-22.
Claims 3, 5, 7, 14,16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Sensharma and Zheng further in view of Martin (US 10,346,990 B2)
Regarding claim 3, the combination of Sensharma and Zheng fails to teach:
wherein a synchronous jitter of selected facial and document features is found in the synchronized video frames for analyzing to verify liveness.
Martin teaches:
wherein a synchronous jitter of selected facial and document features is found in the synchronized video frames for analyzing to verify liveness.
(Martin, [Col 1 Line 33]; “determining a corneal reflection change of the object based on the determined first and second corneal reflections; comparing the determined corneal reflection change of the object to a motion associated with the first and second time points; and determining facial liveliness of the subject based on a result of the comparison.”)
(Martin,[Col 15 Line 13]; “pose of the face of consumer 202 is detected or estimated by analyzing one or more facial landmarks of consumer 202. The facial landmark features can include the nose, the forehead, the eyebrows, the eyes, the eye corners, the lips, and/or the mouths.”)
Examiner Note: The applicant’s specification defines the relevant analysis as comparing the jitter of base points extracted from the video streams, where the base points include facial anchor points and document anchor points. Martin teaches finding the position changes (jitter) of selected facial feature points across the sequential, time-synchronized video frames and analyzing those changes to verify liveness, which reads on the facial-feature portion of the limitation. Zheng already detects and recognizes document feature points in the rear-camera stream. It would have been obvious to one of ordinary skill in the art before the effective filing date to apply Martin’s feature-jitter liveness analysis to the document feature points detected by Zheng in the same manner it is applied to the facial feature points, because doing so applies a known anti-spoofing technique to a known set of detected feature points ready for the improvement, with a reasonable expectation of success, in order to confirm that the synchronized face and document streams are a single live real-time capture. The combined teaching therefore reads on finding a synchronous jitter of selected facial and document features in the synchronized video frames to verify liveness.
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Sensharma, Zheng, with Martin. The motivation for the combination is to find a synchronous jitter of selected facial and document features in the synchronized video frames to verify liveness. (Martin, [Col 1, Line 33]; "determining a corneal reflection change of the object based on the determined first and second corneal reflections; comparing the determined corneal reflection change of the object to a motion associated with the first and second time points; and determining facial liveliness of the subject based on a result of the comparison.")
Regarding claim 5, the combination of Sensharma and Zheng fails to teach:
wherein said at least one motion sensor provides an additional data stream that is used to check correlation with a synchronous jitter, ensuring the authenticity of an interaction with the user.
Martin teaches:
wherein said at least one motion sensor provides an additional data stream that is used to check correlation with a synchronous jitter, ensuring the authenticity of an interaction with the user.
(Martin, [Col 1 Line 41]; “comparing the determined corneal reflection change of the object to a motion includes correlating the determined corneal reflection change of the object to the motion; scoring a matching quality based on a result of the correlation; and comparing the scored matching quality to a predetermined threshold.”)
(Martin, [Col 12 Line 8]; “The motion (and/or orientation and/or relative position) of the camera device can be measured, e.g., by on-board accelerometer or gyroscope. The detected position changes in the corneal reflections of the camera device may be correlated to the predetermined or measured motion of the camera device.”)
Examiner Note: The applicant's specification describes this check as comparing the jitter of the feature points against the motion-sensor data, stating that "the algorithm checks the synchronization of the base points' jitter of the video stream with the sensor data" and that "a gyroscope and/or an accelerometer provide additional data streams that the system uses to check the correlation with the jitter of the video streams, ensuring the authenticity of the interaction with the user" (Data Synchronization and Analysis 15). Martin does this, using an accelerometer or gyroscope as an extra data stream and checking how well that sensor data lines up with the position changes of the detected features, which is the jitter, then scoring the match against a threshold to confirm the user is live. This reads on the claimed motion-sensor data stream checked for correlation with a synchronous jitter to confirm the authenticity of the interaction with the user. It would have been obvious to combine Martin with Sensharma and Zheng for the reasons given for claim 3.
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Sensharma, Zheng, with Martin. The motivation for the combination is to provide a motion-sensor data stream that is checked for correlation with the synchronous jitter to confirm the authenticity of the interaction with the user. (Martin, [Col 1, Line 41]; "comparing the determined corneal reflection change of the object to a motion includes correlating the determined corneal reflection change of the object to the motion; scoring a matching quality based on a result of the correlation; and comparing the scored matching quality to a predetermined threshold.")
Regarding claim 7, the combination of Sensharma, Zheng and Martin teaches:
further comprising performing an analysis to ensure that the video streams and the additional data stream from the at least one motion sensor are in sync for verification of a presence of the user and the identification document together in real-time.
(Zheng, detailed description; “The first verification result may be used to represent a probability at which the identity document image, the first background image, and the second background image are from the same photographing scene.”)
Examiner Note: The applicant’s specification at paragraph describes a “comprehensive analysis” that ensures the video streams and the motion sensor data are in sync to verify the presence of the user and the document together in real time. Zheng’s same-photographing-scene determination, based on the gyroscope position information collected when capturing the document, reads on this limitation when combined with the synchronized video streams of Sensharma.
Regarding claims 14, 16, and 18, the rationale in the rejection of claims 3, 5, and 7 is provided herein. In addition, the method of claims 3, 5, and 7 corresponds to the system of claims 14, 16, and 18.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIVANGI SARKAR whose telephone number is (571)272-7262. The examiner can normally be reached M-F: 7:30-5:00.
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, Emily Terrell can be reached at (571) 270-3717. 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.
/SHIVANGI SARKAR/Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666
Prosecution Insights