DETAILED ACTION
This communication is responsive to the Request for Continued Examination (RCE) filed on August 06, 2026. Claims 1-20 have been canceled and claims 21-40 have been added new. Claims 21-40 are pending and are directed towards BIOMETRIC AUTHENTICATION.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/06/2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/06/2026 was Acknowledge. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 25, 34-35 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 34 recites the limitation "wherein the composite identity evaluation and the strength of the current authentication ". There is insufficient antecedent basis for this limitation in the claim.
Claims 25 and 35 recite acronyms “GPS and RF” that are not defined.
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 (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 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.
Claim(s) 21-25, 29, 31-35 and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Khitrov et al. US 2019/0130168 A1 (hereinafter “Khitrov”) in view of Han et al. US 2019/0228248 A1 (hereinafter “Han”)
As per claims 21 and 31, Khitrov teaches a computer implemented method of authorizing access for access-control requests (A decision module 170 determines the resulting authentication score 175 that determines access. Khitrov, para [0050]), the method comprising:
managing, by a computing device, an authentication status based, at least in part, on passive authentication information, active authentication information, and liveness validation (combining knowledge based authentication, behavioral biometrics and facial recognition in order to create strong authentication and liveness detection, while keeping user experience to simply typing. Khitrov, para [0011]);
comparing, by the computing device, the passive authentication information to a stored user identifier associated with a user (The set of photos taken of the user as the login (and/or the password) information is being entered are compared by a facial recognition module 130 to a photo previously stored in a database. Khitrov, para [0046]);
comparing, by the computing device, the active authentication information to the stored user identifier associated with the user (prompting the user to type a text based login (or/and a password). Khitrov, para [0008])( A user 50 interacts with an electronic device 115 contains a login screen, with login text box where a user manually types into the login space his/her login information.. Khitrov, para [0044]);
generating a liveness evaluation of capture of the passive and active authentication information, wherein the liveness evaluation is based on authentication information that validates that capture of the passive and active authentication information is from a live user (a liveness module interconnected to the facial liveness module, the behavioral recognition module and the decision module, the liveness module for receiving the facial liveness score and the behavioral score and determining a liveness score. Khitrov, para [0024]),
receiving, by the computing device, a request to execute an access-controlled function (the liveness test may be used to determine liveness of a verification object for user verification that is further performed for user log-in, payment services, access control, and the like in various examples. Khitrov, para [0054]); and
granting, by the computing device, the request responsive to determining that a user identity probability satisfies a first identity probability threshold associated with the access-controlled function based on, at least in part, the authentication status (the first liveness test model, the second liveness test model may be a model configured to provide information, as a non-limiting example, a probability value and a feature value, to be used to determine liveness of the test object based on information input to the second liveness model. Khitrov, para [0075])( in a case of the first liveness test result and the second liveness test result being the first score and the second score, respectively, the liveness test apparatus may determine that the test object is a living object in response to a combination value obtained based on the first score and the second score being greater than a threshold value. Khitrov, para [0080]).
Khitrov teaches using neural network to process the authentication information and generate liveness score, but does not explicitly teach wherein generating the liveness evaluation includes an act of processing the authentication information that validates using at least one neural network, wherein the at least one neural network is configured to process the authentication information as input, and generating the liveness evaluation includes establishing a probability that authentication information is obtained from the live user.
However, Han teaches wherein generating the liveness evaluation includes an act of processing the authentication information that validates using at least one neural network, wherein the at least one neural network is configured to process the authentication information as input (perform the first liveness test using a neural network-based first liveness test model using the color image as an input; and perform the second liveness test using a neural network-based second liveness test model using the IR image as an input…The one or more processors may be further configured to: perform a third liveness test based on a combination of the color image and the IR image; and determine the liveness of the object based on a result of the first liveness test, a result of the second liveness test, and a result of the third liveness test. Han, para [0030-[0031]]), and generating the liveness evaluation includes establishing a probability that authentication information is obtained from the live user (Similarly to the first liveness test model, the second liveness test model may be a model configured to provide information, as a non-limiting example, a probability value and a feature value, to be used to determine liveness of the test object based on information input to the second liveness model. The second liveness test model may also be based on a DCNN, or a neural network of another structure different from the DCNN. As a non-limiting example, the second liveness test model may be a neural network-based liveness (or liveness and verification) model and configured so that, upon reading from a memory of its trained parameters by one or more processors, the one or more processors may implement the second liveness test model for an input image to calculate, determine, or extract a liveness score specific to the trained objective, e.g., a liveness score of an IR image. Han, para [0075]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the teaching of Khitrov in view of Han. One would be motivated to do so, to enhance the accuracy of the system using neural network analysis.
As per claims 22 and 32, Khitrov and Han teach the method of claims 21 and 31. Khitrov does not explicitly teach wherein the act of managing the authentication status is based at least in part on a time component, and the method includes an act of adjusting the authentication status over time.
However, Han teaches wherein the act of managing the authentication status is based at least in part on a time component, and the method includes an act of adjusting the authentication status over time (When a certain set of training data is input to the liveness test model, the initial internal parameters of the liveness test model may be adjusted so that the liveness test model may output a desired value corresponding to the input training data. Such a training process may be performed for each set of training data, and thus the initial or interim internal parameters may be adjusted throughout the process such that the liveness test model may gradually output a more desired value. Han, para [0062]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the system of Khitrov in view of Han. One would be motivated to do so, to enhance the accuracy of the system by adjusting the authentication over time.
As per claims 23 and 33, Khitrov and Han teach the method of claims 22 and 32, wherein the authentication status reflects a composite identity evaluation and a strength of current authentication (combining knowledge based authentication, behavioral biometrics and facial recognition in order to create strong authentication and liveness detection, while keeping user experience to simply typing. Khitrov, para [0011]) (This invention allows to drastically simplify user experience of the biometric multi-modal authentication process, while strengthening security of the login process. Khitrov, para [0032]).
As per claims 24 and 34, Khitrov and Han teach the method of claims 23 and 31, wherein the composite identity evaluation and the strength of the current authentication is based on any one or more or any combination of active and passive authentication information, and liveness validation (combining knowledge based authentication, behavioral biometrics and facial recognition in order to create strong authentication and liveness detection, while keeping user experience to simply typing. Khitrov, para [0011]) (This invention allows to drastically simplify user experience of the biometric multi-modal authentication process, while strengthening security of the login process. Khitrov, para [0032]).
As per claims 25 and 35, Khitrov and Han teach the method of claims 24 and 34, wherein the active and passive authentication information includes any one or more of user behavioral information, accelerometer data, GPS data, facial recognition data, voice data, heartbeat data, fingerprint data, proximity sensor data, atmospheric pressure, RF signal data, or gravity data (Behavioral information 125 can include biometrics such as keystroke dynamics, analysis of the data from an accelerometer, gyroscope, geo-location services, environment analysis including Bluetooth and Wi-Fi networks information. Khitrov, para [0045]).
As per claims 29 and 39, Khitrov and Han teach the method of claims 21 and 31. Khitrov does not explicitly teach wherein the at least one neural network is a consolidated neural network model, configured to process the passive authentication information and process the active authentication information.
However, Han teaches wherein the at least one neural network is a consolidated neural network model, configured to process the passive authentication information and process the active authentication information (The processor 1010 may then determine whether the test object is a living object based on the obtained first liveness test result and the obtained second liveness test result. In this example, a neural network-based first liveness test model using the color image as an input may be used to obtain the first liveness test result, and a neural network-based second liveness test model using the IR image as an input may be used to obtain the second liveness test result. In another example, as described with reference to FIG. 3, the processor 1010 may generate test data to perform the liveness test on the test object based on the color image and the IR image, and determine whether the test object is a living object based on an output result of a liveness test model using the generated test data as an input. Han, para [0125-0126]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the system of Khitrov in view of Han. One would be motivated to do so, to enhance the accuracy of the system by using neural network to process the authentication parameters.
Claim(s) 26-28, 30, 36-38 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Khitrov et al. US 2019/0130168 A1 (hereinafter “Khitrov”) in view of Han et al. US 2019/0228248 A1 (hereinafter “Han”) and further in view of Kursun US 2018/0232508 A1 (hereinafter “Kurusn”)
As per claims 26 and 36, Khitrov and Han teach the method of claims 21 and 31. Khitrov does not explicitly teach wherein the method comprises accessing the authentication status over time and determining whether a current authentication status is sufficient for a current access-control request.
However, Kursun teaches wherein the method comprises accessing the authentication status over time and determining whether a current authentication status is sufficient for a current access-control request (periodically re-deriving the multiple time-dependent authentication metrics based on incoming time-varying user-related data from at least some of the plurality of input sources, and periodically generating subsequent authentication signals in response to determining the periodically re-derived multiple time-dependent authentication metrics correspond to the authorized user. Kursun, para [0006])( one or more learning engines may have been trained to generate a confirmatory authentication signal in response to a stream of time-dependent metrics generated from time-dependent input signals. The authentication signal may also be a simple indication of whether the user has been authenticated for a particular request (at a particular time) sent by a particular remote device and/or application. The authentication signal may, in some implementations, have to be provided to the requesting device or application continuously. Kursun, para [0059]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the system of Khitrov in view of Kursun. One would be motivated to do so, to enhance the accuracy of the system by periodically checking the status of the authentication.
As per claims 27 and 37, Khitrov, Han and Kursun teach the method of claims 26 and 36. Khitrov does not explicitly teach wherein the method comprises capturing additional authentication information in response to determining that the current authentication status is insufficient.
However, Kursun teaches wherein the method comprises capturing additional authentication information in response to determining that the current authentication status is insufficient ( If the check of the metrics results in a determination that there is no detected problem, inconsistency, or anomaly, further metric measurements may be obtained for different time instances at which input data (from different sources) is collected. For example, at different time instances (t.sub.1, t.sub.2, . . . , t.sub.i) within some time window interval, different coefficients, defining the metrics, may be used, thus resulting in different metric functions (e.g., different linear sums) at different time instances (as illustrated in block 324). The particular coefficient values used at different time instances may be based on some deterministic relationship that can be used at the PPU 100 and/or at remote device (in order to confirm the correctness of authentications values generated at the PPU). In some embodiments, for different time windows (that may each include multiple time instances), the data streams used for computing/deriving metrics may be varied/swapped. Thus, for example, at block 326, at time intervals t.sub.swap, the metric set may be randomly (or pseudorandomly) changed, and the procedure 300 is repeated with a new set of incoming data streams. Kursun, para [0058]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the system of Khitrov in view of Kursun. One would be motivated to do so, to enhance the accuracy of the system by periodically checking the status of the authentication.
As per claims 28 and 38, Khitrov, Han and Kursun teach the method of claims 27 and 37, wherein the method comprises accepting from an entity managing an access-control request a definition of a threshold sufficiency (The Authentication Score S.authentication calculates as follows: … where T.liveness is a predefined threshold for Liveness Score. Khitrov, para [0051])( in a case of the first liveness test result and the second liveness test result being the first score and the second score, respectively, the liveness test apparatus may determine that the test object is a living object in response to a combination value obtained based on the first score and the second score being greater than a threshold value. Conversely, the liveness test apparatus may determine that the test object is a lifeless object in response to the combination value not being greater than the threshold value. Khitrov, para [0080]).
As per claims 30 and 40, Khitrov and Han teach the method of claims 21 and 31. Khitrov does not explicitly teach wherein the method comprises executing identification and authentication matching in an encrypted space so that plaintext features of any user are not stored
However, Kursun teaches wherein the method comprises executing identification and authentication matching in an encrypted space so that plaintext features of any user are not stored (The implementations described herein rely on dynamically changing composite biometrics, and run-time data, for secure and private authentication of users without requiring user name and passwords, and for secure and private processing of highly sensitive data for high security applications such as medical applications, banking, identification, etc. The implementations described herein also provide a novel technique to digitally stamp and tokenize data so that only authorized applications can process the data under specified rules. Kursun, para [0038]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the system of Khitrov in view of Kursun. One would be motivated to do so, to enhance the security of the authentication system.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHALID M ALMAGHAYREH whose telephone number is (571)272-0179. The examiner can normally be reached Monday - Thursday 8AM-5PM EST & Friday variable.
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, RUPAL DHARIA can be reached at (571)272-3880. 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.
Respectfully submitted
/KHALID M ALMAGHAYREH/ Primary Examiner, Art Unit 2492
