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 .
Claims 1-23 have been examined.
Drawings
The drawings filed on 02/14/2024 are acceptable for examination proceedings.
Specification
The specification filed on 02/14/2024 is acceptable for examination proceedings.
Priority
Application 18441492 filed 02/14/2024 is a Continuation in Part of 17262634 , filed 01/22/2021 ,now U.S. Patent # 11935348
17262634 is a National Stage entry of PCT/US2019/042939 , International Filing Date: 07/23/2019 PCT/US2019/042939 Claims Priority from Provisional Application 62702452 , filed 07/24/2018. Therefore, the effective filling date for the subject matter defined in the pending claims of this application is 07/24/2018.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/06/2024 and 06/27/2025. Accordingly, the information disclosure statement is being considered by the examiner.
Internet Communications
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Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-23 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zheng et al. (US Pub. No.: US 2017/0365259 A1, hereinafter refer as to Zheng).
As per claim 1, Zheng discloses process for granting or denying a user access to a system using authentication data (fig 3 illustrate a processing flow performed by the identity authentication system, for example), the process comprising the steps of:
a. receiving from a device in the possession of the user a unique identifier for the system (fig. 3 furthermore para. 0057 discloses Step S101 : the requester A' completes the login by inputting the correct user ID and login password, and the request receiving module 11 receives the identity authentication request that the requester A' sends to the identity authentication system 1 through the client 2, for example), wherein the unique identifier for the system is displayed at or near an access point and the unique identifier for the system changes from time to time (figure 3 step (S101); it is implicit that the identity of the server or application to be accessed is included in the access request, for example);
b. receiving from the device in the possession of the user a unique identifier associated with the user (para. 0057 discloses the requester A' completes the login by inputting the correct user ID and login password, and the request receiving module 11 receives the identity authentication request that the requester A' sends to the identity authentication system 1 through the client 2, for example);
c. verifying that the user is authorized to access the system; d. transmitting a request for authentication data to the device in the possession of the user (para.0057: Step S101 : the requester A' completes the login by inputting the correct user ID and login password, and the request receiving module 11 receives the identity authentication request that the requester A' sends to the identity authentication system 1 through the client 2, and establishes the interaction between the requester A' and the identity authentication system, for example);
e. receiving from the device in the possession of the user a sample of authentication data of the kind requested in the transmitted request (para.0058: Step S102: the dynamic security code generating module 13 randomly selects several characters from the preset global character codebook to constitute a dynamic security code, and transmits the dynamic security code to the client 2 of the requester A, for example);
f. comparing one or more attributes of the received sample with one or more attributes of a custom authentication print for a user, wherein the custom authentication print is stored and used as a reference to authenticate the user (paras.0060-0064 discloses Step S104: the identity authentication module 14 extracts the voiceprint model of the user A from the voiceprint model storage unit 15 Step S107: the identity authentication module 14 calculates the comprehensive confidence CL of the identity of the requester A', for example); and
g. granting or denying the user access to the system based upon a correlation between the one or more attributes of the received sample and the one or more expected predicted attributes of the custom authentication print (para. 0065: Step S109: since the comprehensive confidence CL of the identity is greater than or equal to the preset confidence threshold, the identity authentication result is that the requester A' is the user A of the server; the requester A' is allowed to access the server as the user A to operate figure 3(S109), for example).
As per claims 2-4, 6 and 10, Zheng discloses wherein the unique identifier for the system is received from a list of pre-defined access points (paras.0060-0064, Step S104 discloses the identity authentication module 14 extracts the voiceprint model of the user A from the voiceprint model storage unit 15 Step S107: the identity authentication module 14 calculates the comprehensive confidence CL of the identity of the requester A', for example), and wherein the user selects a desired pre-defined access point corresponding to the unique identifier for the system from the list of pre-defined access points (fig. 3, furthermore para. 0057 discloses Step S101 : the requester A' completes the login by inputting the correct user ID and login password, and the request receiving module 11 receives the identity authentication request that the requester A' sends to the identity authentication system 1 through the client 2, for example),
As per claim 5, Zheng discloses wherein the authentication data is voice data, and wherein the user is transmitted a randomly generated passcode that the user records in a speech sample, wherein the speech sample is then compared to a custom authentication print comprising data retrieved from an earlier speech sample provided by the user (para.0058, discloses Step S102: the dynamic security code generating module 13 randomly selects several characters from the preset global character codebook to constitute a dynamic security code, and transmits the dynamic security code to the client 2 of the requester A, for example), and wherein the speech sample generated on the device by the user speaking the passcode is an audio file, waveform, or mathematical representation generated based on the user's voice articulating the passcode (fig. 3 , and furthermore 0056-0065, for example).
As per claims 7-9, Zheng discloses wherein the randomly generated passcode is uniquely generated as part of a verification session (para. 0047 discloses the comprehensive confidence CL of the identity in the present invention incorporates the security code content confidence average CL.sub.1 as well as the requester identity speech confidence average CL.sub.2. Thus, a system and method for double identity authentication that can not only verify the dynamic security code content, but also verify the speaker's voiceprint are provided, for example).
As per claim 11, Zheng discloses the step of the mobile application running the user through a verification process after the unique biometric print for the user is built to ensure the on-boarding process was successful (para. 0006 discloses the existing biometric recognition technologies achieve recognition and authentication usually by using the template matching method in the field of pattern recognition, that is, a model reserved by a user is compared with the actual model to be authenticated, and if the similarity of the two models reaches a preset threshold, the authentication passes, otherwise the authentication fails, for example).
As per claim 12, Zheng discloses wherein the system comprises a terminal, website, VPN login, or physical access control point (para. 0132 , discloses the “client” and the “server” are the user's mobile phone and the server of the mobile service provider, respectively; for the latter case, for example, the “client” is the user's mobile phone, and the “server” is the hardware and/or software installed in the user's mobile phone for implementing the identity authentication function. For example, in the case where the present invention is applied to a mobile phone to realize screen-unlock function of the mobile phone, the “client” and the “server” are different logical processing units in the same physical device (mobile phone), for example).
As per claim 13, Zheng discloses wherein the unique identifier for the system is provided in a barcode, in a URL, RFID or any digital identifier or by accessing a link on a webpage (fig. 10 depicted 10 is a schematic diagram of the spatial distribution of the digital model, for example).
As per claim 14, Zheng discloses wherein the custom authentication print for the user is created during an on-boarding process, wherein the user provides an initial sample of the kind of authentication data requested, and wherein the data is then processed to extract the one or more attributes from the data to create the custom authentication print (paras.0060-0064 discloses Step S104: the identity authentication module 14 extracts the voiceprint model of the user A from the voiceprint model storage unit 15 Step S107: the identity authentication module 14 calculates the comprehensive confidence CL of the identity of the requester A', for example).
As per claim 15, Zheng discloses comprising a step of continuously monitoring one or more aspects of the user's use of the system and revoking access to the system if the one or more aspects provide a trigger to do so (para. 0054 discloses the identity authentication module 14 judges whether or not the calculated comprehensive confidence CL of the identity is greater than or equal to the preset confidence threshold. If the comprehensive confidence CL of the identity is greater than or equal to the preset confidence threshold, the identity authentication result is that the requester is the user of the server, the identity authentication result is sent back to the client 2, and the requester is allowed to access the server to operate. Conversely, the identity authentication result is that the requester is not the user of the server, the identity authentication result is sent back to the client 2, and the requester is refused to access the server to operate, for example).
As per claim 16, Zheng discloses a process for preventing fraudulent access to a secure system (fig 3 illustrate a processing flow performed by the identity authentication system, for example), the process comprising the steps of: a. continuously monitoring one or more behaviors of a user while the user is utilizing a mobile application or a device (para. 0054 discloses the identity authentication module 14 judges whether or not the calculated comprehensive confidence CL of the identity is greater than or equal to the preset confidence threshold. If the comprehensive confidence CL of the identity is greater than or equal to the preset confidence threshold, the identity authentication result is that the requester is the user of the server, the identity authentication result is sent back to the client 2, and the requester is allowed to access the server to operate. Conversely, the identity authentication result is that the requester is not the user of the server, the identity authentication result is sent back to the client 2, and the requester is refused to access the server to operate, for example);
b. periodically sampling the one or more behaviors of the user (para. 0054 discloses the identity authentication module 14 judges whether or not the calculated comprehensive confidence CL of the identity is greater than or equal to the preset confidence threshold. If the comprehensive confidence CL of the identity is greater than or equal to the preset confidence threshold, the identity authentication result is that the requester is the user of the server, the identity authentication result is sent back to the client 2, and the requester is allowed to access the server to operate. Conversely, the identity authentication result is that the requester is not the user of the server, the identity authentication result is sent back to the client 2, and the requester is refused to access the server to operate, for example);
c. comparing one or more attributes of the samples of the one or more behaviors of the user to one or more attributes of a stored custom authentication print for the user (para. 0006 discloses the existing biometric recognition technologies achieve recognition and authentication usually by using the template matching method in the field of pattern recognition, that is, a model reserved by a user is compared with the actual model to be authenticated, and if the similarity of the two models reaches a preset threshold, the authentication passes, otherwise the authentication fails, for example);
d. determining a percentage match between the one or more attributes of the samples of the one or more behaviors of the user and the one or more attributes of a stored custom authentication print for the user (paras.0060-0064 discloses Step S104: the identity authentication module 14 extracts the voiceprint model of the user A from the voiceprint model storage unit 15 Step S107: the identity authentication module 14 calculates the comprehensive confidence CL of the identity of the requester A', for example); and
e. implementing a feedback response based on whether the percentage match corresponds to a pre-selected confidence level (paras.0060-0064 discloses Step S104: the identity authentication module 14 extracts the voiceprint model of the user A from the voiceprint model storage unit 15 Step S107: the identity authentication module 14 calculates the comprehensive confidence CL of the identity of the requester A', for example).
As per claim 17, Zheng discloses wherein the feedback response comprises: a. terminating the user's access to the secured system if the percentage match is below the pre-selected confidence level (paras.0060-0064 discloses Step S104: the identity authentication module 14 extracts the voiceprint model of the user A from the voiceprint model storage unit 15 Step S107: the identity authentication module 14 calculates the comprehensive confidence CL of the identity of the requester A', for example);
b. prompting the user to re-authenticate themselves if the percentage match is below the pre-selected confidence level; or c. no further action if the percentage match is above the pre-selected confidence level (paras.0060-0064 discloses Step S104: the identity authentication module 14 extracts the voiceprint model of the user A from the voiceprint model storage unit 15 Step S107: the identity authentication module 14 calculates the comprehensive confidence CL of the identity of the requester A', for example).
As per claim 18, Zheng discloses wherein the pre-selected confidence level is selected from a value of 75% and above, 80% and above, 85% and above, 90% and above, 95% and above, and 100% (para. 0054 discloses the identity authentication module 14 judges whether or not the calculated comprehensive confidence CL of the identity is greater than or equal to the preset confidence threshold. If the comprehensive confidence CL of the identity is greater than or equal to the preset confidence threshold, the identity authentication result is that the requester is the user of the server, the identity authentication result is sent back to the client 2, and the requester is allowed to access the server to operate. Conversely, the identity authentication result is that the requester is not the user of the server, the identity authentication result is sent back to the client 2, and the requester is refused to access the server to operate, for example).
As per claim 19, Zheng discloses a process for averting crises while a user is accessing a secure system (fig 3 illustrate a processing flow performed by the identity authentication system, for example), the process comprising the steps of: a. continuously monitoring one or more behaviors of a user while the user is utilizing a mobile application or a device (para. 0054 discloses the identity authentication module 14 judges whether or not the calculated comprehensive confidence CL of the identity is greater than or equal to the preset confidence threshold. If the comprehensive confidence CL of the identity is greater than or equal to the preset confidence threshold, the identity authentication result is that the requester is the user of the server, the identity authentication result is sent back to the client 2, and the requester is allowed to access the server to operate. Conversely, the identity authentication result is that the requester is not the user of the server, the identity authentication result is sent back to the client 2, and the requester is refused to access the server to operate, for example); b. periodically sampling the one or more behaviors of the user (para. 0054 discloses the identity authentication module 14 judges whether or not the calculated comprehensive confidence CL of the identity is greater than or equal to the preset confidence threshold. If the comprehensive confidence CL of the identity is greater than or equal to the preset confidence threshold, the identity authentication result is that the requester is the user of the server, the identity authentication result is sent back to the client 2, and the requester is allowed to access the server to operate. Conversely, the identity authentication result is that the requester is not the user of the server, the identity authentication result is sent back to the client 2, and the requester is refused to access the server to operate, for example); c. comparing one or more attributes of the samples of the one or more behaviors of the user to one or more attributes of a stored custom authentication print for the user (para. 0006 discloses the existing biometric recognition technologies achieve recognition and authentication usually by using the template matching method in the field of pattern recognition, that is, a model reserved by a user is compared with the actual model to be authenticated, and if the similarity of the two models reaches a preset threshold, the authentication passes, otherwise the authentication fails, for example);
d. predicting if the comparison between the one or more attributes of the samples of the one or more behaviors of the user and the one or more attributes of a stored custom authentication print for the user indicate an ongoing crisis (para. 0065: Step S109: since the comprehensive confidence CL of the identity is greater than or equal to the preset confidence threshold, the identity authentication result is that the requester A' is the user A of the server; the requester A' is allowed to access the server as the user A to operate figure 3(S109), for example); and
e. implementing a feedback response based on whether an ongoing crisis is predicted (paras.0060-0064 discloses Step S104: the identity authentication module 14 extracts the voiceprint model of the user A from the voiceprint model storage unit 15 Step S107: the identity authentication module 14 calculates the comprehensive confidence CL of the identity of the requester A', for example).
As per claim 20, Zheng discloses wherein a crisis is predicted if the sampled behavior of the user displays erratic behaviors when compared to the stored custom authentication print, wherein erratic behaviors comprise exerting more pressure on a screen or a touch pad compared to stored attributes, scrolling on a touch pad more frantically compared to stored attributes, committing more errors when using a keypad compared to stored attributes, and using less keystrokes when using the keypad compared to stored attributes (paras.0060-0064 discloses Step S104: the identity authentication module 14 extracts the voiceprint model of the user A from the voiceprint model storage unit 15 Step S107: the identity authentication module 14 calculates the comprehensive confidence CL of the identity of the requester A', for example).
As per claim 21, Zheng discloses wherein the feedback response comprises: (paras.0060-0064 discloses Step S104: the identity authentication module 14 extracts the voiceprint model of the user A from the voiceprint model storage unit 15 Step S107: the identity authentication module 14 calculates the comprehensive confidence CL of the identity of the requester A', for example); a. terminating the user's access to the secured system if a crisis is predicted (paras.0060-0064 discloses Step S104: the identity authentication module 14 extracts the voiceprint model of the user A from the voiceprint model storage unit 15 Step S107: the identity authentication module 14 calculates the comprehensive confidence CL of the identity of the requester A', for example); b. prompting the user to re-authenticate themselves if a crisis is predicted (paras.0060-0064 discloses Step S104: the identity authentication module 14 extracts the voiceprint model of the user A from the voiceprint model storage unit 15 Step S107: the identity authentication module 14 calculates the comprehensive confidence CL of the identity of the requester A', for example); or
c. taking no further action if a crisis is not predicted (paras.0060-0064 discloses Step S104: the identity authentication module 14 extracts the voiceprint model of the user A from the voiceprint model storage unit 15 Step S107: the identity authentication module 14 calculates the comprehensive confidence CL of the identity of the requester A', for example).
As per claim 22, Zheng discloses wherein the user that is prompted to re-authenticate themselves may be requested to re-authenticate themselves by providing a facial recognition scan, providing a scan of the whole room in which the user is located, providing a voice recognition scan, or a combination thereof (para. 0006 discloses the existing biometric recognition technologies achieve recognition and authentication usually by using the template matching method in the field of pattern recognition, that is, a model reserved by a user is compared with the actual model to be authenticated, and if the similarity of the two models reaches a preset threshold, the authentication passes, otherwise the authentication fails, for example).
As per claim 23, Zheng discloses wherein the feedback response further comprises terminating the user's access to the secured system and contacting the authorities if the re- authentication indicates the user is in danger (paras.0060-0064 discloses Step S104: the identity authentication module 14 extracts the voiceprint model of the user A from the voiceprint model storage unit 15 Step S107: the identity authentication module 14 calculates the comprehensive confidence CL of the identity of the requester A', for example).
Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Rao et al. (US Patent 11,736,447) provides systems, apparatuses, methods, and computer program products are disclosed for providing passive continuous session authentication. An example method includes authenticating a session for a user of a client device. The example method further includes generating a video data structure comprising a video stream, deriving a set of biometric attributes of the user from the video stream, synchronizing temporal information with the set of biometric attributes derived from the video stream, generating an aggregated behavioral attribute data structure comprised of the video data structure and the set of biometric attributes derived from the video stream synchronized to the temporal information, and re-authenticating, by the session authentication circuitry at a second time after the first time, the session for the user of the client device based on the aggregated behavioral attribute data structure.
Skerpac (US Pub. No.: US 2013/0132091 A1) provide an n-dimensional biometric security system as well as a method of identifying and validating a user through the use of a automated random one-time passphrase generation. The use of tailored templates to generate one-time phase phrase text as well as the use of update subscriptions of templates ensures a high level of security. A verification session preferably uses short, text-independent one-time pass phrases and secure audio tokens with master audio generated from an internal text-to-speech security processor. An automated enrollment process may be implemented in an ongoing and seamless fashion with a user's interactions with the system.
Gelardi et al. (US Pub. No.: US 2022/0261466 A1) provides computer implemented methods for enrolling a user as an authenticated user of a computing device and for authenticating a user of a computing device are provided. The methods make use of behavioral biometrics to determine a set of shares that represent a secret credential according to a secret sharing scheme. The set of shares is initially determined when the user is enrolled based on typical measurements of the user's behavioral biometrics and authentication data indicating how to generate the set of shares from a user's behavioral biometrics is generated. When authenticating the user, the computing device can generate the set of shares based on the authentication data and measurements of the current user's behavioral biometrics. The computing device can use the generated set of shares to recreate a copy of the secret credential with which to authenticate the user.
Obaidi (US Pub. No.: US 2019/0377853 A1) provides the use of user-behavior-based adaptive authentication may provide more secure user authentication without sacrificing user convenience. A baseline behavior pattern of a user may be identified using a machine learning algorithm based on user behavior data collected by one or more applications on at least one user device of the particular user for a predetermined time period. One or more events that deviate from the baseline behavior pattern of the user during a specific time period are then detected using the machine learning algorithm based on new user behavior data of the user obtained during the specific time period. In response to receiving a request from an application to authenticate a particular user for access or continued access to a resource, an authentication question and a correct answer for the authentication question are generated based on a detail of an event that deviates from the baseline behavior pattern.
Conclusion
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A.G.
July 9, 2025
/ABIY GETACHEW/ Primary Examiner, Art Unit 2434