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
This Office Action is in response to the Amendment filed on 06/25/2026. claims 1, 4, and 10 have been amended; Claims 1 and 10 are independent claims. Claims 1-10 have been examined and are pending. This Action is made FINAL.
Response to Arguments
The objection to the drawing of figure 6 is maintained. The drawing (Figure 6.) is objected to under 37 CFR 1.83(a) because they fail to show the necessary labelling or explanations of figures, parts or steps as described in the specification. For example, figure.6 does not label any components, and/or do not briefly explain any method steps and/or components. In other words, these drawings lack the necessary structural detail that is essential for a proper understanding of the disclosed invention, which should have been shown in the drawing. MPEP § 608.02(d).
The objection to claims 1 and 10 is withdrawn as the claims have been amended.
Applicant’ arguments in the instant Amendment, filed on 06/25/2026, with respect to limitations listed below, have been fully considered but they are not persuasive.
a. Applicants argue: “The present claims are materially different. The claimed behavioral biometric model is not merely a stored set of biometric data or a template to be matched against a captured biometric sample. Rather, the claimed behavioral biometric model is a computational element configured to receive, as input, values of characteristic parameters of user behavior during interaction actions with an interaction device and to generate, as output, a score representative of a probability …” (Applicant Remarks/Arguments, pages 12-13)
The Examiner respectfully disagrees with the applicant.
Applicant's argument is not persuasive because Malachi is not relied upon merely for a stored set of biometric data. Malachi par. [0077] expressly teaches generating and storing “reference models for all the users” and matching biometric samples with the reference models to generate genuine and impostor scores. Malachi further teaches behavioral characteristic information, including dynamic-signature parameters and keystroke timing information (pars. [0110], [0112]). Accordingly, Malachi teaches or suggests the claimed behavioral biometric model receiving behavioral characteristic information and generating a corresponding biometric matching score.
b. Applicants argue: “Accordingly, Malachi does not teach or suggest at least the claimed behavioral biometric models of the reference users, the claimed behavioral biometric model of the legitimate user, the claimed application of those models to the obtained characteristic parameter values…” (Applicant Remarks/Arguments, pages 13).
The Examiner respectfully disagrees with the applicant.
Applicant's argument is not persuasive because the rejection does not rely upon Malachi alone for respectively applying each reference-user model. Malachi teaches behavioral biometric reference models, behavioral characteristic information, and matching biometric samples with reference models to generate scores (par. [0077]), while Beigi is relied upon for applying test biometric data to the target model and respective competing/reference models to obtain corresponding scores. Thus, the combined teachings of Malachi and Beigi teach or suggest applying the obtained behavioral information to the legitimate-user model and respective reference-user models.
c. Applicants argue: Malachi does not teach “the claimed authentication decision based on both the first score and the second scores.” (Applicant Remarks/Arguments, pages 13).
The Examiner respectfully disagrees with the applicant.
Applicant's argument is not persuasive because it addresses Malachi individually rather than the combined teachings relied upon in the rejection. Malachi teaches determining whether a requesting individual is authenticated based on biometric comparison with stored user information, while Beigi teaches verification using both a target-model score and scores obtained from competing/reference models. Accordingly, Malachi in combination with Beigi teaches or suggests determining the authentication decision based on both the first score and the second scores, as recited in claim 1.
d. Applicants argue: “Beigi does not cure these deficiencies.”, “Beigi does not disclose the claimed behavioral biometric models configured to receive characteristic parameter values of behavior during interaction actions with an interaction device…”, “At most, Beigi teaches using competing biometric models in the context of speaker verification.” (Applicant Remarks/Arguments, pages 13-14).
The Examiner respectfully disagrees with the applicant.
Applicant's argument is not persuasive because it addresses Beigi individually rather than the references as combined. Beigi is relied upon for its teaching of applying test biometric information to a target model and respective competing/reference models and using the resulting scores in verification, while Malachi supplies the behavioral-biometric framework, behavioral characteristic information, reference models, and biometric scoring. The rejection therefore does not require Beigi alone to disclose all limitations of claim 1.
e. Applicants argue:” The combination proposed in the Office Action would also require a substantial reconstruction of Malachi [] Thus, the cited references do not provide a persuasive reason to modify Malachi in the manner required to arrive at the claimed invention.” (Applicant Remarks/Arguments, pages 13-14).
The Examiner respectfully disagrees with the applicant.
Applicant's argument is not persuasive. Malachi itself teaches generating and storing reference models for users and matching biometric samples with reference models to generate genuine and impostor scores (par. 0077]). Thus, incorporating Beigi's teaching of evaluating the test biometric against respective competing/reference models constitutes a predictable use of additional reference-model comparisons within Malachi's existing biometric matching framework, rather than a substantial reconstruction or change in Malachi's principle of operation
Drawing Objections
The drawing (Figure 6.) is objected to under 37 CFR 1.83(a) because they fail to show the necessary labelling or explanations of figures, parts or steps as described in the specification. For example, figure 6 does not label any components, and/or do not briefly explain any method steps and/or components. In other words, these drawings lack the necessary structural detail that is essential for a proper understanding of the disclosed invention, which should have been shown in the drawing. MPEP § 608.02(d). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
Claims 1-3, 7-8, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Malachi (“Malachi,” US 2016/0269411) in view of Beigi (“Beigi,” US 10,042,993)
Regarding claim 1, Malachi teaches a method comprising
obtaining behavioral biometric models of reference users (Malachi: ABSTRACT, During verification phase, a biometric sensor captures the requesting individual biometric template and sends it to the server, that compares the requesting individual biometric template to a formerly captured stored biometric template; par. [0077] states: “First, reference models for all the users are generated and stored in the model database.” Second, some samples are matched with reference models to generate the genuine and impostor scores and calculate the threshold, and then is the testing step; par. [0078] biometric information is captured, features are extracted, and a template is generated as a synthesis of the relevant extracted characteristics), wherein each behavioral biometric model of a reference user among the reference users is configured to receive, as input, values of characteristic parameters of the behavior of the reference user during interaction actions with an interaction device (Malachi: par. [0077], samples are “matched with reference models”; par. [0078], extracted biometric features may comprise a vector of numbers representing relevant characteristics; par. [0110], dynamic signature information includes x(t), y(t), pressure p(t), azimuth, inclination and pen up/down acquired using a digitizing tablet, PDA or smartphone; par. [0112], keystroke dynamics comprises timing information describing when keys are pressed and released while a user types using a keyboard or keypad) and to generate, as output, a score representative of a probability that the behavior represented by the input characteristic parameter values is that of the reference user (Malachi: par. [0077], samples are “matched with reference models to generate the genuine and impostor scores” and a threshold is calculated; par. [0079], the matching phase compares an obtained template with existing templates and estimates the distance between them using an algorithm);
obtaining a behavioral biometric model of a legitimate user, wherein the behavioral biometric model of the legitimate user is configured to receive, as input, values of characteristic parameters of the behavior of the legitimate user during interaction actions with an interaction device and to generate, as output, a score representative of a probability that the behavior represented by the input characteristic parameter values is that of the legitimate user (Malachi: par. [0077], a captured biometric is compared with a specific stored template to verify that the individual is the person he or she claims to be, and samples are matched with stored models to generate genuine and impostor scores; par. [0078], biometric information is captured during enrollment, features are extracted, and a template representing the relevant characteristics is created and stored for subsequent comparison; par. [0110], dynamic signature recognition is a behavioral biometric and includes x(t), y(t), pressure p(t), azimuth, inclination and pen-up/down values acquired using a digitizing tablet, PDA or smartphone; par. [0112], keystroke dynamics comprises timing information describing when keys are pressed and released while the user types using a keyboard/keypad, with the user's keystroke rhythms being measured to develop a unique biometric template of the user's typing pattern for future authentication.);
obtaining values of characteristic parameters of a behavior of a user to be authenticated, wherein the obtained values are determined from events produced by interaction actions with an application system carried out by means of an interaction device by the user to be authenticated (Malachi: ABSTRACT, During verification phase, a biometric sensor captures the requesting individual biometric template and sends it to the server, that compares the requesting individual biometric template to a formerly captured stored biometric template; Malachi: par. [0112] Keystroke dynamics, keystroke biometrics or typing dynamics, is the detailed timing information that describes exactly when each key was pressed and when it was released; par. 0110 tablet, PDA, smartphone; par. 0112, keyboard).
determining a first score by applying the behavioral biometric model of the legitimate user to the obtained values of the characteristic parameters (Malachi: par. [0077], some samples are matched with reference models to generate the genuine and impostor scores and calculate the threshold, and then is the testing step; ABSTRACT, During verification phase, a biometric sensor captures the requesting individual biometric template and sends it to the server, that compares the requesting individual biometric template to a formerly captured stored biometric template; par. 0112, the keystroke rhythms of a user are measured to develop a unique biometric template of the user's typing pattern for future authentication.)
determining second scores the behavioral biometric models of the reference users to the obtained values of the characteristic parameters (Malachi: par. 0077, reference models for all the users are generated and stored in the model database. Second, some samples are matched with reference models to generate the genuine and impostor scores and calculate the threshold, and then is the testing step);
determining a decision to authenticate the user to be authenticated as being the legitimate user on the basis of the first score (Malachi: ABSTRACT, “Upon detecting a match, the identity is considered verified,”; par. 0077, generate the genuine and impostor scores and calculate the threshold, and then is the testing step; abstract, “compares the requesting individual biometric template to a formerly captured stored biometric template.).
Malachi teaches “determining second scores the behavioral biometric models of the reference users to the obtained values of the characteristic parameter”, “determining a decision to authenticate the user to be authenticated as being the legitimate user on the basis of the first score” respectively but does not explicitly disclose “respectively applying each of the behavioral biometric models of the reference users.” and “the second scores.”
However, in an analogous art, Beigi teaches each of the behavioral biometric models of reference users to produce individually traceable second scores (Beigi: Col. 21, lines 22-54, at section 1.3.1.1 Competing Biometric models , "comparison against the target speaker's model is not enough. There is always a need for contrast when making a comparison. Therefore, one or more competing models should also be evaluated to come to a verification decision. The competing model may be a so-called (universal) background model or one or more cohort models. The final decision is made by assessing whether the speech sample given at the time of verification is closer to the target model or to the competing model(s). If it is closer to the target model, then the user is verified and otherwise rejected [..] the state of the art sometimes uses cohorts of the speaker being tested, according to the user ID which is provided by the user [] the cohort [] are a small set of speakers in the database who have similar traits to the target speaker [] in reality, there is more than one comparison for speaker verification — comparison against the target model and the competing model(s).").
determining a decision to authenticate on the basis of both the first score and the second scores (Beigi: Col. 21, lines 22-54, , at section 1.3.1.1 Competing Biometric models; "The final decision is made by assessing whether the speech sample given at the time of verification is closer to the target model or to the competing model(s)." in combination Malachi: Abstract, par. 0077).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Beigi with the method and system of Malachi to include determining second scores by respectively applying each of the behavioral biometric models of the reference users to the obtained values of the characteristic parameters and determining a decision to authenticate the user to be authenticated as being the legitimate user on the basis of the first score and the second scores. One would have been motivated to combine because, as Beigi expressly teaches, “comparison against the target speaker's model is not enough. There is always a need for contrast when making a comparison. Therefore, one or more competing models should also be evaluated to come to a verification decision” (Beigi: Section 1.3.1.1, Competing Biometric Models, Col. 21, lines 24-35). The combination involves only the predictable application of Beigi's known individual competing model architecture to Malachi's existing behavioral biometric reference model matching framework, yielding the predictable result of individually traceable per-user impostor scores that provide the contrast Beigi identifies as necessary for a reliable verification decision. The combination requires no change in the underlying function of either reference. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007) (obvious to combine known elements using known methods to yield predictable results).
Regarding claim 2, the combination of Malachi and Beigi teaches the method as claimed in claim 1. The combination of Malachi and Beigi further teaches wherein the first score represents a probability that the user is the legitimate user (Malachi: par. [0077]: "reference models for all the users are generated and stored in the model database. Second, some samples are matched with reference models to generate the genuine and impostor scores.").
Regarding claim 3, the combination of Malachi and Beigi teaches the method as claimed in claim 1. The combination of Malachi and Beigi further teaches wherein each second score represents a probability that the user is a reference user associated with the behavioral model used to generate the considered second score (Malachi: par. [0077]: "reference models for all the users are generated and stored in the model database. Second, some samples are matched with reference models to generate the genuine and impostor scores; Beigi: Section 1.3.1.1, Competing Biometric Models, Col. 21, lines 24-35, "the cohort... are a small set of speakers in the database who have similar traits to the target speaker".).
Regarding claim 5, the combination of Malachi and Beigi teaches the method as claimed in claim 1. The combination of Malachi and Beigi further teaches, wherein
the decision to authenticate is negative if the first score is below an authentication threshold (Malachi: [0077], "generate the genuine and impostor scores and calculate the threshold, and then is the testing step."; Abstract “Upon detecting a match, the identity is considered verified.”);
the decision to authenticate is negative if the first score is above an authentication threshold and at least one of the second scores is above the authentication threshold (Malachi: par. [0077]: "some samples are matched with reference models to generate the genuine and impostor scores and calculate the threshold, and then is the testing step."; Beigi: Col. 21, lines 22-54, 1.3.1.1: "The final decision is made by assessing whether the speech sample given at the time of verification is closer to the target model or to the competing model(s)." [] "comparison against the target speaker's model is not enough. There is always a need for contrast when making a comparison. Therefore, one or more competing models should also be evaluated to come to a verification decision."); and
the decision to authenticate is positive if the first score is above an authentication threshold and all of the second scores are below the authentication threshold (Malachi: par. [0077], "some samples are matched with reference models to generate the genuine and impostor scores and calculate the threshold, and then is the testing step; Abstract, “Upon detecting a match, the identity is considered verified.”; Beigi: Col. 21, lines 22-54, 1.3.1.1: "The final decision is made by assessing whether the speech sample given at the time of verification is closer to the target model or to the competing model(s)." [] "comparison against the target speaker's model is not enough. There is always a need for contrast when making a comparison. Therefore, one or more competing models should also be evaluated to come to a verification decision.").
Regarding claim 6, the combination of Malachi and Beigi teaches the method as claimed in claim 1. The combination of Malachi and Beigi further teaches, where
the decision to authenticate is negative if the first score is below an authentication threshold (Malachi: [0077], "generate the genuine and impostor scores and calculate the threshold, and then is the testing step."; Abstract “Upon detecting a match, the identity is considered verified.”);
the decision to authenticate is positive if the first score is above an authentication threshold and fewer than N second scores are above the authentication threshold (Malachi: par. [0077]: "some samples are matched with reference models to generate the genuine and impostor scores and calculate the threshold, and then is the testing step; Abstract “Upon detecting a match, the identity is considered verified.”; Beigi: Col. 21, lines 22-54, 1.3.1.1, "The final decision is made by assessing whether the speech sample given at the time of verification is closer to the target model or to the competing model(s).");
the decision to authenticate is negative if the first score is above an authentication threshold (Malachi: par. [0077], "some samples are matched with reference models to generate the genuine and impostor scores and calculate the threshold, and then is the testing step) and at least N or more second scores are above the authentication threshold (Beigi: Col. 21, lines 22-54, 1.3.1.1, "The final decision is made by assessing whether the speech sample given at the time of verification is closer to the target model or to the competing model(s).");
N being an integer strictly greater than 1 (Beigi: Col. 21, lines 22-54, 1.3.1.1, "the cohort [...] are a small set of speakers in the database who have similar traits to the target speaker." Under BRI, “small set of speakers” therefore expressly and necessarily establishes that the cohort contains at least 2 reference users — N ≥ 2 (i.e. N "strictly greater than 1") and smaller than or equal to 10 (Beigi: Col. 21, lines 22-54, 1.3.1.1, "the cohort [...] are a small set of speakers in the database who have similar traits to the target speaker." Under BRI, "N smaller than or equal to 10" constitutes a routine engineering design choice within the range of obvious alternatives for a "small set" of reference users as taught by Beigi. selecting N ≤ 10 as the specific upper bound of a "small set" of reference users is a routine engineering design choice that yields only predictable results).
Regarding claim 7, the combination of Malachi and Beigi teaches the method as claimed in claim 1. The combination of Malachi and Beigi further teaches wherein the reference users are users different from the legitimate user (Malachi: paragraph [0077]: "reference models for all the users are generated and stored in the model database. Second, some samples are matched with reference models to generate the genuine and impostor scores."; Beigi: Col. 21, lines 22-32, at Section 1.3.1.1: "the cohort is selected based on the user ID which he/she provides[...] the cohort [...] are a small set of speakers in the database who have similar traits to the target speaker.").
Regarding claim 8, the combination of Malachi and Beigi teaches the method as claimed in claim 1. The combination of Malachi and Beigi further teaches wherein the behavioral models of the reference users are the most discriminating behavioral models from among a set of reference user behavioral models (Beigi: Col. 21, lines 22-32, at Section 1.3.1.1: "comparison against the target speaker's model is not enough. There is always a need for contrast when making a comparison. [] the cohort is selected based on the user ID [] are a small set of speakers in the database who have similar traits to the target speaker. It is possible that the impostor is closer to the user ID he/she is trying to mimic in relation to the cohort.").
Regarding claim 10, claim 10 is directed to a device comprising at least one processor (Malachi: fig. 1, par. 0003) and at least one memory (Malachi: fig. 1, par. 0003), storing program instructions that, when executed by the at least one processor associated with the method claimed in claim 1; claim 10 is similar in scope to claim 1, and is therefore rejected under similar rationale.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Malachi (“Malachi,” US 2016/0269411) in view of Beigi (“Beigi,” US 10,042,993), and Shahidzadeh et al. (“Shahidzadeh,” US 11, 367,323), and Douglas et al. (“Douglas,” US 2019/0220583), and further in view of Obaidi (“Obaidi,” US 2018/0309792).
Regarding claim 4, the combination of Malachi and Beigi teaches the method as claimed in claim 1. The combination of Malachi and Beigi teaches the step wherein the steps of determining the first score, the second scores and the decision to authenticate but does not explicitly disclose “repeating the scoring and decision steps across a temporal sequence of time interval”.
However, in an analogous art, Shahidzadeh discloses that the steps of determining scores and an authenticate are repeated for characteristic parameter values respectively obtained for a temporal sequence of time intervals (Shahidzadeh: Col. 4, lines 9-19, "The biobehavorial system and method 100 disclosed herein helps tracking of users for not only authentication but also continuous post authorization monitoring [] The post authorization will be continuous both in physical and cyber space where machine learning algorithms may detect, recognize, infer, predict and score every move of the user; Col. 18, lines 11-22, "The approach to continuous cognitive authentication described herein involves biobehavior modeling... Daily behavior is analyzed with mobile and ambient data."; Col. 18, lines 56-60, " Then, the biobehavior becomes determining authentication factor. A loss of the mobile device 200, on the other hand, can quickly be detected and reacted upon actively. Accounts are blocked, data is removed;”; Col. 3, lines 51-53, A "biobehavioral" derived credential is one that is drawn from a combination of human biological features and behavioral activities).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Shahidzadeh with the method and system of Malachi and Beigi to include determining scores and an authenticate are repeated for characteristic parameter values respectively obtained for a temporal sequence of time intervals. One would have been motivated to combine because, as Shahidzadeh expressly teaches, "the post authorization will be continuous both in physical and cyber space where machine learning algorithms may detect, recognize, infer, predict and score every move of the user and hence are ability to identify anomalies" (Shahidzadeh: Col. 4, lines 9-19). One of ordinary skill in the art would have recognized that applying Shahidzadeh's continuous temporal scoring architecture to Malachi's and Beigi's behavioral biometric authentication framework yields the predictable result of a system that continuously monitors whether the current user remains the legitimate user or has been replaced by an impostor —directly addressing the known limitation of Malachi's one-shot authentication approach. The combination requires no change in the underlying function of any reference. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007).
Malachi, Beigi, and Shahidzadeh do not explicitly
updating a current value of a weight for each time interval, the weight being decremented if one of the second scores obtained for this time interval is greater than an authentication threshold. the weight being incremented if the first score obtained for this time interval is greater than the authentication threshold.
However, in an analogous art, Douglas discloses
updating a current value of a weight for each time interval, the weight being decremented if one of the second scores obtained for this time interval is greater than an authentication threshold (Douglas: Claim 32, "maintaining, for each user during one or more corresponding authentication sessions, a sessional confidence score... the generated identified usage patterns utilized to periodically adapt the sessional confidence score to adjust the sessional confidence score to adapt for identified anomalies of tracked behavior."; par. [0012], "The system is configured to track patterns/anomalies in passive authentication mechanisms (e.g., keystrokes, touch patterns, touch force), and increase or decrease the session identity score over a period of time."; par. [0014], "Authentication may be conducted on a continuous basis, whereby a session based identity score is continuously refreshed through the course of a session to monitor for changes in the attributes."; par. [0388]: "For every authentication event, calculate the distance between baseline and the current event. If the event is farther than a configurable threshold reduce the confidence score.") the weight being incremented if the first score obtained for this time interval is greater than the authentication threshold (Douglas: Claim 32: "responsive to one or more successful verifications, increase the sessional confidence score based on an adapted contribution score from each successful verification."; par. [0012], "The system is configured to track patterns/anomalies in passive authentication mechanisms (e.g., keystrokes, touch patterns, touch force), and increase or decrease the session identity score over a period of time."; par. [0024], "the 'identity score' is slightly increased with every successful fingerprint scan when compared to a biometric fingerprint enrollment template.").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Douglas with the method and system of Malachi, Beigi, and Shahidzadeh to include updating a current value of a weight for each time interval, the weight being decremented if one of the second scores obtained for this time interval is greater than an authentication threshold. the weight being incremented if the first score obtained for this time interval is greater than the authentication threshold. One would have been motivated to reduce a burden on user convenience without making major compromises to security, thus improves the user experience. Ensures that the necessary security/identity assurance requirements are met for all authentication requests, while at the same time assessing scenarios in which the identity assurance requirements are low and thus lowering the authentication requirements to the user (Douglas: pars. [0013], [0161]).
The combination of Malachi, Beigi, Shahidzadeh, and Douglas teaches the first score and the current value of the weight but does not explicitly disclose
“modifying the first score by adding the current value of the weight after updating for this time interval, the modified first score being used to determine the decision to authenticate.”
However, in an analogous art, Obaidi discloses
the first score obtained for a time interval being modified by adding the current value of the weight after updating for this time interval, the modified first score being used to determine the decision to authenticate (Obaidi discloses in paragraph [0038]: "the authentication protocol module 226 may select the first and second biometric authentication protocols, in combination, on the basis that a sum of the respective authentication scores may be greater than the requisite authentication score of the security policy."; par. [0038]: "a plurality of biometric authentication protocolsthat in combination generate a total authentication score that is greater than the requisite authentication score of the security policy."; Abstract: "Each biometric authentication protocol may be assigned an authentication score that reflects a confidence that a biometric sample used to gain an access privilege does in fact correspond to the client.").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Obaidi with the method and system of Malachi, Beigi, Shahidzadeh, and Douglas to include the first score obtained for a time interval being modified by adding the current value of the weight after updating for this time interval, the modified first score being used to determine the decision to authenticate. One would have been motivated to provide the biometric authentication system which reduces an overall volume of communication between a client device and the underlying computing resource, which in turn translate into network bandwidth efficiency for the underlying computing resource. The pattern matching module use statistically reliable pattern matching techniques to ensure that biometric samples received from a client device, reliably match a registered biometric template (Obaidi: pars. 0010, 0034).
Claim 9 is under 35 U.S.C. 103 as being unpatentable over Malachi (“Malachi,” US 2016/0269411) in view of Beigi (“Beigi,” US 10,042,993), further in view of Williams et al. (“William,” US 8,353,764)
Regarding claim 9, the combination of Malachi and Beigi teaches the method as claimed in claim 1. Malachi and Beigi do not teaches the method, wherein the application system is a video game system.
However, in an analogous art, Williams discloses wherein the application system is a video game system (Williams: Col. 6, line 66 to Col. 7, line 5, The computing system can, for example, be a machine (e.g., server, gaming machine, or other electronic device) operable in a gaming environment that provides a user interface to interact with one or more games for the one or more entities that are authenticated; Col. 5, lines 43-46, As such, authentication systems that use behavioral biometric data are well suited for non-governmental activities (e.g., gaming activities) where privacy can be a major concern of players).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Beigi with the method and system of Malachi and Beigi to include wherein the application system is a video game system. One would have been motivated to because, as Williams expressly teaches, "modern gaming environments represent an example where the ever increasing use of computing machines (e.g., gaming servers) has resulted in more serious concerns about security" and "authentication systems that use behavioral biometric data are well suited for non-governmental activities (e.g., gaming activities)" (Williams: Col. 5, lines 43-46). Williams expressly identifies gaming environments as presenting heightened security concerns and directly recommends behavioral biometric authentication as the solution for those concerns (Williams: Col. 4, lines 2-5, Col. 6, line 66 to Col. 7, line 5) — providing express motivation to apply Malachi's and Beigi's behavioral biometric authentication framework to a video game system. One of ordinary skill in the art reading Williams's express recommendation that behavioral biometric authentication "is well suited for gaming activities" would have been directly motivated to implement the behavioral biometric authentication method of Malachi and Beigi on a gaming machine as taught by Williams — yielding the predictable result of a video game system that continuously authenticates players based on their behavioral biometric patterns during active gaming sessions, directly addressing the heightened security concerns Williams identifies in gaming environments. The combination requires no change in the underlying function of any reference. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Canh Le/
Examiner, Art Unit 2439
August 11, 2026
/LUU T PHAM/Supervisory Patent Examiner, Art Unit 2439
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