DETAILED ACTION
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 1 is pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/13/2025 was filed after the mailing date of the Claim on 1/17/2025. 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 § 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.
4. Claim(s) 1 is/are rejected under 35 U.S.C. 103 as being unpatentable over Visser, et al. [US 20150301796] in view of Nkambou, et al. [US 20160005323].
As per claim 1: Visser, et al. teaches a method comprising:
receiving user behavioral data from a plurality of channels; [Visser: para 0074; user behavioral data may broadly include in the form of input, audio, or movement. Para 0173, 0280; multiple channels]
analyzing the user behavioral data using machine learning analysis to dynamically adjust domain classifications [Visser: para 0271, 0291-0292, 0440; i.e. model learning, , a deep neural network, ] for a user based on detected changes in behavioral patterns over time; [Visser: para 0076-0078; the microphone capture the audio command signal and an analog-to-digital converter (ADC) at the mobile device convert the captured audio command signal from an analog waveform into a digital waveform comprised of digital audio samples. Gain adjusters operate in either the analog or digital domain. For example, a gain adjuster may operate in the digital domain and adjust the digital audio samples produced by the analog-to-digital converter. Determines whether the audio command signal satisfies the validation criterion, wherein identify the audio command and its association to the security level of access. As such, domain classifications may be analog or digital, which may be adjusted]
**automatically modifying challenge-response templates based on the adjusted domain classifications; [Visser: para 0080-0081; the testing module dynamically generate the test phrase upon test phrase does not correspond. Modifying challenge-response templates includes the test phrase to include one or more of the particular sounds and based on the security level. An example of a challenge-response template may be a model or type that involve a testing phrase of particular sound or security level, or phrase signal or audio signal]
generating success metrics or failure metrics for authentication attempts using modified challenge-response templates; [Visser: para 0084; a higher confidence level threshold increase a false alarm rate and decrease a miss rate. The miss rate correspond to a false acceptance likelihood that the test phrase audio signal satisfy the verification criterion when the user is not the authorized user corresponding to the speaker model. The limitation of success or failure metrics may be in the form of a rate, score, value, etc., such that these metrics provide distinctions of data. Para 0086; the historical success rates include a first number of successful speaker verifications out of a second number of attempted speaker verifications. See also para 0155-0158]
using the success metrics or the failure metrics to further adjust the machine learning analysis; and [Visser: para 0084-0086; The testing module generate the success GUI in response to determining that the test phrase audio signal satisfies the verification criterion. The enrollment module update the speaker model. The testing module update (e.g., increase) the first number and the second number in response to determining that the test phrase audio signal satisfies the verification criterion]
providing updated challenge-response authentication based on adjusted machine learning analysis and adjusted challenge-response templates. [Visser: para 0086; The testing module update the historical success rates to indicate that the speaker verification is successful. Para 0088; The testing module store the alternative modality GUI in the memory, and the testing module provide the alternative modality GUI to the display of the mobile device. The testing module store the alternative modality GUI in the memory, and the testing module provide the alternative modality GUI to the display of the mobile device. The testing module determine whether the alternative test phrase signal satisfies the verification criterion based on a stricter (e.g., higher) confidence level threshold than used to determine whether the test phrase audio signal satisfies the verification criterion]
Visser suggests modifying challenge-response templates based on the adjusted domain classifications, by the testing module dynamically generate the test phrase upon test phrase does not correspond [Visser: para 0080-0081]. Modifying challenge-response templates includes the test phrase to include one or more of the particular sounds and based on the security level. An example of a challenge-response template may be a model or type that involve a testing phrase of particular sound or security level, or phrase signal or audio signal. However, Visser does not “automatically” modify challenge-response templates.
Nkambou teaches the present invention relates to provide an adaptive computerized learning or e-learning system and method in order to guide a learner to excel while significantly reducing training time and cost [Nkambou: para 0008]. Nkambou discusses linking of attributes provides a linking of the attribute semantics through the ontology of the domain that structures them. This way, the attributes (i.e. skills, knowledge, strategies and mental processes) are defined and structured in a domain ontology, which not only allows exploiting the links between them but also allows in depth analysis and inferring new axioms concerning them, for explaining the errors of the learner in the diagnostic record. Managing the assessment of latent abstract attributes. This is done in two ways: 1) by providing the MKS method to estimate the mastery levels of those attributes through an interpretation of semantic links that connect them and 2) by providing a function (into 2 hierarchies) that transforms the domain ontology into a bag of hierarchies (of four types) which is then given as input for an automatic use of a DINA-H model to estimate those attributes [Nkambou: para 0104-0105]. Further, Nkambou obviously suggest the ability to automatically modify challenge-response templates, by testing and validating, and developing an automatic perfection mechanism of the database where one would be motivated to allow an automatic re-assessment or re-adjustment of the model whenever necessary. The adjustment could even lead to a change of the model [Nkambou: para 0125].
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 Nkambou with Visser to teach “automatically” modify challenge-response templates for the reason to allow an automatic re-adjustment of the model whenever necessary, which leads to a change of the model.
Conclusion
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Leynna Truvan
Examiner
Art Unit 2435
/L.TT/Examiner, Art Unit 2435
/EDWARD ZEE/Primary Examiner, Art Unit 2435
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