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 .
The claimed invention of claims 1-10, filed on 9/26/2024, is acknowledged and considered.
Claims 1, 9, and 10 are independent claims. Claims 1-10 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/5/2024 was filed after the mailing date of the Claims on 9/26/2024. 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.
Claim(s) 1-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Molloy, et al. [US 20210287141] in view of Ortiz, et al. [US 20220108026]
As per claim 1: Molloy, et al. teaches an information processing apparatus comprising:
at least one memory that is configured to store instructions; and [Molloy: para 0008]
at least one processor that is configured to execute the instructions to: [Molloy: para 0008]
calculate a degree of similarity between a feature quantity of first information and a feature quantity of second information; [Molloy: para 0036; setting the regularizer strength based on a function of the difference between the similarity measure and the predetermined threshold value, setting the regularizer strength to a default increased value. The difference between the similarity measure and predetermined threshold value suggest the feature quantity of the first and second information. More examples on para 0072, 0075, 0144]
calculate gradient information indicating a gradient of the degree of similarity; [Molloy: para 0034; two gradients are then compared and a similarity between the two gradients is calculated. In another embodiment, if the similarity measure between the two gradients is equal to or greater than a predetermined threshold value, then the two gradients are considered to be similar. More examples on para 0071, 0151]
determine an element serving as a perturbing target in the first information, on the basis of the gradient information; [Molloy: para 0031; other perturbations that are usually constructed by iteratively computing gradients]
apply a perturbation to the element serving as the perturbing target in the first information; and [Molloy: para 0031; The perturbations the attacker uses can be diversified and made discordant among the ensemble AI models so that the attacker cannot transfer attack or find the perturbation that can deceive all AI models in the ensemble at once. With this approach, not just the gradients that are frequently used for evasion attack, but also other perturbations that are usually constructed by iteratively computing gradients, are considered in the defensive mechanisms. See also para 0038]
**assess a risk in authentication processing on the basis of a result of the authentication processing of collating/verifying the first information to which the perturbation is applied, and the second information. [**rejected under a secondary reference, discussion below]
Molloy suggest apply a perturbation to the element serving as the perturbing target in the first information where other perturbations that are usually constructed by iteratively computing gradients are considered in the defensive mechanisms [Molloy: para 0031]. However, Molloy did not clearly teach “assess a risk in authentication processing on the basis of a result of the authentication processing of collating/verifying the first information to which the perturbation is applied, and the second information”..
Ortiz teaches “assess a risk in authentication processing on the basis of a result of the authentication processing of collating/verifying the first information to which the perturbation is applied, and the second information”, by the validation processing steps of the application of formatting, security, or sanity checks, and additional transformation to the data is conducted to perturb specific data values to add a level of uncertainty [Ortiz: para 0050]. As such, one would be motivated to “assess a risk in authentication processing on the basis of a result of the authentication processing of collating/verifying the first information to which the perturbation is applied, and the second information”, so that a set of new data values can be generated with a level of noise applied to perturb the data sets prior to load as an additional layer of information security [Ortiz: para 0237].
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 Ortiz with Molloy to teach “assess a risk in authentication processing on the basis of a result of the authentication processing of collating/verifying the first information to which the perturbation is applied, and the second information” for the reason to provide an additional layer of information security [Ortiz: para 0237].
Claim 2: Molloy: para 0019, 0151 [generate a similarity measure and compared to a threshold to determine if the measure indicates the gradients have a similarity equal to or greater than a threshold similarity]; discussing the information processing apparatus according to claim 1, wherein the first information is a first image including a first living body, the second information is a second image including a second living body, and the at least one processor that is configured to execute the instructions to calculate a degree of similarity between a feature quantity about the first living body extracted from the first image and a feature quantity about the second living body extracted from the second image.
Claim 3: Molloy: para 0062; discussing the information processing apparatus according to claim 1, wherein the at least one processor that is configured to execute the instructions to determine one element with the highest gradient information, to be the element serving as the perturbing target.
Claim 4: Molloy: para 0033 [The gradient for the selected class is computed based on a processing of one or more portions that is dependent upon the training algorithm utilized, e.g., stochastic gradient descent, batch gradient descent, mini-batch gradient descent, etc.]; discussing the information processing apparatus according to claim 1, wherein the at least one processor that is configured to execute the instructions to determine a predetermined number of elements in descending order of the gradient information, to be the element serving as the perturbing target.
Claim 5: Molloy: para 0031, 0071; discussing the information processing apparatus according to claim 1, wherein the at least one processor that is configured to execute the instructions to determine an element in which the gradient information is greater than a predetermined threshold, to be the element serving as the perturbing target.
Claim 6: Molloy: para 0034 in view of Ortiz: para 0422-0424 [suggesting “assess the risk in the authentication processing on the basis of the false authentication probability”, under the same pretext and motivation as in claim 1]; discussing the information processing apparatus according to claim 1,wherein the at least one processor that is configured to execute the instructions to calculate a false authentication probability in the authentication processing and assess the risk in the authentication processing on the basis of the false authentication probability.
Claim 7: Molloy: para 0071; discussing the information processing apparatus according toinstructions to: calculate a degree of similarity between the feature quantity of the first information and a feature quantity of third information, in addition to the degree of similarity between the feature quantity of the first information and the feature quantity of the second informations [Molloy: para 0036; setting the regularizer strength based on a function of the difference between the similarity measure and the predetermined threshold value, setting the regularizer strength to a default increased value. The difference between the similarity measure and predetermined threshold value suggest the feature quantity of the first and second information. More examples on para 0072, 0075, 0144]; calculate the gradient information about a gradient of the degree of similarity between the feature quantity of the first information and the feature quantity of the third information, in addition to the gradient information about the gradient of the degree of similarity between the feature quantity of the first information and the feature quantity of the second information [Molloy: para 0034; two gradients are then compared and a similarity between the two gradients is calculated. In another embodiment, if the similarity measure between the two gradients is equal to or greater than a predetermined threshold value, then the two gradients are considered to be similar. More examples on para 0151]; and determine the element serving as the perturbing target on the basis of the gradient information about the gradient of the degree of similarity between the feature quantity of the first information and the feature quantity of the second information [Molloy: para 0031; other perturbations that are usually constructed by iteratively computing gradients] and the gradient information about the gradient of the degree of similarity between the feature quantity of the first information and the feature quantity of the third information. [Molloy: para 0031; The perturbations the attacker uses can be diversified and made discordant among the ensemble AI models so that the attacker cannot transfer attack or find the perturbation that can deceive all AI models in the ensemble at once. With this approach, not just the gradients that are frequently used for evasion attack, but also other perturbations that are usually constructed by iteratively computing gradients, are considered in the defensive mechanisms. See also para 0038]
Claim 8: Molloy: para 0029-0031, 0075 [gradient similarity evaluations, weighted data]; discussing the Information processing apparatus according to claim 7, wherein the at least one processor that is configured to execute the instructions to determine the element serving as the perturbing target, by using at least one of a product, a weighted sum, and a sum of absolute values of the gradient information about the gradient of the degree of similarity between the feature quantity of the first information and the feature quantity of the second information and the gradient information about the gradient of the degree of similarity between the feature quantity of the first information and the feature quantity of the third information.
As per claim 9: Molloy, et al. teaches an information processing method that is executed by at least one computer, the information processing method comprising:
calculating a degree of similarity between a feature quantity of first information and a feature quantity of second information; [Molloy: para 0036; setting the regularizer strength based on a function of the difference between the similarity measure and the predetermined threshold value, setting the regularizer strength to a default increased value. The difference between the similarity measure and predetermined threshold value suggest the feature quantity of the first and second information. More examples on para 0072, 0075, 0144]
calculating gradient information indicating a gradient of the degree of similarity; [Molloy: para 0034; two gradients are then compared and a similarity between the two gradients is calculated. In another embodiment, if the similarity measure between the two gradients is equal to or greater than a predetermined threshold value, then the two gradients are considered to be similar. More examples on para 0071, 0151]
determining an element serving as a perturbing target in the first information, on the basis of the gradient information; [Molloy: para 0031; other perturbations that are usually constructed by iteratively computing gradients]
applying a perturbation to the element serving as the perturbing target in the first information; and [Molloy: para 0031; The perturbations the attacker uses can be diversified and made discordant among the ensemble AI models so that the attacker cannot transfer attack or find the perturbation that can deceive all AI models in the ensemble at once. With this approach, not just the gradients that are frequently used for evasion attack, but also other perturbations that are usually constructed by iteratively computing gradients, are considered in the defensive mechanisms. See also para 0038]
assessing a risk in authentication processing on the basis of a result of the authentication processing of collating/verifying the first information to which the perturbation is applied, and the second information. [**rejected under a secondary reference, discussion below]
Molloy suggest apply a perturbation to the element serving as the perturbing target in the first information where other perturbations that are usually constructed by iteratively computing gradients are considered in the defensive mechanisms [Molloy: para 0031]. However, Molloy did not clearly teach “assess a risk in authentication processing on the basis of a result of the authentication processing of collating/verifying the first information to which the perturbation is applied, and the second information”..
Ortiz teaches “assess a risk in authentication processing on the basis of a result of the authentication processing of collating/verifying the first information to which the perturbation is applied, and the second information”, by the validation processing steps of the application of formatting, security, or sanity checks, and additional transformation to the data is conducted to perturb specific data values to add a level of uncertainty [Ortiz: para 0050]. As such, one would be motivated to “assess a risk in authentication processing on the basis of a result of the authentication processing of collating/verifying the first information to which the perturbation is applied, and the second information”, so that a set of new data values can be generated with a level of noise applied to perturb the data sets prior to load as an additional layer of information security [Ortiz: para 0237].
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 Ortiz with Molloy to teach “assess a risk in authentication processing on the basis of a result of the authentication processing of collating/verifying the first information to which the perturbation is applied, and the second information” for the reason to provide an additional layer of information security [Ortiz: para 0237].
As per claim 10: Molloy, et al. teaches a non-transitory recording medium on which a computer program that allows at least one computer to execute an information processing method is recorded, the information processing method including:
calculating a degree of similarity between a feature quantity of first information and a feature quantity of second information; [Molloy: para 0036; setting the regularizer strength based on a function of the difference between the similarity measure and the predetermined threshold value, setting the regularizer strength to a default increased value. The difference between the similarity measure and predetermined threshold value suggest the feature quantity of the first and second information. More examples on para 0072, 0075, 0144]
calculating gradient information indicating a gradient of the degree of similarity; [Molloy: para 0034; two gradients are then compared and a similarity between the two gradients is calculated. In another embodiment, if the similarity measure between the two gradients is equal to or greater than a predetermined threshold value, then the two gradients are considered to be similar. More examples on para 0071, 0151]
determining an element serving as a perturbing target in the first information, on the basis of the gradient information; [Molloy: para 0031; other perturbations that are usually constructed by iteratively computing gradients]
applying a perturbation to the element serving as the perturbing target in the first information; and [Molloy: para 0031; The perturbations the attacker uses can be diversified and made discordant among the ensemble AI models so that the attacker cannot transfer attack or find the perturbation that can deceive all AI models in the ensemble at once. With this approach, not just the gradients that are frequently used for evasion attack, but also other perturbations that are usually constructed by iteratively computing gradients, are considered in the defensive mechanisms. See also para 0038]
assessing a risk in authentication processing on the basis of a result of the authentication processing of collating/verifying the first information to which the perturbation is applied, and the second information. [**rejected under a secondary reference, discussion below]
Molloy suggest apply a perturbation to the element serving as the perturbing target in the first information where other perturbations that are usually constructed by iteratively computing gradients are considered in the defensive mechanisms [Molloy: para 0031]. However, Molloy did not clearly teach “assess a risk in authentication processing on the basis of a result of the authentication processing of collating/verifying the first information to which the perturbation is applied, and the second information”..
Ortiz teaches “assess a risk in authentication processing on the basis of a result of the authentication processing of collating/verifying the first information to which the perturbation is applied, and the second information”, by the validation processing steps of the application of formatting, security, or sanity checks, and additional transformation to the data is conducted to perturb specific data values to add a level of uncertainty [Ortiz: para 0050]. As such, one would be motivated to “assess a risk in authentication processing on the basis of a result of the authentication processing of collating/verifying the first information to which the perturbation is applied, and the second information”, so that a set of new data values can be generated with a level of noise applied to perturb the data sets prior to load as an additional layer of information security [Ortiz: para 0237].
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 Ortiz with Molloy to teach “assess a risk in authentication processing on the basis of a result of the authentication processing of collating/verifying the first information to which the perturbation is applied, and the second information” for the reason to provide an additional layer of information security [Ortiz: para 0237].
Conclusion
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Leynna Truvan
Examiner
Art Unit 2435
/L.TT/Examiner, Art Unit 2435
/JOSEPH P HIRL/Supervisory Patent Examiner, Art Unit 2435