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 .
Status of Claims
The following is a Non-Final Office Action in response to applicant’s filing on February 25, 2026. Claim 3 was canceled. Claims 1-2, and 4-15 are pending, of which claims 1, 14, and 15 are in independent form.
Response to Arguments
In view of the remarks, submitted on February 25, 2026, applicant’s arguments have been carefully and respectfully considered but are not persuasive.
On Pages 6-7 of remarks, Applicant argues that “The office action otherwise does not provide any explanation as to how Guralnik also discloses this feature or the combination of the two references somehow synergistically disclose or render obvious the features of claim 3" in amended independent claims 1, 14 and 15. Applicant’s arguments with respect to the rejections of the amended claims 1, 14, and 15 based on the amended limitation “wherein the reference data include at least one of the first score, the second score, and the integrated score that are acquired in a different place from a place where the information processing apparatus is operated, and an authentication result based on the integrated score” have been fully considered and are unpersuasive because the rejection is based on the combined teachings of Kiefer in view of Guralnik, not Kiefer alone. It is noted that each reference teaches specific claim limitations and provides a rationale for their combination consistent with U.S.C. §103 and MPEP §2143.
Kiefer discloses the use of multiple biometric modalities and multiple biometric matching methods in a biometric identification system. Biometric modalities may include (but are not limited to) such methods as fingerprint identification, iris recognition, voice recognition, facial recognition, hand geometry, signature recognition, signature gait recognition, vascular patterns, lip shape, ear shape and palm print recognition (see, paragraph [0002]), further, Kiefer teaches the use of the N-scores reported from N<∞ biometrics is called the test observation (see, paragraph [0052]), which corresponds to the claimed first score, and second score.
Furthermore, regarding the limitation “an authentication result based on the integrated score” Kiefer discloses determining whether a data set is acceptable, such as allowing access (see, paragraph [0012]) comparing FAR/FRR thresholds and accepting or rejection the data set (paragraphs [0013]-[0014]), Additionally, Kiefer explicitly describes accepting a match or rejecting it based on hypothesis testing (paragraphs [0053]-[0054]).
Guralnik explicitly teaches “an integrated (fused) score”. In paragraph [0046] Guralnik taches a single modality score is generated for each of the plurality of biometric modalities. Further, in paragraph [0048] Guralnik teaches scores from a plurality of biometric sampling systems are received, and the scores are first fused from the plurality of biometric sampling systems into a single score, and the fused scores are then aggregated from the plurality of biometric sampling systems with one or more scores from other modalities and in paragraph [0046], Guralnik discloses it is determined whether there is a match between two or more biometric samples. Therefore, Guralnik explicitly discloses “integrated score” and “authentication result based on the integrated score”.
Moreover, the claim recites that scores are required “the integrated score that are acquired in a different place from a place where the information processing apparatus is operated”. Guralnik in paragraph [0009] teaches the context, such as location and time of the biometric samples acquisition, combined with prior knowledge of association of subjects in the galleries. Further, in paragraph [0048], Guralnik teaches the scores are first fused from the plurality of biometric sampling systems into a single score, and the fused scores are then aggregated from the plurality of biometric sampling systems with one or more scores from other modalities. On the other hand, Kiefer relies on multiple biometric modalities and systems in paragraph [0002]. A person of ordinary skill in the art would have understood that such systems inherently involve different environments, which are separate from the central processing apparatus. Therefore, it would have been obvious to a person of ordinary skill in the art to combine the teachings of Kiefer and Guralnik because both references are in the same field of biometric authentication, Kiefer provides the multi score decision farmwork, while Guralnik provides explicit score fusion techniques. Accordingly, Guralnik’s score fusion techniques into Kiefer’s multi-model biometric system would have improve authentication performance. Therefore, the examiner maintains the rejection under 35 USC § 103.
As to the dependent claims 1-13, these claims remain rejected by virtue of dependency to their independent claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, and 4-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Analysis
Step 1 (Statutory Categories) — 2019 PEG pq. 53
Claims 1-2, and 4-15 are directed to the statutory categories of invention.
Step 2A, Prong 1 (Do the claims recite an abstract idea?) — 2019 PEG pq. 54
Claim 14 recites the following types of subject matter that are judicial exceptions: Abstract idea —: The claim recites the abstract idea of collecting information, analyzing that information, and performing a mathematical calculation based on the collected information.
The step of “calculating an integrated score on the basis of the first score, the second score, and the reference data, …) correspond of a mathematical operation or data mental evaluation.
The recites a judicial exception, namely an abstract idea involving data analysis and mathematical computation.
Step 2A, Prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?) - 2019 PEG pq. 54
additional elements do not integrate the exception into a practical application. The limitations “acquiring a first score …;” “acquiring a second score;” and “acquiring reference data …;” constitute insignificant extra-solution activity (mere data gathering). The recited generic computer components (a processor and memory) provide only a conventional environment, and the “authentication result based on the integrated score” is post-solution output. Although the claim recites biometric recognition and authentication, the additional limitations, amounts to merely linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h), and including “at least one memory” and “at least one processor” configured to execute instructions, are recited at a high level of generality and merely implement the abstract idea on generic computer components.
Therefore, claim 14 is directed to an abstract idea and is not integrated into a practical application under Step 2A.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?) - 2019 PEG pq. 56
The additional elements “a computer executing the method” and “generic steps of acquiring and processing data”, individually and in combination, are well-understood, routine, and conventional and do not amount to significantly more than the abstract idea.
Accordingly, under Step 2B of the PEG, the claim 14 is not patent eligible.
Claim 1 includes all the limitations of claim 14. Therefore, claim 1 recites the same abstract idea of claim 14. Claim 1 recites the additional limitations “An information processing apparatus comprising: at least one memory that is configured to store instructions”, which in Step 2A, Prong 2, the limitations are merely elaborating on the abstract idea, by further specifying an additional limitation at a high-level of generality, therefore, does not amount to significantly more than the abstract idea.
Claim 2 depends from claim 13 includes all the limitations of claim 13. Therefore, claim 2 recites the same abstract idea of claim 13. Claim 2 recites the additional limitations “a storage medium configured to store computer-executable instructions; and one or more processors coupled to the storage medium and configured to execute computer-executable instructions”, which in Step 2A, Prong 2, the limitations are merely elaborating on the abstract idea, by further specifying an additional limitation at a high-level of generality, therefore, does not amount to significantly more than the abstract idea.
Claim 4 depends from claim 13 includes all the limitations of claim 13. Therefore, claim 4 recites the same abstract idea of claim 13. Claim 4 recites the additional limitations “a storage medium configured to store computer-executable instructions; and one or more processors coupled to the storage medium and configured to execute computer-executable instructions”, which in Step 2A, Prong 2, the limitations are merely elaborating on the abstract idea, by further specifying an additional limitation at a high-level of generality, therefore, does not amount to significantly more than the abstract idea.
Claim 5 depends from claim 13 includes all the limitations of claim 13. Therefore, claim 5 recites the same abstract idea of claim 13. Claim 5 recites the additional limitations “a storage medium configured to store computer-executable instructions; and one or more processors coupled to the storage medium and configured to execute computer-executable instructions”, which in Step 2A, Prong 2, the limitations are merely elaborating on the abstract idea, by further specifying an additional limitation at a high-level of generality, therefore, does not amount to significantly more than the abstract idea.
Claim 6 depends from claim 1 includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of claim 1. Claim 6 recites the additional limitations “a storage medium configured to store computer-executable instructions; and one or more processors coupled to the storage medium and configured to execute computer-executable instructions”, which in Step 2A, Prong 2, the limitations are merely elaborating on the abstract idea, by further specifying an additional limitation at a high-level of generality, therefore, does not amount to significantly more than the abstract idea.
Claim 7 depends from claim 6 includes all the limitations of claim 6. Therefore, claim 7 recites the same abstract idea of claim 6. Claim 7 recites the additional limitations “a storage medium configured to store computer-executable instructions; and one or more processors coupled to the storage medium and configured to execute computer-executable instructions”, which in Step 2A, Prong 2, the limitations are merely elaborating on the abstract idea, by further specifying an additional limitation at a high-level of generality, therefore, does not amount to significantly more than the abstract idea.
Claim 8 depends from claim 6 includes all the limitations of claim 6. Therefore, claim 8 recites the same abstract idea of claim 6. Claim 8 recites the additional limitations “a storage medium configured to store computer-executable instructions; and one or more processors coupled to the storage medium and configured to execute computer-executable instructions”, which in Step 2A, Prong 2, the limitations are merely elaborating on the abstract idea, by further specifying an additional limitation at a high-level of generality, therefore, does not amount to significantly more than the abstract idea.
Claim 9 depends from claim 8 includes all the limitations of claim 8. Therefore, claim 9 recites the same abstract idea of claim 8. Claim 9 recites the additional limitations “a storage medium configured to store computer-executable instructions; and one or more processors coupled to the storage medium and configured to execute computer-executable instructions”, which in Step 2A, Prong 2, the limitations are merely elaborating on the abstract idea, by further specifying an additional limitation at a high-level of generality, therefore, does not amount to significantly more than the abstract idea.
Claim 10 depends from claim 9 includes all the limitations of claim 9. Therefore, claim 10 recites the same abstract idea of claim 9. Claim 10 recites the additional limitations “a storage medium configured to store computer-executable instructions; and one or more processors coupled to the storage medium and configured to execute computer-executable instructions”, which in Step 2A, Prong 2, the limitations are merely elaborating on the abstract idea, by further specifying an additional limitation at a high-level of generality, therefore, does not amount to significantly more than the abstract idea.
Claim 11 depends from claim 1 includes all the limitations of claim 1. Therefore, claim 11 recites the same abstract idea of claim 1. Claim 11 recites the additional limitations “a storage medium configured to store computer-executable instructions; and one or more processors coupled to the storage medium and configured to execute computer-executable instructions”, which in Step 2A, Prong 2, the limitations are merely elaborating on the abstract idea, by further specifying an additional limitation at a high-level of generality, therefore, does not amount to significantly more than the abstract idea.
Claim 12 depends from claim 6 includes all the limitations of claim 6. Therefore, claim 12 recites the same abstract idea of claim 6. Claim 12 recites the additional limitations “a storage medium configured to store computer-executable instructions; and one or more processors coupled to the storage medium and configured to execute computer-executable instructions”, which in Step 2A, Prong 2, the limitations are merely elaborating on the abstract idea, by further specifying an additional limitation at a high-level of generality, therefore, does not amount to significantly more than the abstract idea.
Claim 13 depends from claim 6 includes all the limitations of claim 6. Therefore, claim 13 recites the same abstract idea of claim 6. Claim 13 recites the additional limitations “a storage medium configured to store computer-executable instructions; and one or more processors coupled to the storage medium and configured to execute computer-executable instructions”, which in Step 2A, Prong 2, the limitations are merely elaborating on the abstract idea, by further specifying an additional limitation at a high-level of generality, therefore, does not amount to significantly more than the abstract idea.
Claim 15 depends from claims 1 and 14 includes all the limitations of claims 1 and 14. Therefore, claim 15 recites the same abstract idea of claims 1 and 14. Claim 15 recites the additional limitations “A non-transitory recording medium”, which in Step 2A, Prong 2, the limitations are merely elaborating on the abstract idea, by further specifying an additional limitation at a high-level of generality, therefore, does not amount to significantly more than the abstract idea.
Accordingly, claims 1-2, and 4-15 are rejected under 35 U.S.C. § 101.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-2, and 4-15 are rejected under 35 U.S.C. 103 as being unpatentable over Kiefer (US 2008/0104006 A1), hereinafter Kiefer in view of Guralnik et al. (US 2010/0228692 A1), hereinafter Guralnik.
Regarding claim 1, Kiefer discloses an information processing apparatus comprising:
at least one memory that is configured to store instructions (Kiefer, Paras 0158); and
at least one processor that is configured to execute the instructions to (Kiefer, Paras 0158):
acquire a first score indicating a matching score of first biometric recognition using first biometric information about a target (Kiefer, Paras 0105—0106, The scores for each modality were collected independently from essentially disjoint subsets of the general population);
acquire a second score indicating a matching score of second biometric recognition using second biometric information about the target(Kiefer, Paras 0105—0106, The scores for each modality were collected independently from essentially disjoint subsets of the general population); wherein the reference data include at least one of the first score, the second score (Kiefer, Para. 0002, the use of multiple biometric modalities and multiple biometric matching methods in a biometric identification system. Biometric modalities may include (but are not limited to) such methods as fingerprint identification, iris recognition, voice recognition, facial recognition, hand geometry, signature recognition, signature gait recognition, vascular patterns, lip shape, ear shape and palm print recognition) and (Kiefer, Para. 00052) and (Kiefer, Para. 0054) and (Kiefer, Para. 0069),
Kiefer does not explicitly disclose acquire reference data about at least one of the first biometric recognition and the second biometric recognition; and
calculate an integrated score on the basis of the first score, the second score, and the reference data, and the integrated score that are acquired in a different place from a place where the information processing apparatus is operated, and an authentication result based on the integrated score.
However, Guralnik teaches acquire reference data about at least one of the first biometric recognition and the second biometric recognition (Guralnik, Para. 0060, a process comprises receiving a plurality of biometric samples relating to a plurality of biometric modalities, generating a single modality score for each of the plurality of biometric modalities, selecting a classifier from a database of multi-modal classifiers, applying a multi-modal fusion to the single modality scores and the classifier, aggregating the single modality scores, generating a context dependent model and applying a measure of the context in which the biometric samples were obtained to the aggregated single modality scores); and
calculate an integrated score on the basis of the first score, the second score, and the reference data (Guralnik, Para. 0015, Fig. 1, Key elements of this embodiment include an intra-modal fusion that leverages similarities among registered subjects in various biometric sensor galleries within each modality to improve matching regardless of type of biometric system used at matching time. Another element relates to a multimodal fusion classifier that aggregates scores using an appropriate classifier from a small bank that covers all possible subsets of biometric modalities and biometric systems. A context-aware data fusion analyzes biometric samples and their scores in the perspective of the context in which the biometrics were taken, as well as prior knowledge of events and associations of registered subjects in the galleries), and the integrated score that are acquired in a different place from a place where the information processing apparatus is operated, and an authentication result based on the integrated score (Guralnik, Para. 0046, a single modality score is generated for each of the plurality of biometric modalities) and (Guralnik, Para. 0048, scores from a plurality of biometric sampling systems are received, and the scores are first fused from the plurality of biometric sampling systems into a single score, and the fused scores are then aggregated from the plurality of biometric sampling systems with one or more scores from other modalities).
Kiefer and Guralnik are both considered to be analogous to the claim invention because they are in the same field of biometric authentication. Therefore, it would have been obvious to someone ordinary skill in the art before the effective filling date of the claimed invention to have modified Kiefer to incorporate the teachings of Guralnik to include acquire reference data about at least one of the first biometric recognition and the second biometric recognition (Guralnik, Para. 0060); and calculate an integrated score on the basis of the first score, the second score, and the reference data (Guralnik, Para. 0015) and the integrated score that are acquired in a different place from a place where the information processing apparatus is operated, and an authentication result based on the integrated score (Guralnik, Para. 0046) and (Guralnik, Para. 0048). Doing so would aid to improve recognition performance is to consider the context in which particular subjects are observed, since biometric probes are rarely acquired in isolation. The context, such as location and time of the biometric samples acquisition, combined with prior knowledge of association of subjects in the galleries, can provide ancillary information and can be used to further improve recognition and verification accuracy (Guralnik, Para. 0009).
Regarding claim 2, the combination of Kiefer in view of Guralnik teaches the Information processing apparatus according to claim 1, wherein the referenced data include at least one of the first score, the second score, the integrated score that are acquired in a past, and an authentication result based on the integrated score (Guralnik, Para. 0048, at 470, scores from a plurality of biometric sampling systems are received, and the scores are first fused from the plurality of biometric sampling systems into a single score, and the fused scores are then aggregated from the plurality of biometric sampling systems with one or more scores from other modalities. At 475, the biometric samples comprise subjects of interest. At 480, there exists a gallery of registered subjects system comprises relationships among the registered subjects and relationships among the subjects of interest) and (Guralnik, Para. 0033, subjects of interest can be related to each other through location and/or time at which their biometric samples were taken or through an event which triggered the collection of samples to determine subjects' identities). Therefore, it would have been obvious to someone ordinary skill in the art before the effective filling date of the claimed invention to have modified Kiefer to incorporate the teachings of Guralnik to include wherein the referenced data include at least one of the first score, the second score, the integrated score that are acquired in a past, and an authentication result based on the integrated score (Guralnik, Para. 0048). Doing so would aid to improve recognition performance is to consider the context in which particular subjects are observed, since biometric probes are rarely acquired in isolation. The context, such as location and time of the biometric samples acquisition, combined with prior knowledge of association of subjects in the galleries, can provide ancillary information and can be used to further improve recognition and verification accuracy (Guralnik, Para. 0009)
Regarding claim 4, the combination of Kiefer in view of Guralnik teaches the information processing apparatus according to claim 3, wherein, in a case of using main machine reference data that are the reference data acquired by the information processing apparatus, and other machine reference data that are the reference data acquired in a different place from the place where the information processing apparatus is operated (Guralnik, Fig. 1, and Para. 0019, FIG. 1 illustrates an example embodiment of a multi-modal biometric system 100. The system 100 first aggregates scores from various biometric systems of biometric samples 105 within a single modality 110, thereby leveraging information in all galleries within that modality to expand coverage of available biometric systems. Each modality 110 can be associated with one or more biometric systems 115A/115B. A modality 110 can further include a module 120 for intra-modal gallery expansion and score aggregation), the at least one processor is configured to execute the instructions to set a weight of the main machine reference data to be larger than a weight of the other machine reference data, thereby calculating the integrated score (Guralnik, Para. 0031, the higher is the weight. Similarly, the edge exists between a subject of interest and a registered subject for each match based on ancillary information. The weight of the edge represents the strength of the relationship. For example, the weight of a signature relationship represents a similarity score between a signature of subject of interest and a signature of registered user, the weight of the hair color edge represents a similarity score between the hair color of the subject of interest and the hair color of registered user, etc.). Therefore, it would have been obvious to someone ordinary skill in the art before the effective filling date of the claimed invention to have modified Kiefer to incorporate the teachings of Guralnik to include wherein, in a case of using main machine reference data that are the reference data acquired by the information processing apparatus, and other machine reference data that are the reference data acquired in a different place from the place where the information processing apparatus is operated (Guralnik, Fig. 1, and Para. 0019, FIG. 1), the at least one processor is configured to execute the instructions to set a weight of the main machine reference data to be larger than a weight of the other machine reference data, thereby calculating the integrated score (Guralnik, Para. 0031). Doing so would aid to improve recognition performance is to consider the context in which particular subjects are observed, since biometric probes are rarely acquired in isolation. The context, such as location and time of the biometric samples acquisition, combined with prior knowledge of association of subjects in the galleries, can provide ancillary information and can be used to further improve recognition and verification accuracy (Guralnik, Para. 0009).
Regarding claim 5, the combination of Kiefer in view of Guralnik teaches the information processing apparatus according to claim 3, wherein in a case of using similar reference data that are the reference data acquired in another place where a degree of similarity with the place where the information processing apparatus is operated is higher than a predetermined value (Guralnik, Para. 0021, if two individuals have similar scores according to one biometric system, there is a high probability they will have similar scores in another biometric system that measures the same modality, (e.g. optical and ultrasonic fingerprint sensors, electro-optical and near infrared face cameras)), and dissimilar reference data that are the reference data acquired in another place where the degree of similarity with the place where the information processing apparatus is operated is lower than the predetermined value (Guralnik, Para. 0023, any log-scaled score above 5 represents a good match. The plot demonstrates that, in general, dissimilar individuals will have lower scores in both IR and RGB galleries, while more similar individuals will have higher scores in both galleries), the at least one processor is configured to execute the instructions to set a weight of the similar reference data to be larger than a weight of the dissimilar reference data, thereby calculating the integrated score (Guralnik, Para. 0025, In the context of combining scores from different modalities, several schemes can adaptively weigh individual matchers based on the quality scores. These approaches show that adaptation of the fusion functions at the score level in multimodal biometrics can report significant verification improvements. Prior systems have presented a likelihood ratio-based approach to perform quality-based fusion of match scores in a multi-biometric system. Other prior systems have implemented adaptive weight estimation components for the face biometrics using a user's head pose and image illumination as well as for finger biometrics using users' positioning and image clarity). Therefore, it would have been obvious to someone ordinary skill in the art before the effective filling date of the claimed invention to have modified Kiefer to incorporate the teachings of Guralnik to include wherein in a case of using similar reference data that are the reference data acquired in another place where a degree of similarity with the place where the information processing apparatus is operated is higher than a predetermined value (Guralnik, Para. 0021), and dissimilar reference data that are the reference data acquired in another place where the degree of similarity with the place where the information processing apparatus is operated is lower than the predetermined value (Guralnik, Para. 0023), the at least one processor is configured to execute the instructions to set a weight of the similar reference data to be larger than a weight of the dissimilar reference data, thereby calculating the integrated score (Guralnik, Para. 0025). Doing so would aid to improve recognition performance is to consider the context in which particular subjects are observed, since biometric probes are rarely acquired in isolation. The context, such as location and time of the biometric samples acquisition, combined with prior knowledge of association of subjects in the galleries, can provide ancillary information and can be used to further improve recognition and verification accuracy (Guralnik, Para. 0009).
Regarding claim 6, the combination of Kiefer in view of Guralnik teaches the information processing apparatus according to claim 1, wherein the at least one processor is configured to execute the instructions to determine a specific gravity value corresponding to the first score and the second score by using the reference data, and calculate the integrated score on the basis of the first score, the second score, and the specific gravity value (Kiefer, Para. 0095, score pairs that result in the failure to be authenticated at either biometric station must fall within the region R.sub.3=(R.sub.1.orgate.R.sub.2).sup.C, from which it is shown that FRR 2 = .intg. R 3 .times. f .function. ( x | Au ) .times. d x FRR 1 ##EQU43##) and (Kiefer, Para. 0088, it is assumed that all N biometric scores are simultaneously available for fusion. The Neyman-Pearson Lemma guarantees that this provides the most powerful test for a fixed FAR).
Regrading claim 7, the combination of Kiefer in view of Guralnik teaches the information processing apparatus according to claim 6, wherein the at least one processor is configured to execute the instructions to determine a first specific gravity value corresponding to the first score and a second specific gravity value corresponding to the second score, by using the reference data (Kiefer, Para. 0095, score pairs that result in the failure to be authenticated at either biometric station must fall within the region R.sub.3=(R.sub.1.orgate.R.sub.2).sup.C, from which it is shown that FRR 2 = .intg. R 3 .times. f .function. ( x | Au ) .times. d x FRR 1 ##EQU43##); and correct the first score with the first specific gravity value and corrects the second score with the second specific gravity value, thereby calculating the integrated score (Kiefer, Para. 0095, score pairs that result in the failure to be authenticated at either biometric station must fall within the region R.sub.3=(R.sub.1.orgate.R.sub.2).sup.C, from which it is shown that FRR 2 = .intg. R 3 .times. f .function. ( x | Au ) .times. d x FRR 1 ##EQU43##) and (Kiefer, Para. 0176, biometric applications can be divided into two types: verification tasks and identification tasks. For verification tasks a single matching score is produced, and an application accepts or rejects a matching attempt by thresholding the matching score. Based on the threshold value, performance characteristics of FAR and FRR can be estimated. For identification tasks a set of N matching scores {s.sub.1, s.sub.2, . . . , s.sub.N}, s.sub.1>s.sub.2> . . . >s.sub.N is produced for N enrolled persons. The person can be identified as an enrollee corresponding to the best ranked score s.sub.1, or the identification attempt can be rejected. Based on the decision algorithm FAR and FRR can be estimated. The usual decision algorithm for verification and identification involves setting some threshold t.sub.0 and accepting a matching score s if this score is greater than threshold: s>t.sub.0).
Regarding claim 8, the combination of Kiefer in view of Guralnik teaches the information processing apparatus according to claim 6, wherein the at least one processor is configured to execute the instructions to determine the specific gravity value such that a false acceptance rate or a false rejection rate satisfies a predetermined condition, by using the referenced data (Kiefer, Para. 0158, brevity, the steps of such a method will not be outlined here, but it should suffice to say that any of the methods outlined above may be translated into computer readable code which will cause a computer to determine and compare FAR.sub.Z.infin. and/or FRR as part of a system designed to determine whether a data set is acceptable for making a decision) and (Kiefer, Para. 0093, for a given test method, there exists a value of FAR.sub.1 that yields the smallest cost and we present an algorithm to find that value).
Regarding claim 9, the combination of Kiefer in view of Guralnik teaches the information processing apparatus according to claim 8, wherein the at least one processor is configured to execute the instructions to determine the specific gravity value so as to minimize the false rejection ratio when a predetermined false acceptance ratio is satisfied, by using the referenced data (Kiefer, Para. 0094, an algorithm is outlined below. The algorithm seeks to optimally minimize Equation 30. To do so, we (a) set the initial cost estimate to infinity and, (b) for a specified FARsys, loop over all possible values of FAR1≦FARsys. In practice, the algorithm may use a uniformly spaced finite sample of the infinite possible values. The algorithm may proceed as follows: (c) set FAR1=FARsys, (d) set Cost=∞, and (e) loop over possible FAR1 values. For the first biometric at the current FAR1 value, the algorithm may proceed to (f) find the optimal Match-Zzone, R1, and (g) compute the correct-acceptance-rate over R1).
Regarding claim 10, the combination of Kiefer in view of Guralnik teaches the information processing apparatus according to claim 9, wherein the at least one processor is configured to execute the instructions to generate distributions of the integrated scores of an identical person pair and another person pair, by using the reference data (Kiefer, Para. 0195, the functions F and G are the probability distributions or cumulative distribution functions of match scores X and Y. They represent the characteristic behaviors of the biometric system); and calculate the false rejection rate when the predetermined false acceptance rate is satisfied, on the basis of the distributions of the integrated scores (Kiefer, Para. 0175, if s is the score of a matched pair P=P′, we refer to s as a match (genuine) score; if s is the score of a mismatched pair P≠P′, we refer to s as a non-match or mismatch (impostor) score. Deciding H0 when H1 is true gives a false accept (FA), erroneously accepting an individual (false positive). Deciding H1 when H0 is true, on the other hand, results in a false reject (FR), incorrectly rejecting an individual (false negative). The False Accept Rate (FAR) and False Reject Rate (FRR) together characterize the accuracy of a recognition system, which can be expressed in terms of cumulative probability distributions of scores. The FAR and FRR are two interrelated variables and depend very much on decision threshold to (see FIG. 19)).
Regarding claim 11, the combination of Kiefer in view of Guralnik teaches the information processing apparatus according to claim 10, wherein the at least one processor is configured to execute the instructions to: use a first histogram illustrating the distribution of the integrated score of the other person pair, and set a threshold score such that a false acceptance rate; which is obtained as a ratio, to a whole, of a part that is greater than or equal to the threshold score in the first histogram, satisfies the predetermined false acceptance rate (Kiefer, Para. 0124, That is, it is possible to build a 2-dimensional histogram, which is stored in a 2-dimensional array of appropriate dimensions for the partition. If we divide each array element by the total number of samples, we have an approximation to the probability of a score pair falling within the associated sub-square. We call this type of an array the probability partition array (PPA). Let P.sub.fm be the PPA for the joint false match distribution and let P.sub.m be the PPA for the authentic match distribution. Then, the probability of an impostor's score pair, (s.sub.1, s.sub.2).epsilon.p.sub.ij, resulting in a match is P.sub.fm(i, j). Likewise, the probability of a score pair resulting in a match when it should be a match is P.sub.m(i,j). The PPA for a false reject (does not match when it should) is P.sub.fr=1-P.sub.m), and use a second histogram illustrating the distribution of the integrated score of the identical person pair (Kiefer, Fig. 14), and calculate a false rejection rate, which is a ratio, to a whole, of a part that is less than the threshold score in the second histogram (Kiefer, Para. 0124, that is, it is possible to build a 2-dimensional histogram, which is stored in a 2-dimensional array of appropriate dimensions for the partition. If we divide each array element by the total number of samples, we have an approximation to the probability of a score pair falling within the associated sub-square. We call this type of an array the probability partition array (PPA). Let P.sub.fm be the PPA for the joint false match distribution and let P.sub.m be the PPA for the authentic match distribution. Then, the probability of an impostor's score pair, (s.sub.1, s.sub.2).epsilon.p.sub.ij, resulting in a match is P.sub.fm(i, j). Likewise, the probability of a score pair resulting in a match when it should be a match is P.sub.m(i,j). The PPA for a false reject (does not match when it should) is P.sub.fr=1-P.sub.m).
Regarding claim 12, the combination of Kiefer in view of Guralnik teaches the information processing apparatus according to any one of The information processing apparatus according to any one of wherein the integrated score calculation unit determines the specific gravity value on the basis of at least one of target information, which is information about the target, and environmental information, which is information about an environment for performing the first biometric recognition and the second biometric recognition, in addition to the reference data (Kiefer, Para. 0066, if one accepts that accuracy dominates the decision-making process and cost dominates the combination strategy, certain conclusions may be drawn. Consider a two-biometric verification system for which a fixed FAR has been specified. In that system, a person's identity is authenticated if H1 is accepted by the first biometric OR if accepted by the second biometric. If H1 is rejected by the first biometric AND the second biometric, then manual intervention is required. If a cost is associated with each stage of the verification process, a cost function can be formulated. It is a reasonable assumption to assume that the fewer people that filter down from the first biometric sensor to the second to the manual check, the cheaper the system) and (Kiefer, Para. 0105, Tests were conducted on individual and fused biometric systems in order to determine whether the theory presented above accurately predicts what will happen in a real-world situation).
Regarding claim 13, the combination of Kiefer in view of Guralnik teaches the information processing apparatus according to claim 6, wherein the at least one processor is configured to execute the instructions to determine a new specific gravity value by using the specific gravity value determined in a past (Kiefer, Para. 0095, next, the algorithm may test against the second biometric. Note that the region R.sub.1 of the score space is no longer available since the first biometric test used it up. The Neyman-Pearson test may be applied to the reduced decision space, which is the compliment of R.sub.1. So, at this time, (h) the algorithm may compute FAR.sub.2=FAR.sub.sys-FAR.sub.1, and FAR.sub.2 may be (i) used in the Neyman-Pearson test to determine the most powerful test, CAR.sub.2, for the second biometric fused with the first biometric over the reduced decision space R.sub.1.sup.C. The critical region for CAR.sub.2 is R.sub.2, which is disjoint from R.sub.1 by our construction. Score pairs that result in the failure to be authenticated at either biometric station must fall within the region R.sub.3=(R.sub.1.orgate.R.sub.2).sup.C, from which it is shown that FRR 2 = .intg. R 3 .times. f .function. ( x | Au ) .times. d x FRR 1 ##EQU43##).
Regarding claim 14, an information processing method of claim 14 is similarly analyzed and rejected as the system claim 1.
Regarding claim 15, a non-transitory recording medium of claim 15 is similarly analyzed and rejected as the system claim 1 and the method claim 14.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
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/GITA FARAMARZI/Examiner, Art Unit 2496
/JORGE L ORTIZ CRIADO/Supervisory Patent Examiner, Art Unit 2496
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