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
Claims 1-10 are presented for examination in this application, 18/267,730, filed 06/15/2023, having an effective filing date of 12/24/2020 via PCT/JP2020/048472.
The Examiner cites particular sections in the references as applied to the claims below for the convenience of the applicant(s). Although the specified citations are
representative of the teachings in the art and are applied to the specific limitations within
the individual claim, other passages and figures may apply as well. It is respectfully
requested that, in preparing responses, the applicant(s) fully consider the references in
their entirety as potentially teaching all or part of the claimed, as well as the context of
the passage as taught by the prior art or disclosed by the Examiner.
Drawings
The drawings submitted on 06/15/2023 have been considered and accepted.
Information Disclosure Statement
Acknowledgement is made of the information disclosure statements filed 06/15/2023 and 08/16/2024. All patents and non-patent literature have been considered.
Claim Objections
The following claims are objected to for the following reasons:
Claims 5 and 6 recite “…wherein the loss function includes a logistic function as a nonlinear function that effects the likelihood ratio.” (emphasis added). “Effects” should be “affects”. Corrections should be made to the claims to ensure grammatical correctness.
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 1-10 are rejected under 35 U.S.C 101 as being unpatentable because the claimed invention in these claims is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1 – Is the claim directed to a process, machine, manufacture, or a composition of matter?
Yes, the claim is directed to a system.
Step 2 – Prong 1 – Does the claim recite an abstract idea, law of nature, or a natural phenomenon?
Yes, the claim recites an abstract idea:
calculate a likelihood ratio indicating a likelihood of a class to which the series data belong, on the basis of at least two consecutive elements of the plurality of elements — this limitation is directed to a mathematical calculations 2106.04(a)(2) I. C.)
classify the series data into at least one class of a plurality of classes that are classification candidates, on the basis of the likelihood ratio — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2 – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
an information processing system comprising: at least one memory that is configured to store instructions; and at least one processor that is configured to execute the instructions — this limitation is directed to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106).
obtain a plurality of elements included in series data — this limitation amounts to data gathering which is an insignificant extra-solution activity (see MPEP 2106.05(g)(3)) which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity.
perform learning related to calculation of the likelihood ratio, by using a loss function in which the likelihood ratio increases when a correct answer class to which the series data belong is in a numerator of the likelihood ratio and the likelihood ratio decreases when the correct answer class is in a denominator of the likelihood ratio — this limitation is directed to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than judicial exception. Any additional elements that were determined to be insignificant extra-solution activities in step 2A prong 2 are further evaluated in step 2B on whether they well-understood, routine, and conventional activities. The “obtain a plurality of elements included in series data” limitation was found to be an insignificant extra-solution activity in claim 1. This limitation is directed at a high-level of generality and amounts to transmitting data over a network, which is a well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.). Thus the claim is not patent eligible.
Regarding claims 9 and 10:
Independent claims 9 and 10 recite analogous limitations to claim and therefore are rejection the same grounds. In addition, claim 10 recites additional elements analyzed under step 2A prong 2 and step 2B:
Claim 10: “a non-transitory recording medium on which a computer program that allows a computer to execute an information processing method is recorded”. This limitation is directed to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computers merely as a tool to perform an existing process (see MPEP 2106.05(f) (2)). The processor is considered a generic computer component. Further, under step 2B, including instructions corresponds to storing information in the memory, being well-understood, routine, and conventional (see MPEP 2106.05(d) II. (IV)).
Regarding claim 2:
Claim 2 recites further details of the mathematical formula of the loss function used which amount to a mathematical concept.
Regarding claim 3:
Claim 3 recites further details of the mathematical formula of the loss function used which amount to a mathematical concept.
Regarding claim 4:
Claim 4 recites further details of the mathematical formula of the loss function used which amount to a mathematical concept.
Regarding claim 5:
Claim 5 recites further details of the mathematical formula of the loss function used which amount to a mathematical concept.
Regarding claim 6:
Claim 6 recites further details of the mathematical formula of the loss function used which amount to a mathematical concept.
Regarding claim 7:
Claim 7 recites further details of the calculations used to result in an integrated likelihood ratio which amount to a mathematical concept.
Regarding claim 8:
Claim 8 recites further details of the calculations used to result in an integrated likelihood ratio which amount to a mathematical concept. Furthermore, the claim recites obtaining a plurality of elements included in the series data which amounts to mere data gathering, considered a pre-solution activity which is an insignificant extra-solution activity (see MPEP 2106.05(g) (3)).
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.
Claims 1, 5, 6, 9, and 10 are rejected under 35 U.S.C 103 as being unpatentable over Yamamoto et al. (US20070088548A1 hereinafter, Yamamoto) in view of Kato (US8612227B2 hereinafter, Kato).
Regarding claim 1:
Yamamoto teaches an information processing system comprising: at least one memory that is configured to store instructions; and at least one processor that is configured to execute the instructions to obtain a plurality of elements included in (see para [0062]: “a central processing unit (CPU) 52 that controls each section of the speech-section detecting device 10 according to a program stored in ROM 52; a random access memory (RAM) 53 that stores therein various data necessary for a control of the speech-section detecting device 10; ”);
calculate a likelihood ratio indicating a likelihood of a class to which the (see para [0039]: “Next, the model comparing unit 108 calculates an evaluation value LR indicative of the likelihood of speech (log-likelihood ratio) using the m-dimensional feature vector and speech/non-speech Gaussian Mixture Model (GMM) acquired through learning in advance (step S108) as follows:LR=g(y|speech)−g(y|nonspeech) (6)where g(|speech) is the log-likelihood of the speech GMM, and g(|nonspeech) is the log-likelihood of the non-speech GMM.”. Also see para [0054]: “Data is classified into either one of the two classes: speech (C1) and non-speech (C2)”);
classify the (see para [0054]: “Data is classified into either one of the two classes: speech (C1) and non-speech (C2)”. Also see para [0060]: “Moreover, the EM algorithm is based on the maximum likelihood criteria of a sample acquired through learning. These methods are not the best to acquire parameters through learning for the speech/non-speech determination.”); and
perform learning related to calculation of the likelihood ratio, by using a loss function in which the likelihood ratio increases when a correct answer class to which the (see para [0056]: “Dk(y:Λ) in Equation 9 is a log-likelihood between gk and gi. Dk(y:Λ) becomes negative when an acoustic signal, which is a sample acquired through learning, is classified as belonging to the right-answer category. On the other hand, Dk(y:Λ) becomes positive when an acoustic signal, which is a sample acquired through learning, is classified as belonging to the wrong-answer category.”. Also see para [0057]: “The loss lk provided by the loss function is closer to 1 (one) when the rate of wrong recognition is larger, and to 0 (zero) when the error rate is smaller. Learning of the parameter set Λ is performed so as to lower the value provided by the loss function”).
Yamamoto does not explicitly teach series data with likelihood ratios.
Kato, however, analogously teaches series data with likelihood ratios (see col 7 lines 32-38: “A likelihood calculation unit 153 calculates an acoustic likelihood by matching time series data of acoustic feature parameters against a lexical tree stored in a second database 20 and an acoustic model stored in a third database 21 in the self-transition and LR transition to determine an accumulated likelihood by accumulating the acoustic likelihood in a time direction..”)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Yamamoto and Kato before him or her, to modify the system of claim 1 to include attributes of series data with likelihood ratios in order to operate on ordered data that has regular intervals (see col 7 lines 19-22: “ Acoustic feature parameters are a feature vector obtained by analyzing an input speech at regular intervals (for example, 10 ms; hereinafter, denoted as frames). Therefore, the audio signal is converted into a time series”).
Regarding claim 5:
Yamamoto in view of Kato teaches the system of claim 1.
Yamamoto further teaches wherein the loss function includes a sigmoid function as a nonlinear function that effects the likelihood ratio (see para [0056]: “ loss lk due to a classification error (y;Λ) is defined by Equation 10. Also see equation 10:
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).
Regarding claim 6:
Yamamoto in view of Kato teaches the system of claim 1.
Yamamoto further teaches wherein the loss function includes logistic function as a nonlinear function that effects the likelihood ratio (see para [0056]: “ loss lk due to a classification error (y;Λ) is defined by Equation 10. Also see equation 10:
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).
Regarding claim 9:
Yamamoto teaches an information processing method comprising: obtaining a plurality of elements included in (see para [0062]: “a central processing unit (CPU) 52 that controls each section of the speech-section detecting device 10 according to a program stored in ROM 52; a random access memory (RAM) 53 that stores therein various data necessary for a control of the speech-section detecting device 10; ”);
calculating a likelihood ratio indicating a likelihood of a class to which the (see para [0039]: “Next, the model comparing unit 108 calculates an evaluation value LR indicative of the likelihood of speech (log-likelihood ratio) using the m-dimensional feature vector and speech/non-speech Gaussian Mixture Model (GMM) acquired through learning in advance (step S108) as follows:LR=g(y|speech)−g(y|nonspeech) (6)where g(|speech) is the log-likelihood of the speech GMM, and g(|nonspeech) is the log-likelihood of the non-speech GMM.”. Also see para [0054]: “Data is classified into either one of the two classes: speech (C1) and non-speech (C2)”);
classifying the (see para [0054]: “Data is classified into either one of the two classes: speech (C1) and non-speech (C2)”. Also see para [0060]: “Moreover, the EM algorithm is based on the maximum likelihood criteria of a sample acquired through learning. These methods are not the best to acquire parameters through learning for the speech/non-speech determination.”); and
performing learning related to calculation of the likelihood ratio, by using a loss function in which the likelihood ratio increases when a correct answer class to which the (see para [0056]: “Dk(y:Λ) in Equation 9 is a log-likelihood between gk and gi. Dk(y:Λ) becomes negative when an acoustic signal, which is a sample acquired through learning, is classified as belonging to the right-answer category. On the other hand, Dk(y:Λ) becomes positive when an acoustic signal, which is a sample acquired through learning, is classified as belonging to the wrong-answer category.”. Also see para [0057]: “The loss lk provided by the loss function is closer to 1 (one) when the rate of wrong recognition is larger, and to 0 (zero) when the error rate is smaller. Learning of the parameter set Λ is performed so as to lower the value provided by the loss function”).
Yamamoto does not explicitly teach series data with likelihood ratios.
Kato, however, analogously teaches series data with likelihood ratios (see col 7 lines 32-38: “A likelihood calculation unit 153 calculates an acoustic likelihood by matching time series data of acoustic feature parameters against a lexical tree stored in a second database 20 and an acoustic model stored in a third database 21 in the self-transition and LR transition to determine an accumulated likelihood by accumulating the acoustic likelihood in a time direction..”)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Yamamoto and Kato before him or her, to modify the method of claim 9 to include attributes of series data with likelihood ratios in order to operate on ordered data that has regular intervals (see col 7 lines 19-22: “ Acoustic feature parameters are a feature vector obtained by analyzing an input speech at regular intervals (for example, 10 ms; hereinafter, denoted as frames). Therefore, the audio signal is converted into a time series”).
Regarding claim 10:
Yamamoto teaches an information processing method including: obtaining a plurality of elements included in (see para [0062]: “a central processing unit (CPU) 52 that controls each section of the speech-section detecting device 10 according to a program stored in ROM 52; a random access memory (RAM) 53 that stores therein various data necessary for a control of the speech-section detecting device 10; ”);
calculating a likelihood ratio indicating a likelihood of a class to which the (see para [0039]: “Next, the model comparing unit 108 calculates an evaluation value LR indicative of the likelihood of speech (log-likelihood ratio) using the m-dimensional feature vector and speech/non-speech Gaussian Mixture Model (GMM) acquired through learning in advance (step S108) as follows:LR=g(y|speech)−g(y|nonspeech) (6)where g(|speech) is the log-likelihood of the speech GMM, and g(|nonspeech) is the log-likelihood of the non-speech GMM.”. Also see para [0054]: “Data is classified into either one of the two classes: speech (C1) and non-speech (C2)”);
classifying the (see para [0054]: “Data is classified into either one of the two classes: speech (C1) and non-speech (C2)”. Also see para [0060]: “Moreover, the EM algorithm is based on the maximum likelihood criteria of a sample acquired through learning. These methods are not the best to acquire parameters through learning for the speech/non-speech determination.”); and
performing learning related to calculation of the likelihood ratio, by using a loss function in which the likelihood ratio increases when a correct answer class to which the (see para [0056]: “Dk(y:Λ) in Equation 9 is a log-likelihood between gk and gi. Dk(y:Λ) becomes negative when an acoustic signal, which is a sample acquired through learning, is classified as belonging to the right-answer category. On the other hand, Dk(y:Λ) becomes positive when an acoustic signal, which is a sample acquired through learning, is classified as belonging to the wrong-answer category.”. Also see para [0057]: “The loss lk provided by the loss function is closer to 1 (one) when the rate of wrong recognition is larger, and to 0 (zero) when the error rate is smaller. Learning of the parameter set Λ is performed so as to lower the value provided by the loss function”).
Yamamoto does not explicitly teach series data with likelihood ratios or a non-transitory recording medium.
Kato, however, analogously teaches series data with likelihood ratios (see col 7 lines 32-38: “A likelihood calculation unit 153 calculates an acoustic likelihood by matching time series data of acoustic feature parameters against a lexical tree stored in a second database 20 and an acoustic model stored in a third database 21 in the self-transition and LR transition to determine an accumulated likelihood by accumulating the acoustic likelihood in a time direction..”) and
a non-transitory recording medium (see claim 8: “A non-transitory computer-readable recording medium storing a pattern recognition program, executed by a computer to perform the method of claim 7.”)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Yamamoto and Kato before him or her, to modify the non-transitory recording medium of claim 10 to include attributes of series data with likelihood ratios in order to operate on ordered data that has regular intervals (see col 7 lines 19-22: “ Acoustic feature parameters are a feature vector obtained by analyzing an input speech at regular intervals (for example, 10 ms; hereinafter, denoted as frames). Therefore, the audio signal is converted into a time series”).
Claims 2, 3, and 4 are rejected under 35 U.S.C 103 as being unpatentable over Yamamoto et al. (US20070088548A1 hereinafter, Yamamoto) in view of Kato (US8612227B2 hereinafter, Kato) in further view of Hunter (“MM Algorithms for Generalized Bradley-Terry Models” hereinafter, Hunter).
Regarding claim 2:
Yamamoto in view of Kato teaches the system of claim 1.
Yamamoto further teaches wherein the at least one processor is configured to execute the instructions to perform the learning by using a loss function that takes into account the likelihood ratios of (see para [0039]: “Next, the model comparing unit 108 calculates an evaluation value LR indicative of the likelihood of speech (log-likelihood ratio) using the m-dimensional feature vector and speech/non-speech Gaussian Mixture Model (GMM) acquired through learning in advance (step S108) as follows:LR=g(y|speech)−g(y|nonspeech) (6)where g(|speech) is the log-likelihood of the speech GMM, and g(|nonspeech) is the log-likelihood of the non-speech GMM.”. Also see para [0057]: “The loss lk provided by the loss function is closer to 1 (one) when the rate of wrong recognition is larger, and to 0 (zero) when the error rate is smaller. Learning of the parameter set Λ is performed so as to lower the value provided by the loss function”).
Yamamoto does not explicitly teach likelihood ratios of Nx(N-1) patterns.
Hunter, however, analogously teaches likelihood ratios of Nx(N-1) patterns (see pg. 390 eq. 15. Also see pg. 388: “One feature of the function Qk(γ) defined in (10) that makes it easier to maximize than the original log-likelihood is the fact that it separates the components of the parameter vector γ.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Yamamoto, Kato, and Hunter before him or her, to modify the system of claim 2 to include attributes of likelihood ratios of Nx(N-1) patterns in order to aid in maximizing the log-likelihood ratios (see pg. 388-389 section 3: “One feature of the function Qk(γ) defined in (10) that makes it easier to maximize than the original log-likelihood is the fact that it separates the components of the parameter vector γ.”).
Regarding claim 3:
Yamamoto in view of Kato in further view of Hunter teaches the system of claim 1.
Yamamoto does not explicitly teach wherein the at least one processor is configured to execute the instructions to perform the learning by using a loss function that takes into account a part of the likelihood ratios of the Nx(N-1) patterns.
Hunter, however, analogously teaches wherein the at least one processor is configured to execute the instructions to perform the learning by using a loss function that takes into account a part of the likelihood ratios of the Nx(N-1) patterns (see pg. 390 eq. 15. Also see pg. 388: “One feature of the function Qk(γ) defined in (10) that makes it easier to maximize than the original log-likelihood is the fact that it separates the components of the parameter vector γ.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Yamamoto, Kato, and Hunter before him or her, to modify the system of claim 3 to include attributes of performing the learning by using a loss function that takes into account a part of the likelihood ratios of the Nx(N-1) patterns in order to aid in maximizing the log-likelihood ratios (see pg. 388-389 section 3: “One feature of the function Qk(γ) defined in (10) that makes it easier to maximize than the original log-likelihood is the fact that it separates the components of the parameter vector γ.”).
Regarding claim 4:
Yamamoto in view of Kato in further view of Hunter teaches the system of claim 3.
Yamamoto further teaches wherein the at least one processor is configured to execute the instructions to perform the learning by using a loss function that takes into account the likelihood ratio in which the correct answer class is in the numerator, out of the (see para [0039]: “Next, the model comparing unit 108 calculates an evaluation value LR indicative of the likelihood of speech (log-likelihood ratio) using the m-dimensional feature vector and speech/non-speech Gaussian Mixture Model (GMM) acquired through learning in advance (step S108) as follows:LR=g(y|speech)−g(y|nonspeech) (6)where g(|speech) is the log-likelihood of the speech GMM, and g(|nonspeech) is the log-likelihood of the non-speech GMM.”).
Yamamoto does not explicitly teach likelihood ratios of Nx(N-1) patterns.
Hunter, however, analogously teaches likelihood ratios of Nx(N-1) patterns (see pg. 390 eq. 15. Also see pg. 388: “One feature of the function Qk(γ) defined in (10) that makes it easier to maximize than the original log-likelihood is the fact that it separates the components of the parameter vector γ.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Yamamoto, Kato, and Hunter before him or her, to modify the system of claim 4 to include attributes of likelihood ratios of Nx(N-1) patterns in order to aid in maximizing the log-likelihood ratios (see pg. 388-389 section 3: “One feature of the function Qk(γ) defined in (10) that makes it easier to maximize than the original log-likelihood is the fact that it separates the components of the parameter vector γ.”).
Claims 7 and 8 is rejected under 35 U.S.C 103 as being unpatentable over Yamamoto et al. (US20070088548A1 hereinafter, Yamamoto) in view of Kato (US8612227B2 hereinafter, Kato) in further view of Hunter (“MM Algorithms for Generalized Bradley-Terry Models” hereinafter, Hunter) and further in view of Varin et al. (“Pairwise likelihood inference for ordinal categorical time series” hereinafter, Varin).
Regarding claim 7:
Yamamoto in view of Kato teaches the system of claim 1.
Yamamoto does not explicitly teach wherein the likelihood ratio is an integrated likelihood ratio that is calculated by taking into account a plurality of individual likelihood ratios that are calculated on the basis of two consecutive elements included in the series data.
Varin, however, analogously teaches wherein the likelihood ratio is an integrated likelihood ratio that is calculated by taking into account a plurality of individual likelihood ratios that are calculated on the basis of two consecutive elements included in the series data (see pg. 2368 section 3: “When we consider an AOP(1) model, in order to compute the first-order pairwise likelihood, we only require the calculation of the following bivariate joint probabilities
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variances σ^2/(1-ɣ^2)and correlation . Computing the first-order pairwise likelihood requires the approximation (n – 1) of bivariate Gaussian integrals instead of an often prohibitive single n-dimensional Gaussian integral, as in ordinary likelihood inference.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Yamamoto, Kato, Hunter, and Varin before him or her, to modify the system of claim 7 to include attributes of wherein the likelihood ratio is an integrated likelihood ratio that is calculated by taking into account a plurality of individual likelihood ratios that are calculated on the basis of two consecutive elements included in the series data in order to not be as prohibitive as ordinary likelihood inferences (see pg. 2368 section 3 : “ Computing the first-order pairwise likelihood requires the approximation of bivariate Gaussian integrals instead of an often prohibitive single n-dimensional Gaussian integral, as in ordinary likelihood inference.”).
Regarding claim 8:
Yamamoto in view of Kato in further view of Hunter and further in view of Varin teaches the system of claim 1.
Yamamoto further teaches wherein the at least one processor is configured to execute the instructions to sequentially obtain a plurality of elements included in the (see para [0033]: “Namely, information that is more effective for performing the speech/non-speech determination is included in the time-varying information as compared to information included in the feature value (such as MFCC) extracted from a single frame.”. Also see para [0034]: “It is also possible to use a vector obtained by combining a plurality of a single-frame feature values. In this case, the feature vector x(t) at time t is expressed by:z(t)=[x i(t), . . . , x N(t)]T (3)x(t)=[z(t−Z)T , . . . , z(t−1)T , z(t)T , z(t+1)T , . . . , z(t+Z)T]T (4)where z(t) is the MFCC at time t; and Z is the number of frames that are used in combining both before and after the frame corresponding to time t.”).
Yamamoto does not exclusively teach the use of series data with likelihood ratios.
Kato, however, analogously teaches series data with likelihood ratios (see col 7 lines 32-38: “A likelihood calculation unit 153 calculates an acoustic likelihood by matching time series data of acoustic feature parameters against a lexical tree stored in a second database 20 and an acoustic model stored in a third database 21 in the self-transition and LR transition to determine an accumulated likelihood by accumulating the acoustic likelihood in a time direction..”)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Yamamoto, Kato, Hunter, and Varin before him or her, to modify the system of claim 8 to include attributes of series data with likelihood ratios in order to operate on ordered data that has regular intervals (see col 7 lines 19-22: “ Acoustic feature parameters are a feature vector obtained by analyzing an input speech at regular intervals (for example, 10 ms; hereinafter, denoted as frames). Therefore, the audio signal is converted into a time series”).
Yamamoto does not explicitly teach wherein the at least one processor is configured to execute the instructions to calculate a new integrated likelihood ratio by using the individual likelihood ratio that is calculated on the basis of the newly obtained element and the integrated likelihood ratio calculated in the past.
Varin, however, analogously teaches to calculate a new integrated likelihood ratio by using the individual likelihood ratio that is calculated on the basis of the newly obtained element and the integrated likelihood ratio calculated in the past (see pg. 2368 section 3: “When we consider an AOP(1) model, in order to compute the first-order pairwise likelihood, we only require the calculation of the following bivariate joint probabilities
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variances σ^2/(1-ɣ^2)and correlation . Computing the first-order pairwise likelihood requires the approximation (n – 1) of bivariate Gaussian integrals instead of an often prohibitive single n-dimensional Gaussian integral, as in ordinary likelihood inference.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Yamamoto, Kato, Hunter, and Varin before him or her, to modify the system of claim 8 to include attributes of wherein the likelihood ratio is an integrated likelihood ratio that is calculated by taking into account a plurality of individual likelihood ratios that are calculated on the basis of two consecutive elements included in the series data in order to not be as prohibitive as ordinary likelihood inferences (see pg. 2368 section 3 : “ Computing the first-order pairwise likelihood requires the approximation of bivariate Gaussian integrals instead of an often prohibitive single n-dimensional Gaussian integral, as in ordinary likelihood inference.”).
Conclusion
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/ANDREW BRACERO/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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