DETAILED ACTION
Introduction
Applicant's submission filed on 07/11/2024 has been entered. Claims 1-10 are pending in the application and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 3 objected to because of the following informalities: set the threshold based on two different provisional voice segment lengths, however, it is confusing how the threshold set is related to the first and second length if one is longer than the other. Appropriate correction is required.
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 because the claimed invention is directed to an extra solution activity abstract idea without significantly more.
According to USPTO guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, method of manufacture, or composition of matter), or
STEP 2: the claim recites a judicial exception (e.g. an abstract idea) without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (Prong 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? The guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts- mathematical relationships, formulas or equations, calculations
Certain methods of organizing human activity- fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships or interactions between people
Mental processes- concepts that are practicably performed in the human mind (including an observation, evaluation, judgement, or opinions)
STEP 2A (Prong 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? The guidelines provide the following exemplary considerations that are indicative than an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? Consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, or conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Using the two-step inquiry, claim 1 is directed to an abstract idea as show below:
STEP 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
YES. Claim 1 is directed to an apparatus.
STEP 2A (Prong 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
YES. The claim recites an abstract idea:
The limitation of determines a beginning of a voice segment including a voice that appears in a voice signal, as drafted, is a process that, under its broadest reasonable interpretation, is a human activity listening to the start of a voice.
The limitation determines an end of the voice segment by determining whether or not a length of a non-voice segment that appears after the beginning is determined, is greater than or equal to a threshold as drafted, is a process that, under its broadest reasonable interpretation, recites a time limit value that is used to detect a valid voice by a human. The limitation of set the threshold on the basis of a property of a provisional voice segment starting from the beginning, as drafted, is a process that, under its broadest reasonable interpretation, recites a human activity to note the threshold of time to detect of voice in a notebook.
STEP 2A (Prong 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO.
Claim 1 recites the additional element of generating through a “processor”, which are recited at a high level of generality and amounts to merely using a computer as a tool to perform an abstract idea or mere instructions to apply the exception using a generic computer component. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the insignificant extra-solution activities abstract idea but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. This judicial exception is not integrated into a practical application.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
NO.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor and voice audio signal amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claim 1 is not patent eligible.
Claim 2 further specifies providing the length of the provisional voice signal and is a process that, under its broadest reasonable interpretation, is a data gathering process (insignificant extra-solution activity) and does not reflect an improvement in the functioning of a technology or computer . The claim is not patent eligible.
Claim 3 further recites process of comparing with a threshold. This limitation, under its broadest reasonable interpretation, is a data gathering process (insignificant extra-solution activity), using well-understood, routine, and conventional components recited at a high level of generality and does not reflect an improvement in the functioning of a technology or computer. The claim is not patent eligible.
Claim 4 further describes the processing of voice segment and is a process that, under its broadest reasonable interpretation, is insignificant extra-solution activity and does not reflect an improvement in the functioning of a technology or computer. The claim is not patent eligible.
Claim 5 further describes processing of the voice signal using a CTC model and is a process that, under its broadest reasonable interpretation, is a mathematical computation process that can be performed by a human using mathematical computational tools and does not reflect an improvement in the functioning of a technology or computer. The claim is not patent eligible.
Claim 6 further describes the transcribing and is a process that, under its broadest reasonable interpretation, is a computation process that can be performed by a human and does not reflect an improvement in the functioning of a technology or computer. The claim is not patent eligible.
Claim 7-8 further describes the processing of voice segment and is a process that, under its broadest reasonable interpretation, is insignificant extra-solution activity and does not reflect an improvement in the functioning of a technology or computer. The claims are patent eligible.
Claims 9-10 are analogous to claim 1 respectively, as directed to a method and processing device, the processing device to perform the operations set forth in claim 1 and are subjected to the same rejections as claims 1 respectively. Claims 9 and 10 merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The claims are not patent eligible.
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.
Claims 1-2, 4-6 and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Vaidya et. al. US Patent 11,817,117 in view of Arakawa et. al. US Patent 8,694,308.
Regarding claim 1, Vaidya teaches a voice detection apparatus comprising:
at least one memory configured to store instructions; and at least one processor configured to execute the instructions to (see Vaidya, col 3 lines 31-36): determines a beginning of a voice segment including a voice that appears in a voice signal (see Vaidya col 6 lines 50-53, In at least one embodiment, the EOS detector 210 can be used to detect a start of speech (SOS) and an EOS segment for output of an automatic speech recognition (ASR) model based on the output of the CTC function 208 ); determines an end of the voice segment by determining whether or not a length of a non-voice segment that appears after the beginning is determined, is greater than or equal to a threshold (see Vaidya, col 12, lines 53-65In step 606, the system performing the method 600, determines a percentage of blank symbols (non-voice) within the first sliding window. In an embodiment, the percentage of blank symbols is determined by dividing the number of blank symbols in the sliding window by the total number of symbols within the sliding window. In block 608, the system performing the method 600, determines if the EOS threshold has been satisfied based at least in part on the percentage of blank symbols determined in block 606); and set the threshold on the basis of a property of a provisional voice segment starting from the beginning (see Vaidya, col 2, lines 15-26 , To determine the EOS threshold for a particular speaker, intervals between successive words (e.g., inter-word time) is calculated based at least in part on the output of the CTC as described in greater detail below. In one example, the EOS threshold is calculated(set the threshold) as a function of a set of inter-word times (provisional voice segment) based at least in part on a string of characters outputted by the CTC).
Vaidya teaches set the threshold on the basis of a property of a provisional voice segment starting from the beginning, to further compact prosecution, to teach a property of a provisional voice segment, Arakawa is used to further teach determines a beginning of a voice segment including a voice that appears in a voice signal (see Arakawa Fig. 2, col 6 lines 38-45, As the feature value calculated, for example, the following may be used. SNR, zero point crossing, ratio of a voice likelihood to a non-voice likelihood, a first derivative or a second derivative of a voice power, and a smoothed version of a feature value ); determines an end of the voice segment by determining whether or not a length of a non-voice segment that appears after the beginning is determined, is greater than or equal to a threshold (see Arakawa, col 5 lines 63-67 a feature threshold value/provisional duration threshold value storage unit 4 in which a threshold value for feature values found on a per frame basis a threshold value for a provisional voiced interval duration, and a threshold value for a provisional non-voice duration, are stored); and set the threshold on the basis of a property of a provisional voice segment starting from the beginning (see Arakawa, col 7 lines 1-7, The duration threshold value determination unit 5 then determines a duration threshold value from the feature value found on a per frame basis and from the threshold value for the feature value, and from the provisional duration threshold value(starting from the beginning). The threshold value for the feature value and the provisional duration threshold value are stored in the feature threshold value/provisional duration threshold value storage unit 4 (step S4); col 8 lines 61-65 discusses the leading and trailing ends of the frame of interest ( starting from the beginning)).
Vaidya and Arakawa are considered to be analogous to the claimed invention because they relate to speech detection. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Vaidya on processing adaptive end of speech detection with the provisional voice /non voice teachings of Arakawa is used to improve performance, or reduce processing amount ( see Arakawa, col 1 lines 34-35).
Regarding claim 2, Vaidya in view of Arakawa teaches the voice detection apparatus according to claim 1. Arakawa further teaches wherein the property of the provisional voice segment includes a length of the provisional voice segment (see Arakawa, col 7 lines 1-7, The duration threshold value determination unit 5 then determines a duration threshold value from the feature value found on a per frame basis and from the threshold value for the feature value, and from the provisional duration threshold value( length of provisional voice segment). The threshold value for the feature value and the provisional duration threshold value are stored in the feature threshold value/provisional duration threshold value storage unit 4 (step S4)). The same motivation to combine as claim 1 applies here.
Regarding claim 3, Vaidya in view of Arakawa teaches the voice detection apparatus according to claim 2. Arakawa further teaches wherein the at least one processor is configured to execute the instructions to set the threshold such that the threshold set when the length of the provisional voice segment is a first length, is greater than the threshold set when the length of the provisional voice segment is a second length that is longer than the first length (see Arakawa, col 27 lines 48-67 teaches the threshold for voiced/non voiced are based on provisional voiced interval duration threshold(determined voiced interval duration/threshold value) and feature value ( provisional voice segment length); non-voiced threshold set based the provisional voice segment of first length or second length (different lengths)). Further, Vaidya teaches wherein the at least one processor is configured to execute the instructions to set the threshold such that the threshold set when the length of the provisional voice segment is a first length, is greater than the threshold set when the length of the provisional voice segment is a second length that is longer than the first length (see Vaidya, col 11 lines 45-63, teaches computing the EOS threshold based on the computed inter word interval and can be computed by subsequent inter-word intervals ( different provisional voice segment lengths)).
Regarding claim 4, Vaidya in view of Arakawa teaches the voice detection apparatus according to claim 1. Vaidya further teaches wherein the property of the provisional voice segment includes at least one of a number of characters of the voice included in the provisional voice segment, a number of words of the voice included in the provisional voice segment, and a speaking speed of the voice included in the provisional voice segment (see Vaidya, col 2 lines 21-26 When performing automatic speech recognition using speaker adaptive EOS, a certain number of words spoken by the particular speaker may be captured prior to estimating a rate of speech for the particular speaker. In one example, a set of twenty-five inter-word times is obtained prior to calculating the rate of speech).
Regarding claim 5, Vaidya in view of Arakawa teaches the voice detection apparatus according to claim 1. Vaidya further teaches wherein the at least one processor is configured to execute the instructions to: generate, determine the beginning on the basis of the symbol data, and determine the end on the basis of the symbolic data, and the non-voice segment includes a segment in which the blank symbol appears continuously (see Vaidya, col 2 line 46- col 3 line 3 The CTC, in various embodiments, provides a probability distribution for possible characters, or samples, contained in that audio frame or time step of an audio input. In one example, these characters may include any appropriate alphanumerical character, as well as potentially one or more special characters such as blanks to represent time steps or audio frames in which no other character is detected. The EOS detector may then determine if the percentage of blank symbols (e.g., non-speech states) satisfies the EOS threshold.).
Regarding claim 6, Vaidya in view of Arakawa teaches the voice detection apparatus according to claim 5. Vaidya further teaches wherein the property of the provisional voice segment includes a number of character symbols included in the provisional voice segment( see Vaidya, col 6 line 1-14 In at least one embodiment, an output of the neural acoustic model 206 is provided to a connectionist temporal classification (CTC) function 208 to, given these extracted audio features, provide a probability distribution for possible characters, or samples, contained in a particular audio frame(provisional voice segment) or time step of the input audio 202. In various embodiments, the characters outputted by the neural acoustic model 206 include any appropriate alphanumerical character (e.g., A-Z), as well as potentially one or more special characters such as blanks to represent time steps or audio frames in which no other character is detected. In other words, the blank character, in an embodiment, represents a time step or audio frame in which speech was not detected (e.g., the speaker was silent such as a pause between words).).
Regarding claim 9, is directed to a method claim corresponding to the apparatus claim presented in claim 1 and is rejected under the same grounds stated above regarding claim 1.
Regarding claim 10, is directed to a non-transitory recording medium claim corresponding to the apparatus claim presented in claim 1 and is rejected under the same grounds stated above regarding claim 1.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Vaidya et. al. US Patent 11,817,117 in view of Arakawa et. al. US Patent 8,694,308 further in view of Li et. al. US PgPub. 2022/0335947.
Regarding claim 7, Vaidya in view of Arakawa teaches the voice detection apparatus according to claim 1. Vaidya further teaches sets the threshold on the basis of the speaker information corresponding to the identified speaker (see Vaidya, col 2 lines 21-26 When performing automatic speech recognition using speaker adaptive EOS, a certain number of words spoken by the particular speaker may be captured prior to estimating a rate of speech for the particular speaker. In one example, a set of twenty-five inter-word times is obtained prior to calculating the rate of speech; see Vaidya, col 2, lines 15-26 , To determine the EOS threshold for a particular speaker, intervals between successive words (e.g., inter-word time) is calculated based at least in part on the output of the CTC as described in greater detail below. In one example, the EOS threshold is calculated(set the threshold) as a function of a set of inter-word times (provisional voice segment) based at least in part on a string of characters outputted by the CTC). However, Vaidya in view of Arakawa fail to teach wherein the voice detection apparatus further comprises a storage unit that stores, for each speaker, speaker information about characteristics of a voice uttered by the speaker, and the at least one processor is configured to execute the instructions to identify the setting unit identifies a speaker from whom the voice signal is acquired.
However, Li teaches wherein the voice detection apparatus further comprises a storage unit that stores, for each speaker, speaker information about characteristics of a voice uttered by the speaker, and the at least one processor is configured to execute the instructions to identify the setting unit identifies a speaker from whom the voice signal is acquired (see Li, [0012] analyzes vocal characteristics of speech sounds in each data fragment to identify the speaker of a set of speakers, [0299, 0308-0309] discusses further on generation of the speaker vector for the respective speaker ID).
Vaidya in view Arakawa teaches adaptive end of speech detection based on particular speaker, however does not teach particular speaker identification. Li teaches using the speaker diarization techniques to identify the speaker. Using the known technique of speaker identification taught by Li (see Li, Fig. 19A), to provide the speaker identification in the reference Vaidya in view Arakawa would have been obvious to one of ordinary skill in the art.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Vaidya et. al. US Patent 11,817,117 in view of Arakawa et. al. US Patent 8,694,308 further in view of O'Hart Kinney et. al. US PgPub 2019/0325898 (henceforth Kinney).
Regarding claim 8, Vaidya in view of Arakawa teaches the voice detection apparatus according to claim 1. However, Vaidya in view of Arakawa fail to teach convert the voice signal into text data by analyzing the voice signal by using dictionary data, and set the threshold on the basis of a property of the dictionary data.
However, Kinney teaches convert the voice signal into text data by analyzing the voice signal by using dictionary data (see Kinney, [0058] A step 42 uses the audio sequence, in real time, to compute a disfluency score according to an appropriate approach; Kinney [0083] the phonetic dictionary( pronunciation dictionary) is used to provide the transcription and the phonetic disfluency model is used to compute the score (analyzing the voice signal) ), and set the threshold on the basis of a property of the dictionary data (see Kinney, [0058] A step 43 adapts the EOU timeout ( End of Unit of speech; threshold) as a function of the disfluency score. Doing so enables the process to prevent an improper timeout that disrupts receiving a complete sentence in the audio sequence; Kinney, [0075] discusses pronunciation dictionaries used to compute the disfluency score).
Vaidya in view Arakawa teaches adaptive end of speech detection based on particular speaker, however does not teach changing the threshold of speech detection based on the dictionary data. Kinney teaches changing the end of utterance detection based on the phonetic dictionary data. Using the known technique of changing the end of utterance detection based on the disfluency score which is taught by Kinney (see Kinney, Fig. 9), to provide the change in threshold for speech detection in the reference Vaidya in view Arakawa would have been obvious to one of ordinary skill in the art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Arakawa US Patent 8,812,313 , teaches the active/non-active duration threshold is updated based on the number of active voice segments/non-active segments (Arakawa ‘312, col 18 lines 47-57, Fig. 10).
Hauenstein et. al. US Patent 7,035,799 teaches different possible system parameters for threshold settings including acoustic look-ahead and in language models to compute word ends(see Hauenstein, Fig. 4).
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/NANDINI SUBRAMANI/Examiner, Art Unit 2656
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