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 Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated below. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
a transforming unit in claims 1, 8
a weighting unit in claim 1-7
a recognizing unit in claims 8
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter without significantly more. The claims as whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea.
Independent claims 1, 9 and 17 recite a transforming unit that transforms an utterance by an utterer into a feature vector; a weighting unit that weights the feature vector with importance of the utterance based on situation information representing a situation at time of the utterance by the utterer; and a recognizing unit that recognizes a new utterance by the utterer based on the weighted feature vector.
The limitations of transforming, weighting, recognizing as drafted cover a mental process when a human hears a speaker, writes a count of the words in his notebook, notes the tone was emotional and multiplies the earlier score by 2. The next time the person speaks, the human assigns a score to the speech and compares it to the notebook to determine emotional level.
This judicial exception is not integrated into a practical application. In particular claim 20 recites additional element of computer, which is a form of generic equipment. In the as-filed Specifications ¶0085] recite he abovementioned programs can be stored using various types of non-transitory computer-readable mediums and provided to a computer. The non-transitory computer-readable medium includes various types of tangible storage mediums. Examples of non-transitory computer-readable medium include magnetic recording medium (e.g., flexible disk, magnetic tape, hard disk drive), magneto-optical recording medium (e.g., magneto-optical disk), read only memory (CD-ROM), CD-R, CD-R/W, semiconductor memory (e.g., mask ROM, programmable ROM, Erasable PROM, flash ROM, random access memory (RAM)). In addition, a program may be provided to a computer by various types of temporary computer-readable medium. Examples of temporary computer-readable medium include electrical signals, optical signals, and electromagnetic wave. The temporary computer-readable medium may provide a program to the computer via a wired communication channel, such as an electric wire and an optical fiber, or a wireless communication channel.. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a computer is noted as a general computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Claims 2 and 10 recite wherein the weighting unit estimates an emotion at the time of the utterance of the utterer based on the situation information, calculates the importance of the utterance based on the emotion, and weights the feature vector with the importance. This amounts to the human hears a speaker, writes a count of the words in his notebook, notes the tone was emotional and multiplies the earlier score by 2 if it is emotional. No other limitations are present.
Claims 3 and 11 recite wherein the weighting unit estimates the emotion of the utterer from speech or video of the utterer that is the situation information, calculates the importance of the utterance based on the emotion, and weights the feature vector with the importance. This amounts to the human hears a speaker, writes a count of the words in his notebook, notes the tone was emotional and multiplies the earlier score by 2 if it is emotional. No other limitations are present.
Claims 4 and 12 recite wherein the weighting unit calculates the importance so that the importance is larger as a degree of a preset emotion at the time of the utterance of the utterer is larger. This amounts to the human hears a speaker, assigns a score of 2 times the number of words because of emotional nature as it compares to a preset emotion of no emotion. No other limitations are present.
Claims 5 and 13 recite wherein the weighting unit calculates the importance of speech based on time information at the time of the utterance by the utterer that is the situation information, and weights the feature vector with the importance. This amounts to the human hears a speaker, assigns a score based on time of day of the speech. No other limitations are present.
Claims 6 and 14 recite wherein the weighting unit calculates the importance so that the importance is smaller as time is more previous based on the time information at the time of the utterance by the utterer. This amounts to the human hears a speaker assigns a score 2x than the score of similar speech an hour earlier. No other limitations are present.
Claims 7 and 15 recite wherein the weighting unit calculates the importance of the utterance based on the time information for each lapse of time, and weights the feature vector with the importance. This amounts to the human hears a speaker assigns a different score each time he hears the speaker. No other limitations are present.
Claims 8 and 16 recite wherein the transforming unit transforms the utterance by the utterer into the feature vector based on a preset basis. This amounts to the assigning a score of 10 to a speech if it is highly emotional irrespective of the number of words. No other limitations are present
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-5, 8, 9-13, 16, 17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kalinli (US 20140114655 A1)
With respect to claims 1, 9 and 17 Kalinli teaches [Claim 1] A speech recognition apparatus comprising: [Claim 9] A speech recognition method comprising: [Claim 17] A non-transitory computer-readable storage medium storing a program, the program comprising instructions for causing a computer to execute processes to (0076] According to another embodiment, instructions for emotion recognition using auditory attention cues may be stored in a computer readable storage medium. By way of example, and not by way of limitation, FIG. 4 illustrates an example of a non-transitory computer readable storage medium 400 in accordance with an embodiment of the present invention. The storage medium 400 contains computer-readable instructions stored in a format that can be retrieved, interpreted, and executed by a computer processing device. By way of example, and not by way of limitation, the computer-readable storage medium 400 may be a computer-readable memory, such as random access memory (RAM) or read only memory (ROM), a computer readable storage disk for a fixed disk drive (e.g., a hard disk drive), or a removable disk drive. In addition, the computer-readable storage medium 400 may be a flash memory device, a computer-readable tape, a CD-ROM, a DVD-ROM, a Blu-Ray, HD-DVD, UMD, or other optical storage medium):
a transforming unit that transforms an utterance by an utterer into a feature vector (Kalinli ¶[0017] The method and system described in the present disclosure are inspired by the human auditory attention system and uses auditory attention features for emotion recognition [emotion feature vector]);
a weighting unit that weights the feature vector with importance of the utterance based on situation information representing a situation at time of the utterance by the utterer (Kalinli ¶¶[0017] The method and system described in the present disclosure are inspired by the human auditory attention system and uses auditory attention features for emotion recognition [emotion feature vector],¶[0067] According to certain aspects of the present disclosure, one can train and adjust speech recognition models based on recognized emotion [situation at time]. For example, suppose people speak faster when they are happy. One may tweak the parameters [weighted feature] of the speech recognition based on the recognized emotion to better match to the spoken utterance which may be affected by the speaker's emotion.);
and a recognizing unit that recognizes a new utterance by the utterer based on the weighted feature vector (Kalinli ¶[0067] Then, at runtime, based on user's estimated emotion state [based on weighted feature vector], the matching acoustic model can be used to improve speech recognition performance. Similarly, the language model and dictionary can be adapted based on the emotion. )
With respect to claims 2 and 10 Kalinli teaches wherein the weighting unit estimates an emotion at the time of the utterance of the utterer based on the situation information, calculates the importance of the utterance based on the emotion, and weights the feature vector with the importance ( ¶(Kalinli ¶[0067] According to certain aspects of the present disclosure, one can train and adjust speech recognition models based on recognized emotion [situation at time]. For example, suppose people speak faster when they are happy. One may tweak the parameters [weighted feature]of the speech recognition based on the recognized emotion to better match to the spoken utterance which may be affected by the speaker's emotion. By way of example, and not by way of limitation, many acoustic models can be pre-trained where each is tuned to a specific emotion class. For example, during training, an acoustic model can be tuned for "excited" emotion class by using data collected from users who is excited. Then, at runtime, based on user's estimated emotion state [estimates and emotion at the time], the matching acoustic model can be used to improve speech recognition performance. Similarly, the language model and dictionary can be adapted [] based on the emotion. For example, when people are bored they tend to speak slower whereas excited people tend to speak faster, which eventually changes word pronunciations. The dictionary, which consists of the pronunciation of words as a sequence of phonemes, can also be dynamically adapted based on the user's emotion to better match the user's speech characteristic due to his/her emotion. Again, multiple dictionaries tuned to certain emotion classes can be created offline, and then used based on the estimated user emotion to improve speech recognition performance.)
With respect to claims 3 and 11 Kalinli teaches wherein the weighting unit estimates the emotion of the utterer from speech or video of the utterer that is the situation information, calculates the importance of the utterance based on the emotion, and weights the feature vector with the importance (Kalinli ¶[0017] The method and system described in the present disclosure are inspired by the human auditory attention system and uses auditory attention features for emotion recognition [emotion feature vector],¶[0067] According to certain aspects of the present disclosure, one can train and adjust speech recognition models based on recognized emotion [situation at time]. For example, suppose people speak faster when they are happy. One may tweak the parameters [weighted feature]of the speech recognition based on the recognized emotion to better match to the spoken utterance which may be affected by the speaker's emotion. By way of example, and not by way of limitation, many acoustic models can be pre-trained where each is tuned to a specific emotion class. For example, during training, an acoustic model can be tuned for "excited" emotion class by using data collected from users who is excited. Then, at runtime, based on user's estimated emotion state, the matching acoustic model can be used to improve speech recognition performance. Similarly, the language model and dictionary can be adapted based on the emotion. For example, when people are bored they tend to speak slower whereas excited people tend to speak faster, which eventually changes word pronunciations. The dictionary, which consists of the pronunciation of words as a sequence of phonemes, can also be dynamically adapted based on the user's emotion to better match the user's speech characteristic due to his/her emotion. Again, multiple dictionaries tuned to certain emotion classes can be created offline, and then used based on the estimated user emotion to improve speech recognition performance.)
With respect to claims 4 and 12 Kalinli teaches wherein the weighting unit calculates the importance so that the importance is larger as a degree of a preset emotion at the time of the utterance of the utterer is larger (Kalinli ¶[0063] For the top down model 200 the most salient locations of the saliency maps 249 may be selected and used for emotion recognition. A maximum of a saliency map 249 may define the most salient portion of a feature map. For example, the top N salient events 206 can be selected for further processing for emotion recognition, where N can be determined with experiments. Alternatively, a saliency threshold [degree of preset emotion]can be set and event/s with a saliency score that is above the determined threshold may be selected for further analysis and emotion [emotion]recognition. Once the salient event has been identified, feature extraction 208 may be performed on a window of sound, W, around the salient events 206. When the number of selected salient events; e.g. N, is smaller than the number of segments in an utterance or sound clip, this will result in computational cost reduction. Features that may be extracted at this stage include, but are not limited to prosodic features (e.g., pitch, intensity, duration, and variations thereon), auditory attention features (e.g., intensity, frequency, contrast temporal contrast, orientation, pitch variation following dimension reduction to remove redundant features and reduce dimension), MEL filterbank energy, MFCC, etc. or some combination of prosodic features, auditory attention features, and MEL filterbank energy, MFCC, etc. The extracted features may be sent to a machine learning algorithm to predict emotion based on salient events.)
With respect to claims 5 and 13 Kalinli teaches wherein the weighting unit calculates the importance of speech based on time information at the time of the utterance by the utterer that is the situation information, and weights the feature vector with the importance ([0051] A salient event detector 202 analyzes the input window of sound to detect salient events and returns a saliency score as a function of time 204. Then, audio events with sufficient saliency score can be selected [based on time information] for further analysis, while other portions of sound may be ignored. By way of example, for example, selection can be done in a decreasing order of saliency score; e.g., the top N saliency scores, where N can be determined experimentally. Alternatively, events with a saliency score that's exceeding a saliency threshold may be selected. Next, to capture the audio event corresponding to a salient event, the sound around each salient point is extracted using a window of duration W creating selected segments 206. In particular, portions 206 having a sufficient saliency score may be subject to feature extraction 208, e.g., in a manner similar to that described above with respect to FIG. 1A. )
With respect to claims 8 and 16 Kalinli teaches wherein the transforming unit transforms the utterance by the utterer into the feature vector based on a preset basis (Kalinli ¶( ¶[0067] According to certain aspects of the present disclosure, one can train and adjust speech recognition models based on recognized emotion. For example, suppose people speak faster when they are happy. One may tweak the parameters of the speech recognition based on the recognized emotion to better match to the spoken utterance which may be affected by the speaker's emotion. By way of example, and not by way of limitation, many acoustic models can be pre-trained where each is tuned to a specific emotion class [preset basis]. For example, during training, an acoustic model can be tuned for "excited" emotion class by using data collected from users who is excited))
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 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.
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 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 6-7, 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Kalinli in further view of Bou (US 20200372891 A1).
With respect to claims 6 and 14 Kalinli does not explicitly disclose however Bou teaches wherein the weighting unit calculates the importance so that the importance is smaller as time is more previous based on the time information at the time of the utterance by the utterer (Bou ¶[0007] During a transition from double-talk to single-talk, however, a shorter time smoothing constant (e.g., one applying a relatively higher weight to a current or most recent interval) [importance is lower for distant past events] may be selected, allowing the speech component to be smoothed out of the spectral mismatch more quickly. As the transition proceeds into a single-talk period, one or more longer time smoothing constants may be applied, allowing for more accurate spectral mismatch estimates as the risk of corruption from the speech component recedes.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify the utterance transformer of Kalinli to include time based importance of Bou in order to provide contextual relevance based on time.
With respect to claims 7 and 15 Bou further teaches wherein the weighting unit calculates the importance of the utterance based on the time information for each lapse of time, and weights the feature vector with the importance (Bou ¶[0007] In some examples, time smoothing constants may be selected based on a current status of being in a single-talk period or a transition from a double-talk period to a single-talk period. During the single-talk period, a “longer” time smoothing constant (e.g., one that applies a relatively lower weight [lower weight for this interval]to a current or most recent interval) may be selected, allowing for more accurate spectral mismatch estimates. During a transition from double-talk to single-talk, however, a shorter time smoothing constant (e.g., one applying a relatively higher weight [higher weight for this interval]to a current or most recent interval) may be selected, allowing the speech component to be smoothed out of the spectral mismatch more quickly. As the transition proceeds into a single-talk period, one or more longer time smoothing constants may be applied, allowing for more accurate spectral mismatch estimates as the risk of corruption from the speech component recedes.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ATHAR N PASHA whose telephone number is (408)918-7675. The examiner can normally be reached on Monday-Thursday Alternate Fridays, 7:30-4:30 PT.
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/ATHAR N PASHA/Primary Examiner, Art Unit 2657
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