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 .
Compact Prosecution
The examiner suggests scheduling a telephone interview to discuss potential examiner amendments. The purpose of this interview is to explore possible claim amendments that could help advance the prosecution of this application.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 11 and 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant argues that the combination of the references Chae ( US20200111482) in view of Jung et al (US20160062983) fails to disclose Comparing two candidate recognition results (“first text” and “second text”) for the same input. Using a thresholded difference to select which to output and using the difference/relevance to drive training data acquisition.
In response Examiner respectfully disagree because the combination of Chae and Jung discloses Jung however discloses identifying a second text similar to the first text among textual information obtained from at least one of the personal information of the user or application information stored in the memory. (Section 0071 and 0072 two text such as Ki Myeon Moon and Kim Hyeong Moon reads on the two text information. Understand that Kim Hyeong Moon is identified from memory 220 to determine the similarity)
identify a difference between the first text and the second text based on identifying that the difference between the first text and the second text is less than or equal to a threshold value, (Sections 0122 and 0134 teaches that Jung compares a recognized named entity (from speech/text) to reference information (e.g contact list), calculating a similarity score (Levenshtein distance))
(NB: Understand that both the recognized entity and the reference data are all recognition results of utterance- also see Section 0065).
based on identifying that the difference between the first text and the second text is more than the threshold value, output the first text as the speech recognition result of the speech data, (Section 0110 and 0136- thus if no reference info is above threshold, the system uses the originally recognized named entity)
obtain relevance information based on the difference between the first text and the second text being less than the threshold value, ((Section 0086 and 0136- if similarity between recognized named entity and reference info is above threshold, the system corrects the named entity to the reference info. This is similar to substituting the recognized entity with a more likely candidate from the reference set)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to include the teaching of modifying the captured speech data to be from a user. The motivation is that it will mimic a person’s speech.
Chae, in view of Jung, fails to disclose acquiring training data for speech recognition based on relevance information that is determined from the difference between the first text and the second text with respect to the speech data—specifically, when the relevance information is based on the difference between the first and second texts being less than a threshold value.
In other words, the cited references do not teach or suggest selecting or acquiring training data for recognizing a user's speech by using relevance information that is calculated from comparing the first text (e.g., the initial recognition result) and the second text (e.g., a candidate or reference text), where this relevance information is only used if the difference between the two texts is below a specified threshold.
Eakin disclose acquiring training data for speech recognition based on relevance information that is determined from the difference between the first text and the second text with respect to the speech data—specifically, when the relevance information is based on the difference between the first and second texts being less than a threshold value. (Col. 36 lines 44-49- thus the ambiguity learning component is retrained using the dialog session data (relevance information) , (Also in Col. 40 lines 52-56 the system retrained other models to recognize the out of vocabulary)
(Col 42 lines 40-50 The model of the ambiguity is encounter by multiple models to be used for retraining or updating new models)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to include the teaching of updating models with disambiguate or relevance information where the difference is below the threshold as described above. The motivation is that incorporating new relevance information into the training process will improve the effectiveness of newly developed models. By using updated relevance information, the models can be better trained and achieve higher accuracy.
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, 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chae ( US20200111482) in view of Jung et al (US20160062983) and further in view of Eakin (US11211058).
Claim 1, Chae discloses an electronic device ( El. 10 in Fig. 4) comprising:
a microphone (Microphone 122 in fig 4, Section 0063, lines 5-6) ;
memory storing at least one instruction (Memory 170 shown in Fig. 4, Section 0066)
and at least one processor configured to execute the at least one instruction to (Processor 180 shown in Fig. 4) acquire, through the microphone, speech data corresponding to a user's speech, (Obtain speech data S601 in fig. 6)
acquire a first text based on the speech data by at least partially performing at least one of automatic speech recognition (ASR), or natural language understanding (NLU), (Section 0035, lines 1-5- thus artificial neural network and Section 0144, audio information for the AI apparatus 100, also see Section 0197).
based on acquiring the first text, identify a second text stored in the memory based on the first text, (Section 0205, lines 4-8- thus mapping the extracted utterance feature vector in the model)
output the first text or the second text as a speech recognition result of the speech data based on a difference between the first text and the second text, (Section 0196- thus the second speech data corresponding to a second style and a second speech recognition result)
(NB: Section 0208, Fig. 7 utterance feature vectors 720_1 reads on first text and the feature vectors 720_1 maps to feature space reads on the second text)
and acquire training data for recognition based on a relevance information between the first text and the second text with respect to the speech data. (Section 0058- thus a training data for model learning is acquired which reads on the training data)
Chae does not clearly disclose to identify a second text corresponding to the first text among textual information obtained from at least one of personal information of the user or application information stored in the memory.
identify a difference between the first text and the second text based on identifying that the difference between the first text and the second text is less than or equal to a threshold value,
based on identifying that the difference between the first text and the second text is more than the threshold value, output the first text as the speech recognition result of the speech data, obtain relevance information based on the difference between the first text and the second text being less than the threshold value,
Jung however discloses identifying a second text similar to the first text among textual information obtained from at least one of the personal information of the user or application information stored in the memory. (Section 0071 and 0072 two text such as Ki Myeon Moon and Kim Hyeong Moon reads on the two text information. Understand that Kim Hyeong Moon is identified from memory 220 to determine the similarity)
identify a difference between the first text and the second text based on identifying that the difference between the first text and the second text is less than or equal to a threshold value, (Sections 0122 and 0134 teaches that Jung compares a recognized named entity (from speech/text) to reference information (e.g contact list), calculating a similarity score (Levenshtein distance))
(NB: Understand that both the recognized entity and the reference data are all recognition results of utterance- also see Section 0065).
based on identifying that the difference between the first text and the second text is more than the threshold value, output the first text as the speech recognition result of the speech data, (Section 0110 and 0136- thus if no reference info is above threshold, the system uses the originally recognized named entity)
obtain relevance information based on the difference between the first text and the second text being less than the threshold value, ((Section 0086 and 0136- if similarity between recognized named entity and reference info is above threshold, the system corrects the named entity to the reference info. This is similar to substituting the recognized entity with a more likely candidate from the reference set)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to include the teaching of modifying the captured speech data to be from a user. The motivation is that it will mimic a person’s speech.
Chae, in view of Jung, fails to disclose acquiring training data for speech recognition based on relevance information that is determined from the difference between the first text and the second text with respect to the speech data—specifically, when the relevance information is based on the difference between the first and second texts being less than a threshold value.
In other words, the cited references do not teach or suggest selecting or acquiring training data for recognizing a user's speech by using relevance information that is calculated from comparing the first text (e.g., the initial recognition result) and the second text (e.g., a candidate or reference text), where this relevance information is only used if the difference between the two texts is below a specified threshold.
Eakin disclose acquiring training data for speech recognition based on relevance information that is determined from the difference between the first text and the second text with respect to the speech data—specifically, when the relevance information is based on the difference between the first and second texts being less than a threshold value. (Col. 36 lines 44-49- thus the ambiguity learning component is retrained using the dialog session data (relevance information) , (Also in Col. 40 lines 52-56 the system retrained other models to recognize the out of vocabulary)
(Col 42 lines 40-50 The model of the ambiguity is encounter by multiple models to be used for retraining or updating new models)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to include the teaching of updating models with disambiguate or relevance information where the difference is below the threshold as described above. The motivation is that incorporating new relevance information into the training process will improve the effectiveness of newly developed models. By using updated relevance information, the models can be better trained and achieve higher accuracy.
Claim 2, Chae in view of Jung and further in view of Eakin discloses that the electronic device of claim 1, wherein the at least one processor is further configured to execute the at least one instruction based on accumulating a designated amount of the training data, learn a feature vector analysis model for recognizing the user's speech, based on the training data. (Chae, Section 0217: learning processor 130 determine utterance style of each training data for a speech recognition model which includes feature vectors)
Claim 3, Chae in view of Jung and further in view of Eakin discloses that the electronic device of claim 1, wherein the at least one processor is further configured to execute the at least one instruction based on the difference between the first text and the second text being equal to or less than a designated value acquire the training data for recognition of the speech data as the second text. (Chae, Section 0232, lines 10-15- thus if the input utterance feature vector is not similar to the utterance feature vector belonging to the previously learned- See also that closet cluster is less than a predetermined value)
Claim 4, Chae in view of Jung and further in view of Eakin discloses that the electronic device of claim 3, wherein the at least one processor is further configured to execute the at least one instruction to determine a relationship between the first text and the second text to be an utterance characteristic of the user. (Chae: Section 0233, lines 14-15- thus utterance feature vector reads on the characteristic)
Claims 5 and 6, (Cancelled)
Claim 7, Chae in view of Jung and further in view of Eakin discloses that the electronic device of claim 1, wherein the at least one processor is further configured to execute the at least one instruction based on the first text, identify at least one utterance intent included in the speech data; (Chae: Section 0193- thus intent information) and based on the at least one utterance intent, identify the second text from among a plurality of texts stored in the memory. (Chae: Section 0164 generate intent analysis based on the question to generate at least one answer)
Claim 8, Chae in view of Jung and further in view of Eakin discloses that the electronic device of claim 7, wherein the at least one processor is further configured to execute the at least one instruction to: based on the at least one utterance intent, identify an utterance pattern of the speech data; (Chae, Section 0195 thus the speech data corresponds to the received speech model corresponding to the utterance style (Pattern) and store, in the memory, the utterance pattern as information on an utterance characteristic of the user. (Utterance style reads on the characteristic of the user).
Claim 9, Chae in view of Jung and further in view of Eakin discloses that the electronic device of claim 1, wherein the at least one processor is further configured to execute the at least one instruction to: divide each of the first text and the second text in units of phonemes; (Chae: Section 0236 – thus the processor may divide the speech data into predetermined units and determine the utterance style in the predetermined units) and identify the difference between the first text and the second text, based on similarities between a plurality of first phonemes in the first text and a plurality of second phonemes in the second text. (Chae: Section 0232, lines 1-2 “The second utterance style determination model may determine whether the input utterance feature vector is similar to an utterance feature vector belonging to the previously learned clusters, unlike the first utterance style determination model”)
Claim 10, Chae in view of Jung and further in view of Eakin discloses The electronic device of claim 1, wherein the at least one processor is further configured to execute the at least one instruction to extract features of the speech data acquired from the user based on the features, extract a feature vector of the speech data; (Chae: Section 0181, lines 1-3 thus the extracted utterance feature includes at least one of a gender of a speaker, speech speed and pronunciation and even pronunciation) based on the feature vector, acquire speech-recognized multiple speech recognition candidates; (Chae: Section 0267- thus the first to m-th clusters reads on the various candidates) determine the first text, based on matching probabilities of the multiple speech recognition candidates determined by at least one language model; (Chae: Section 0198, lines 4-7 “Convert the speech data into text based on the calculated probability”) determine whether to replace the first text with the second text, as the speech recognition result from among the multiple speech recognition candidates, (Chae: Section 0272- thus adding the text data 1540 means that new dataset is added to replace some old dataset) based on information of at least one utterance characteristic of the user and a personal information of the user stored in the memory; (Chae: Section 0275-0276- thus the new utterance style being recognized means that the old one has been replaced) and display, through a display of the electronic device, the speech recognition result of the speech data. (Chae: display unit 151 shown in Fig. 4 as part of the Output unit 150)
Claim 11, Chae discloses a method of operating an electronic device, ( El. 10 in Fig. 4) comprising:
acquiring, through a microphone of the electronic device, (Microphone 122 in fig 4, Section 0063, lines 5-6) speech data corresponding to a user's speech; , (Obtain speech data S601 in fig. 6) acquiring a first text based on the speech data by at least partially performing at least one of automatic speech recognition (ASR) or natural language understanding (NLU); (Section 0035, lines 1-5- thus artificial neural network and Section 0144, audio information for the AI apparatus 100, also see Section 0197).
Based on acquiring the first text, identifying a second text stored in the electronic device based on the first text; (Section 0205, lines 4-8- thus mapping the extracted utterance feature vector in the model)
outputting the first text or the second text as a speech recognition result of the speech data based on a difference between the first text and the second text; (Section 0196- thus the second speech data corresponding to a second style and a second speech recognition result)
(NB: Section 0208, Fig. 7 utterance feature vectors 720_1 reads on first text and the feature vectors 720_1 maps to feature space reads on the second text)
and acquiring training data for recognition based on a relevance between the first text and the second text with respect to the speech data. (Section 0058- thus a training data for model learning is acquired which reads on the training data)
Chae does not clearly disclose to identify a second text similar to the first text among textual information obtained from at least one of personal information of the user or application information stored in the memory.
identifying a difference between the first text and the second text; based on identifying that the difference between the first text and the second text is less than or equal to a threshold value,
based on identifying that the difference between the first text and the second text is more than the threshold value, outputting the first text as the speech recognition result of the speech data;
obtaining relevance information based on the difference between the first text and the second text being less than the threshold value;
and
acquiring training data for recognition of the user's speech based on the relevance information a relevance between the first text and the second text with respect to the speech data.
Jung however discloses identifying a second text similar to the first text among textual information obtained from at least one of the personal information of the user or application information stored in the memory. (Section 0071 and 0072 two text such as Ki Myeon Moon and Kim Hyeong Moon reads on the two text information. Understand that Kim Hyeong Moon is identified from memory 220 to determine the similarity)
identifying a difference between the first text and the second text; based on identifying that the difference between the first text and the second text is less than or equal to a threshold value, (Sections 0122 and 0134 teaches that Jung compares a recognized named entity (from speech/text) to reference information (e.g contact list), calculating a similarity score (Levenshtein distance))
(NB: Understand that both the recognized entity and the reference data are all recognition results of utterance- also see Section 0065).
based on identifying that the difference between the first text and the second text is more than the threshold value, outputting the first text as the speech recognition result of the speech data;
(Section 0110 and 0136- thus if no reference info is above threshold, the system uses the originally recognized named entity)
obtaining relevance information based on the difference between the first text and the second text being less than the threshold value;
((Section 0086 and 0136- if similarity between recognized named entity and reference info is above threshold, the system corrects the named entity to the reference info. This is similar to substituting the recognized entity with a more likely candidate from the reference set)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to include the teaching of modifying the captured speech data to be from a user. The motivation is that it will mimic a person’s speech.
acquiring training data for recognition of the user's speech based on the relevance information a relevance between the first text and the second text with respect to the speech data.
Chae, in view of Jung, fails to disclose acquiring training data for speech recognition based on relevance information that is determined from the difference between the first text and the second text with respect to the speech data—specifically, when the relevance information is based on the difference between the first and second texts being less than a threshold value.
In other words, the cited references do not teach or suggest selecting or acquiring training data for recognizing a user's speech by using relevance information that is calculated from comparing the first text (e.g., the initial recognition result) and the second text (e.g., a candidate or reference text), where this relevance information is only used if the difference between the two texts is below a specified threshold.
Eakin disclose acquiring training data for speech recognition based on relevance information that is determined from the difference between the first text and the second text with respect to the speech data—specifically, when the relevance information is based on the difference between the first and second texts being less than a threshold value. (Col. 36 lines 44-49- thus the ambiguity learning component is retrained using the dialog session data (relevance information) , (Also in Col. 40 lines 52-56 the system retrained other models to recognize the out of vocabulary)
(Col 42 lines 40-50 The model of the ambiguity is encounter by multiple models to be used for retraining or updating new models)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to include the teaching of updating models with disambiguate or relevance information where the difference is below the threshold as described above. The motivation is that incorporating new relevance information into the training process will improve the effectiveness of newly developed models. By using updated relevance information, the models can be better trained and achieve higher accuracy.
Claim 12, Chae in view of Jung and further in view of Eakin discloses that the method of claim 11, further comprising based on accumulating a designated amount of the training data, training a feature vector analysis model for recognizing the user's speech, based on the training data. (Chae, Section 0217: learning processor 130 determine utterance style of each training data for a speech recognition model which includes feature vectors)
Claim 13, Chae in view of Jung and further in view of Eakin discloses that the method of claim 11, wherein the acquiring the training data comprises based on the difference between the first text and the second text being equal to or less than a designated value, acquiring the training data for recognition of the speech data as the second text. (Chae, Section 0232, lines 10-15- thus if the input utterance feature vector is not similar to the utterance feature vector belonging to the previously learned- See also that closet cluster is less than a predetermined value)
Claim 14, Chae in view of Jung and further in view of Eakin discloses that the method of claim 13, wherein the acquiring the training data comprises: determining a relationship between the first text and the second text to be an utterance characteristic of the user. (Chae: Section 0233, lines 14-15- thus utterance feature vector reads on the characteristic)
Claims 15-16 (Canceled)
Claim 17, Chae in view of Jung and further in view of Eakin discloses that the method of claim 11, further comprising: based on the first text, identifying at least one utterance intent included in the speech data; (Chae: Section 0193- thus intent information) and based on the at least one utterance intent, identifying the second text among multiple texts stored in the memory. (Chae: Section 0164 generate intent analysis based on the question to generate at least one answer)
Claim 18, Chae in view of Jung and further in view of Eakin discloses that the method of claim 17, further comprising: based on the at least one utterance intent, identifying an utterance pattern of the speech data; (Chae, Section 0195 thus the speech data corresponds to the received speech model corresponding to the utterance style (Pattern) and storing, in the memory, the utterance pattern as information on an utterance characteristic of the user. (Utterance style reads on the characteristic of the user).
Claim 19, Chae in view of Jung and further in view of Eakin discloses that the method of claim 11, further comprising: dividing each of the first text and the second text in units of phonemes; (Chae: Section 0236 – thus the processor may divide the speech data into predetermined units and determine the utterance style in the predetermined units) and identifying the difference between the first text and the second text, based on similarities between a plurality of first phonemes in the first text and a plurality of second phonemes in the second text. (Chae: Section 0232, lines 1-2 “The second utterance style determination model may determine whether the input utterance feature vector is similar to an utterance feature vector belonging to the previously learned clusters, unlike the first utterance style determination model”)
Claim 20, Chae discloses a non-transitory computer readable medium for storing computer readable program code or instructions which are executable by a processor to perform a method for operating an electronic device , ( El. 10 in Fig. 4) the method comprising acquiring, through a microphone of the electronic device, (Microphone 122 in fig 4, Section 0063, lines 5-6) speech data corresponding to a user's speech; (Obtain speech data S601 in fig. 6)
Chae discloses a method of operating an electronic device, ( El. 10 in Fig. 4) comprising:
acquiring, through a microphone of the electronic device, (Microphone 122 in fig 4, Section 0063, lines 5-6) speech data corresponding to a user's speech; , (Obtain speech data S601 in fig. 6) acquiring a first text based on the speech data by at least partially performing at least one of automatic speech recognition (ASR) or natural language understanding (NLU); (Section 0035, lines 1-5- thus artificial neural network and Section 0144, audio information for the AI apparatus 100, also see Section 0197).
Based on acquiring the first text, identifying a second text stored in the electronic device based on the first text; (Section 0205, lines 4-8- thus mapping the extracted utterance feature vector in the model)
outputting the first text or the second text as a speech recognition result of the speech data based on a difference between the first text and the second text; (Section 0196- thus the second speech data corresponding to a second style and a second speech recognition result)
(NB: Section 0208, Fig. 7 utterance feature vectors 720_1 reads on first text and the feature vectors 720_1 maps to feature space reads on the second text)
and acquiring training data for recognition based on a relevance between the first text and the second text with respect to the speech data. (Section 0058- thus a training data for model learning is acquired which reads on the training data)
Chae does not clearly disclose to identify a second text similar to the first text among textual information obtained from at least one of personal information of the user or application information stored in the memory.
identifying a difference between the first text and the second text; based on identifying that the difference between the first text and the second text is less than or equal to a threshold value,
based on identifying that the difference between the first text and the second text is more than the threshold value, outputting the first text as the speech recognition result of the speech data;
obtaining relevance information based on the difference between the first text and the second text being less than the threshold value;
and
acquiring training data for recognition of the user's speech based on the relevance information a relevance between the first text and the second text with respect to the speech data.
Jung however discloses identifying a second text similar to the first text among textual information obtained from at least one of the personal information of the user or application information stored in the memory. (Section 0071 and 0072 two text such as Ki Myeon Moon and Kim Hyeong Moon reads on the two text information. Understand that Kim Hyeong Moon is identified from memory 220 to determine the similarity)
identifying a difference between the first text and the second text; based on identifying that the difference between the first text and the second text is less than or equal to a threshold value, (Sections 0122 and 0134 teaches that Jung compares a recognized named entity (from speech/text) to reference information (e.g contact list), calculating a similarity score (Levenshtein distance))
(NB: Understand that both the recognized entity and the reference data are all recognition results of utterance- also see Section 0065).
based on identifying that the difference between the first text and the second text is more than the threshold value, outputting the first text as the speech recognition result of the speech data;
(Section 0110 and 0136- thus if no reference info is above threshold, the system uses the originally recognized named entity)
obtaining relevance information based on the difference between the first text and the second text being less than the threshold value;
((Section 0086 and 0136- if similarity between recognized named entity and reference info is above threshold, the system corrects the named entity to the reference info. This is similar to substituting the recognized entity with a more likely candidate from the reference set)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to include the teaching of modifying the captured speech data to be from a user. The motivation is that it will mimic a person’s speech.
acquiring training data for recognition of the user's speech based on the relevance information a relevance between the first text and the second text with respect to the speech data.
Chae, in view of Jung, fails to disclose acquiring training data for speech recognition based on relevance information that is determined from the difference between the first text and the second text with respect to the speech data—specifically, when the relevance information is based on the difference between the first and second texts being less than a threshold value.
In other words, the cited references do not teach or suggest selecting or acquiring training data for recognizing a user's speech by using relevance information that is calculated from comparing the first text (e.g., the initial recognition result) and the second text (e.g., a candidate or reference text), where this relevance information is only used if the difference between the two texts is below a specified threshold.
Eakin disclose acquiring training data for speech recognition based on relevance information that is determined from the difference between the first text and the second text with respect to the speech data—specifically, when the relevance information is based on the difference between the first and second texts being less than a threshold value. (Col. 36 lines 44-49- thus the ambiguity learning component is retrained using the dialog session data (relevance information) , (Also in Col. 40 lines 52-56 the system retrained other models to recognize the out of vocabulary)
(Col 42 lines 40-50 The model of the ambiguity is encounter by multiple models to be used for retraining or updating new models)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to include the teaching of updating models with disambiguate or relevance information where the difference is below the threshold as described above. The motivation is that incorporating new relevance information into the training process will improve the effectiveness of newly developed models. By using updated relevance information, the models can be better trained and achieve higher accuracy.
Cited Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zelenko (US20210201913) discloses a method and system for translating speech to text the speech having been received by a client device. A user utterance corresponding to the speech is received. A second predicted text corresponding to the user utterance.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Akwasi M Sarpong whose telephone number is (571)270-3438. The examiner can normally be reached Mon-Fri. 8:00am-4:00pm.
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/AKWASI M SARPONG/ SPE, Art Unit 2681
6/3/2026
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