VIRTUAL ASSISTANT WITH ERROR IDENTIFICATION
9 Assignments
0 Petitions
Accused Products
Abstract
Virtual assistants provide results in response to user commands and analyze user utterances in response to the result. The analysis can interpret words, recognized from the utterance, as being negative indicators that imply user dissatisfaction. Virtual assistants request follow-up information from users. Analysis also interprets words as indicators of clarification and collect information to add to a knowledgebase. Machine learning algorithms use recognized words to train a behavioral model to improve results. Virtual assistants also infer, from replacement of words in successive commands, that earlier commands had word recognition errors and infer, from addition of words, that earlier commands had interpretation errors. Virtual assistants act locally or as devices in communication with servers.
35 Citations
36 Claims
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1-13. -13. (canceled)
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14. A non-transitory computer readable medium comprising code that, if executed by at least one computer processor comprised by a virtual assistant, would cause the virtual assistant to:
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receive a first utterance; recognize a first sequence of words and an alternative sequence of words from the first utterance; receive a second utterance; recognize a second sequence of words from the second utterance; identify that the second sequence of words matches the alternative sequence of words; and conclude that the first sequence of words had a speech recognition error. - View Dependent Claims (25, 26)
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15-22. -22. (canceled)
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23. A non-transitory computer readable medium comprising code that, if executed by at least one computer processor comprised by a virtual assistant, would cause the virtual assistant to:
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receive a first utterance; recognize a first sequence of words from the first utterance; interpret the first sequence of words to create a first interpretation; interpret the first sequence of words to create an alternative interpretation; receive a second utterance; recognize a second sequence of words from the second utterance; interpret the second sequence of words to create a second interpretation; identify that the second interpretation matches the alternative interpretation; and conclude that the first interpretation had an interpretation error. - View Dependent Claims (24)
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27. A method of identifying speech recognition errors, the method comprising:
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receiving a first utterance; recognizing a first sequence of words and an alternative sequence of words from the first utterance; receiving a second utterance; recognizing a second sequence of words from the second utterance; identifying that the second sequence of words matches the alternative sequence of words; and concluding that the first sequence of words had a speech recognition error. - View Dependent Claims (28, 29)
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30. A method of identifying speech recognition errors, the method comprising:
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receiving a first utterance; recognizing a first sequence of words from the first utterance; interpreting the first sequence of words to create a first interpretation; interpreting the first sequence of words to create an alternative interpretation; receiving a second utterance; recognizing a second sequence of words from the second utterance; interpreting the second sequence of words to create a second interpretation; identifying that the second interpretation matches the alternative interpretation; and concluding that the first interpretation had an interpretation error. - View Dependent Claims (31)
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32. An error-detecting speech recognition device comprising:
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a speech recognition module that; from a first speech utterance, produces a first sequence of words and an alternative sequence of words; and from a second speech utterance, produces a second sequence of words; and an identification module that identifies that the second sequence of words matches the alternative sequence of words, wherein it can be concluded that the first sequence of words had a speech recognition error. - View Dependent Claims (33, 34)
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35. An error-detecting speech recognition device comprising:
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a speech recognition module that; from a first speech utterance, produces a first sequence of words and an alternative sequence of words; and from a second speech utterance, produces a second sequence of words; an interpretation module that; interprets the first sequence of words to create a first interpretation; interprets the alternative sequence of words to create an alternative interpretation; and interprets the second sequence of words to create a second interpretation; and an identification module that identifies that the second interpretation matches the alternative interpretation, wherein it can be concluded that the first sequence of words had a speech recognition error. - View Dependent Claims (36)
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Specification