DETAILED ACTION
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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 5-6, 10-11, and 16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2, 8, 11-12 and 17-18 of U.S. Patent No. 10,311,860. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1, 5-6, 10-11, and 16 of the instant application are similar in scope and content of the patented claims 1-2, 8, 11-12 and 17-18 of the patent issued to the same Applicant.
It is clear that all the elements of the application claims 1, 5-6, 10-11, and 16 are to be found in patented claims 1-2, 8, 11-12 and 17-18 (as the application claims 1, 5-6, 10-11, and 16 fully encompasses patented claims 1-2, 8, 11-12 and 17-18). The difference between the application claims and the patent claims lies in the fact that the patent claim includes many more elements and is thus much more specific. Thus the invention of claims 1-2, 8, 11-12 and 17-18 of the patent is in effect a “species” of the “generic” invention of the application claims 1, 5-6, 10-11, and 16. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Since application claims 1, 5-6, 10-11, and 16 is anticipated by claims 1-2, 8, 11-12 and 17-18 of the patent, it is not patentably distinct from of the patented claims.
Application No: 19/001,061
Patent No: 10,311,860
1. A computer-implemented method when executed on data processing hardware causes the data processing hardware to perform operations comprising: obtaining, from a speech recognizer, a speech recognition candidate of a spoken utterance; receiving context data submitted by a user that includes one or more words; biasing a language model based on the one or more words of the context data submitted by the user to increase a likelihood of output of the one or more words; and processing, using the biased language model, the speech recognition candidate to determine a transcription of the spoken utterance.
1. A computer-implemented method comprising: receiving audio data corresponding to a user utterance and context data for the user utterance; identifying, based on the context data, an initial set of one or more n-grams including one or more n-grams that do not represent speech preceding the user utterance; generating an expanded set of one or more n-grams based at least on the initial set of n-grams, the expanded set of n-grams comprising one or more n-grams that are different from the n-grams in the initial set of n-grams; based at least on the expanded set of n-grams, adjusting a language model trained to predict a first set of n-grams to be able to predict an additional n-gram in the expanded set of n-grams; determining one or more speech recognition candidates for at least a portion of the user utterance using the adjusted language model, wherein each speech recognition candidate comprises one or more words; after determining the one or more speech recognition candidates, adjusting a score for a particular speech recognition candidate based on determining that the particular speech recognition candidate is included in the expanded set of n-grams; after adjusting the score for the particular speech recognition candidate, determining, a transcription for the user utterance that includes at least one of the one or more speech recognition candidates; and providing the transcription of the user utterance for output.
11. The computer-implemented method of claim 1, wherein adjusting the language model is performed in response to receiving the context data for the user utterance, the context data indicating a context at a time the user utterance is spoken.
2. The computer-implemented method of claim 1, wherein the operations further comprise providing, for output from a user device associated with the user, the transcription of the spoken utterance.
3. The computer-implemented method of claim 1, wherein the operations further comprise: receiving audio data corresponding to the spoken utterance; and processing, using the speech recognizer, the audio data to obtain the speech recognition candidate of the spoken utterance.
4. The computer-implemented method of claim 3, wherein the one or more words of the context data submitted by the user are received before the audio data corresponding to the spoken utterance is received.
5. The computer-implemented method of claim 3, wherein the spoken utterance is detected by a user device providing an interface to the user.
12. The method of claim 1, wherein receiving the audio data comprises receiving audio data detected by a user device; and wherein the initial set of n-grams comprises one or more words or phrases displayed on a screen of the user device.
6. The computer-implemented method of claim 1, wherein the language model comprises a probabilistic language model.
2. The computer-implemented method of claim 1, wherein adjusting the language model based at least on the expanded set of n-grams comprises adjusting recognition scores for one or more of the n-grams of the expanded set of n-grams in the language model.
7. The computer-implemented method of claim 1, wherein the language model comprises a continuous space language model.
8. The computer-implemented method of claim 1, wherein the language model is previously trained and is not specific to a particular context.
9. The computer-implemented method of claim 1, wherein the data processing hardware resides on a server system in communication with a user device associated with the user over a communication network.
10. The computer-implemented method of claim 1, wherein the processing the speech recognition candidate using the biased language model includes adjusting a recognition score assigned to the speech recognition candidate.
8. The computer-implemented method of claim 1, wherein the audio data corresponding to the user utterance corresponds to a particular segment of a spoken user input that comprises multiple segments; and wherein the language model is a general language model or a general language model that has been influenced during processing of a segment preceding the particular segment of the spoken user input.
11. A system comprising: data processing hardware; and memory hardware in communication with the data processing hardware and storing instructions that when executed on the data processing hardware causes the data processing hardware to perform operations comprising: obtaining, from a speech recognizer, a speech recognition candidate of a spoken utterance; receiving context data submitted by a user that includes one or more words; biasing a language model based on the one or more words of the context data submitted by the user to increase a likelihood of output of the one or more words; and processing, using the biased language model, the speech recognition candidate to determine a transcription of the spoken utterance.
17. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving audio data corresponding to a user utterance and context data for the user utterance; identifying, based on the context data, an initial set of one or more n-grams including one or more n-grams that do not represent speech preceding the user utterance; generating an expanded set of one or more n-grams based at least on the initial set of n-grams, the expanded set of n-grams comprising one or more n-grams that are different from the n-grams in the initial set of n-grams; based at least on the expanded set of n-grams, adjusting a language model trained to predict a first set of n-grams to be able to predict an additional n-gram in the expanded set of n-grams; determining one or more speech recognition candidates for at least a portion of the user utterance using the adjusted language model, wherein each speech recognition candidate comprises one or more words; after determining the one or more speech recognition candidates, adjusting a score for a particular speech recognition candidate based on determining that the particular speech recognition candidate is included in the expanded set of n-grams; after adjusting the score for the particular speech recognition candidate, determining, a transcription for the user utterance that includes at least one of the one or more speech recognition candidates; and providing the transcription of the user utterance for output.
12. The system of claim 11, wherein the operations further comprise providing, for output from a user device associated with the user, the transcription of the spoken utterance.
13. The system of claim 11, wherein the operations further comprise: receiving audio data corresponding to the spoken utterance; and processing, using the speech recognizer, the audio data to obtain the speech recognition candidate of the spoken utterance.
14. The system of claim 13, wherein the one or more words of the context data submitted by the user are received before the audio data corresponding to the spoken utterance is received.
15. The system of claim 13, wherein the spoken utterance is detected by a user device providing an interface to the user.
16. The system of claim 11, wherein the language model comprises a probabilistic language model.
18. The system of claim 17, wherein adjusting the language model based at least on the expanded set of n-grams comprises adjusting the language model to generate scores for candidate transcriptions using adjusted probability scores different from probability scores determined through training of the language model, the adjusted probability scores indicating increased probabilities for one or more of the n-grams of the expanded set of n-grams compared to the probability scores determined through training of the language model.
17. The system of claim 11, wherein the language model comprises a continuous space language model.
18. The system of claim 11, wherein the language model is previously trained and is not specific to a particular context.
19. The system of claim 11, wherein the data processing hardware resides on a server system in communication with a user device associated with the user over a communication network.
20. The system of claim 11, wherein the processing the speech recognition candidate using the biased language model includes adjusting a recognition score assigned to the speech recognition candidate.
Claims 1-2, 5, 9 and 11 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 6, 13 and 17 of U.S. Patent No. 11,037,551. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-2, 5, 9 and 11 of the instant application are similar in scope and content of the patented claims 1-4, 6, 13 and 17 of the patent issued to the same Applicant.
It is clear that all the elements of the application claims 1-2, 5, 9 and 11 are to be found in patented claims 1-4, 6, 13 and 17 (as the application claims 1-2, 5, 9 and 11 fully encompasses patented claims 1-4, 6, 13 and 17). The difference between the application claims and the patent claims lies in the fact that the patent claim includes many more elements and is thus much more specific. Thus the invention of claims 1-4, 6, 13 and 17 of the patent is in effect a “species” of the “generic” invention of the application claims 1-2, 5, 9 and 11. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Since application claims 1-2, 5, 9 and 11 is anticipated by claims 1-4, 6, 13 and 17 of the patent, it is not patentably distinct from of the patented claims.
Application No: 19/001,061
Patent No: 11,037,551
1. A computer-implemented method when executed on data processing hardware causes the data processing hardware to perform operations comprising: obtaining, from a speech recognizer, a speech recognition candidate of a spoken utterance; receiving context data submitted by a user that includes one or more words; biasing a language model based on the one or more words of the context data submitted by the user to increase a likelihood of output of the one or more words; and processing, using the biased language model, the speech recognition candidate to determine a transcription of the spoken utterance.
1. A computer-implemented method comprising: obtaining a dialog state of a user device associated with a user; before the user speaks an utterance to the user device: identifying, from an n-gram cache, an initial set of n-grams that represent one or more words or phrases corresponding to the dialog state of the user device; and biasing a language model on the initial set of n-grams to increase a likelihood of output of the one or more words or phrases corresponding to the dialog state of the user device; receiving audio data indicating the utterance of the user; processing the audio data using the biased language model to generate a transcription of the utterance; and providing the transcription of the utterance for output.
2. The method of claim 1, further comprising: receiving context data for the user device wherein obtaining the dialog state comprises identifying the dialog state based on the context data.
3. The method of claim 2, wherein the utterance is detected by the user device providing an interface to the user, and wherein the context data comprises data that indicates a topic corresponding to the interface.
2. The computer-implemented method of claim 1, wherein the operations further comprise providing, for output from a user device associated with the user, the transcription of the spoken utterance.
4. The method of claim 2, wherein the utterance is detected by the user device providing an interface to the user, and wherein the context data comprises data indicating a task to be performed using the interface.
3. The computer-implemented method of claim 1, wherein the operations further comprise: receiving audio data corresponding to the spoken utterance; and processing, using the speech recognizer, the audio data to obtain the speech recognition candidate of the spoken utterance.
4. The computer-implemented method of claim 3, wherein the one or more words of the context data submitted by the user are received before the audio data corresponding to the spoken utterance is received.
5. The computer-implemented method of claim 3, wherein the spoken utterance is detected by a user device providing an interface to the user.
6. The method of claim 2, wherein the context data indicates one or more words or phrases included in a graphical user interface of the user device at a time that the utterance was spoken.
6. The computer-implemented method of claim 1, wherein the language model comprises a probabilistic language model.
7. The computer-implemented method of claim 1, wherein the language model comprises a continuous space language model.
8. The computer-implemented method of claim 1, wherein the language model is previously trained and is not specific to a particular context.
9. The computer-implemented method of claim 1, wherein the data processing hardware resides on a server system in communication with a user device associated with the user over a communication network.
13. The method of claim 1, wherein receiving the audio data comprises receiving, by a server system, audio data provided by the user device over a communication network.
10. The computer-implemented method of claim 1, wherein the processing the speech recognition candidate using the biased language model includes adjusting a recognition score assigned to the speech recognition candidate.
11. A system comprising: data processing hardware; and memory hardware in communication with the data processing hardware and storing instructions that when executed on the data processing hardware causes the data processing hardware to perform operations comprising: obtaining, from a speech recognizer, a speech recognition candidate of a spoken utterance; receiving context data submitted by a user that includes one or more words; biasing a language model based on the one or more words of the context data submitted by the user to increase a likelihood of output of the one or more words; and processing, using the biased language model, the speech recognition candidate to determine a transcription of the spoken utterance.
17. A system comprising: one or more computers; and one or more computer-readable media storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising: obtaining a dialog state of a user device associated with a user; before the user speaks an utterance to the user device: identifying, from an n-gram cache, an initial set of n-grams that represent one or more words or phrases corresponding to the dialog state of the user device; and biasing a language model on the initial set of n-grams to increase a likelihood of output of the one or more words or phrases corresponding to the dialog state of the user device; receiving audio data indicating the utterance of the user; processing the audio data using the biased language model to generate a transcription of the utterance; and providing the transcription of the utterance for output.
12. The system of claim 11, wherein the operations further comprise providing, for output from a user device associated with the user, the transcription of the spoken utterance.
13. The system of claim 11, wherein the operations further comprise: receiving audio data corresponding to the spoken utterance; and processing, using the speech recognizer, the audio data to obtain the speech recognition candidate of the spoken utterance.
14. The system of claim 13, wherein the one or more words of the context data submitted by the user are received before the audio data corresponding to the spoken utterance is received.
15. The system of claim 13, wherein the spoken utterance is detected by a user device providing an interface to the user.
16. The system of claim 11, wherein the language model comprises a probabilistic language model.
17. The system of claim 11, wherein the language model comprises a continuous space language model.
18. The system of claim 11, wherein the language model is previously trained and is not specific to a particular context.
19. The system of claim 11, wherein the data processing hardware resides on a server system in communication with a user device associated with the user over a communication network.
20. The system of claim 11, wherein the processing the speech recognition candidate using the biased language model includes adjusting a recognition score assigned to the speech recognition candidate.
Claims 1, 5, 9, 11-12, and 19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 6, 10-11, 16 and 20 of U.S. Patent No. 11,682,383. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1, 5, 9, 11-12, and 19 of the instant application are similar in scope and content of the patented claims 1, 6, 10-11, 16 and 20 of the patent issued to the same Applicant.
It is clear that all the elements of the application claims 1, 5, 9, 11-12, and 19 are to be found in patented claims 1, 6, 10-11, 16 and 20 (as the application claims 1, 5, 9, 11-12, and 19 fully encompasses patented claims 1, 6, 10-11, 16 and 20). The difference between the application claims and the patent claims lies in the fact that the patent claim includes many more elements and is thus much more specific. Thus the invention of claims 1, 6, 10-11, 16 and 20, of the patent is in effect a “species” of the “generic” invention of the application claims 1, 5, 9, 11-12, and 19. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Since application claims 1, 5, 9, 11-12, and 19 is anticipated by claims 1, 6, 10-11, 16 and 20 of the patent, it is not patentably distinct from of the patented claims.
Application No: 19/001,061
Patent No: 11,682,383
1. A computer-implemented method when executed on data processing hardware causes the data processing hardware to perform operations comprising: obtaining, from a speech recognizer, a speech recognition candidate of a spoken utterance; receiving context data submitted by a user that includes one or more words; biasing a language model based on the one or more words of the context data submitted by the user to increase a likelihood of output of the one or more words; and processing, using the biased language model, the speech recognition candidate to determine a transcription of the spoken utterance.
1. A computer-implemented method when executed on data processing hardware causes the data processing hardware to perform operations comprising: receiving context data for a user device associated with a user: identifying an initial set of n-grams from the context data; receiving audio data corresponding to an utterance detected by the user device; processing, using a speech recognizer, the audio data to generate speech recognition candidates for the utterance spoken by the user, each speech recognition candidate associated with a respective speech recognition score; adjusting, using the initial set of n-grams, one or more of the speech recognition scores associated with the speech recognition candidates; and after adjusting the one or more speech recognition scores, determining a transcription of the utterance by selecting the speech recognition candidate that is associated with the highest respective speech recognition score.
2. The computer-implemented method of claim 1, wherein the operations further comprise providing, for output from a user device associated with the user, the transcription of the spoken utterance.
3. The computer-implemented method of claim 1, wherein the operations further comprise: receiving audio data corresponding to the spoken utterance; and processing, using the speech recognizer, the audio data to obtain the speech recognition candidate of the spoken utterance.
4. The computer-implemented method of claim 3, wherein the one or more words of the context data submitted by the user are received before the audio data corresponding to the spoken utterance is received.
5. The computer-implemented method of claim 3, wherein the spoken utterance is detected by a user device providing an interface to the user.
6. The computer-implemented method of claim 1, wherein: the utterance is detected by the user device providing an interface to the user; and the context data comprises data indicating a task to be performed using the interface.
6. The computer-implemented method of claim 1, wherein the language model comprises a probabilistic language model.
7. The computer-implemented method of claim 1, wherein the language model comprises a continuous space language model.
8. The computer-implemented method of claim 1, wherein the language model is previously trained and is not specific to a particular context.
9. The computer-implemented method of claim 1, wherein the data processing hardware resides on a server system in communication with a user device associated with the user over a communication network.
10. The computer-implemented method of claim 1, wherein: the data processing hardware resides on the user device; or the data processing hardware resides on a server system in communication with the user device over a communication network.
10. The computer-implemented method of claim 1, wherein the processing the speech recognition candidate using the biased language model includes adjusting a recognition score assigned to the speech recognition candidate.
11. A system comprising: data processing hardware; and memory hardware in communication with the data processing hardware and storing instructions that when executed on the data processing hardware causes the data processing hardware to perform operations comprising: obtaining, from a speech recognizer, a speech recognition candidate of a spoken utterance; receiving context data submitted by a user that includes one or more words; biasing a language model based on the one or more words of the context data submitted by the user to increase a likelihood of output of the one or more words; and processing, using the biased language model, the speech recognition candidate to determine a transcription of the spoken utterance.
11. A system comprising: data processing hardware; and memory hardware in communication with the data processing hardware and storing instructions that when executed on the data processing hardware causes the data processing hardware to perform operations comprising: receiving context data for a user device associated with a user: identifying an initial set of n-grams from the context data; receiving audio data corresponding to an utterance detected by the user device; processing, using a speech recognizer, the audio data to generate speech recognition candidates for the utterance spoken by the user, each speech recognition candidate associated with a respective speech recognition score; adjusting, using the initial set of n-grams, one or more of the speech recognition scores associated with the speech recognition candidates; and after adjusting the one or more speech recognition scores, determining a transcription of the utterance by selecting the speech recognition candidate that is associated with the highest respective speech recognition score.
12. The system of claim 11, wherein the operations further comprise providing, for output from a user device associated with the user, the transcription of the spoken utterance.
16. The system of claim 11, wherein: the utterance is detected by the user device providing an interface to the user; and the context data comprises data indicating a task to be performed using the interface.
13. The system of claim 11, wherein the operations further comprise: receiving audio data corresponding to the spoken utterance; and processing, using the speech recognizer, the audio data to obtain the speech recognition candidate of the spoken utterance.
14. The system of claim 13, wherein the one or more words of the context data submitted by the user are received before the audio data corresponding to the spoken utterance is received.
15. The system of claim 13, wherein the spoken utterance is detected by a user device providing an interface to the user.
16. The system of claim 11, wherein the language model comprises a probabilistic language model.
17. The system of claim 11, wherein the language model comprises a continuous space language model.
18. The system of claim 11, wherein the language model is previously trained and is not specific to a particular context.
19. The system of claim 11, wherein the data processing hardware resides on a server system in communication with a user device associated with the user over a communication network.
20. The system of claim 11, wherein: the data processing hardware resides on the user device; or the data processing hardware resides on a server system in communication with the user device over a communication network.
20. The system of claim 11, wherein the processing the speech recognition candidate using the biased language model includes adjusting a recognition score assigned to the speech recognition candidate.
Claims 1, 5, 9, 11, 15 and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 7, 9-11, 17 and 19-20 of U.S. Patent No. 12,183,328. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1, 5, 9, 11, 15 and 20 of the instant application are similar in scope and content of the patented claims 1, 7, 9-11, 17 and 19-20 of the patent issued to the same Applicant.
It is clear that all the elements of the application claims 1, 5, 9, 11, 15 and 20 are to be found in patented claims 1, 7, 9-11, 17 and 19-20 (as the application claims 1, 5, 9, 11, 15 and 20 fully encompasses patented claims 1, 7, 9-11, 17 and 19-20). The difference between the application claims and the patent claims lies in the fact that the patent claim includes many more elements and is thus much more specific. Thus the invention of claims 1, 7, 9-11, 17 and 19-20, of the patent is in effect a “species” of the “generic” invention of the application claims 1, 5, 9, 11, 15 and 20. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Since application claims 1, 5, 9, 11, 15 and 20 is anticipated by claims 1, 7, 9-11, 17 and 19-20 of the patent, it is not patentably distinct from of the patented claims.
Application No: 19/001,061
Patent No: 12,183,328
1. A computer-implemented method when executed on data processing hardware causes the data processing hardware to perform operations comprising: obtaining, from a speech recognizer, a speech recognition candidate of a spoken utterance; receiving context data submitted by a user that includes one or more words; biasing a language model based on the one or more words of the context data submitted by the user to increase a likelihood of output of the one or more words; and processing, using the biased language model, the speech recognition candidate to determine a transcription of the spoken utterance.
1. A computer-implemented method when executed on data processing hardware that causes the data processing hardware to perform operations comprising: receiving an initial set of n-grams, the initial set of n-grams designated by an application developer of an application, each n-gram of the initial set of n-grams comprises n adjacent letters, symbols, or words; biasing, using the initial set of n-grams, a trained speech recognizer by modifying one or more parameters of a trained language model used by the trained speech recognizer, the one or more parameters modified to increase prediction probabilities associated with the trained speech recognizer recognizing n-grams in audio data that are included in the initial set of n-grams; receiving audio data corresponding to an utterance detected by a user device associated with a user; and processing, using the biased trained speech recognizer and the modified trained language model, the received audio data corresponding to the utterance to determine a transcription of the utterance that is biased toward the initial set of n-grams.
2. The computer-implemented method of claim 1, wherein the operations further comprise providing, for output from a user device associated with the user, the transcription of the spoken utterance.
3. The computer-implemented method of claim 1, wherein the operations further comprise: receiving audio data corresponding to the spoken utterance; and processing, using the speech recognizer, the audio data to obtain the speech recognition candidate of the spoken utterance.
4. The computer-implemented method of claim 3, wherein the one or more words of the context data submitted by the user are received before the audio data corresponding to the spoken utterance is received.
5. The computer-implemented method of claim 3, wherein the spoken utterance is detected by a user device providing an interface to the user.
7. The computer-implemented method of claim 3, wherein the context data comprises data indicating a task to be performed using an interface provided to the user by the user device.
9. The computer-implemented method of claim 1, wherein the utterance is detected by the user device providing an interface to the user.
6. The computer-implemented method of claim 1, wherein the language model comprises a probabilistic language model.
7. The computer-implemented method of claim 1, wherein the language model comprises a continuous space language model.
8. The computer-implemented method of claim 1, wherein the language model is previously trained and is not specific to a particular context.
9. The computer-implemented method of claim 1, wherein the data processing hardware resides on a server system in communication with a user device associated with the user over a communication network.
10. The computer-implemented method of claim 1, wherein: the data processing hardware resides on the user device; or the data processing hardware resides on a server system in communication with the user device over a communication network.
10. The computer-implemented method of claim 1, wherein the processing the speech recognition candidate using the biased language model includes adjusting a recognition score assigned to the speech recognition candidate.
11. A system comprising: data processing hardware; and memory hardware in communication with the data processing hardware and storing instructions that when executed on the data processing hardware causes the data processing hardware to perform operations comprising: obtaining, from a speech recognizer, a speech recognition candidate of a spoken utterance; receiving context data submitted by a user that includes one or more words; biasing a language model based on the one or more words of the context data submitted by the user to increase a likelihood of output of the one or more words; and processing, using the biased language model, the speech recognition candidate to determine a transcription of the spoken utterance.
11. A system comprising: data processing hardware; and memory hardware in communication with the data processing hardware and storing instructions that, when executed on the data processing hardware, causes the data processing hardware to perform operations comprising: receiving an initial set of n-grams, the initial set of n-grams designated by an application developer of an application, each n-gram of the initial set of n-grams comprises n adjacent letters, symbols, or words; biasing, using the initial set of n-grams, a trained speech recognizer by modifying one or more parameters of a trained language model used by the trained speech recognizer, the one or more parameters modified to increase prediction probabilities associated with the trained speech recognizer recognizing n-grams in audio data that are included in the initial set of n-grams; receiving audio data corresponding to an utterance detected by a user device associated with a user; and processing, using the biased trained speech recognizer and the modified trained language model, the received audio data corresponding to the utterance to determine a transcription of the utterance that is biased toward the initial set of n-grams.
12. The system of claim 11, wherein the operations further comprise providing, for output from a user device associated with the user, the transcription of the spoken utterance.
13. The system of claim 11, wherein the operations further comprise: receiving audio data corresponding to the spoken utterance; and processing, using the speech recognizer, the audio data to obtain the speech recognition candidate of the spoken utterance.
14. The system of claim 13, wherein the one or more words of the context data submitted by the user are received before the audio data corresponding to the spoken utterance is received.
15. The system of claim 13, wherein the spoken utterance is detected by a user device providing an interface to the user.
17. The system of claim 13, wherein the context data comprises data indicating a task to be performed using an interface provided to the user by the user device.
19. The system of claim 13, wherein the utterance is detected by the user device providing an interface to the user.
16. The system of claim 11, wherein the language model comprises a probabilistic language model.
17. The system of claim 11, wherein the language model comprises a continuous space language model.
18. The system of claim 11, wherein the language model is previously trained and is not specific to a particular context.
19. The system of claim 11, wherein the data processing hardware resides on a server system in communication with a user device associated with the user over a communication network.
20. The system of claim 11, wherein the processing the speech recognition candidate using the biased language model includes adjusting a recognition score assigned to the speech recognition candidate.
20. The system of claim 13, wherein: the data processing hardware resides on the user device; or the data processing hardware resides on a server system in communication with the user device over a communication network.
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.
Claims 1-20 are rejected under 35 U.S.C. 102(a1) as being anticipated by Biadsy et al., (US 2015/0228279 A1).
As per claims 1 and 11 a computer-implemented method/system when executed on data processing hardware causes the data processing hardware to perform operations comprising:
obtaining, from a speech recognizer, a speech recognition candidate of a spoken utterance (0027-0028);
receiving context data submitted by a user that includes one or more words (Fig.1, - the computing system 120 receives audio data 112 and context data 116 from client device);
biasing a language model based on the one or more words of the context data submitted by the user to increase a likelihood of output of the one or more words (Fig.1, - language model 150 can determine a posterior probability of a current word, e.g., the first word of the utterance 104, given information about the context for the utterance 104, which may include linguistic context, e.g., the prior words "Let's meet at," and/or non-linguistic context, e.g., location, device state, application, user characteristics, etc., (0041)); and,
using the biased language model, the speech recognition candidate to determine a transcription of the spoken utterance (the speech recognizer module 130 uses an acoustic model and a language model to identify the candidate transcriptions 135, (0027)).
As per claims 2 and 12, Biadsy et al., teach the computer-implemented method/system of claims 1 and 11, wherein the operations further comprise providing, for output from a user device associated with the user, the transcription of the spoken utterance (the speech recognizer module 130 uses an acoustic model and a language model to identify the candidate transcriptions 135, (0027)).
As per claims 3 and 13, Biadsy et al., teach the computer-implemented method/system of claims 1 and 11, wherein the operations further comprise: receiving audio data corresponding to the spoken utterance; and processing, using the speech recognizer, the audio data to obtain the speech recognition candidate of the spoken utterance (Fig.1, - the computing system 120 receives audio data 112 and context data 116 from client device); (Fig.1, - language model 150 can determine a posterior probability of a current word, e.g., the first word of the utterance 104, given information about the context for the utterance 104, which may include linguistic context, e.g., the prior words "Let's meet at," and/or non-linguistic context, e.g., location, device state, application, user characteristics, etc., (0041)).
As per claims 4 and 14, Biadsy et al., teach the computer-implemented method/system of claims 3 and 13, wherein the one or more words of the context data submitted by the user are received before the audio data corresponding to the spoken utterance is received (0041-0045 0057-0062).
As per claims 5 and 15, Biadsy et al., teach the computer-implemented method/system of claims 3 and 13, wherein the spoken utterance is detected by a user device providing an interface to the user (0018, Fig.1, item 110).
As per claims 6 and 16, Biadsy et al., teach the computer-implemented method/system of claims 1 and 11, computer-implemented method of claim 1, wherein the language model comprises a probabilistic language model (Fig.1, items 150 and 160, 0045).
As per claims 7 and 17, Biadsy et al., teach the computer-implemented method/system of claims 1 and 11, wherein the language model comprises a continuous space language model (Fig.1, items 150 and 160, 0045).
As per claims 8 and 18, Biadsy et al., teach the computer-implemented method/system of claims 1 and 11, wherein the language model is previously trained and is not specific to a particular context (Fig.1, items 150 and 160, 0045).
As per claims 9 and 19, Biadsy et al., teach the computer-implemented method/system of claims 1 and 11, wherein the data processing hardware resides on a server system in communication with a user device associated with the user over a communication network (0098, 0019, 0103).
As per claims 10 and 20, Biadsy et al., teach the computer-implemented method/system of claims 1 and 11, wherein the processing the speech recognition candidate using the biased language model includes adjusting a recognition score assigned to the speech recognition candidate (Fig.1, - language model 150 can determine a posterior probability of a current word, e.g., the first word of the utterance 104, given information about the context for the utterance 104, which may include linguistic context, e.g., the prior words "Let's meet at," and/or non-linguistic context, e.g., location, device state, application, user characteristics, etc., (0041)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form PTO-892.
Chelba et al (US 8,175, 878 B1) teach systems, methods, and apparatuses, including computer program products, are provided for representing language models. In some implementations, a computer-implemented method is provided. The method includes generating a compact language model including receiving a collection of n-grams from the corpus, each n-gram of the collection having a corresponding first probability of occurring in the corpus and generating a tree representing the collection of n-grams. The method also includes using the language model to identify a second probability of a particular string of words occurring.
Bellegarda et al., (US 10,067,938 B2) teach systems and processes for multilingual word prediction are provided. In accordance with one example, a method includes, at an electronic device having one or more processors and memory, receiving context information associated with a current word; determining, for each of a plurality of languages, a set of monolingual probabilities based on the context information; determining a set of language weights based on the context information; determining a set of multilingual probabilities based on the respective sets of monolingual probabilities and the set of language weights; and providing a plurality of candidate words based on the set of multilingual probabilities.
Salvador et al., (US 9,153,231 B1) teach that Neural networks may be used in certain automatic speech recognition systems. To improve performance of these neural networks, they may be updated/retrained during run time by training the neural network based on the output of a speech recognition system or based on the output of the neural networks themselves. The outputs may include weighted outputs, lattices, weighted N-best lists, or the like. The neural networks may be acoustic model neural networks or language model neural networks. The neural networks may be retrained after each pass through the network, after each utterance, or in varying time scales.
Zhou et al., (US 2003/0149561 A1) teach a spoken dialog system using a best-fit language model and a spoken dialog system using best-fit grammar are disclosed. A spoken dialog system implementing both a best-fit language model and best-fit grammar is further disclosed. Regarding the language model, likelihood scores from a large vocabulary continuous speech recognition ("LVCSR") module are used to select the best-fit language model among a general task language model and dialog-state dependent language models. Based on the chosen language model, a dialog manager can implement different strategies to improve general dialog performance and recognition accuracy. Regarding grammar, the best-fit grammar method improves performance and user experience of dialog systems by choosing the best-fit grammar among a general purpose grammar and dialog-state dependent sub-grammars. Based on the selected grammar pattern, the dialog system can choose from varying dialog strategies, resulting in an increase in user acceptance of spoken dialog systems.
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/VIJAY B CHAWAN/Primary Examiner, Art Unit 2658
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