DETAILED ACTION
1. This action is responsive to remarks filed 6/16/2026.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
3. Independent claims 1, 11 have been amended.
Response to Arguments
4. Applicant’s arguments filed have been fully considered but are moot based on the new grounds of rejection responsive to the amendments.
Claim Rejections - 35 USC § 103
5. 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.
6. 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.
7. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Reavely et al (10,453,117) In view of Mohri et al (7,398,197).
Regarding claim 1 Reavely teaches A computer-implemented method when executed by data processing hardware causes the data processing hardware to perform operations (abstract: system; figs 1, 2, 7, 8; col 25 l 47-col 26 l. 9 – system; device) comprising:
receiving audio data of an utterance spoken by a user and captured by a computing device associated with the user (figure 1 input audio; col 2 l. 9: user utterance may include a query; col 4 l. 4-10 system include one or more devices; l. 23-24: system may receive audio data corresponding to a query);
determining, from the audio data, phonemes corresponding to the utterance (col 6 l. 35-39: input audio data with models for sounds e.g phonemes; col 7 l. 30: phonemes);
generating, using the phonemes corresponding to the utterance, a word lattice that includes one or more candidate transcriptions of the utterance, each candidate transcription of the one or more candidate transcriptions having a corresponding transcription likelihood score (fig 2 250 ASR, acoustic model, language model; col 6 l. 25-30: ASR module may convert the audio data into text; ASR transcribes audio data into text data; col 6 l. 35-39; col 6 l. 40-58 probability or a confidence score representing the likelihood; each potential textual interpretation of the spoken utterance (hypothesis) is associated with a confidence score; may also output multiple hypothesis in the form of a lattice…with each hypothesis corresponding to a confidence score or other score);
determining a context indicating that a user is listening to music through the computing device (col 8 l. 4545-49; col 18 l. 11-20 user context; dialog context, application context; Col 21 l 30-34: customer ID, context; system may consider when any particular applications are currently active (such as music being played);
based on the context indicating that the user is listening to music through the computing device, selecting a grammar corresponding to a specific user intent of issuing a media playing command
(col 8 l. 45 – col 9 l 3: To correctly perform NLU processing of speech input, an NLU process 260 may be configured to determine a “domain” of the utterance so as to determine and narrow down which services offered by the endpoint device (e.g., server 120 or device 110) may be relevant. For example, an endpoint device may offer services relating to interactions with a telephone service, a contact list service, a calendar/scheduling service, a music player service, etc. Words in a single text query may implicate more than one service, and some services may be functionally linked (e.g., both a telephone service and a calendar service may utilize data from the contact list).
The named entity recognition (NER) module 262 receives a query in the form of ASR results and attempts to identify relevant grammars and lexical information that may be used to construe meaning. To do so, the NER module 262 may begin by identifying potential domains that may relate to the received query. The NLU storage 273 includes a databases of devices (274a-274n) identifying domains associated with specific devices. For example, the device 110 may be associated with domains for music, telephony, calendaring, contact lists, and device-specific communications, but not video. In addition, the entity library may include database entries about specific services on a specific device, either indexed by Device ID, User ID, or Household ID, or some other indicator.);
{compiling the selected grammar into a weighted finite-state transducer (wFST); and}
for the respective candidate transcription of the one or more candidate transcriptions having the highest corresponding transcription likelihood score, parsing, using the {wFST compiled from the } selected grammar corresponding to the specific user intent of issuing the media playing command, the respective candidate transcription to identify an action for the computing device to perform (col 8 l. 16-30: takes textual input; determines the meaning; derive an intent or a desired action …that allow a device to complete that action; l. 35-36: may be configured to parsed; 45-56: to correctly perform NLU processing, determine a domain of the utterance, music player service; col 9 l 4-10: each domain associated with a grammar; col 10 – “play music”, parse query to identify words and intent; col 11 l. 5-17: output from NLU processing may then be sent to a command processor; if the NLU output includes a command to play music, the destination command processor may be a music playing application…configured to execute a music playing command
col 13 l. 40-57: [0.78] Video [0.13] Books [0.07] Music;
Col 17 l. 58-63; col 19 l.22-25: application domain selector, score; col 21 l. 46-47: highest scoring result passed to command processor for execution).
Reavely Does not specifically teach where Mohri et al (7,398,197) teaches
compiling the selected grammar into a weighted finite-state transducer (wFST) (Abstract: A context-free grammar can be represented by a weighted finite-state transducer. This representation can be used to efficiently compile that grammar into a weighted finite-state automaton that accepts the strings allowed by the grammar with the corresponding weights.; The topology can be fully expanded or dynamically expanded as required to recognize a particular input string.;
Col 1 l. 53-55 This invention provides systems and methods that generate, from a context-free grammar, a finite-state automaton or transducer that represents that context-free grammar.;
Col 2 l 4-8: This invention separately provides systems and methods that compile a finite-state automaton or transducer that represents a context-free grammar into a delayed acceptor that recognizes the strings described by that context-free grammar.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate Mohri to allow for improved processing of the selected grammar to identify the action, while also presenting a reasonable expectation of success.
The incorporation would then allow for
parsing, using the wFST compiled from the selected grammar corresponding to the specific user intent of issuing the media playing command, the respective candidate transcription to identify an action for the computing device to perform.
Regarding claim 2 Reavely teaches The computer-implemented method of claim 1, wherein the operations further comprise instructing the computing device to perform the identified action (col 2 l. 9-12: user utterance may include a query, input to the system to execute a command).
Regarding claim 3 Reavely teaches The computer-implemented method of claim 1, wherein the operations further comprise selecting the grammar from among a plurality of grammars based on the context of the computing device (abstract: domains; fig 2 domain grammar; col 2 l. 58-67: domain; col 4 l. 25-49: NLU domains, built-in; supplemental applications;
col 8 l. 45-56: process may be configured to determine a domain of the utterance so as to determine and narrow down which services offered…may be relevant; words in a single text query may implicate more than one service; col 13 l. 40-57: cross-domain ranker;
Col 18 l. 10-20 supplemental intent category recognizer may also consider…user context).
Regarding claim 4 Reavely teaches The computer-implemented method of claim 3, wherein each grammar of the plurality of grammars comprises a different specified structure of terms (fig 2 domain grammar; col 9 l 4-10: each domain associated with a grammar).
Regarding claim 5 Reavely teaches The computer-implemented method of claim 1, wherein the computing device comprises a mobile phone (fig 8; col 1 l. 19-22; col 25 l. 54-66 multiple devices contain components of the system; smart phone).
Regarding claim 6 Reavely teaches The computer-implemented method of claim 1, wherein the computing device comprises or a wearable device (fig 8; col 25 l. 54-66 multiple devices contain components of the system; smart watch)
Regarding claim 7 Reavely teaches The computer-implemented method of claim 1, wherein each candidate transcription of the one or more candidate transcriptions comprises multiple terms (col 6 l. 42-44: confidence score representing the likelihood that a particular set of words matches those spoken in the utterance; col 7 l. 29-42).
Regarding claim 8 Reavely teaches The computer-implemented method of claim 7, wherein each term of the multiple terms comprises a corresponding term confidence score (col 6 l. 42-44: confidence score representing the likelihood that a particular set of words matches those spoken in the utterance; col 7 l. 29-42).
Regarding claim 9 Reavely teaches The computer-implemented method of claim 1, wherein the transcription likelihood score indicates a likelihood that the candidate transcription matches the utterance spoken by the user (col 6 l. 42-44: confidence score representing the likelihood that a particular set of words matches those spoken in the utterance).
Regarding claim 10 Reavely teaches The computer-implemented method of claim 1, wherein the data processing hardware resides on the computing device (fig 1, 2, 8; col 1 l. 19-22; col 24 l. 45-67: device may include…ASR…NLU…command processor; col 25 l. 54-66 multiple devices contain components of the system).
Regarding claim 11 Reavely and Mohri teach A system comprising:
data processing hardware; and
memory hardware in communication with the data processing hardware, the memory hardware storing instructions that when executed on the data processing hardware cause the data processing hardware to perform operations (Reavely figures 1-2, 6-8) comprising:
receiving audio data of an utterance spoken by a user and captured by a computing device associated with the user;
determining, from the audio data, phonemes corresponding to the utterance;
generating, using the phonemes corresponding to the utterance, a word lattice that includes one or more candidate transcriptions of the utterance, each candidate transcription of the one or more candidate transcriptions having a corresponding transcription likelihood score;
determining a context indicating that a user is listening to music through the computing device;
based on the context indicating that the user is listening to music through the computing device, selecting a grammar corresponding to a specific user intent of issuing a media playing command;
compiling the selected grammar into a weighted finite-state transducer (wFST); and
for the respective candidate transcription of the one or more candidate transcriptions having the highest corresponding transcription likelihood score, parsing, using the wFST compiled from the selected grammar corresponding to the specific user intent of issuing the media playing command, the respective candidate transcription to identify an action for the computing device to perform.
Claim recites limitations similar to claim 1 and is rejected for similar rationale and reasoning
Claim 12 recites limitations similar to claim 2 and is rejected for similar rationale and reasoning
Claim 13 recites limitations similar to claim 3 and is rejected for similar rationale and reasoning
Claim 14 recites limitations similar to claim 4 and is rejected for similar rationale and reasoning
Claim 15 recites limitations similar to claim 5/6 and is rejected for similar rationale and reasoning
Regarding claim 16 Reavely teaches the system of claim 11, where the grammar comprises a default grammar (abstract: default domains; col 3 l 63-67: built-in system domains).
Claim 17 recites limitations similar to claim 7 and is rejected for similar rationale and reasoning
Claim 18 recites limitations similar to claim 8 and is rejected for similar rationale and reasoning
Claim 19 recites limitations similar to claim 9 and is rejected for similar rationale and reasoning
Claim 20 recites limitations similar to claim 10 and is rejected for similar rationale and reasoning
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SHAUN ROBERTS/
Primary Examiner, Art Unit 2655
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