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 .
Status of Claims
Claims 1-11 and 16-20 are pending in this application.
Claims 12-15 are canceled.
Response to Arguments
Regarding Rejection under 35 U.S.C. 101
Applicant’s arguments with respect to rejections have been fully considered but they are not persuasive.
Regarding Claim 1, the Applicant argues that the rejection under 35 U.S.C. 101 is improper because the claims recite significantly more than the abstract idea because the claimed method improves the reliability and responsiveness of voice-controlled devices (Applicant Specification, [0042]-[0051]) (REMARKS, on page 8, 3rd paragraph – page 11 of 15, 1st paragraph).
However, Examiner respectfully disagrees that the rejection under 35 U.S.C. 101 is improper because the newly amended claim 1 is still directed to abstract idea. The patent-eligibility analysis below follows 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, dated July, 2024.
The steps of claim 1 describe the collection, processing, and analysis of data relating to voice commands, followed by training a model based on the analyzed data. Courts have consistently held that such concepts—collecting information, analyzing or processing data, and adjusting a model or system based on the results—are abstract ideas. See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (data collection, analysis, and display are abstract ideas); BASCOM Global Internet Servs., Inc. v. AT&T Mobility LLC, 827 F.3d 1341, 1348 (Fed. Cir. 2016) (filtering content is an abstract idea).
Here, the claim is directed to the abstract idea of collecting and processing data, analyzing that data (generating a confidence score), and using that analysis to update a model (training the speech recognizer).
The claim does not recite any technical details about how the confidence model operates differently from conventional models, nor does it describe any novel hardware or software architecture that improves computer functionality. The mere invocation of “training a model” without particularity does not demonstrate an unconventional machine or technique or a specific improvement in computer technology. Even though the disclosed invention is described in the background as improving computer technology, the claim provides no meaningful limitations such that this improvement is realized. Therefore, the claim 1 does not amount to significantly more than the abstract idea itself.
Regarding independent claims 18-20, the claims 18-20 are similar to claim 1.
Regarding dependent claims 2-11 and 16-17, the claims 2-11 and 16-17 are also directed to processes which manipulate data which are processes which can be performed by a human and implemented by a generic computer. Accordingly, the limitations of the Claims are not sufficient to add significantly more to improve technological functionality.
As such, claims 1-11 and 16-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. Thus, the rejection is maintained at this time. Please see the rejection below for the whole analysis.
Regarding Rejection under 35 U.S.C. 103
Applicant’s arguments with respect to rejections have been fully considered but are moot because the arguments do not apply to any of the references being used in the current rejection. The amended limitations raise new grounds for rejections and further that the Examiner is therefore applying a new reference.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11 and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claim 1 recites “obtaining to-be-recognized audio; separately training a wake-up model using at least two groups of wake-up word training sets to obtain at least two groups of training data; processing the to-be-recognized audio, using the wake-up model and the at least two groups of training data separately, to obtain at least two confidence levels and respective confidence level thresholds corresponding to the at least two confidence levels, and controlling an operating state of the voice apparatus by selectively triggering a wake-up event of the voice apparatus based on a comparison result between the at least two confidence levels and the respective confidence level thresholds corresponding to the at least two confidence levels
The limitation of “obtaining…”, “processing…”, and “controlling” is a process that, under its broadest reasonable interpretation, covers a human organizing of activities. More specifically, a human listens to a command and operates to trigger a device based on recognizing a command.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a computer amounts to no more than mere instructions to apply an exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
With respect to claims 18-20, the claim is similar to claim 1 and claims 18-20 recite additional element of “processors” and “storage medium”. The processor and memory are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions and being used as an applying) such that it amounts no more than mere instructions to apply the exception using a generic computer component as well. These claims further do not remedy the judicial exception being integrated into a practical application and further fail to include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to dependent claims 2-11 and 16-17, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, claims 1-11 and 16-20 are rejected.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 9, and 16-20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Sharifi et al., (US Pat. 9,711,148) in view of Deller et al, (US 10,127,908 B1).
Regarding claim 1, Sharifi discloses a wake-up processing method, applicable in a voice apparatus, the method comprising:
obtaining to-be-recognized audio (Figs. 1 and 2, Col. 11, lines 31-40, receiving an audio signal 122 from the user 102);
separately training a wake-up model using at least two groups of wake-up word training sets to obtain at least two groups of training data (Col. 10, line 63-Col. 11, line 10, training the text-dependent and/or text-independent models using the utterance of the user after identifying a particular user; “store additional instances of the keyword that it determines have been uttered by a particular user in association with that user's account in a retraining process”);
processing the to-be-recognized audio, using the wake-up model and the at least two groups of training data separately, to obtain at least two confidence levels and respective confidence level thresholds corresponding to the at least two confidence levels, wherein the at least two groups of training data are obtained by separately training with at least two groups of wake up word training sets using the wake up model (Figs. 1 and 2, Col. 7, lines 3-27, “the keyword detector module 124 may use speaker-agnostic speech recognition models such as HMMs to identify the keyword”; Col. 11, line 43-Col. 12, line 65, determining a confidence level associated with one or more text-dependent models by a text-dependent analyzer module 126 and a confidence level for one or more text-independent models by text-independent analyzer module 130; “the text-dependent model and the text-independent models may have separate thresholds for triggering”); and
controlling [an operating state of the voice apparatus by selectively] triggering a wake-up event of the voice apparatus based on a comparison result between the at least two confidence levels and the respective confidence level thresholds corresponding to the at least two confidence levels (Figs. 1 and 2, Col. 5, lines 5-27, Col. 11, line 43-Col. 12, line 65, “analyze the confidence for the text-dependent models to trigger an update of the text-dependent models and analyze the confidence for the text-independent models to trigger updates of the text-independent models. If the confidence is greater than the threshold, the server may initiate an update of a text-dependent model associated with the particular speaker, a text-independent model associated with the particular speaker, or both, using the audio signal encoding the utterance”).
Sharifi does not explicitly teach the bracketed limitation however Deller does explicitly teach including the bracketed limitation:
controlling [an operating state of the voice apparatus by selectively] triggering a wake-up event of the voice apparatus (Deller, Col. 11, lines 12-53, controlling an apparatus using a wakeword detection module which compares audio data to detect and process a wakeword based on a user profile, language and/or accent).
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to incorporate the method of speech recognition as taught by Sharifi with the method of triggering an event of apparatus based on user’s profile as taught by Deller to provide personalized information in order to improves entity resolution. (Deller, Col. 15, liens 35-38).
Regarding claim 2, Sharifi in view of Deller discloses the method according to claim 1, and Sharifi further discloses: wherein said obtaining the to-be-recognized audio comprises: performing a data collection through a sound collection device to obtain initial voice data (Sharifi, Fig. 1 Col. 11, lines 31-40, receiving an audio signal 122 from the user 102); and
pre-processing the initial voice data to obtain the to-be-recognized audio (Sharifi, Fig. 1 Col. 11, lines 31-40, encoding the received audio signal).
Regarding claim 3, Sharifi in view of Deller discloses the method according to claim 1, and Sharifi further discloses: wherein: each of the at least two groups of training data comprises a model parameter and a confidence level threshold (Col. 9, lines 20-65, the confidence levels associated with the text-dependent model and text-independent model have weight); and
said processing the to-be-recognized audio using the wake-up model and the at least two groups of training data separately, to obtain the at least two confidence levels and the respective confidence level thresholds corresponding to the at least two confidence levels comprises: processing the to-be-recognized audio using the wake-up model and the model parameters in the at least two groups of training data separately to obtain the at least two confidence levels, and obtaining the respective confidence level thresholds corresponding to the at least two confidence levels from the at least two groups of training data (Sharifi, Col. 2, lines 10-25, “assigning a first weight to the first confidence and a second weight to the second confidence, the first weight being greater than the second weight, and combining the weighted first confidence and the weighted second confidence to generate the combined confidence”).
Regarding claim 4, Sharifi in view of Deller discloses the method according to claim 1, and Sharifi further discloses:
wherein: the at least two groups of training data comprise a first group of training data and a second group of training data, the first group of training data comprising a first model parameter and a first confidence level threshold, and the second group of training data comprising a second model parameter and a second confidence level threshold (Col. 9, lines 20-65, the confidence levels associated with the text-dependent model and text-independent model have weight); and
said processing the to-be-recognized audio using the wake-up model and the at least two groups of training data separately, to obtain the at least two confidence levels and the respective confidence level thresholds corresponding to the at least two confidence levels comprises: processing the to-be-recognized audio using the wake-up model and the first model parameter in the first group of training data to obtain a first confidence level, and determining the first confidence level threshold corresponding to the first confidence level from the first group of training data; and processing the to-be-recognized audio using the wake-up model and the second model parameter in the second group of training data to obtain a second confidence level, and determining the second confidence level threshold corresponding to the second confidence level from the second group of training data (Col. 11, line 43-Col. 12, line 65, determining a confidence level associated with one or more text-dependent models by a text-dependent analyzer module 126 and a confidence level for one or more text-independent models by text-independent analyzer module 130; “the text-dependent model and the text-independent models may have separate thresholds for triggering an update”).
Regarding claim 5, Sharifi in view of Deller discloses the method according to claim 4, and Sharifi further discloses:
wherein said triggering the wake-up event of the voice apparatus based on the comparison result between the at least two confidence levels and the respective confidence level thresholds corresponding to the at least two confidence levels comprises: controlling the operating state of the voice apparatus by triggering the wake-up event of the voice apparatus when the first confidence level is greater than or equal to the first confidence level threshold or the second confidence level is greater than or equal to the second confidence level threshold (Figs. 1 and 2, Col. 5, lines 5-27, Col. 11, line 43-Col. 12, line 65, If the confidence is greater than the threshold, the server may initiate an update of a text-dependent model associated with the particular speaker, a text-independent model associated with the particular speaker, or both, using the audio signal encoding the utterance”).
Regarding claim 6, Sharifi in view of Deller discloses the method according to claim 5, and Sharifi further discloses:
wherein the wake-up event comprises a first wake-up event and/or a second wake-up event, the first wake-up event having an association relation with a wake-up word corresponding to the first group of training data, and the second wake-up event having an association relation with a wake-up word corresponding to the second group of training data (Col. 5, lines 5-64, performing speech recognition techniques to determine whether the keyword was spoken).
Regarding claim 9, Sharifi in view of Deller discloses the method according to claim 6, and Althaus further discloses:
wherein said triggering the wake-up event of the voice apparatus based on the comparison result between the at least two confidence levels and the respective confidence level thresholds corresponding to the at least two confidence levels comprises: calculating, when the first confidence level is greater than or equal to the first confidence level threshold and the second confidence level is greater than or equal to the second confidence level threshold, a first value by which the first confidence level exceeds the first confidence level threshold and a second value by which the second confidence level exceeds the second confidence level threshold, and controlling the operating state of the voice apparatus by triggering a target wake-up event of the voice apparatus based on the first value and the second value (Ath[0044]-[0050][0059]-[0062][0064] triggering a system based on identified confidence scores and thresholds).
Regarding claim 16, Sharifi in view of Deller discloses the method according to claim 1, and Sharifi further discloses:
obtaining the at least two groups of wake-up word training sets; and training the wake-up model using the at least two groups of wake-up word training sets, to obtain the at least two groups of training data, wherein each of the at least two groups of training data comprises a model parameter and a confidence level threshold (Sharifi, Col. 11, line 43-Col. 12, line 65, determining a confidence level associated with one or more text-dependent models by a text-dependent analyzer module 126 and a confidence level for one or more text-independent models by text-independent analyzer module 130; “the text-dependent model and the text-independent models may have separate thresholds for triggering”).
Regarding claim 17, Sharifi in view of Deller discloses the method according to claim 16, and Sharifi further discloses:
wherein said obtaining the at least two groups of wake-up word training sets comprises: obtaining an initial training set, wherein the initial training set comprises at least two wake-up words; and grouping the initial training set based on different wake-up words to obtain the at least two groups of wake-up word training sets (Sharifi, Col. 5, lines 6-65, obtaining an training set which includes keywords and query).
Regarding claim 19, Sharifi discloses a voice apparatus comprising a memory and one or more processors, wherein:
the memory is configured to store one or more computer programs executable by the processor; and the one or more processors are configured to perform, when executing the one or more computer programs, a wake-up processing method applicable in a voice apparatus, the method comprising (Col. 13, lines 36-52, processors and memory):
obtaining a to-be-recognized audio (Figs. 1 and 2, Col. 11, lines 31-40, receiving an audio signal 122 from the user 102);
separately training a wake-up model using at least two groups of wake-up word training sets to obtain at least two groups of training data (Col. 10, line 63-Col. 11, line 10, training the text-dependent and/or text-independent models using the utterance of the user after identifying a particular user; “store additional instances of the keyword that it determines have been uttered by a particular user in association with that user's account in a retraining process”);
processing the to-be-recognized audio, using the wake-up model and the at least two groups of training data separately, to obtain at least two confidence levels and respective confidence level thresholds corresponding to the at least two confidence levels, wherein the at least two groups of training data are obtained by separately training with at least two groups of wake up word training sets using the wake up model (Figs. 1 and 2, Col. 7, lines 3-27, “the keyword detector module 124 may use speaker-agnostic speech recognition models such as HMMs to identify the keyword”; Col. 11, line 43-Col. 12, line 65, determining a confidence level associated with one or more text-dependent models by a text-dependent analyzer module 126 and a confidence level for one or more text-independent models by text-independent analyzer module 130; “the text-dependent model and the text-independent models may have separate thresholds for triggering”); and
controlling [an operating state of the voice apparatus by selectively] triggering a wake-up event of the voice apparatus based on a comparison result between the at least two confidence levels and the respective confidence level thresholds corresponding to the at least two confidence levels (Figs. 1 and 2, Col. 5, lines 5-27, Col. 11, line 43-Col. 12, line 65, “analyze the confidence for the text-dependent models to trigger an update of the text-dependent models and analyze the confidence for the text-independent models to trigger updates of the text-independent models. If the confidence is greater than the threshold, the server may initiate an update of a text-dependent model associated with the particular speaker, a text-independent model associated with the particular speaker, or both, using the audio signal encoding the utterance”).
Sharifi does not explicitly teach the bracketed limitation however Deller does explicitly teach including the bracketed limitation:
controlling [an operating state of the voice apparatus by selectively] triggering a wake-up event of the voice apparatus (Deller, Col. 11, lines 12-53, controlling an apparatus using a wakeword detection module which compares audio data to detect and process a wakeword based on a user profile, language and/or accent).
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to incorporate the method of speech recognition as taught by Sharifi with the method of triggering an event of apparatus based on user’s profile as taught by Deller to provide personalized information in order to improves entity resolution. (Deller, Col. 15, liens 35-38).
Regarding claims 18 and 20, Claims 18 and 20 are the corresponding system and medium claims to method claim 1. Therefore, claims 18 and 20 are rejected using the same rationale as applied to claim 1 above.
Claims 7 and 8 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Sharifi et al., (US Pat. 9,711,148) in view of Deller et al, (US 10,127,908 B1) and further in view of Smith et al., (US Pub. 2020/0090646).
Regarding claim 7, Sharifi in view of Deller discloses the method according to claim 6. Sharifi in view of Deller does not explicitly teach however Smith does explicitly teach: controlling the operating state of the voice apparatus by triggering the first wake-up event of the voice apparatus when the first confidence level is greater than or equal to the first confidence level threshold and the second confidence level is smaller than the second confidence level threshold (Smith, Figs. 5 and 6, [0129] “a sensitivity level takes the form of a confidence threshold that defines a minimum confidence (i.e., probability) level for a wake-word engine that serves as a dividing line between triggering or not triggering a wake-word event when the wake-word engine is analyzing detected sound for its particular wake word“; [0135] each of wake-word engines, 570 and 571, has the respective confidence thresholds).
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to incorporate the method of speech recognition as taught by Sharifi in view of Deller with the method of multiple thresholds as taught by Smith to improve the quality of the detected sound (Smith, [0109]).
Regarding claim 8, Sharifi in view of Deller discloses the method according to claim 6. Sharifi in view of Deller does not explicitly teach however Smith does explicitly teach: wherein said triggering the wake-up event of the voice apparatus based on the comparison result between the at least two confidence levels and the respective confidence level thresholds corresponding to the at least two confidence levels comprises: controlling the operating state of the voice apparatus by triggering the second wake-up event of the voice apparatus when the second confidence level is greater than or equal to the second confidence level threshold and the first confidence level is smaller than the first confidence level threshold (Smith, Figs. 5 and 6, [0129] “a sensitivity level takes the form of a confidence threshold that defines a minimum confidence (i.e., probability) level for a wake-word engine that serves as a dividing line between triggering or not triggering a wake-word event when the wake-word engine is analyzing detected sound for its particular wake word“; [0135] each of wake-word engines, 570 and 571, has the respective confidence thresholds).
Allowable Subject Matter
Claim-s 10-11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form PTO-892.
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEONG-AH A. SHIN whose telephone number is (571)272-5933. The examiner can normally be reached 9 AM-3PM.
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Seong-ah A. Shin
Primary Examiner
Art Unit 2659
/SEONG-AH A SHIN/ Primary Examiner, Art Unit 2659
Prosecution Insights