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 .
DETAILED ACTION
Response to Arguments
Applicant's arguments with respect to claims 1-6, 8-18, and 20 have been considered but are moot in view of the new ground(s) of rejection. Applicant’s arguments are directed to the amended subject matter; new prior art is provided below.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6, 8-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210043191 A1 Chao; Pu-sen et al. (hereinafter Chao) in view of US 20220122614 A1 Pelecanos; Jason et al. (hereinafter Pelecanos) and further in view of US 20210082401 A1 KIM; Heejae (hereinafter Kim).
Re claim 1, Chao teaches
1. An electronic device comprising: at least one processor including processing circuitry; and memory storing instructions, wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic device to: (fig. 1 and 5)
receive an utterance from a user; (input at fig. 2 e.g. user request)
perform speaker recognition on the utterance; (using a speaker recognition model to determine if a speaker is the person speaking and updated thereof as new inputs come in and are added to past inputs 0016… and for instance at the client device or other devices 0061-0063 using fig. 2-4, a user inputs a request as TD speech in, if it fails, then TI speech input, if that fails the system proceeds as a guest user and still obtain context/intent to handle a non-user model general/guest speech recognition operation 0082 and learning thereof to update the model 0012, false positives can occur between TD speech input and TI speech input, thereby learning takes place 0008-0009 with 0012)
However, while learning and model updates take place following false positive/rejection, as well as the ability to still process a speech query, Chao fails to teach:
when the speaker recognition on the utterance fails, detect a related event which is related to an action corresponding to an intent of the utterance for which the speaker recognition failed (Pelecanos although intent is established and a command can be processed as in fig. 1c, output information when a false reject is established would be “sorry, I could not verify your voice: the event is the action to be performed e.g. use command “what is next on my calendar?” as in fig. 1b where a false reject is established in context with a command that the system can process e.g. accessing a calendar and providing the results to a user e.g. intent 0035-0036 and also in fig. 1c evidencing the systems ability to understand intent… the event is the action to be performed e.g. use command “what is next on my calendar?” as in fig. 1b where a false reject is established in context with a command that the system can process e.g. accessing a calendar and providing the results to a user e.g. intent 0035-0036)
obtain feedback from the user on the information (Pelecanos the second input by the user is a form of feedback e.g. repeating the command and slower… following the premise that although intent is established and a command can be processed as in fig. 1c, output information when a false reject is established would be “sorry, I could not verify your voice: the event is the action to be performed e.g. use command “what is next on my calendar?” as in fig. 1b where a false reject is established in context with a command that the system can process e.g. accessing a calendar and providing the results to a user e.g. intent 0035-0036 and also in fig. 1c evidencing the systems ability to understand intent)
wherein the feedback is used to (Pelecanos updating a user model by the user re-enrolling such as due to false rejects e.g. 0040, based on false failures via subsequent inputs by the user is a form of feedback e.g. repeating the command and slower… following the premise that although intent is established and a command can be processed as in fig. 1c, output information when a false reject is established would be “sorry, I could not verify your voice: the event is the action to be performed e.g. use command “what is next on my calendar?” as in fig. 1b where a false reject is established in context with a command that the system can process e.g. accessing a calendar and providing the results to a user e.g. intent 0035-0036 and also in fig. 1c evidencing the systems ability to understand intent)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chao to incorporate the above claim limitations as taught by Pelecanos to allow for a simple substitution of one known element such as direct user feedback through repetition and re-enrollment to update the existing speaker model of Chao, which allows for system to update the user's voice model with new, high-quality, or representative samples, which directly reduces the false rejection rate, such as when over time, a user's voice may change, or they may use different devices, causing the initial enrolment to become obsolete, where re-enrollment or repetition allows the model to adapt to these changes and also prevent false acceptance, in addition to accuracy and security.
However, while the combination teaches verifying the user and notifying the user that the system could not verify the voice and multiple follow-up attempts to avoid false rejection i.e. Pelecanos, it fails to teach repeating back the erroneous portion per se and providing an option for a user to provide feedback or a selection of what the system is questioning, thus failing to teach:
in response to the related event being detected, output information corresponding to the related event by outputting a user interface comprising the information corresponding to the related event, wherein the user interface is configured to (i) playback an audio signal of the utterance for which the speaker recognition failed and (ii) output a positive or negative selection option indicating whether a voice corresponds to the user; (Kim repeating back on a speaker a portion of the user utterance which needs clarification or disambiguation, and providing the user an option to select yes/no with respect to the disambiguation 0062-0065)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chao in view of Pelecanos to incorporate the above claim limitations as taught by Kim to allow for combining prior art elements according to known methods to yield predictable results such as using the user verification methods of Pelecanos with the disambiguation audible repeat on speaker with yes/no selection of Kim, thereby improving Pelecanos to have a user selection as another option to truly avoid a false rejection when the system is not recognizing the identity properly, such that for the above claim limitations, the disambiguation concept as claimed is covered by Kim with the addition of manual user input, applied to the verification concepts of Pelecanos, i.e. user manual selection of the command as well as identity check embedded thereof.
Re claim 13, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope
Re claim 14, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope.
For instance, fig. 1 and fig. 5 of Chao.
Re claims 2 and 15, Chao teaches
2. The electronic device of claim 1, wherein the utterance for which the speaker recognition has failed is an utterance for which intent recognition is successful. (for instance at the client device or other devices 0061-0063using fig. 2-4, a user inputs a request as TD speech in, if it fails, then TI speech input, if that fails the system proceeds as a guest user and still obtain context/intent to handle a non-user model general/guest speech recognition operation 0082 and learning thereof to update the model 0012, false positives can occur between TD speech input and TI speech input, thereby learning takes place 0008-0009 with 0012)
Re claims 3 and 16, while learning and model updates take place following false positive/rejection, as well as the ability to still process a speech query, Chao fails to teach:
3. The electronic device of claim 1, wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic device to generate a detection event corresponding to the related event, in response to the related event is detecteupdating a user model by the user re-enrolling such as due to false rejects e.g. 0040, based on false failures via subsequent inputs by the user is a form of feedback e.g. repeating the command and slower… following the premise that although intent is established and a command can be processed as in fig. 1c, output information when a false reject is established would be “sorry, I could not verify your voice: the event is the action to be performed e.g. use command “what is next on my calendar?” as in fig. 1b where a false reject is established in context with a command that the system can process e.g. accessing a calendar and providing the results to a user e.g. intent 0035-0036 and also in fig. 1c evidencing the systems ability to understand intent)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chao to incorporate the above claim limitations as taught by Pelecanos to allow for a simple substitution of one known element such as direct user feedback through repetition and re-enrollment to update the existing speaker model of Chao, which allows for system to update the user's voice model with new, high-quality, or representative samples, which directly reduces the false rejection rate, such as when over time, a user's voice may change, or they may use different devices, causing the initial enrolment to become obsolete, where re-enrollment or repetition allows the model to adapt to these changes and also prevent false acceptance, in addition to accuracy and security.
Re claims 4 and 17, while learning and model updates take place following false positive/rejection, as well as the ability to still process a speech query, Chao fails to teach:
4. The electronic device of claim 1, wherein the detection event comprises an event for detecting input which comprises the action corresponding to the intent of the utterance for which the speaker recognition failed, wherein the user input is received after the utterancethat the system can process e.g. accessing a calendar and providing the results to a user e.g. intent 0035-0036 and also in fig. 1c evidencing the systems ability to understand intent)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chao to incorporate the above claim limitations as taught by Pelecanos to allow for a simple substitution of one known element such as direct user feedback through repetition and re-enrollment to update the existing speaker model of Chao, which allows for system to update the user's voice model with new, high-quality, or representative samples, which directly reduces the false rejection rate, such as when over time, a user's voice may change, or they may use different devices, causing the initial enrolment to become obsolete, where re-enrollment or repetition allows the model to adapt to these changes and also prevent false acceptance, in addition to accuracy and security.
Re claim 5, Chao teaches
5. The electronic device of claim 1, wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic devicedetection event based on an occurrence time of the utterance for which the speaker recognition has failed and an intent of the utterance for which the speaker recognition has failed. (when there is failure, intent can be included, using time as a factor in analysis 0085, for instance at the client device or other devices 0061-0063using fig. 2-4, a user inputs a request as TD speech in, if it fails, then TI speech input, if that fails the system proceeds as a guest user and still obtain context/intent to handle a non-user model general/guest speech recognition operation 0082 and learning thereof to update the model 0012, false positives can occur between TD speech input and TI speech input, thereby learning takes place 0008-0009 with 0012)
Re claims 6 and 18, Chao teaches
6. The electronic device of claim 1, wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic deviceuser sends a query as in device in fig. 1… for instance at the client device or other devices 0061-0063using fig. 2-4, a user inputs a request as TD speech in, if it fails, then TI speech input, if that fails the system proceeds as a guest user and still obtain context/intent to handle a non-user model general/guest speech recognition operation 0082 and learning thereof to update the model 0012, false positives can occur between TD speech input and TI speech input, thereby learning takes place 0008-0009 with 0012)
Re claims 8 and 20, while the combination teaches verifying the user and notifying the user that the system could not verify the voice and multiple follow-up attempts to avoid false rejection i.e. Pelecanos, it fails to teach repeating back the erroneous portion per se and providing an option for a user to provide feedback or a selection of what the system is questioning, thus failing to teach a question output related to the user i.e. about, under BRI pertinent to the question the user asks:
8. The electronic device of claim 7, wherein the user interface is configured to output a question about the user (Kim repeating back on a speaker a portion of the user utterance which needs clarification or disambiguation, and providing the user an option to select yes/no with respect to the disambiguation 0062-0065)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chao in view of Pelecanos to incorporate the above claim limitations as taught by Kim to allow for combining prior art elements according to known methods to yield predictable results such as using the user verification methods of Pelecanos with the disambiguation audible repeat on speaker with yes/no selection of Kim, thereby improving Pelecanos to have a user selection as another option to truly avoid a false rejection when the system is not recognizing the identity properly, such that for the above claim limitations, the disambiguation concept as claimed is covered by Kim with the addition of manual user input, applied to the verification concepts of Pelecanos, i.e. user manual selection of the command as well as identity check embedded thereof.
Re claim 9, while learning and model updates take place following false positive/rejection, as well as the ability to still process a speech query, Chao fails to teach:
9. The electronic device of claim 1, wherein the feedback is for determining whether the utterance for which the speaker recognition has failed is false-rejected. (Pelecanos updating a user model by the user re-enrolling such as due to false rejects e.g. 0040, based on false failures via subsequent inputs by the user is a form of feedback e.g. repeating the command and slower… following the premise that although intent is established and a command can be processed as in fig. 1c, output information when a false reject is established would be “sorry, I could not verify your voice: the event is the action to be performed e.g. use command “what is next on my calendar?” as in fig. 1b where a false reject is established in context with a command that the system can process e.g. accessing a calendar and providing the results to a user e.g. intent 0035-0036 and also in fig. 1c evidencing the systems ability to understand intent)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chao to incorporate the above claim limitations as taught by Pelecanos to allow for a simple substitution of one known element such as direct user feedback through repetition and re-enrollment to update the existing speaker model of Chao, which allows for system to update the user's voice model with new, high-quality, or representative samples, which directly reduces the false rejection rate, such as when over time, a user's voice may change, or they may use different devices, causing the initial enrolment to become obsolete, where re-enrollment or repetition allows the model to adapt to these changes and also prevent false acceptance, in addition to accuracy and security.
Re claim 10, while learning and model updates take place following false positive/rejection, as well as the ability to still process a speech query, Chao fails to teach:
10. The electronic device of claim 1, wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic device“what is next on my calendar?” as in fig. 1b where a false reject is established in context with a command that the system can process e.g. accessing a calendar and providing the results to a user e.g. intent 0035-0036 and also in fig. 1c evidencing the systems ability to understand intent)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chao to incorporate the above claim limitations as taught by Pelecanos to allow for a simple substitution of one known element such as direct user feedback through repetition and re-enrollment to update the existing speaker model of Chao, which allows for system to update the user's voice model with new, high-quality, or representative samples, which directly reduces the false rejection rate, such as when over time, a user's voice may change, or they may use different devices, causing the initial enrolment to become obsolete, where re-enrollment or repetition allows the model to adapt to these changes and also prevent false acceptance, in addition to accuracy and security.
Re claim 11, Chao teaches
11. The electronic device of claim 10, wherein the instructions, when executed by the at least one processor individually or collectively, cause the electronic device
Re claim 12, while learning and model updates take place following false positive/rejection, as well as the ability to still process a speech query, Chao fails to teach:
12. The electronic device of claim 1, wherein the speaker model is updated based on the utterance for which the speaker recognition has failed. (Pelecanos updating a user model by the user re-enrolling such as due to false rejects e.g. 0040, based on false failures via subsequent inputs by the user is a form of feedback e.g. repeating the command and slower… following the premise that although intent is established and a command can be processed as in fig. 1c, output information when a false reject is established would be “sorry, I could not verify your voice: the event is the action to be performed e.g. use command “what is next on my calendar?” as in fig. 1b where a false reject is established in context with a command that the system can process e.g. accessing a calendar and providing the results to a user e.g. intent 0035-0036 and also in fig. 1c evidencing the systems ability to understand intent)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chao to incorporate the above claim limitations as taught by Pelecanos to allow for a simple substitution of one known element such as direct user feedback through repetition and re-enrollment to update the existing speaker model of Chao, which allows for system to update the user's voice model with new, high-quality, or representative samples, which directly reduces the false rejection rate, such as when over time, a user's voice may change, or they may use different devices, causing the initial enrolment to become obsolete, where re-enrollment or repetition allows the model to adapt to these changes and also prevent false acceptance, in addition to accuracy and security.
Conclusion
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/27/2026 has been entered.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20180374486 A1 CHEN Z et al.
Speaker recognition training
US 20110054899 A1 Phillips; Michael S. et al.
Model updates
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/MICHAEL COLUCCI/Primary Examiner, Art Unit 2655 (571)-270-1847
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