DETAILED ACTION
This office action is in response to Applicant’s Request for Continued Examination (RCE), received on 01/27/2026. Claims 1-2, 10-12, and 20 have been amended. Claims 1-20 are pending and have been considered.
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/27/2026 has been entered.
Response to Arguments
Applicant’s arguments, see pg. 8, filed 01/16/2026, with respect to the rejection(s) of claim(s) 1 and 11 under 35 U.S.C. 103 (Xu in view of Mao) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Li et al. (US-20180233151-A1), hereinafter Li. Li discloses “Processing circuitry of an information processing apparatus obtains a set of identity vectors that are calculated according to voice samples from speakers. The identity vectors are classified into speaker classes respectively corresponding to the speakers. The processing circuitry selects, from the identity vectors, first subsets of interclass neighboring identity vectors respectively corresponding to the identity vectors and second subsets of intraclass neighboring identity vectors respectively corresponding to the identity vectors. The processing circuitry determines an interclass difference based on the first subsets of interclass neighboring identity vectors and the corresponding identity vectors; and determines an intraclass difference based on the second subsets of intraclass neighboring identify vectors and the corresponding identity vectors. Further, the processing circuitry determines a set of basis vectors to maximize a projection of the interclass difference on the basis vectors and to minimize a projection of the intraclass difference on the basis vectors” (abstract). Specifically, Li discloses performing feature transformation on identity vectors to be compared to verification vectors ([0056]). See updated rejections below.
In response to applicant's arguments against the references individually, see pg. 9, “Xu does not operate on embeddings to generate embeddings”, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Mao is incorporated to disclose the concept of embeddings as applied to the feature templates of Xu, wherein the examiner asserts that representation of features as embeddings is a well-known technique in the art.
Applicant's arguments filed 01/16/2026, see pg. 9, with respect to “Moreover, the Examiner’s embodiment of Xu…Such a combination of feature extraction models in Xu neither suggests, nor could be modified to practice…” have been fully considered but they are not persuasive.
Applicant’s representative asserts, “Moreover, the Examiner's embodiment of Xu applies combined feature extraction model (resulting from combining the feature extraction models) having the same machine-learning layers for both obtaining enrollment embeddings and converting the enrollment embeddings. See Xu, [0106]-[0108]. Such a combination of feature extraction models in Xu neither suggests, nor could be modified to practice, a first embedding extractor comprising a first plurality of machine- learning layers trained to generate enrollment embeddings and an embedding convertor comprising a second plurality of machine-learning layers trained to generate converted embeddings. As such, Xu's combination of feature extraction models therefore fails to suggest the features of generating ‘a plurality of converted embeddings ... by applying ... an embedding convertor comprising a second plurality of machine-learning layers, where the plurality of enrollment embeddings are extracted ‘by applying a first embedding extractor comprising a first plurality of machine-learning layers,’ as described in the claims.”
In response, the examiner would like to refer to the broadest reasonable interpretation (BRI) of the claimed plurality of machine-learning layers as currently claimed. Specifically, the examiner respectfully asserts that a “first embedding extractor comprising a first plurality of machine-learning layers” and an “embedding convertor comprising a second plurality of machine-learning layers” does not require the extractor and convertor from being separate elements in an overall machine-learning architecture. The examiner respectfully asserts that one ‘model’ (or similarly defined component) which performs both the operations of the extractor and convertor maps to the claims. The examiner understands that Applicant’s intention may be to bring the structure of Fig. 4 into the claims, but the breadth with which the machine-learning layers are claimed does not require the structural interpretation as disclosed in Fig. 4.
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 (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 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.
Claim(s) 1, 5, 9, 10-11, 15, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. (US-20240071392-A1), hereinafter Xu, in view of Mao et al. (US-20210183358-A1), hereinafter Mao, further in view of Li et al. (US-20180233151-A1), hereinafter Li.
Regarding claim 1, Xu discloses: A computer-implemented method ([0143] an embodiment of this application further provides a computer program product including instructions) comprising:
at an enrollment ([0042] a registration procedure, [Registration and enrollment are synonymous terms in the context of speaker verification]):
Xu does not disclose:
obtaining a plurality of enrollment embeddings.
Mao discloses:
obtaining a plurality of enrollment embeddings ([0023] The user device 110a may process (132) the audio data using a first neural network to determine first embedding data representing characteristics of the utterance. This first embedding data may include a data vector that represents vocal characteristics of the voice of the user 10, [Consider “enrollment” audio data of [0045] of Mao]).
Xu and Mao are considered analogous art within audio signal conversion. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Xu to incorporate the teachings of Mao, because of the novel way to convert audio data into formats capable of being sent/handled by (Mao, [0001]).
Xu further discloses:
obtaining, by a computer ([0054] the electronic device includes, but is not limited to, a mobile phone, a tablet computer), a plurality of enrollment embeddings extracted using a plurality of enrollment signals for an enrolled user by applying a first embedding extractor comprising a first plurality of machine-learning layers trained to generate the plurality of enrollment embeddings for a first attribute-type corresponding to a first type of machine learning architecture ([Fig. 3, S302, “The electronic device processes the first registration voice by using a pre-stored first model, to obtain a first user feature template…”], [0042] a voiceprint recognition system acquires a registration voice entered by the user, extracts a voiceprint feature from the registration voice according to a pre-trained depth model (referred to as a “voiceprint feature extraction model” or “model” in this specification), and stores the voiceprint feature in an electronic device as a user feature template, [In view of the previously disclosed plurality of embeddings representing vocal characteristics of Mao, features extracted from a voice to form a voiceprint indicates the features of Xu to be representative of embeddings. Further, a pre-stored first model tracks to a first type of machine learning architecture with a first attribute-type corresponding to the first feature template. The first user feature template is the plurality of enrollment embeddings, see [0103] defining feature vectors of user template features. Processing using a model suggests the model to be comprised of layers]).
Xu in view of Mao does not disclose:
generating, by the computer, a plurality of converted embeddings corresponding to the plurality of enrollment embeddings by
applying, to each enrollment embedding of the plurality of enrollment embeddings, an embedding convertor comprising a second plurality of machine-learning layers trained to generate a converted embedding having a second attribute-type corresponding to a second type of machine learning architecture,
the converted embedding generated for an enrollment embedding having the first attribute-type.
Li discloses:
generating, by the computer ([Considering the previously disclosed computer of Xu]), a plurality of converted embeddings corresponding to the plurality of enrollment embeddings by
applying, to each enrollment embedding of the plurality of enrollment embeddings ([0123] In the registration stage, a voice is a target speaker needs to be obtained. After front-end preprocessing, feature extraction, and model training, the voices is mapped into a given identity vector of a determined length, [An identity vector (wherein that vector is comprising embeddings in view of Mao) for a registration tracks to a plurality of enrollment embeddings]), an embedding convertor comprising a second plurality of machine-learning layers trained to generate a converted embedding having a second attribute-type corresponding to a second type of machine learning architecture ([Fig. 9, Identity Vector Regulating Module], [0124] Both the given identity vector of the target speaker and the identity vector to be verified in the recognition stage subsequently pass through a back-end regulator, for performing regulation and optimization operations including feature transformation, [Performing a feature transform on a registration identity vector indicates generating a converted embedding having a second attribute type via the transform, wherein performing model training for generating the identity vectors ([0123]) indicates a transformed identity vector (also necessarily generated) to be performed using the same trained model, necessarily comprising layers]),
the converted embedding generated for an enrollment embedding having the first attribute-type ([As previously disclosed, transforming a registration identity vector indicates conversion of an enrollment embedding having a first attribute-type]); and
Xu, Mao, and Li are considered analogous art within speaker identity verification. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Xu in view of Mao to incorporate the teachings of Li, because of the novel way to perform feature transformation on base identity vector sets to be processed for identity verification, allowing for inter- and intraclass difference comparisons of voice samples which improves accuracy of speaker recognition for samples belonging to different class sets, i.e. attribute-types (Li, [0057]).
Xu further discloses:
generating, by the computer ([In view of the previously disclosed computer of Xu]), a converted enrolled voiceprint having the second attribute-type for the enrolled user based upon the plurality of converted embeddings ([0105] updates, based on the second voiceprint feature, the first user feature template stored in the electronic device, [Updating a first user feature, generated based on enrollment/registration audio, using a second voiceprint feature gathered through a second model having a second-attribute type indicates the updated user feature to be a converted enrolled voiceprint having the second-attribute type, i.e. that resulting from the combination of the first and second features as previously disclosed, in view of the transformed identity vectors of Li which track to feature templates ([0003] of Li discloses identity vectors may represent voice)]); and
at a deployment ([0042] In the verification procedure, [In the context of speech/speaker recognition, deployment and verification are synonymous terms]):
generating, by the computer ([In view of the previously disclosed computer of Xu]), an inbound voiceprint for an inbound user extracted using an inbound signal for an inbound user by applying a second embedding extractor for the second attribute-type ([0105] the electronic device processes the first verification voice by using the second model, to obtain a second voiceprint feature); and,
generating, by the computer ([In view of the previously disclosed computer of Xu]), a similarity score for the inbound signal based upon a distance between the converted enrolled voiceprint and the inbound voiceprint ([0103] For example, in cosine distance model scoring, a cosine value between a feature vector of a to-be-verified first voiceprint feature and a feature vector of the user template feature is calculated, and the cosine value is used as a similarity score in other words, a scoring result)), the similarity score indicating a likelihood that the inbound user is the enrolled user ([0050] obtain a verification result, [A verification result tracks to a decision as to whether or not the two voiceprints verified to be from the same speaker]).
Regarding claim 5, Xu in view of Mao, further in view of Li discloses: The method according to claim 1.
Mao further discloses:
training a plurality of embedding convertors according to a plurality of attribute-types ([0031] As explained in greater detail with reference to FIG. 3, the feature conversion component 208 may be trained such that it outputs sufficiently different converted embedding data 210 given embedding data 206 that corresponds to different users [Outputting data corresponding to a plurality of users indicates those users have different embedding features, i.e. voice features, which track to attribute type in view of [0040] of the instant application, i.e. other attributes (specific voice/frequency features) of signals]).
Regarding claim 9, Xu in view of Mao, further in view of Li discloses: the method according to claim 1,
Mao further discloses:
wherein generating the converted enrolled voiceprint having the second attribute-type includes algorithmically combining, by the computer, the converted enrollment embeddings having the second attribute-type ([0076] the feature extraction component 204 may accumulate or otherwise combine the audio data 202 as it comes in. That is, for a certain frame's worth of audio data 202 that comes in, the feature extraction component 204 may combine that frame's worth of data to the previous data received for the particular utterance).
Regarding claim 10, Xu in view of Mao, further in view of Li discloses: the method according to claim 1.
Mao further discloses:
extracting, by the computer, a set of enrollment features from an enrollment signal ([Fig. 2A, 204], [0030] The feature extraction component 204 may be trained to process the audio data 202 and output corresponding embedding data 206, which may be a vector of values representing audio characteristics of the audio data 202).
extracting, by the computer, an enrollment embedding based upon the set of features extracted from the enrollment signal by applying the first embedding extractor for the first attribute-type ([Fig. 2A, 206], [0030] In other words, the feature extraction component 204 extracts features associated with the voice type associated with the audio data 202 but not necessarily the particular words represented in the audio data 202 [i.e. they are extracting embeddings of features for an input signal 202 having a first attribute-type]).
Regarding claim 11, Xu discloses:
a system ([0042] the voiceprint recognition system) comprising:
a non-transitory machine-readable memory configured to store machine-readable instructions for one or more neural networks ([0056] The mobile phone 100 includes a processor 110, an internal memory 121, [Internal memory on a physical device will be non-transitory]); and,
a computer comprising a processor ([0148] These computer program instructions may be provided for a general-purpose computer, a dedicated computer, an embedded processor) configured to:
at an enrollment ([0042] a registration procedure, [Registration and enrollment are synonymous terms in the context of speaker verification]):
Xu does not disclose:
obtain a plurality of enrollment embeddings.
Mao discloses:
obtain a plurality of enrollment embeddings ([0023] The user device 110a may process (132) the audio data using a first neural network to determine first embedding data representing characteristics of the utterance. This first embedding data may include a data vector that represents vocal characteristics of the voice of the user 10, [Consider “enrollment” audio data of [0045] of Mao]).
Xu and Mao are considered analogous art within audio signal conversion. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Xu to incorporate the teachings of Mao, because of the novel way to convert audio data into formats capable of being sent/handled by (Mao, [0001]).
Xu further discloses:
obtain a plurality of enrollment embeddings extracted using a plurality of enrollment signals for an enrolled user by applying a first embedding extractor comprising a first plurality of machine-learning layers trained to generate the plurality of enrollment embeddings for a first attribute-type corresponding to a first type of machine learning architecture ([Fig. 3, S302, “The electronic device processes the first registration voice by using a pre-stored first model, to obtain a first user feature template…”], [0042] a voiceprint recognition system acquires a registration voice entered by the user, extracts a voiceprint feature from the registration voice according to a pre-trained depth model (referred to as a “voiceprint feature extraction model” or “model” in this specification), and stores the voiceprint feature in an electronic device as a user feature template, [In view of the previously disclosed plurality of embeddings representing vocal characteristics of Mao, features extracted from a voice to form a voiceprint indicates the features of Xu to be representative of embeddings. Further, a pre-stored first model tracks to a first type of machine learning architecture with a first attribute-type corresponding to the first feature template. The first user feature template is the plurality of enrollment embeddings, see [0103] defining feature vectors of user template features. Processing using a model suggests the model to be comprised of layers]).
Xu in view of Mao does not disclose:
generate a plurality of converted embeddings corresponding to the plurality of enrollment embeddings by
applying, to each enrollment embedding of the plurality of enrollment embeddings, an embedding convertor comprising a second plurality of machine-learning layers trained to generate a converted embedding having a second attribute-type corresponding to a second type of machine learning architecture,
the converted embedding generated for an enrollment embedding having the first attribute-type.
Li discloses:
generate a plurality of converted embeddings corresponding to the plurality of enrollment embeddings by
applying, to each enrollment embedding of the plurality of enrollment embeddings ([0123] In the registration stage, a voice is a target speaker needs to be obtained. After front-end preprocessing, feature extraction, and model training, the voices is mapped into a given identity vector of a determined length, [An identity vector (wherein that vector is comprising embeddings in view of Mao) for a registration tracks to a plurality of enrollment embeddings]), an embedding convertor comprising a second plurality of machine-learning layers trained to generate a converted embedding having a second attribute-type corresponding to a second type of machine learning architecture ([Fig. 9, Identity Vector Regulating Module], [0124] Both the given identity vector of the target speaker and the identity vector to be verified in the recognition stage subsequently pass through a back-end regulator, for performing regulation and optimization operations including feature transformation, [Performing a feature transform on a registration identity vector indicates generating a converted embedding having a second attribute type via the transform, wherein performing model training for generating the identity vectors ([0123]) indicates a transformed identity vector (also necessarily generated) to be performed using the same trained model, necessarily comprising layers]),
the converted embedding generated for an enrollment embedding having the first attribute-type ([As previously disclosed, transforming a registration identity vector indicates conversion of an enrollment embedding having a first attribute-type]); and
Xu, Mao, and Li are considered analogous art within speaker identity verification. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Xu in view of Mao to incorporate the teachings of Li, because of the novel way to perform feature transformation on base identity vector sets to be processed for identity verification, allowing for inter- and intraclass difference comparisons of voice sample which improves accuracy of speaker recognition for samples belonging to different class sets, i.e. attribute-types (Li, [0057]).
Xu further discloses:
generate a converted enrolled voiceprint having the second attribute-type for the enrolled user based upon the plurality of converted embeddings ([0105] updates, based on the second voiceprint feature, the first user feature template stored in the electronic device, [Updating a first user feature, generated based on enrollment/registration audio, using a second voiceprint feature gathered through a second model having a second-attribute type indicates the updated user feature to be a converted enrolled voiceprint having the second-attribute type, i.e. that resulting from the combination of the first and second features as previously disclosed, in view of the transformed identity vectors of Li which track to feature templates ([0003] of Li discloses identity vectors may represent voice)]); and
at a deployment ([0042] In the verification procedure, [In the context of speech/speaker recognition, deployment and verification are synonymous terms]):
generate an inbound voiceprint for an inbound user extracted using an inbound signal for an inbound user by applying a second embedding extractor for the second attribute-type ([0105] the electronic device processes the first verification voice by using the second model, to obtain a second voiceprint feature); and,
generate a similarity score for the inbound signal based upon a distance between the converted enrolled voiceprint and the inbound voiceprint ([0103] For example, in cosine distance model scoring, a cosine value between a feature vector of a to-be-verified first voiceprint feature and a feature vector of the user template feature is calculated, and the cosine value is used as a similarity score in other words, a scoring result)), the similarity score indicating a likelihood that the inbound user is the enrolled user ([0050] obtain a verification result, [A verification result tracks to a decision as to whether or not the two voiceprints verified to be from the same speaker]).
Regarding claim 15, Xu in view of Mao, further in view of Li discloses the system according to claim 11.
Mao further discloses:
train a plurality of embedding convertors according to a plurality of attribute-types ([0031] As explained in greater detail with reference to FIG. 3, the feature conversion component 208 may be trained such that it outputs sufficiently different converted embedding data 210 given embedding data 206 that corresponds to different users [Outputting data corresponding to a plurality of users indicates those users have different embedding features, i.e. voice features, which track to attribute types in view of [0040] of the instant application, i.e. other attributes (specific voice/frequency features) of signals]).
Regarding claim 19, Xu in view of Mao, further in view of Li discloses: the system according to claim 11.
Mao further discloses:
wherein when generating the converted enrolled voiceprint having the second attribute-type the computer is further configured to store the converted enrolled voiceprint into a user profile database ([0076] the feature extraction component 204 may accumulate or otherwise combine the audio data 202 as it comes in. That is, for a certain frame's worth of audio data 202 that comes in, the feature extraction component 204 may combine that frame's worth of data to the previous data received for the particular utterance).
Regarding claim 20, Xu in view of Mao, further in view of Li discloses: the system according to claim 11.
Mao further discloses:
extract a set of enrollment features from an enrollment signal ([Fig. 2A, 204], [0030] The feature extraction component 204 may be trained to process the audio data 202 and output corresponding embedding data 206, which may be a vector of values representing audio characteristics of the audio data 202).
extract an enrollment embedding based upon the set of features extracted from the enrollment signal by applying the first embedding extractor for the first attribute-type ([Fig. 2A, 206], [0030] In other words, the feature extraction component 204 extracts features associated with the voice type associated with the audio data 202 but not necessarily the particular words represented in the audio data 202 [i.e. they are extracting embeddings of features for an input signal 202 having a first attribute-type]).
Claim(s) 2, 4, 8, 12, 14, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Mao, further in view of Li, further in view of Thomson et al. (US-20200175961-A1), hereinafter Thomson.
Regarding claim 2, Xu in view of Mao, further in view of Li discloses the method according to claim 1.
Xu in view of Mao, further in view of Li does not disclose:
obtaining, by the computer, a plurality of training embeddings extracted using a plurality of training signals by applying the first embedding extractor for the first attribute-type; and,
training, by the computer, the embedding convertor by applying the second plurality of machine-learning layers of the embedding convertor on the plurality of training embeddings.
Thomson discloses:
obtaining, by the computer, a plurality of training embeddings extracted using a plurality of training signals by applying the first embedding extractor for the first attribute-type ([1023] The training set 5810 may be provided to feature extractors 5840).
training, by the computer, the embedding convertor by applying the second plurality of machine-learning layers of the embedding convertor on the plurality of training embeddings ([1023] The feature extractors 5840 may be configured to determine features such as n-grams or word embeddings of the training set 5810 that may be provided to the model trainer).
Xu, Mao are considered analogous art within speaker embedding conversion. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Xu in view of Mao, further in view of Li to incorporate the teachings of Thomson, because of the novel way to incorporate a plurality of training voice profiles using live audio for improved automatic speech recognition system accuracy (Thomson, [0096]).
Regarding claim 4, Xu in view of Mao, further in view of Li, further in view of Thomson discloses the method according to claim 2.
Thomson further discloses:
training the embedding extractor includes executing, by the computer, one or more data augmentation operations on at least of a training audio signal and an enrollment signal ([0766] Additionally or alternatively, audio in a first format may be converted to a second format and presented to an ASR system 3720 configured for the second format. For example, wideband audio may be downsampled to 8 kHz and processed by an ASR system 3720 configured to recognize 8 kHz speech. [Down-sampling is listed as one of a non-limiting example of a augmentation operation according to the spec, [0042]]).
Regarding claim 8, Xu in view of Mao, further in view of Li discloses the method according to claim 1.
Xu in view of Mao, further in view of Li does not disclose:
wherein generating the converted enrolled voiceprint having the second attribute-type includes storing, by the computer, the converted enrolled voiceprint into a user profile database.
Thomson discloses:
wherein generating the converted enrolled voiceprint having the second attribute-type includes storing, by the computer, the converted enrolled voiceprint into a user profile database ([0163] In some embodiments, the configuration service may include a business server, a user profile system, and a subscription management system. The configuration service may store information on the individual devices or on a server [Storing user profiles containing voice information ([0163]) in view of the voice prints of Mao, indicates that a converted enrolled voiceprint having the second attribute type is stored in a user profile]).
Xu, Mao, Li, and Thomson are considered analogous art within speaker embedding conversion. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Xu in view of Mao, further in view of Li to incorporate the teachings of Thomson, because of the novel way to incorporate a plurality of training voice profiles using live audio for improved automatic speech recognition system accuracy (Thomson, [0096]).
Regarding claim 12, Xu in view of Mao, further in view of Li discloses: the system according to claim 11.
Xu in view of Mao, further in view of Li does not disclose:
obtain, by the computer, a plurality of training embeddings extracted using a plurality of training signals by applying the first embedding extractor for the first attribute-type; and,
train, by the computer, the embedding convertor by applying the second plurality of machine-learning layers of the embedding convertor on the plurality of training embeddings.
Thomson discloses:
obtain, by the computer, a plurality of training embeddings extracted using a plurality of training signals by applying the first embedding extractor for the first attribute-type ([1023] The training set 5810 may be provided to feature extractors 5840).
train, by the computer, the embedding convertor by applying the second plurality of machine-learning layers of the embedding convertor on the plurality of training embeddings ([1023] The feature extractors 5840 may be configured to determine features such as n-grams or word embeddings of the training set 5810 that may be provided to the model trainer).
Xu, Mao, Li, and Thomson are considered analogous art within speaker embedding conversion. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Xu in view of Mao, further in view of Li to incorporate the teachings of Thomson, because of the novel way to incorporate a plurality of training voice profiles using live audio for improved automatic speech recognition system accuracy (Thomson, [0096]).
Regarding claim 14, Xu in view of Mao, further in view of Li, further in view of Thomson discloses: the system according to claim 12.
Thomson further discloses:
training the embedding extractor includes executing, by the computer, one or more data augmentation operations on at least of a training audio signal and an enrollment signal ([0766] Additionally or alternatively, audio in a first format may be converted to a second format and presented to an ASR system 3720 configured for the second format. For example, wideband audio may be downsampled to 8 kHz and processed by an ASR system 3720 configured to recognize 8 kHz speech. [Down-sampling is listed as one of a non-limiting example of a augmentation operation according to the spec, [0042]]).
Regarding claim 18, Xu in view of Mao, further in view of Li discloses: the system according to claim 11.
Xu in view of Mao, further in view of Li does not disclose:
wherein when generating the converted enrolled voiceprint having the second attribute-type the computer is further configured to store the converted enrolled voiceprint into a user profile database.
Thomson discloses:
wherein when generating the converted enrolled voiceprint having the second attribute-type the computer is further configured to store the converted enrolled voiceprint into a user profile database ([0163] In some embodiments, the configuration service may include a business server, a user profile system, and a subscription management system. The configuration service may store information on the individual devices or on a server [Storing user profiles containing voice information ([0163]) in view of the voice prints of Mao, indicates that a converted enrolled voiceprint having the second attribute type is stored in a user profile]).
Xu, Mao, Li, and Thomson are considered analogous art within speaker embedding conversion. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Xu in view of Mao, further in view of Li to incorporate the teachings of Thomson, because of the novel way to incorporate a plurality of training voice profiles using live audio for improved automatic speech recognition system accuracy (Thomson, [0096]).
Claim(s) 3 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Mao, further in view of Li, further in view of Thomson, further in view of Khoury et al. (US-20190392842-A1), hereinafter Khoury.
Regarding claim 3, Xu in view of Mao, further in view of Li, further in view of Thomson discloses: the method according to claim 2.
Xu in view of Mao, further in view of Li, further in view of Thomson does not disclose:
performing, by the computer, a loss function of the embedding extractor according to a predicted converted embedding outputted by the embedding extractor for a training audio signal, the loss function instructing the computer to update one or more hyper-parameters of one or more layers of the embedding extractor.
Khoury teaches:
performing, by the computer, a loss function of the embedding extractor according to a predicted converted embedding outputted by the embedding extractor for a training audio signal, the loss function instructing the computer to update one or more hyper-parameters of one or more layers of the embedding extractor ([0034] After a given batch is processed, these embedding vectors are used to compute a loss, and the loss is used to modify connection weights in the three feed-forward neural networks 212, 222, 232 according to a back-propagation technique. Claim 15: …the loss function to modify one or more connection weights [Batch tracks to sample(s) and connection weights track to hyper-parameters]).
Xu, Mao, Li, Thomson, and Khoury are considered analogous art because they are both in the language processing art, so 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 teachings of Xu in view of Mao, further in view of Li, further in view of Thomson to incorporate a loss function. A loss function may facilitate a more accurate speech-processing system during training and therefore a more accurate implementation (Khoury, [0045]).
Regarding claim 13, Xu in view of Mao, further in view of Li, further in view of Thomson discloses: the system according to claim 12,
Xu in view of Mao, further in view of Li, further in view of Thomson does not disclose:
perform, by the computer, a loss function of the embedding extractor according to a predicted converted embedding outputted by the embedding extractor for a training audio signal, the loss function instructing the computer to update one or more hyper-parameters of one or more layers of the embedding extractor.
Khoury teaches:
perform, by the computer, a loss function of the embedding extractor according to a predicted converted embedding outputted by the embedding extractor for a training audio signal, the loss function instructing the computer to update one or more hyper-parameters of one or more layers of the embedding extractor ([0034] After a given batch is processed, these embedding vectors are used to compute a loss, and the loss is used to modify connection weights in the three feed-forward neural networks 212, 222, 232 according to a back-propagation technique. Claim 15: …the loss function to modify one or more connection weights [Batch tracks to sample(s) and connection weights track to hyper-parameters]).
Xu, Mao, Li, Thomson, and Khoury are considered analogous art because they are both in the language processing art, so 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 teachings of Xu in view of Mao, further in view of Li, further in view of Thomson to incorporate a loss function. A loss function may facilitate a more accurate speech-processing system during training and therefore a more accurate implementation (Khoury, [0045]).
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Mao, further in view of Li, further in view of He et al (CN-102270451-A) with reference to the English machine translation provided, hereinafter He.
Regarding claim 6, Xu in view of Mao, further in view of Li discloses: the method according to claim 5.
Xu in view of Mao, further in view of Li fails to disclose:
wherein the computer generates a plurality of converted enrolled voiceprints by applying the plurality of embedding convertors corresponding to the plurality of attribute-types on the plurality of embedding signals.
He discloses:
wherein the computer generates a plurality of converted enrolled voiceprints by applying the plurality of embedding convertors corresponding to the plurality of attribute-types on the plurality of embedding signals ([0130] In the embodiment of the invention, the feature extraction unit 402 extracting each voice print characteristic sequence are corresponding to the one specific channel, correspondingly, the model training unit 403 may firstly respectively training to each voice print characteristic sequence to obtain the corresponding voiceprint model; a plurality of voiceprint model then, corresponding to the different channels obtained. [0071] each of the specific channel in the embodiment of the invention corresponds to a group of voice print characteristic sequence, so it can firstly respectively training the voice print characteristic sequence at each channel to obtain the corresponding voiceprint model).
Xu, Mao, Li, and He are all considered analogous art within language processing. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Xu in view of Mao, further in view of Li to incorporate generation of more than one voiceprint responding to the plurality of converted embeddings generated as disclosed in He. He specifically mentions compatibility as a reason for generating multiple voiceprints. (He, [0129]).
Regarding claim 16, Xu in view of Mao, further in view of Li discloses: the system according to claim 15.
Xu in view of Mao, further in view of Li fails to disclose:
wherein the computer generates a plurality of converted enrolled voiceprints by applying the plurality of embedding convertors corresponding to the plurality of attribute-types on the plurality of embedding signals.
He discloses:
wherein the computer generates a plurality of converted enrolled voiceprints by applying the plurality of embedding convertors corresponding to the plurality of attribute-types on the plurality of embedding signals ([0130] In the embodiment of the invention, the feature extraction unit 402 extracting each voice print characteristic sequence are corresponding to the one specific channel, correspondingly, the model training unit 403 may firstly respectively training to each voice print characteristic sequence to obtain the corresponding voiceprint model; a plurality of voiceprint model then, corresponding to the different channels obtained. [0071] each of the specific channel in the embodiment of the invention corresponds to a group of voice print characteristic sequence, so it can firstly respectively training the voice print characteristic sequence at each channel to obtain the corresponding voiceprint model).
Xu, Mao, Li, and He are all considered analogous art within language processing. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Xu in view of Mao, further in view of Li to incorporate generation of more than one voiceprint responding to the plurality of converted embeddings generated as disclosed in He. He specifically mentions compatibility as a reason for generating multiple voiceprints. (He, [0129]).
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Mao, further in view of Li, further in view of Thomson, further in view of Carbune et al. (US-20220157300-A1), hereinafter Carbune.
Regarding claim 7, Xu in view of Mao, further in view of Li discloses: the method according to claim 5.
Xu in view of Mao, further in view of Li does not disclose:
identifying, by the computer, the second attribute-type of the inbound embedding.
Thomson discloses:
identifying, by the computer, the second attribute-type of the inbound embedding ([0766] Additionally or alternatively, a first one of the ASR systems 3720 may be configured for audio sampled and encoded in a first format and a second one of the ASR systems 3720 may be configured for audio sampled and encoded in a second format. Additionally or alternatively, audio in a first format may be converted to a second format and presented to an ASR system 3720 configured for the second format. For example, wideband audio may be downsampled to 8 kHz and processed by an ASR system 3720 configured to recognize 8 kHz speech [Identifying the need to downsample indicates identification of type of embeddings]).
Xu, Mao, Li, and Thomson are considered analogous art within speaker embedding conversion. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Xu in view of Mao, further in view of Li to incorporate the teachings of Thomson, because of the novel way to incorporate a plurality of training voice profiles using live audio for improved automatic speech recognition system accuracy (Thomson, [0096]).
Xu in view of Mao, further in view of Li, further in view of Thomson does not disclose:
selecting, by the computer, the converted enrolled voiceprint from a plurality of converted enrolled voiceprints according to the second attribute-type.
Carbune discloses:
selecting, by the computer, the converted enrolled voiceprint from a plurality of converted enrolled voiceprints according to the second attribute-type ([0063] Filtering based on format or modality may allow selection of digital component objects that have a format that is compatible or optimized for computing device 140 [Format or modality tracks to attribute-type, digital component object tracks to a voiceprint]).
Xu, Mao, Li, Thomson, and Carbune are all analogous art in the speech processing art. Therefore, it would have been obvious prima facie to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Xu in view of Mao, further in view of Li, further in view of Thomson to incorporate the teachings of Carbune, using a filter within a data processing system to determine/match the correct voiceprint based on a series of different voiceprints available with different attribute types (Carbune, [0063]).
Regarding claim 17, Xu in view of Mao, further in view of Li discloses: the system according to claim 15.
Xu in view of Mao, further in view of Li does not disclose:
identify, by the computer, the second attribute-type of the inbound embedding.
Thomson discloses:
identify, by the computer, the second attribute-type of the inbound embedding ([0766] Additionally or alternatively, a first one of the ASR systems 3720 may be configured for audio sampled and encoded in a first format and a second one of the ASR systems 3720 may be configured for audio sampled and encoded in a second format. Additionally or alternatively, audio in a first format may be converted to a second format and presented to an ASR system 3720 configured for the second format. For example, wideband audio may be downsampled to 8 kHz and processed by an ASR system 3720 configured to recognize 8 kHz speech [Identifying the need to downsample indicates identification of type of embeddings]).
Xu, Mao, Li, and Thomson are considered analogous art within speaker embedding conversion. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Xu in view of Mao, further in view of Li to incorporate the teachings of Thomson, because of the novel way to incorporate a plurality of training voice profiles using live audio for improved automatic speech recognition system accuracy (Thomson, [0096]).
Xu in view of Mao, further in view of Li, further in view of Thomson does not disclose:
select, by the computer, the converted enrolled voiceprint from a plurality of converted enrolled voiceprints according to the second attribute-type.
Carbune discloses:
select, by the computer, the converted enrolled voiceprint from a plurality of converted enrolled voiceprints according to the second attribute-type ([0063] Filtering based on format or modality may allow selection of digital component objects that have a format that is compatible or optimized for computing device 140 [Format or modality tracks to attribute-type, digital component objects are listed as audio content items and therefore track to voiceprints]).
Xu, Mao, Li, Thomson, and Carbune are all analogous art in the speech processing art. Therefore, it would have been obvious prima facie to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Xu in view of Mao, further in view of Li, further in view of Thomson to incorporate the teachings of Carbune, using a filter within a data processing system to determine/match the correct voiceprint based on a series of different voiceprints available with different attribute types (Carbune, [0063]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Stafylakis et al. (US-20200312337-A1) discloses “A system includes an interactive voice recognition (IVR) module arranged to perform a speech conversation with a first user and receive a first user identifier, where the speech conversation has an interaction context based on a subject matter of the speech conversation. The system includes a datastore arranged to store a group of active words associated with the interaction context and store first user voiceprints derived from pre-captured audio of the first user, where each active word is selected based on one or more selection criterion derived from conversations of a population of users. An automated speech recognition (ASR) module is arranged to perform speech recognition of the first user audio provided during the speech conversation. A voice biometric (VB) module is arranged to generate captured voiceprints and determine a similarity score based on comparisons of captured voiceprints with first user voiceprints” (abstract). Specifically, [0105] discloses converting voiceprints into vector representations before determining correlation between registration and verification voices. See entire document.
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/THEODORE WITHEY/Examiner, Art Unit 2655
/ANDREW C FLANDERS/Supervisory Patent Examiner, Art Unit 2655
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