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 .
Response to Arguments
1. Regarding the objections to claims 1, 8, and 14, Applicant has amended the claims to address the minor informalities. Accordingly, the objections are withdrawn.
2. Regarding the rejection under 35 U.S.C. § 112(b), Applicant has amended independent claims 1, 8, and 14 to address the antecedent basis issues. Accordingly, the rejection is withdrawn.
3. Regarding the rejections under 35 U.S.C. § 103, Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
4. Claims 1, 4, 6-8, 12-14, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Cutler (US 2023/0421702 A1) in view of Fanelli et al. (PGPUB No. 2024/0160849, hereinafter Fanelli) and further in view of Kim et al. (US 2023/0419979 A1, hereinafter Kim).
Regarding claim 1, Cutler discloses A computer-implemented method comprising: receiving audio data associated with a first user in a virtual meeting (para. 0052 “A signal from a microphone 502 on a client device can be processed using the teleconferencing architecture for playback on a speaker 504 of another client device.”; para. 0045 “A first user P1 speaks into client device 110, and the microphone of client device 110 picks up microphone signal 212.”), wherein one or more other users are also connected to the virtual meeting via one or more client devices (see Fig. 2, additional users (for example, P2 and P3) connected to the meeting via client devices 120 and 130); collecting one or more segments of voice content of the first user connected to the virtual meeting (para. 0052 “Enrollment information 506 can be obtained from a user via speech clips of words spoken by the user during an enrollment session.”)…; using an audio embedding model to generate one or more speaker embeddings based on the one or more collected segments of voice content of the first user (para. 0052 “The speech clips can be used to derive a vector embedding representing acoustic characteristics of the user's speech.”; para. 0085 “For instance, a local teleconferencing client application can instruct the user to read words shown on the screen in a normal voice. The user's speech can be used as a speech example by the client device or by server 140 to determine a vector embedding representing acoustic characteristics of that user's speech. The embedding can be employed to personalize an enhancement model for that specific user as described previously.”); providing the… speaker embedding and the audio data associated with the virtual meeting to a denoise model to generate personalized denoised voice content of the first user (Fig. 6, audio data 602 and speaker embedding 610 are both provided to a personalized enhancement model 600 to generate enhanced audio 622; para. 0052 “A signal from a microphone 502 on a client device can be processed using the teleconferencing architecture for playback on a speaker 504 of another client device. Enrollment information 506 can be obtained from a user via speech clips of words spoken by the user during an enrollment session. The speech clips can be used to derive a vector embedding representing acoustic characteristics of the user's speech. The embedding can be employed by personalized enhancement models such as echo cancellation model 508, noise suppression model 510, and/or de-reverberation model 512 to suppress sounds other than those made by the user's voice speaking into microphone 502.”); and transmitting the personalized denoised voice content of the first user to the one or more client devices associated with the one or more other users (para. 0052 “The embedding can be employed by personalized enhancement models such as echo cancellation model 508, noise suppression model 510, and/or de-reverberation model 512 to suppress sounds other than those made by the user's voice speaking into microphone 502.”; para. 0053 “Digital gain control 516 can be employed to automatically adjust the gain of the microphone signal. Then, an encoder 518 can encode the resulting sound signal for transmission over network 150, where the sound can be mixed by server 140 with signals from other devices. Jitter buffer management 520 can manage network jitter of a packet stream carrying the encoded audio by automatically delaying arriving packets to reduce audio quality impairments as a result of network jitter. Decoder 522 can decode the packets. Lost packets can be mitigated using packet loss concealment 524 prior to playback over speaker 504.”; para. 0045 “A first user P1 speaks into client device 110…The server mixes the received microphone signals together and communicates them back to the individual devices as playback signal 224 for playback on client device 120 and playback signal 234 for playback on client device 130.”).
Cutler discloses receiving audio data from a virtual meeting and collecting segments of voice content of a first user during the virtual meeting. However, Cutler does not specifically disclose filtering [the one or more segments] to discard multi-speaker voice content segments and one or more non-voice content segments. Furthermore, Cutler does not specifically disclose generating an average embedding based on the one or more speaker embeddings.
Fanelli discloses filtering the one or more segments to discard multi-speaker voice content segments and one or more non-voice segments (Speech segments are isolated from non-speech segments (e.g. Fig. 2A, “Music” and “Noise” blocks) using speech detections (VAD, speaker change detection, and overlapped speech detection) to obtain speech segments (Fig. 2B, SEGM(s)1-7), which reads on the operation of filtering the audio data to discard multi-speaker segments (via overlapped speech detection) and non-voice segments (via voice activity detection); para. 0055 “The blocks of each audio channel are processed by block-based embeddings extraction component 102, which performs feature extraction 109 on the blocks and applies voice activity detection (VAD) 113 to the features. In some embodiments, VAD 113 detects the speech of multiple speakers and performs overlapped speech detection 110. The speech detections are used to isolate label speech segments, i.e., portions of the blocks containing speech. The speech detections are based on a combination of results from VAD 113, speaker change detection 112 and overlapped speech detection 110.”; para. 0056 “The isolated speech segments are input into embedding extraction component 111 together with data for overlapped speech detection 110 and speaker change detection 112. Embedding extraction component 111 computes embeddings for each segment that is identified by speaker change detection 112 and the overlapped speech is discarded so that no embeddings are extracted from overlapped speech.”; see Fig. 2). Fanelli further teaches generating an average embedding based on the one or more speaker embeddings (para. 0060-0061 “Multiple embeddings of speech segment 1 are generated and features are extracted from each embedding of SEGM1, and the embeddings are statistically combined (e.g., by computing an average embedding from the multiple embeddings). An audio block is a uniform segmentation of the audio file (e.g., 1s, 2s, 3s , etc.). The above process is repeated for Block 2 and Block 3”).
Cutler and Fanelli are considered to be analogous to the claimed invention as they are both in the same field of speech processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the collection of audio segments disclosed in Cutler to include a step where multi-speaker segments and non-voice segments are discarded before speaker embeddings are generated, and to further generate an average embedding based on the one or more speaker embeddings. Doing so would be beneficial, this would reduce noise and improve accuracy in the speaker embeddings (Fanelli, para. 0068).
Cutler in view of Fanelli does not specifically disclose generating the average embedding determining the one or more speaker embeddings satisfy a similarity threshold.
Kim teaches determining the one or more speaker embeddings satisfy a similarity threshold, generating an average embedding based on the one or more speaker embeddings (para. 0077 “As a particular example of this, an average or other speaker vector may be produced for each speaker based on that speaker's cluster of embedding vectors 306 for a completed local or global clustering operation. During a subsequent local or global clustering operation, cosine similarity values or other similarity values may be determined between centroids of clusters of embedding vectors 306 produced during the subsequent local or global clustering operation and the speaker vectors from at least one prior local or global clustering operation. If a similarity value is larger than a specified threshold (such as 0.65), the cluster identifier for the subsequent local or global clustering operation can be assigned to match the same identifier as used in the prior local or global clustering operation.”).
Cutler, Fanelli, and Kim are considered to be analogous to the claimed invention as they are both in the same field of speech processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kim in order to generate the average embedding in response to determining the one or more speaker embeddings satisfy a similarity threshold. Doing so would be beneficial, as similarity metrics help to determine whether vectors relate to the same speaker (Kim, para. 0077).
Regarding claim 4, Cutler in view of Fanelli and Kim discloses wherein the audio data comprises: ambient audio content associated with a current physical location of an individual accessing the virtual meeting via the first user (Cutler, para. 0046 “Note that the microphone on client device 120 picks up various sounds, including speech by the user P1 in the same room as client device 120, playback signal 224 played back by a loudspeaker of client device 120, as well as any ambient noise in the room.”).
Regarding claim 6, Cutler in view of Fanelli and Kim discloses wherein the voice content of the first user comprises: voice content in audio data captured by a pre-defined audio capture device currently in use by an individual accessing the virtual meeting via the first user (Cutler, para. 0052 “A signal from a microphone 502 on a client device can be processed using the teleconferencing architecture for playback on a speaker 504 of another client device.”; para. 0045 “A first user P1 speaks into client device 110, and the microphone of client device 110 picks up microphone signal 212.”); and wherein the computer-implemented method further comprises based on verifying current use of the pre-defined audio capture device (Cutler, para. 0086 “Instead, an embedding can be determined for each user in an online manner, as the user participates in a call.”), initiating collection of the one or more segments of voice content of the first user (Cutler, para. 0086 “For instance, in some cases, the dominant speaker on a given device can be detected automatically and the dominant speaker can automatically be enrolled for personalized enhancement on that device.”).
Regarding claim 7, Cutler in view of Fanelli and Kim discloses wherein the pre-defined audio capture device comprises at least one microphone disposed on a headset device (Cutler, para. 0031 “Note that a microphone that provides a microphone signal to a computing device can be an integrated component of that device (e.g., included in a device housing) or can be an external microphone in wired or wireless communication with that computing device. Similarly, when a computing device plays back a signal over a loudspeaker, that loudspeaker can be an integrated component of the computing device or in wired or wireless communication with the computing device. In the case of a wired or wireless headset, a microphone and one or more loudspeakers can be integrated into a single peripheral device that sends microphone signals to a corresponding computing device and outputs a playback signal received from the computing device.”).
Regarding claim 8, claim 8 is a non-transitory computer-readable medium claim with limitations similar to those in method claim 1, and thus is rejected under similar rationale.
Additionally, Cutler discloses A non-transitory computer-readable medium having a computer-readable program code embodied therein (para. 0095 “In contrast, the term “computer-readable storage media” excludes signals. Computer-readable storage media includes “computer-readable storage devices.” Examples of computer-readable storage devices include volatile storage media, such as RAM, and non-volatile storage media, such as hard drives, optical discs, and flash memory, among others.”; para. 0094 “Computer-readable instructions and/or data can be stored on storage, such as storage/memory and or the datastore.”) to be executed by one or more processors (para. 0094 “Processing capability can be provided by one or more hardware processors (e.g., hardware processing units/cores) that can execute computer-readable instructions to provide functionality.”), the program code including instructions for (para. 0116 “Another example includes a system comprising a processor, and a storage medium storing instructions which, when executed by the processor, cause the system to…”).
Regarding claim 12, Cutler in view of Fanelli and Kim discloses wherein the audio data comprises: ambient voice content associated with a current physical location of an individual accessing the virtual meeting via the first user, the ambient voice content different than the voice content of the first user (Cutler, para. 0046 “Note that the microphone on client device 120 picks up various sounds, including speech by the user P1 in the same room as client device 120, playback signal 224 played back by a loudspeaker of client device 120, as well as any ambient noise in the room.”).
Regarding claim 13, claim 13 is rejected for analogous reasons to claim 6.
Regarding claim 14, claim 14 is a system claim with limitations similar to method claim 1, and is thus rejected under similar rationale.
Additionally, Cutler discloses A communication system comprising one or more processors configured to perform the operations of: (para. 0094 “The term “device”, “computer,” “computing device,” “client device,” and or “server device” as used herein can mean any type of device that has some amount of hardware processing capability and/or hardware storage/memory capability. Processing capability can be provided by one or more hardware processors (e.g., hardware processing units/cores) that can execute computer-readable instructions to provide functionality...”).
Regarding claim 18, Cutler in view of Fanelli and Kim discloses wherein the audio data comprises at least one of: non-voice content and additional voice content different than the voice content of the first user (Cutler, Cutler, para. 0046 “Note that the microphone on client device 120 picks up various sounds, including speech by the user P1 in the same room as client device 120, playback signal 224 played back by a loudspeaker of client device 120, as well as any ambient noise in the room.”).
Regarding claim 19, claim 19 is rejected for analogous reasons to claim 4.
Regarding claim 20, claim 20 is rejected for analogous reasons to claim 6.
5. Claims 2-3, 9-11, and 15-17 are rejected under 35. U.S.C. 103 as being unpatentable over Cutler in view of Fanelli and Kim, and further in view of Tan et al. (PGPUB No. 2022/0375477, hereinafter Tan).
Regarding claim 2, Cutler in view of Fanelli and Kim discloses teaches collecting one or more segments of voice content of the first user. Cutler in view of Fanelli and Kim does not specifically disclose:
filtering respective segments of voice content of the first user according to a segment similarity criterion.
Tan teaches collecting one or more segments of voice content (Fig. 6, 605 and 610; Paragraph 0052 “At 605, biometric system 102 … receives audio. The audio may correspond to a call between a user and one or more call agents…”; Paragraph 0053 “At 610, biometric system 102 … divides the audio into segments.”), further comprising filtering the respective segments of voice content of the first user according to a segment similarity criterion (Voice content may be further filtered by comparing segments to a signature vector generated from user’s voice (Paragraph 0022 “Referring to FIG. 1, the ML subsystem 114 may generate a signature vector indicative of voice audio of the user.”), and filtering out segments that are not similar to user’s voice; Paragraph 0025 “The voice biometric subsystem 116 may remove segments from the audio, for example, if it does not match the signature vector. The voice biometric subsystem 116 may determine that a segment does not match the signature vector, for example, if the distance (e.g., the similarity score) between the signature vector and the vector representation of the segment does not satisfy a threshold (e.g., a similarity threshold).”).
Cutler, Fanelli, Kim, and Tan are considered to be analogous to the claimed invention as they are all in the same field of speech processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the collection of segments of voice content in Cutler to incorporate the teachings of Tan in order to filter the voice segments. Doing so would separate out portions of audio similar to a particular user’s voice from other interfering noise, which could improve quality in audio processing applications such as voice biometrics (Tan, Abstract).
Regarding claim 3, Cutler in view of Fanelli and Kim and further in view of Tan discloses wherein generating personalized denoised voice content of the first user account comprises: sending input to the audio embedding model based on one or more filtered respective similar segments of voice content (Cutler, audio input to personalized enhancement model 600 (Fig. 6, 602 and 600); by combining the teachings of Tan, filtered respective similar segments of voice content are sent as input to the audio embedding model).
Cutler, Fanelli, Kim, and Tan are considered to be analogous to the claimed invention as they are all in the same field of speech processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Tan in order to send input to the audio embedding model based on one or more filtered respective similar segments of voice content. Doing so would be beneficial given the same rationale as claim 2.
Regarding claim 9, Cutler in view of Fanelli and Kim teaches collecting one or more segments of voice content of the first user account comprises: grouping respective segments of voice content of the first user in a buffer (Fanelli, Fig. 6, “Embeddings Extraction” 603 outputs embeddings (604) for segments of voice content (Paragraph 0056); the embeddings representing these segments of voice content are stored in a buffer 604 (Paragraph 80 “Audio block segmentation 108 is another step in pipeline 100 (see FIGS. 1, 2 and 6). The embeddings extracted by each speech segment (segment is a time window of speech where a unique speaker is talking), are stored and clustered after all the embeddings have been extracted from each segment over multiple audio blocks.”)).
Cutler, Fanelli, and Kim are considered to be analogous to the claimed as they are all in the same field of speech processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Fanelli in order group respective segments of voice content in a buffer. Implementing this would limit the processing and memory usage required to process audio files (Fanelli, Paragraph 0081).
Cutler in view of Fanelli and Kim does not specifically disclose:
filtering the respective segments of voice content of the first user account according to a segment similarity criterion.
Tan teaches collecting one or more segments of voice content (Fig. 6, 605 and 610; Paragraph 0052 “At 605, biometric system 102 … receives audio. The audio may correspond to a call between a user and one or more call agents…”; Paragraph 0053 “At 610, biometric system 102 … divides the audio into segments.”), further comprising filtering the respective segments of voice content of the first user account according to a segment similarity criterion (Voice content may be further filtered by comparing segments to a signature vector generated from user’s voice (Paragraph 0022 “Referring to FIG. 1, the ML subsystem 114 may generate a signature vector indicative of voice audio of the user.”), and filtering out segments that are not similar to user’s voice; Paragraph 0025 “The voice biometric subsystem 116 may remove segments from the audio, for example, if it does not match the signature vector. The voice biometric subsystem 116 may determine that a segment does not match the signature vector, for example, if the distance (e.g., the similarity score) between the signature vector and the vector representation of the segment does not satisfy a threshold (e.g., a similarity threshold).”).
Cutler, Fanelli, Kim, and Tan are considered to be analogous to the claimed invention as they are all in the same field of speech processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Tan in order to filter the voice segments. Doing so would separate out portions of audio similar to a particular user’s voice from other interfering noise, which could improve quality in audio processing applications such as voice biometrics (Tan, Abstract).
Regarding claim 10, Cutler in view of Fanelli and Kim and further in view of Tan discloses filtering the respective segments of voice content upon determining a current amount of buffered segments meets a threshold amount (Tan, Paragraph 0027 “In some embodiments, the system may determine that a biometric should not be generated because the audio (e.g., as a whole) is not suitable for use as a biometric… In this example, the voice biometric subsystem 116 may determine that the audio 211 should not be used to generate a voice biometric because the threshold number of segments that need to remain to use the audio for generating a biometric may be three segments. Additionally or alternatively, the threshold may be a percentage (e.g., 10%, 30%, 65%, etc.). For example, if more than 30% of the segments are removed from the audio, the biometric system 102 may determine that the audio should not be used for generating a biometric for the user.”).
Cutler, Fanelli, Kim, and Tan are considered to be analogous to the claimed invention as they are all in the same field of speech processing. Therefore, it would have been obvious to specifically filter respective segments of voice content upon determining a threshold amount of buffered segments has been reached. Doing so would be beneficial, as this would provide a metric for determining if sufficient data has been collected and prevent unsuitable data from being used for generating speaker embeddings, improving the accuracy of the model.
Regarding claim 11, Cutler in view of Fanelli and Kim and further in view of Tan discloses sending input to the audio embedding model based on one or more filtered respective similar segments of voice content (Cutler, audio input to personalized enhancement model 600 (Fig. 6, 602 and 600); by combining the teachings of Tan and Fanelli, filtered respective similar segments of voice content are sent as input to the audio embedding model).
Cutler, Fanelli, Kim, and Tan are considered to be analogous to the claimed invention as they are all in the same field of speech processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Tan in order to send input to the audio embedding model based on one or more filtered respective similar segments of voice content. Doing so would be beneficial given the same rationale as claim 9.
Regarding claim 15, Cutler in view of Fanelli and Kim teaches collecting one or more segments of voice content of the first user. Cutler in view of Fanelli and Kim does not specifically disclose:
filtering respective segments of voice content of the first user according to a segment similarity criterion.
Tan teaches collecting one or more segments of voice content (Fig. 6, 605 and 610; Paragraph 0052 “At 605, biometric system 102 … receives audio. The audio may correspond to a call between a user and one or more call agents…”; Paragraph 0053 “At 610, biometric system 102 … divides the audio into segments.”), further comprising filtering respective segments of voice content of the first user according to a segment similarity criterion (Voice content may be further filtered by comparing segments to a signature vector generated from user’s voice (Paragraph 0022 “Referring to FIG. 1, the ML subsystem 114 may generate a signature vector indicative of voice audio of the user.”), and filtering out segments that are not similar to user’s voice; Paragraph 0025 “The voice biometric subsystem 116 may remove segments from the audio, for example, if it does not match the signature vector. The voice biometric subsystem 116 may determine that a segment does not match the signature vector, for example, if the distance (e.g., the similarity score) between the signature vector and the vector representation of the segment does not satisfy a threshold (e.g., a similarity threshold).”).
Cutler, Fanelli, Kim, and Tan are considered to be analogous to the claimed invention as they are all in the same field of speech processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Tan in order to filter the voice segments. Doing so would separate out portions of audio similar to a particular user’s voice from other interfering noise, which could improve quality in audio processing applications such as voice biometrics (Tan, Abstract).
Regarding claim 16, Cutler in view of Fanelli and Kim and further in view of Tan discloses grouping the respective segments of voice content of the first user account in a buffer (Fanelli, Fig. 6, “Embeddings Extraction” 603 outputs embeddings (604) for segments of voice content (Paragraph 0056); the embeddings representing these segments of voice content are stored in a buffer 604 (Paragraph 80 “Audio block segmentation 108 is another step in pipeline 100 (see FIGS. 1, 2 and 6). The embeddings extracted by each speech segment (segment is a time window of speech where a unique speaker is talking), are stored and clustered after all the embeddings have been extracted from each segment over multiple audio blocks.”)); and filtering the respective segments of voice content upon determining a current amount of buffered segments meet a threshold amount (Tan, Paragraph 0027 “In some embodiments, the system may determine that a biometric should not be generated because the audio (e.g., as a whole) is not suitable for use as a biometric… In this example, the voice biometric subsystem 116 may determine that the audio 211 should not be used to generate a voice biometric because the threshold number of segments that need to remain to use the audio for generating a biometric may be three segments. Additionally or alternatively, the threshold may be a percentage (e.g., 10%, 30%, 65%, etc.). For example, if more than 30% of the segments are removed from the audio, the biometric system 102 may determine that the audio should not be used for generating a biometric for the user.”).
Cutler, Tan, Kim, and Fanelli are considered to be analogous to the claimed invention as they are all in the same field of speech processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have grouped respective segments of voice content in a buffer as taught in Fanelli. Implementing this would limit the processing and memory usage required to process audio files (Fanelli, Paragraph 0081). Additionally, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have filtered the respective segments of voice content upon determining that a threshold amount of buffered segments have been collected. Doing so would be beneficial, as this would provide a metric for determining if sufficient data has been collected and prevent unsuitable data from being used for generating speaker embeddings, improving the accuracy of the model.
Regarding claim 17, Cutler in view of Fanelli and Kim and further in view of Tan discloses sending input to the audio embedding model based on one or more filtered respective similar segments of voice content (Cutler, Fig. 2, audio signals 215 are partitioned into audio segments (Paragraph 0043), and are fed into noise removal model 222 containing the audio embedding model (Paragraph 0049); by combining the teachings of Tan, filtered respective similar segments of voice content are sent as input to the audio embedding model).
Cutler, Fanelli, Kim, and Tan are considered to be analogous to the claimed invention as they are all in the same field of speech processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Tan in order to send input to the audio embedding model based on one or more filtered respective similar segments of voice content. Doing so would be beneficial given the same rationale as claim 15.
Allowable Subject Matter
6. Claim 21 is 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:
Ostrand et al. (US 2022/0199102 A1): generation of customized acoustic profiles of users, apply customized acoustic model to speaker to generate optimize audio stream (Fig. 7)
Rudberg et al. (US 2020/0388297 A1): noise reduction of first audio data by selection of appropriate model for either reducing noise of one speaker or for reducing noise when two speakers present (Fig. 3, para. 0031)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CODY DOUGLAS HUTCHESON/Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659
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