CTNF 18/842,325 CTNF 100567 DETAILED ACTION This communication is in response to the Application filed on August 28, 2024. Claims 1 - 15 are pending and have been examined. Claims 1, 13, 14 and 15 are independent. Foreign priority: March 7, 2022. PCT/JP2023/000764 was filed on January 13, 2023. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statement The information disclosure statement (IDS) submitted on August 28, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Drawings The drawings filed on August 28, 2024 have been accepted and considered by the Examiner. Double Patenting Note The Examiner notes that previously published patent application publication U.S. 2023/0007434 was analyzed for Double Patenting. However, based on the current claim scope no Double patenting was found. Claim Rejections - 35 USC § 103 07-20-fti The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action: (a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made. 07-21 AIA Claim s 1 - 2, 5 - 6, 8 - 15 are rejected under 35 U.S.C. 103(a) as being unpatentable over Wang et al (U.S. Patent Application Publication 2020/0389728) , hereinafter referred to as Wang, in view of Marzorati et al., (U.S. Patent Application Publication 2022/0303654), hereinafter referred to as Marzorati . Regarding Claim 1, 13 and 14, Wang teaches: 1. An information processing apparatus, comprising, 13. An information processing method, comprising, and 14. An information processing program that allows an information processing apparatus to operate as: PNG media_image1.png 406 303 media_image1.png Greyscale a first speech extraction processing section that generates a first speech extraction signal by extracting an utterance speech component from a speech signal including an utterance speech uttered by a user; [Wang, Figure 11 “11 Speech signal obtaining module”; “a speech signal obtaining module (i.e., the claimed “first speech extraction processing section”) , configured to obtain a speech signal collected by an acoustic microphone,” Par. 0010] a correction signal generation section that generates a correction signal from a vibration signal indicating vibration of a part of the user that vibrates in conjunction with a user's utterance; and [Wang, Figure 11 “12 Speech activity detecting module”; “In embodiments of the present disclosure, the speech signals simultaneously (i.e., the claimed “in conjunction”) collected by the acoustic microphone and the non-acoustic (i.e., the claimed “vibration”) microphone are obtained. The non-acoustic microphone is capable of collecting a speech signal in a manner unrelated to ambient noise (for example, by detecting vibration of human skin or vibration of human throat bones (i.e., the claimed “vibration of a part of the user”) ). Thereby, speech activity detection based on the speech signal collected by the non-acoustic microphone can be used to reduce an influence of the ambient noise and improve detection accuracy, in comparison with that based on the speech signal collected by the acoustic microphone.” Par. 0050; “a speech activity detecting module (i.e., the claimed “correction signal generation section”) , configured to detect speech activity based on the speech signal collected by the non- acoustic (i.e., the claimed “vibration of a part of the user”) microphone, to obtain a result of speech activity detection;” Par. 0011] a post-processing section that generates an utterance speech signal indicating the utterance speech by post-processing the first speech extraction signal based on the correction signal. [Wang, Figure 11 “13 Speech denoising module”; “a speech denoising module (i.e., the claimed “post-processing section”) , configured to denoise (i.e., the claimed “post-processing”) the speech signal collected by the acoustic microphone (i.e., the claimed “first speech extraction signal”) based on the result of speech activity detection (i.e., the claimed “correction signal”) , to obtain a denoised speech signal.” Par. 0012] Wang fails to explicitly teach user. However, Marzorati teaches: a first speech extraction processing section that generates a first speech extraction signal by extracting an utterance speech component from a speech signal including an utterance speech uttered by a user; [Marzorati, “An acoustic signal is received from a first user. The acoustic signal of the first user includes a verbal utterance related to the first user (i.e., the claimed “utterance speech component from a speech signal including an utterance speech uttered by a user”) . A set of one or more non-verbal sensor readings may be detected from a set of one or more sensors of a wearable device. The wearable device is worn by the user concurrent with the verbal utterance. A verbal distortion factor may be determined based on an acoustic adjustment model and in response to the set of non-verbal sensor readings. An adjusted verbal utterance related to the first user may be generated. The adjusted verbal utterance may be generated from the verbal utterance and based on the verbal distortion factor. The adjusted verbal utterance may be provided to a voice output.” Par. 0004; “The RAA 270 may include receiving from microphone 234, or 242 one or more verbal utterances and other acoustic information from the user 210.” Par. 0040; “At 405 method 400 begins, when one or more acoustic signals are received at 410. The one or more acoustic signals may include non-verbal sound such as the movement of air and vibration of vocal chords. The received verbal acoustics may include verbal utterances from a user, such as words, phrases, phonetics, or other parts of speech. The acoustic signals (i.e., the claimed “first speech extraction signal”) may be received (i.e., the claimed “extracted”) by an electronic device (i.e., the claimed “first speech extraction processing section”) and may be provided by a respirator (e.g., such as electronic device 240 receiving sound from respirator 220).” Par. 0068] a correction signal generation section that generates a correction signal from a vibration signal indicating vibration of a part of the user that vibrates in conjunction with a user's utterance; and [Marzorati, “An acoustic signal is received from a first user. The acoustic signal of the first user includes a verbal utterance related to the first user. A set of one or more non-verbal sensor readings (i.e., the claimed “correction signal”) may be detected from a set of one or more sensors of a wearable device. The wearable device is worn by the user concurrent with the verbal utterance (i.e., the claimed “in conjunction with a user's utterance”) . A verbal distortion factor may be determined based on an acoustic adjustment model and in response to the set of non-verbal sensor readings. An adjusted verbal utterance related to the first user may be generated. The adjusted verbal utterance may be generated from the verbal utterance and based on the verbal distortion factor. The adjusted verbal utterance may be provided to a voice output.” Par. 0004; “For example, concurrent with being worn by a user, one or more non-verbal sensors (i.e., the claimed “correction signal generation section”) such as airflow sensors, air pressure sensors, vibration sensors, or other relevant non-verbal sensors may be measuring various sounds, air movements, and vibrations of a user (i.e., the claimed “vibration of a part of the user that vibrates”) .” Par. 0070] a post-processing section that generates an utterance speech signal indicating the utterance speech by post-processing the first speech extraction signal based on the correction signal. [Marzorati, “An acoustic signal is received from a first user. The acoustic signal of the first user includes a verbal utterance related to the first user. A set of one or more non-verbal sensor readings may be detected from a set of one or more sensors of a wearable device. The wearable device is worn by the user concurrent with the verbal utterance. A verbal distortion factor may be determined based on an acoustic adjustment model (i.e., the claimed “post-processing section”) and in response to the set of non-verbal sensor readings (i.e., the claimed “correction signal”) . An adjusted verbal utterance related to the first user may be generated. The adjusted verbal utterance (i.e., the claimed “utterance speech signal”) may be generated from the verbal utterance (i.e., the claimed “first speech extraction signal”) and based on the verbal distortion factor (i.e., the claimed “correction signal”) . The adjusted verbal utterance may be provided to a voice output.” Par. 0004] Wang and Marzorati acoustic adjustment/voice denoising systems and are analogous to the instant application. Accordingly, it would have been obvious to one of ordinary skill in the acoustic adjustment/voice denoising systems art to modify Wang’s teachings of “speech signal obtaining module” (i.e., the claimed “first speech extraction processing section”), “speech activity detecting module” (i.e., the claimed “correction signal generation section”) and “acoustic adjustment model” (i.e., the claimed “post-processing section”) (Wang, Par. 0010, Par. 0011, Par. 0012) with the explicit teachings of “user” (Marzorati, Par. 0004) taught by Marzorati in order to “provide clarity to a user's voice while speaking” (Marzorati, Par. 0018). Regarding Claim 2, Wang in view of Marzorati has been discussed above. The combination further teaches: wherein the correction signal generation section includes a second speech extraction processing section that generates a second speech extraction signal by extracting the utterance speech component from the vibration signal, and [Wang, see mapping applied to claim 1; Marzorati, see mapping applied to claim 1; Claim is directed to repeating the subject matter for a second speech extraction processing section and a second speech extraction signal. However, second speech extraction processing section/second speech extraction signal/repeating steps known from prior art is straightforward, amounts to the normal use of the teachings of Wang in view of Marzorati and are rejected under similar rationale.] the post-processing section generates the utterance speech signal by post-processing the first speech extraction signal based on the second speech extraction signal. [Wang, see mapping applied to claim 1; Marzorati, see mapping applied to claim 1; Claim is directed to repeating the subject matter for a second speech extraction signal. However, second speech extraction signal/repeating steps known from prior art is straightforward, amounts to the normal use of the teachings of Wang in view of Marzorati and are rejected under similar rationale.] Regarding Claim 5, Wang in view of Marzorati has been discussed above. The combination further teaches: wherein the first speech extraction processing section generates the first speech extraction signal by inputting the speech signal to a first learning model learned to output a first speech extraction signal using the speech signal as training data. [Wang, see mapping applied to claim 1; Marzorati, see mapping applied to claim 1; Marzorati, “If the electronic device is operating in a training mode at 420:Y, the received acoustic information (i.e., the claimed “first speech extraction signal by inputting the speech signal”) may be used to update a model at 430 (i.e., the claimed “first learning model”) . Updating of the model may include inputting non-verbal information from the sensor readings, such as acoustic information received from the respirator. Updating of the model may include inputting verbal information such as detected or determined words or phrases based on performing natural language processing on the data.” Par. 0069; “The example network is provided a given number of training inputs n in a subset (or entirety) of training data that have input values x.” Par. 0064; Wang, “The unvoiced sound predicting model is obtained by pre-training based on a training speech signal (i.e., the claimed “first speech extraction signal using the speech signal as training data”) .” Par. 0250] Regarding Claim 6, Wang in view of Marzorati has been discussed above. The combination further teaches: wherein the second speech extraction processing section generates the second speech extraction signal by inputting the vibration signal to a second learning model learned to output a second speech extraction signal using the speech signal and the vibration signal as the training data. [Wang, see mapping applied to claim 1; Marzorati, see mapping applied to claim 1; Marzorati, “If the electronic device is operating in a training mode at 420:Y, the received acoustic information may be used to update a model at 430. Updating of the model may include inputting non-verbal information from the sensor readings, such as acoustic information (i.e., the claimed “speech signal and the vibration signal”) received from the respirator. Updating of the model may include inputting verbal information such as detected or determined words or phrases based on performing natural language processing on the data.” Par. 0069; “The example network is provided a given number of training inputs n in a subset (or entirety) of training data that have input values x.” Par. 0064; Claim is directed to repeating the subject matter for a second speech extraction processing section, second speech extraction signal and second learning model. However, speech extraction processing section/ second speech extraction signal/ second learning model / repeating steps known from prior art is straightforward, amounts to the normal use of the teachings of Wang in view of Marzorati and are rejected under similar rationale.] Regarding Claim 8, Wang in view of Marzorati has been discussed above. The combination further teaches: wherein the part of the user that vibrates in conjunction with the user's utterance is a part of a human body located in or around a larynx, an artificial organ, or a medical device. [Wang, see mapping applied to claim 1; Marzorati, see mapping applied to claim 1; Wang, “The non-acoustic microphone is capable of collecting a speech signal in a manner independent from ambient noise (for example, by detecting vibration of human skin or vibration of human throat bones (i.e., the claimed “part of a human body located in or around a larynx”) ).” Par. 0019; Marzorati, “The one or more acoustic signals may include non-verbal sound such as the movement of air and vibration of vocal chords (i.e., the claimed “part of a human body located in or around a larynx”) .” Par. 0068] Regarding Claim 9, Wang in view of Marzorati has been discussed above. The combination further teaches: wherein the post-processing section outputs the utterance speech signal, [Wang, see mapping applied to claim 1; Marzorati, see mapping applied to claim 1; Wang, “a speech denoising module (i.e., the claimed “post-processing section”) , configured to denoise (i.e., the claimed “post-processing”) the speech signal collected by the acoustic microphone (i.e., the claimed “first speech extraction signal”) based on the result of speech activity detection (i.e., the claimed “correction signal”) , to obtain (i.e., the claimed “output”) a denoised speech signal (i.e., the claimed “utterance speech signal”) .” Par. 0012] Regarding Claim 10, Wang in view of Marzorati has been discussed above. The combination further teaches: wherein the vibration signal is generated by a vibration signal processing section that processes vibration input to a vibration input device to which the vibration of the part is input and generates the vibration signal. [Wang, see mapping applied to claim 1; Marzorati, see mapping applied to claim 1; Wang, “(for example, the bone conduction microphone is pressed against a facial bone or a throat bone detects vibration of the bone, and converts the vibration (i.e., the claimed “vibration input”) into a speech signal (i.e., the claimed “processes vibration input to which the vibration of the part is input and generates the vibration signal”); ” Par. 0038; “The non-acoustic microphone (i.e., the claimed “vibration input device”) is capable of collecting a speech signal in a manner unrelated to ambient noise (for example, by detecting vibration of human skin or vibration of human throat bones). Thereby, speech activity detection (i.e., the claimed “vibration signal processing”) based on the speech signal collected by the non-acoustic microphone (i.e., the claimed “vibration input device”) can be used to reduce an influence of the ambient noise and improve detection accuracy, in comparison with that based on the speech signal collected by the acoustic microphone.” Par. 0050; Marzorati, “plurality of vibration sensors (i.e., the claimed “vibration signal processing section”) ” Par. 0035] Regarding Claim 11, Wang in view of Marzorati has been discussed above. The combination further teaches: wherein the vibration input device is a sensor that directly detects the vibration of the part, and is built into a device worn on the human body, or detects the vibration of the part by irradiating the part with a laser. [Wang, see mapping applied to claims 1, 10; Marzorati, see mapping applied to claims 1, 10; Wang, “(hereinafter referred to as a non-acoustic (i.e., the claimed “vibration”) microphone (i.e., the claimed “vibration input device”) , such as a bone conduction microphone or an optical microphone),” Par. 0038; “optical microphone also called a laser microphone emits a laser onto a throat skin or a facial skin via a laser emitter (i.e., the claimed “irradiated the part with a laser”) , receives a reflected signal caused by skin vibration via a receiver, analyzes a difference between the emitted laser and the reflected laser, and converts the difference into a speech signal), thereby greatly reducing the noise-generated interference on speech communication or speech recognition.” Par. 0038; Marzorati, “For example, concurrent with being worn by a user (i.e., the claimed “worn on the human body”) , one or more non-verbal (i.e., the claimed “vibration”) sensors such as airflow sensors, air pressure sensors, vibration sensors, or other relevant non-verbal sensors may be measuring various sounds, air movements, and vibrations of a user .” Par. 0070] Regarding Claim 12, Wang in view of Marzorati has been discussed above. The combination further teaches: wherein the speech signal is generated by a speech signal processing section that processes a speech input to a speech input device to which the utterance speech uttered by the user is input and generates the speech signal. [Wang, see mapping applied to claim 1; Marzorati, see mapping applied to claim 1; Wang, “a speech signal collected by an acoustic microphone (i.e., the claimed “speech input device”) ” Par. 0070; “the speech signal collected by the acoustic microphone (i.e., the claimed “speech input to a speech input device to which the utterance speech uttered by the user is input) is denoised based on the result of speech activity detection (i.e., the claimed “speech signal processing section”) , to obtain a denoised speech signal (i.e., the claimed “generates a speech signal”) .” Par. 0048] Regarding Claim 15, Wang in view of Marzorati has been discussed above. The combination further teaches: 15. An information processing system, comprising: a speech input device that inputs an utterance speech uttered by a user; [Wang, “a speech signal (i.e., the claimed “utterance speech uttered by a user”) collected by an acoustic microphone (i.e., the claimed “speech input device”) ” Par. 0070; a vibration input device that inputs vibration of a part of the user that vibrates in conjunction with a user's utterance; and [Wang, “(hereinafter referred to as a non-acoustic (i.e., the claimed “vibration”) microphone (i.e., the claimed “vibration input device”) , such as a bone conduction microphone or an optical microphone),” Par. 0038; “optical microphone also called a laser microphone emits a laser onto a throat skin or a facial skin via a laser emitter (i.e., the claimed “irradiated the part with a laser”) , receives a reflected signal caused by skin vibration via a receiver, analyzes a difference between the emitted laser and the reflected laser, and converts the difference into a speech signal), thereby greatly reducing the noise-generated interference on speech communication or speech recognition.” Par. 0038; Marzorati, “For example, concurrent with being worn by a user (i.e., the claimed “worn on the human body”) , one or more non-verbal (i.e., the claimed “vibration”) sensors such as airflow sensors, air pressure sensors, vibration sensors, or other relevant non-verbal sensors may be measuring various sounds, air movements, and vibrations of a user .” Par. 0070] an information processing apparatus, including a first speech extraction processing section that generates a first speech extraction signal by extracting an utterance speech component from a speech signal including the utterance speech, a correction signal generation section that generates a correction signal from a vibration signal indicating the vibration of the part, and a post-processing section that generates an utterance speech signal indicating the utterance speech by post-processing the first speech extraction signal based on the correction signal. [Wang, see mapping applied to claim 1; Marzorati, see mapping applied to claim 1] 07-21 AIA Claim s 3 - 4 are rejected under 35 U.S.C. 103(a) as being unpatentable over Wang in view of Marzorati and Zheng et al., (CN114067818A), hereinafter referred to as Zheng . Regarding Claim 3, Wang in view of Marzorati has been discussed above. The combination further teaches: wherein the correction signal generation section includes an utterance detection section that generates a masking signal indicating presence or absence and intensity of the utterance speech from the vibration signal, and [Wang, see mapping applied to claim 1; Marzorati, see mapping applied to claim 1; Wang, “The signal intensity of the speech signal (i.e., the claimed “intensity of the utterance speech”-) collected by the acoustic microphone is further detected in response to determining that there is no fundamental frequency information, so as to improve the accuracy of the determination that there is no voice signal (i.e., the claimed “absence”) in the speech frame corresponding to the fundamental frequency information, in the speech signal collected by the acoustic microphone. In this embodiment, the fundamental frequency information is derived from the speech signal collected by the non-acoustic microphone (i.e., the claimed “utterance speech from the vibration signal”) , and the non-acoustic microphone is capable to collect a speech signal in a manner independent from ambient noise. It can be detected whether there is a voice signal (i.e., the claimed “presence”) in the speech frame corresponding to the fundamental frequency information. An influence of the ambient noise on the detection is reduced, and accuracy of the detection is improved.” Par. 0075-0076] the post-processing section generates the utterance speech signal by post-processing the first speech extraction signal based on the masking signal. [Wang, see mapping applied to claim 1; Marzorati, see mapping applied to claim 1] The combination fails to teach masking. However, Zheng teaches: wherein the correction signal generation section includes an utterance detection section that generates a masking signal indicating presence or absence and intensity of the utterance speech from the vibration signal, and [Zheng, “based on the flexible vibration sensor voice loss mechanism, constructing equalization module and generating module, the equalization coefficient obtained by the equalization module directly operates the voice low-frequency characteristic, the value is similar to the masking value in the voice de-noising, it can effectively inhibit the noise in the voice, the generating module is concerned with the loss component recovery, through the clear division of the module, further improving the voice quality, and the interpretability is strong;” Par. n0049; “ masking value in the speech de-noising model to realize enhancement, also can be called equalization coefficient. In the high frequency band, the flexible vibration sensor speech component (i.e., the claimed “utterance speech from the vibration signal”) is almost all lost (i.e., the claimed “absence”) , so it is necessary to estimate the lost speech component. Therefore, the enhancement of the flexible vibration sensor voice can be viewed as realizing the balance of the voice and the process of losing component.” Par. n0062] the post-processing section generates the utterance speech signal by post-processing the first speech extraction signal based on the masking signal. [Zheng, “based on the flexible vibration sensor voice loss mechanism, constructing equalization module and generating module, the equalization coefficient obtained by the equalization module directly operates the voice low-frequency characteristic, the value is similar to the masking value in the voice de-noising, it can effectively inhibit the noise in the voice, the generating module is concerned with the loss component recovery, through the clear division of the module, further improving the voice quality, and the interpretability is strong;” Par. n0049; “ masking value in the speech de-noising model (i.e., the claimed “post-processing section”) to realize enhancement, also can be called equalization coefficient. In the high frequency band, the flexible vibration sensor speech component (i.e., the claimed “utterance speech from the vibration signal”) is almost all lost (i.e., the claimed “absence”) , so it is necessary to estimate the lost speech component. Therefore, the enhancement of the flexible vibration sensor voice can be viewed as realizing the balance of the voice and the process of losing component.” Par. n0062] Wang, Marzorati and Zheng acoustic adjustment/voice denoising systems and are analogous to the instant application. Accordingly, it would have been obvious to one of ordinary skill in the acoustic adjustment/voice denoising systems art to modify Wang’s teachings of “speech signal obtaining module” (i.e., the claimed “first speech extraction processing section”), “speech activity detecting module” (i.e., the claimed “correction signal generation section”) and “acoustic adjustment model” (i.e., the claimed “post-processing section”) (Wang, Par. 0010, Par. 0011, Par. 0012) with the explicit teachings of “user” (Marzorati, Par. 0004) taught by Marzorati and the teachings of “masking” (Zheng, Par. 0075-0076) taught by Zheng in order to “provide clarity to a user's voice while speaking” (Marzorati, Par. 0018) and “address the problems existing in the prior art by providing a method and system for speech enhancement” (Zheng, n0004). Regarding Claim 4, Wang in view of Marzorati and Zheng has been discussed above. The combination further teaches: wherein the correction signal generation section includes a second speech extraction processing section that generates a second speech extraction signal by extracting the utterance speech component from the vibration signal, and [Wang, see mapping applied to claims 1 - 3; Marzorati, see mapping applied to claim 1 - 3; Zheng, see mapping applied to claim 1- 3; Claim is directed to repeating the subject matter for a second speech extraction processing section and a second speech extraction signal. However, second speech extraction processing section/second speech extraction signal/repeating steps known from prior art is straightforward, amounts to the normal use of the teachings of Wang in view of Marzorati and are rejected under similar rationale.] an utterance detection section that generates a masking signal indicating presence or absence and intensity of the utterance speech from the vibration signal, and [Wang, see mapping applied to claims 1 - 3; Marzorati, see mapping applied to claim 1 - 3; Zheng, see mapping applied to claim 1- 3] the post-processing section generates the utterance speech signal by post-processing the first speech extraction signal based on the second speech extraction signal and the masking signal. [Wang, see mapping applied to claims 1 - 3; Marzorati, see mapping applied to claim 1 - 3; Zheng, see mapping applied to claim 1- 3] 07-21 AIA Claim 7 is rejected under 35 U.S.C. 103(a) as being unpatentable over Wang in view of Marzorati, Zheng and Blampoix (WO2004049303A1) . Regarding Claim 7, Wang in view of Marzorati and Zheng has been discussed above. The combination further teaches: wherein the utterance detection section generates envelope information as the masking signal. [Wang, see mapping applied to claims 1 - 3; Marzorati, see mapping applied to claim 1 - 3; Zheng, see mapping applied to claim 1- 3] The combination fails to teach envelope. However, Blampoix teaches: wherein the utterance detection section generates envelope information as the masking signal. [Blampoix, “a mask (n), n being an integer between 0 and N-l, set to infinity in negative values or to a high negative value in absolute value, takes into account the amplitude of the mask induced by each peak detected, z(n) advantageously being expressed in decibels. Advantageously, the vocal signal x(n) as module input is smoothed. After the smoothing operation, various envelopes representing signals that can constitute the vocal signal are then sought.” Pg. 17, Ln.16-22] Wang, Marzorati, Zheng and Blampoix acoustic adjustment/voice denoising/voice optimization systems and are analogous to the instant application. Accordingly, it would have been obvious to one of ordinary skill in the acoustic adjustment/voice denoising/ voice optimization systems art to modify Wang’s teachings of “speech signal obtaining module” (i.e., the claimed “first speech extraction processing section”), “speech activity detecting module” (i.e., the claimed “correction signal generation section”) and “acoustic adjustment model” (i.e., the claimed “post-processing section”) (Wang, Par. 0010, Par. 0011, Par. 0012) with the explicit teachings of “user” (Marzorati, Par. 0004) taught by Marzorati and the teachings of “masking” (Zheng, Par. 0075-0076) taught by Zheng and the teachings of “envelope” (Blampoix, Pg. 17, Ln.16-22) taught by Blampoix in order to “provide clarity to a user's voice while speaking” (Marzorati, Par. 0018), “address the problems existing in the prior art by providing a method and system for speech enhancement” (Zheng, n0004), and advance “voice analysis, especially by voice signal processing” (Blampoix, Pg. 1, Ln. 9-10) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Rothenberg et al., (U.S. Patent 11,295,759) teaches masking and envelopes. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EUNICE LEE whose telephone number is 571-272-1886. The examiner can normally be reached M-F 8:00 AM - 5:00 PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh Mehta can be reached on 571-272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /EUNICE LEE/Examiner, Art Unit 2656 /BHAVESH M MEHTA/ Supervisory Patent Examiner, Art Unit 2656 Application/Control Number: 18/842,325 Page 2 Art Unit: 2656 Application/Control Number: 18/842,325 Page 3 Art Unit: 2656 Application/Control Number: 18/842,325 Page 4 Art Unit: 2656 Application/Control Number: 18/842,325 Page 5 Art Unit: 2656 Application/Control Number: 18/842,325 Page 6 Art Unit: 2656 Application/Control Number: 18/842,325 Page 7 Art Unit: 2656 Application/Control Number: 18/842,325 Page 8 Art Unit: 2656 Application/Control Number: 18/842,325 Page 9 Art Unit: 2656 Application/Control Number: 18/842,325 Page 10 Art Unit: 2656 Application/Control Number: 18/842,325 Page 11 Art Unit: 2656 Application/Control Number: 18/842,325 Page 12 Art Unit: 2656 Application/Control Number: 18/842,325 Page 13 Art Unit: 2656 Application/Control Number: 18/842,325 Page 14 Art Unit: 2656 Application/Control Number: 18/842,325 Page 15 Art Unit: 2656 Application/Control Number: 18/842,325 Page 16 Art Unit: 2656 Application/Control Number: 18/842,325 Page 18 Art Unit: 2656 Application/Control Number: 18/842,325 Page 19 Art Unit: 2656
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