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 .
Priority
Receipt is acknowledged or paper submitted under 35 U.S.C. 119(a)-(d), which papers have been places of record in the file.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/20/2024 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 were submitted on 07/17/2024. These drawings are reviewed and accepted by the examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3 and 13-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mani (US 20190318733 A1).
Regarding claims 1, 13, and 14, Mani teaches:
“receiving a current audio input signal having a speech portion and a non-speech portion” (par. 0023; ‘At times, the first signal may comprise at least one silence region between the speech signal and the one or more noise signals.’);
“determining a speech feature of the speech portion in the current audio input signal” (par. 0035, gain of speech signal; ‘The processed signal may correspond to a beamformed signal which may be characterized by amplification in the gain of the speech signal and stability (or improvement) in the SNR of the first signal.’);
“determining a speech quality of the current audio input signal” (par. 0035, SNR; ‘The processed signal may correspond to a beamformed signal which may be characterized by amplification in the gain of the speech signal and stability (or improvement) in the SNR of the first signal.’);
“evaluating whether the speech quality meets a predetermined speech quality requirement” (par. 0078; ‘The threshold value may be a lower limit for the SNR, below which the output and response may be considered as “poor”.’); and
“creating or updating, in response to the speech quality meeting the predetermined speech quality requirement, a reference speech feature by using the speech feature, wherein the reference speech feature is used for enhancing the speech portion in an audio input signal” (par. 0096; ‘In accordance with the first condition, the second pre-processor 210 may be assumed to provide the filtered first signal (X.sub.E(z)) that is modeled as a clear reference speech signal.’; par. 0101; ‘The processed speech signal (S.sub.E(z)) may be characterized as directional, amplified, or enhanced speech signal with significant clarity and intelligibility as compared to unprocessed speech signals or noisy speech signals.’).
Regarding claim 2 (dep. on claim 1), Mani further teaches:
“wherein the determining the speech quality of the current audio input signal comprises: determining a speech signal-to-noise ratio of the current audio input signal, wherein the speech signal-to-noise ratio represents a ratio of a power of the speech portion to a power of the non-speech portion” (par. 0103; ‘In accordance with an embodiment, the SegSNR is evaluated for each frame of a noisy speech signal, such as the first signal (X(z)) or the second signal (Y(z)), and subsequent frame of the processed speech signal (S.sub.E(z)). The SegSNR is finally evaluated for the entire processed speech signal from an average of SegSNR for each frame of the noisy speech signal and the processed speech signal.’; See also par. 0105).
Regarding claim 3 (dep. on claim 2), Mani further teaches:
“wherein the evaluating whether the speech quality meets the predetermined speech quality requirement comprises: comparing the speech signal-to-noise ratio with a predetermined speech signal-to-noise ratio threshold” (par. 0132; ‘An SNR associated with the processed signal may be greater than or equal to a threshold value.’); and
“determining, in response to the speech signal-to-noise ratio being greater than the predetermined speech signal-to-noise ratio threshold, that the speech quality meets the predetermined speech quality requirement” (par. 0132; ‘An SNR associated with the processed signal may be greater than or equal to a threshold value.’).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 4-12 and 15-22 are rejected under 35 U.S.C. 103 as being unpatentable over Mani in view of Gao (US 20200202852 A1).
Regarding claim 4 (dep. on claim 1), Mani does not expressly teach:
“obtaining one or more prestored reference speech features”; and
“retrieving the reference speech feature matching the speech feature from the one or more prestored reference speech features.”
In a similar field of endeavor (improving SNR of voice data), Gao teaches:
“obtaining one or more prestored reference speech features” (par. 0050, preset voice feature; ‘The matching analysis unit 143 is configured to compare the target voice feature acquired from the feature extraction unit 142 with the preset voice feature in the storage module 16, and send a feedback signal to the infrared emitter 15 according to the comparison result, and the infrared emitter 15 receives and parses the feedback signal to generate an infrared signal and transmits the infrared signal to the electronic device to control the electronic device.’); and
“retrieving the reference speech feature matching the speech feature from the one or more prestored reference speech features” (par. 0051; ‘Specifically, If the ratio between the target voice feature obtained from feature extraction unit 142 and the preset voice feature in storage module 16 is greater than or equal to the voice feature matching threshold set in threshold setting unit 141, the matching analysis unit 143 will send a positive feedback signal to the infrared transmitter 15, and the positive feedback signal includes specific information of the infrared signal to be transmitted, namely the code corresponding to the infrared signal, thus different infrared signals can be sent through different voice commands to control different electrical appliances or different functions of the same appliance.’).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Mani’s method of adaptive enhancement of a speech signal by incorporating Gao’s feature extraction unit and matching analysis unit in order to recognize a target voice and voice commands. (Gao: par. 0036, 0051)
Regarding claim 5 (dep. on claim 4), the combination of Mani in view of Gao further teaches:
“creating, in response to the reference speech feature matching the speech feature not being retrieved, a new reference speech feature by using the speech feature of the current audio input signal” (Gao: par. 0066, self-setting voice feature; ‘For example, the preset voice command “opening fan” corresponds to turning on the fan, when the user wants to turn on the fan with the self-setting voice command “OPEN”, the self-setting voice command “OPEN” is collected by the self-set voice collection module 28, the voice binding unit 271 binds or associates the self-set voice feature of the “OPEN” with the preset voice feature of the preset voice command “opening fan”, and sends a voice prompt “Congratulation, tie” after the binding is successful. Therefore, the user can realize the function of turning on the fan through “OPEN”.’) and
“enhancing the speech portion in the current audio input signal by using the speech feature of the speech portion in the current audio input signal” (Gao: par. 0066; ‘… the voice binding unit 271 binds or associates the self-set voice feature of the “OPEN” with the preset voice feature of the preset voice command “opening fan”…’).
Regarding claim 6 (dep. on claim 5), the combination of Mani in view of Gao further teaches:
“comparing a duration of the current audio input signal with a predetermined duration threshold” (Mani: par. 0103; ‘In accordance with an exemplary scenario, (X(i)) and (S(i)) may represent a frame (i) of the noisy speech signal, such as the first signal and the processed speech signal, (N) and (M) may be a total number of frames and a length of each frame, for example “20 milliseconds”.’); and
“creating, in response to the duration of the current audio input signal being greater than the predetermined duration threshold, a reference speech feature by using the speech feature of the current audio input signal” (Gao: par. 0066, self-setting voice feature; ‘For example, the preset voice command “opening fan” corresponds to turning on the fan, when the user wants to turn on the fan with the self-setting voice command “OPEN”, the self-setting voice command “OPEN” is collected by the self-set voice collection module 28, the voice binding unit 271 binds or associates the self-set voice feature of the “OPEN” with the preset voice feature of the preset voice command “opening fan”, and sends a voice prompt “Congratulation, tie” after the binding is successful. Therefore, the user can realize the function of turning on the fan through “OPEN”.’).
Regarding claim 7 (dep. on claim 4), the combination of Mani in view of Gao further teaches:
“comparing, in response to the reference speech feature matching the speech feature being retrieved, the speech quality of the current audio input signal with a speech quality corresponding to the matching reference speech feature” (Mani: par. 0101; ‘The processed speech signal (S.sub.E(z)) may be characterized as directional, amplified, or enhanced speech signal with significant clarity and intelligibility as compared to unprocessed speech signals or noisy speech signals.’);
“updating, in response to the speech quality of the current audio input signal being superior to the speech quality corresponding to the matching reference speech feature, the matching reference speech feature by using the speech feature of the current audio input signal” (Mani: par. 0099; ‘The first speech estimation error (E.sub.1) may be used to update a set of filter coefficients of the second filter 2108. The set of filter coefficients may be updated until the optimum response (H.sub.SO(z)) of the second filter 210B is obtained.’); and
“enhancing the speech portion in the current audio input signal by using the speech feature of the speech portion in the current audio input signal” (Mani: par. 0101; ‘The processed speech signal (S.sub.E(z)) may be characterized as directional, amplified, or enhanced speech signal with significant clarity and intelligibility as compared to unprocessed speech signals or noisy speech signals.’).
Regarding claim 8 (dep. on claim 7), the combination of Mani in view of Gao further teaches:
“enhancing, in response to the speech quality of the current audio input signal not being superior to the speech quality corresponding to the matching reference speech feature, the speech portion in the current audio input signal by using the speech feature of the speech portion in the current audio input signal and the matching reference speech feature” (Mani: par. 0101; ‘The processed speech signal (S.sub.E(z)) may be characterized as directional, amplified, or enhanced speech signal with significant clarity and intelligibility as compared to unprocessed speech signals or noisy speech signals.’).
Regarding claim 9 (dep. on claim 4), the combination of Mani in view of Gao further teaches:
“enhancing, in response to the reference speech feature matching the speech feature not being retrieved and the speech quality not meeting the predetermined speech quality requirement, the speech portion in the current audio input signal by using the speech feature of the speech portion in the current audio input signal” (Mani: par. 0101; ‘The processed speech signal (S.sub.E(z)) may be characterized as directional, amplified, or enhanced speech signal with significant clarity and intelligibility as compared to unprocessed speech signals or noisy speech signals.’).
Regarding claim 10 (dep. on claim 4), the combination of Mani in view of Gao further teaches:
“enhancing, in response to the reference speech feature matching the speech feature being retrieved and the speech quality not meeting the predetermined speech quality requirement, the speech feature by using the speech feature of the speech portion in the current audio input signal and the matching reference speech feature” (Mani: par. 0101; ‘The processed speech signal (S.sub.E(z)) may be characterized as directional, amplified, or enhanced speech signal with significant clarity and intelligibility as compared to unprocessed speech signals or noisy speech signals.’).
Regarding claim 11 (dep. on claim 1), the combination of Mani in view of Gao further teaches:
“wherein the speech feature comprises a pitch period or a Mel-frequency cepstral coefficient” (Gao: par. 0048; ‘The feature extraction unit 142 is configured to extract the target voice feature acquired by the preprocessing module 13, and the voice feature may be any one of a sound intensity feature, a loudness feature, a pitch feature, a pitch cycle feature, a combination of the two, or a combination of the three.’).
Regarding claim 12 (dep. on claim 1), the combination of Mani in view of Gao further teaches:
“wherein the determining the speech feature of the speech portion in the current audio input signal comprises: determining a speech enhancement feature and a speech comparison feature of the speech portion in the current audio input signal” (Gao: par. 0048; ‘For example, the voice recognition used by the voice recognition module 14 may be a voice recognition algorithm based on a convolutional neural network algorithm and/or voice recognition based on a voiceprint feature extraction.’; par. 0083; ‘At the same time, the first collecting module is used to acquire the user's voice data, and the interference suppression unit of the preprocessing module first suppresses the interference signal of the voice data, improves the signal-to-noise ratio of the voice data, and then uses the noise filtering unit to further eliminate the interference signal, effectively retain the user's voice in the voice data, that is, the target voice;’),
“wherein the reference speech feature comprises a reference speech enhancement feature and a reference speech comparison feature, the speech enhancement feature and the reference speech enhancement feature being used for enhancing the speech portion of the audio input signal, and the speech comparison feature being used for matching the reference speech comparison feature” (Gao: par. 0048; ‘For example, the voice recognition used by the voice recognition module 14 may be a voice recognition algorithm based on a convolutional neural network algorithm and/or voice recognition based on a voiceprint feature extraction.’; par. 0083; ‘At the same time, the first collecting module is used to acquire the user's voice data, and the interference suppression unit of the preprocessing module first suppresses the interference signal of the voice data, improves the signal-to-noise ratio of the voice data, and then uses the noise filtering unit to further eliminate the interference signal, effectively retain the user's voice in the voice data, that is, the target voice;’).
Regarding claim 15, Mani teaches:
“receiving a current audio input signal having a speech portion and a non-speech portion” (par. 0023; ‘At times, the first signal may comprise at least one silence region between the speech signal and the one or more noise signals.’);
“determining a speech feature of the speech portion in the current audio input signal” (par. 0035, gain of speech signal; ‘The processed signal may correspond to a beamformed signal which may be characterized by amplification in the gain of the speech signal and stability (or improvement) in the SNR of the first signal.’);
“determining a speech quality of the current audio input signal” (par. 0035, SNR; ‘The processed signal may correspond to a beamformed signal which may be characterized by amplification in the gain of the speech signal and stability (or improvement) in the SNR of the first signal.’);
“evaluating whether the speech quality meets a predetermined speech quality requirement” (par. 0132; ‘An SNR associated with the processed signal may be greater than or equal to a threshold value.’).
Mani does not expressly teach:
“retrieving a reference speech feature matching the speech feature from one or more prestored reference speech features”; and
“enhancing, in response to an evaluation result for the predetermined speech quality requirement and a matching result for the one or more reference speech features, the speech portion in the current audio input signal by using one or two of the speech feature of the speech portion in the current audio input signal and the matching reference speech feature.”
In a similar field of endeavor (improving SNR of voice data), Gao teaches:
“retrieving a reference speech feature matching the speech feature from one or more prestored reference speech features” (par. 0051; ‘Specifically, If the ratio between the target voice feature obtained from feature extraction unit 142 and the preset voice feature in storage module 16 is greater than or equal to the voice feature matching threshold set in threshold setting unit 141, the matching analysis unit 143 will send a positive feedback signal to the infrared transmitter 15, and the positive feedback signal includes specific information of the infrared signal to be transmitted, namely the code corresponding to the infrared signal, thus different infrared signals can be sent through different voice commands to control different electrical appliances or different functions of the same appliance.’).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Mani’s method of adaptive enhancement of a speech signal by incorporating Gao’s feature extraction unit and matching analysis unit in order to recognize a target voice and voice commands. (Gao: par. 0036, 0051)
The combination of Mani in view of Gao teaches:
“enhancing, in response to an evaluation result for the predetermined speech quality requirement and a matching result for the one or more reference speech features, the speech portion in the current audio input signal by using one or two of the speech feature of the speech portion in the current audio input signal and the matching reference speech feature” (Mani: par. 0101; ‘The processed speech signal (S.sub.E(z)) may be characterized as directional, amplified, or enhanced speech signal with significant clarity and intelligibility as compared to unprocessed speech signals or noisy speech signals.’).
Regarding claim 16 (dep. on claim 15), the combination of Mani in view of Gao further teaches:
“in response to the speech quality meeting the predetermined speech quality requirement and the reference speech feature matching the speech feature not being retrieved, enhancing the speech portion in the current audio input signal by using the speech feature of the speech portion in the current audio input signal, and creating a new reference speech feature by using the speech feature of the current audio input signal” (Mani: par. 0101; ‘The processed speech signal (S.sub.E(z)) may be characterized as directional, amplified, or enhanced speech signal with significant clarity and intelligibility as compared to unprocessed speech signals or noisy speech signals.’).
Regarding claim 17 (dep. on claim 15), the combination of Mani in view of Gao further teaches:
“comparing, in response to the speech quality meeting the predetermined speech quality requirement and the reference speech feature matching the speech feature being retrieved, the speech quality of the current audio input signal with a speech quality of the matching reference speech feature” (Mani: par. 0101; ‘The processed speech signal (S.sub.E(z)) may be characterized as directional, amplified, or enhanced speech signal with significant clarity and intelligibility as compared to unprocessed speech signals or noisy speech signals.’);
“updating, in response to the speech quality of the current audio input signal being superior to the speech quality of the matching reference speech feature, the matching reference speech feature by using the speech feature of the current audio input signal” (Mani: par. 0099; ‘The first speech estimation error (E.sub.1) may be used to update a set of filter coefficients of the second filter 2108. The set of filter coefficients may be updated until the optimum response (H.sub.SO(z)) of the second filter 210B is obtained.’); and
“enhancing the speech portion in the current audio input signal by using the speech feature of the speech portion in the current audio input signal” (Mani: par. 0101; ‘The processed speech signal (S.sub.E(z)) may be characterized as directional, amplified, or enhanced speech signal with significant clarity and intelligibility as compared to unprocessed speech signals or noisy speech signals.’).
Regarding claim 18 (dep. on claim 17), the combination of Mani in view of Gao further teaches:
“enhancing, in response to the speech quality of the current audio input signal not being superior to the speech quality of the matching reference speech feature, the speech portion in the current audio input signal by using the speech feature of the speech portion in the current audio input signal and the matching reference speech feature” (Mani: par. 0101; ‘The processed speech signal (S.sub.E(z)) may be characterized as directional, amplified, or enhanced speech signal with significant clarity and intelligibility as compared to unprocessed speech signals or noisy speech signals.’).
Regarding claim 19 (dep. on claim 15), the combination of Mani in view of Gao further teaches:
“enhancing, in response to the speech quality not meeting the predetermined speech quality requirement and the reference speech feature matching the speech feature not being retrieved, the speech portion in the current audio input signal by using the speech feature of the speech portion in the current audio input signal” (Mani: par. 0101; ‘The processed speech signal (S.sub.E(z)) may be characterized as directional, amplified, or enhanced speech signal with significant clarity and intelligibility as compared to unprocessed speech signals or noisy speech signals.’).
Regarding claim 20 (dep. on claim 15), the combination of Mani in view of Gao further teaches:
“enhancing, in response to the speech quality not meeting the predetermined speech quality requirement and the reference speech feature matching the speech feature being retrieved, the speech feature by using the speech feature of the speech portion in the current audio input signal and the matching reference speech feature” (Mani: par. 0101; ‘The processed speech signal (S.sub.E(z)) may be characterized as directional, amplified, or enhanced speech signal with significant clarity and intelligibility as compared to unprocessed speech signals or noisy speech signals.’).
Regarding claim 21 (dep. on claim 15), the combination of Mani in view of Gao further teaches:
“wherein the determining the speech feature of the speech portion in the current audio input signal comprises: determining a speech enhancement feature and a speech comparison feature of the speech portion in the current audio input signal” (Gao: par. 0048; ‘For example, the voice recognition used by the voice recognition module 14 may be a voice recognition algorithm based on a convolutional neural network algorithm and/or voice recognition based on a voiceprint feature extraction.’; par. 0083; ‘At the same time, the first collecting module is used to acquire the user's voice data, and the interference suppression unit of the preprocessing module first suppresses the interference signal of the voice data, improves the signal-to-noise ratio of the voice data, and then uses the noise filtering unit to further eliminate the interference signal, effectively retain the user's voice in the voice data, that is, the target voice;’),
“wherein the reference speech feature comprises a reference speech enhancement feature and a reference speech comparison feature, the speech enhancement feature and the reference speech enhancement feature being used for enhancing the speech portion of the audio input signal, and the speech comparison feature being used for matching the reference speech comparison feature” (Gao: par. 0048; ‘For example, the voice recognition used by the voice recognition module 14 may be a voice recognition algorithm based on a convolutional neural network algorithm and/or voice recognition based on a voiceprint feature extraction.’; par. 0083; ‘At the same time, the first collecting module is used to acquire the user's voice data, and the interference suppression unit of the preprocessing module first suppresses the interference signal of the voice data, improves the signal-to-noise ratio of the voice data, and then uses the noise filtering unit to further eliminate the interference signal, effectively retain the user's voice in the voice data, that is, the target voice;’).
Regarding claim 22, the combination of Mani in view of Gao teaches:
“receiving a current audio input signal having a speech portion and a non-speech portion” (par. 0023; ‘At times, the first signal may comprise at least one silence region between the speech signal and the one or more noise signals.’);
“determining a speech feature of the speech portion in the current audio input signal, wherein the speech feature comprises a speech enhancement feature and a speech comparison feature” (Gao: par. 0048; ‘For example, the voice recognition used by the voice recognition module 14 may be a voice recognition algorithm based on a convolutional neural network algorithm and/or voice recognition based on a voiceprint feature extraction.’; par. 0083; ‘At the same time, the first collecting module is used to acquire the user's voice data, and the interference suppression unit of the preprocessing module first suppresses the interference signal of the voice data, improves the signal-to-noise ratio of the voice data, and then uses the noise filtering unit to further eliminate the interference signal, effectively retain the user's voice in the voice data, that is, the target voice;’);
“determining a speech quality of the current audio input signal” (par. 0035, SNR; ‘The processed signal may correspond to a beamformed signal which may be characterized by amplification in the gain of the speech signal and stability (or improvement) in the SNR of the first signal.’);
“evaluating whether the speech quality meets a predetermined speech quality requirement” (par. 0132; ‘An SNR associated with the processed signal may be greater than or equal to a threshold value.’);
“obtaining one or more prestored reference speech features, each comprising a reference speech enhancement feature and a reference speech comparison feature” (Gao: par. 0050, preset voice feature; ‘The matching analysis unit 143 is configured to compare the target voice feature acquired from the feature extraction unit 142 with the preset voice feature in the storage module 16, and send a feedback signal to the infrared emitter 15 according to the comparison result, and the infrared emitter 15 receives and parses the feedback signal to generate an infrared signal and transmits the infrared signal to the electronic device to control the electronic device.’);
“retrieving, based on comparison between the speech comparison feature and the reference speech comparison feature, a reference speech feature matching the speech feature from the one or more prestored reference speech features” (Gao: par. 0051; ‘Specifically, If the ratio between the target voice feature obtained from feature extraction unit 142 and the preset voice feature in storage module 16 is greater than or equal to the voice feature matching threshold set in threshold setting unit 141, the matching analysis unit 143 will send a positive feedback signal to the infrared transmitter 15, and the positive feedback signal includes specific information of the infrared signal to be transmitted, namely the code corresponding to the infrared signal, thus different infrared signals can be sent through different voice commands to control different electrical appliances or different functions of the same appliance.’); and
“enhancing, in response to an evaluation result for the predetermined speech quality requirement and a matching result for the one or more reference speech features, the speech portion in the current audio input signal by using one or two of the speech enhancement feature of the speech portion in the current audio input signal and the reference speech enhancement feature of the matching reference speech feature” (Mani: par. 0101; ‘The processed speech signal (S.sub.E(z)) may be characterized as directional, amplified, or enhanced speech signal with significant clarity and intelligibility as compared to unprocessed speech signals or noisy speech signals.’).
Conclusion
Other pertinent prior art are cited in the PTO-892 for the applicant's consideration.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK VILLENA whose telephone number is (571)270-3191. The examiner can normally be reached 10 am - 6pm EST Monday through Friday.
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, Richemond Dorvil can be reached at (571) 272-7602. 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.
MARK . VILLENA
Examiner
Art Unit 2658
/MARK VILLENA/Examiner, Art Unit 2658
Prosecution Insights