DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is in response to amendment filed on November 11, 2025 and wherein claims 1, 21, 22 amended and claims 11-20, 23-24 remain cancelation status.
In virtue of this communication, claims 1-10, 21-22, 25-32 are currently pending in this Office Action.
The Office appreciates the explanation of the amendment and analyses of the prior arts, and however, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993) and MPEP 2145.
In the response to this office action, the Examiner respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting this application.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-5, 7-10, 21-22, 25, 27-28, 30-32 are rejected under 35 U.S.C. 103 as being unpatentable over Hassan (US 20170323653 A1) and in view of reference Ravindran et al (US 20160284349 A1, hereinafter Ravindran)
Claim 1: Hassan teaches an audio recognition method (title and abstract, ln 1-9, method steps in figs. 6, 8), comprising:
acquiring a to-be-detected audio signal (a signal 300, as a mixture of uttered “How do I replace the ignition system?” and background noise components, para 4, and acquired via a microphone in fig. 1A/1B, para 28);
determining that an acquisition scenario of the to-be-detected audio signal is a first scenario (a repair workshop scenario is determined in at least a speech enhancement, para 27); and
selecting, according to the determination of the first scenario (based on the repair workshop scenario by sending the clean speech 340 “How do I replace the ignition system”, para 39), a sound event recognition model corresponding to the first scenario from among multiple sound event recognition models (selection based on the likelihood scores on the sound event such as drill sound, wrench sound, and hammer sound via the DNN decoding unit 234, e.g., drill sound is selected with highest likelihood score 1.5 as most audio event from likelihood scores of drill sound, wrench sound, and hammer sound event, para 47);
determining, based on the to-be-detected audio signal (the signal 300 also inputted into an audio event detection unit AED 230 through a noise extractor 210 in fig. 4, para 35) and the sound event recognition model (the event detection unit AED 230 through a DNN decoding unit 234, para 46-47) corresponding to the first scenario (by taking the clean speech 340 “How do I replace the ignition system”, para 33 and for assuring that the clean speech is excluded from the extracted noise via noise extractor 212, para 41, and for classifying audio event corresponding to a type of noise, from labels “DRILL”, “WRENCH”, “HAMMER”, etc., corresponding to auto repair workshop scenario, para 47), a sound event (audio event data 390 represented as label “DRILL” from multiples above, para 47) identified by the to-be-detected audio signal (the signal 300 as input to the noise extractor 212 in fig. 4), wherein the sound event recognition model corresponding to the first scenario is a neural network model (at least including DNN decoding unit 234 for decoding extract audio features 370 of the noise signal 360 by using DNN data 380, para 46 and speech enhancement and audio event detection system 130 are associated with an auto repair workshop environment, para 27) trained with an audio signal (trained by using audio samples 1, 2., …, N in fig. 7, para 70) in the first scenario (training in the auto repair workshop scenario, para 72) and configured to identify a sound event (represented by audio labels, including names of the auto repair tools, the auto repair sounds, sources of noise, etc., e.g., audio label “HAMMER” corresponding to hammer sound in the auto repair workshop, para 72) in the first scenario (the auto repair workshop scenario, para 72) based on the audio signal including noisy speech samples yi(t), i=1, 2, …, N in fig. 7).
However, Hassan does not explicitly teach it is according to the to-be-detected audio signal for the disclosed determination of that the acquisition scenario of the to-be-detected audio signal is a first scenario.
Ravindran teaches an analogous field of endeavor by disclosing an audio recognition method (title and abstract, ln 1-2 and method steps in fig. 3 and implemented in an environment-sensitive system in fig. 2) and wherein a plurality of acquisition scenarios of the to-be-detected audio signal (an analog front end 204 receives and processes the audio signal in fig. 2 and at the step 302, from one or more microphones, para 43) is identified (identifying SNR setting as high, medium, or low in element 408, para 55 and identifying the types of sound such as wind in the background or noise of the audio data by classify sounds in audio data by type of sound 416 in fig. 4, para 44, and e.g., the identified scenarios including environment of user’s heavy breathing, crowd or traffic noise, airplane, etc., para 35) according to the to-be-detected audio signal (based on at least obtained audio data 402 through computing SNR 404 in fig. 4, para 49 and through classify sounds in audio data by Type of sound 416 in fig. 4, para 55, 68) for benefits of obtaining optimization of audio signal processing and recognition (by improving efficiency in balance between accuracy and workload, para 54, and improving quality of word recognition from the audio signal with lower power consumption, para 2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied the determination of that the acquisition scenario of the to-be-detected audio signal is the first scenario according to the to-be-detected audio signal, as taught by Ravindran, determining that the acquisition scenario of the to-be-detected audio signal is the first scenario in the audio recognition method, as taught by Hassan, for the benefits discussed above.
Claim 21 has been analyzed and rejected according to claim 1 above, and the combination of Hassan and Ravindran further teaches an audio recognition apparatus (Hassan, the system 130 in fig. 1A-1B, and Ravindran, implemented on a smartphone, smartwatches, smart-writ bands, smart headsets and smart glasses, etc., para 17), comprising: one or more processors (Hassan, computing system includes at least one processor and at least one electronic storage medium, para 25, and Ravindran, microprocessor or CPU, para 112) and one or more memories (Hassan, at least one electronic storage device, para 23, and Ravindran, memory RAM, DRAM, SRAM, flash memory, para 113-114) coupled to the one or more processors (Hassan, at least one processor, para 25, and Ravindran, instruction sets, and software conveyed in the machine-readable medium, para 98) of the method of claim 1 (Hassan, implemented by computer hardware and computer software, para 82, and Ravindran, instructions stored in the memory, para 98, and executed by the processor, para 98).
Claim 22 has been analyzed and rejected according to claims 1, 21 above, and the combination of Hassan and Ravindran further teaches at least one chip (Hassan, at least one processor as a chip, para 25, and Ravindran, integrated into a chipset, para 117 or system-on-a-chip SoC, para 17).
Claim 2: the combination of Hassan and Ravindran further teaches, according to claim 1 above, wherein the to-be-detected audio signal comprises a plurality of audio frames (Hassan, framing input at 602 and audio frames are provided, para 69, and Ravindran, the audio signal is divided into frames, by 10ms frames by one example, para 25), and wherein determining that the acquisition scenario of the to-be-detected audio signal is the first scenario according to the to-be-detected audio signal (Hassan, the repair workshop scenario is determined in at least a speech enhancement, para 27 and Ravindran, one of the SNR ratio levels low, medium, and high, para 55) comprises:
inputting each of the plurality of audio frames into a scenario recognition model (Hassan, speech enhancement module 200 with DNN data 240A and DNN decoding unit 204 in fig. 4, and Ravindran, including compute SNR 404, classify sounds in audio data by type of sound 416, select parameter values depending on the SNR, etc., in fig. 4), to obtain scenario information of each audio frame (Ravindran, a-priori probability of acoustic events like heavy breathing, para 73 and frame or super-frame containing frames with each of 10ms to 30ms, para 49), wherein the scenario recognition model is a neural network model (Hassan, DNN training for the speech enhancement in fig. 5 and the speech enhancement includes at least DNN decoding unit 204 in fig. 4, and Ravindran, artificial neural network algorithms to classify the data, para 77) trained with audio frames in a plurality of scenarios (Hassan, trained by using training data include noisy speech sample and noise sample and through a DNN training for speech in fig. 5 and Ravindran, feature templates saved during the training for each class for the classification process, para 77) and configured to determine an acquisition scenario of an audio frame (Hassan, the repair workshop scenario is determined in at least a speech enhancement, para 27, and under such scenario, the clean speech 340 is derived in fig. 3 and Ravindran, SNR calculate 404 and classify sounds in audio data by type of sound 416 are used for determining scenarios including SNR levels low, medium, high and sound types discussed in claim 1 above), and wherein the scenario information of each audio frame indicates a probability that an acquisition scenario of each audio frame is each of the plurality of scenarios (Ravindran, the a-priori probability of acoustic events as values representing a relative frequency of such events in an environment of that type, para 73 and frame or super-frame containing frames with each of 10ms to 30ms, para 49); and
determining that the acquisition scenario of the to-be-detected audio signal is the first scenario in the plurality of scenarios according to the scenario information of each audio frame (Ravindran, such as heavy beathing scenario is determined based on whether the acoustic environment contains such events, para 73).
Claim 4: the combination of Hassan and Ravindran further teaches, according to claim 2 above, wherein the scenario recognition model is trained based on audio frames in at least one of a road scenario, a subway scenario, a home scenario, or an office scenario (Hassan, repair workshop scenario, para 27, and the discussion in claim 1 above, and Ravindran, background noise type includes traffic, or other vehicle noise, para 35).
Claim 5: the combination of Hassan and Ravindran further teaches, according to claim 1 above, wherein the to-be-detected audio signal comprises a plurality of audio frames (Hassan, framing input at 602 and audio frames are provided, para 69, and Ravindran, the audio signal is divided into frames, by 10ms frames by one example, para 25), the sound event recognition model corresponding to the first scenario comprises at least one sound event recognition model (Hassan, including noise extractor 212,audio feature extractor 232, DNN decoding unit 234, etc., in fig. 4 and Ravindran, including acoustic model, language model, etc. and wherein an acoustic model is selected for de-emphasizing one or more particular identified sounds in the audio data, and setting parameters for language model to emphasize a relevant sub-vocabulary based on the environmental information of the user, etc., para 24), the at least one sound event recognition model comprises a first sound event recognition model (Hassan, DNN decoding unit 234 in fig. 4 and Ravindran, the decoder 23 in fig. 1 and 232 in fig. 2 as deep neural networks DNNs, para 28), and the first sound event recognition model is trained with an audio frame identifying a first sound event in the first scenario (Hassan, training in fig. 7, and Ravindran, training is performed to create feature templates saved, para 77), and wherein determining, based on the to-be-detected audio signal (Hassan, through noise extraction module 210, takes the audio input 300 and Ravindran, through feature extraction unit 224 by taking the output of acoustic font end processing 205 in fig. 2) by using the sound event recognition model corresponding to the first scenario (Hassan, e.g., identified “DRILL” sound event corresponding to repair auto workshop scenario, the discussion in claim 1 above, and Ravindran, by using the updated parameters through parameter refinement unit 214 in fig. 2), the sound event identified by the to-be-detected audio signal comprises: inputting the plurality of audio frames into the first sound event recognition model respectively (Hassan, audio input 300 inputted to the noise extraction module 210 in fig. 3, and Ravindran, the output from acoustic front end 205 to the feature extraction unit 224 in fig. 2), to obtain sound event information identified by the plurality of audio frames (Hassan, likelihood scores associated with the provided different audio labels by DNN decoding unit 234, para 47 and Ravindran, the decoder 23 uses acoustic scores to identify utterance hypotheses and their scores, para 28), wherein sound event information of each audio frame of the plurality of audio frames indicates a probability that the each audio frame identifies the first sound event (Hassan, likelihood scores associated with the provided different audio labels and discussed above, and Ravindran, the scores are calculated at the decoder and outputting hypothetic word lattice providing confidence measures, para 28); and
determining, when sound event information identified by a first audio frame in the plurality of audio frames meets a third preset condition, the first sound event as the sound event identified by the to-be-detected audio signal (Hassan, the likelihood scores are measured with a certain value or a predetermined threshold to determine whether an audio event corresponding to the noise extracted from element 212, etc., in fig. 4 occurred or not, para 51).
Claim 7: the combination of Hassan and Ravindran further teaches, according to claim 4 above, wherein the first scenario is the road scenario (Hassan, target environment can be a construction site, or any environment for the training data of the speech enhancement module 200, para 26 and Ravindran, crowd noise, traffic, vehicle noise, etc., as non-speech patterns identified by audio classification unit 210, para 35, e.g., starting a vehicle by stating words, etc., para 33), and sound event recognition models (Hassan, including trained modules for DRILL, WRENCH, and HAMMER, etc., audio events corresponding to the workshop scenario, para 47, and upon the training data, para 51 and Ravindran, individual vehicle sounds whether from the inside or outside of an automobile or airplane, para 70 and corresponding to selected acoustic model for de-emphasizing and selected language model for emphasizing, through the ASR parameters or parameter refinement unit 214, and corresponding to outputs from at least SNR 208 and acoustic classification 210 of the audio data in fig. 4, para 24 and including models for traffic noise, individual vehicle noise, biking with vehicle noise, etc., para 63, 70), except explicitly teaching wherein the sound event recognition models include a sound event recognition model for whistles, a sound event recognition model for alarm sounds, a sound event recognition model for crash sounds, a sound event recognition model for car passing sounds, or a combination thereof.
It has been a recognized problem and need in the art, which may include a design need to solve the problem to have sound event recognition models corresponding to variety of potential and possible traffic noises in the vehicle traffic scenario, respectively and there had been a finite number of identifiable and predictable potential solutions to the sound event recognition models corresponding to the variety of vehicle traffic acoustic noise events:
a sound event recognition model for individual vehicle sound,
a sound event recognition model for break operation,
a sound event recognition model for whistles,
a sound event recognition model for alarm sounds,
a sound event recognition model for car crash,
a sound event recognition model for car passing,
a sound event recognition model for a combination of sounds above,
it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have pursued the known potential solutions with a reasonable expectation of success or obvious to try, see MPEP 2141, III, so that the variety sounds can be recognized in the vehicle traffic scenario for effectively taking measures and actions for safety, security, and conveniences.
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have applied that the sound event recognition models include the sound event recognition model for the whistles, the sound event recognition model for the alarm sounds, the sound event recognition model for the car crash, the sound event recognition model for the car passing, and the sound event recognition model for a combination of sounds above, as taught by obvious to try, to the sound event recognition models in the audio recognition method, as taught by the combination of Hassan and Ravindran, for the benefits discussed above.
Claim 8 has been analyzed and rejected according to claims 4, 7 above (claimed “subway scenario” under which, sound recognition models for “train passing sounds”, “compartment crash sounds”, “subway station announcements”, or “a combination thereof” would be in obvious to try, MPEP 2141, III and similar to the traffic scenario under which the sound recognition models as discussed in claim 7 above).
Claim 9: the combination of Hassan and Ravindran further teaches, according to claims 4, 7 above (claimed “the home scenario” under which, sound recognition models for “vacuum cleaning sounds of vacuum cleaners”, “washing sounds of washing machines”, “dish collision sounds”, “infant crying”, “faucet dripping sounds”, or a combination thereof, would be in obvious to try, MPEP 2141, III and similar to the traffic scenario under which the sound recognition models as discussed in claim 7 above).
Claim 10 has been analyzed and rejected according to claims 4, 7-9 above.
Claim 25 has been analyzed and rejected according to claims 21, 2.
Claim 27 has been analyzed and rejected according to claims 25, 4.
Claim 28 has been analyzed and rejected according to claims 21, 5.
Claim 30 has been analyzed and rejected according to claims 22, 2.
Claim 31: the combination of Hassan and Ravindran further teaches, according to claim 1 above, further comprising providing an output to a user or implementing a control response based on the determined sound event (Hassan, audio event data 390, e.g., “DRILL” label and triggered to provide safety notifications to the user, para 33 and Ravindran, a user intent is determined based on the audio data input from element 14, 16, etc., and outputting result to speaker component 26, display component to display the result, and from a language interpreter execution 24, or initiate an action in light of the speech command or request, para 33).
Claim 32: the combination of Hassan and Ravindran further teaches, according to claim 31 above, wherein the output to the user comprises a visual notification (Hassan, providing a message “Please complete the drill operation …” to the user if “DRILL” label is created, etc., para 33-34 and Ravindran, through display component 28, fig. 1), an audible notification (Ravindran, through a speaker component 26 in fig. 1), a tactile notification, or a combination thereof (Markush claim, MPEP 2117).
Claims 3, 6, 26, 29 are rejected under 35 U.S.C. 103 as being unpatentable over Hassan (above) and in view of references Ravindran (above) and Mitchell et al. (US 20210104230 A1, hereinafter Mitchell).
Claim 3: the combination of Hassan and Ravindran further teaches, according to claim 2 above, wherein determining that the acquisition scenario of the to-be-detected audio signal is the first scenario in the plurality of scenarios according to the scenario information of each audio frame (discussed above in claims 1-2 above), except explicitly teaching collecting statistics on a quantity of audio frames belonging to each of the plurality of scenarios in the plurality of audio frames; and determining the first scenario as the acquisition scenario of the to-be-detected audio signal when a) quantity of audio frames belonging to the first scenario in the plurality of scenarios in the plurality of audio frames meets a first preset condition, and b) a probability indicated by scenario information corresponding to the audio frames belonging to the first scenario in the plurality of audio frames meets a second preset condition.
Mitchell teaches an analogous field of endeavor by disclosing an audio recognition method (title and abstract, ln 1-13 and method steps in figs. 2-4) and wherein a plurality of scenarios is disclosed (e.g., a sound scene of railway station, a family dinner, loud crash, noisy room, baby cry, etc., para 14) and Mitchell further teaches wherein collecting statistics on a quantity of audio frames belonging to each of the plurality of scenarios in the plurality of audio frames (grouping the sound class decisions for each frame into single long-term event and/or scene indicators with a start time and an end time, e.g., 0.4 seconds/25 frames, para 43 and calculated based on frame-level classification, para 42); and determining the first scenario as the acquisition scenario of the to-be-detected audio signal when a) quantity of audio frames belonging to the first scenario in the plurality of scenarios in the plurality of audio frames meets a first preset condition (the indicator of the identified scene and/or event lasted and met at least duration threshold, para 44, and step 213 in fig. 2, para 108), and b) a probability indicated by scenario information corresponding to the audio frames belonging to the first scenario in the plurality of audio frames meets a second preset condition (smoothed scores are thresholded to turn the scores into class decisions, e.g., baby cry score is above the threshold, it is determined to the frame of baby cry, para 93) for benefits of improving accuracy of audio recognition (by probability of identification of majority of the frames having the class associated with sound event and/or scene in a time period, para 138).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied wherein collecting statistics on the quantity of audio frames belonging to each of the plurality of scenarios in the plurality of audio frames; and determining the first scenario as the acquisition scenario of the to-be-detected audio signal when a) quantity of audio frames belonging to the first scenario in the plurality of scenarios in the plurality of audio frames meets the first preset condition, and b) the probability indicated by scenario information corresponding to the audio frames belonging to the first scenario in the plurality of audio frames meets the second preset condition, as taught by Mitchell, to determining that the acquisition scenario of the to-be-detected audio signal is the first scenario in the plurality of scenarios according to the scenario information of each audio frame in the audio recognition method, as taught by the combination of Hassan and Ravindran, for the benefit discussed above.
Claim 6: the combination of Hassan, Ravindran, and Mitchell further teaches, according to claim 5 above, wherein when the sound event information identified by the first audio frame meets the third preset condition (the discussion in claim 5 above, and Mitchell, (smoothed scores are thresholded to turn the scores into class decisions, e.g., baby cry score is above the threshold, it is determined to the frame of baby cry, para 93), and sound event information identified by a second audio frame in a preset quantity of frames meets a fourth preset condition (Mitchell, the indicator of the identified scene and/or event lasted and met at least duration threshold, para 44, and step 213 in fig. 2, para 108), then a time point corresponding to the second audio frame is a start time point of the first sound event (Mitchell, grouping the sound class decisions for each frame so that a single long-term event and/or scene indicators having a start time, an end time and a duration, para 43).
However, the combination of Hassan, Ravindran, and Mitchell does not explicitly teach wherein the present quantity of frames is previous to the first audio frame.
It has been a recognized problem and need in the art, which may include a design need or choice to solve the problem to have the stable and reliable probability of identified sound event of the frame through a sequence of frames for meeting the threshold and there had been a finite number of identifiable and predictable potential solutions to the sound event recognition by sound event information or probability through sequence of frames:
The first audio frame is at a start of preset quantity of frames,
The first audio frame is at last of preset quantity of frames,
The first audio frame is one of the preset quantity of frames,
it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have pursued the known potential solutions with a reasonable expectation of success or obvious to try, see MPEP 2141, III, so that the stable and reliable audio event recognition is obtained.
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have applied that the preset quantity of frames is previous to the first audio frame, as taught by obvious to try, to the first frame and the preset quantity of audio frames in the audio recognition method, as taught by the combination of Hassan, Ravindran, and Mitchell, for the benefits discussed above.
Claim 26 has been analyzed and rejected according to claims 25, 3 above
Claim 29 has been analyzed and rejected according to claims 28, 6 above
Response to Arguments
Applicant's arguments filed on November 11, 2025 have been fully considered and but are moot in view of the new ground(s) of rejection necessitated by the applicant amendment. Although a new ground of rejection has been used to address additional limitations that have been added to claims 1, 21, 22, a response is considered necessary for several of applicant’s arguments since references Hassan and Ravindran will continue to be used to meet several claimed limitations.
With respect to the prior art rejection of independent claim 1, similar to claims 21, 22, under 35 USC §103(a) and newly added feature “selecting, according to the determination of the first scenario, a sound vent recognition model corresponding to the first scenario from among multiple sound event recognition models”, applicant argued: Hassan does not teach the newly added feature above because “Hassan includes only a single even detection unit 230, which includes the DNN decoding unit 234. See Hassan at FIG. 4. Hassan does not describe multiple event detection units 230 or DNN decoding units 234 among which one is selected corresponding to the determined first scenario”, as asserted in paragraph 2 of page 12 in Remarks filed on November 11, 2025.
In response to the argument cited above, the Office respectfully disagrees because claim failed to recite what “sound event recognition model is” and “multiple sound event recognition models” are, and thus, as described in office action above, Hassan’s DNN decoding unit 234 with the specific data provided by DNN data 240B (fig. 4) corresponding to determined scenario (auto repair workshop scenario, para 27) is mapped to the claimed sound event recognition model, e.g., the unit 234 with the DNN data 240B as the model is corresponding to drill sound event model if the feature of drill sound data from the audio feature extractor 232, with the DNN data (380) corresponding to hammer sound event model if the feature of hammer sound data from the audio feature extractor 232, and similar to wrench sound (para 47), and thus, in lack of what “model” are “models” are, the Hssan’s disclosure above is essentially equivalent to the claimed “multiple sound event recognition models” and further teaches selecting one from the “multiple” above (based on likelihood score, para 47), but applicant is in silence and therefore, the argument above is moot. For the at least similar reasons described above, the prior art rejection of other independent claims 21, 22 and dependent claims 2-10, 25-32 also maintained.
In the response to this office action, the Office respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the Office in prosecuting this application.
Conclusion
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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/LESHUI ZHANG/
Primary Examiner,
Art Unit 2695
Prosecution Insights