DETAILED ACTION
The Response filed on 02/23/2026 has been correspondingly accepted and considered in the office action. Claims 1-7 are pending. Claims 1 and 5 are independent and amended.
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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a noise removal unit, a first sound recognition unit, and a second sound recognition unit in Claims 1 and 5; a coherence function generation unit, a spatial filter coefficient calculation unit, and a beamforming performance unit in Claims 2 and 6; a coherence calculation unit, a coherence average calculation unit, and a filter unit in Claim 3; a pattern recognition unit in Claims 4 and 7; an image input unit and control unit in Claim 5.
Response to Arguments
Claims 1-7 stand rejected under 35 U.S.C. § 103. Applicant’s arguments with respect to Claims 1-7 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
In order to expedite prosecution, and as to the material from the Specifications that are not in the Claim and are argued by the Applicant, please note Lopatka et al., (US Pub No. 2020/0213728, hereinafter, Lopatka).
For at least the supra provided reasons, Applicant's arguments have been fully considered but they are not persuasive.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Jeong, (KR20200104203A, hereinafter, Jeong) in view of Maeng et al., (US Pub No. 2021/0327447, hereinafter, Maeng) further in view of Lopatka et al., (US Pub No. 2020/0213728, hereinafter, Lopatka).
Regarding Claim 1,
Jeong discloses an artificial intelligence (AI)-based sound recognition module (Jeong, Figs. 1 and 2, pars [033], "…FIGS. 1 and 2, the apparatus 10 for recognizing a location of a moving object using a sound signal includes a camera 100, a microphone 200, a speaker 500..."; par [055], "…The processor 400 of the present embodiment may perform learning through deep learning...") comprising:
wherein the second sound recognition unit is configured to extract a feature from the recognized audio signal, convert the extracted feature into a pattern vector for teaching or recognition, store the pattern vector in a neuron library (Jeong, par [057], "…Neural networks are, for example, artificial neural networks that learn sound signals and video signals through deep learning..."; par [040], "…The memory 300 may store sound information input through the microphone 200 and may further store an image captured through the camera 100. The memory 300 may store a sound determination value calculated by learning a sound of a direction indicator pressed before the moving object moves in the learning mode 440 of the processor 400..."),
recognize a pattern of the pattern vector using a sound recognition model generated through library teaching, standardize the recognized pattern, make a global decision on the standardized pattern, and output the sound detection signal based on the global decision (par [057], "…the neural network may be provided to extract feature values through a plurality of convolutional filtering and pooling...The neural network may receive the above training data and perform learning to match sound signals based on an image signal having attribute information..."; par [090], "…A plurality of input sound information may be classified as hidden (hidden layer) according to the sound type..."; par [093], "…Output (output layer) calculates one output value by comparing sound information input from the input layer with sound information from the hidden layer...the output value by the output layer is a sound formed by a direction indicator, and a sound determination value may be calculated..." ; par [097], "…The signal generation unit 430 may finally output the first warning signal formed by the comparison determination unit 420..."), and
Jeong does not explicitly disclose the limitations regarding the noise reduction and voice recognition using a beamforming technique. However, Maeng, in the analogous field of endeavor, discloses a noise removal unit configured to remove a noise waveform from a sound input through a microphone based on direction information, and output a noise-removed signal (Maeng, Fig.7, par [333], "…the electronic device 100 may include a plurality of microphones and may adopt a beamforming technique to allow a beam to be formed in a specific direction using the plurality of microphones...noise signals may be removed by subtracting the audio signal received via the first beam from the audio signals received via the first beam and the second beam..."; par [334], "…an adaptive digital filter-applied beamforming method may apply...");
A first sound recognition configured to recognize a sound from the noise-removed signal, and output a recognized audio signal (Maeng, par [335], "…the processor 180 may determine whether the sound contains a human speech..."); and
a second sound recognition unit configured to process the recognized audio signal output from the first sound recognition unit through a neuron artificial neural network and output a sound detection signal (Maeng, par [0341], "…The processor 180 may perform beamforming of the microphone based on the speaker's direction to thereby execute an audio zoom function (S750)..."; par [342], "…the weight used to perform the beamforming operation may be determined by a DNN model...").
Therefore, it would have been obvious to one of ordinary skill in the art, before effective filing date of the claimed invention, to have modified the apparatus or recognizing a location of a moving object using a sound signal of Jeong with a visual data enhanced audio zoom of Maeng with a reasonable expectation of success to enhance the accuracy of the sound collection and speech recognition by incorporation image analysis captured via the camera for the control of the beamforming of the microphone (Maeng, paras. [003-020]).
But Jeong in view of Maeng does not explicitly disclose receiving sound from the object approaching the AI-based sound recognition module and subsequent removal of the ambient noise.
However, Lopatka, in the analogous field of endeavor, discloses wherein the sound signal input through the microphone is generated by a sound of an object approaching the AI-based sound recognition module, and the noise removal unit is configured to filter out ambient noise sound signals from the sound signal (Lopatka, Figs.1-3, par [016], "…a detection and tracking system 130...The microphone array 120 is configured to receive acoustic signals that are present in the operating environment of the autonomous vehicle 110 and provides an audio channel that can be used for beamforming by the detection and tracking system 130…", "…The detection and tracking system 130 is also configured to determine a direction of motion of the acoustic source relative to the autonomous vehicle 110, for example, whether the emergency vehicle is approaching or receding from the autonomous vehicle..."; par [017], "…The beamforming employs time-frequency masks to reduce noise in the beam signal spectra…The method also includes applying a deep neural network (DNN) classifier to detect an acoustic event, associated with the acoustic source...", "…estimating a direction of motion of the acoustic source relative to the array of microphones based on a Doppler effect frequency shift of the acoustic event...").
Therefore, it would have been obvious to one of ordinary skill in the art, before effective filing date of the claimed invention, to have modified an audio zoom system with beamforming of the multiple microphone enhanced by the image recognition from captured image of Jeong in view of Maeng with an acoustic event detection and tracking with beamforming and time-frequency mask noise reduction of Lopatka with a reasonable expectation of success to improve the reliability of sound detection and tracking when the acoustic source and/or the detection platform are in motion (Lopatka, paras [1, 14-15]).
Regarding Claim 4,
Jeong in view of Maeng further in view of Lopatka discloses the AI-based sound recognition module of claim 1, wherein the second sound recognition unit includes:
Maeng further discloses a complementary metal-oxide-semiconductor (CMOS) connector configured to receive the audio signal (Maeng, Fig.4A, par [197], "…The input unit 120 includes a camera 121 for obtaining images or video, a microphone 122, which is one type of audio input device for inputting an audio signal...");
a field-programmable gate array (FPGA) configured to extract the feature from the audio signal input through the CMOS connector and convert the extracted feature into the pattern vector (Fig.6, par [308], "…The AI processor 21 can learn a neural network using programs stored in the memory 25..."; par [311], "…the AI processor 21 may include a data learning unit 22 that learns a neural network for data classification/recognition..."); and
a pattern recognition unit configured to store the pattern vector converted by the FPGA in the neuron library, recognize the pattern of the pattern vector using the sound recognition model generated through the library (paras [315-316], "…The model learning unit 24 can perform learning such that a neural network model has a determination reference about how to classify predetermined data, using the acquired learning data…When a neural network model is learned, the model learning unit 24 can store the learned neural network model in the memory..."),
Jeong further discloses teaching, and transmit a recognition result to the FPGA, and wherein the FPGA is configured to standardize the pattern recognized by the pattern recognition unit, makes the global decision on the pattern, and outputs sound detection signal based on the global decision (Jeong, par [057], "…the neural network may be provided to extract feature values through a plurality of convolutional filtering and pooling...The neural network may receive the above training data and perform learning to match sound signals based on an image signal having attribute information..."; par [090], "…A plurality of input sound information may be classified as hidden (hidden layer) according to the sound type..."; par [093], "…Output (output layer) calculates one output value by comparing sound information input from the input layer with sound information from the hidden layer...the output value by the output layer is a sound formed by a direction indicator, and a sound determination value may be calculated..." ; par [097], "…The signal generation unit 430 may finally output the first warning signal formed by the comparison determination unit 420...").
Regarding Claim 5,
Jeong discloses An artificial intelligence (AI)-based sound recognition camera (Jeong, Figs. 1 and 2, pars [033], "…FIGS. 1 and 2, the apparatus 10 for recognizing a location of a moving object using a sound signal includes a camera 100, a microphone 200, a speaker 500..."; par [043], "…the processor 400 may include an image analysis unit 410, a comparison determination unit 420, and a signal generation unit 430…"; par [055], "…The processor 400 of the present embodiment may perform learning through deep learning...") comprising:
an image input unit configured to receive an image acquired through a camera, and transmit the recognition image to the sound recognition module (par [028], "…FIGS. 1 and 2, the apparatus 10 for recognizing a location of a moving object using a sound signal includes a camera 100, a microphone 200, a speaker 500..."; par [034], "…The camera 100 is located in front of the apparatus 10 for recognizing the position of the moving object using a sound signal, and may photograph the front of the moving object...");
a control unit configured to control warning and display based on the sound detection signal output from the sound recognition module (par [042], "…The processor 400 may compare a sound determination value formed based on sound information input through the microphone 200 and a path determination value formed based on a front image of a moving object photographed through the camera 100..."; par [047], "…The comparison determination unit 420 compares the path determination value output by analyzing the moving path by the image analysis unit 410 and the sound determination value to determine whether they match..."; par [050], "…The signal generator 430 may generate a first warning signal by determining whether to generate the first warning signal based on whether the comparison determination unit 420 matches..."); and
a warning and display device configured to perform the warning and display based on sound detection according to a warning and display control signal generated by the control unit, wherein the sound recognition module is configured to recognize the recognition image converted by the image input unit and output an image detection signal based on the recognized recognition image, wherein the control unit is configured to control the warning and display based on the image detection signal, and wherein the warning and display device is configured to perform the warning and display based on image detection (par [058], "…The processor 400 inputs the sound signal acquired through the microphone to the neural network, compares the sound determination value acquired through the neural network with the path determination value acquired for the currently acquired image signal, and if the comparison result is inconsistent, through the speaker, the first warning sound may be provided to the driver or the user. This may be done by the comparison determination unit 420 and the signal generation unit 430 of the processor 400...").
Jeong does not explicitly disclose the limitations regarding the noise reduction and voice recognition using a beamforming technique, preprocessing the received image, and display devices. However, Maeng, in the analogous field of endeavor, discloses the sound recognition module including a noise removal unit configured to remove a noise waveform from a sound input through a microphone based on direction information, output a noise-removed signal (Maeng, Fig.7, par [333], "…the electronic device 100 may include a plurality of microphones and may adopt a beamforming technique to allow a beam to be formed in a specific direction using the plurality of microphones...noise signals may be removed by subtracting the audio signal received via the first beam from the audio signals received via the first beam and the second beam..."; par [334], "…an adaptive digital filter-applied beamforming method may apply..."), and
a first sound recognition unit configured to recognize a sound from the noise-removed signal and output a recognized audio signal (Maeng, par [335], "…the processor 180 may determine whether the sound contains a human speech..."), and
a second sound recognition unit configured to process the recognized audio signal output from the first sound recognition unit through a neuron artificial neural network to output a sound detection signal (Maeng, par [0341], "…The processor 180 may perform beamforming of the microphone based on the speaker's direction to thereby execute an audio zoom function (S750)..."; par [342], "…the weight used to perform the beamforming operation may be determined by a DNN model...");
Maeng discloses preprocess the received image, convert the preprocessed image into a recognition image (Maeng, paras [318-319], "…The learning data preprocessor can preprocess acquired data such that the acquired data can be used in learning for situation determination..."; "…the learning data preprocessed by the preprocessor. The selected learning data can be provided to the model learning unit 24..."),
Maeng discloses a warning and display device configured to performed display based on sound detection according to a warning and display control signal generated by the control unit (Maeng, Fig.4: Display unit 151, paras [200-201], "…The display unit 151 may have an inter-layered structure or an integrated structure with a touch sensor..."; Fig.14, par [381], "…upon recognizing the speaker's direction, the processor 180 may display guide information for setting the beamforming direction of microphone on the display..."),
Lopatka, in the analogous field of endeavor, discloses wherein the sound signal input through the microphone is generated by a sound of an object approaching the AI-based sound recognition module, and the noise removal unit is configured to filter out ambient noise sound signals from the sound signal (Lopatka, Figs.1-3, par [016], "…a detection and tracking system 130...The microphone array 120 is configured to receive acoustic signals that are present in the operating environment of the autonomous vehicle 110 and provides an audio channel that can be used for beamforming by the detection and tracking system 130…", "…The detection and tracking system 130 is also configured to determine a direction of motion of the acoustic source relative to the autonomous vehicle 110, for example, whether the emergency vehicle is approaching or receding from the autonomous vehicle..."; par [017], "…The beamforming employs time-frequency masks to reduce noise in the beam signal spectra…The method also includes applying a deep neural network (DNN) classifier to detect an acoustic event, associated with the acoustic source...", "…estimating a direction of motion of the acoustic source relative to the array of microphones based on a Doppler effect frequency shift of the acoustic event...").
Rationale for combination is similar to that provided for Claim 1.
Claim 7 is an apparatus claim with limitations similar to the limitations of Claim 4 and is rejected under similar rationale. Additionally,
Jeong discloses a complementary metal-oxide-semiconductor (CMOS) connector configured to receive an image signal (Jeong, par [028], "…FIGS. 1 and 2, the apparatus 10 for recognizing a location of a moving object using a sound signal includes a camera 100, a microphone 200, a speaker 500..."; par [034], "…The camera 100 is located in front of the apparatus 10 for recognizing the position of the moving object using a sound signal, and may photograph the front of the moving object...");
Maeng discloses preprocess the image signal, and recognize the image signal using an image recognition model (Maeng, paras [318-319], "…The learning data preprocessor can preprocess acquired data such that the acquired data can be used in learning for situation determination..."; "…the learning data preprocessed by the preprocessor. The selected learning data can be provided to the model learning unit 24...");
outputs an image recognition result obtained through the recognition of the image signal using the image recognition model (Maeng, par [338], "…The processor 180 may recognize the person's face by applying the input image to a pre-trained neural network model..."; par [353], "…the processor 180 may extract features necessary for recognition from the captured image…."; par [349], "…The processor 180 may apply a plurality of video frames as inputs to the artificial neural network (ANN) and perform control to perform image recognition (facial recognition) via the ANN...").
Rationale for combination is similar to that provided for Claim 1.
Claims 2-3 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Jeong in view of Maeng further in view of Lopatka further in view of Nesta et al., (US Pub No. 2019/0355373, hereinafter, Nesta).
Regarding Claim 2,
Jeong in view of Maeng further in view of Lopatka discloses the AI-based sound recognition module of claim 1, wherein the microphone includes a plurality of microphones, and the noise removal unit includes:
Maeng further discloses a plurality of microphones spaced apart from each other at a predetermined interval to determine directionality sounds input to the plurality of microphones (Maeng, Fig.7, par [333], "…the electronic device 100 may include a plurality of microphones and may adopt a beamforming technique to allow a beam to be formed in a specific direction using the plurality of microphones...");
a spatial filter coefficient calculation unit configured to calculate a spatial filter coefficient using the filtered averages of the coherences to output the calculated spatial filter coefficient (par [334], "…an adaptive digital filter-applied beamforming method may apply"); and
a beamforming performance unit configured to performed beamforming on an input signal using the spatial filter coefficient to output a noise-processed signal (par [333], "…noise signals may be removed by subtracting the audio signal received via the first beam from the audio signals received via the first beam and the second beam...").
But, Jeong nor Maeng explicitly discloses coherence function calculation in association with the spatial filter of the beamformer in a neural network-based speech recognition system.
However, Nesta, in the analogous field of endeavor, discloses a coherence function generation unit configured to calculate coherences of the sounds according to intervals between each of the plurality of microphones (Nesta, Fig.1, par [007], "… methods and systems for detecting, tracking and/or enhancing a target audio source, such as human speech, in a noisy audio signal..."; par [027], "…Systems and methods configured to track a plurality of concurrent sources in 360° by using a microphone array ( e.g., with 3 or 4 microphones)..."; par [028], "…FIG. 1 illustrates an audio processing device 100...the audio sensor array 105 includes a plurality of microphones 105a-105n, each generating one audio channel of a multi-channel audio signal..."; Fig.3: target activity detection system 300, par [050], "…define an acoustic map capturing the dominant direction of propagations...a multidimensional spatial coherence likelihood function is defined...and define the spatial coherence function"),
calculate averages of the coherences for each identical distance , filter the calculated averages of the coherences, and output the filtered averages of the coherences (Fig.5, par [052], "…Next, the dominant source direction indexes are computed from the first Nb maxima of the average spatial coherence function..."; paras [053-056], "…The source tracker 320 tracks the most dominant N source locations by assuming that each source does not occupy the same angular region…", "…Trackerj (l). lik: average spatial coherence likelihood value..."; par [060], "…a voice activity detector may be implemented based on spectral features and/or use a Neural-Networks..."; par [068], "…with G(k)=[Gi(k), ... , G,v(k)]r represent the multichannel spatial filter…");
Therefore, it would have been obvious to one of ordinary skill in the art, before effective filing date of the claimed invention, to have modified an DNN-based audio detection and tracking system with beamforming of the multiple microphone enhanced by the image recognition from captured image taught in combination of Jeong, Maeng, and Lopatka with the beamforming technique with multidimensional coherence function of Nesta with a reasonable expectation of success to construct appropriate spatial filters from determined coherent noise source and to improve detection, tracking and/or enhancement of audio from audio targets in noisy environments with little supervision (Nesta, paras [003-006]).
Regarding Claim 3,
Jeong in view of Maeng further in view of Lopatka further in view of Nesta discloses the AI-based sound recognition module of claim 2, wherein the coherence function generation unit includes:
Nesta further discloses a coherence calculation unit configured to calculate the coherences of the input signal according to the intervals between each of the plurality of microphones in a noise period, , and output the calculated coherences (Nesta, Fig.1, par [007], "… methods and systems for detecting, tracking and/or enhancing a target audio source, such as human speech, in a noisy audio signal..."; par [027], "…Systems and methods configured to track a plurality of concurrent sources in 360° by using a microphone array ( e.g., with 3 or 4 microphones)..."; par [028], "…FIG. 1 illustrates an audio processing device 100...the audio sensor array 105 includes a plurality of microphones 105a-105n, each generating one audio channel of a multi-channel audio signal..."; Fig.3: target activity detection system 300, par [050], "…define an acoustic map capturing the dominant direction of propagations...a multidimensional spatial coherence likelihood function is defined...and define the spatial coherence function");
a coherence average calculation unit configured to calculate average values of the coherences input from the coherence calculation unit for each identical distance, and output the calculated average values of the coherences (Fig.5, par [052], "…Next, the dominant source direction indexes are computed from the first Nb maxima of the average spatial coherence function..."; paras [053-056], "…The source tracker 320 tracks the most dominant N source locations by assuming that each source does not occupy the same angular region…", "…Trackerj (l). lik: average spatial coherence likelihood value..."); and
a filter unit configured to filter the average values of the coherences to smooth out a rapid change according to a frequency, and output the filtered average values of the coherences (Fig.5, par [060], "…a voice activity detector may be implemented based on spectral features and/or use a Neural-Networks..."; par [068], "…with G(k)=[Gi(k), ... , G,v(k)]r represent the multichannel spatial filter…").
Claim 6 is an apparatus claim with limitations similar to the limitations of Claim 2 and is rejected under similar rationale. Additionally,
Jeong discloses the sound recognition camera (Jeong, Figs. 1 and 2, pars [033], "…FIGS. 1 and 2, the apparatus 10 for recognizing a location of a moving object using a sound signal includes a camera 100, a microphone 200, a speaker 500..."; par [043], "…the processor 400 may include an image analysis unit 410, a comparison determination unit 420, and a signal generation unit 430…")
Rationale for combination is similar to that provided for Claim 2.
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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/JANGWOEN LEE/Examiner, Art Unit 2656
/BHAVESH M MEHTA/Supervisory Patent Examiner, Art Unit 2656
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