DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments filed on 12/18/2025 are being considered by the examiner. No amendments are made.
Regarding the 35 U.S.C 101 rejections examiners note as follows.
Applicant asserts:
“First, the claims include elements that cannot practically be performed in the
human mind. A claim does not recite a judicial exception, an abstract idea, under Prong One of
Step 2A, if it is merely based on or involves an abstract idea. See M.P.E.P. § 2106.04(a)(l). For
mental processes, "[c]laims do not recite a mental process when they do not contain limitations
that can practically be performed in the human mind, for instance when the human mind is not
equipped to perform the claim limitations." M.P.E.P. § 2106.04(a)(2)(11l)(A).” (Remark page 8, lines 8-13)
Examiner Note: Examiner respectfully disagree with applicant’s assertion. Not all of the limitations were interpreted under mental processes as asserted by the applicant. Please see each of the claim limitation below.
Obtaining audio data is just a data collection which is pre solution activity. Human mental process can think if there is any inter-spike frequency in the audio signal/spectrogram which can be done after collecting audio data. Here is each of the claim 1 limitation:
“obtaining audio data encoding sound comprising at least one frequency;” - This limitation is collecting data which is pre-solution activity.
“calculating a calculated frequency based at least on a value associated with any of the at least one frequency that is within a frequency band;” - This is math algorithm. Please see MPEP at § 210.6.04(a)(2)(1) (C.)(vi)
“determining, based on the calculated frequency, the sound comprises at least one of a presence of undesirable sound or an absence of desirable sound; and” - mental processes that can be performed by a human.
“removing at least a portion of the sound encoded in the audio data corresponding to the at least one of the presence of undesirable sound or the absence of desirable sound from a stream of audio data.” - mental processes of a human
Human can mentally visualize sound frequency distribution/spectral distribution from prior knowledge, also human can mentally think if that distribution has any frequency missing or noise or any outlier present or absent which needs to be removed to get clean speech/sound.
Applicant further asserts:
(“For example, the human mind is not practically equipped to perform the
following operations: "calculating a calculated frequency based at least on a value associated
with any of the at least one frequency that is within a frequency band," "determining, based on
the calculated frequency, the sound comprises at least one of a presence of undesirable sound or an absence of desirable sound," or "removing at least a portion of the sound encoded in the audio data corresponding to the at least one of the presence of undesirable sound or the absence of desirable sound from a stream of audio data." That is, the Office has not articulated any coherent analysis as to how a human mind can 1) calculate a calculated frequency based on a value associated with a singular frequency within a frequency band, 2) use that calculate frequency to determine presence of undesirable sound or an absence of desirable sound, and 3) remove a portion of sound encoded in audio data that includes an undesirable sound or the absence of a desirable sound from a stream of audio data.”) (Remark page 8, lines 14-25)
Examiner Notes: Examiner respectfully disagree with applicant’s assertion. Not all of the limitations were interpreted under mental processes as asserted by the applicant. Please see each of the claim limitation below and previous office action.
“obtaining audio data encoding sound comprising at least one frequency;” - This limitation is collecting data which is pre-solution activity.
“calculating a calculated frequency based at least on a value associated with any of the at least one frequency that is within a frequency band;” - This is math algorithm. Please see MPEP at § 210.6.04(a)(2)(1) (C.)(vi)
“determining, based on the calculated frequency, the sound comprises at least one of a presence of undesirable sound or an absence of desirable sound; and” - mental processes that can be performed by a human.
“removing at least a portion of the sound encoded in the audio data corresponding to the at least one of the presence of undesirable sound or the absence of desirable sound from a stream of audio data.” - mental processes of a human
The claim clearly recites math calculation steps which are math algorithm. “determining”, and ““removing” steps in the claim can be performed in the human mind. Human Knows speech spectrogram, silence spectrogram ranges, values and histogram from prior knowledge. From human prior knowledge, human can mentally visualize frequency spectral distribution and determine/identify speech or silence in the spectrogram.
Applicant further asserts:
(“Rather, the Office improperly condenses determining the presence of undesirable
sound or an absence of desirable sound based on the calculated frequency to merely
"determining" and simply states it can be performed in the human mind without any reason or
rationale as to how that could be performed in the human mind. The Office further states that
calculating and removing are mathematical algorithms, and ends the analysis there. Applicant
respectfully submits that for the mathematical concepts grouping of abstract ideas, "[a] claim
does not recite a mathematical concept (i.e., the claim limitations do not fall within the
mathematical concept grouping), if it is only based on or involves a mathematical concept." Id at
§ 210.6.04(a)(2)(1). These claim elements are only based on a mathematical concept to be used
for removing portions of audio data that are undesirable, and do not preempt the mathematical
concept by claiming a mathematical operation untethered to a specific use case. The Office has
provided no rationale as to how the claimed calculating and removing elements of the claim
preempt or monopolize a mathematical algorithm. Consequently, the Office has not provided any supportable analysis that claim 1 recites a judicial exception under Step 2A Prong One.”) (Remark page 8-9, last para of page 8, and first para of page 9)
Examiner Notes: This is math algorithm. Please see MPEP § 210.6.04(a)(2)(1) (C.)(vi). The claim clearly mention mathematical step “… calculating a calculated frequency based at least on a value associated with any of the at least one frequency that is within a frequency band; …”.
Therefore, examiner maintains 101 rejection.
Applicant further asserts:
(“Second, claim 1 integrates any alleged abstract idea into a practical application, thereby satisfying Step 2A, Prong Two. Claim 1 recites technical features that provide a practical application of the alleged abstract idea, such as enabling a specific way to identify portions of audio data of a stream of audio data that includes an undesirable sound of the absence of a desirable sound using a calculated frequency that is determined based on a value associated with a frequency value in the audio data. As explained by M.P.E.P. § 2106.04, under Step 2A, Prong Two, claims are not directed to an abstract idea if an alleged abstract idea is integrated into a practical application. Here, the claims recite aspects that enable more efficient manipulation of audio data to remove undesirable sounds. As such, claim 1 has a practical application and should therefore not be considered to be directed to an abstract idea.”) (Remark page 9, first full para.)
Examiner Notes: The claims do not integrate the judicial exception into a practical application. The additional elements recited—generic computing components in claim 11 (processors, memory, computing device). The claims do not describe a specific improvement to the functioning of the computer or another technological improvement. They instead use generic computer implementation to carry out the abstract idea.
Regarding the 35 U.S.C 103 rejections examiners note as follows.
Applicant asserts:
(“Claims 1, 4, 9-12, 17, and 20 stand rejected under 35 U.S.C. § 103 as allegedly
being unpatentable Munoz in view of Wang, and further in view of DeBiasio. Applicant
respectfully disagrees for the reasons discussed below.
With regard to rejections under 35 U.S.C. § 103, the Examiner must provide
evidence which as a whole shows that the legal determination sought to be proved (i.e., the
reference teachings establish a prima facie case of obviousness) is more probable than not.
M.P.E.P. § 2142. Accordingly, "the key to supporting any rejection under 35 U.S.C. § 103 is the
clear articulation of the reason(s) why the claimed invention would have been obvious."
M.P.E.P. § 2142; see KSR international Co. v. Teleflex, Inc., 550 U.S. 398, 82 U.S.P.Q. 2d 1385,
1395-97 (2007).”) (Remark page 10, lines 1-10)
MUNOZ et al. US 20240031765 A1- Munoz teaches mono audio signal. Munoz even teaches unwanted audio signal can be removed partially or completely to get enhanced mono audio signal. Munoz not only teaches audio signal but also teach type of audio signal and removing partially or completely to enhance mono audio signal. Please see paragraph [0057], [0076], [0090] and [0100].
WANG, US 20160379670 A1- WANG teaches sub-band of sound signal and determining sub-band of SNR. And A sub-band SNR of each sub-band of the input audio signal is calculated. (“[0004] … comparing the energy of the audio signal on each sub-band with estimated energy of a background noise signal on each sub-band, so as to obtain a signal-to-noise ratio (SNR) of the audio signal on each sub-band; and then determining an SSNR according to a sub-band SNR of each sub-band, and comparing the SSNR with a preset VAD decision threshold, where if the SSNR exceeds the VAD decision threshold, the audio signal is an active signal, or if the SSNR does not exceed the VAD decision threshold, the audio signal is an inactive signal.”) (”[0008] Embodiments disclosed herein provide a method for detecting an audio signal and an apparatus, which can accurately distinguish between an active voice and an inactive voice.”) [0004], [0008], [0069] , [0071], and [0085]. Note: SNR is strength of the audio data.
DeBiasio et al. US 20070015485 A1 - DeBiasio teaches FIG. 4A, element 426, (“[0082] The receiver module, in turn, receives the streaming audio signal, decodes and demodulates the signal as necessary, and places the data representing the audio content into a temporary buffer (step 426), in preparation to transmit the audio signal onto the bus. …”) DeBiasio even teaches decoding incoming or outgoing signals in paragraph [0100]. In para[0062], DeBiasio teaches ADC converter to convert analog signal to digital signal, amplify signal, and digitized stereo signals are encoded using encoding techniques.
Applicant further asserts:
(“Wang describes a process for detecting speech m audio by companng a
signal-to-noise ratio of the audio signal to a voice activity threshold to determine if speech is
present. See Wang at [0069]. A neural network is then used to identify and remove noise from
portions of the audio signal that are determined to contain speech. To increase accuracy of
detection of speech, sub-band signal to noise ratios are calculated to ensure that noise in high
frequency sub-bands is not misidentified as not containing speech. See Wang at [0071].
However, Wang does not determine that the sound comprises at least one of a presence of
undesirable sound or an absence of desirable sound, as it only detects if speech, a wanted sound, is present.” (Remark page 11, 2nd paragraph)
Examiner Note: Wang teaches detecting zero crossing rate (ZCR) of the audio signal. detecting an audio signal in fig 1-4 and enhanced segmental signal-to-noise ratio (SSNR) of the audio signal, and misdetection of an active signal can be reduced. Wang further teaches LSNR of the audio signal is an average SNR or a weighted SNR within a relatively long period of time. See para [0051], [0081], [0094].
Applicant further asserts:
(“It would not have been obvious to combine Munoz, Wang, and DeBiasio to arrive
at the claimed subject matter, as it would alter the principle of operation of both Munoz and
Wang, by analyzing audio to identify the presence of undesirable sound or the absence of
desirable sound from a stream of audio data, rather than trying to identify speech, i.e., the
presence of a desirable sound. There is nothing in Munoz or Wang that indicates that the inverse could be performed using the described techniques, such as identifying the presence of
undesirable sound or the absence of desirable sound.”) (Remark page 11, last paragraph)
Examiner Note: In 103, combination examiner used Munoz, Wang and DeBiasio are obvious because, Munoz teaches mono audio signal and unwanted audio signal can be removed partially or completely to get enhanced mono audio signal. Wang relates to the field of signal processing technologies, and more specifically, to a method for detecting an audio signal and an apparatus. DeBiasio receive stream audio signal, has ADC converter and bandpass filter to eliminate certain band frequency.
Examiner used above three reference and made 103 rejection and all three references are determining or identifying sound/audio. Examiner is not convinced with applicant’s arguments.
MUNOZ et al. US 20240031765 A1- Munoz teaches mono audio signal. Munoz even teaches unwanted audio signal can be removed partially or completely to get enhanced mono audio signal. Munoz not only teaches audio signal but also teach type of audio signal and removing partially or completely to enhance mono audio signal. Please see paragraph [0057], [0076], [0090] and [0100].
Wang teaches detecting zero crossing rate (ZCR) of the audio signal. detecting an audio signal in fig 1-4 and enhanced segmental signal-to-noise ratio (SSNR) of the audio signal, and misdetection of an active signal can be reduced. Wang further teaches LSNR of the audio signal is an average SNR or a weighted SNR within a relatively long period of time and generating stereo audio signals. See para [0051], [0081], [0094], [0131].
DeBiasio receive stream audio signal, has ADC converter and bandpass filter to eliminate certain band frequency.
The rejection of claim 1 as obvious over MUNOZ et al. US 20240031765 A1 in view of WANG, US 20160379670 A1and further view of DeBiasio et al. US 20070015485 A1, is therefore maintained.
Arguments on page 12 regarding claims 11 and 17 are the same arguments as in claim 1, Please see above examiner notes for claim 1
Therefore, examiner maintained 103 rejections.
Examiner used above three reference and made 103 rejection and all three references are determining or identifying certain type of sound/audio. Examiner is not convinced with applicant’s arguments.
Applicant further asserts:
(“For example, claim 3 recites, in part, " wherein the value corresponds to an intensity value, and the calculated frequency is a mean frequency calculated based at least in part on the intensity value associated with any of the at least one frequency that is within the frequency band." Claim 13 recites similar subject matter. The Office relies on Patel to allegedly describe this claimed subject matter. See Office Action at 17. The cited sections of Patel describe techniques for processing audio in monitoring a user respiratory condition, but do not describe
techniques for removing unwanted noise from speech in audio by calculating a mean frequency
value based on intensity values of individual frequencies, as claimed.” (Remark page 13, first paragraph)
Examiner Note: : Patel teaches signal preparation processor 26066 may apply a rolling median filter to smooth outliers and normalize features. A rolling-median filter may be applied, using a window of three samples. A z-score may be utilized to normalized the feature values in para [0085]. Patel further teaches trimming the audio sample data, frequency filtering, normalization, removing background noise, intermittent spikes, other acoustic artifacts, in paragraph [0036]. Not only that Patel has noise analyzer which is used to analyze noise and eliminate those. Please see para [0077], and [0085]-[0086].
Applicant’s arguments are not persuasive. Therefore, examiner maintained 103 rejections.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claims are directed to an abstract idea without significantly more.
Regarding claims 1, 11, and 17 the limitation(s) of “obtaining”, and “determining” as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. More specifically, the mental processes of a human receiving dialog related data, collecting audio data and identifying if there is speech present or not, it is just data gathering. Also, “calculating”, and “removing” in the claim, reads on a mathematical algorithm, like finding mean, average value, cut-off frequency and zero insertion. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the --Mental Processes-- grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application because the recitation of “a processor”, in claim 11-16 and 17-20 reads to generalized computer components, based upon the claim interpretation wherein the structure is interpreted using [0023] in the specification. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using generalized computer components to obtain, calculate, determine, and remove amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
With respect to claims 2, the claim recites “adjusting sound value”, which reads on a human adjusting value on a graph where noise/silence can be modified on a piece of paper. No additional limitations are present.
With respect to claims 3 and 13, the claim recites calculating mean or average based on intensity value in frequency band/range. The claim recites mathematical algorithm. No additional limitations are present.
With respect to claims 4, comparing calculated value to a threshold which reads on a human data analyzing after math calculation. No additional limitations are present.
With respect to claims 5, 14, and 18 “determining …” which reads on a human drawing frequency graph on a sheet of paper and labelling each frequency range to different name, and determining noise or unwanted sound present or not, and figure out one frequency band is different from other. No additional limitations are present.
With respect to claims 6 the claim recites one/some frequency/frequencies on a specific frequency band are bigger than other frequency band. No additional limitations are present.
With respect to claims 7 and 16, “desirable sound corresponds to a unit of human speech …” which reads on a human mind drawing spectrogram on a sheet of paper, recognizing different range and specific range of frequency in the band are human sound frequency representation. No additional limitations are present.
With respect to claims 8, 15 and 19, the claim recites “compare …” compare each frequency band/range with threshold value after defining different frequency bands. Which reads on a human can analyze data and compare values in their mind. No additional limitations are present.
With respect to claims 9, “the audio data is generated using one or more neural networks …”), include additional elements beyond those recited with respect to independent claim 1. “ Neural networks” is well-understood, routine, and conventional computer function in view of MUNOZ et al. US 20240031765 A1, describing the additional element in such a manner as to indicate that the element is sufficiently well-known in the art. (“[0076] … For example, the signal enhancer 142 may be configured to use the neural network 152 (e.g., a GAN) to perform generative audio techniques to generate the one or more enhanced mono audio signals 143. …”) by MUNOZ et al. US 20240031765 A1.
With respect to claims 10, “presenting an audio stream …” is just post solution activity. No additional limitations are present.
With respect to claim 12, “audio signal is a streaming audio signal….obtaining the segment from the streaming audio signal” is just a user sampling and remembering audio from a conversation between two people. No additional elements are present.
These claims further do not remedy the judicial exception being integrated into a practical application and further fail to include additional elements that are sufficient to amount to significantly more than the judicial exception.
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 1, 4, 9, 10, 11, 12, 17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over MUNOZ et al. US 20240031765 A1 in view of WANG, US 20160379670 A1and further view of DeBiasio et al. US 20070015485 A1
Regarding Claim 1,MUNOZ teaches:
1. A method comprising: obtaining audio data encoding sound comprising at least one frequency; MUNOZ teaches (“[0052] In some examples, an audio signal, such as music audio, includes various frequency components. Signal enhancement in the present disclosure may refer to equalization, where balance of the frequency components is adjusted. In other examples within this disclosure, the equalization of frequency components of an audio signal may be based on using one or more generative networks.”) (“[0059] In some examples, the enhanced mono audio signal may be an equalized signal (e.g., a music signal captured by one or more microphones or decoded from encoded audio data), wherein balance of different frequency components is adjusted from the corresponding one or more input audio signals. (“[0079] … the one or more processors 190 are configured to decode encoded audio data to generate the one or more input audio signals 125, as further described with reference to FIG. 7. … the encoded audio data corresponds to audio received during a call with a second user of a second device, and the audio analyzer 140 provides the one or more stereo audio signals 149 to one or more speakers for playback to the user 101, as further described with reference to FIG. 7”) by MUNOZ et al. US 20240031765 A1
MUNOZ further teaches amplitude envelope of a signal, root mean square energy “[0131] … Audio feature extraction (not shown) may be performed on the one or more input audio signals to extract the one or more input feature values. Exemplary features include but are not limited to amplitude envelope of a signal, root mean square energy, and zero-crossing rate. The neural network 258 processes the one or more input feature values to generate one or more output feature values of the one or more stereo audio signals 149.”) by MUNOZ et al. US 20240031765 A1
MUNOZ teaches:
removing at least a portion of the sound encoded in the audio data corresponding to the at least one of the presence of undesirable sound or the absence of desirable sound from a stream of audio data. MUNOZ teaches (“[0047] Specifically, the one or more input signals may be audio signals captured by one or more microphones in a soundscape that includes a source of a primary (target) audio signal such as a speech signal, e.g., uttered by a person, and one or more sources of secondary (unwanted) audio signals, e.g., other speech signals, directional noise, diffuse noise, etc. Signal enhancement in the present disclosure may refer to at least partially removing the secondary audio signals from the input audio signals. As described above, in some examples, the secondary audio signals may be removed using one or more generative networks. “) (“[0053] Furthermore, generative audio techniques may be used to generate an enhanced mono audio signal. In some examples, the enhanced mono audio signal may be a noise suppressed speech signal (e.g., a speech signal captured by one or more microphones or decoded from encoded audio data), wherein noise has been partially or completely removed from the corresponding one or more input audio signals …”) (“[0057] In some examples, the enhanced mono audio signal may be a source separated signal (e.g., a target signal from a particular audio source captured by one or more microphones or decoded from encoded audio data), wherein unwanted audio signals have been partially or completely removed from the corresponding one or more input audio signals. As mentioned above, the source separation may involve one or more generative networks (e.g., GANs), as further described with respect to FIGS. 1A and 1F.”) (“[0076] … For example, the signal enhancer 142 may be configured to use the neural network 152 (e.g., a GAN) to perform generative audio techniques to generate the one or more enhanced mono audio signals 143. To illustrate, the signal enhancer 142 may use the neural network 152 to partially or completely remove noise for noise suppression, to adjust signal gains for audio zoom, to perform beamforming for audio focus, to perform dereverberation for removing the effects of reverberation, to partially or fully separate audio for source separation, to perform bass adjustment for increasing or decreasing bass, to perform equalization for adjusting a balance of different frequency components, or a combination thereof.”) (“[0090] … performs gain adjustment of a plurality of input audio signals …”) (“[0100] … the one or more input audio signals 125 can correspond to decoded audio data, an audio stream, …”) by MUNOZ et al. US 20240031765 A1
MUNOZ talks about amplitude envelope of a signal, root mean square energy but does not explicitly teach calculating a calculated frequency based at least on a value associated with any of the at least one frequency that is within a frequency band;
MUNOZ does not explicitly teach calculating a calculated frequency based at least on a value associated with any of the at least one frequency that is within a frequency band.
WANG teaches:
calculating a calculated frequency based at least on a value associated with any of the at least one frequency that is within a frequency band; WANG teaches (“[0069] … A sub-band SNR of each sub-band of the input audio signal is calculated, where the sub-band SNR is a ratio of energy of the sub-band to energy of background noise on the sub-band. …”) by WANG, US 20160379670 A1
based on the calculated frequency, WANG teaches (“[0069] … A sub-band SNR of each sub-band of the input audio signal is calculated, where the sub-band SNR is a ratio of energy of the sub-band to energy of background noise on the sub-band. The energy of the background noise on the sub-band generally is an estimated value obtained by estimation by a background noise estimator. …”) (“[0071] 201. Determine a sub-band SNR of an input audio signal.”) (“[0085]… In conclusion, when the enhanced segmental signal-to-noise ratio (SSNR) is determined by increasing the quantity of high-frequency end sub-bands whose sub-band SNRs are greater than the first preset threshold, calculation may be performed using the following formula: …”) by WANG, US 20160379670 A1
WANG is considered to be analogous to the claimed invention because it relates to the field of signal processing technologies, and more specifically, to a method for detecting an audio signal and an apparatus.
Therefore, it would have been obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Munoz to incorporate the teachings of WANG in order to determine segmental signal-to-noise ratio (SSNR).
One could have been motivated to do so because the system can detect an audio signal, which can accurately distinguish between an active voice and an inactive voice. (”[0008] Embodiments disclosed herein provide a method for detecting an audio signal and an apparatus, which can accurately distinguish between an active voice and an inactive voice.”) by WANG, US 20160379670 A1
The combination does not explicitly teach determine unwanted signal.
DeBiasio teaches:
the sound comprises at least one of a presence of undesirable sound or an absence of desirable sound; and FIG. 4A, element 426, DeBiasio teaches the digitized stereo signals (i.e an audio stream) are encoded using, for example SBC encoding techniques. (“[0061] … the appropriate encoding protocol (such as, e.g., SBC) for use in transmitting the signal. …”) (“[0062] … Thereafter, the digitized stereo signals are encoded using, for example SBC encoding techniques. The SBC encoded digital signal may be sampled at 44.1 KHz or 48 KHz, per Bluetooth standards.”) (“[0063] … The modulated signal may then be passed through a digital-to-analog converter 216 for converting the modulated signal into the analog domain. Low pass filter 218 may be used to limit the permissible frequency spectrum of the signal, and hence reduce unwanted noise. … … Frequency synthesizer 232 may be used in conjunction with a crystal oscillator to recover the proper signal carrier frequency and reject signals having unwanted or spurious frequencies …”) (“[0064] The resulting signal is then amplified by amplifier 222, after which certain signal processing may occur relative to the signal by tuner/switch 224. Such processing may include balancing of the signal and segregation of the signal into discrete time slots, etc. The resulting signal may then pass through band-pass filter 234, where unwanted frequencies are rejected. …”) (“[0082] The receiver module, in turn, receives the streaming audio signal, decodes and demodulates the signal as necessary, …”) by DeBiasio et al. US 20070015485 A1
DeBiasio is considered to be analogous to the claimed invention because it relates to wireless devices for use with vehicles, and more particularly to a wireless media source capable of communicating with devices on a data bus of a vehicle.
Therefore, it would have been obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Munoz and WANG to incorporate the teachings of DeBiasio in order to determine noise and remove noise.
One could have been motivated to do so because the system can remove noise/unwanted signal. (”[0064] The resulting signal may then pass through band-pass filter 234, where unwanted frequencies are rejected. ...”) by DeBiasio et al. US 20070015485 A1
Claim 11 is a processor claim with limitations similar to the limitations of method Claim 1 and is rejected under similar rationale. Additionally,
Munoz further teaches:
11. A processor comprising one or more processing units to perform operations comprising: (“Abstract A device includes a processor configured to perform signal enhancement of an input audio signal to generate an enhanced mono audio signal. The processor is also configured to mix a first audio signal and a second audio signal to generate a stereo audio signal. …”) by Munoz et al. US 20240031765 A1
Claim 17 is a processor claim with limitations similar to the limitations of method Claim 1 and is rejected under similar rationale. Additionally,
Munoz further teaches:
17. A system comprising: one or more processing units . …(“Abstract A device includes a processor configured to perform signal enhancement of an input audio signal to generate an enhanced mono audio signal. The processor is also configured to mix a first audio signal and a second audio signal to generate a stereo audio signal. …”) by Munoz et al. US 20240031765 A1
Regarding Claim 4, the combination teaches the method claim 1, as identified above.
WANG further teaches:
4. The method of claim 1, wherein determining the sound comprises at least one of a presence of undesirable sound or an absence of desirable sound comprises comparing the calculated frequency to a threshold value. WANG teaches (“[0004] … comparing the energy of the audio signal on each sub-band with estimated energy of a background noise signal on each sub-band, so as to obtain a signal-to-noise ratio (SNR) of the audio signal on each sub-band; and then determining an SSNR according to a sub-band SNR of each sub-band, and comparing the SSNR with a preset VAD decision threshold, where if the SSNR exceeds the VAD decision threshold, the audio signal is an active signal, or if the SSNR does not exceed the VAD decision threshold, the audio signal is an inactive signal.”) by WANG, US 20160379670 A1
WANG is considered to be analogous to the claimed invention because it relates to the field of signal processing technologies, and more specifically, to a method for detecting an audio signal and an apparatus.
Therefore, it would have been obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Munoz to incorporate the teachings of WANG in order to determine segmental signal-to-noise ratio (SSNR).
One could have been motivated to do so because the system can detect an audio signal, which can accurately distinguish between an active voice and an inactive voice. (”[0008] Embodiments disclosed herein provide a method for detecting an audio signal and an apparatus, which can accurately distinguish between an active voice and an inactive voice.”) by WANG, US 20160379670 A1
Regarding Claim 9, the combination teaches the method claim 1, as identified above.
Munoz further teaches:
9. The method of claim 1, wherein: the audio data is generated using one or more neural networks, the audio data comprising a time-frequency representation of an audio signal. Munoz teaches (“[0140] In FIG. 2C, ellipses between nodes of the input layer 270 indicate that while four nodes are illustrated (corresponding to one node per signal that is to be input to the neural network 258), the input layer 270 may include more than four nodes. For example, one or more of the audio signals 143A, 143B, 167, or 125 may be encoded into a multibit feature vector for input to the neural network 258. As an example, the background audio signal 167 may be sampled to generate time-windowed samples.”) (“[0141] Each time-windowed sample may be transformed to a frequency domain signal. Each frequency domain signal may be encoded as an N-bit, e.g., 16-bit, feature vector with N being a non-zero power of two.”) by Munoz et al. US 20240031765 A1
Regarding Claim 10, the combination teaches the method claim 1, as identified above.
Munoz further teaches
10. The method of claim 1, further comprising: presenting an audio stream that excludes at least a portion of the sound encoded in the audio data. Munoz teaches (“[0079] … the encoded audio data corresponds to audio received during a call with a second user of a second device, and the audio analyzer 140 provides the one or more stereo audio signals 149 to one or more speakers for playback to the user 101, as further described with reference to FIG. 7.”) (“[0221] As described with reference to FIG. 1A, the audio analyzer 140 processes the one or more input audio signals 125 based on the image data 127, the location data 163, or both, to generate the one or more stereo audio signals 149. The audio analyzer 140 provides the one or more stereo audio signals 149 to one or more speakers 722 to output sounds 785. The one or more stereo audio signals 149 (e.g., the sounds 785) include less noise than the one or more input audio signals 125 and more audio context than the one or more enhanced mono audio signals 143, as described with reference to FIG. 1A.”) by Munoz et al. US 20240031765 A1.
Regarding Claim 12, the combination teaches claim 11 as identified above.
Wang further teaches:
12. The processor of claim 11, the operations further comprise: obtaining the segment from the streaming audio signal. Wang teaches (“[0104] … In this way, the 20 sub-bands that are originally obtained through division are re-divided into 24 sub-bands. …”) (“[0112] Further, the determining an input audio signal as a to-be-determined audio signal includes: determining the audio signal as a to-be-determined audio signal according to a sub-band SNR of the audio signal.”) by WANG, US 20160379670 A1
WANG is considered to be analogous to the claimed invention because it relates to the field of signal processing technologies, and more specifically, to a method for detecting an audio signal and an apparatus.
Therefore, it would have been obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Munoz to incorporate the teachings of WANG in order to determine segmental signal-to-noise ratio (SSNR).
One could have been motivated to do so because the system can detect an audio signal, which can accurately distinguish between an active voice and an inactive voice. (”[0008] Embodiments disclosed herein provide a method for detecting an audio signal and an apparatus, which can accurately distinguish between an active voice and an inactive voice.”) by WANG, US 20160379670 A1
wherein the audio signal is a streaming audio signal, and 12FIG. 4A, element 426, DeBiasio teaches (“[0082] The receiver module, in turn, receives the streaming audio signal, decodes and demodulates the signal as necessary, …”) by DeBiasio et al. US 20070015485 A1
DeBiasio is considered to be analogous to the claimed invention because it relates to wireless devices for use with vehicles, and more particularly to a wireless media source capable of communicating with devices on a data bus of a vehicle.
Therefore, it would have been obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Munoz and WANG to incorporate the teachings of DeBiasio in order to determine noise and remove noise.
One could have been motivated to do so because the system can remove noise/unwanted signal. (”[0064] The resulting signal may then pass through band-pass filter 234, where unwanted frequencies are rejected. ...”) by DeBiasio et al. US 20070015485 A1
Regarding Claim 20, MUNOZ further teaches:
20. The system of claim 17, wherein the system is comprised in at least one of:
a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine;
an in-vehicle infotainment system for an autonomous or semi-autonomous machine;
a first system for performing simulation operations; a second system for performing digital twin operations; a third system for performing light transport simulation; a fourth system for performing collaborative content creation for 3D assets; a fifth system for performing deep learning operations; a sixth system implemented using an edge device; a seventh system implemented using a robot; an eighth system for performing conversational Artificial Intelligence operations; a nineth system for generating synthetic data; a tenth system incorporating one or more virtual machines (VMs); an eleventh system implemented at least partially in a data center; or a twelfth system implemented at least partially using cloud computing resources. MUNOZ teaches (“ [0038] FIG. 13 is a diagram of a voice-controlled speaker system operable to perform audio signal enhancement, in accordance with some examples of the present disclosure.”) (“[0049] One way to perform an audio zoom operation is to perform a beamforming operation that includes generating a virtual audio beam formed by two or more microphones in the direction of the primary (target) audio signal and/or a null beam in the direction of the secondary (unwanted) audio signals. Thus, signal enhancement in the present disclosure may also refer to at least performing an audio zoom operation. As described above, in some examples, the zoom operations to increase perceptibility of the target signal may be based on using one or more generative networks.”) (“[0060] Systems and methods of audio signal enhancement are disclosed.”) by MUNOZ et al. US 20240031765 A1
Claim 2, is/are rejected under 35 U.S.C. 103 as being unpatentable over Munoz, Wang and DeBiasio in view of. Song et al. US 20180075859 A1
Regarding Claim 2, the combination teaches method claim 1 as identified above.
The combination does not explicitly teach adjusting a sound value associated with at least a portion of the at least one frequency after determining the sound comprises at least one the presence of undesirable sound or the absence of desirable sound.
Song teaches:
2. The method of claim 1, further comprising: adjusting a sound value associated with at least a portion of the at least one frequency after determining the sound comprises at least one the presence of undesirable sound or the absence of desirable sound Song teaches (“[0006] A voice activity detector (VAD) is derived from a probability of speech presence (SPP) for every frequency analyzed. As a second step, the noise model created in the first step is updated at the audio signal's frame rate, if voice activity detection (VAD) permits.”) (“[0033] The noisy signal, X, is processed using conventional prior art noise detection steps 304 but the noisy signal, X, is also processed by new steps 305 that essentially determine whether a noise should also be suppressed by analyzing the similarity metric or a “distance” between a higher order LPC and a lower order LPC, as well as comparing the LPC content of the noisy signal X, to the linear predictive coefficients (LPCs) of the noise model, that are created and updated on the fly. Signal X is classified as either noise or speech at step 320. Referring now to the prior steps, at the step identified by reference numeral 306, noise characteristics are determined using statistical analysis. At step 308, a speech presence probability is calculated. At step 310, noise estimate in the form of power spectral density or PSD, is calculated.”) (“[0045] The second PSD noise calculator 408 updates a calculation of the noise power spectral density (PSD) responsive to the determination that the noise in the signal X, …”) (“The log spectrum distance is calculated by two set of cepstral coefficient sets derived from the higher and lower order LPC coefficient sets; A speech/noise classifier that compares the distance and its short time trajectory against a set of thresholds to determine the frame of signal being speech or noise; The thresholds used for the speech/noise classifier is updated based on the classification statistics and/or in consultation with other voice activity detection methods; generating a plurality of linear predictive coding (LPC) coefficient sets as on line created noise models at run time. each set of LPC coefficients representing a corresponding noise, Noise model is created and updated under conditions that the current frame of signal is classified as noise by conventional methods (e.g. probability of speech presence) or the LPC speech/noise classifier;a separate but parallel noise/speech classification is also put in place based on evaluating the distance of the LPC coefficients of the input signal against the noise models represented by LPC coefficients sets. ….”) by Song et al. US 20180075859 A1
Song is considered to be analogous to the claimed invention because it directed to noise estimation.
Therefore, it would have been obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Munoz, Wang and DeBiasio to incorporate the teachings of Song in order to include feature adjusting spectrogram.
One could have been motivated to do so because the system can offers widespread, affordable medical screening and real-time awareness of physical and mental states of people by profiling voice as a service (“Abstract … Speech in a motor vehicle is improved by suppressing transient, “non-stationary” noise using pattern matching. ….”) by Song et al. US 20180075859 A1
Claim 3 and 13, is/are rejected under 35 U.S.C. 103 as being unpatentable over Munoz, Wang and DeBiasio in view of Patel et al. US 20230329630 A1.
Regarding Claim 3, the combination teaches method claim 1 as identified above.
The combination does not explicitly teach the value corresponds to an intensity value, and the calculated frequency is a mean frequency calculated based at least in part on the intensity value associated with any of the at least one frequency that is within the frequency band.
Patel teaches:
3. The method of claim 1, wherein the value corresponds to an intensity value, and the calculated frequency is a mean frequency calculated based at least in part on the intensity value associated with any of the at least one frequency that is within the frequency band. Patel teaches (“[0108] … Spectral entropy indicates the entropy of a spectrum in a particular frequency band. Spectral contrast may be determined by sorting power spectrum values by intensity in a particular frequency band and computing a ratio of a highest quartile of values (peaks) to a lowest quartile of values (troughs) in the frequency band. Spectral flatness may be determined by computing the ratio of the geometric mean to the arithmetic mean of spectrum values in a given frequency band. Spectral entropy, spectral contrast, and spectral flatness each may be computed for specific frequency bands. In one embodiment, spectral entropy is determined at 1.5-2.5 kilohertz (kHz) and 1.6-3.2 kHz; spectral flatness is determined at 1.5-2.5 kHz; spectral contrast is determined at 1.6 to 3.2 kHz and 3.2-6.4 kHz. …”) by Patel et al. US 20230329630 A1
Patel is considered to be analogous to the claimed invention because it is monitoring a user's respirator, condition and provide decision support by analyzing a user's audio data. Spoken phonemes may be detected within audio data, and acoustic features may be extracted for the phonemes.
Therefore, it would have been obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Munoz, Wang and DeBiasio to incorporate the teachings of Patel in order to include intensity value associated with any of the at least one frequency that is within the frequency band.
One could have been motivated to do so because the system can determine Spectral flatness or noise. Easily determine noise by calculating mean from flatness of spectrum. (“[0108] … Spectral flatness may be determined by computing the ratio of the geometric mean to the arithmetic mean of spectrum values in a given frequency band..”) by Patel et al. US 20230329630 A1
Claim 13 is a processor claim with limitations similar to the limitations of method Claim 3 and is rejected under similar rationale.
Claim 5, 6, 7, 14, 16, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over MUNOZ, WANG and DeBiasio in view of Nomura et al. US 20110002225 A1.
Regarding Claim 5, the combination teaches method claim 1 as identified above.
The combination does not explicitly teach first calculated frequency, and second calculated frequency are within the frequency band.
Nomura teaches:
5. The method of claim 1, wherein the calculated frequency is a first calculated frequency, the frequency band is a first frequency band, determining the sound comprises at least one of the presence of undesirable sound or the absence of desirable sound comprises calculating a second calculated frequency based at least on the value associated with any of the at least one frequency that is within a second frequency band, and Nomura teaches (“[0127 … At first, the signal versus background sound ratios are grouped in the time direction and the frequency direction, or in one direction of them, an average value of the signal versus background sound ratios within each group is calculated as the average signal versus background sound ratio of the above group. Thereafter, the coefficient correction lower-limit value preset responding to the average signal versus background sound ratio is calculated. In addition, the grouping in the frequency direction may be fitted to an auditory feature of a human being in such a manner that a small number of the signal versus background sound ratios are grouped in a low-frequency band and a large number of the signal versus background sound ratios are grouped in a high-frequency band. This grouping may be preset in some cases and may be calculated responding to the suppression coefficient or the signal versus background sound ratio in some cases.”) (“[0134] In addition, a scheme of, after grouping the background sound in the time direction and the frequency direction, or in one direction of them, and calculating an average value of the background sound within each group as an average background sound of the above group, calculating the coefficient correction lower-limit value preset according to the average background sound may be employed. The grouping in the frequency direction can be fitted to an auditory feature of a human being in such a manner that a small number of the background sounds are grouped in a low-frequency band and a large number of the background sounds are grouped in a high-frequency band. This grouping may be preset in some cases, and may be calculated responding to the suppression coefficient, the signal versus background sound ratio, and the background sound in some cases.”) (“[0135] Further, the analysis information explained above may be calculated as analysis information common to a plurality of the frequency bands. For example, the transmission unit 10 may divide the frequency band at an equal interval, and calculate the analysis information for each divided frequency band. In addition, the transmission unit 10 may calculate the analysis information to an auditory feature of a human being. That is, the transmission unit 10 may divide the low-frequency band at a fine interval and the high-frequency band at a coarse interval, and calculate the analysis information in a divided unit. This enables the information quantity of the analysis information to be curtailed.”) by Nomura et al. US 20110002225 A1 the second frequency band is different from the first frequency band. Nomura teaches low-frequency band (i.e. first frequency band ) and high-frequency band (i.e. second frequency band). (“[0135] Further, the analysis information explained above may be calculated as analysis information common to a plurality of the frequency bands. For example, the transmission unit 10 may divide the frequency band at an equal interval, and calculate the analysis information for each divided frequency band. In addition, the transmission unit 10 may calculate the analysis information to an auditory feature of a human being. That is, the transmission unit 10 may divide the low-frequency band at a fine interval and the high-frequency band at a coarse interval, and calculate the analysis information in a divided unit. This enables the information quantity of the analysis information to be curtailed.”) by Nomura et al. US 20110002225 A1
Nomura is considered to be analogous to the claimed invention because it relates to a method of a signal analysis and a signal control for controlling an input signal, which is configured of a plurality of sound sources, for each component element being included in the signal.
Therefore, it would have been obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Munoz, Wang and DeBiasio to incorporate the teachings of Nomura in order to include First and second frequency band and their calculated value.
One could have been motivated to do so because this enables the information quantity of the analysis information to be curtailed. (“[0135] … That is, the transmission unit 10 may divide the low-frequency band at a fine interval and the high-frequency band at a coarse interval, and calculate the analysis information in a divided unit. This enables the information quantity of the analysis information to be curtailed. by Nomura et al. US 20110002225 A1
Claim 14 is a processor claim with limitations similar to the limitations of method Claim 5 and is rejected under similar rationale.
Claim 18 is a system claim with limitations similar to the limitations of method Claim 5 and is
rejected under similar rationale.
Regarding Claim 6, the combination teaches method claim 5 as identified above.
Nomura further teaches:
6. The method of claim 5, wherein the second frequency band comprises one or more frequencies greater than the first frequency band. Nomura teaches low-frequency band (i.e. first frequency band ) and high-frequency band (i.e. second frequency band). (“[0135] Further, the analysis information explained above may be calculated as analysis information common to a plurality of the frequency bands. For example, the transmission unit 10 may divide the frequency band at an equal interval, and calculate the analysis information for each divided frequency band. In addition, the transmission unit 10 may calculate the analysis information to an auditory feature of a human being. That is, the transmission unit 10 may divide the low-frequency band at a fine interval and the high-frequency band at a coarse interval, and calculate the analysis information in a divided unit. This enables the information quantity of the analysis information to be curtailed.”) by Nomura et al. US 20110002225 A1
Nomura is considered to be analogous to the claimed invention because it relates to a method of a signal analysis and a signal control for controlling an input signal, which is configured of a plurality of sound sources, for each component element being included in the signal.
Therefore, it would have been obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Munoz, Wang and DeBiasio to incorporate the teachings of Nomura in order to include First and second frequency band and their calculated value.
One could have been motivated to do so because this enables the information quantity of the analysis information to be curtailed. (“[0135] … That is, the transmission unit 10 may divide the low-frequency band at a fine interval and the high-frequency band at a coarse interval, and calculate the analysis information in a divided unit. This enables the information quantity of the analysis information to be curtailed. by Nomura et al. US 20110002225 A1
Claim 14 is a processor claim with limitations similar to the limitations of method Claim 5 & Claim 6 and is rejected under similar rationale.
Regarding Claim 7, the combination teaches method claim 6 as identified above.
Nomura further teaches :
7. The method of claim 6, wherein the desirable sound corresponds to a unit of human speech, the first frequency band corresponds to a first portion of the unit of human speech, and the second frequency band corresponds to a different second portion of the unit of human speech. Nomura teaches (“[0135] Further, the analysis information explained above may be calculated as analysis information common to a plurality of the frequency bands. For example, the transmission unit 10 may divide the frequency band at an equal interval, and calculate the analysis information for each divided frequency band. In addition, the transmission unit 10 may calculate the analysis information to an auditory feature of a human being. That is, the transmission unit 10 may divide the low-frequency band at a fine interval and the high-frequency band at a coarse interval, and calculate the analysis information in a divided unit. This enables the information quantity of the analysis information to be curtailed.”) by Nomura et al. US 20110002225 A1
Nomura is considered to be analogous to the claimed invention because it relates to a method of a signal analysis and a signal control for controlling an input signal, which is configured of a plurality of sound sources, for each component element being included in the signal.
Therefore, it would have been obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Munoz, Wang and DeBiasio to incorporate the teachings of Nomura in order to include First and second frequency band and their calculated value.
One could have been motivated to do so because this enables the information quantity of the analysis information to be curtailed. (“[0135] … That is, the transmission unit 10 may divide the low-frequency band at a fine interval and the high-frequency band at a coarse interval, and calculate the analysis information in a divided unit. This enables the information quantity of the analysis information to be curtailed. by Nomura et al. US 20110002225 A1
Claim 16 is a processor claim with limitations similar to the limitations of method Claim 7 and is rejected under similar rationale.
Claim 8 and 15, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over MUNOZ, WANG, DeBiasio and Nomura in view of Bonnet et al. US 20210236339 A1
Regarding Claim 8, the combination teaches the method claim 5.
WANG further teaches:
8. The method of claim 5, wherein determining the sound comprises at least one of a presence of undesirable sound or an absence of desirable sound comprises: comparing the first calculated frequency to a first . WANG teaches (“[0004] … comparing the energy of the audio signal on each sub-band with estimated energy of a background noise signal on each sub-band, so as to obtain a signal-to-noise ratio (SNR) of the audio signal on each sub-band; and then determining an SSNR according to a sub-band SNR of each sub-band, and comparing the SSNR with a preset VAD decision threshold, where if the SSNR exceeds the VAD decision threshold, the audio signal is an active signal, or if the SSNR does not exceed the VAD decision threshold, the audio signal is an inactive signal.”) by WANG, US 20160379670 A1
WANG is considered to be analogous to the claimed invention because it relates to the field of signal processing technologies, and more specifically, to a method for detecting an audio signal and an apparatus.
Therefore, it would have been obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Munoz, Wang and DeBiasio, Nomura to incorporate the teachings of WANG in order to determine segmental signal-to-noise ratio (SSNR).
One could have been motivated to do so because the system can detect an audio signal, which can accurately distinguish between an active voice and an inactive voice. (”[0008] Embodiments disclosed herein provide a method for detecting an audio signal and an apparatus, which can accurately distinguish between an active voice and an inactive voice.”) by WANG, US 20160379670 A1
The combination does not explicitly teach compare threshold to frequency.
Bonnet teaches :
Compare frequency to threshold Bonnet teaches (“[0117] To be consistent with Eq. (2), the in-ear SPLs and threshold value L.sub.th are compared within the same frequency range used to calculate the mean coherence function Δ(f.sub.min<f<f.sub.max). Whenever L.sub.i>L.sub.th, any detected WID is considered as “high-level” (i.e. having a significant impact on L.sub.OEM,i(f)), which implies that method (2b) should be used rather than method (1). Finally, method (2b) requires a prior knowledge of L.sub.tmp(f) and NR.sub.tmp(f), which implies that the “Δ<Δ.sub.th” criterion should be met beforehand. When WIDs are detected (Δ>Δ.sub.th) and the variables L.sub.tmp(f) and NR.sub.tmp (f) are not yet initialized, L.sub.IEM,i*(f) can be estimated using the following expression:”) by Bonnet et al. US 20210236339 A1
Bonnet is considered to be analogous to the claimed invention because it relates to the field of noise exposure measurement.
Therefore, it would have been obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Munoz, Wang and DeBiasio, Nomura to incorporate the teachings of Bonnet in order to determine compare frequency with threshold.
One could have been motivated to do so because the system can detect noise accurately. (”[0007] Several have developed systems that continuously monitor an individual's noise exposure under the HPD. But to accurately determine if an individual is properly protected against noise, the influence of self-induced sounds on in-ear noise dosimeter measurements needs to be considered, since the SPLs measured below HPDs may be significantly affected by noise emitted by the wearer. …”) by Bonnet et al. US 20210236339 A1
Claim 15 is a processor claim with limitations similar to the limitations of method Claim 8 and is rejected under similar rationale.
Claim 19 is a system claim with limitations similar to the limitations of method Claim 8 and is rejected under similar rationale.
Conclusion
THIS ACTION IS MADE FINAL. 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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/F.H.S./Examiner, Art Unit 2653
/Paras D Shah/Supervisory Patent Examiner, Art Unit 2653
04/17/2026
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