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 .
DETAILED ACTION
This is the initial office action that has been issued in response to patent application, 18/437,463, filed on 02/09/2024. Claims 1-15 are currently pending and have been considered below. Claims 1, 13-14 are independent claims.
Priority
The application claims foreign priority of Europe 23163788.5, filed on 03/23/2023.
Drawings
The drawings filed on 02/09/2024 are accepted by the examiner.
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.
Claims 1-3, 8, 10-14 are rejected under 35 U.S.C. 103 as being unpatentable over Du(US Publication No. 20210125628 A1) in view of Kirovski(US Publication No. 20050055214 A)in further view of Wentz(US Patent No. 11379263 B2)
Regarding Claim 1:
Du discloses:
A method of providing a signed bitstream, where an audio signal is captured and encoded as a bitstream the bitstream having a sequence of data units representing time segments of the audio signal, the method comprising(DU, [0024]… the device for audio recognition according to the present disclosure calculate, based on audio variation trends among frames and within each of the frames of audio data to be recognized, to obtain a characteristic value of each frame of the audio data to be recognized, and then match the characteristic value of each frame with a pre-established audio characteristic value comparison table to obtain a recognition result. A comparative relationship between frames and within a frame is used to obtain a relatively stable encoding result)):
Du does not disclose:
assigning a score to each data unit
and resetting the reference point, wherein the score assigned to a data unit is based on at least one of
a) a detected content of the time segment of the audio signal corresponding to the data unit b) contextual information which relates the time segment to a history of the audio signal, wherein the assigned score includes a positive contribution corresponding to the time segment's deviation from a model of the history of the audio signal, or c) information relating to the conditions of capturing the time segment
Kirovski discloses:
assigning a score to each data unit(Kirovski [0048], each insertion operation modifies its magnitude frequency components XMAG(k) with the strong watermark value w(k) if the magnitude frequency component exceeds the hearing threshold z(k) and alternatively,);
and resetting the reference point, wherein the score assigned to a data unit is based on at least one of(Kirovski, [0093], the watermark is present and a decision flag D is set to one (steps 174 and 176). Otherwise, the watermark is not present and the decision flag D is reset to zero (step 178). The watermark detector 130 writes the decision value D and the process concludes (steps 180 and 182).):
a) a detected content of the time segment of the audio signal corresponding to the data unit b) contextual information which relates the time segment to a history of the audio signal, wherein the assigned score includes a positive contribution corresponding to the time segment's deviation from a model of the history of the audio signal, or c) information relating to the conditions of capturing the time segment(Kirovski, [0021], audio watermarking technology for inserting and detecting strong and weak watermarks in audio signals. The strong watermark identifies the content producer, [0070], Since the watermark values w(i) have zero mean, the numerator in Equation (3) will be a sum of negative and positive values, whereas the denominator will be equal to Q2 times the number of indices in the set I. Therefore, for a large K, the measure NC0 will be a random variable with an approximately normal (Gaussian) probability distribution, with an expected value of zero and a variance much smaller than one.),
monitoring an accumulated score, which is a sum of the scores assigned to all preceding data units back to a reference point in the bitstream(Wentz, [0067], upon initialization of a first core, a cryptographic measurement root code may be booted from resistant hardware, such as, without limitation, on-chip read-only memory (ROM), and/or other hardcoded memory or circuitry. Software monitor may subsequently be loaded into memory from at least a non-volatile programmable memory. In an embodiment, all other memory address space may be cleared, zeroed, and/or set to a uniform value to achieve a known initial state.);
when the accumulated score reaches a threshold, performing the steps of: inserting into the bitstream a signature unit including a cryptographic digital signature of fingerprints of a subsequence of the data units back to the reference point(Wentz, [0061], a basic input/output system (BIOS) that initiates upon startup of selection device 104 may compute a cryptographic hash of a boot loader of an operating system running on selection device 104; cryptographic hash may include boot drivers of one or more processes that initiate when selection device 104 starts up. Secure computing module 116 may then digitally sign cryptographic hash; cryptographic hash with or without digital signature, may be stored in memory);
Regarding Claim 2:
The method of claim 1, Du in view of Kirovski in further view of Wentz teaches wherein the assigned score includes a predefined positive contribution if content of a predefined content type is detected(Du, [0024] … the device for audio recognition according to the present disclosure calculate, based on audio variation trends among frames and within each of the frames of audio data to be recognized, to obtain a characteristic value of each frame of the audio data to be recognized, and then match the characteristic value of each frame with a pre-established audio characteristic value comparison table to obtain a recognition result).
Regarding Claim 3:
The method of claim 2, Du in view of Kirovski in further view of Wentz teaches where the predefined content type is one or more of: voice activity, speech, screams, silence, noise from mechanical destruction, noise from a particular vehicle maneuver, noise from firearms(Du, [0038], The audio data to be recognized can be a segment of speech recorded by audio recognition software, or a segment of speech from, e.g., interactive TV.).
Regarding Claim 4:
The method of claim 1, Du in view of Kirovski in further view of Wentz teaches wherein: the model is a probabilistic model; and the positive contribution is included in the assigned score if the time segment represents a significant deviation or an anomaly in view of the probabilistic model(Du, [0039], deviation may occur during matching and recognition due to the impact of frame division. To reduce deviation, during frame division, the frames may be set to overlap with one another… Therefore, the audio data to be recognized is divided into frames according to a division rule that adjacent frames are set to have an overlap of a preset number of milliseconds.).
Regarding Claim 8:
The method of claim 1, Du in view of Kirovski in further view of Wentz’s teaches wherein: the assigned score is based on information relating to the conditions of capturing the time segment, said information including a performance indicator for a network utilized for transferring the bitstream(Du, [0079], represents the vector value in the jth dimension of the (i+2)th frame of audio data, di+2, j+1 represents the vector value in the (j+1)th dimension of the (i+2)th frame of audio data, t1, t2, t3, t4 and diff represent intermediate variables, Biti,j represents the value of the jth bit of the binary sequence of the ith frame of audio data, and n, m are constant coefficients.);
and the assigned score includes a positive contribution corresponding to a temporary drop in the performance indicator(Du, [0043], Assuming that the vector is a 12-dimensional vector, then as shown in FIG. 3, signals in the ith frame can be expressed as (di0, di1 . . . di10, di11), d being a float type data. Furthermore, to make subsequent comparison and matching simpler, the obtained vector can be encoded and converted to a binary sequence or a positive number corresponding to the binary sequence. For example, the expression of frame characteristic vector shown in FIG. 3 can be encoded and converted in the following manner).
Regarding Claim 10:
The method of claim 1, Du in view of Kirovski in further view of Wentz’s teaches wherein the score assigned to a data unit includes a minimum value(DU, [0067], Since the audio data to be recognized typically comprises multiple frames of data, the comparison results for the multiple frames can be added, and the voting position corresponding to the minimum value of the ultimate counting results is determined to be the recognition result.).
Regarding Claim 11:
The method of claim 1, Du in view of Kirovski in further view of Wentz’s teaches wherein the signature unit to be inserted into the bitstream includes a digital signature of fingerprints which pertain to a subsequence of data units which ends earlier than the accumulated score reaches the threshold, if the threshold is reached at an increased rate of change; or to a subsequence of data units which ends where the accumulated score reaches the threshold(Du, [0014], one or more frames of sample audio that have the same characteristic value as that of the current frame of the audio data to be recognized, and marking a voting label at positions in the voting matrix corresponding to the one or more frames of sample audio that have the same audio characteristic value as that of the current frame of the audio data to be recognized; and using the segment of sample audio having the highest number of voting labels that exceeds a preset threshold as the recognition result.).
Regarding Claim 12:
The method of claim 1, Du in view of Kirovski in further view of Wentz’s teaches which is performed in real time relative to said audio capturing and encoding process(Du, [0065], otes are cast at the first matched position of a sample audio for all matching results. In other words, if the 3rd frame of the audio data to be recognized and the 6th frame of the 8th sample audio have the same characteristic value through matching (which is the first time of matching with this sample audio)).
Regarding Claim 14:
Du teaches:
A non-transitory computer readable recording medium comprising a computer program comprising instructions to cause a controller to execute a method of providing a signed bitstream, where an audio signal is captured and encoded as a bitstream, the bitstream having a sequence of data units representing time segments of the audio signal, the method comprising(DU, [0024]… the device for audio recognition according to the present disclosure calculate, based on audio variation trends among frames and within each of the frames of audio data to be recognized, to obtain a characteristic value of each frame of the audio data to be recognized, and then match the characteristic value of each frame with a pre-established audio characteristic value comparison table to obtain a recognition result. A comparative relationship between frames and within a frame is used to obtain a relatively stable encoding result):
Du does not disclose:
assigning a score to each data unit
and resetting the reference point, wherein the score assigned to a data unit is based on at least one of: a) a detected content of the time segment of the audio signal corresponding to the data unit, b) contextual information which relates the time segment to a history of the audio signal, wherein the assigned score includes a positive contribution corresponding to the time segment’s deviation from a model of the history of the audio signal, or c) information relating to the conditions of capturing the time segment
Kirovski discloses:
assigning a score to each data unit (Kirovski [0048], each insertion operation modifies its magnitude frequency components XMAG(k) with the strong watermark value w(k) if the magnitude frequency component exceeds the hearing threshold z(k) and alternatively,);
and resetting the reference point, wherein the score assigned to a data unit is based on at least one of: a) a detected content of the time segment of the audio signal corresponding to the data unit, b) contextual information which relates the time segment to a history of the audio signal, wherein the assigned score includes a positive contribution corresponding to the time segment’s deviation from a model of the history of the audio signal, or c) information relating to the conditions of capturing the time segment(Kirovski, [0021], audio watermarking technology for inserting and detecting strong and weak watermarks in audio signals. The strong watermark identifies the content producer, [0070], Since the watermark values w(i) have zero mean, the numerator in Equation (3) will be a sum of negative and positive values, whereas the denominator will be equal to Q2 times the number of indices in the set I. Therefore, for a large K, the measure NC0 will be a random variable with an approximately normal (Gaussian) probability distribution, with an expected value of zero and a variance much smaller than one.),
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Du’s method and device for audio recognition by enhancing Du’s audio data to be recognized to obtain a plurality of frames of audio data to capture audio content by the users device and generating audio signatures as taught by Kirovski in order to enhance the accuracy and robustness of even detection while maintaining predictable and efficient operation.
The motivation is to ensure that scores increase correspond to meaningful content rather than routine signal variations. Furthermore, to ensure that downstream signing operations are triggered only by significant audio conditions.
Du in view of Kirovski do not disclose:
monitoring an accumulated score, which is a sum of the scores assigned to all preceding data units back to a reference point in the bitstream
when the accumulated score reaches a threshold, performing the steps of: inserting into the bitstream a signature unit including a cryptographic digital signature of fingerprints of a subsequence of the data units back to the reference point
Wentz discloses:
monitoring an accumulated score, which is a sum of the scores assigned to all preceding data units back to a reference point in the bitstream(Wentz, [0067], upon initialization of a first core, a cryptographic measurement root code may be booted from resistant hardware, such as, without limitation, on-chip read-only memory (ROM), and/or other hardcoded memory or circuitry. Software monitor may subsequently be loaded into memory from at least a non-volatile programmable memory. In an embodiment, all other memory address space may be cleared, zeroed, and/or set to a uniform value to achieve a known initial state.);
when the accumulated score reaches a threshold, performing the steps of: inserting into the bitstream a signature unit including a cryptographic digital signature of fingerprints of a subsequence of the data units back to the reference point(Wentz, [0061], a basic input/output system (BIOS) that initiates upon startup of selection device 104 may compute a cryptographic hash of a boot loader of an operating system running on selection device 104; cryptographic hash may include boot drivers of one or more processes that initiate when selection device 104 starts up. Secure computing module 116 may then digitally sign cryptographic hash; cryptographic hash with or without digital signature, may be stored in memory);
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Du in view of Kirovski’s method and device for audio recognition by enhancing Kirovski’s audio data to be recognized to obtain a plurality of frames of audio data to capture audio content by the users device and generating audio signatures as taught by Wentz in order to ensure integrity and authenticity while avoiding the overhead of signing every individual unit
The motivation is to enhance processing efficiency by limiting signature insertion to meaningful accumulated conditions while minimizing signing overhead.
Regarding Claim 15:
The method of claim 1, wherein the reference point remains stationary during an execution of the method except in the step of resetting the reference point in response to the accumulated score having reached the threshold(Kirovski, [0091], the watermark detector 130 tests for a condition where there is no watermark by setting the watermark vector w(i) to zero, such that the watermarked input vector Y(i) is less than the hearing threshold by buffer value B. The watermark detector 130 then computes the correlation value NC for the sync point r (step 164). The process of computing correlation values NC continues for subsequent sync points, each incremented from the previous point by step R (i.e., r=r+R) (step 166), until correlation values for a maximum number of sync points has been collected (step 168).).
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Du’s method and device for audio recognition by enhancing Du’s audio data to be recognized to obtain a plurality of frames of audio data to capture audio content by the user’s device and generating audio signatures as taught by Kirovski in order to ensure consistent evaluation across a defined interval.
The motivation is to enhance robustness and consistency of accumulated-score evaluation and control of scoring intervals in a continuous data stream.
Claims 5-7 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Du(US Publication No. 20210125628 A1) in view of Kirovski(US Publication No. 20050055214 A)in further view of Wentz(US Patent No. 11379263 B2)and in further view of Kalampoukas(US Publication No. 20180062778 A1)
Regarding Claim 5:
Du in view of Kirovski in further view of Wentz disclose:
The method of claim 4…
Du in view of Kirovski in further view of Wentz do not disclose:
wherein the positive contribution is included in the assigned score for a deviating time segment only if content of a predefined content type is detected in that time segment
Kalampoukas discloses:
wherein the positive contribution is included in the assigned score for a deviating time segment only if content of a predefined content type is detected in that time segment(Kalampoukas, [0094], it may be anticipated that content identification will be more likely to be successful under certain conditions. The mobile user device 205 may schedule automatic content recognition operation for efficiency based on detected conditions such as time, location, etc.).
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Du in view of Kirovski in further view of Wentz’s method and device for audio recognition by enhancing Kirovski in further view of Wentz’s audio data to be recognized to obtain a plurality of frames of audio data to capture audio content by the users device and generating audio signatures as taught by Kalampoukas in order to selectively control subsequent processing and reduce unnecessary operations during non-relevant audio segments.
The motivation is to enhance the accuracy of detecting meaningful audio events while reducing false positives and enhancing the reliability of the scoring mechanism used to trigger signing operations.
Regarding Claim 6:
Du in view of Kirovski in further view of Wentz disclose:
The method of claim 4…
Du in view of Kirovski in further view of Wentz do not disclose:
wherein the model is frequency-selective
Kalampoukas discloses:
wherein the model is frequency-selective(Kalampoukas, [0052], The scheduling and frequency of monitoring audio information and generating audio signatures may be influenced or controlled based on the recorded conditions or rules derived from the recorded conditions.).
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Du’s Method and device for audio recognition by enhancing Du’s audio data to be recognized to obtain a plurality of frames of audio data to capture audio content by the users device and generating audio signatures as taught by Kalampoukas in order to enhance the robustness and responsiveness of the system to localized threats and data inconsistencies.
The motivation of a frequently selective model enhances security by enabling adaptive and unpredictable evaluations of data segments. Also, this strengthens the system’s ability to detect and respond to threats in real time.
Regarding Claim 7:
Du in view of Kirovski in further view of Wentz disclose:
The method of claim 1…
Du in view of Kirovski in further view of Wentz do not disclose:
wherein the assigned score is based on one or more of the following conditions of capturing the time segment: a time of day, a direction of incidence on an audio recording device, a geo-position of a mobile audio recording device, a meteorological condition
Kalampoukas discloses:
wherein the assigned score is based on one or more of the following conditions of capturing the time segment: a time of day, a direction of incidence on an audio recording device, a geo-position of a mobile audio recording device, a meteorological condition(Kalampoukas, [0009], An automatic content recognition system is provided that includes a user device for the purpose of capturing audio and generating an audio signature. The user device may be a smartphone or tablet. The system is also capable of determining the conditions present at the time of capture of the audio information, including environmental conditions. The environmental conditions may include one or more of day, date, time, location, network, motion and orientation.).
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Du in view of Kirovski in further view of Wentz’s Method and device for audio recognition by enhancing Du in view of Kirovski in further view of Wentz’s audio data to be recognized to obtain a plurality of frames of audio data to capture audio content by the user’s device and generating audio signatures as taught by Kalampoukas in order to evaluate audio segments in light of external factors increasing the precision of integrity scoring.
The motivation is to ensure that the trust model is not only data-driven but environmentally and situationally aware in context blind systems when assigning scores to audio segments and evidentiary value of recorded data.
Regarding Claim 9:
The method of claim 7, Du in view of Kirovski in further view of Wentz’s teaches wherein said information relating to the conditions of capturing the time segment is used to reinforce a basic score that is based on the detected content or contextual information(Kalampoukas, [0067], Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range is encompassed within the invention.).
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Du in view of Kirovski in further view of Wentz’s Method and device for audio recognition by enhancing Du in view of Kirovski in further view of Wentz’s audio data to be recognized to obtain a plurality of frames of audio data to capture audio content by the user’s device and generating audio signatures as taught by Kalampoukas in order to evaluate audio segments in light of external factors increasing the precision of integrity scoring.
The motivation is to ensure that the trust model is not only data-driven but environmentally and situationally aware in context blind systems when assigning scores to audio segments and evidentiary value of recorded data.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Du(US Publication No. 20210125628 A1) in view of Kirovski(US Publication No. 20050055214 A)in further view of Wentz(US Patent No. 11379263 B2) and in further view of Kalampoukas(US Publication No. 20180062778 A1)
Regarding Claim 13:
Du discloses:
an audio encoder configured to encode the audio signal as a bitstream(Du, [0043], encoded and converted to a binary sequence or a positive number corresponding to the binary sequence.);
and processing circuitry configured to perform a method of providing a signed bitstream, where an audio signal is captured and encoded as a bitstream, the bitstream having a sequence of data units representing time segments of the audio signal, the method comprising(DU, [0024]… the device for audio recognition according to the present disclosure calculate, based on audio variation trends among frames and within each of the frames of audio data to be recognized, to obtain a characteristic value of each frame of the audio data to be recognized, and then match the characteristic value of each frame with a pre-established audio characteristic value comparison table to obtain a recognition result. A comparative relationship between frames and within a frame is used to obtain a relatively stable encoding result)):
Du does not disclose:
assigning a score to each data unit
and resetting the reference point, wherein the score assigned to a data unit is based on at least one of:a) a detected content of the time segment of the audio signal corresponding to the data unit, b) contextual information which relates the time segment to a history of the audio signal, wherein the assigned score includes a positive contribution corresponding to the time segment's deviation from a model of the history of the audio signal, or c) information relating to the conditions of capturing the time segment
Kirovski discloses:
assigning a score to each data unit(Kirovski [0048], each insertion operation modifies its magnitude frequency components XMAG(k) with the strong watermark value w(k) if the magnitude frequency component exceeds the hearing threshold z(k) and alternatively,);
and resetting the reference point, wherein the score assigned to a data unit is based on at least one of:a) a detected content of the time segment of the audio signal corresponding to the data unit, b) contextual information which relates the time segment to a history of the audio signal, wherein the assigned score includes a positive contribution corresponding to the time segment's deviation from a model of the history of the audio signal, or c) information relating to the conditions of capturing the time segment(Kirovski, [0021], audio watermarking technology for inserting and detecting strong and weak watermarks in audio signals. The strong watermark identifies the content producer, [0070], Since the watermark values w(i) have zero mean, the numerator in Equation (3) will be a sum of negative and positive values, whereas the denominator will be equal to Q2 times the number of indices in the set I. Therefore, for a large K, the measure NC0 will be a random variable with an approximately normal (Gaussian) probability distribution, with an expected value of zero and a variance much smaller than one.).
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Du’s method and device for audio recognition by enhancing Du’s audio data to be recognized to obtain a plurality of frames of audio data to capture audio content by the users device and generating audio signatures as taught by Kirovski in order to enhance the accuracy and robustness of even detection while maintaining predictable and efficient operation.
The motivation is to ensure that scores increase correspond to meaningful content rather than routine signal variations. Furthermore, to ensure that downstream signing operations are triggered only by significant audio conditions
Du in view of Kirovski do not disclose:
a score counter
monitoring an accumulated score, which is a sum of the scores assigned to all preceding data units back to a reference point in the bitstream
when the accumulated score reaches a threshold, performing the steps of: inserting into the bitstream a signature unit including a cryptographic digital signature of fingerprints of a subsequence of the data units back to the reference point
Wentz discloses:
a score counter(Wentz, [0044], the counters count the number of oscillations per a time period, and the output is set to 0 if one counter has a higher value and 1 if another counter has a higher value.);
monitoring an accumulated score, which is a sum of the scores assigned to all preceding data units back to a reference point in the bitstream(Wentz, [0067], upon initialization of a first core, a cryptographic measurement root code may be booted from resistant hardware, such as, without limitation, on-chip read-only memory (ROM), and/or other hardcoded memory or circuitry. Software monitor may subsequently be loaded into memory from at least a non-volatile programmable memory. In an embodiment, all other memory address space may be cleared, zeroed, and/or set to a uniform value to achieve a known initial state.);
when the accumulated score reaches a threshold, performing the steps of: inserting into the bitstream a signature unit including a cryptographic digital signature of fingerprints of a subsequence of the data units back to the reference point(Wentz, [0061], a basic input/output system (BIOS) that initiates upon startup of selection device 104 may compute a cryptographic hash of a boot loader of an operating system running on selection device 104; cryptographic hash may include boot drivers of one or more processes that initiate when selection device 104 starts up. Secure computing module 116 may then digitally sign cryptographic hash; cryptographic hash with or without digital signature, may be stored in memory);
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Du in view of Kirovski’s method and device for audio recognition by enhancing Kirovski’s audio data to be recognized to obtain a plurality of frames of audio data to capture audio content by the users device and generating audio signatures as taught by Wentz in order to ensure integrity and authenticity while avoiding the overhead of signing every individual unit
The motivation is to enhance processing efficiency by limiting signature insertion to meaningful accumulated conditions while minimizing signing overhead.
Du in view of Kirovski in further view of Wentz do not disclose:
A controller for use in association with an audio capturing device configured to capture an audio signal
and a signature generator operable to insert signature units into the bitstream, the controller comprising: an input interface for monitoring the audio signal and/or the bitstream
Kalampoukas discloses:
A controller for use in association with an audio capturing device configured to capture an audio signal(Kalampoukas, [0087], The server may include a receiver 102 to receive an audio signature and a database controller 130. The receiver receives audio signatures and any metadata associated with the audio signatures that is transmitted by a remote user device 180.);
and a signature generator operable to insert signature units into the bitstream, the controller comprising: an input interface for monitoring the audio signal and/or the bitstream(Kalampoukas, [0090], … The remote user device 180, as previously discussed, may be utilized to monitor audio information at a user location. A device 180 may also be provided to monitor reference content and generate reference audio fingerprints and audio signatures with metadata which may be passed to the database controller 130 and stored in database 140 as a reference. The metadata may be a time stamp,);
an output interface towards the signature generator(Kalampoukas, [0026], … an audio signature generator connected to the processor and responsive to the microphone, and a transmitter connected to the processor capable of transmitting an audio signature to a communications channel.)
Before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify Du in view of Kirovski in further view of Wentz’s method and device for audio recognition by enhancing Kirovski in further view of Wentz’s audio data to be recognized to obtain a plurality of frames of audio data to capture audio content by the users device and generating audio signatures as taught by Kalampoukas in order to ensure proper coordination between audio monitoring and signature insertion operations
The motivation is to enhance modularity and interoperability of the system allowing the audio capture, analysis and signature insertion functions to be implemented as separate components while being centrally coordinated by the controller
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAYASA SHAAWAT whose telephone number is (571)272-3939. The examiner can normally be reached on M-F, 8 AM TO 5 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, JEFFREY PWU can be reached on (571)272-6789. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MAYASA SHAAWAT/
Examiner, Art Unit 2433
/JEFFREY C PWU/Supervisory Patent Examiner, Art Unit 2433