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 .
This action is responsive to communication filed on 12/22/2025.
Claims 1-20 are subject to examination.
This amendment and applicant’s arguments has been fully considered and entered 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.
Claim(s) 1, 6-8, 13-15, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sabripour et al. U.S. Patent Publication # 2020/0385116 (hereinafter Sabripour) in view of Falcao et al. U.S. Patent Publication # 2019/0228093 (hereinafter Falcao) further in view of Ryhorchuk et al. U.S. Patent Publication # 2014/0293993 (hereinafter Ryhorchuk) further in view of Zadeh et al. U.S. Patent Publication # 2022/0121884 (hereinafter Zadeh)
With respect to claim 1, Sabripour teaches a method of identifying data to be stored on an edge computing device in an edge computing environment, executable by a processor, comprising:
receiving data from one or more sensors (i.e. vehicular camera or vehicular sensors) operatively coupled to the edge computing device (i.e. vehicle computing device) (Paragraph 18-22);
-extracting one or more features (i.e. video features) and metadata (i.e. motion data set and/or vehicle environment data obtained from vehicular sensors) from the received data (Paragraph 28, 40) wherein the extracted features comprises an object in an image (i.e. object of interest from a drone camera) (Paragraph 46) and particular audio in an audio recording (i.e. reproducing audio that is decoded from a voice or audio streams of calls received via communication from other devices from digital audio stored at the vehicular computing device)(Paragraph 31)
-accounting for computational constraints of the edge computing device (i.e. computes a measure of the video quality by processing in real time, the video captured by the vehicular camera based on analysis of one or more video features that are extracted from video captured by the camera)within a computation timeframe (i.e. a period during which the vehicle was operating on a smooth road surface without any potholes or bumps) to create a pre-defined tolerance range for benchmark validation (i.e. video quality threshold which was created under acceptable conditions may corresponds to a period during which the vehicle was operating on a smooth road surface without any potholes or bumps)(Paragraph 40, 42);
comparing the extracted features and metadata to a pre-defined tolerance range (i.e. threshold) and one or more exogenous variables (i.e. motion) (Paragraph 40); and
causing the received data to be stored on a second edge computing device based on a determination that the compared features and metadata falls outside of the tolerance range (i.e. moving images are uploaded on the storage devices which is implemented in the vehicular computing device or on a remote cloud storage server and the vehicular computing device processes the video captured by the vehicular camera and further computes a measures of the video quality of the video captured by the vehicular camera) (Paragraph 19, 21-22, 33).
Sabripour does not explicitly teach causing the received data to be stored on the edge computing device after a determination that the compared features and the metadata falls inside of the tolerance range stored on a second computing device and deleted from the edge computing device ONLY AFTER determination that the compared features and metadata falls outside of the tolerance range.
Falcao teaches causing the received data to be stored on the edge computing device after a determination that the compared features and the metadata falls inside of the tolerance range (i.e. data received from clients may be parsed and evaluated prior to stored on the database. A copy of the received data file may be generated and the original data maybe stored as a master version of the data while copy is stored in another database apart from the database which is the functionally equivalent to the storing on the computing device after determination that compared features and the metadata falls inside tolerance range )(Paragraph 36) and stored on a second computing device (i.e. storage of the data at a different server class) ONLY AFTER determination that the compared features and metadata (i.e. characteristic falls outside (i.e. aged) of the tolerance range (i.e. aged past first twenty four hours) (Paragraph 34, 36-37). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Falcao’s teaching in Sabripour’s teaching to come up with causing data to be stored on second computing device only after a determination is made that compared features and metadata falls outside of the tolerance range. The motivation for doing so would be this is enable responding to large numbers of queries due to increased read-write-access capabilities and provide increased performance by assigned to faster processing, storage and retrieval of resources faster.
Sabripour and Falcao does not explicitly teach storing only after a determination that the compared features and metadata falls outside of the tolerance range.
Ryhorchuk teaches storing only after a determination that the compared features and metadata falls outside of the tolerance range (paragraph 85-87, 32). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Ryhorchuk’s teaching in Sabripour and Falcao’s teaching to come up with deleting from the computing device sensor data including distance comparison including position information if the received data is outside of the predetermined threshold of the parameters. The motivation for doing so would be to create storage space in the memory to store relevant features and metadata.
Sabripour teaches wherein the extracted features comprises an object in an image (i.e. object of interest from a drone camera) (Paragraph 46) and particular audio in an audio recording (i.e. reproducing audio that is decoded from a voice or audio streams of calls received via communication from other devices from digital audio stored at the vehicular computing device)(Paragraph 31) but does not explicitly state a particular sound in an audio recording.
Zadeh teaches wherein the extracted features comprises an object in an image (i.e. extracting different poses of an object) (Paragraph 1907) and particular audio in an audio recording (i.e. extracting potential images at a different poses of that speaker from the video track in sync with audio and extracting analysis of sound track of audio/video tracks, identifies/distinguishes speakers and associates those entities of time segments in the audio/video track)(Paragraph 31). It would have been obvious to one of ordinary skill in the art at the time of applicant’s invention to implement Zadeh’s teaching in Sabripour, Falcao and Ryhorchuk’s teaching to come up with having a particular sound in an audio recording. The motivation for doing so would be to identifying/distinguishing particular speakers and associates those entities to time segments in the audio/video tracks.
With respect to claim 6, Sabripour teaches the method of claim 1, the wherein comparing the extracted features and metadata to the pre-defined tolerance range and the one or more exogenous variables comprises a selection from the group consisting of: verifying the received data against a union and average of other data having a same timestamp as the received data (i.e. periodically such that past and present motion dataset can be compared) (Paragraph 41)
With respect to claim 7, Sabripour teaches the method of claim 1, wherein the received data corresponds a selection from the group consisting of: sensor data (Paragraph 22), audio data (Paragraph 31), an image (Paragraph 31), and a video (Paragraph 13)
With respect to claim 8, Sabripour teaches a computer system for identifying data to be stored on an edge computing device in an edge computing environment, the computer system comprising: one or more computer-readable storage media configured to store computer program code; and one or more computer processors configured to access said computer program code and operate as instructed by said computer program code, said computer program code including:
receiving code configured to cause the one or more computer processors to receive data from one or more sensors operatively coupled to the edge computing device(i.e. vehicle computing device) (Paragraph 18-22);
accounting code configured to cause the one or more computer processors to account for computational constraints of the edge computing device (i.e. computes a measure of the video quality by processing in real time, the video captured by the vehicular camera based on analysis of one or more video features that are extracted from video captured by the camera)within a computation timeframe (i.e. a period during which the vehicle was operating on a smooth road surface without any potholes or bumps) to create a pre-defined tolerance range for benchmark validation (i.e. video quality threshold which was created under acceptable conditions may corresponds to a period during which the vehicle was operating on a smooth road surface without any potholes or bumps)(Paragraph 40, 42);
extracting code configured to cause the one or more computer processors to extract one or more features(i.e. video features) and metadata (i.e. motion data set and/or vehicle environment data obtained from vehicular sensors) from the received data (Paragraph 28, 40) wherein the extracted features comprises an object in an image (i.e. object of interest from a drone camera) (Paragraph 46) and particular audio in an audio recording (i.e. reproducing audio that is decoded from a voice or audio streams of calls received via communication from other devices from digital audio stored at the vehicular computing device)(Paragraph 31)
comparing code configured to cause the one or more computer processors to compare the extracted features and metadata to a pre-defined tolerance range (i.e. threshold) and one or more exogenous variables (i.e. motion) (Paragraph 40);
storing code configured to cause the one or more computer processors to cause the received data to be stored on (i.e. moving images are uploaded on the storage devices which is implemented in the vehicular computing device )(Paragraph 19, 21-22) a second edge computing device based on a determination that the compared features and metadata falls outside of the tolerance range, respectively (i.e. moving images are uploaded on the storage devices which is implemented in the vehicular computing device and the vehicular computing device processes the video captured by the vehicular camera and further computes a measures of the video quality of the video captured by the vehicular camera) (Paragraph 19, 21-22).
Sabripour does not explicitly teach causing the received data to be stored on the edge computing device after a determination that the compared features and the metadata falls inside of the tolerance range stored on a second computing device and deleted from the edge computing device ONLY AFTER determination that the compared features and metadata falls outside of the tolerance range.
Falcao teaches causing the received data to be stored on the edge computing device after a determination that the compared features and the metadata falls inside of the tolerance range (i.e. data received from clients may be parsed and evaluated prior to stored on the database. A copy of the received data file may be generated and the original data maybe stored as a master version of the data while copy is stored in another database apart from the database which is the functionally equivalent to the storing on the computing device after determination that compared features and the metadata falls inside tolerance range )(Paragraph 36) and stored on a second computing device (i.e. storage of the data at a different server class) ONLY AFTER determination that the compared features and metadata (i.e. characteristic falls outside (i.e. aged) of the tolerance range (i.e. aged past first twenty four hours) (Paragraph 34, 36-37). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Falcao’s teaching in Sabripour’s teaching to come up with causing data to be stored on second computing device only after a determination is made that compared features and metadata falls outside of the tolerance range. The motivation for doing so would be this is enable responding to large numbers of queries due to increased read-write-access capabilities and provide increased performance by assigned to faster processing, storage and retrieval of resources faster.
Sabripour and Falcao does not explicitly teach only after a determination that the compared features and metadata falls outside of the tolerance range.
Ryhorchuk teaches storing only after a determination that the compared features and metadata falls outside of the tolerance range (paragraph 85-87, 32). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Ryhorchuk’s teaching in Sabripour and Falcao’s teaching to come up with deleting from the computing device sensor data including distance comparison including position information if the received data is outside of the predetermined threshold of the parameters. The motivation for doing so would be to create storage space in the memory to store relevant features and metadata.
Sabripour teaches wherein the extracted features comprises an object in an image (i.e. object of interest from a drone camera) (Paragraph 46) and particular audio in an audio recording (i.e. reproducing audio that is decoded from a voice or audio streams of calls received via communication from other devices from digital audio stored at the vehicular computing device)(Paragraph 31) but does not explicitly state a particular sound in an audio recording.
Zadeh teaches wherein the extracted features comprises an object in an image (i.e. extracting different poses of an object) (Paragraph 1907) and particular audio in an audio recording (i.e. extracting potential images at a different poses of that speaker from the video track in sync with audio and extracting analysis of sound track of audio/video tracks, identifies/distinguishes speakers and associates those entities of time segments in the audio/video track)(Paragraph 31). It would have been obvious to one of ordinary skill in the art at the time of applicant’s invention to implement Zadeh’s teaching in Sabripour, Falcao and Ryhorchuk’s teaching to come up with having a particular sound in an audio recording. The motivation for doing so would be to identifying/distinguishing particular speakers and associates those entities to time segments in the audio/video tracks.
With respect to claims 13-14 respectively, teaches same limitations as claims 6-7 respectively, therefore rejected under same basis.
With respect to claim 15, Sabripour teaches a computer program product for identifying data to be stored on an edge computing device in an edge computing environment, comprising: one or more computer-readable storage devices; and program instructions stored on at least one of the one or more computer-readable storage devices, the program instructions configured to cause one or more computer processors to:
receive data from one or more sensors (i.e. vehicular camera or vehicular sensors) operatively coupled to the edge computing device (i.e. vehicle computing device) (Paragraph 18-22)
account for computational constraints of the edge computing device (i.e. computes a measure of the video quality by processing in real time, the video captured by the vehicular camera based on analysis of one or more video features that are extracted from video captured by the camera) within a computation timeframe (i.e. a period during which the vehicle was operating on a smooth road surface without any potholes or bumps) to create a pre-defined tolerance range for benchmark validation (i.e. video quality threshold which was created under acceptable conditions may corresponds to a period during which the vehicle was operating on a smooth road surface without any potholes or bumps)(Paragraph 40, 42)
extract one or more features(i.e. video features) and metadata (i.e. motion data set and/or vehicle environment data obtained from vehicular sensors) from the received data (Paragraph 28, 40); wherein the extracted features comprises an object in an image (i.e. object of interest from a drone camera) (Paragraph 46) and particular audio in an audio recording (i.e. reproducing audio that is decoded from a voice or audio streams of calls received via communication from other devices from digital audio stored at the vehicular computing device)(Paragraph 31)
compare the extracted features and metadata to a pre-defined tolerance range (i.e. threshold) and one or more exogenous variables (i.e. motion) (Paragraph 40);
cause the received data to be stored on (i.e. moving images are uploaded on the storage devices which is implemented in the vehicular computing device )(Paragraph 19, 21-22) or a second edge computing device based on a determination that the compared features and metadata falls within the tolerance range or outside of the tolerance range, respectively (i.e. moving images are uploaded on the storage devices which is implemented in the vehicular computing device and the vehicular computing device processes the video captured by the vehicular camera and further computes a measures of the video quality of the video captured by the vehicular camera) (Paragraph 19, 21-22).
Sabripour does not explicitly teach causing the received data to be stored on the edge computing device after a determination that the compared features and the metadata falls inside of the tolerance range stored on a second computing device and deleted from the edge computing device ONLY AFTER determination that the compared features and metadata falls outside of the tolerance range.
Falcao teaches causing the received data to be stored on the edge computing device after a determination that the compared features and the metadata falls inside of the tolerance range (i.e. data received from clients may be parsed and evaluated prior to stored on the database. A copy of the received data file may be generated and the original data maybe stored as a master version of the data while copy is stored in another database apart from the database which is the functionally equivalent to the storing on the computing device after determination that compared features and the metadata falls inside tolerance range )(Paragraph 36) and stored on a second computing device (i.e. storage of the data at a different server class) ONLY AFTER determination that the compared features and metadata (i.e. characteristic falls outside (i.e. aged) of the tolerance range (i.e. aged past first twenty four hours) (Paragraph 34, 36-37). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Falcao’s teaching in Sabripour’s teaching to come up with causing data to be stored on second computing device only after a determination is made that compared features and metadata falls outside of the tolerance range. The motivation for doing so would be this is enable responding to large numbers of queries due to increased read-write-access capabilities and provide increased performance by assigned to faster processing, storage and retrieval of resources faster.
Sabripour and Falcao does not explicitly teach storing only after a determination that the compared features and metadata falls outside of the tolerance range.
Ryhorchuk teaches storing only after a determination that the compared features and metadata falls outside of the tolerance range (paragraph 85-87, 32). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Ryhorchuk’s teaching in Sabripour and Falcao’s teaching to come up with deleting from the computing device sensor data including distance comparison including position information if the received data is outside of the predetermined threshold of the parameters. The motivation for doing so would be to create storage space in the memory to store relevant features and metadata.
Sabripour teaches wherein the extracted features comprises an object in an image (i.e. object of interest from a drone camera) (Paragraph 46) and particular audio in an audio recording (i.e. reproducing audio that is decoded from a voice or audio streams of calls received via communication from other devices from digital audio stored at the vehicular computing device)(Paragraph 31) but does not explicitly state a particular sound in an audio recording.
Zadeh teaches wherein the extracted features comprises an object in an image (i.e. extracting different poses of an object) (Paragraph 1907) and particular audio in an audio recording (i.e. extracting potential images at a different poses of that speaker from the video track in sync with audio and extracting analysis of sound track of audio/video tracks, identifies/distinguishes speakers and associates those entities of time segments in the audio/video track)(Paragraph 31). It would have been obvious to one of ordinary skill in the art at the time of applicant’s invention to implement Zadeh’s teaching in Sabripour, Falcao and Ryhorchuk’s teaching to come up with having a particular sound in an audio recording. The motivation for doing so would be to identifying/distinguishing particular speakers and associates those entities to time segments in the audio/video tracks.
With respect to claim 20 respectively, teaches same limitations as claim 7 respectively, therefore rejected under same basis.
Claim(s) 2, 9, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sabripour et al. U.S. Patent Publication # 2020/0385116 (hereinafter Sabripour) in view of Falcao further in view of Ryhorchuk further in view of Zadeh further in view of Babenko et al. U.S. Patent # 8,238,671 (hereinafter Babenko)
With respect to claim 2, Sabripour, Falcao, Ryhorchuk and Zadeh teaches the method of claim 1, but fails to teaches further comprising updating the tolerance range based on minimizing an occurrence of false positives and false negatives associated with the received data and the tolerance range.
Babenko teaches updating the tolerance range based on minimizing an occurrence of false positives and false negatives associated with the received data and the tolerance range (column 7 lines 59-67)(column 8 lines 1-19). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Babenko’s teaching in Sabripour, Falcao, Ryhorchuk and Zadeh’s teaching to come up with updating the tolerance range based on minimizing an occurrence of false positive and false negatives associated with received data and the tolerance range. The motivation for doing so would be to provide more accurate analysis/results to the user/admin.
With respect to claims 9 & 16, teaches same limitations as claim 2, therefore rejected under same basis.
Claim(s) 3, 10, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sabripour et al. U.S. Patent Publication # 2020/0385116 (hereinafter Sabripour) in view of Falcao further in view of Ryhorchuk further in view Zadeh further in view of Babenko et al. U.S. Patent # 8,238,671 (hereinafter Babenko) further in view of Pfenning et al. U.S. Patent Publication # 2022/0235102 (hereinafter Pfenning)
With respect to claim 3, Sabripour, Falcao, Ryhorchuk, Zadeh and Babenko teaches the method of claim 2, but fails to further teach wherein the one or more features are extracted through a selection from the group consisting of: a region-based convolutional neural network and bidirectional generative adversarial network.
Pfenning teaches wherein the one or more features are extracted through a region-based convolutional neural network (Paragraph 10) and bidirectional generative adversarial network (Paragraph 10). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Pfenning’s teaching in Sabripour, Falcao, Ryhorchuk, Zadeh and Babenko’s teaching to come up with one or more feature are extracted through region based convolutional neural network. The motivation for doing so would be so convolutional neural network can learn from raw data without requiring preprocessing.
With respect to claims 10 & 17, teaches same limitations as claim 2, therefore rejected under same basis.
Claim(s) 4-5, 11-12, 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sabripour et al. U.S. Patent Publication # 2020/0385116 (hereinafter Sabripour) in view of Falcao further in view of Ryhorchuk further in view of Zadeh further in view of Cook et al. U.S. Patent Publication # 20180183874 (hereinafter Cook)
With respect to claim 4, Sabripour teaches the method of claim 1, wherein the sensors comprise a selection from the group consisting of: a camera (Paragraph 22), a microphone (Paragraph 30), a seismic sensor (i.e. shaking and rotation)(Paragraph 40), and a motion detector (Paragraph 20).
Sabripour, Falcao, Ryhorchuk and Zadeh does not teach sensors comprise a thermometer, a pressure sensor, a humidity sensor, seismic, accelerometer and motion detector.
Cook teaches sensors comprise a thermometer, a pressure sensor, a humidity sensor, and accelerometer (Paragraph 21, 59). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Cook’s teaching in Sabripour, Falcao, Ryhorchuk and Zadeh’s teaching to come up with having sensors comprises, a thermometer, a pressure sensor, a humidity sensor, seismic sensor, accelerometer and motion detector. The motivation for doing so would be to get accurate result/reading from the IoT devices, wearables, household devices etc. so the data/metrics can be used to provide accurate analysis.
With respect to claim 5, Sabripour teaches the wherein the one or more exogenous variables correspond to one or more sensor inputs, comprising a selection from the group consisting of: a temperature value (Paragraph 40), an amount or presence of vibration (i.e. shaking and rotation)(Paragraph 40), and an amount or presence of motion (Paragraph 20).
Sabripour, Falcao, Ryhorchuk and Zadeh does not teach a selection from group consisting of: pressure value, a humidity value.
Cook teaches a selection from the group consisting of: a temperature value (Paragraph 21, 59), pressure value, humidity value, and an amount or presence of motion (Paragraph 21, 59). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Cook’s teaching in Sabripour, Falcao, Ryhorchuk and Zadeh’s teaching to come up with having sensors exogenous variable including pressure value and humidity value. The motivation for doing so would be to get accurate result/reading from the IoT devices, wearables, household devices etc. so the data/metrics can be used to provide accurate analysis.
With respect to claims 11-12 respectively, teaches same limitations as claim 4-5 respectively, therefore rejected under same basis.
With respect to claims 18-19 respectively, teaches same limitations as claim 4-5 respectively, therefore rejected under same basis.
Response to Arguments
A). Applicant’s arguments filed 11/24/2025 with respect to amended claims limitation “wherein the extracted features comprises an object in an image and a particular sound in an audio recording “ 1, 8, 15, have been fully considered but deemed moot in view of new grounds of rejection.
With respect to remark A, Sabripour teaches a method of identifying data to be stored on an edge computing device in an edge computing environment, executable by a processor, comprising:
receiving data from one or more sensors (i.e. vehicular camera or vehicular sensors) operatively coupled to the edge computing device (i.e. vehicle computing device) (Paragraph 18-22);
-extracting one or more features (i.e. video features) and metadata (i.e. motion data set and/or vehicle environment data obtained from vehicular sensors) from the received data (Paragraph 28, 40) wherein the extracted features comprises an object in an image (i.e. object of interest from a drone camera) (Paragraph 46) and particular audio in an audio recording (i.e. reproducing audio that is decoded from a voice or audio streams of calls received via communication from other devices from digital audio stored at the vehicular computing device)(Paragraph 31)
-accounting for computational constraints of the edge computing device (i.e. computes a measure of the video quality by processing in real time, the video captured by the vehicular camera based on analysis of one or more video features that are extracted from video captured by the camera)within a computation timeframe (i.e. a period during which the vehicle was operating on a smooth road surface without any potholes or bumps) to create a pre-defined tolerance range for benchmark validation (i.e. video quality threshold which was created under acceptable conditions may corresponds to a period during which the vehicle was operating on a smooth road surface without any potholes or bumps)(Paragraph 40, 42);
comparing the extracted features and metadata to a pre-defined tolerance range (i.e. threshold) and one or more exogenous variables (i.e. motion) (Paragraph 40); and
causing the received data to be stored on a second edge computing device based on a determination that the compared features and metadata falls outside of the tolerance range (i.e. moving images are uploaded on the storage devices which is implemented in the vehicular computing device or on a remote cloud storage server and the vehicular computing device processes the video captured by the vehicular camera and further computes a measures of the video quality of the video captured by the vehicular camera) (Paragraph 19, 21-22, 33).
Sabripour does not explicitly teach causing the received data to be stored on the edge computing device after a determination that the compared features and the metadata falls inside of the tolerance range stored on a second computing device and deleted from the edge computing device ONLY AFTER determination that the compared features and metadata falls outside of the tolerance range.
Falcao teaches causing the received data to be stored on the edge computing device after a determination that the compared features and the metadata falls inside of the tolerance range (i.e. data received from clients may be parsed and evaluated prior to stored on the database. A copy of the received data file may be generated and the original data maybe stored as a master version of the data while copy is stored in another database apart from the database which is the functionally equivalent to the storing on the computing device after determination that compared features and the metadata falls inside tolerance range )(Paragraph 36) and stored on a second computing device (i.e. storage of the data at a different server class) ONLY AFTER determination that the compared features and metadata (i.e. characteristic falls outside (i.e. aged) of the tolerance range (i.e. aged past first twenty four hours) (Paragraph 34, 36-37). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Falcao’s teaching in Sabripour’s teaching to come up with causing data to be stored on second computing device only after a determination is made that compared features and metadata falls outside of the tolerance range. The motivation for doing so would be this is enable responding to large numbers of queries due to increased read-write-access capabilities and provide increased performance by assigned to faster processing, storage and retrieval of resources faster.
Sabripour and Falcao does not explicitly teach storing only after a determination that the compared features and metadata falls outside of the tolerance range.
Ryhorchuk teaches storing only after a determination that the compared features and metadata falls outside of the tolerance range (paragraph 85-87, 32). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Ryhorchuk’s teaching in Sabripour and Falcao’s teaching to come up with deleting from the computing device sensor data including distance comparison including position information if the received data is outside of the predetermined threshold of the parameters. The motivation for doing so would be to create storage space in the memory to store relevant features and metadata.
Sabripour teaches wherein the extracted features comprises an object in an image (i.e. object of interest from a drone camera) (Paragraph 46) and particular audio in an audio recording (i.e. reproducing audio that is decoded from a voice or audio streams of calls received via communication from other devices from digital audio stored at the vehicular computing device)(Paragraph 31) but does not explicitly state a particular sound in an audio recording.
Zadeh teaches wherein the extracted features comprises an object in an image (i.e. extracting different poses of an object) (Paragraph 1907) and particular audio in an audio recording (i.e. extracting potential images at a different poses of that speaker from the video track in sync with audio and extracting analysis of sound track of audio/video tracks, identifies/distinguishes speakers and associates those entities of time segments in the audio/video track)(Paragraph 31). It would have been obvious to one of ordinary skill in the art at the time of applicant’s invention to implement Zadeh’s teaching in Sabripour, Falcao and Ryhorchuk’s teaching to come up with having a particular sound in an audio recording. The motivation for doing so would be to identifying/distinguishing particular speakers and associates those entities to time segments in the audio/video tracks.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
A). Niuwenhuys et al. U.S. Patent Publication # 2018/0034884 which in Paragraph 82 teaching about interaction pattern to associated metadata information that details characteristics of the interaction pattern.
B). Zhu et al. U.S. Patent Publication # 2022/0188695 which in Paragraph 51 teaches about labeling false positive and/or false negative and adjusting the threshold to minimize the errors.
C). Savvides et al. U.S. Patent Publication # 2018/0096457.
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DHAIRYA A. PATEL
Primary Examiner
Art Unit 2453
/DHAIRYA A PATEL/Primary Examiner, Art Unit 2453