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, see Remarks, filed March 11, 2026, with respect to the rejection(s) of claim(s) 1 under Davis et al (US 11,887,386) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Raber (US 2005/0206741).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1 – 4, 7 – 11, 15, 16, and 18 - 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bostick et al US (10,931,912, hereafter Bostick) in view of Raber (US 2005/0206741) in further view of Davis et al (US 11,887,386, hereafter Davis) in further view of Kirsch (US 2019/0007626) in further view of Schmidt et al (US 10,686,984, hereafter Schmidt).
As per claim 1, Bostick discloses a method, comprising:
capturing, by a camera associated with an automobile, video data representative of an operational environment of the automobile (column 2 lines 42 – 43; Accordingly, vehicles may include a suite of cameras to capture video as the vehicle is being driven.);
determining whether an event has occurred at a given time point included in the video data (column 2 lines 61 – 64; For example, an anomalous event for which video may be uploaded and stored may include a traffic accident, bicycle accident, etc. Accordingly, aspects of the present invention may include systems and/or methods that detect the occurrence of a particular event (e.g., an anomalous event) and, in turn, upload a portion of the video at a particular time prior to the event and after the event to a cloud server for analysis.);
responsive to determining that the event has occurred at the given time point, identifying a video segment included in the first data storage device that corresponds to the event (column 10 lines 32 – 34; For example, event detection component 415 may upload video from a local storage device implemented within the vehicle to video storage server 425.);
storing the video segment corresponding to the event using a second data storage device that includes a second storage capacity larger than the first storage capacity (column 2 lines 60 – 64 and column 10 lines 32 – 34; Here the examiner argues based on applicant’s specification that a first storage is a local storage device and a second storage is cloud-based storage and the capacity of the cloud-based storage would be greater than local storage);
identifying a reviewing entity to which the video segment is to be sent, the identifying being based on video content included in the video segment (column 10 lines 55 – 60); and
sending, from the second data storage device, the video segment to the identified reviewing entity (column 10 lines 55 – 60).
However, Bostick does not explicitly teach wherein storing the video data using the first data storage device includes overwriting older video data included in the first data storage device with newer video data upon the video data exceeding the first storage capacity;
determining, by an artificial intelligence system, whether an event has occurred at a given time point included in the video data.
In the same field of endeavor, Raber teaches wherein storing the video data using the first data storage device includes overwriting older video data included in the first data storage device with newer video data upon the video data exceeding the first storage capacity (¶ 56; As a practical matter, video/audio storage device 40 of the vehicle subsystem 15 has a limited storage capacity… For example, if the total amount of data stored on storage system 40 exceeds x%, the old media files are overwritten/discarded. ).
In the same field of endeavor, Davis teaches determining, by an artificial intelligence system, whether an event has occurred at a given time point included in the video data (column 17 lines 18 - 34; As another example, in some embodiments, the selective media transmission system 106 can identify trigger events at the trigger event determination 304 by applying a motion trigger machine-learning model to the motion data 314. In doing so, the motion trigger machine-learning model can identify a velocity pattern, an acceleration/deceleration pattern, an inertial pattern, a driving behavior pattern, etc. and generate a corresponding collision event prediction. In addition, generating the collision event prediction can include the motion trigger machine-learning model determining whether the collision event prediction satisfies one or more probability thresholds indicating a likelihood of a collision event.).
However, Bostick or Davis or Raber does not explicitly teach capturing, in response to one or more of a motion detection sensor detecting motion and a user performing a voice-activated command.
In the same field of endeavor, Kirsch teaches capturing, in response to one or more of a motion detection sensor detecting motion and a user performing a voice-activated command (¶ 103; Sensor subsystem 910 of in-vehicle computing system 900 may communicate with and receive inputs from various vehicle sensors and may further receive user inputs. For example, the inputs received by sensor subsystem 910 may include inputs from an audio sensor detecting voice commands issued by a user, a light sensor detecting light directed toward the vehicle (e.g., impinging on a vehicle camera and/or display), a vehicle-mounted camera detecting images in an environment of the vehicle, a motion sensor indicating motion of the vehicle and/or vehicle-mounted cameras, etc.).
However, Bostick, Davis, or Kirsch or Raber does not teach wherein the artificial intelligence system is located the camera.
In the same field of endeavor, Schmidt teaches wherein the artificial intelligence system is located the camera (column 14 lines 1 – 7).
Therefore, it would have been obvious for one of ordinary skill in the art at the time the invention was effectively filed to modify the invention of Bostick in view of Raber in further view of Davis in further view of Kirsch in further view of Schmidt. The advantage being optimizing automobile trigger event capture.
As per claim 2, Bostick discloses the method of claim 1, wherein the second data storage device includes a second storage capacity larger than the first storage capacity and facilitates storage of more video segments than the first data storage device (column 2 lines 60 – 64 and column 10 lines 32 – 39).
As per claim 3, Bostick discloses the method of claim 1, wherein the reviewing entity to which the video segment is sent includes a law enforcement agency or an insurance company (column 10 lines 55 – 60; As another example, events categorized as an accident (e.g., data from impact sensors and/or video/image data identifying damaged vehicles) may be provided to a video storage server 425 associated with a law enforcement agency, insurance company, roadside assistance company, news organization, traffic reporting organization, etc).
As per claim 4, Bostick discloses the method of claim 1, wherein sending the video segment includes initiating a post on a social network (column 10 lines 55 – 60).
As per claim 5, Bostick discloses the method of claim 4, wherein initiating the post on the social network is performed after receiving a single user input on a user interface (column 10 lines 55 – 60).
As per claim 7, Bostick discloses the method of claim 1, further comprising obtaining metadata associated with the video data, wherein the metadata is used in determining whether the event has occurred and identifying the reviewing entity to which the video segment is to be sent (column 13 lines 40 – 45 and lines 54 – 58).
As per claim 9, Bostick discloses the method of claim 8, wherein the artificial intelligence system is located locally within the camera (column 13 lines 31 – 34; In embodiments, the video uploading module 540 may upload video in a directionally intelligent manner in which only video captured by cameras 405 facing a direction that relevant to the event.).
As per claim 10, Bostick discloses the method of claim 1, wherein the second data storage device is a cloud service (column 2 lines 60 – 64).
As per claim 11, Bostick discloses a network, comprising:
a camera adapted for use within an automobile for capturing video data during operation of the automobile (column 2 lines 42 – 43; Accordingly, vehicles may include a suite of cameras to capture video as the vehicle is being driven.);
an artificial intelligence system having access to the video data to identify an event (column 13 lines 31 – 34);
a first data storage device configured to receive and temporarily store the video data in an ongoing manner during operation a second data storage device configured to store a portion of the video data that has been designated as being associated with the event included with the video data stored in the first data storage device (column 2 lines 60 – 64 and column 10 lines 32 – 39); and
a user interface configured to perform functions that include:
notifying a user of detection of the event detected by the artificial intelligence system; and receiving user input for indicating a user-detected event, wherein the user interface includes an element for sending a video segment associated with the event detected by the artificial intelligence system or the user-detected event to a reviewing entity (column 10 lines 55 – 60 and column 13 lines 63 – column 14 lines 2; column 13 lines 63 – column 14 lines 2: In embodiments, the video uploading module 540 may receive a manual instruction (e.g., via a physical button or via a user device) to upload video to video storage server 425. For example, a user or driver may select to upload video storage server 425 if the video uploading module 540 has not detected an event but if the user wishes to upload video. In embodiments, the video uploading module 540 may, over a period of time, learn the conditions of the vehicle surroundings in which a manual instruction is received to upload video. Further, the video uploading module 540 may create a new rule identifying the conditions so that events can be automatically detected in the future and video can be automatically uploaded.; Bostick’s patent discloses that further artificial intelligence is being applied by generating rules through user data and video analysis of vehicle surrounding data).
However, Bostick does not explicitly teach a smartphone, wherein the artificial intelligence system is located locally on smartphone.
In the same field of endeavor, Schmidt teaches a smartphone, wherein the artificial intelligence system is located locally on smartphone (column 1 lines 46 – 50 and column 17 lines 8 – 15).
Therefore, it would have been obvious for one of ordinary skill in the art at the time the invention was effectively filed to modify the invention of Bostick in view of Schmidt. The advantage being optimizing automobile trigger event capture.
As per claim 15, Bostick discloses the network of claim 11, wherein the element configured to send the video segment associated with the event is a button included with the user interface that initiates sending of the video segment with only a single user input (column 13 lines 63 – column 14 lines 2; In embodiments, the video uploading module 540 may receive a manual instruction (e.g., via a physical button or via a user device) to upload video to video storage server 425. For example, a user or driver may select to upload video storage server 425 if the video uploading module 540 has not detected an event but if the user wishes to upload video. In embodiments, the video uploading module 540 may, over a period of time, learn the conditions of the vehicle surroundings in which a manual instruction is received to upload video. Further, the video uploading module 540 may create a new rule identifying the conditions so that events can be automatically detected in the future and video can be automatically uploaded.; Bostick’s patent discloses that further artificial intelligence is being applied by generating rules through user data and video analysis of vehicle surrounding data).
As per claim 18, Bostick discloses the network of claim 11, wherein the second data storage device is included in a cloud service that enables the video data to be stored and accessed (column 2 lines 60 – 64).
As per claim 19, Bostick discloses The network of claim 11, wherein the first data storage device is in communication with a cloud service that can receive the video segment of the video data to enable the video segment to be stored and accessed (column 2 lines 60 – 64 and column 10 lines 32 – 34; column 10 lines 32 – 34: For example, event detection component 415 may upload video from a local storage device implemented within the vehicle to video storage server 425.).
As per claim 20, Bostick discloses the network of claim 11, wherein the reviewing entity to which the video segment is sent includes a law enforcement agency, an insurance company, or a social media platform (column 10 lines 55 – 60; As another example, events categorized as an accident (e.g., data from impact sensors and/or video/image data identifying damaged vehicles) may be provided to a video storage server 425 associated with a law enforcement agency, insurance company, roadside assistance company, news organization, traffic reporting organization, etc).
Claim(s) 6 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bostick in view Davis in further view of Kirsch in further view of Schmidt in further view of (hereafter Bostick) in further view of Cronin (US 2015/0356853).
As per claim 6, Bostick teaches the method of claim 1.
However, Bostick does not explicitly teach wherein determining whether the event has occurred includes determining changes in motion of the automobile using sensor data captured by an accelerometer, wherein the changes in motion of the automobile exceeding a threshold value indicates that the event has occurred.
In the same field of endeavor, Cronin teaches wherein determining whether the event has occurred includes determining changes in motion of the automobile using sensor data captured by an accelerometer, wherein the changes in motion of the automobile exceeding a threshold value indicates that the event has occurred (¶ 64).
Therefore, it would have been obvious for one of ordinary skill in the art at the time the invention was effectively filed to modify the invention of Bostick in view of Cronin. The advantage is enhanced video analysis.
Regarding claim 17, arguments analogous to those presented for claim 6 are applicable for claim 17.
Claim(s) 12 - 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bostick in further view of Renkis (US 9,686,514).
As per claim 12, Bostick discloses the network of claim 11.
However, Bostick does not explicitly teach wherein the camera is integrated into a smartphone.
In the same field of endeavor, Renkis teaches wherein the camera is integrated into a smartphone (column 7 lines 23 – 26).
Therefore, it would have been obvious for one of ordinary skill in the art at the time the invention was effectively filed to modify the invention of Bostick in view of Renkis. The advantage is enhanced video analysis.
As per claim 13, Bostick discloses the network of claim 12, wherein the artificial intelligence system is located locally on the smartphone (column 13 lines 31 – 34; In embodiments, the video uploading module 540 may upload video in a directionally intelligent manner in which only video captured by cameras 405 facing a direction that relevant to the event.).
As per claim 14, Bostick discloses the network of claim 11, wherein the artificial intelligence system (column 13 lines 31 – 34).
However, Bostick does not explicitly teach is located remotely from the automobile.
In the same field of endeavor, Renkis is located remotely from the automobile (column 7 lines 23 – 26; Renkis disclose that a smartphone is part of the network that is remote from the automobile and further the camera is integrated with the smartphone).
Therefore, it would have been obvious for one of ordinary skill in the art at the time the invention was effectively filed to modify the invention of Bostick in view of Renkis. The advantage is enhanced video analysis.
Other Prior Art Cited
Campbell (US 10,984,275) - storing, at the in-cabin media capture device, the media recording in a first memory partition of the in-cabin media capture device set to overwrite after a predetermined overwrite looping duration; identifying a storage trigger event based on the data associated with the transportation service; and in response to identifying the storage trigger event, storing a subset of the media recording corresponding to the storage trigger event in a second memory partition of the in-cabin media capture device.
Sambo et al (US 2020/0057894) - As shown in FIG. 1D and by reference number 116, the camera, which continuously captures video data, can capture the video data in one-second increments and store the video data as one-second video files in the circular buffer. The circular buffer can store up to 50 one-second video files because the circular buffer has a 50-second capacity. Once the 50-second capacity is reached, the circular buffer can overwrite the oldest one-second video file in the circular buffer with a new one-second video file (¶ 28).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHIKAODILI E ANYIKIRE whose telephone number is (571)270-1445. The examiner can normally be reached 8 am - 4:30 pm.
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/CHIKAODILI E ANYIKIRE/Primary Examiner, Art Unit 2487
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