DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending.
Claims 4, 9-11, 17 and 19-20 are withdrawn from consideration.
Claims 1-3, 5-8, 12-16 and 18 are being examined.
Response to Arguments (Election/Restrictions)
Applicant’s election without traverse of Species 1 (claims 2 and 16, with generic claims 1, 8, 15, and 18) in the reply filed on 4/30/2026 is acknowledged.
In the interest of compact prosecution, the examiner withdraws the restriction in part and agrees to examine Species 1-2, 4-6 and 10-12 together (claims 1-3, 5-8, 12-16 and 18 with generic claims included) in the present application. The restriction is maintained as to Species 3 and 7-9 (claims 4, 9-11, 17 and 19-20), as their cumulative search burden remains undue. Claims directed solely to Species 3 and 7-9 are withdrawn from consideration per 37 C.F.R. § 1.142(b).
Prosecution continues on all claims directed to Species 1-2, 4-6 and 10-12.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claim(s) 1-3, 5-8, 15-16 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mimar (US20140300739A1) in view of Tiong et al (US20230419652A1) and further in view of Shi et al (US20220377421A1).
Regarding claims 1, 8 and 15, Mimar teaches a method, comprising:
receiving, by a device, video data associated with a vehicle experiencing an event;
(Mimar, "a compact cell-phone sized vehicle telematics device with one or more cameras embedded in the same package for evidentiary audio-video recording, automatic accident detection and emergency help request", [0083]; "Because the present method and system uses three or four HDR cameras continuously recording the surrounding and inside to an embedded SD Card non-volatile storage, the present system contains a three or four channel HD Digital Video Recorder (DVR), it sends H.264 or H.265 compressed audio-video information from seconds before the accident to seconds after the accident occurrence.", [0212]; receiving and continuously recording video data by a telematics device embedded in a vehicle that is used for capturing an event, such as a vehicle accident)
determining, by the device, object data identifying objects depicted in the video data;
(Mimar, "The front-view camera is accurate to view the License Plate Number Recognition (LPNR) of the vehicle ahead of the trigger event.", [0214], "Also, number of passengers is detected using face detection using both rear view cameras.", [0207]; processing the video data to detect and identify objects depicted within it, specifically recognizing license plates and using face detection to identify passengers)
calculating, by the device and based on the video data and the object data, sensitivity scores indicating a likelihood of injury or a dangerousness of the event;
(Mimar, "Magnitude of the acceleration: Clearly, the higher the acceleration, the more likely it is to cause injury.", [0145], "The SI measures the area under the acceleration crash curve using weighted by a power value of n. This takes into account both duration and weighted value of deceleration for the T time period.", [0191], "The SI equation above provides a very accurate estimation of accident detection and also determines the severity level of a given accident.", [0192]; calculating severity scores (e.g., Severity Index) based on the crash pulse and deceleration/acceleration values, which explicitly indicates the dangerousness of the event and the likelihood of injury to the occupants)
aggregating, by the device, the sensitivity scores to generate an aggregated score;
(Mimar, "A severe accident is detected by the formula for Severity Index (SI)", [0190]; "The acceleration value A is summed for t=t1 to t2, where n=2.5 is used in one embodiment.", [0191]; "The severity index level SI is scaled to 0 to 10 as a measurement of severity level of an accident", [0193]; aggregating the individual acceleration/sensitivity values by summing them over the duration of the crash to generate a single aggregated score, known as the Severity Index)
determining, by the device, whether the aggregated score satisfies a threshold;
(Mimar, "When SI≧P1Accident accident is detected, where P1Accident is a threshold parameter.", [0191]; determining whether the aggregated Severity Index (SI) score meets or exceeds a predefined threshold parameter to confirm the accident)
selectively:
horizontally concatenating, by the device and based on the aggregated score satisfying the threshold, a subset of frames of the video data to generate an input image, or
discarding, by the device, the video data based on the aggregated score failing to satisfy the threshold;
(Mimar, "The multiplex compressed audio-video is stored on part of USB memory key in a continuous loop", [0087]; "When SI≧P1Accident accident is detected, where P1Accident is a threshold parameter.", [0191]; The claim requires either horizontally concatenating frames OR discarding the video data if the threshold fails. Mimar teaches the latter. By storing the video in a "continuous loop," the device automatically overwrites (discards) the previously recorded video data unless the aggregated severity score meets the threshold parameter to trigger an accident save)
Mimar does not expressly disclose but Tiong and Shi teach:
generating, by the device and based on the sensitivity scores, one or more queries about whether the video data contains graphic content;
(Tiong, "For example, given an input image of a bowl of salad and a query “what are the black objects” in the image, a VQA model is expected to generate an answer based on the visual content", [0003]; Shi, "The content of the target image data may be determined as offensive content in the case that the content relates to terrorism, violence, pornography, gambling, and the like", [0129]; Tiong teaches generating contextual queries about visual content. Shi teaches analyzing videos for graphic content (i.e., violence, pornography, etc.). Combining Shi's objective of evaluating graphic/offensive content with Tiong's querying mechanism teaches generating queries about whether the video contains graphic content based on the severity/offense score)
It would have been obvious to a person having ordinary skill in the art to incorporate Tiong and Shi into the vehicle telematics device of Mimar in order to automatically assess the recorded evidentiary accident footage for graphic or sensitive content by generating targeted visual queries based on the accident's severity score, thereby allowing the system to proactively flag or filter potentially traumatic visual data (such as severe injuries or violence) before the video link is accessed and reviewed by emergency responders or cloud operators. The combination of Mimar, Tiong and Shi also teaches other enhanced capabilities.
The combination of Mimar, Tiong and Shi further teaches:
prompting, by the device, a multi-modal large language model (MMLLM), with the input image and the one or more queries, to determine whether the video data contains graphic content; and
(Tiong, "Specifically, a pretrained vision-language model (PVLM) that describes visual information with textual captions is employed to bridge the vision and language modalities ... Finally, a PLM is employed to generate a text answer in response to the question based on the captions.", [0018]; Shi, "The content of the target image data may be determined as offensive content in the case that the content relates to terrorism, violence, pornography, gambling, and the like", [0129]; Tiong teaches prompting an MMLLM (which is constituted by the combination of the Pretrained Vision-Language Model and Pretrained Language Model) with the image and a query to determine an answer. When combined with Shi, this multi-modal model is prompted to determine whether the video data contains graphic/offensive content)
performing, by the device, one or more actions based on the video data containing graphic content.
(Mimar, Fig. 34; "uploading said captured accident video to a drop box in a cloud storage, and providing a URL link to access it in an emergency help request message;", "composing an emergency help request message which includes accident information including said scaled severity index value, location and time of accident, vehicle information, said nearest address, and link to accident video;", "sending said emergency help request message to IP based emergency services of Public Safety Answering (PSAP) that has jurisdiction over a caller's location, using a messaging system that includes but not limited to one of SMS 911, NG-9-1-1, and eCall.", [claim 1]; performing actions (uploading video, composing/sending emergency message with video link) based on video containing graphic content (accident footage deemed severe/graphic via SI threshold; Shi, " some target video files that do not comply with the review criteria are filtered out (for example, the content of the target video file involving terrorism, violence, pornography, gambling, and the like)", [0123]; "In the case that the file offense score is greater than the file score threshold, it is highly probable that the target video file content is offensive, and the target video file may be distributed to a designated client as a moderation task.", [0146]; performing actions, such as filtering the video out or distributing it to a moderation client, based on the video data containing graphic content);
Regarding claims 2 and 16, the combination of Mimar, Tiong and Shi teaches its/their respective base claim(s).
The combination further teaches the method of claim 1, wherein determining the object data comprises:
utilizing an object detection model and an object tracking model to determine the object data identifying bounding boxes, tracks, and labels for the objects depicted in the video data.
(Mimar, "performing facial processing to determine driver's face gaze direction and level of eyes closed", [claim 11]; facial processing utilizes object detection for faces and eyes (labels), implying bounding boxes for feature extraction, and object tracking for gaze direction over time (tracks); "The present system counts the number of faces in the vehicle for reporting.", [0223]; object detection model identifies and labels faces, with implied bounding boxes for counting; "driver biometric signals (i.e., face gaze direction, level of eyes closed, etc.).", [0122]; gaze tracking requires object tracking model for eye movements (tracks); Fig. 13; "facial processing", [0083]; processing block implies models for detection and tracking of facial objects in video data)
Regarding claim 3, the combination of Mimar, Tiong and Shi teaches its/their respective base claim(s).
The combination further teaches the method of claim 1, further comprising:
discarding the video data and ceasing processing of the video data based on the event not being assigned a major severity or a critical severity.
(Mimar, " The scaled SI index is then calculated by: Scaled SI Index=min(10,(SI/SI MAX))", [0193, 0194]; if scaled SI (severity) is low (not major/critical), no emergency notification, processing ceases, video data overwrites in loop (discarded))
Regarding claim 5, the combination of Mimar, Tiong and Shi teaches its/their respective base claim(s).
The combination further teaches the method of claim 1, wherein performing the one or more actions comprises one or more of:
disabling a preview of the video data in a video list page based on the video data containing graphic content; or
disabling viewing of the video data based on the video data containing graphic content.
(Shi, "the video with offensive content is filtered out.", [0005]; disables viewing/preview of graphic (offensive) video; "The content of the target video file is determined to be legal in the case that first moderation information is received from the client.", [0148]; only legal (non-graphic) enabled for viewing; "In the case that the file offense score is greater than the file score threshold, it is highly probable that the target video file content is offensive, and the target video file may be distributed to a designated client as a moderation task. The client is managed by a specialized moderator ", [0146]; “before or after publishing of the video, the content of the video needs to be moderated, and the video with offensive content is filtered out”, [0005]; “Prior to publishing the target video file, the content of the target video file is moderated based on the moderation criteria, some target video files that do not comply with the review criteria are filtered out (for example, the content of the target video file involving terrorism, violence, pornography, gambling, and the like)”, [0123]; filtering out (Discarding/Blocking) offensive video content)
Regarding claim 6, the combination of Mimar, Tiong and Shi teaches its/their respective base claim(s).
The combination further teaches the method of claim 1, wherein performing the one or more actions comprises one or more of:
preventing downloading of the video data based on the video data containing graphic content; or
displaying information warning that the video data contains graphic content and should only be viewed by approved personnel.
(Shi, "the target video file may be distributed to a designated client as a moderation task. The client is managed by a specialized moderator.", [0146]; graphic content only to approved (moderator), implies warning and prevents general download; "the ratio of the quantity of target video files with the offensive content ...", [0152]; offensive (graphic) filtered, prevents download)
Regarding claim 7, the combination of Mimar, Tiong and Shi teaches its/their respective base claim(s).
The combination further teaches the method of claim 1, wherein performing the one or more actions comprises:
retraining the MMLLM based on the video data containing graphic content.
(Shi, "By backpropagation, the deep neural network is trained as the content moderation model based on the image region data, the sample image data, and the offense category.", [0069]; training the DNN using multi-modal data)
Regarding claim 18, the combination of Mimar, Tiong and Shi teaches its/their respective base claim(s).
The combination further teaches the non-transitory computer-readable medium of claim 15, wherein the one or more instructions, that cause the device to perform the one or more actions, cause the device to one or more of:
disable a preview of the video data in a video list page based on the video data containing graphic content;
disable viewing of the video data based on the video data containing graphic content;
prevent downloading of the video data based on the video data containing graphic content;
display information warning that the video data contains graphic content and should only be viewed by approved personnel; or
retrain the MMLLM based on the video data containing graphic content.
(Shi, see comments on claims 5-7)
Allowable Subject Matter
Claim(s) 12-14 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening Claim(s).
The following is a statement of reasons for the indication of allowable subject matter:
Claim(s) 12-14 recite(s) limitation(s) related to calculating external injury risk by tracking rapid shrinkage and upward movement of pedestrian or animal bounding boxes; enabling interactive two-way communication by receiving, generating, and providing responses to follow-up requests from the MMLLM; and moderating offensive material by selectively blurring video previews or specific graphic portions within the video data. There are no explicit teachings to the above limitation(s) found in the prior art cited in this office action and from the prior art search.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIANXUN YANG whose telephone number is (571)272-9874. The examiner can normally be reached on MON-FRI: 8AM-5PM Pacific Time.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached on (571)272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JIANXUN YANG/
Primary Examiner, Art Unit 2662 6/27/2026
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