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 .
Claim Status
Claims 21-50 are cancelled. Claims 1-20 are currently pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05-09-2025; 01-09-2026; and 08-19-2026 have been considered by the examiner.
Response to Arguments
Applicant's arguments filed 08-19-2026 have been fully considered but they are not persuasive. Applicant argues that US Patent 12,570,318 to BLOOMQUIST does not base a visual presentation on the events criticality level because Bloomquist does not describe visual characteristics of the notification. However, claim 1 does not expressly require the visual presentation to be defined by any particular visual characteristic such as color, size, font or placement etc., and furthermore, does not exclude textual presentation. The newly amended “visual presentation…on the criticality level” therefore encompasses the textual content of the human-readable description which is presented visually.
As set forth in the following detailed analysis of the claim, Bloomquist’s human-readable description of the event is relied upon as the notification which is provided for visual presentation. Bloomquist further teaches that its video-to-text description may describe the seriousness or level of criticality of the event as being a high level of criticality (col. 57: lines 55-65) or as having a criticality level which is low (col. 57: lines 65-67). Thus, the text content of the description reflects the events criticality level as required by the amendment.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 9-13, 19 and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by BLOOMQUIST (US Patent 12,570,318).
Regarding claims 1 and 11,
Bloomquist teaches a computer implemented method and its associated apparatus, the method comprising:
receiving a first video captured using one or more capturing devices (col. 35:22-28 teaches capture devices 104a-104n which may be implemented by one or more of the camera systems 100a-100n. The capture devices 104a-104n may be configured to capture video data of the environment 40 near the vehicle 50. For example, the capture devices 104a-104n may be configured to generate video data corresponding to the all-around view 62a-62d shown in association with FIG. 1.; col. 42:3-8 teaches that camera systems 100a-100n implemented by the vehicle 50 may be configured to capture pixel data of the exterior environment 360 and/or of an interior of the vehicle 50. The video processing pipeline 252 may be configured to generate video frames in response to the pixel data);
analyzing the first video to determine an event that occurred in the first video;
generating a textual summary describing the event (col 35:22-28 teaches that video data is generated by the capture devices 104a-104n may enable the AI module 230 to determine contextual information about the environment 40 during an intervention event. For example, the video data generated by the capture devices 104a-104n may provide data about a number, size and/or location of other vehicles on the road, a number, size and/or location of pedestrians, the shape of the roadway, distances to various obstacles (e.g., trees, signs, buildings, etc.), roadway information (e.g., signs), road markings (e.g., lane lines, turn lane markers, pedestrian crosswalks, etc.), pothole locations, street light status (e.g., an active light on a street light, pedestrian light indicators, railway crossing status, etc.), weather conditions, etc. );
generating a textual summary describing the event (col.42:12-15 teaches that the processor 102 may be configured to generate a natural text description of the video frames and/or detect objects in the video frames)
determining, based at least in part on the generated textual summary describing the event both a criticality level corresponding to the event (col 4:12-15 teaches that the present invention may determine an event significance (e.g., a level of criticality); col. 53:25-30 teaches a video-to-text description 454 may describe the seriousness (or level of criticality). Based on the video-to-text description 454 the level of criticality described, the driving AI model 256 may determine weight values; col. 57:54-59 teaches that the level of criticality may be determined in response to the objects detected in the video frames; col. 44:3-12 teaches that the video-to-text AI model 254 may analyze the video frames to generate the natural text description that may describe the contents of the video data) and a notification corresponding to the event (col 32:21-27 teaches that based upon detected events and following an encountered scenario the system may request input from the driver 60 (e.g., in response to detecting emergency vehicles, the vehicle 50 may ask “How do you think we should drive near the emergency vehicle”), said request for input based upon an encountered event is interpreted as corresponding to "notification corresponding to an event"); and
causing the notification to be provided for presentation at a device, wherein a visual presentation of the notification is based at least in part on the criticality level (col.47:1-10 teaches providing a human readable description of the contents of the video frames with sufficient detail to enable the visually impaired to understand the contents of the video frames (e.g., using a screen reader). Bloomquist further teaches that analysis of a text description, in combination with reference to known critical events, may result in the determination of a high or a low criticality level event (col. 57:lines 55-65); and that the video-to-text description may describe the seriousness, or level of criticality of the event as low (col. 57:lines 65-67). Therefore, Bloomquist teaches that the text content of the human-readable description reasonably corresponds to “based at least in part of the criticality level” as amended. Because the claim does not explicitly require that “visual presentation … based … on the criticality level” correspond to a particular visual characteristic e.g., color, size, placement etc.,. And given that the human-readable text content i.e., “…event as low” is based at least in part on the criticality level, said human readable description corresponds to a “visual presentation … based … on the criticality level”).
Regarding claims 2 and 12,
Bloomquist teaches determining a feature in the first video, wherein the feature corresponds to one or more of a person type, an object type, a company type, a profession type, an event type, an action type, a historical event type, or an alert associated with the first video; wherein: generating the textual summary is based at least in part on the feature (col.22:61-col. 23:3 teaches CNN module 190b may be configured to implement recognition of objects through multiple layers of feature detection. The CNN module 190b may be configured to calculate descriptors based on the feature detection performed. The descriptors may enable the processor 102 to determine a likelihood that pixels of the video frames correspond to particular objects (e.g., a particular make/model/year of a vehicle, identifying a person as a particular individual, detecting a type of animal, detecting characteristics of a face, etc.); col. 30:9-13 teaches the AI module 230 may be a neural processing unit (NPU). In one example, the AI module 230 may be configured to perform computer vision operations. In another example, the AI module 230 may be configured to perform video-to-text operations.)
Regarding claims 3 and 13,
Bloomquist teaches receiving feedback information, wherein the feedback information comprises information indicative of an accuracy of one or more of the textual summary, the criticality level, or the feature (col. 20:27-28);
and analyzing the feedback information in a later analysis of a second received video (col. 43:50-53 teaches a looped computer vision technique to perform iterative object detection).
Regarding claims 9 and 19,
Bloomquist teaches determining the criticality level corresponding to
the event further comprises based on determining that the event corresponds to a predetermined event from an external application (col. 57:62-64 teaches critically determined following comparison to known events), determining that the event corresponds to a non-threat (col. 4:17-20 teaches determining an event is low criticality e.g., 1 on a scale of 1 to 10).
Regarding claims 10 and 20,
Bloomquist teaches determining the criticality level corresponding to
the event further comprises based on determining that the event corresponds to a routine event, determining that the event corresponds to a non-threat (col. 57:62-64 teaches criticality is determined based on a comparison to known criticality events: col. 4:17-20 teaches a criticality scale of 1 to 10 i.e., low to high).
Allowable Subject Matter
Claims 4-8 and 14-18 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 claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIONNE PENDLETON whose telephone number is (571)272-7497. The examiner can normally be reached M-F 9a-5pm.
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/DIONNE PENDLETON/Primary Examiner, Art Unit 2689
