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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-4, 6-12, 14, 16-19 are rejected under 35 U.S.C. 102 (a)(1) as being anticiapted by Goldman (WO 2021/198772).
As to claim 1 representative of claim 12, Goldman teaches a detailed vehicle navigation system with crowdsourcing by, flagging, and rectifying unexpected traffic sign data (section 0003, discusses autonomous vehicles based on signs) comprising:
a plurality of vehicles, each with a front sensor, configured to capture traffic sign data (section 0198, sparse maps are created from multiple vehicles by crowdsourcing);
a traffic sign data aggregation system configured to collect and store the captured traffic
sign data from the plurality of vehicles over a span of time (section 0198, GPS sensors from one or more vehicles along a particular roadway);
the traffic sign data aggregation system further configured to identify and flag one or more potential false traffic sign detections, from the captured traffic sign data, based on a road category and an associated rule (section 0204 specific signs are detected and identified based on road features and locations, classify the image as a sign and specific type of sign);
a time and spatial filtering system configured to determine, for one or more traffic
zones, an average vehicle speed during a non-maximal traffic period (section 0287 the velocity and speed of the vehicles are determined for navigation trajectory determination);
the time and spatial filtering system further configured to filter the captured traffic sign
data based on a traffic sign category (section 0207 discloses identifying speed limit sign, yield signs, stop signs based on the recognized type);
the traffic sign data aggregation system further configured to determine from the
filtered captured traffic sign data, based on the time and spatial filtering system
determinations, a most likely traffic sign legend (0206 to 0207 creates sparse maps based on crowdsourcing and object classification using positional data and section 0219 discusses harvesting the vehicle images collected)
As to claims 3 and 14, Goldman teaches wherein the traffic sign category includes a speed limit sign, a stop sign, or a yield sign (section 0207 teaches speed signs, stop signs, and yield signs).
As to claim 4, Goldman teaches wherein the road category includes a primary, a secondary, and a tertiary (section 0217 identifies road profile data and landmarks).
As to claims 6 and 16, Goldman teaches comprising the traffic sign data aggregation system further configured to perform data clustering by grouping traffic sign data associated with a single particular traffic sign (sections 0235 to 0240 identify traffic signs through a recognition of locations and landmarks and a standardized set of characteristics).
As to claims 7 and 17, Goldman teaches wherein the captured traffic sign data from the plurality of vehicles over a span of time comprises data collected and analyzed by a crowdsourcing algorithm (sections 0243 to 0245 and section 0259).
As to claims 8 and 18, Goldman teaches where the time and spatial filtering system is further configured to apply a distribution method to process crowdsourced telemetry data including an estimated confidence score (section 0245, uses a ratio of images and thresholds for crowdsourcing).
As to claims 9 and 18, Goldman estimating a confidence score based on determining a maximum peak value and a qualified peak value from the crowdsourced telemetry data (section 0245, uses a ratio of images and thresholds for crowdsourcing, thresholds inherently have qualified max and min values)
As to claims 10 and 19, Goldman teaches where the time and spatial filtering system is further configured to perform a de-duplication process based on the determined most likely traffic sign legend (section 0311 teaches accepting and rejecting possible landmarks associated with road segments based enabled crowdsourcing, see also sections 30-31).
As to claim 11, Goldman teaches comprising where the time and spatial filtering system is further configured to filter data based on a speed limit category by filtering out speed values less
than a threshold value (section 0245, uses a ratio of images and thresholds for crowdsourcing, thresholds inherently have qualified max and min values, see also section 30-31 low speed)
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 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Goldman (WO 2021/198772) in view of Oliveira et al (Misbehaviour Detection System for Intelligent Speed Assistance)
As to claims 5 and 15, Goldman teaches identify traffic signs through a recognition of locations and landmarks and a standardized set of characteristics (sections 0235 to 0240). Goldman also teaches removing ghost landmarks (i.e., removal of signs) (section 0283).
Goldman does not flag one or more false traffic sign detections as a high risk when the road
category is not a primary level and the speed limit is greater than a threshold value.
` Oliveira teaches an automatic driving assistance method and detecting road signs (pg. 1792, 1-30). Oliveria teaches an object decoder tracker (fig. 6), Vehicle Node detector (fig. 7), Perception detector (fig. 8), and Traffic Properties extractor fig. 9) that are used to flag misbehaving vehicles based on traffic signs detected and speed/velocity of the vehicle (page 1796, col. 1, Perception detector and col. 2, IVIM detector). Oliveria flags vehicles based on traffic density or mis-behaving vehicles (page 1796, col. 2, IVIM detector paragraph 1 discloses accident, danger, or roadwork and traffic velocity).
It would have been obvious to one of ordinary skill in the art for Goldman to use the automative vehicle flag detection, as taught by Oliveria, to slow cars in dangerous situations.
Allowable Subject Matter
Claims 2 and 13 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.
Claim 20 is allowed.
The following is a statement of reasons for the indication of allowable subject matter: Claim 2 and 13 further allows the traffic sign data aggregation system to filter unfit data selected from a selected vehicle (from a plurality vehicles in independent claims) as a source of error in determining a traffic sign speed limit value). Independent claim 20 incorporates this feature in combination with the other features of the claim.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW C BELLA whose telephone number is (571)272-7778. The examiner can normally be reached Mon-Fri: 8:00am-5:00pm.
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/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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