Prosecution Insights
Last updated: August 15, 2026
Application No. 19/226,600

GRAPHICAL NEURAL NETWORK IN ALIGNMENT AND ROAD FEATURE GENERATOR

Non-Final OA §102
Filed
Jun 03, 2025
Priority
Jun 04, 2024 — provisional 63/655,687 +1 more
Examiner
NGUYEN, TAN QUANG
Art Unit
Tech Center
Assignee
Mobileye Vision Technologies Ltd.
OA Round
1 (Non-Final)
91%
Grant Probability
Favorable
1-2
OA Rounds
11m
Est. Remaining
98%
With Interview

Examiner Intelligence

Grants 91% — above average
91%
Career Allowance Rate
1050 granted / 1159 resolved
+30.6% vs TC avg
Moderate +7% lift
Without
With
+7.1%
Interview Lift
resolved cases with interview
Fast prosecutor
2y 2m
Avg Prosecution
26 currently pending
Career history
1173
Total Applications
across all art units

Statute-Specific Performance

§101
3.8%
-36.2% vs TC avg
§103
37.3%
-2.7% vs TC avg
§102
44.3%
+4.3% vs TC avg
§112
5.0%
-35.0% vs TC avg
Black line = Tech Center average estimate • Based on career data from 1159 resolved cases

Office Action

§102
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 . DETAIL ACTION Notice to Applicant(s) This application has been examined. Claims 1-26 are pending. The prior arts submitted on September 29, 2025 and February 12, 2026 have been considered. 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 1-26 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Taieb et al. (2021/0372808). As per claim 1, Taieb et al. disclose a system for generating a map for use in navigating a host vehicle relative to a road segment which includes at least one processor comprising circuitry and a memory, wherein the memory includes instructions that when executed by the circuitry cause the at least one processor (see at least figures 8, 12; paragraph 0213) to receive drive information from each of a plurality of harvesting vehicles that traversed the road segment, wherein the drive information received from each of the plurality of harvesting vehicles includes at least one location indicator associated with an actual trajectory traveled by the harvesting vehicle, as the harvesting vehicle traversed the road segment (see at least figures 11A, 12, 13); provide the drive information received from each of the plurality of harvesting vehicles to a trained model, wherein the trained model is configured to receive the drive information as input and output normalized drive information for each of the plurality of harvesting vehicles, wherein the normalized drive information includes the at least one location indicator aligned relative to a predetermined reference location (see at least figures 11A, 12; paragraphs 0246, 0434, 0438, 0481); aggregate the normalized drive information provided for each of the plurality of harvesting vehicles to determine one or more target drivable paths through the road segment (see at least figure 14); store in the map the one or more target drivable paths (see at least paragraph 0220); and distribute the map data to at least one host vehicle navigation system for use in navigating the host vehicle along the road segment relative to the one more mapped target drivable paths (see at least paragraph 0287). As per claim 2, Taieb et al. disclose that the drive information also includes at least one identifier indicative of a detected road sign along with at least one indicator of a position of the detected road sign (see at least figures 36, 37A; paragraph 0486, 0494). As per claim 3, Taieb et al. disclose that the normalized drive information includes the at least one indicator of the position of the detected road sign aligned relative to the predetermined reference location (see at least figure 38; paragraphs 0498, 0505). As per claim 4, Taieb et al. disclose that the memory includes instructions that when executed by the circuitry cause the at least one processor to aggregate the normalized drive information to determine a refined position for the detected road sign (see at least figures 16, 17,37A, paragraph 0412); store the refined position for the detected road sign in the map (see at least figure 38; paragraphs 0498, 0261); and distribute the map data to the at least one host vehicle navigation system for use in navigating the host vehicle along the road segment relative to the refined position for the detected road sign (see at least paragraphs 0261, 0287). As per claim 5, Taieb et al. disclose that the navigation of the host vehicle relative to the refined position for the detected sign includes localizing the host vehicle in the real world based on the refined position for the detected road sign stored in the map and based on a location of a representation of the detected road sign in at least one image acquired by an image capture device onboard the host vehicle (see at least 0310, 0498). As per claim 6, Taieb et al. disclose that the drive information also includes one or more location indicators associated with each of a plurality of detected lane markings (see at least paragraph 0348). As per claim 7, Taieb et al. disclose that the normalized drive information includes the one or more location indicators associated with each of the plurality of detected lane markings aligned relative to the predetermined reference location (see at least paragraphs 0324, 0325). As per claim 8, Taieb et al. disclose that the memory includes instructions that when executed by the circuitry cause the at least one processor to aggregate the normalized drive information to determine a refined path for each of the plurality of detected lane markings; store indicators of the refined path for each of the plurality of detected lane markings in the map; and distribute the map data to the at least one host vehicle navigation system for use in navigating the host vehicle along the road segment relative to the refined paths associated with the plurality of detected lane markings (see at least paragraphs 0348, 0350). As per claim 9, Taieb et al. disclose that the drive information also includes one or more location indicators associated with a detected road edge (see paragraph 0348). As per claim 10, Taieb et al. disclose that the normalized drive information includes the one or more location indicators associated with the detected road edge aligned relative to the predetermined reference location (see at least paragraph 0348, 0350). As per claim 11, Taieb et al. disclose that the memory includes instructions that when executed by the circuitry cause the at least one processor to aggregate the normalized drive information to determine a refined path representative of the detected road edge; store indicators of the refined path representative of the detected road edge in the map; and distribute the map data to the at least one host vehicle navigation system for use in navigating the host vehicle along the road segment relative to the refined path representative of the detected road edge (see at least paragraphs 0221, 0348). As per claim 12, Taieb et al. disclose that the at least one location indicator associated with the actual trajectory traveled by one of the harvesting vehicles includes a plurality of point locations along the actual trajectory (see at least figures 24A-D; paragraphs 0345, 0340, 0348). As per claim 13, Taieb et al. disclose that the plurality of point locations includes 3D GPS coordinates (see at least paragraph 0218). As per claim 14, Taieb et al. disclose that the plurality of point locations includes 3D real world coordinates (see at least paragraph 0348). As per claim 15, Taieb et al. disclose that the 3D real world coordinates are determined, at least in part, based on localization of the harvesting vehicle relative to one or more recognized landmarks represented in one or more captured images (see at least paragraphs 0273, 0279). As per claim 16, Taieb et al. disclose that the predetermined reference location is an origin associated with the map (see paragraph 0505). As per claim 17, Taieb et al. disclose that the origin is associated with a segment of the map (see paragraph 0505). As per claim 18, Taieb et al. disclose that the predetermined reference location is a 3D real world point location (see paragraph 0505). As per claim 19, Taieb et al. disclose that the one more target drivable paths are stored in the map as a 3D spline (see at least paragraph 0220). As per claim 20, Taieb et al. disclose that the 3D spline approximates a refined actual trajectory determined based on crowdsourced aggregation of the drive information received from the plurality of harvesting vehicles (see at least paragraphs 0218, 0220). As per claim 21, Taieb et al. disclose that the trained model includes a graph neural network (see paragraph 0166). As per claim 22, Taieb et al. disclose that each of the one more target drivable paths is associated with one or more lanes of the road segment (see paragraph 0220). As per claim 23, wherein the association of each of the one more target drivable paths with the one or more lanes of the road segment is stored in the map (see paragraph 0220). With respect to claims 24-26, the limitations of these claims have been noted in the rejections above. They are therefore considered rejected as set forth above. Conclusion All claims are rejected. The following references are cited as being of general interest: Graefe et al. (2019/0132709), Graves (2025/0012576) and Nayak et al. (2025/0335278). Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAN QUANG NGUYEN whose telephone number is (571) 272-6966. The examiner can normally be reached on Monday to Thursday from 7:00am to 5:30pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Nolan, can be reached at 570-270-7016. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://portal.uspto.gov/external/portal. Should you have questions about access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. July 16, 2026 /TAN Q NGUYEN/Primary Examiner, Art Unit 3661
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Prosecution Timeline

Jun 03, 2025
Application Filed
Jul 21, 2026
Non-Final Rejection mailed — §102 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
91%
Grant Probability
98%
With Interview (+7.1%)
2y 2m (~11m remaining)
Median Time to Grant
Low
PTA Risk
Based on 1159 resolved cases by this examiner. Grant probability derived from career allowance rate.

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