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 .
Status of the Claims
This Office Action is in response to applicant’s filing on 12/18/2025. Claims 1-3, 5, 7 and 8 have been amended. Claims 1-13 are currently pending and addressed below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on December 18th, 2025 has been entered.
Response to Arguments
Applicant's amendments filed 12/18/2025 in regards to the 35 U.S.C. 112(b) have been fully considered and are therefore withdrawn.
Applicant's amendments filed 12/18/2025 in regards to the 35 U.S.C. 101 have been fully considered and are therefore withdrawn.
Applicant’s arguments with respect to claim(s) 1 and 7 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claim(s) 1-5 and 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al., U.S. Pub. No. 2023/0213623A1 in view of Liu et al., U.S. Pub. No. 2023/0356753A1, hereinafter referred to as Li and Liu, respectively.
As per Claims 1 and 7, Li discloses a method and system comprising:
a processor which is configured to extract movement data relating to the captured road users from the sensor data and is further configured to learn a high-definition map of the predefined road section by a machine learning method based on the movement data (see at least Figure 8 and 0048, 0050-0051, 0064, 0128, 0133, “ machine learning HD MAP); and
wherein the processor is further configured to determine, based on an input of current movement data relating to a road user, a prediction of at least one future position of the road user in the predefined road section, using the learned high-definition map (see at least Figure 8 and 0050-0051, 0107 and 0112);
wherein the system, based on determining the prediction, interacts with a warning system that selectively issues warnings in the predefined road section to the road user (see at least 0051).
Li fails to explicitly disclose historical movement data relating to the road user, wherein the historical sensor data relates to captured users in a predefined road section, wherein the high-definition map includes road configuration information defined by the historical movement data, wherein the learned high-definition map is in the form of a trained artificial neural network and the trained artificial neural network is one of a plurality of neural networks, wherein each of the plurality of neural networks is associated with a different road segment and represents a different map. Li does disclose if based on historical data the vehicle is approaching an intersection where multiple fatal accidents happened recently, any person-like or vehicle-like object may have a higher priority. However, Liu teaches historical movement data relating to the road user, wherein the historical sensor data relates to captured users in a predefined road section, wherein the high-definition map includes road configuration information defined by the historical movement data (see at least 0006, 0033 “…predicting near future behavior of road users…historic data associated with the road users states and/or lane segments…and segment-related data. ….The assessment system further feeds the respective states-related data and segment-related data to one or more neural networks…”)
Further, Liu teaches wherein the learned high-definition map is in the form of a trained artificial neural network and the trained artificial neural network is one of a plurality of neural networks, wherein each of the plurality of neural networks is associated with a different road segment and represents a different map (see at least 0011, 0035 “…one or more neural networks configured to encode the road users states in view of the lane segments spatially and temporally, and output spatial-and temporal-processed respective states-related data and segment-related data…)
Thus, Li discloses determining future trajectories using a trained neural network while Liu also teaches using historical data of road users and more than one neural network for road segments.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Li to include historical movement data and more than one neural network as taught by Liu, with a reasonable expectation of success because for behavior and/or trajectory prediction in general, intentions of traffic participants are a key factor. Knowing the intentions of other traffic participants may reduce uncertainties and improve performance of a driving system (Liu 0012).
As per Claims 2 and 8, Li discloses wherein the road user comprises a plurality of road users and the at least one future position comprises a plurality of future positions of the road users, wherein the learned high-definition map is in the form of a trained artificial neural network, and wherein the system further comprises a processor configured for inputting the current movement data relating to the road users in the predefined road section into the trained artificial neural network generated in this way for predicting the future positions of the road users in the predefined road section (see at least Figure 8 and 0050-0051, 0107 and 0112).
As per Claims 3 and 9, Li discloses wherein the processor is configured to generate the learned high-definition map as the trained artificial neural network at least based on positions of the road users as movement data, and wherein the processor is configured to generate the future positions of each road user when inputting the current movement data relating to the road users into the trained artificial neural network generated in this way (see at least Figure 8 and 0135 “relative positions”).
Li fails to disclose relative historical positions. Li does disclose relative positions of the regions of interest (ROI). However, Liu teaches relative historical positions (see at least 0032, “the data associated with the road users states may be represented by any feasible data relating the obtained states, such as positions, orientations and/or velocities etc of surrounding road users 3, for instance including the vehicle 2.”).
Thus, Li discloses determining future trajectories using a trained neural network while Liu also teaches determining future trajectories using past/historical data for a neural network.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Li to include historical movement data to determine future trajectories as taught by Liu, with a reasonable expectation of success because for behavior and/or trajectory prediction in general, intentions of traffic participants are a key factor. Knowing the intentions of other traffic participants may reduce uncertainties and improve performance of a driving system (Liu 0012).
As per Claims 4 and 10, Li fails to explicitly disclose wherein the movement data of the road users comprises, in addition to the relative historical positions of the road users, at least one of relative historical speeds of the road users, relative historical direction of the road users, or relative historical accelerations of the road users.
Li does disclose GPS (absolute) location. However, Liu teaches wherein the movement data of the road users comprises, in addition to the relative historical positions of the road users, at least one of relative historical speeds of the road users, relative historical direction of the road users, or relative historical accelerations of the road users (see at least 0011, “since there is obtained states of road users in the vehicle's surroundings, there is acquired—for instance based on sensor data and/or perception data e.g. from surrounding detecting sensors and/or a perception system—states data and/or object information of one or more traffic participants in vicinity of the vehicle, such as respective current or essentially current position(s), orientation(s), vehicle speed(s), acceleration(s) and/or deceleration(s) etc.”).
Thus, Li discloses determining future trajectories using a trained neural network while Liu also teaches determining speed, acceleration and deceleration.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Li to include historical speed, direction and acceleration data to determine future trajectories as taught by Liu, with a reasonable expectation of success because for behavior and/or trajectory prediction in general, intentions of traffic participants are a key factor. Knowing the intentions of other traffic participants may reduce uncertainties and improve performance of a driving system (Liu 0012).
As per Claim 5, Li discloses wherein the movement data comprises absolute positions coupled at least to the relative positions, and wherein the processor is configured to generate an absolute high-definition map as the trained artificial neural network at least using the relative positions and the absolute positions of the road users, and wherein the processor is configured to generate absolute future positions of the road user when inputting the current movement data relating to a road user into the trained artificial neural network generated in this way (see at least 0050-0051 and 0120, “GPS Location”).
Li fails to explicitly disclose historical positions. Li does disclose if based on historical data the vehicle is approaching an intersection where multiple fatal accidents happened recently, any person-like or vehicle-like object may have a higher priority. However, Liu teaches historical locations (historical movement data relating to the road user, wherein the historical sensor data relates to captured users in a predefined road section, wherein the high-definition map includes road configuration information defined by the historical movement data (see at least 0006, 0033 “…predicting near future behavior of road users…historic data associated with the road users states and/or lane segments…and segment-related data. ….The assessment system further feeds the respective states-related data and segment-related data to one or more neural networks…”)
Thus, Li discloses determining future trajectories using a trained neural network while Liu also teaches using historical data of road users.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Li to include historical movement data as taught by Liu, with a reasonable expectation of success because for behavior and/or trajectory prediction in general, intentions of traffic participants are a key factor. Knowing the intentions of other traffic participants may reduce uncertainties and improve performance of a driving system (Liu 0012).
Claims 6 and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Liu as applied to claims 1 and 7 above, and further in view of Stewart et al., U.S. Pub. No. US2021/0086784A1, hereinafter referred to as Stewart.
As per Claims 6 and 11, Li fails to disclose wherein the processor is configured to generate historical swarm trajectories from the historical movement data and is further configured to generate a vector map from the historical swarm trajectories as the learned high-definition map for the road section, and wherein the system further comprises a processor configured to generate the prediction of the at least one future position of the road user in the predefined road section using the current movement data and the vector map as the learned high-definition map. Ng does teach vector maps (see at least Figure 5c, 0057)
However, Stewart teaches historical swarm trajectories (see at least 0031 and 0037, “the path segment may describe the path (e.g., the paths 115 in FIG. 1) that each swarming vehicle 110 will follow over time. The VCU 210 may compare the position, velocity, or heading of the swarming vehicle 110 with the position, velocity, or heading specified in the test plan for past time intervals or for future time intervals”).
Thus, Li and Liu teach future trajectories based on historical data while Stewart teaches using vehicle swarm data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Li to include historical swarm data to determine future trajectories as taught by Stewart, with a reasonable expectation of success because utilizing multiple vehicle data in the same location will provide more accurate trajectories (see at least Stewart 0044).
As per Claim 12 and 13, Li fails to disclose generating historical movement trajectories with a predefined length from the detected historical movement data as relevant swarm trajectories, clustering the same or similar historical movement trajectories and creating routes from the respective clusters.
However, Stewart teaches historical swarm trajectories (see at least 0004, 0030, “wherein the second test plan is created based on the system test plan”, “a modified test plan may include trajectory data for a single or specific swarming vehicle”).
Thus, Li and Liu teach future trajectories based on historical data while Stewart teaches using vehicle swarm data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Li to include swarm data to determine future trajectories as taught by Stewart, with a reasonable expectation of success because utilizing multiple vehicle data in the same location will provide more accurate trajectories (see at least Stewart 0044).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Fadey S Jabr whose telephone number is (571)272-1516. The examiner can normally be reached Monday-Friday 8:30am-4:30pm.
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.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fadey S Jabr can be reached at 571-272-1516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
FADEY S. JABR
Supervisory Patent Examiner
Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
Prosecution Insights