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 .
Response to Amendment
The amendment filed on 02/19/2026 has been entered. Claims 1-7, 9-20 remain pending in the application.
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 1, 2, 3, 15, 16, 17, 18 are rejected under 35 U.S.C. 103 as being unpatentable by Newman (US20200200855) in view of Li (US20220009488) and Ellis (US20040083035).
Regarding claim 1, Newman teaches a computer device comprising at least one processor and at least one memory device, wherein the computer is associated with a host vehicle, wherein the at least one processor is programmed to ([0049] disclosing a computer with codes):
Receive sensor information from one or more sensors associated with the host vehicle ([0038]-[0043], [0053]-[0054] disclosing localization of other vehicles via the use of host vehicle sensor data);
Build a local object ([0043] disclosing building an object map that defines preceding vehicles and adjacent vehicles and following vehicles that are potential target vehicles. [0095]-[0097] disclosing a location of each vehicle around the own vehicle as a local object map based on localization data);
compare the local object ([0043] disclosing the determining the direction of the other vehicle with respect to own vehicle. [0043] disclosing determining based preceding vehicles and following vehicles. [0095]-[0097] disclosing based on localization to determine which vehicles are the front vehicles thus they are the target leading vehicle, i.e., matched scenario, and which vehicle in the back which would match a following vehicle scenario as the target following vehicle. see also [0120] disclosing an predicted intended driving operation to avoid an accident to changing to an adjacent lane thus notifying an adjacent target vehicle of the vehicle’s intention based on this scenario).
identify, from the plurality of predefined driving scenarios based on the comparing, a matching driving scenario that includes (i) a driving operation that matches the predicted intended driving operation, and (ii) a pre-defined potential target vehicle location that matches the current location of the at least one potential vehicle with respect to the host vehicle ([0043] disclosing the determining the direction of the other vehicle with respect to own vehicle. [0043] disclosing determining based preceding vehicles and following vehicles. [0095]-[0097] disclosing based on localization to determine which vehicles are the front vehicles thus they are the target leading vehicle, i.e., matched scenario, and which vehicle in the back which would match a following vehicle scenario as the target following vehicle. [0120] disclosing a predicted intended driving operation to avoid an accident to changing to an adjacent lane thus notifying an adjacent target vehicle of the vehicle’s intention based on this scenario. Since vehicle’s store all control action and scenarios in memory, thus this scenario is identified to match what is stored in the control memory and thus the action is taken to notify the neighboring vehicle, or the front vehicle or the rear vehicle depending on each of the scenarios above).
identify, based on the identified matching scenario, the at least one potential target vehicle as being a target vehicle to which to transmit one or more messages (at least [0095]-[0097] disclosing identifying the target vehicles based on the matching scenarios to be the leading and following vehicle), and
transmit the one or more messages to the target vehicle about the identified matching driving scenario, wherein the target vehicle is controlled based at least in part of the one or more messages ([0095]-[0097] disclosing strongly braking the rear vehicle based on the message).
While Newman does not explicitly disclose an object map. Predict, based on at least one control input received from an operator of the host vehicle, an intended driving operation of the host vehicle.
Li teaches an object map ([0053]-[0064] disclosing updating a map distribution of vehicles around host vehicle, i.e., object map).
Newman already teaches the localization of leading/front/rear/adjacent vehicles around the host vehicle, thus It would be obvious to combine the object map of Li with the localization technique of Newman yielding predictable results and improving the localization of other vehicles within lanes.
Ellis teaches Predict, based on at least one control input received from an operator of the host vehicle, an intended driving operation of the host vehicle ([0153] disclosing based on the intended predicted lane change based on a driver input, the signal is transmitted to other vehicles as intent to change lanes).
The combination of the teaching of Ellis obvious yielding predictable results in order to predict a driver intention thus improving the alert transmission of Newman to incorporate the intention of a driver thus improving safety by alerting other vehicles of a driver’s intention.
Regarding claim 2, Newman as modified by Li teaches the computer device of claim 1, wherein the at least one processor is further programmed to receive one or more status messages from a first vehicle within a predetermined distance of the host vehicle (Newman [0085] disclosing the surrounding vehicles sending status message as localization signals to a receiving vehicle, [0095]-[0097] disclosing localizing vehicles within a predetermined range based on the localization signals including their codes).
Regarding claim 3, Newman as modified by Li and Ellis further teaches the computer device of claim 2, wherein the at least one processor is further programmed to update the local object map of the surroundings of the host vehicle based upon the received one or more status messages.
Specifically Li teaches wherein the at least one processor is further programmed to update the local object map of the surroundings of the host vehicle based upon the received one or more status messages ([0053]-[0064] disclosing the vehicle acquiring the movement information received from other vehicles which includes the position information of other vehicles within a predetermined region from the vehicle and update a map distribution of vehicles. [0064] disclosing receiving the data from gps of other vehicles).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching Newman as modified by Li and Ellis and Li to incorporate the teaching of Li of updating the map based on the received status message in order to determine positional relationship to all vehicles within a predetermined region around the vehicle as taught by Li [0053]-[0064].
Regarding claim 15, Newman as modified by Li and Ellis teaches computer device of Claim 1, wherein the at least one processor is further programmed to determine if each potential target vehicle is in a same lane at the host vehicle.
Specifically, Li teaches wherein the at least one processor is further programmed to determine if each potential target vehicle is in a same lane at the host vehicle ([0061] disclosing the positional information of other vehicles in lanes).
The combination of lane information as taught by Li is obvious yielding predictable results and improving driving safety by determining where vehicles are located and their movements to inform a driver at least as taught by Li [0104].
Regarding claim 18, Newman as modified by Li and Ellis teaches the computer device of claim 1, wherein the at least one processor is further programmed to: one or more status messages from a first vehicle within a predetermined distance of the vehicle (Newman [0085] disclosing the surrounding vehicles sending status message as localization signals to a receiving vehicle, [0095]-[0097] disclosing localizing vehicles within a predetermined range based on the localization signals including their codes).
Li further teaches update the local object map of the surroundings of the vehicle based upon the one or more status messages ([0053]-[0064] disclosing the vehicle acquiring the movement information received from other vehicles which includes the position information of other vehicles within a predetermined region from the vehicle. [0064] disclosing receiving the data from gps of other vehicles).
It would have been obvious to one of ordinary skill in the art to have modified the teaching of Newman as modified by Li and Ellis to incorporate the teaching of Li of update the local object map of the surroundings of the vehicle based upon the one or more status messages in order to update positional relationship to all vehicles within a predetermined region around the vehicle as taught by Li [0053]-[0064]. Newman already teaches the localization of leading/front/rear/adjacent vehicles around the host vehicle, thus It would be obvious to combine the object map of Li with the localization technique of Newman yielding predictable results and improving the localization of other vehicles within lanes.
Claims 16, 17 are rejected for similar reasons as claim 1, see above rejection.
Claims 4, 19 are rejected under 35 U.S.C. 103 as being unpatentable by Newman (US20200200855) in view of Li (US20220009488) and Ellis (US20040083035) and Kim el (US20230278555).
Regarding claim 4, Newman as modified by Li and Ellis teaches the computer device of claim 1, wherein the at least one processor is further programmed to: Receive a plurality of status messages from a plurality of vehicles within a predetermined distance of the host vehicle (Newman [0095]-[0097], [0085] disclosing receiving localization signal “status message” from a plurality of vehicles within a range).
Newman as modified by Li and Ellis does not teach Build the local object map of the surroundings of the hist vehicle based upon the received plurality of status messages status messages and the received sensor information.
Kim el teaches Build the local object map of the surroundings of the hist vehicle based upon the received plurality of status messages and the received sensor information ([0433] discloses fusing the map data and the sensed data into a map data based on the received speeds and locations of the other vehicles).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Kim el of Build the local object map of the surroundings of the hist vehicle based upon the received one or more status messages and the received sensor information, yielding predictable results in order to achieve more accurate control by basing the control on a current status of the other vehicle as taught by Kim [0433].
Claim 19 is rejected for similar reasons as claim 4, see above rejection.
Claims 5, 6, 20 are rejected under 35 U.S.C. 103 as being unpatentable by Newman (US20200200855) in view of Li (US20220009488) and Ellis (US20040083035) and Nagase (US20070213924).
Regarding claim 5, Newman as modified by Li and Ellis teaches the computer device of claim 1. Newman as modified by Li and Ellis further teach wherein the at least one processor is further programmed to: Detect a plurality of vehicles within a predetermined distance of the host vehicle based upon the local object map; Determine a corresponding distance for each of the plurality of vehicles; Identify the target vehicle based upon the identified driving scenario and the plurality of distances for the plurality of vehicles.
Li teaches Detect a plurality of vehicles within a predetermined distance of the host vehicle based upon the local object map messages ([0053]-[0064] disclosing the vehicle acquiring the movement information received from other vehicles which includes the position information of other vehicles within a predetermined region from the vehicle and update a map distribution of vehicles. [0053]-[0064] further disclose determining distance relationship between the vehicle and surrounding vehicle based on the vehicle distribution map. [0064] disclosing receiving the data from gps of other vehicles).
Determine a corresponding distance for each of the plurality of vehicles ([0053]-[0064] further disclose determining distance relationship between the vehicle and surrounding vehicle based on the vehicle distribution map);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Newman as modified by Li and Ellis to incorporate the teaching of Li of a plurality of vehicles within a predetermined distance of the host vehicle based upon the local object map and Determine a corresponding distance for each of the plurality of vehicles in order to determine positional relationship to all vehicles within a predetermined region around the vehicle as taught by Li [0053]-[0064].
Newman as modified by Li and Ellis does not teach Identify the target vehicle based upon the identified driving scenario and the plurality of distances for the plurality of vehicles
Nagase teaches Identify the target vehicle based upon the identified driving scenario and the plurality of distances for the plurality of vehicles ([0051]-[0054] disclosing determining a plurality of vehicles leading and trailing the host vehicle and determining distance to the vehicles that are within a threshold distance from the host vehicle based on a determined scenario of no traffic determined and a plausibility of blocking the other vehicle, the trailing vehicle is the target vehicle that is within a distance from the host vehicle).
It would have been obvious to one of ordinary skill in the art to have modified the teaching of Newman as modified by Li and Ellis to incorporate the teaching of Nagase of Identify the target vehicle based upon the identified driving scenario and the plurality of distances for the plurality of vehicles in order to change lane and avoid obstructing another vehicle as taught by Nagase [0051]-[0054].
Regarding claim 6, Newman as modified by Li and Ellis and Nagase teaches the computer device of claim 5, wherein the at least one processor is further programmed to categorize the plurality of vehicles into a subset of trailing vehicles and a subset of leading vehicles.
Nagase teaches wherein the at least one processor is further programmed to categorize the plurality of vehicles into a subset of trailing vehicles and a subset of leading vehicles ([0051]-[0054] disclosing the vehicle is preceding in front of the host vehicle and following behind the host vehicle, i.e., categorized as following or leading).
It would have been obvious to one of ordinary skill in the art to have modified the teaching of Newman as modified by Li and Ellis and Nagase to incorporate the teaching of Nagase of wherein the at least one processor is further programmed to categorize the plurality of vehicles into a subset of trailing vehicles and a subset of leading vehicles in order to change lane and avoid obstructing another vehicle as taught by Nagase [0051]-[0054].
Claim 20 is rejected for similar reasons as claim 5, see above rejection.
Claims 7 are rejected under 35 U.S.C. 103 as being unpatentable by Newman (US20200200855) in view of Li (US20220009488) and Ellis (US20040083035) and Sato (US20200357283).
Regarding claim 7, Newman as modified by Li and Ellis does not teach the computer device of Claim 1, wherein the at least one processor is further programmed to store a plurality of the plurality of pre-defined driving scenarios in the at least one memory device, the plurality of pre-defined driving scenarios including one or more of Lane Change; Ambiguous Right-of-Way at Stop-Sign Intersections; Slow Traffic Ahead; Tailgating; Late Start at a Green Light; and Curved Roads.
Sato teaches further programmed to store a plurality of the plurality of pre-defined driving scenarios in the at least one memory device, the plurality of pre-defined driving scenarios including one or more of Lane Change; Ambiguous Right-of-Way at Stop-Sign Intersections; Slow Traffic Ahead; Tailgating; Late Start at a Green Light; and Curved Roads ([0388] disclosing a slow traffic situation. [0433] disclosing a scenario of passing another vehicle. [0120] disclosing identifying the scenario of traffic jam to determine to share information).
It would be obvious to combine the predefined driving scenarios of Sato yielding predictable results and improving driving safety by sharing specific dangerous traffic situations to avoid collisions for a following vehicle as taught by Sato [0120].
Claims 9 are rejected under 35 U.S.C. 103 as being unpatentable by Newman (US20200200855) in view of Li (US20220009488) and Ellis (US20040083035) and Masui (US11975716).
Regarding claim 9, Newman as modified by Li and Ellis teaches the computer device of Claim 1, Newman as modified by Li and Ellis does not teach wherein the at least one processor is further programmed to identify the target vehicle based upon a lateral distance between the host vehicle and the target vehicle.
Masui teaches wherein the at least one processor is further programmed to identify the target vehicle based upon a lateral distance between the host vehicle and the target vehicle (col 8 last paragraph discloses the target is identified based on the lateral distance less than a threshold).
It would have been obvious to one of ordinary skill in the art to have modified the teaching of Newman as modified by li and Ellis to incorporate the teaching of Masui of wherein the at least one processor is further programmed to identify the target vehicle based upon a lateral distance between the host vehicle and the target vehicle in order to properly identify the target for appropriate driving assistance control as taught by Masui col 7. The combination yields predictable results of identifying vehicles within a threshold lateral distance as target vehicles improving driving safety.
Claims 10 are rejected under 35 U.S.C. 103 as being unpatentable by Newman (US20200200855) in view of Li (US20220009488) and Ellis (US20040083035) and Nemoto (US20220194368).
Regarding claim 10, Newman as modified by Li and Ellis teaches the computer device of Claim 1. Newman as modified by Li and Ellis does not teach wherein the at least one processor is further programmed to: store a plurality of historical locations for a plurality of vehicles; and identify the target vehicle that will be affected by the identified driving scenario based upon the plurality of historical locations for the plurality of vehicles.
Nemoto teaches store a plurality of historical locations for a plurality of vehicles store a plurality of historical locations for a plurality of vehicles ([0042]-[0044] disclosing storing a plurality of history of positions of the second vehicle).
and identify the target vehicle that will be affected by the identified driving scenario based upon the plurality of historical locations for the plurality of vehicles ([0042]-[0044] disclosing identifying the second vehicle “target vehicle” based on the predicted trajectory from the history of positions of the second vehicle stored, and determining the second vehicle will collide with an object on the road “scenario”).
It would have been obvious to one of ordinary skill in the art to have modified the teaching of Newman as modified by Li and Ellis to incorporate the teaching of Nemoto of store a plurality of historical locations for a plurality of vehicles; and identify the target vehicle that will be affected by the identified driving scenario based upon the plurality of historical locations for the plurality of vehicles in order to alert the second vehicle of a possible collision as taught by Nemoto [0042]-[0044]. The combination is obvious yielding predictable results of predicting a scenario before happening and thus avoiding collisions improving driving safety.
Claims 11 are rejected under 35 U.S.C. 103 as being unpatentable by Newman (US20200200855) in view of Li (US20220009488) and Ellis (US20040083035) and Nemoto (US20220194368) and Shimakage (US20190202459).
Regarding claim 11, Newman as modified by Li and Ellis and Nemoto teaches the computer device of Claim 10. Newman as modified by Li and Ellis and Nemoto does not teach wherein the host vehicle in on a curved road and the target vehicle is identified based upon a point where the host vehicle was closest to the target vehicle.
Shimakage teaches wherein the host vehicle in on a curved road and the target vehicle is identified based upon a point where the host vehicle was closest to the target vehicle ([0047] disclosing the preceding vehicle is always identified based on the distance is chosen as a closest distance D1 smaller than other distances while the vehicle travels along a curve).
It would have been obvious to one of ordinary skill in the art to have modified the teaching of Newman as modified by Li and Ellis and Nemoto to incorporate the teaching of Shimakage of wherein the host vehicle in on a curved road and the target vehicle is identified based upon a point where the host vehicle was closest to the target vehicle in order safely detect and identify and follow a preceding vehicle based on curved information as taught by Shimakage [0047].
Claims 12 are rejected under 35 U.S.C. 103 as being unpatentable by Newman (US20200200855) in view of Li (US20220009488) and Ellis (US20040083035) and Nemoto (US20220194368) and Shimakage (US20190202459) and Habu (US20180022351).
Regarding claim 12, Newman as modified by Li and Ellis and Nemoto and Shimakage teaches the computer device of Claim 11. Newman as modified by Li and Ellis and Nemoto and Shimakage does not teach wherein the at least one processor is further programmed to calculate an initial lateral distance between the host vehicle at the point closest to the target vehicle and a current location of the target vehicle.
Habu teaches wherein the at least one processor is further programmed to calculate an initial lateral distance between the host vehicle at the point closest to the target vehicle and a current location of the target vehicle ([0147] disclosing determining the lateral position of the vehicle that is closest to the own vehicle in the lateral direction, i.e., the lateral distance is calculated in order to determine that the preceding vehicle is the closest to the own vehicle in the lateral direction).
It would have been obvious to one of ordinary skill in the art to have combine the teaching of Habu of wherein the at least one processor is further programmed to calculate an initial lateral distance between the host vehicle at the point closest to the target vehicle and a current location of the target vehicle, yielding predictable results in order to accurately determine a target preceding vehicle based on the shortest lateral distance as taught by Habu [0147].
Claims 13 are rejected under 35 U.S.C. 103 as being unpatentable by Newman (US20200200855) in view of Li (US20220009488)) and Ellis (US20040083035) and Masui (US11975716) and Kamrani (US20200327807).
Regarding claim 13, Newman as modified by Li and Ellis and Masui teaches the computer device of Claim 9. Newman as modified by Li and Ellis and Masui does not teach wherein the at least one processor is further programmed to determine if the host vehicle performed a lane change within the plurality of historical locations based upon the host vehicle’s yaw rates through the plurality of historical locations.
Kamrani teaches wherein the at least one processor is further programmed to determine if the host vehicle performed a lane change within the plurality of historical locations based upon the host vehicle’s yaw rates through the plurality of historical locations ([0022]-[0024] disclosing determining a lane change occurred based on number of obtained values of yaw rates at multiple points as the vehicle drives, i.e., at multiple locations).
It would have been obvious to one of ordinary skill in the art to have modified the teaching of Newman as modified by Li Ellis and Masui to combine the teaching of Kamrani of wherein the at least one processor is further programmed to determine if the host vehicle performed a lane change within the plurality of historical locations based upon the host vehicle’s yaw rates through the plurality of historical locations in order to detect a lane change has occurred based on the yaw rate sequence as taught by Kamrani [0022]-[0024], the combination is obvious yielding predictable results and identifying driving scenarios improving driving safety.
Claims 14 are rejected under 35 U.S.C. 103 as being unpatentable by Newman (US20200200855) in view of Li (US20220009488) and Ellis (US20040083035) and Masui (US11975716) and Ishioka (US20190143972).
Regarding claim 14, Newman as modified by Li and Ellis and Masui teaches the computer device of Claim 9. Newman as modified by Li and Ellis and Masui does not teach wherein the at least one processor is further programmed to determine if the host vehicle performed a lane change within the plurality of historical locations.
Ishioka teaches wherein the at least one processor is further programmed to determine if the ([0091] disclosing predicting lane change based on historic positions).
It would have been obvious to one of ordinary skill in the art to have combined the teaching of Ishioka of wherein the at least one processor is further programmed to determine if the in order to predict a lane change based on historic location which improves driving safety. It would have been obvious to try to use the method of determining a lane change by historical positions to predict a host vehicle lane change with a reasonable expectation of success.
Response to Arguments
Applicant’s arguments filed on 05/27/2025 have been fully considered but they are not Fully persuasive.
Newman teaches comparing the predicted intended driving operation to a plurality of predefined scenarios, wherein the scenarios include a driving operation and a target vehicle and identifying the target vehicle based on the matching of the driving scenarios including matching the driving operation and target vehicle at the predefined location… at least [0094]-[0095] and [0120] disclosing the scenarios that include an intended operation of braking the vehicle or swerving and identifying that scenario and alerting a target vehicle being either in a location in front, rear or adjacent based on each scenario differently… it is interpreted that a vehicle is operated based on these scenarios stored in the memory otherwise the vehicle cannot make these determinations. Ellis further teaches the prediction of an intended driver operation based on the input, the combination is obvious to alert the vehicle that corresponds to the direction of intended driver moving to thus improving safety and improving the alerting system to the driver’s intention.
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.
The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure. The prior art cited in PTO-892 and not mentioned above disclose related devices and methods.
US20190382004 discloses sharing road information with other vehicles.
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/MOHAMAD O EL SAYAH/Examiner, Art Unit 3658A
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