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 .
Priority
The applicant’s claim to priorities KR10-2023-0059520 on 05/09/2023 and KR10-2023-0075345 on 06/16/2023 are acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/23/2026 complies with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 102
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 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 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.
Claim(s) 1-5, 7-8, 11, and 12-13 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yang et al. (20210295705; hereinafter Yang).
Regarding claim 1, Yang teaches a method performed by a first device (Yang: Abstract), the method comprising:
collecting sensing data regarding a moving object (Yang: “the vehicle data may include one or more vehicle dynamics describing the vehicle movements of the connected vehicle 103 ... Non-limiting examples of the vehicle dynamics of the connected vehicle 103 include ... the vehicle location (e.g., GPS coordinates)” ¶ 36);
acquiring information regarding a movement path of the object based on the sensing data (Yang: “the vehicle data may include multiple sets of vehicle dynamics of the connected vehicle 103, each set of vehicle dynamics may be associated with a timestamp and may describe the vehicle movements of the connected vehicle 103 at the timestamp” ¶ 36);
generating a message for sharing the sensing data regarding the object (Yang: “the connected vehicle 103 may transmit and receive sensor data, vehicle dynamics, etc., to and from other connected vehicles 103” ¶ 28); and
transmitting the message to a second device (Yang: “the connected vehicle 103 may transmit trip data, vehicle dynamics, requests associated with various vehicle functions, etc. to the server 101, and receive vehicle instructions, function data associated with vehicle functions, etc., from the server 101” ¶ 28),
wherein the message comprises N data sets configured based on the information regarding the movement path of the object (Yang: “the vehicle data may include one or more vehicle dynamics describing the vehicle movements of the connected vehicle 103. The vehicle dynamics may be determined from the vehicle operation data and/or other sensor data of the connected vehicle 103. Non-limiting examples of the vehicle dynamics of the connected vehicle 103 include the vehicle speed, the acceleration/deceleration rate, the vehicle location (e.g., GPS coordinates), the lane number, etc., of the connected vehicle 103” ¶ 36), and
wherein the N data sets are related to a position of the object at each of N different time points (Yang: “each set of vehicle dynamics may be associated with a timestamp and may describe the vehicle movements of the connected vehicle 103 at the timestamp” ¶ 36).
Regarding claim 2, Yang teaches the method of claim 1, wherein the position of the object at each of the N different time points is determined by the first device based on the information regarding the movement path of the object (Yang: “the vehicle data may include one or more vehicle dynamics describing the vehicle movements of the connected vehicle 103 ... the vehicle data may include multiple sets of vehicle dynamics of the connected vehicle 103, each set of vehicle dynamics may be associated with a timestamp and may describe the vehicle movements of the connected vehicle 103 at the timestamp” ¶ 36).
Regarding claim 3, Yang teaches the method of claim 1, wherein the movement path of the object is a path along which the object is predicted to move (Yang: “The connected vehicle status may also include... estimated vehicle dynamics of the connected vehicle 103” ¶ 43), and
wherein the N data sets are related to the position of the object predicted at N future time points (Yang: “estimated vehicle dynamics of the connected vehicle 103 in the geographic region as the connected vehicle 103 proceeds through the geographic region during a future time period (e.g., predicted vehicle speed, predicted vehicle location, etc., of the connected vehicle 103 at multiple timestamps within the future time period)” ¶ 43).
Regarding claim 4, Yang teaches the method of claim 1, wherein the movement path of the object is a path of movement history of the object, and
wherein the N data sets are related to the position of the object at N past time points (Yang: “a past traffic condition may describe the past traffic on the road segment at a past timestamp” ¶ 50, see also ¶ 41, 42).
Regarding claim 5, Yang teaches the method of claim 1, wherein the sensing data regarding the moving object is acquired from vehicle-to-everything (V2X) messages received from other devices (Yang: “The network 105 may also be coupled to or include portions of a telecommunication network for sending data in a variety of different communication protocols ... vehicle-to-vehicle (V2V) networks, vehicle-to-infrastructure/infrastructure-to-vehicle (V2I/I2V) networks, vehicle-to-infrastructure/vehicle-to-everything (V2I/V2X) networks” ¶ 27).
Regarding claim 7, Yang teaches the method of claim 1, wherein the message further comprises at least one data set configured based on the sensing data in addition to the N data sets (Yang: “The sensor(s) 113 may be configured to collect any type of signal data suitable to determine characteristics of the connected vehicle 103 and/or its internal and external environments” ¶ 34, see also ¶ 3).
Regarding claim 8, Yang teaches the method of claim 7, wherein the message comprises information for distinguishing whether each data set is a data set configured based on the information regarding the movement path or a data set configured based on the sensing data (Yang: “the vehicle data may also include vehicle dynamics describing the vehicle movements of proximate vehicles that are captured by the sensors 113 of the connected vehicle 103. In some embodiments, the connected vehicle 103 may analyze its sensor data that monitors the proximate environment, and determine the vehicle dynamics of the proximate vehicles based on the vehicle dynamics of the connected vehicle 103 and based on the relative position of the proximate vehicles on the road segment, the relative distance between the connected vehicle 103 and the proximate vehicles, etc., as reflected in the sensor data” ¶ 36).
Regarding claim 11, Yang teaches a non-transitory computer-readable recording medium having recorded thereon a program for executing the method of claim 1 (Yang: “As described in further detail below, the technology includes various aspects, such as function deployment methods, systems, computing devices, computer program products, and apparatuses, among other aspects” ¶ 20).
Regarding claim 12, Yang teaches a first device comprising:
a memory configured to store instructions (Yang: “The connected vehicle 103 may include computing device(s) 152 having sensor(s) 113, processor(s) 115, memory(ies) 117” ¶ 29); and
a processor configured to perform operations by executing the instructions (Yang: “The connected vehicle 103 may include computing device(s) 152 having sensor(s) 113, processor(s) 115, memory(ies) 117” ¶ 29),
...
In regards to the remainder of claim 12, the claim recites analogous limitations to claim 1, and is therefore rejected under the same premise.
Regarding claim 13, Yang teaches the first device of claim 12, wherein the first device is a user equipment (UE), a vehicle (“The connected vehicle 103 may include computing device(s) 152 having sensor(s) 113, processor(s) 115, memory(ies) 117” ¶ 29), a road side unit (RSU), or a server.
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 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) 6 and 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Pazhayampallil et a. (20190137287; hereinafter Pazhayampallil).
Regarding claim 6, Yang teaches the method of claim 1, ...
However, Yang fails to disclose wherein the message comprises information regarding a confidence level for the N data sets configured based on the movement path of the object.
In a similar field of endeavor, Pazhayampallil teaches wherein the message comprises information regarding a confidence level for the N data sets configured based on the movement path of the object (Pazhayampallil: “The computer system can thus inform autonomous vehicles moving toward a Type 1B or Type 1C discrepancy of this discrepancy, thereby enabling these autonomous vehicles to both calculate their locations with a greater degree of confidence based on the known location of the discrepancy and to adjust navigational actions according to this discrepancy” ¶ 69, see also ¶ 70).
As such, it would have been obvious to one of ordinary skill in the art, at the time of effective filing and with a reasonable expectation for success, to have modified the message system of Yang so that it also includes the element of a confidence level, as taught by Pazhayampallil, in order to improve localization accuracy (Pazhayampallil: ¶ 72).
Regarding claim 9, Yang teaches the method of claim 1, wherein the sensing data comprises first sensing data acquired within sensing coverage of the first device (Yang: “an example geographic region 750 of a server 101 in a roadway area 700. As depicted, the roadway area 700 may include the plurality of vehicles traveling on various road segments that are located within or proximate to the geographic region 750 of the server 101” ¶ 47) and ...
However, Yang fails to disclose second sensing data acquired outside the sensing coverage of the first device.
In a similar field of endeavor, Pazhayampallil teaches second sensing data acquired outside the sensing coverage of the first device (Pazhayampallil: “the computer system can also identify a second subset of autonomous vehicles—in this first list of autonomous vehicles—outside of the threshold distance of the geospatial location of the discrepancy, outside of the threshold time of this geospatial location” ¶ 76).
As such, it would have been obvious to one of ordinary skill in the art, at the time of effective filing and with a reasonable expectation for success, to have modified the sensor system of Yang so that it also includes the element of a second sensing data outside the coverage of the first device, as taught by Pazhayampallil, in order to improve localization accuracy utilizing a plurality of data points (Pazhayampallil: ¶ 79).
Regarding claim 10, Yang teaches the method of claim 9, ...
However, Yang fails to teach wherein the second sensing data is received from another device having sensing coverage different from the sensing coverage of the first device.
In a similar field of endeavor Pazhayampallil teaches wherein the second sensing data is received from another device having sensing coverage different from the sensing coverage of the first device (“the computer system can also: query the autonomous vehicle fleet manager for a second list of autonomous vehicles currently commissioned to the geographic region containing the geospatial location of the discrepancy but currently parked or currently executing rideshare routes disjoint (e.g., offset by more than fifty meters) from the geospatial location of the discrepancy ... transmit the localization map update to a second autonomous vehicle—via the low-bandwidth wireless network—within five minutes of a first autonomous vehicle first detecting this discrepancy” ¶ 79).
As such, it would have been obvious to one of ordinary skill in the art, at the time of effective filing and with a reasonable expectation for success, to have modified the sensor system of Yang so that it also includes the element of second sensing data different from the first device, as taught by Pazhayampallil, in order to improve and verify localization accuracy and data (Pazhayampallil: ¶ 79).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Choi et al. (20220159428) is in the similar field of endeavor as the claimed invention of vehicle data information.
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/C.P./Examiner, Art Unit 3663
/ABBY J FLYNN/Supervisory Patent Examiner, Art Unit 3663
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