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 .
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.
Response to Amendment
This FINAL action is in response to amendment filed on 10/15/2025.
Claims 1, 5, 12, 19 are amended.
Claims 2-4, 6-11, 13-18, 20 are original.
Specification
The disclosure is objected to because of the following informalities:
"the one or more forward vehicles in the vicinity of the vehicle 306a may be the vehicle 306b" should read "the one or more forward vehicles in the vicinity of the vehicle 306a may be the vehicle 306c" [0088]
"the one or more backward vehicles in vicinity of the vehicle 306a may be the vehicle 306c" should read "the one or more backward vehicles in vicinity of the vehicle 306a may be the vehicle 306b" [0088]
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed inventions are directed to a judicial exception without significantly more, as determined by the Subject Matter Eligibility Test
detailed below.
Step 1
Step 1 of the Subject Matter Eligibility Test entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter.
Independent claims -1, 12, 19 are directed towards a system, a method, and a non-transitory computer readable medium, respectively. Therefore, each of the independent claims 1, 12, and 19, and the corresponding dependent claims 2-11, 13-18, and 20 are directed to a statutory category of invention under step 1.
Step 2A, Prong 1
If the claim recites a statutory category of invention, the claim requires further analysis in Step
2A. Step 2A of the Subject Matter Eligibility Test is a two-prong inquiry. In Prong 1, examiners evaluate whether the claim recites a judicial exception.
Regarding Prong 1, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 recites abstract limitations, including those shown in bold below.
A system comprising:
a memory configured to store computer executable instructions; and
one or more processors configured to execute the instructions to:
obtain sensor data of each of a plurality of vehicles associated with a lane segment, the sensor data comprising at least one of: forward distance data of one or more vehicles in vicinity of each of the plurality of vehicles in the lane segment, and backward distance data of the one or more vehicles in the vicinity of each of the plurality of vehicles in the lane segment;
determine lane distance data for the lane segment based on the sensor data, wherein the lane distance data comprises forward average distance data, and backward average distance data; and
determine traffic data for the lane segment based on the lane distance data; and
provide lane-level navigation instructions based on the determined traffic data.
These limitations, as drafted, describe a system that, under its broadest reasonable interpretation, covers performance of the limitations in the mind, or by a human using pen and paper, and therefore recites mental processes. For example, “determine lane distance data for the lane segment, wherein the lane distance data comprises forward average distance data, and backward average distance data; and determine traffic data for the lane segment based on the lane distance data.” may be interpreted as a mental determination made according to observable data, such as determining that traffic is bad, based on observation that vehicles are close to each other compared to historical vehicle distance. Thus, the claim recites an abstract idea.
Claims -12 and 19 recite abstract limitations analogous to those identified above with respect to claim 1, and therefore recite abstract ideas per the same analysis.
Step 2A, Prong 2
If the claim recites a judicial exception in Step 2A, Prong 1, the claim requires further analysis in Step 2A, Prong 2. In Step 2A, Prong 2, examiners evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
Regarding Prong 2, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in MPEP § 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra-solution activity, or generally linking the use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”.
Claim 1 recites additional elements including those underlined below.
A system comprising:
a memory configured to store computer executable instructions; and
one or more processors configured to execute the instructions to:
obtain sensor data of each of a plurality of vehicles associated with a lane segment, the sensor data comprising at least one of: forward distance data of one or more vehicles in vicinity of each of the plurality of vehicles in the lane segment, and backward distance data of the one or more vehicles in the vicinity of each of the plurality of vehicles in the lane segment;
determine lane distance data for the lane segment based on the sensor data, wherein the lane distance data comprises forward average distance data, and backward average distance data; and
determine traffic data for the lane segment based on the lane distance data; and
provide lane-level navigation instructions based on the determined traffic data.
The recitation of obtain sensor data of each of a plurality of vehicles associated with a lane segment, the sensor data comprising at least one of: forward distance data of one or more vehicles in vicinity of each of the plurality of vehicles in the lane segment, and backward distance data of the one or more vehicles in the vicinity of each of the plurality of vehicles in the lane segment amounts to mere data receiving, which is a form of insignificant extra-solution activity. Furthermore, the recitation of communicating information based on the recited abstract ideas amounts to sending or displaying information, which is a form of insignificant extra-solution activity. The recitation of a memory configured to store computer executable instructions; and one or more processors configured to execute the instructions and based on the sensor data amounts to mere instructions to implement an abstract idea or other exception on a computer. Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B
If the additional elements do not integrate the exception into a practical application in step 2A Prong 2, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B to determine whether it provides an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
As discussed above, the additional elements of a memory configured to store computer executable instructions; and one or more processors configured to execute the instructions and based on the sensor data amount to mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
As discussed above, communicating information based on the abstract ideas amounts to insignificant extra-solution activity. MPEP 2106.05(d)(II), and the cases cited therein, including in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data (i.e., providing navigation instructions) is a well understood, routine, and conventional function.
As discussed above, obtain sensor data of each of a plurality of vehicles associated with a lane segment, the sensor data comprising at least one of: forward distance data of one or more vehicles in vicinity of each of the plurality of vehicles in the lane segment, and backward distance data of the one or more vehicles in the vicinity of each of the plurality of vehicles in the lane segment amounts to insignificant extra-solution activity. MPEP § 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Thus, even when viewed as an ordered combination, nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea.
Dependent claims 2-11, 13-18, and 20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the various limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine, and conventional additional elements that do not integrate the judicial exception into a practical application (i.e., further characterizing the collection of data and mental processes regarding congestion levels). Therefore, dependent claims 2-11, 13-18, and 20 are not patent eligible under the same rationale as provided for in the rejection of independent claims 1, 12, and 19.
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.
Claim(s) 1, 12, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230036770 A1 ELDESSOUKI; Wael Mohammad et al. (hereinafter Eldessouki), in view of US 11631331 B1 Palmer; Jason (hereinafter Palmer).
Regarding claim 1, Eldessouki discloses: A system (see Eldessouki at least [0001] system and computer program product for evaluating traffic density) comprising:
a memory configured to store computer executable instructions (see Eldessouki at least [0039] instructions stored in memory); and
one or more processors configured to execute the instructions (see Eldessouki at least [0106] processing circuitry 1061 which can include without limitation one or more processors) to:
obtain sensor data of each of a plurality of vehicles (see Eldessouki at least [0100] each floating vehicle of a fleet of floating vehicles 920 may similarly connect to the Internet 980 in order to access and communicate with the TMS 800. In an example, longitudinal sensor data from a plurality of sensors of the floating vehicle 901 can be stored in a data storage center 992 of a cloud-computing environment 990), the sensor data comprising at least one of: forward distance data of one or more vehicles in vicinity of each of the plurality of vehicles in the lane segment, and backward distance data of the one or more vehicles in the vicinity of each of the plurality of vehicles in the lane segment (see Eldessouki at least [0038] the distance-type sensors can be used to determine the distance between the floating vehicle 201 and a neighboring vehicle (i.e., leading vehicle, trailing vehicle, adjacent vehicle));
determine lane distance data for the lane segment based on the sensor data, wherein the lane distance data comprises forward average distance data, and backward average distance data (see Eldessouki at least [0088] R.sub.Back 851 is the average distance between the trailing vehicle and the floating vehicle 801, and R.sub.Front 850 is the average distance between the leading vehicle and the floating vehicle 801).
Eldessouki does not teach: obtain sensor data of each of a plurality of vehicles associated with a lane segment; determine traffic data for the lane segment based on the lane distance data; and provide lane-level navigation instructions based on the determined traffic data.
However, Palmer teaches: obtain sensor data of each of a plurality of vehicles associated with a lane segment (see Palmer at least [col. 3, lines 49-51] individual vehicles may be configured to detect vehicle events, e.g., based on output signals generated by one or more sensors and [col. 3, lines 54-58] vehicle events include information gathered by monitoring the operation of one or more vehicles, including but not limited to information related to average speed in a particular traffic lane, average following distance in a particular traffic lane);
determine traffic data for the lane segment based on the lane distance data (see Palmer at least [col. 6, line 67 – col. 7, line 4] the lane-specific information may include information regarding previously detected fuel-efficiency, previously detected travel duration, previously detected average vehicle speed, previously detected average following distance and [col. 7, lines 41-45] the lane-specific information may be specific and/or particular to different levels or kinds of traffic (e.g., different detected vehicle usage in heavy traffic versus light traffic, or high number versus number level of detected pedestrians)); and
provide lane-level navigation instructions based on the determined traffic data (see Palmer at least [col. 8, lines 8-13] recommendation component 120 may be configured to determine a particular lane-specific recommendation to vehicle 12. In some implementations, determinations by recommendation component 120 may be based on the obtained lane-specific information and the obtained goal(s)).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle average distance determination system disclosed by Eldessouki to include the lane-information-specific lane recommendations of Palmer. One of ordinary skill in the art would have been motivated to make this modification because information gathered from each traffic lane provides a basis for fuel-efficient, easy, and safe travel recommendations, as suggested by Palmer (see Palmer at least [col. 8, lines 25-29] a particular lane-specific recommendation may be not provided to limit the number of future lane changes, but rather to improve one or more of fuel-efficiency, expected trip duration, likelihood of occurrences of (certain types of) vehicle events, ease of vehicle operation).
Regarding claim 12, Eldessouki discloses: A method comprising:
obtaining sensor data of each of a plurality of vehicles (see Eldessouki at least [0100] each floating vehicle of a fleet of floating vehicles 920 may similarly connect to the Internet 980 in order to access and communicate with the TMS 800. In an example, longitudinal sensor data from a plurality of sensors of the floating vehicle 901 can be stored in a data storage center 992 of a cloud-computing environment 990), the sensor data comprising at least one of: forward distance data of one or more vehicles in vicinity of each of the plurality of vehicles in the lane segment and backward distance data of the one or more vehicles in vicinity of each of the plurality of vehicles in the lane segment (see Eldessouki at least [0038] the distance-type sensors can be used to determine the distance between the floating vehicle 201 and a neighboring vehicle (i.e., leading vehicle, trailing vehicle, adjacent vehicle));
determining lane distance data for the lane of the link segment based on the sensor data, wherein the lane distance data comprises forward average distance data and backward average distance data (see Eldessouki at least [0088] R.sub.Back 851 is the average distance between the trailing vehicle and the floating vehicle 801, and R.sub.Front 850 is the average distance between the leading vehicle and the floating vehicle 801).
Eldessouki does not teach: obtaining sensor data of each of a plurality of vehicles associated with a lane segment; determining traffic data for the lane segment based on the lane distance data; and providing lane-level navigation instructions based on the determined traffic data.
However, Palmer teaches: obtaining sensor data of each of a plurality of vehicles associated with a lane segment (see Palmer at least [col. 3, lines 49-51] individual vehicles may be configured to detect vehicle events, e.g., based on output signals generated by one or more sensors and [col. 3, lines 54-58] vehicle events include information gathered by monitoring the operation of one or more vehicles, including but not limited to information related to average speed in a particular traffic lane, average following distance in a particular traffic lane);
determining traffic data for the lane segment based on the lane distance data (see Palmer at least [col. 6, line 67 – col. 7, line 4] the lane-specific information may include information regarding previously detected fuel-efficiency, previously detected travel duration, previously detected average vehicle speed, previously detected average following distance and [col. 7, lines 41-45] the lane-specific information may be specific and/or particular to different levels or kinds of traffic (e.g., different detected vehicle usage in heavy traffic versus light traffic, or high number versus number level of detected pedestrians)); and
providing lane-level navigation instructions based on the determined traffic data (see Palmer at least [col. 8, lines 8-13] recommendation component 120 may be configured to determine a particular lane-specific recommendation to vehicle 12. In some implementations, determinations by recommendation component 120 may be based on the obtained lane-specific information and the obtained goal(s)).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle average distance determination method disclosed by Eldessouki to include the lane-information-specific lane recommendations of Palmer. One of ordinary skill in the art would have been motivated to make this modification because information gathered from each traffic lane provides a basis for fuel-efficient, easy, and safe travel recommendations, as suggested by Palmer (see Palmer at least [col. 8, lines 25-29] a particular lane-specific recommendation may be not provided to limit the number of future lane changes, but rather to improve one or more of fuel-efficiency, expected trip duration, likelihood of occurrences of (certain types of) vehicle events, ease of vehicle operation).
Regarding claim 19, Eldessouki discloses: A computer programmable product (see Eldessouki at least [0001] computer program product for evaluating traffic density) comprising a non-transitory computer readable medium having stored thereon computer executable instructions (see Eldessouki at least [0113] computer-readable medium), which when executed by one or more processors (see Eldessouki at least [0106] processing circuitry 1061 which can include without limitation one or more processors), cause the one or more processors to conduct operations comprising:
obtaining sensor data of each of a plurality of vehicles (see Eldessouki at least [0100] each floating vehicle of a fleet of floating vehicles 920 may similarly connect to the Internet 980 in order to access and communicate with the TMS 800. In an example, longitudinal sensor data from a plurality of sensors of the floating vehicle 901 can be stored in a data storage center 992 of a cloud-computing environment 990), the sensor data comprising at least one of: forward distance data of one or more vehicles in vicinity of each of the plurality of vehicles in the lane segment and backward distance data of the one or more vehicles in vicinity of each of the plurality of vehicles in the lane segment (see Eldessouki at least [0038] the distance-type sensors can be used to determine the distance between the floating vehicle 201 and a neighboring vehicle (i.e., leading vehicle, trailing vehicle, adjacent vehicle));
determining lane distance data for the lane of the link segment based on the sensor data, wherein the lane distance data comprises forward average distance data and backward average distance data (see Eldessouki at least [0088] R.sub.Back 851 is the average distance between the trailing vehicle and the floating vehicle 801, and R.sub.Front 850 is the average distance between the leading vehicle and the floating vehicle 801).
Eldessouki does not teach: obtaining sensor data of each of a plurality of vehicles associated with a lane segment; determining traffic data for the lane segment based on the lane distance data; providing lane-level navigation instructions based on the determined traffic data.
However, Palmer teaches: obtaining sensor data of each of a plurality of vehicles associated with a lane segment (see Palmer at least [col. 3, lines 49-51] individual vehicles may be configured to detect vehicle events, e.g., based on output signals generated by one or more sensors and [col. 3, lines 54-58] vehicle events include information gathered by monitoring the operation of one or more vehicles, including but not limited to information related to average speed in a particular traffic lane, average following distance in a particular traffic lane);
determining traffic data for the lane segment based on the lane distance data (see Palmer at least [col. 6, line 67 – col. 7, line 4] the lane-specific information may include information regarding previously detected fuel-efficiency, previously detected travel duration, previously detected average vehicle speed, previously detected average following distance and [col. 7, lines 41-45] the lane-specific information may be specific and/or particular to different levels or kinds of traffic (e.g., different detected vehicle usage in heavy traffic versus light traffic, or high number versus number level of detected pedestrians)); and
providing lane-level navigation instructions based on the determined traffic data (see Palmer at least [col. 8, lines 8-13] recommendation component 120 may be configured to determine a particular lane-specific recommendation to vehicle 12. In some implementations, determinations by recommendation component 120 may be based on the obtained lane-specific information and the obtained goal(s)).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle average distance determination non-transitory computer readable medium disclosed by Eldessouki to include the lane-information-specific lane recommendations of Palmer. One of ordinary skill in the art would have been motivated to make this modification because information gathered from each traffic lane provides a basis for fuel-efficient, easy, and safe travel recommendations, as suggested by Palmer (see Palmer at least [col. 8, lines 25-29] a particular lane-specific recommendation may be not provided to limit the number of future lane changes, but rather to improve one or more of fuel-efficiency, expected trip duration, likelihood of occurrences of (certain types of) vehicle events, ease of vehicle operation).
Claim(s) 2, 13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eldessouki, in view of Palmer, and further in view of US 20200344820 A1 FOWE JAMES (hereinafter Fowe).
Regarding claim 2, Eldessouki and Palmer teach: The system of claim 1, wherein to obtain the sensor data of the plurality vehicles associated with the lane segment, the one or more processors are further configured to:
determine, based on the obtained sensor data, vehicle location data for each of the plurality of vehicles (see Eldessouki at least [0037] High fidelity determinations of traffic speed and traffic density require high quality data related to: (1) speed of the floating car, (2) time and location coordinates of the floating car and [0038] the plurality of sensors 210 includes a satellite positioning system (SPS) receiver for determining location. The SPS receiver can be a type of Global Navigation Satellite System (GNSS), such as a Global Positioning System (GPS), for real-time determination of current vehicle coordinates).
Eldessouki and Palmer do not teach: obtain, based on map data, lane location data relating to the lane segment; generate map matched data for each of the plurality of vehicles based on the lane location data and the corresponding vehicle location data, the map matched data indicating a matching between the vehicle location data and the lane location data; and determine the plurality of vehicles of the map matched data as the vehicles corresponding to the lane segment.
However, Fowe teaches: obtain, based on map data, lane location data relating to the lane segment (see Fowe at least [0032] the device 122 may determine a current lane position based on image recognition techniques and a stored HD map);
generate map matched data for each of the plurality of vehicles based on the lane location data and the corresponding vehicle location data, the map matched data indicating a matching between the vehicle location data and the lane location data (see Fowe at least [0060] the location of the vehicle is map matched to a lane using the sensor data. A GPS value may be used to identify the road segment using a map matching algorithm to match the GPS coordinates to a stored map and road segment. Lane level map matching may provide a good estimate of what lane a vehicle is on given a sequence of GPS probes coming from the vehicle); and
determine the plurality of vehicles of the map matched data as the vehicles corresponding to the lane segment (see Fowe at least [0009] A sub cluster is generated with vehicles driving on the same lane based on a proximity vehicle distance).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination system disclosed by Eldessouki and Palmer to include the sensor-based location sensing and map-matching vehicle lane determination technique of Fowe. One of ordinary skill in the art would have been motivated to make this modification because determining which groups of vehicles travel share lanes allows to group vehicles into clusters that are more viable for V2V communication as suggested by Fowe (see Fowe at least [0028] Creating VANET clusters this way provides better V2V communication, accuracy and faster reactions for navigation applications and vehicles on the roadway. For example, in FIG. 2, a separate VANET cluster may be formed on the HOV lane. This cluster moves faster and will keep the vehicles on the HOV lanes coordinated and connected, while a VANET cluster on the other lanes would keep the vehicles in the respective group coordinated and connected).
Regarding claim 13, Eldessouki and Palmer teach: The method of claim 12, wherein to obtain the sensor data of the plurality vehicles associated with the lane segment, the method further comprises:
determine, based on the obtained sensor data, vehicle location data for each of the plurality of vehicles (see Eldessouki at least [0037] High fidelity determinations of traffic speed and traffic density require high quality data related to: (1) speed of the floating car, (2) time and location coordinates of the floating car and [0038] the plurality of sensors 210 includes a satellite positioning system (SPS) receiver for determining location. The SPS receiver can be a type of Global Navigation Satellite System (GNSS), such as a Global Positioning System (GPS), for real-time determination of current vehicle coordinates).
Eldessouki and Palmer do not teach: obtaining, based on map data, lane location data relating to the lane segment; generating map matched data for each of the plurality of vehicles based on the lane location data and the vehicle location data, the map matched data indicating a matching between the vehicle location data and the lane location data; and determining the plurality of vehicles of the map matched data as the vehicles corresponding to the lane segment.
However, Fowe teaches: obtaining, based on map data, lane location data relating to the lane segment (see Fowe at least [0032] the device 122 may determine a current lane position based on image recognition techniques and a stored HD map);
generating map matched data for each of the plurality of vehicles based on the lane location data and the vehicle location data, the map matched data indicating a matching between the vehicle location data and the lane location data (see Fowe at least [0060] the location of the vehicle is map matched to a lane using the sensor data. A GPS value may be used to identify the road segment using a map matching algorithm to match the GPS coordinates to a stored map and road segment. Lane level map matching may provide a good estimate of what lane a vehicle is on given a sequence of GPS probes coming from the vehicle); and
determining the plurality of vehicles of the map matched data as the vehicles corresponding to the lane segment (see Fowe at least [0009] A sub cluster is generated with vehicles driving on the same lane based on a proximity vehicle distance).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination method disclosed by Eldessouki and Palmer to include the sensor-based location sensing and map-matching vehicle lane determination technique of Fowe. One of ordinary skill in the art would have been motivated to make this modification because determining which groups of vehicles travel share lanes allows to group vehicles into clusters that are more viable for V2V communication as suggested by Fowe (see Fowe at least [0028] Creating VANET clusters this way provides better V2V communication, accuracy and faster reactions for navigation applications and vehicles on the roadway. For example, in FIG. 2, a separate VANET cluster may be formed on the HOV lane. This cluster moves faster and will keep the vehicles on the HOV lanes coordinated and connected, while a VANET cluster on the other lanes would keep the vehicles in the respective group coordinated and connected).
Regarding claim 20, Eldessouki and Palmer teach: The computer programmable product of claim 19, the method further comprising:
determine, based on the obtained sensor data, vehicle location data for each of the plurality of vehicles (see Eldessouki at least [0037] High fidelity determinations of traffic speed and traffic density require high quality data related to: (1) speed of the floating car, (2) time and location coordinates of the floating car and [0038] the plurality of sensors 210 includes a satellite positioning system (SPS) receiver for determining location. The SPS receiver can be a type of Global Navigation Satellite System (GNSS), such as a Global Positioning System (GPS), for real-time determination of current vehicle coordinates).
Eldessouki and Palmer do not teach: obtaining, based on map data, lane location data relating to the lane segment; generating map matched data for each of the plurality of vehicles based on the lane location data and the vehicle location data, the map matched data indicating a matching between the vehicle location data and the lane location data; and determining the plurality of vehicles of the map matched data as the vehicles corresponding to the lane segment.
However, Fowe teaches: obtaining, based on map data, lane location data relating to the lane segment (see Fowe at least [0032] the device 122 may determine a current lane position based on image recognition techniques and a stored HD map);
generating map matched data for each of the plurality of vehicles based on the lane location data and the vehicle location data, the map matched data indicating a matching between the vehicle location data and the lane location data (see Fowe at least [0060] the location of the vehicle is map matched to a lane using the sensor data. A GPS value may be used to identify the road segment using a map matching algorithm to match the GPS coordinates to a stored map and road segment. Lane level map matching may provide a good estimate of what lane a vehicle is on given a sequence of GPS probes coming from the vehicle); and
determining the plurality of vehicles of the map matched data as the vehicles corresponding to the lane segment (see Fowe at least [0009] A sub cluster is generated with vehicles driving on the same lane based on a proximity vehicle distance).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination computer programmable product disclosed by Eldessouki and Palmer to include the sensor-based location sensing and map-matching vehicle lane determination technique of Fowe. One of ordinary skill in the art would have been motivated to make this modification because determining which groups of vehicles travel share lanes allows to group vehicles into clusters that are more viable for V2V communication as suggested by Fowe (see Fowe at least [0028] Creating VANET clusters this way provides better V2V communication, accuracy and faster reactions for navigation applications and vehicles on the roadway. For example, in FIG. 2, a separate VANET cluster may be formed on the HOV lane. This cluster moves faster and will keep the vehicles on the HOV lanes coordinated and connected, while a VANET cluster on the other lanes would keep the vehicles in the respective group coordinated and connected).
Claim(s) 3, 4, 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eldessouki, in view of Palmer, further in view of JP 2018132529 A SEKIYA HIROYUKI et al. (hereinafter Sekiya), and further in view of US 20200241547 A1 Hashimoto; Ryuta et al. (hereinafter Hashimoto).
Regarding claim 3, Eldessouki and Palmer teach: The system of claim 1.
Eldessouki and Palmer do not teach: wherein the one or more processors are further configured to: determine an average distance threshold for the lane segment, based on map data; determine a traffic congestion value for the lane segment based on a comparison of the average distance threshold and the lane distance data; and update a map database based on the comparison.
However, Sekiya teaches: wherein the one or more processors are further configured to:
determine a traffic congestion value for the lane segment based on a comparison of the average distance threshold and the lane distance data (see Sekiya at least [0017] a predetermined threshold is used and the area exceeding the threshold is determined to be a congested section. By providing multiple threshold levels, it is possible to determine congested sections according to the degree of congestion at multiple levels); and
update a memory based on the comparison (see Sekiya at least [0056] congestion information according to the degree of congestion using a threshold value is stored in the storage unit 104).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination system disclosed by Eldessouki and Palmer to include the threshold comparison technique of determining traffic congestion degree of Sekiya. One of ordinary skill in the art would have been motivated to make this modification because determining the degree of congestion occurring in an area and storing this information allows it to be accessed in the future when congestion information is to be displayed for a given route or lane, as suggested by Sekiya (see Sekiya at least [0020] The traffic congestion information processing unit 105 reads out from the storage unit 104 traffic congestion information for a position or area corresponding to a request for traffic congestion display from the traffic congestion display device 110 , and transmits it to the traffic congestion display device 110 via the communication unit 106 . At this time, the traffic congestion information processing unit 105 transmits traffic congestion information including location information (section) where traffic congestion is occurring and the degree of traffic congestion. In addition, since the degree of congestion for each lane of a road can be determined, the congestion information transmitted includes the congested sections for each lane).
Eldessouki, Palmer, and Sekiya do not teach: determine an average distance threshold for the lane segment, based on map data; and a map database.
However, Hashimoto teaches: determine an average distance threshold for the lane segment, based on map data (see Hashimoto at least [0065] The threshold density calculation section 132 calculates a threshold density from the forward inter-vehicle distance, by using a map in which a relation illustrated in FIG. 7 is specified); and
a map database (see Hashimoto at least [0038] The map database 5 is formed in a storage such as an HDD and an SSD mounted on the vehicle, for example. Map information which the map database 5 has includes, for example, positional information of roads, information on road shapes, positional information of intersections and branch points, lane information of roads and the like).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination system disclosed by Eldessouki, Palmer, and Sekiya to include the map database and map-based vehicle spacing threshold determination of Hashimoto. One of ordinary skill in the art would have been motivated to make this modification because map databases provide valuable information in the road and traffic recognition processes, as suggested by Hashimoto (see Hashimoto at least [0045] a method that performs traveling road recognition based on map information concerning the road on which the own vehicle is traveling and positional information of the own vehicle. The map information is acquired from the map database 5).
Regarding claim 4, Eldessouki, Palmer, Sekiya, and Hashimoto teach: The system of claim 3, wherein the one or more processors are further configured to:
determine a color for displaying the lane segment on a map based display interface, based on the traffic congestion value (see Sekiya at least [0026] the traffic congestion state is displayed in stages by changing the color to be more noticeable as the traffic congestion level increases, for example from blue to yellow to red); and
cause to display the lane segment in association with the determined color during a time period (see Sekiya at least [0026] the congestion display processing unit 112 displays the congested sections and congested lanes indicated by the congestion information on a map or image by overlaying a color indicating congestion on a lane-by-lane basis).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination system disclosed by Eldessouki, Palmer, Sekiya, and Hashimoto to include the lane-level congestion-based color display of Sekiya. One of ordinary skill in the art would have been motivated to make this modification because coloring a map lane-by-lane based on specific lane congestion levels allows navigation guidance to be tailored by lane as opposed to by an entire route, as suggested by Sekiya (see Sekiya at least [0085] since the system can show congestion by lane and display congested sections by the severity of congestion, it can show the start and end areas of the congestion even when driving on the same road. If part of a lane is congested, guidance can be provided that allows the driver to continue on the same road by changing lanes without having to detour onto other roads).
Regarding claim 10, Eldessouki and Palmer teach: The system of claim 1, wherein the one or more processors are further configured to:
determine traffic data for one or more lane segments associated with a link segment, based on corresponding lane distance data (see Palmer at least [col. 7, lines 20-22] for a particular section of a particular roadway, perhaps the vehicle speed is smoother and/or more consistent in the right lane than the left lane).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified vehicle spacing determination system disclosed by Eldessouki and Palmer to include the consideration of multiple lanes in the same section of a roadway of Palmer. One of ordinary skill in the art would have been motivated to make this modification because comparing different lanes within a roadway section can help determine which lane’s characteristics best align with the operation goals, as suggested by Palmer (see Palmer at least [col. 7, lines 22-24] a lower average speed but more consistent and uniform speed may be advantageous for fuel efficiency and/or other vehicle operator goals).
Eldessouki and Palmer do not teach: generate navigation instructions for regulating traffic on the one or more lane segments, based on the corresponding traffic data.
However, Sekiya teaches: generate navigation instructions for regulating traffic on the one or more lane segments, based on the corresponding traffic data (see Sekiya at least [0086] during route guidance, it will be possible to provide guidance on roads where it is possible to change lanes based on congestion information for each lane).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle spacing determination system disclosed by Eldessouki and Palmer to include the lane-level traffic determination and lane-changing guidance of Sekiya. One of ordinary skill in the art would have been motivated to make this modification because lane-specific traffic information allows for informed lane changing decisions, avoiding changes that would be detrimental to drivers, as suggested by Sekiya (see Sekiya at least [0086] This makes it possible to display the route before the change, without making unnecessary changes to another route due to congestion).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eldessouki, in view of Palmer, further in view of Sekiya, further in view Hashimoto, and further in view of CN 113947897 A ZHOU, Shi-qi et al. (hereinafter Zhou).
Regarding claim 5, Eldessouki, Palmer, Sekiya, and Hashimoto teach: The system of claim 3.
Eldessouki, Palmer, Sekiya, and Hashimoto do not teach: wherein the one or more processors are further configured to: on determining the traffic congestion value to be greater or equal to than a predefined threshold, generate updated navigation instructions for a vehicle travelling in the lane segment to re-route the vehicle.
However, Zhou teaches: wherein the one or more processors are further configured to: on determining the traffic congestion value to be greater or equal to than a predefined threshold, generate updated navigation instructions for a vehicle travelling in the lane segment to re-route the vehicle (see Zhou at least [pg. 8, para. 3, beginning with “In addition, after the main”] when the traffic condition average value is greater than the preset traffic congestion threshold value, considering the main vehicle is in the traffic congestion road; it can prompt the driver vehicle driving risk, or prompting the driver to change the driving route, and so on).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination system disclosed by Eldessouki, Palmer, Sekiya, and Hashimoto to include the instruction to change a driving route based on traffic exceeding a threshold of Zhou. One of ordinary skill in the art would have been motivated to make this modification because adjusting driving plans based on traffic conditions decreases the risks of driving, as suggested by Zhou (see Zhou at least [pg. 2, para. 1, beginning with “Road traffic condition”] obtaining the road traffic condition around the vehicle is very necessary, which is good for reasonably planning the driving route of the vehicle, avoiding the driving risk of the vehicle).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eldessouki, in view of Palmer, and further in view of Sekiya.
Regarding claim 6, Eldessouki and Palmer teach: The system of claim 1.
Eldessouki and Palmer do not teach: wherein the traffic data comprises at least one of: a traffic congestion value for the lane segment, a vehicle density value for the lane segment, or a combination thereof.
However, Sekiya teaches: wherein the traffic data comprises at least one of: a traffic congestion value for the lane segment, a vehicle density value for the lane segment, or a combination thereof (see Sekiya at least [0018] congestion information for each lane can be obtained).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle spacing determination system disclosed by Eldessouki and Palmer to include the congestion degree determination of Sekiya. One of ordinary skill in the art would have been motivated to make this modification because determining the amount of traffic congestion occurring along a route allows a system to display congestion levels along a driving route such that a vehicle operator may be informed of traffic congestion levels, as suggested by Sekiya (see Sekiya at least [0019] This congestion information is used, in response to a request from the congestion display device 110, to display congestion on the driving route to the destination).
Claim(s) 7 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eldessouki, in view of Palmer, and further in view of CN 108819942 A ZHANG, Jing-yi et al. (hereinafter Zhang).
Regarding claim 7, Eldessouki and Palmer teach: The system of claim 1.
Eldessouki and Palmer do not teach: wherein the one or more processors are further configured to: determine an average error value associated with at least one of: the forward distance data of each of the plurality of vehicles in the lane segment, and the backward distance data of each of the plurality of vehicles in the lane segment; and update the lane distance data for the lane segment based on the average error value.
However, Zhang teaches: wherein the one or more processors are further configured to: determine an average error value associated with at least one of: the forward distance data of each of the plurality of vehicles in the lane segment, and the backward distance data of each of the plurality of vehicles in the lane segment (see Zhang at least [0056] the intelligent vehicle system of each vehicle can determine whether the distance between the own vehicle and the adjacent preceding vehicle is greater than the average distance of the target driving section area); and
update the lane distance data for the lane segment based on the average error value (see Zhang at least [0052] When the distance between each vehicle and the vehicle in front is greater than the average distance between each vehicle, a distance reduction signal is sent to reduce the distance between the vehicle and the vehicle in front).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination system disclosed by Eldessouki and Palmer to include the consideration of the differences between the current distance of the own vehicle and another vehicle and the average distance between vehicles in the same segment of Zhang. One of ordinary skill in the art would have been motivated to make this modification because the difference between vehicle separations and the average vehicle separation can be used to normalize vehicle spacing and reduce overall space taken up by vehicles, minimizing congestion, as suggested by Zhang (see Zhang at least [0075] by sending a distance reduction signal, the average distance to the vehicle immediately ahead is reduced, thereby improving road utilization and reducing urban congestion).
Regarding claim 8, Eldessouki, Palmer, and Zhang teach: The system of claim 7, wherein the one or more processors are further configured to:
combine the forward average distance data and the backward average distance data based on corresponding average gap value (see Eldessouki at least [0078] With reference to FIG. 4C, average traffic stream density can be determined by calculating a distance between vehicles in adjacent lanes at sub process 435 of sub process 428 and by calculating a distance, from the floating vehicle, to a leading vehicle and a trailing vehicle at sub process 436 of sub process 428 and [0089] Considered in metric units, the average traffic stream density can be estimated as
PNG
media_image1.png
69
390
media_image1.png
Greyscale
and [0090] where D.sub.avg is the average traffic stream density of a segment of the highway (veh/km/ln), Gap.sub.Left 852 is the average observed gaps (m) between successive, adjacent vehicles passing the floating vehicle 801 on the left side, Gap.sub.Right 853 is the average observed gaps (m) between successive, adjacent vehicles passing the floating vehicle 801 on the right side); and
determine the lane distance data for the lane segment based on the combination (see Eldessouki at least [0090] D.sub.avg is the average traffic stream density of a segment of the highway (veh/km/ln)).
Zhang teaches determine the distance data based on corresponding average error value (see Zhang at least [0056] the intelligent vehicle system of each vehicle can determine whether the distance between the own vehicle and the adjacent preceding vehicle is greater than the average distance of the target driving section area).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination system considering nearby traffic gaps disclosed by Eldessouki, Palmer, and Zhang to include the consideration of the differences between the current distance of the own vehicle and another vehicle and the average distance between vehicles in the same segment of Zhang. One of ordinary skill in the art would have been motivated to make this modification because doing so would decrease the length of gaps between vehicles in front of and behind the own vehicle, decreasing the overall space taken up by vehicles and increasing the usefulness of the road, while minimizing congestion, as suggested by Zhang (see Zhang at least [0028] by sending a distance reduction signal, the average distance to the vehicle immediately ahead is reduced, thereby improving road utilization and reducing urban congestion).
Claim(s) 9 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eldessouki, in view of Palmer, and further in view of US 20250146833 A1 HAN; Zuoyue et al. (hereinafter Han).
Regarding claim 9, Eldessouki and Palmer teach: The system of claim 1, wherein the one or more processors are further configured to: obtain historical lane distance data for the lane segment, the historical lane distance data comprising at least one of: historical forward average distance data and historical backward average distance data (see Palmer at least [col. 6, line 57 - col. 7, line 4] the lane-specific information may include information regarding previously detected fuel-efficiency, previously detected travel duration, previously detected average vehicle speed, previously detected average following distance).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination system disclosed by Eldessouki and Palmer to include the previously collected information of Palmer. One of ordinary skill in the art would have been motivated to make this modification because obtaining such information allows for predictions of lane-based trends such as a specific lane typically having hard braking, as suggested by Palmer (see Palmer at least [col. 7, lines 16-19] for a particular section of a particular roadway, perhaps more vehicles experience hard braking in the right lane than the left lane (e.g., due to the particular exits and/or on-ramps in the particular section of the particular roadway)).
Eldessouki and Palmer do not teach: determine the traffic data for the lane segment during a first time period based on the lane distance data and the historical lane distance data.
However, Han teaches: determine the traffic data for the lane segment during a first time period based on the lane distance data and the historical lane distance data (see Han at least [0123] determining a congestion state switching result based on the number, the average vehicle speed and the average vehicle distance of the effective vehicles, and the historical congestion state, wherein the congestion state switching result represents a change in the congestion state).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination system disclosed by Eldessouki and Palmer to include the lane-specific congestion determination of Han. One of ordinary skill in the art would have been motivated to make this modification because the highly specific lane-level congestion information allows for more targeted autonomous driving features, as suggested by Han (see Han at least [0022] The sensing result is highly targeted, and can provide a better service for the driving (especially automatic driving) of the host vehicle).
Regarding claim 17, Eldessouki and Palmer teach: The method of claim 12, the method further comprising: obtaining historical lane distance data for the lane segment, the historical lane distance data comprising at least one of: historical forward average distance data and historical backward average distance data (see Palmer at least [col. 6, line 57 - col. 7, line 4] the lane-specific information may include information regarding previously detected fuel-efficiency, previously detected travel duration, previously detected average vehicle speed, previously detected average following distance).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination method disclosed by Eldessouki and Palmer to include the previously collected information of Palmer. One of ordinary skill in the art would have been motivated to make this modification because obtaining such information allows for predictions of lane-based trends such as a specific lane typically having hard braking, as suggested by Palmer (see Palmer at least [col. 7, lines 16-19] for a particular section of a particular roadway, perhaps more vehicles experience hard braking in the right lane than the left lane (e.g., due to the particular exits and/or on-ramps in the particular section of the particular roadway)).
Eldessouki and Palmer do not teach: the method further comprising: determining the traffic data for the lane segment during a first time period based on the lane distance data and the historical lane distance data.
Han teaches: determining the traffic data for the lane segment during a first time period based on the lane distance data and the historical lane distance data (see Han at least [0123] determining a congestion state switching result based on the number, the average vehicle speed and the average vehicle distance of the effective vehicles, and the historical congestion state, wherein the congestion state switching result represents a change in the congestion state).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination method disclosed by Eldessouki and Palmer to include the lane-specific congestion determination of Han. One of ordinary skill in the art would have been motivated to make this modification because the highly specific lane-level congestion information allows for more targeted autonomous driving features, as suggested by Han (see Han at least [0022] The sensing result is highly targeted, and can provide a better service for the driving (especially automatic driving) of the host vehicle).
Claim(s) 11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eldessouki, in view of Palmer, further in view of US 20210253103 A1 KUMAR; Balaji Sunil et al. (hereinafter Kumar), and further in view of CN 113734167 A FANG, Xiao et al. (hereinafter Fang).
Regarding claim 11, Eldessouki and Palmer teach: The system of claim 1.
Eldessouki and Palmer do not teach: wherein the one or more processors are further configured to: determine forward vehicle distance data for a vehicle from the plurality of vehicles based on the sensor data, the forward vehicle distance data comprising at least a first distance between the vehicle and a first forward vehicle, and a second distance between the first forward vehicle and a second forward vehicle; and on determining the first distance to be less than a first threshold and the second distance to be greater than or equal to a second threshold, generate navigation instructions for the vehicle for overtaking the first forward vehicle.
However, Kumar teaches: wherein the one or more processors are further configured to: determine forward vehicle distance data for a vehicle from the plurality of vehicles based on the sensor data, the forward vehicle distance data comprising at least a first distance between the vehicle and a first forward vehicle (see Kumar at least [0004] determining, by a trajectory determining device, a plurality of dynamic separation distances of an autonomous vehicle from a first vehicle ahead of the autonomous vehicle), and a second distance (see Kumar at least [0006] an available overtaking region for the autonomous vehicle, based on at least one dimension feature associated with at least one adjacent lane and a region ahead of the first vehicle on the first lane); and
on determining the first distance to be less than a first threshold and the second distance to be greater than or equal to a second threshold, generate navigation instructions for the vehicle for overtaking the first forward vehicle (see Kumar at least [0006] generating a trigger for the autonomous vehicle to overtake the first vehicle, when the dynamic separation distance at a current time instance is below a first distance threshold… a trajectory for the autonomous vehicle to overtake the first vehicle based on the overtaking distance, when the available overtaking region is above a second distance threshold).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination system disclosed by Eldessouki and Palmer to include the threshold determinations of multiple zones nearby the host vehicle to determine overtaking capacity of Kumar. One of ordinary skill in the art would have been motivated to make this modification because speed, position, and separation distance of the host vehicle must be adjusted both to optimize travel of the host vehicle and to maintain safe levels of separation from surrounding vehicles, as suggested by Kumar (see Kumar at least [0038] the autonomous vehicle 104 may adjust its current velocity in order to maintain a pre-decided safe distance from the first vehicle 106).
Eldessouki, Palmer, and Kumar do not teach: a second distance between the first forward vehicle and a second forward vehicle.
However, Fang teaches: a second distance between the first forward vehicle and a second forward vehicle (see Fang at least [0036] Since the vehicle enhancement signal takes into account the relative distance and relative acceleration between all the second vehicles in front of the first vehicle in the convoy and the first vehicle, the first vehicle can adjust the driving speed of the first vehicle according to the driving status of at least one second vehicle in front).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination system disclosed by Eldessouki, Palmer, and Kumar to include the second passing-viability zone between multiple vehicles preceding the host vehicle of Fang. One of ordinary skill in the art would have been motivated to make this modification because considering not only the vehicle directly in front, but vehicles farther in front of the host vehicle allows for overall traffic adjustments and control, as suggested by Fang (see Fang at least [0036] It has strong adaptability, flexible adjustment method and strong stability).
Regarding claim 18, Eldessouki and Palmer teach: The method of claim 12.
Eldessouki and Palmer do not teach: the method further comprising: determining forward vehicle distance data for a vehicle from the plurality of vehicles based on the sensor data, the forward vehicle distance data comprising at least a first distance between the vehicle and a first forward vehicle, and a second distance between the first forward vehicle and a second forward vehicle; and on determining the first distance to be less than a first threshold and the second distance to be greater than or equal to a second threshold, generating navigation instructions for the vehicle for overtaking the first forward vehicle.
However, Kumar teaches: the method further comprising: determining forward vehicle distance data for a vehicle from the plurality of vehicles based on the sensor data, the forward vehicle distance data comprising at least a first distance between the vehicle and a first forward vehicle (see Kumar at least [0004] determining, by a trajectory determining device, a plurality of dynamic separation distances of an autonomous vehicle from a first vehicle ahead of the autonomous vehicle), and a second distance (see Kumar at least [0006] an available overtaking region for the autonomous vehicle, based on at least one dimension feature associated with at least one adjacent lane and a region ahead of the first vehicle on the first lane); and
on determining the first distance to be less than a first threshold and the second distance to be greater than or equal to a second threshold, generating navigation instructions for the vehicle for overtaking the first forward vehicle (see Kumar at least [0006] generating a trigger for the autonomous vehicle to overtake the first vehicle, when the dynamic separation distance at a current time instance is below a first distance threshold… a trajectory for the autonomous vehicle to overtake the first vehicle based on the overtaking distance, when the available overtaking region is above a second distance threshold).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination method disclosed by Eldessouki and Palmer to include the threshold determinations of multiple zones nearby the host vehicle to determine overtaking capacity of Kumar. One of ordinary skill in the art would have been motivated to make this modification because speed, position, and separation distance of the host vehicle must be adjusted both to optimize travel of the host vehicle and to maintain safe levels of separation from surrounding vehicles, as suggested by Kumar (see Kumar at least [0038] the autonomous vehicle 104 may adjust its current velocity in order to maintain a pre-decided safe distance from the first vehicle 106).
Eldessouki, Palmer, and Kumar do not teach: a second distance between the first forward vehicle and a second forward vehicle.
However, Fang teaches: a second distance between the first forward vehicle and a second forward vehicle (see Fang at least [0036] Since the vehicle enhancement signal takes into account the relative distance and relative acceleration between all the second vehicles in front of the first vehicle in the convoy and the first vehicle, the first vehicle can adjust the driving speed of the first vehicle according to the driving status of at least one second vehicle in front).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination method disclosed by Eldessouki, Palmer, and Kumar to include the second passing-viability zone between multiple vehicles preceding the host vehicle of Fang. One of ordinary skill in the art would have been motivated to make this modification because considering not only the vehicle directly in front, but vehicles farther in front of the host vehicle allows for overall traffic adjustments and control, as suggested by Fang (see Fang at least [0036] It has strong adaptability, flexible adjustment method and strong stability).
Claim(s) 14 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eldessouki, in view of Palmer, further in view of Fowe, further in view of Sekiya, and further in view of Hashimoto.
Regarding claim 14, Eldessouki, Palmer, and Fowe teach: The method of claim 13.
Eldessouki, Palmer, and Fowe do not teach: the method further comprising: determining a traffic congestion value for the lane segment based on a comparison of the average distance threshold and the lane distance data; and updating a map database based on the comparison.
However, Sekiya teaches: the method further comprising:
determining a traffic congestion value for the lane segment based on a comparison of the average distance threshold and the lane distance data (see Sekiya at least [0017] a predetermined threshold is used and the area exceeding the threshold is determined to be a congested section. By providing multiple threshold levels, it is possible to determine congested sections according to the degree of congestion at multiple levels); and
updating a memory based on the comparison (see Sekiya at least [0056] congestion information according to the degree of congestion using a threshold value is stored in the storage unit 104).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination method disclosed by Eldessouki Palmer, and Fowe to include the threshold comparison technique of determining traffic congestion degree of Sekiya. One of ordinary skill in the art would have been motivated to make this modification because determining the degree of congestion occurring in an area and storing this information allows it to be accessed in the future when congestion information is to be displayed for a given route or lane, as suggested by Sekiya (see Sekiya at least [0020] The traffic congestion information processing unit 105 reads out from the storage unit 104 traffic congestion information for a position or area corresponding to a request for traffic congestion display from the traffic congestion display device 110, and transmits it to the traffic congestion display device 110 via the communication unit 106. At this time, the traffic congestion information processing unit 105 transmits traffic congestion information including location information (section) where traffic congestion is occurring and the degree of traffic congestion. In addition, since the degree of congestion for each lane of a road can be determined, the congestion information transmitted includes the congested sections for each lane).
Eldessouki, Palmer, Fowe, and Sekiya do not teach: determining an average distance threshold for the lane segment, based on map data; and a map database.
However, Hashimoto teaches: determining an average distance threshold for the lane segment, based on map data (see Hashimoto at least [0065] The threshold density calculation section 132 calculates a threshold density from the forward inter-vehicle distance, by using a map in which a relation illustrated in FIG. 7 is specified); and
a map database (see Hashimoto at least [0038] The map database 5 is formed in a storage such as an HDD and an SSD mounted on the vehicle, for example. Map information which the map database 5 has includes, for example, positional information of roads, information on road shapes, positional information of intersections and branch points, lane information of roads and the like).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination method disclosed by Eldessouki, Palmer, Fowe, and Sekiya to include the map database and map-based vehicle spacing threshold determination of Hashimoto. One of ordinary skill in the art would have been motivated to make this modification because map databases provide valuable information in the road and traffic recognition processes, as suggested by Hashimoto (see Hashimoto at least [0045] a method that performs traveling road recognition based on map information concerning the road on which the own vehicle is traveling and positional information of the own vehicle. The map information is acquired from the map database 5).
Regarding claim 15, Eldessouki, Palmer, Fowe, Sekiya, and Hashimoto teach: The method of claim 14, the method further comprising:
determining a color for displaying the lane segment on a map based display interface, based on the traffic congestion value (see Sekiya at least [0026] the traffic congestion state is displayed in stages by changing the color to be more noticeable as the traffic congestion level increases, for example from blue to yellow to red); and
causing to display the lane segment in association with the determined color during a time period (see Sekiya at least [0026] the congestion display processing unit 112 displays the congested sections and congested lanes indicated by the congestion information on a map or image by overlaying a color indicating congestion on a lane-by-lane basis).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination method disclosed by Eldessouki, Palmer, Fowe, Sekiya, and Hashimoto to include the lane-level congestion-based color display of Sekiya. One of ordinary skill in the art would have been motivated to make this modification because coloring a map lane-by-lane based on specific lane congestion levels allows navigation guidance to be tailored by lane as opposed to by an entire route, as suggested by Sekiya (see Sekiya at least [0085] since the system can show congestion by lane and display congested sections by the severity of congestion, it can show the start and end areas of the congestion even when driving on the same road. If part of a lane is congested, guidance can be provided that allows the driver to continue on the same road by changing lanes without having to detour onto other roads).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eldessouki, in view of Palmer, further in view of Fowe, further in view of Sekiya, further in view of Hashimoto, and further in view of Zhou.
Regarding claim 16, Eldessouki, Palmer, Fowe, Sekiya, and Hashimoto teach: The method of claim 14.
Eldessouki, Palmer, Fowe, Sekiya, and Hashimoto do not teach: on determining the traffic congestion value to be greater than or equal to a predefined threshold, generating updated navigation instructions for a vehicle travelling in the lane segment to re-route the vehicle.
However, Zhou teaches: on determining the traffic congestion value to be greater than or equal to a predefined threshold, generating updated navigation instructions for a vehicle travelling in the lane segment to re-route the vehicle (see Zhou at least [pg. 8, para. 3, beginning with “In addition, after the main”] when the traffic condition average value is greater than the preset traffic congestion threshold value, considering the main vehicle is in the traffic congestion road; it can prompt the driver vehicle driving risk, or prompting the driver to change the driving route, and so on).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the traffic data determination method disclosed by Eldessouki, Palmer, Fowe, Sekiya, and Hashimoto to include the instruction to change a driving route based on traffic exceeding a threshold of Zhou. One of ordinary skill in the art would have been motivated to make this modification because adjusting driving plans based on traffic conditions decreases the risks of driving, as suggested by Zhou (see Zhou at least [pg. 2, para. 1, beginning with “Road traffic condition”] obtaining the road traffic condition around the vehicle is very necessary, which is good for reasonably planning the driving route of the vehicle, avoiding the driving risk of the vehicle).
Response to Arguments
Applicant's arguments filed 10/15/2025 have been fully considered.
Applicant's amendments overcome the objections to the claims.
Applicant has not responded regarding the objections to the specification as described in the non-final office action dated 07/15/2025. As such, the objections to the specification are reiterated.
Regarding the arguments provided for the 35 U.S.C. §103 rejection of claims 1-20, the applicant's arguments have been considered but are not persuasive.
(A) Applicant argues, "On page 8-9 of the Office Action, the Examiner asserts that paragraph [0100] and [0038] of Eldessouki discloses feature 1 of Applicant's claim 1… From the above paragraphs it's clear that Applicant's claimed subject matter relates to sensor data collection with respect to lane segment, whereas Eldessouki fails to appreciate such difference. Therefore, Applicant respectfully submits that Eldessouki fails to disclose the feature of ‘obtain sensor data of each of a plurality of vehicles associated with a lane segment, the sensor data comprising at least one of: forward distance data of one or more vehicles in vicinity of each of the plurality of vehicles in the lane segment, and backward distance data of the one or more vehicles in the vicinity of each of the plurality of vehicles in the lane segment’, as recited by Applicant's claim 1." (from remarks pages 12-15)
As to point (A), Examiner is partially persuaded, but overall respectfully disagrees. Examiner acknowledges that Eldessouki does not explicitly recite obtaining sensor data from a plurality of vehicles associated with a specific lane segment. However, Eldessouki does recite collecting sensor data from vehicles in order to determine traffic density by using distances between vehicles. While not explicitly recited as lane-specific data collection, Eldessouki describes collecting sensor information relating to the vehicles driving in the same lane as the probe vehicle (see Eldessouki at least [0037] A floating car, or floating vehicle 101, travels in a middle lane, for instance, of a highway. A leading vehicle 103 and a trailing vehicle 104 travel in front of and behind the floating vehicle 101, respectively, within the middle lane… High fidelity determinations of traffic speed and traffic density require high quality data related to: … (6) distance between the floating car and the trailing vehicle, and (5) distance between the floating car and the leading vehicle.). Additionally, newly cited reference Palmer recites collecting data of distances between vehicles in a particular traffic lane, in order to determine lane-specific information which is used to inform lane-specific travel recommendations. Accordingly, the inter-vehicle distance determination technique of Eldessouki, when viewed in combination with Palmer’s lane-specific data collection, renders obvious the independent claim.
(B) Applicant argues, “Further, on page 9 of the Office Action, the Examiner asserts that paragraph [0078] of Eldessouki teaches feature 2 of Applicant's claim 1… Therefore, the Applicant submits that Eldessouki fails to teach the feature of ‘determine lane distance data for the lane segment based on the sensor data, wherein the lane distance data comprises forward average distance data, and backward average distance data’, as recited by the Applicant's claim 1.” (from remarks page 15)
As to point (B), Examiner respectfully disagrees. Examiner notes that, while Eldessouki does teach calculating distance between vehicles in adjacent lanes, Eldessouki also teaches – in the same paragraph – calculating distances from the floating vehicle to the leading and trailing vehicles. Eldessouki recites determining average distances between the floating vehicle and leading vehicle and between the floating vehicle and trailing vehicle, as part of the determination of traffic information. As such, though reciting more detail than claimed in the claimed invention, Eldessouki does recite the detection of distances between a vehicle and the vehicles in front and behind it, and using those averaged forward distance and averaged backward distances to determine traffic data, reading on the claimed invention.
(C) Applicant argues, “Further, on page 9 of the Office Action, the Examiner asserts that paragraph [0018] of Sekiya teaches the feature of "determine traffic data for the lane segment based on the lane distance data", as recited in claim 1… Therefore, Applicant respectfully submits that Sekiya fails to teach the feature of ‘determine traffic data for the lane segment based on the lane distance data’, as recited by Applicant's independent claim 1.” (from remarks pages 15-16)
As to point (C), Applicant’s argument has been considered but is moot because of the new ground of rejection over Eldessouki in view of Palmer, instead of Eldessouki in view of Sekiya, as necessitated by amendment.
Regarding the arguments provided for the 35 U.S.C. §101 rejections of claims 1-20, the applicant's arguments have been considered but are not persuasive.
(D) Applicant argues, “Applicant submits that the following features of claim 1 provides a practical solution of technical problem… In view of the above, it is clear that the Applicant's claimed invention provides the inventive concept and has a practical application and therefore, the Applicant's claimed invention significantly more than the abstract idea.” (from remarks pages 9-12)
As to point (D), Examiner respectfully disagrees. Applicant argues that the human mind cannot “process traffic data for thousands of lanes…” Examiner notes that the evaluation of subject matter eligibility is based on the recited language of the claimed invention. The claimed invention as recited determines forward average distance data and backward average distance data for a lane segment. A human occupant of a vehicle could mentally estimate these average distances for a plurality of traffic lanes by looking around and comparing the distances they observe. As such, this limitation as claimed can be considered a mental process. The claimed invention as recited collects, processes, and sends data, ultimately resulting in presenting navigation instructions, but not realizing the performance of those navigation instructions. Applicant states that lane-level traffic data allows “both manual and autonomous drivers to track and respond to real-time lane congestion.” Examiner notes that if a human driver tracks and responds to real-time lane congestion, then the so-called improvement is performed in the mind of the driver as they assess the traffic situation, and as such is considered a judicial exception. However, Examiner notes that the recitation of an autonomous vehicle control operation performed based on the currently recited system could possibly overcome a rejection under 35 U.S.C. 101. For example, the final limitation of the independent claim 1 currently recites “provide lane-level navigation instructions based on the determined traffic data,” which amounts to sending data. However, if the claim language were amended to include lane-level navigation instructions being provided to a driving system of an autonomous vehicle and subsequently the autonomous vehicle being actively controlled to drive based on the lane-level navigation instructions, such an amendment would likely incorporate the judicial exception into practical application and the claimed invention would have eligible subject matter. As such, Examiner recommends reciting an active vehicle control step in the independent claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20250033640 A1 Bai; Fan et al. discloses collecting data from multiple vehicles to determine lane-level congestion.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELLE ROSE KNUDSON whose telephone number is (703)756-1742. The examiner can normally be reached 1000-1700 ET M-F.
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, Hitesh Patel can be reached at (571) 270-5442. 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.
/ELLE ROSE KNUDSON/Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
1/15/26