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 .
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 1/9/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
This Office Action is in response to the amendment filed on 9/15/2025. Claims 1, 9, 16, and 24 are amended. Claims 1-7, 9-22, and 24-30 are presently pending and are presented for examination.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Such claim limitations is/are:
means for identifying a first set of vehicle trajectories… found in claim 16. Corresponding structure for the claimed limitation is found in the specification at [0004] (processor) and [0006] (code).
means for identifying a second set of vehicle trajectories… found in claim 16. Corresponding structure for the claimed limitation is found in the specification at [0004] (processor) and [0006] (code).
means for determining that the second set of vehicle trajectories correlates with… found in claim 16. Corresponding structure for the claimed limitation is found in the specification at [0004] (processor) and [0006] (code).
means for determining the traffic signal-to-lane association… found in claim 16. Corresponding structure for the claimed limitation is found in the specification at [0004] (processor) and [0006] (code).
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.
Claim(s) 1-7, 9-22, and 24-30 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim 1 is directed toward a method, independent claim 9 is directed toward a server, independent claim 16 is directed to an apparatus, and independent claim 24 is directed to a computer-readable medium storing instructions. Therefore, each of the independent claim(s) 1, 9, 16, and 24 along with the corresponding dependent claims 10-15, 17-22, and 25-30 are directed to a statutory category of invention under Step 1.
Under Step 2A, Prong 1, the claims are analyzed to determine whether one or more of the claims recites subject matter that falls within one of the following groups of abstract ideas: (1) mental processes, (2) certain methods of organizing human activity, and/or (3) mathematical concepts. In this case, the independent claim(s) 1, 9, 16, and 24 is/are directed to an abstract idea without significantly more. Specifically, the claim(s), under its/their broadest reasonable interpretation(s) cover(s) certain mental processes. The language of independent claim 9 is used for illustration:
A method comprising:
determining a traffic signal-to-lane association for a traffic signal at a traffic intersection comprising (This limitation recites a mental process because a person could visually observe traffic patterns and use them to estimate an association between a lane and traffic signal.):
i) processing image data from one or more traffic cameras associated with the traffic intersection to extract vehicle motion data and roadway delineation information, wherein the vehicle motion data represents movement of one or more vehicles through the traffic intersection and the roadway delineation information represents roadway features including lane boundaries (This limitation recites a mental process because a person could visually estimate lane boundaries or other roadway delineations from lane boundary markers or other roadway features.);
ii) associating the vehicle motion data with a specific lane based on the roadway delineation information (This limitation recites a mental process because a person could visually estimate what lane a vehicle is in based on the vehicle’s location and the roadway markers.);
iii) obtaining traffic light information that indicates states of the traffic signal during a time period corresponding to the vehicle motion data from a road side unit (RSU); and
iv) correlating the associated vehicle motion data extracted from the image data from the one or more traffic cameras with the traffic light information obtained from the RSU to determine a relationship between vehicle behavior in the specific lane and the states of the traffic signal, wherein the correlating comprises (This limitation recites a mental process because a person could, e.g. visually, observe traffic patterns and traffic light states, then use them to estimate what kind of directions the traffic light provides, e.g. red, green, turn, etc. and the lane based on the state of the traffic light at the time a vehicle accelerated or stopped.);
identifying a first set of vehicle trajectories associated with vehicles that stopped in the specific lane (A human could, e.g. visually, determine if a vehicle trajectory is associated with stopping in a specific lane. This is a mental process.);
determining that the first set of vehicle trajectories correlates with the traffic signal being in a red light state (A human could, e.g. visually, determine the traffic light state. This is a mental process.);
identifying a second set of vehicle trajectories associated with vehicles that accelerated from the specific lane through the traffic intersection (A human could, e.g. visually, determine if a vehicle trajectory is associated with accelerating in a specific lane. This is a mental process.);
determining that the second set of vehicle trajectories correlates with the traffic signal being in a green light state (A human could, e.g. visually, determine the traffic light state. This is a mental process.); and
determining the traffic signal-to-lane association based on both (i) the correlation between vehicle stopping and red light states and (ii) the correlation between vehicle acceleration and green light states (A human could, e.g. visually, use the vehicle light state and vehicle acceleration or stopping to estimate which lane the vehicle is in and thereby infer a light/lane association. This is a mental process.); and
disseminating, to an automated vehicle (AV), information describing the traffic signal-to-lane association for the traffic signal at the traffic intersection over a Vehicle-to-Everything (V2X) wireless communication protocol to enable autonomous navigation decisions during approach to the traffic intersection, wherein the AV maneuvers the traffic intersection based on the information describing the traffic signal-to-lane association.
As explained above, independent claim 1 recites at least one abstract idea. The other independent claim(s), claim(s) 1, 9, 16 and 24, which is/are similar in scope to claim 1 likewise recite(s) at least one abstract idea under Step 2A, Prong 1.
Under Step 2A, Prong 2, the claims are analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, 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 such as merely using a computer to implement an abstract idea, adding insignificant extra-solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a "practical application"; see at least MPEP 2106.04(d).
In this case, the mental processes are not integrated into a practical application. For example, independent claims 1, 9, 16, and 24 recite additional elements. These/this limitation(s) amount to implementing the abstract idea on a computer, add insignificant extra-solution activity, and/or generally link use of the judicial exception to a particular technological environment or field of use; see at least MPEP 2106.04(d). More specifically,
A server, comprising: a transceiver; a memory; and one or more processors communicatively coupled with the transceiver and the memory… found in independent claim(s) 9. This limitation amounts to implementing the abstract idea on a computer.
An apparatus… found in independent claim(s) 16. This limitation amounts to implementing the abstract idea on a computer.
A non-transitory computer-readable medium storing instructions… found in independent claim(s) 24. This limitation amounts to implementing the abstract idea on a computer.
disseminating, to an automated vehicle (AV), … found in independent claim(s) 1, 9, 16, and 24. This limitation amounts to insignificant extra-solution activity.
obtaining traffic light information that indicates states of the traffic signal… found in independent claim(s) 1, 9, 16, and 24. This limitation amounts to insignificant extra-solution activity.
a traffic camera… found in independent claims 1, 9, 16, and 24. This limitation amounts to generally linking the use of the abstract idea to a particular technological environment or field of use.
a road side unit (RSU)… found in independent claims 1, 9, 16, and 24. This limitation amounts to generally linking the use of the abstract idea to a particular technological environment or field of use.
to enable autonomous navigation decisions… found in independent claims 1, 9, 16, and 24. This limitation amounts to generally linking the use of the abstract idea to a particular technological environment or field of use.
Therefore, taken alone, the additional elements do not integrate the abstract idea into a practical application. Furthermore, looking at the additional limitation(s) as an ordered combination or as a whole, the limitations add nothing significant that is not already present when looking at the elements taken individually. Because the additional elements do not integrate the abstract idea into a practical application by imposing meaningful limits on practicing the abstract idea, independent claim(s) 1, 9, 16, and 24 is/are directed to an abstract idea.
Under Step 2B, the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application in Step 2A, Prong Two, the additional element of limiting the use of the idea to one particular environment employs generic computer functions to execute an abstract idea and, therefore, does not add significantly more. Mere instruction to apply an exception using generic computer components or limiting the use of the abstract idea to a particular environment or field of use cannot provide an inventive concept. Additionally, as discussed above, several limitation(s) as recited above, is/are considered insignificant extra-solution activity.
A conclusion that an additional element is insignificant extra-solution activity in Step 2A must be re-evaluated in Step 2B to determine if the element is more than what is well-understood, routine, and conventional in the field. In this case, the additional limitations of a A server, An apparatus, and A non-transitory computer-readable medium are well-understood, routine, and conventional activity, because the specification does not provide any indication that the server, apparatus, and non-transitory computer-readable medium is/are anything more than conventional computer(s). Additionally, the remaining element(s) has/have been deemed insignificant extra-solution activity by one or more courts; see at least MPEP 2106.05(d) and MPEP 2106.05(g):
disseminating, to an automated vehicle (AV), … is considered well-understood, routine, and conventional activity under buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
obtaining traffic light information that indicates states of the traffic signal… is considered well-understood, routine, and conventional activity under buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Because the claims fail to recite anything sufficient to amount to significantly more than the judicial exception, independent claim(s) 1, 9, 16, and 24 is/are patent ineligible under 35 U.S.C. 101.
Claims 2, 10, 17, and 25 recite determining the AV is present at the traffic intersection before obtaining motion information. This is a mental process.
Claims 3, 11, 18, and 26 recite use of several measurement variables to determine an AV is present at an intersection. This is a mental process.
Claims 4, 12, 19, and 27 recite several methods for determining to send a traffic signal-to-lane association to an AV. These amount to a mental process (location-based knowledge provisioning process) or generally linking the abstract idea to a field of endeavor.
Claims 5, 13, 20, and 28 recite determining an AV is located in a traffic lane before sending the signal-to-lane association. This is a mental process.
Claim 6, 14, 21, and 29 recite sending the signal-to-lane association by cellular communication. This is generally linking the idea to a field of endeavor.
Claims 7, 15, 22, and 30 recite using specific on-board unit messaging protocols for sending the signal-to-lane association. This is insignificant extra-solution activity (sending and receiving data over a network.)
Dependent claims 2-7, 10-15, 17-22, and 25-30 have been given the full two-part analysis, including analyzing the additional limitations, both individually and in combination. Dependent claims 2-7, 10-15, 17-22, and 25-30, when analyzed both individually and in combination, are also patent ineligible under 35 U.S.C. 101 based on the same analysis as above. The additional limitations recited in the dependent claims fail to establish that the dependent claims are not directed to an abstract idea. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea. Accordingly, claims 2-7, 10-15, 17-22, and 25-30 are patent ineligible under 35 U.S.C. 101.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7, 9-22, and 24-30 are rejected under 35 U.S.C. 103 as being obvious over US 20200126408 A1, hereinafter “Gigengack”, in view of US 20200211371 A1, hereinafter “Frankfurth”, and CN 112639813 A, hereinafter “Qiao”.
Regarding claim 1, Gigengack discloses A method comprising:
determining a traffic signal-to-lane association for a traffic signal at a traffic intersection (See [0003], a data set containing traffic light/traffic lane allocation is created. See Fig. 3 and [0024], the method is used at a traffic intersection.) comprising:
i) processing image data from one or more traffic cameras associated with the traffic intersection to extract vehicle motion data and roadway delineation information, wherein the vehicle motion data represents movement of one or more vehicles through the traffic intersection and the roadway delineation information represents roadway features including lane boundaries (See [0017], vehicle trajectories while the vehicles pass through the traffic intersection are extracted from camera data. Using these observations, the traffic lights are allocated to traffic lanes, i.e. used to determine a traffic signal-to-lane association. See Fig.3 and [0029], the system uses road markings to detect traffic lanes, which is roadway delineation information. The camera records the intersection and is therefore associated with it. Identification of the lanes is used to analyze the vehicle trajectories in part of “method 1”. See [0026], “method 1” is used to produce and update the data set, which comprises the traffic light/lane allocation.);
ii) associating the vehicle motion data with a specific lane based on the roadway delineation information (See [0029], the system determines whether the vehicles are in specific lanes in order to associate their behavior with the lane and the corresponding signal, i.e. associates the vehicle motion data with the specific lane.);
iii) obtaining traffic light information that indicates states of the traffic signal during a time period corresponding to the vehicle motion data (See [0017], the switching state of the traffic light, i.e. the state of the traffic light, is detected. See [0029], the switching state is used in combination with the motion of the vehicle, i.e. the vehicle motion data, to estimate the kind of lane.); and
iv) correlating the associated vehicle motion data extracted from the image data from the one or more traffic cameras with the traffic light information to determine a relationship between vehicle behavior in the specific lane and the states of the traffic signal, wherein the correlating comprises analyzing timing correlations between vehicle acceleration patterns and light states (See Fig. 3 and [0029], the switching state is used in combination with the motion of the vehicle, i.e. the vehicle motion data, to estimate the kind of lane. The vehicle motion data is estimated from camera data. The combination of vehicle behavior and switching state information is used to estimate a relationship between the vehicle behavior in the specific lanes and the states of the traffic signal, in this case the car in the right lane turning right and the cars in the three center lanes driving straight ahead. See further [0008], correlation between the vehicle’s trajectory, which comprises starting to drive, i.e. an acceleration pattern, and the time the light’s state changes is used. The time corresponding to a change in the light’s state inherently defines time information on the states of the light themselves, i.e. the states before and after the change.) wherein the correlating comprises:
identifying a first set of vehicle trajectories associated with vehicles that stopped in the specific lane (See [0008], a vehicle in a lane is observed to start driving as the traffic light switching state changes. This means the vehicle was identified as having stopped in the lane before.);
determining that the first set of vehicle trajectories correlates with the traffic signal being in a red light state (See [0008], the vehicle movement is used with the switching state of the traffic light is used in determining the signal to lane allocation. “corresponding movement” indicates that the trajectory while the vehicle is stopped is associated with the red light state of the traffic light and the trajectory when the vehicle starts to drive and turn is associated with a green light state.);
identifying a second set of vehicle trajectories associated with vehicles that accelerated from the specific lane through the traffic intersection (See [0008], the vehicle movement is used with the switching state of the traffic light is used in determining the signal to lane allocation. “corresponding movement” indicates that the trajectory while the vehicle is stopped is associated with the red light state of the traffic light and the trajectory when the vehicle starts to drive and turn is associated with a green light state.);
determining that the second set of vehicle trajectories correlates with the traffic signal being in a green light state (See [0008], the vehicle movement is used with the switching state of the traffic light is used in determining the signal to lane allocation. “corresponding movement” indicates that the trajectory while the vehicle is stopped is associated with the red light state of the traffic light and the trajectory when the vehicle starts to drive and turn is associated with a green light state.). and
determining the traffic signal-to-lane association based on both (i) the correlation between vehicle stopping and red light states and (ii) the correlation between vehicle acceleration and green light states (See [0008], the correlation between stopping and red light states and acceleration and green light states is used for the allocation. See [0009], the data from different observations are used in the traffic light to lane allocation, i.e. signal-to-lane association, including information indicating the traffic light phase or switching phase during which vehicles pass through the intersection.)
disseminating, to an automated vehicle (AV) information describing the traffic signal-to-lane association for the traffic signal at the traffic intersection over a Vehicle-to-Everything (V2X) wireless communication protocol to enable autonomous navigation decisions during approach to the traffic intersection, wherein the AV maneuvers the traffic intersection based on the information describing the traffic signal-to-lane association (See [0003], a data set containing traffic light/traffic lane allocation is created. See [0014], the data set is stored in the HAD map, and automated vehicles access the HAD map. See [0018], the HAD map is sent from the server to the vehicle. Transmission of the HAD map to a vehicle inherently takes place over a V2X protocol because it is communication between a server and a vehicle. See Fig. 2 and [0028], the communication between the vehicle-external communications device associated with the server and the vehicle’s communication device is illustrate. No wired connection between the communication devices is shown. Examiner asserts that the communication protocol is therefore wireless. See [0027], the traffic light/traffic lane allocation is used to optimally navigate in the vicinity of traffic lights. Examiner asserts that optimally navigating an autonomous vehicle using a traffic light/traffic lane allocation comprises navigating the traffic intersection and during approach to the traffic intersection. See specifically [0009], the measurements by different vehicles are transmitted to an external server unit for evaluation to determine the traffic lane to light allocation. The data from the vehicles therefore describes the light/lane allocation. Transmission from vehicle to a server necessarily takes place over a V2X protocol.).
Gigengack does not explicitly disclose a traffic camera, obtaining traffic light information that indicates states of the traffic signal during a time period corresponding to the vehicle motion data from a road side unit (RSU), or traffic light information obtained from the RSU.
Frankfurth, in the same field of endeavor and solving a related problem, discloses use of a traffic camera (See the abstract and the application generally, an intersection is mapped based on movement patterns found in sensor data; see at least [0013], the sensors used include a camera; and [0014], the sensors are mounted on a traffic light. A camera mounted on a traffic light is at traffic camera). 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 method for associating lanes with traffic signals disclosed by Gigengack to include the traffic camera of Frankfurth. One of ordinary skill in the art would have been motivated to make this modification to put the camera in a position that would allow data to be continuously gathered from the area of the traffic light of interest, as suggested by Frankfurth at [0032].
Gigengack combined with Frankfurth does not explicitly disclose obtaining traffic light information that indicates states of the traffic signal during a time period corresponding to the vehicle motion data from a road side unit (RSU), or traffic light information obtained from the RSU.
Qiao, in the same field of endeavor and solving a related problem, discloses obtaining traffic light information that indicates states of the traffic signal during a time period corresponding to the vehicle motion data from a road side unit (RSU) (See page 12 paragraph 5, the self-driving vehicle uses the status information to perform driving control. Examiner asserts that the status information of the traffic therefore corresponds to the time that the vehicle is navigation the intersection. See further page 19 paragraph 5-7, the vehicle begins receiving current lighting color, i.e. traffic light information indicating the state of the traffic signal, continuously, specifically every .5 seconds. This means that the traffic light information indicates the state of the light during the time period corresponding to the vehicle’s motion in the vicinity of the intersection.), and traffic light information obtained from the RSU (See page 12 paragraph 5, the self-driving vehicle uses the status information to perform driving control. Examiner asserts that the status information of the traffic therefore corresponds to the time that the vehicle is navigation the intersection. See further page 19 paragraph 5-7, the vehicle begins receiving current lighting color, i.e. traffic light information indicating the state of the traffic signal, continuously, specifically every .5 seconds. This means that the traffic light information indicates the state of the light during the time period corresponding to the vehicle’s motion in the vicinity of the intersection.).
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 method for associating lanes with traffic signals disclosed by Gigengack and Frankfurth to include receiving the traffic light information from an RSU as disclosed by Qiao. One of ordinary skill in the art would have been motivated to make this modification in order to avoid misidentifying the state of the traffic light by using visual data, as suggested by Qiao at page 9 paragraph 6.
Regarding claim 2, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 1. Qiao further discloses wherein information regarding a motion of the AV is obtained in response to a determination AV is present at the traffic intersection (see at least page 3 paragraphs 6-7, when the autonomous vehicle is within a threshold distance of the intersection, i.e. at the intersection, the vehicle determines the identity of the lane it is currently located. See page 12 paragraph 2, this occurs when the vehicle is about to arrive at the intersection, i.e. is in motion. The identity of the lane is therefore information regarding a motion of the AV.). 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 system for mapping intersections, including signal to lane association, disclosed by Gigengack, Frankfurth, and Omari to include sending signal to lane association data to an autonomous vehicle based on a determination that the vehicle is approaching the intersection disclosed by Qiao. One of ordinary skill in the art would have been motivated to make this modification in order to provide improved navigation of the intersection for autonomous vehicles, as suggested by as suggested by Qiao at page 1 paragraphs 3-page 2 paragraph 1.
Regarding claim 3, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 2. Qiao further discloses wherein the determination that the AV is present at the traffic intersection is based on: a static position of the AV; a proximity of the AV to the traffic intersection; a velocity of the AV; or any combination thereof (See page 12 paragraphs 2 and 7, and page 13 paragraph 3; when the autonomous vehicle is within a threshold distance of the intersection, the vehicle obtains traffic lane information and performs driving control according to the state information. This process inherently comprises determining that the vehicle is at the intersection based on the threshold distance, i.e. proximity of the AV to the traffic intersection.). 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 system for mapping intersections, including signal to lane association, disclosed by Gigengack, Frankfurth, and Omari to include sending signal to lane association data to an autonomous vehicle based on a determination that the vehicle is approaching the intersection disclosed by Qiao. One of ordinary skill in the art would have been motivated to make this modification in order to provide improved navigation of the intersection for autonomous vehicles, as suggested by as suggested by Qiao at page 1 paragraphs 3-page 2 paragraph 1.
Regarding claim 4, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 1. Gigengack further discloses wherein sending the traffic signal-to-lane association to the AV is performed based on: a priori provisioning method; a location-based knowledge provisioning method; a request-response communication mechanism; or any combination thereof (See [0003], a data set containing traffic light/traffic lane allocation is created. See [0014], the data set is stored in the HAD map, and automated vehicles can access the HAD map. This is a request-response communication mechanism.).
Regarding claim 5, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 1. Qiao further discloses wherein disseminating the traffic signal-to-lane association to the AV is responsive to a determination that the AV is located in a traffic lane at the traffic intersection (See page 3 paragraphs 6-7, when the autonomous vehicle is within a threshold distance of the intersection, i.e. at the intersection, the vehicle determines the identity of the lane it is currently located, i.e. determines that the AV is located in a traffic lane at the intersection. page 2 paragraphs 9-11, the vehicle uses the lane it is in to request traffic light information from the traffic light associated with the lane in a networked device or cloud. Information from the associated light inherently comprises the signal-to-lane association itself.). 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 system for mapping intersections, including signal to lane association, disclosed by Gigengack to include sending signal to lane association data to an autonomous vehicle based on a determination that the vehicle is approaching the intersection disclosed by Qiao. One of ordinary skill in the art would have been motivated to make this modification in order to provide improved navigation of the intersection for autonomous vehicles, as suggested by as suggested by Qiao at page 1 paragraphs 3-page 2 paragraph 1.
Regarding claim 6, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 1. Further disclosure from Frankfurth renders obvious wherein sending the traffic signal-to-lane association to the AV is performed by cellular communication interfaces using safety messages (see at least [0034], a safety message comprising position, speed, and direction information from the vehicle is sent to the communication system, the communication from the system to the vehicle comprises the map of the intersection, and communication can occur by LTE-V2X, a form of cellular communication interface). 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 intersection mapping and map dissemination method disclosed by Gigengack, Qiao, and Omari to include the communication by cellular communication interfaces using safety messages of Frankfurth. One of ordinary skill in the art would have been motivated to make this modification to allow the wireless exchange of information between vehicles and infrastructure, as suggested by Frankfurth at [0034].
Regarding claim 7, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 1. Gigengack additionally discloses wherein the traffic signal-to-lane association is embedded in on-board unit (OBU) application-layer messages or OBU subscription services (see at least [0013]-[0014], where updated maps are sent to the vehicles from the server and used for navigation; and [0009], the maps include the signal-to-lane association. The map data being sent to the vehicle and used for navigation indicates that is being stored in an on-board unit or additional storage associated with the on-board unit.).
Regarding claim 9, Gigengack discloses A server (See [0009]-[0013], where a server executes the method for signal-to-lane association) comprising: a transceiver; a memory; and one or more processors communicatively coupled with the transceiver and the memory (see at least [0028], the vehicle comprises a vehicle-external communications device used for receiving and transmitting data, i.e. a transceiver. Servers inherently comprise one or more processors and a memory. Analysis of the received data indicates that the transceiver is coupled with the transceiver and the memory.), wherein the one or more processors are configured to:
determine a traffic signal-to-lane association for a traffic signal at a traffic intersection (See [0003], a data set containing traffic light/traffic lane allocation is created. See Fig. 3 and [0024], the method is used at a traffic intersection.) comprising:
i) processing image data from one or more traffic cameras associated with the traffic intersection to extract vehicle motion data and roadway delineation information, wherein the vehicle motion data represents movement of one or more vehicles through the traffic intersection and the roadway delineation information represents roadway features including lane boundaries (See [0017], vehicle trajectories while the vehicles pass through the traffic intersection are extracted from camera data. Using these observations, the traffic lights are allocated to traffic lanes, i.e. used to determine a traffic signal-to-lane association. See Fig.3 and [0029], the system uses road markings to detect traffic lanes, which is roadway delineation information. The camera records the intersection and is therefore associated with it. Identification of the lanes is used to analyze the vehicle trajectories in part of “method 1”. See [0026], “method 1” is used to produce and update the data set, which comprises the traffic light/lane allocation.);
ii) associating the vehicle motion data with a specific lane based on the roadway delineation information (See [0029], the system determines whether the vehicles are in specific lanes in order to associate their behavior with the lane and the corresponding signal, i.e. associates the vehicle motion data with the specific lane.);
iii) obtaining traffic light information that indicates states of the traffic signal during a time period corresponding to the vehicle motion data (See [0017], the switching state of the traffic light, i.e. the state of the traffic light, is detected. See [0029], the switching state is used in combination with the motion of the vehicle, i.e. the vehicle motion data, to estimate the kind of lane.); and
iv) correlating the associated vehicle motion data extracted from the image data from the one or more traffic cameras with the traffic light information to determine a relationship between vehicle behavior in the specific lane and the states of the traffic signal (See Fig. 3 and [0029], the switching state is used in combination with the motion of the vehicle, i.e. the vehicle motion data, to estimate the kind of lane. The vehicle motion data is estimated from camera data. The combination of vehicle behavior and switching state information is used to estimate a relationship between the vehicle behavior in the specific lanes and the states of the traffic signal, in this case the car in the right lane turning right and the cars in the three center lanes driving straight ahead. See further [0008], correlation between the vehicle’s trajectory, which comprises starting to drive, i.e. an acceleration pattern, and the time the light’s state changes is used. The time corresponding to a change in the light’s state inherently defines time information on the states of the light themselves, i.e. the states before and after the change.), wherein the correlating comprises
identifying a first set of vehicle trajectories associated with vehicles that stopped in the specific lane (See [0008], a vehicle in a lane is observed to start driving as the traffic light switching state changes. This means the vehicle was identified as having stopped in the lane before.);
determining that the first set of vehicle trajectories correlates with the traffic signal being in a red light state (See [0008], the vehicle movement is used with the switching state of the traffic light is used in determining the signal to lane allocation. “corresponding movement” indicates that the trajectory while the vehicle is stopped is associated with the red light state of the traffic light and the trajectory when the vehicle starts to drive and turn is associated with a green light state.);
identifying a second set of vehicle trajectories associated with vehicles that accelerated from the specific lane through the traffic intersection (See [0008], the vehicle movement is used with the switching state of the traffic light is used in determining the signal to lane allocation. “corresponding movement” indicates that the trajectory while the vehicle is stopped is associated with the red light state of the traffic light and the trajectory when the vehicle starts to drive and turn is associated with a green light state.);
determining that the second set of vehicle trajectories correlates with the traffic signal being in a green light state (See [0008], the vehicle movement is used with the switching state of the traffic light is used in determining the signal to lane allocation. “corresponding movement” indicates that the trajectory while the vehicle is stopped is associated with the red light state of the traffic light and the trajectory when the vehicle starts to drive and turn is associated with a green light state.).
and determining the traffic signal-to-lane association based on both (i) the correlation between vehicle stopping and red light states and (ii) the correlation between vehicle acceleration and green light states (See [0008], the correlation between stopping and red light states and acceleration and green light states is used for the allocation. See [0009], the data from different observations are used in the traffic light to lane allocation, i.e. signal-to-lane association, including information indicating the traffic light phase or switching phase during which vehicles pass through the intersection.);
disseminating to an automated vehicle (AV), information describing the traffic signal-to-lane association for the traffic signal at the traffic intersection over a Vehicle-to-Everything (V2X) wireless communication protocol to enable autonomous navigation decisions during approach to the traffic intersection, wherein the AV maneuvers the traffic intersection based on the information describing the traffic signal-to-lane association (See [0003], a data set containing traffic light/traffic lane allocation is created. See [0014], the data set is stored in the HAD map, and automated vehicles access the HAD map. See [0018], the HAD map is sent from the server to the vehicle. Transmission of the HAD map to a vehicle inherently takes place over a V2X protocol because it is communication between a server and a vehicle. See Fig. 2 and [0028], the communication between the vehicle-external communications device associated with the server and the vehicle’s communication device is illustrate. No wired connection between the communication devices is shown. Examiner asserts that the communication protocol is therefore wireless. See [0027], the traffic light/traffic lane allocation is used to optimally navigate in the vicinity of traffic lights. Examiner asserts that optimally navigating an autonomous vehicle using a traffic light/traffic lane allocation comprises navigating the traffic intersection and during approach to the traffic intersection. See specifically [0009], the measurements by different vehicles are transmitted to an external server unit for evaluation to determine the traffic lane to light allocation. The data from the vehicles therefore describes the light/lane allocation. Transmission from vehicle to a server necessarily takes place over a V2X protocol.).
Gigengack does not explicitly disclose a traffic camera, obtaining traffic light information that indicates states of the traffic signal during a time period corresponding to the vehicle motion data from a road side unit (RSU), or traffic light information obtained from the RSU.
Frankfurth, in the same field of endeavor and solving a related problem, discloses use of a traffic camera (See the abstract and the application generally, an intersection is mapped based on movement patterns found in sensor data; see at least [0013], the sensors used include a camera; and [0014], the sensors are mounted on a traffic light. A camera mounted on a traffic light is at traffic camera). 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 method for associating lanes with traffic signals disclosed by Gigengack to include the traffic camera of Frankfurth. One of ordinary skill in the art would have been motivated to make this modification to put the camera in a position that would allow data to be continuously gathered from the area of the traffic light of interest, as suggested by Frankfurth at [0032].
Gigengack combined with Frankfurth does not explicitly disclose obtaining traffic light information that indicates states of the traffic signal during a time period corresponding to the vehicle motion data from a road side unit (RSU), or traffic light information obtained from the RSU.
Qiao, in the same field of endeavor and solving a related problem, discloses obtaining traffic light information that indicates states of the traffic signal during a time period corresponding to the vehicle motion data from a road side unit (RSU) (See page 12 paragraph 5, the self-driving vehicle uses the status information to perform driving control. Examiner asserts that the status information of the traffic therefore corresponds to the time that the vehicle is navigation the intersection. See further page 19 paragraph 5-7, the vehicle begins receiving current lighting color, i.e. traffic light information indicating the state of the traffic signal, continuously, specifically every .5 seconds. This means that the traffic light information indicates the state of the light during the time period corresponding to the vehicle’s motion in the vicinity of the intersection.), and traffic light information obtained from the RSU (See page 12 paragraph 5, the self-driving vehicle uses the status information to perform driving control. Examiner asserts that the status information of the traffic therefore corresponds to the time that the vehicle is navigation the intersection. See further page 19 paragraph 5-7, the vehicle begins receiving current lighting color, i.e. traffic light information indicating the state of the traffic signal, continuously, specifically every .5 seconds. This means that the traffic light information indicates the state of the light during the time period corresponding to the vehicle’s motion in the vicinity of the intersection.).
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 method for associating lanes with traffic signals disclosed by Gigengack and Frankfurth to include receiving the traffic light information from an RSU as disclosed by Qiao. One of ordinary skill in the art would have been motivated to make this modification in order to avoid misidentifying the state of the traffic light by using visual data, as suggested by Qiao at page 9 paragraph 6.
Regarding claim 10, Gigengack combined with Frankfurth, Qiao, and Omari obvious the limitations of claim 9. Qiao further discloses wherein information regarding a motion of the AV is obtained in response to a determination that the AV is present at the traffic intersection (see at least page 3 paragraphs 6-7, when the autonomous vehicle is within a threshold distance of the intersection, i.e. at the intersection, the vehicle determines the identity of the lane it is currently located. See page 12 paragraph 2, this occurs when the vehicle is about to arrive at the intersection, i.e. is in motion. The identity of the lane is therefore information regarding a motion of the AV.). 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 system for mapping intersections, including signal to lane association, disclosed by Gigengack, Frankfurth, and Omari to include sending signal to lane association data to an autonomous vehicle based on a determination that the vehicle is approaching the intersection disclosed by Qiao. One of ordinary skill in the art would have been motivated to make this modification in order to provide improved navigation of the intersection for autonomous vehicles, as suggested by as suggested by Qiao at page 1 paragraphs 3-page 2 paragraph 1.
Regarding claim 11, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 10. Qiao further discloses wherein the determination that the AV is present at the traffic intersection is based on: a static position of the AV; a proximity of the AV to the traffic intersection; a velocity of the AV; or any combination thereof (See page 12 paragraphs 2 and 7, and page 13 paragraph 3; when the autonomous vehicle is within a threshold distance of the intersection, the vehicle obtains traffic lane information and performs driving control according to the state information. This process inherently comprises determining that the vehicle is at the intersection based on the threshold distance, i.e. proximity of the AV to the traffic intersection.). 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 system for mapping intersections, including signal to lane association, disclosed by Gigengack, Frankfurth, and Omari to include sending signal to lane association data to an autonomous vehicle based on a determination that the vehicle is approaching the intersection disclosed by Qiao. One of ordinary skill in the art would have been motivated to make this modification in order to provide improved navigation of the intersection for autonomous vehicles, as suggested by as suggested by Qiao at page 1 paragraphs 3-page 2 paragraph 1.
Regarding claim 12, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 9. Gigengack further discloses wherein disseminating the traffic signal-to-lane association to the AV is performed based on: a priori provisioning method; a location-based knowledge provisioning method; a request-response communication mechanism; or any combination thereof (See [0003], a data set containing traffic light/traffic lane allocation is created. See [0014], the data set is stored in the HAD map, and automated vehicles can access the HAD map. This is a request-response communication mechanism.).
Regarding claim 13, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 9. Qiao, in the same field of endeavor and solving a related problem, discloses wherein disseminating the traffic signal-to-lane association to the AV is responsive to a determination that the AV is located in a traffic lane at the traffic intersection (See page 3 paragraphs 6-7, when the autonomous vehicle is within a threshold distance of the intersection, i.e. at the intersection, the vehicle determines the identity of the lane it is currently located, i.e. determines that the AV is located in a traffic lane at the intersection. page 2 paragraphs 9-11, the vehicle uses the lane it is in to request traffic light information from the traffic light associated with the lane in a networked device or cloud. Information from the associated light inherently comprises the signal-to-lane association itself.). 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 system for mapping intersections, including signal to lane association, disclosed by Gigengack, Frankfurth, and Omari to include sending signal to lane association data to an autonomous vehicle based on a determination that the vehicle is approaching the intersection disclosed by Qiao. One of ordinary skill in the art would have been motivated to make this modification in order to provide improved navigation of the intersection for autonomous vehicles, as suggested by as suggested by Qiao at page 1 paragraphs 3-page 2 paragraph 1.
Regarding claim 14, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 9. Further disclosure from Frankfurth renders obvious wherein disseminating the traffic signal-to-lane association to the AV is performed by cellular communication interfaces using safety messages (see at least [0034], a safety message comprising position, speed, and direction information from the vehicle is sent to the communication system, the communication from the system to the vehicle comprises the map of the intersection, and communication can occur by LTE-V2X, a form of cellular communication interface). 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 intersection mapping and map dissemination method disclosed by Gigengack, Qiao, and Omari to include the communication by cellular communication interfaces using safety messages of Frankfurth. One of ordinary skill in the art would have been motivated to make this modification to allow the wireless exchange of information between vehicles and infrastructure, as suggested by Frankfurth at [0034].
Regarding claim 15, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 9. Gigengack additionally discloses wherein the traffic signal-to-lane association is embedded in on-board unit (OBU) application-layer messages or OBU subscription services (see at least [0013]-[0014], where updated maps are sent to the vehicles from the server and used for navigation; and [0009], the maps include the signal-to-lane association. The map data being sent to the vehicle and used for navigation indicates that is being stored in an on-board unit or additional storage associated with the on-board unit.).
Regarding claim 16, Gigengack discloses An apparatus (See [0009]-[0013], where a server executes the method for signal-to-lane association. This is an apparatus.) comprising:
means for determining a traffic signal-to-lane association for a traffic signal at a traffic intersection (See [0003], a data set containing traffic light/traffic lane allocation is created. See Fig. 3 and [0024], the method is used at a traffic intersection. See [0009]-[0013], where a server executes the method for signal-to-lane association.) comprising:
i) means for processing image data from one or more traffic cameras associated with the traffic intersection to extract vehicle motion data and roadway delineation information, wherein the vehicle motion data represents movement of one or more vehicles through the traffic intersection and the roadway delineation information represents roadway features including lane boundaries (See [0017], vehicle trajectories while the vehicles pass through the traffic intersection are extracted from camera data. Using these observations, the traffic lights are allocated to traffic lanes, i.e. used to determine a traffic signal-to-lane association. See Fig.3 and [0029], the system uses road markings to detect traffic lanes, which is roadway delineation information. The camera records the intersection and is therefore associated with it. Identification of the lanes is used to analyze the vehicle trajectories in part of “method 1”. See [0026], “method 1” is used to produce and update the data set, which comprises the traffic light/lane allocation.);
ii) means for associating the vehicle motion data with a specific lane based on the roadway delineation information (See [0029], the system determines whether the vehicles are in specific lanes in order to associate their behavior with the lane and the corresponding signal, i.e. associates the vehicle motion data with the specific lane.);
iii) means for obtaining traffic light information that indicates states of the traffic signal during a time period corresponding to the vehicle motion data (See [0017], the switching state of the traffic light, i.e. the state of the traffic light, is detected. See [0029], the switching state is used in combination with the motion of the vehicle, i.e. the vehicle motion data, to estimate the kind of lane.); and
iv) means for correlating the associated vehicle motion data extracted from the image data from the one or more traffic cameras with the traffic light information to determine a relationship between vehicle behavior in the specific lane and the states of the traffic signal, wherein the correlating comprises (See Fig. 3 and [0029], the switching state is used in combination with the motion of the vehicle, i.e. the vehicle motion data, to estimate the kind of lane. The vehicle motion data is estimated from camera data. The combination of vehicle behavior and switching state information is used to estimate a relationship between the vehicle behavior in the specific lanes and the states of the traffic signal, in this case the car in the right lane turning right and the cars in the three center lanes driving straight ahead. See further [0008], correlation between the vehicle’s trajectory, which comprises starting to drive, i.e. an acceleration pattern, and the time the light’s state changes is used. The time corresponding to a change in the light’s state inherently defines time information on the states of the light themselves, i.e. the states before and after the change.), wherein the correlating comprises
means for identifying a first set of vehicle trajectories associated with vehicles that stopped in the specific lane (See [0008], a vehicle in a lane is observed to start driving as the traffic light switching state changes. This means the vehicle was identified as having stopped in the lane before.);
means for determining that the first set of vehicle trajectories correlates with the traffic signal being in a red light state (See [0008], the vehicle movement is used with the switching state of the traffic light is used in determining the signal to lane allocation. “corresponding movement” indicates that the trajectory while the vehicle is stopped is associated with the red light state of the traffic light and the trajectory when the vehicle starts to drive and turn is associated with a green light state.);
means for identifying a second set of vehicle trajectories associated with vehicles that accelerated from the specific lane through the traffic intersection;
means for determining that the second set of vehicle trajectories correlates with the traffic signal being in a green light state (See [0008], the vehicle movement is used with the switching state of the traffic light is used in determining the signal to lane allocation. “corresponding movement” indicates that the trajectory while the vehicle is stopped is associated with the red light state of the traffic light and the trajectory when the vehicle starts to drive and turn is associated with a green light state.); and
means for determining the traffic signal-to-lane association based on both (i) the correlation between vehicle stopping and red light states and (ii) the correlation between vehicle acceleration and green light states (See [0008], the vehicle movement is used with the switching state of the traffic light is used in determining the signal to lane allocation. “corresponding movement” indicates that the trajectory while the vehicle is stopped is associated with the red light state of the traffic light and the trajectory when the vehicle starts to drive and turn is associated with a green light state.);
means for disseminating, to an automated vehicle (AV), information describing the traffic signal-to-lane association for the traffic signal at the traffic intersection over a Vehicle-to-Everything (V2X) wireless communication protocol to enable autonomous navigation decisions during approach to the traffic intersection, wherein the AV maneuvers the traffic intersection based on the information describing the traffic signal-to-lane association (See [0003], a data set containing traffic light/traffic lane allocation is created. See [0014], the data set is stored in the HAD map, and automated vehicles access the HAD map. See [0018], the HAD map is sent from the server to the vehicle. Transmission of the HAD map to a vehicle inherently takes place over a V2X protocol because it is communication between a server and a vehicle. See Fig. 2 and [0028], the communication between the vehicle-external communications device associated with the server and the vehicle’s communication device is illustrate. No wired connection between the communication devices is shown. Examiner asserts that the communication protocol is therefore wireless. See [0027], the traffic light/traffic lane allocation is used to optimally navigate in the vicinity of traffic lights. Examiner asserts that optimally navigating an autonomous vehicle using a traffic light/traffic lane allocation comprises navigating the traffic intersection and during approach to the traffic intersection. See specifically [0009], the measurements by different vehicles are transmitted to an external server unit for evaluation to determine the traffic lane to light allocation. The data from the vehicles therefore describes the light/lane allocation. Transmission from vehicle to a server necessarily takes place over a V2X protocol.).
Gigengack does not explicitly disclose a traffic camera, obtaining traffic light information that indicates states of the traffic signal during a time period corresponding to the vehicle motion data from a road side unit (RSU), or traffic light information obtained from the RSU.
Frankfurth, in the same field of endeavor and solving a related problem, discloses use of a traffic camera (See the abstract and the application generally, an intersection is mapped based on movement patterns found in sensor data; see at least [0013], the sensors used include a camera; and [0014], the sensors are mounted on a traffic light. A camera mounted on a traffic light is at traffic camera). 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 method for associating lanes with traffic signals disclosed by Gigengack to include the traffic camera of Frankfurth. One of ordinary skill in the art would have been motivated to make this modification to put the camera in a position that would allow data to be continuously gathered from the area of the traffic light of interest, as suggested by Frankfurth at [0032].
Gigengack combined with Frankfurth does not explicitly disclose obtaining traffic light information that indicates states of the traffic signal during a time period corresponding to the vehicle motion data from a road side unit (RSU), or traffic light information obtained from the RSU.
Qiao, in the same field of endeavor and solving a related problem, discloses obtaining traffic light information that indicates states of the traffic signal during a time period corresponding to the vehicle motion data from a road side unit (RSU) (See page 12 paragraph 5, the self-driving vehicle uses the status information to perform driving control. Examiner asserts that the status information of the traffic therefore corresponds to the time that the vehicle is navigation the intersection. See further page 19 paragraph 5-7, the vehicle begins receiving current lighting color, i.e. traffic light information indicating the state of the traffic signal, continuously, specifically every .5 seconds. This means that the traffic light information indicates the state of the light during the time period corresponding to the vehicle’s motion in the vicinity of the intersection.), and traffic light information obtained from the RSU (See page 12 paragraph 5, the self-driving vehicle uses the status information to perform driving control. Examiner asserts that the status information of the traffic therefore corresponds to the time that the vehicle is navigation the intersection. See further page 19 paragraph 5-7, the vehicle begins receiving current lighting color, i.e. traffic light information indicating the state of the traffic signal, continuously, specifically every .5 seconds. This means that the traffic light information indicates the state of the light during the time period corresponding to the vehicle’s motion in the vicinity of the intersection.).
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 method for associating lanes with traffic signals disclosed by Gigengack and Frankfurth to include receiving the traffic light information from an RSU as disclosed by Qiao. One of ordinary skill in the art would have been motivated to make this modification in order to avoid misidentifying the state of the traffic light by using visual data, as suggested by Qiao at page 9 paragraph 6.
Regarding claim 17, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 16. Qiao further discloses wherein information regarding a motion of the AV is obtained in response to a determination that the AV is present at the traffic intersection (see at least page 3 paragraphs 6-7, when the autonomous vehicle is within a threshold distance of the intersection, i.e. at the intersection, the vehicle determines the identity of the lane it is currently located. See page 12 paragraph 2, this occurs when the vehicle is about to arrive at the intersection, i.e. is in motion. The identity of the lane is therefore information regarding a motion of the AV.). 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 system for mapping intersections, including signal to lane association, disclosed by Gigengack, Frankfurth, and Omari to include sending signal to lane association data to an autonomous vehicle based on a determination that the vehicle is approaching the intersection disclosed by Qiao. One of ordinary skill in the art would have been motivated to make this modification in order to provide improved navigation of the intersection for autonomous vehicles, as suggested by as suggested by Qiao at page 1 paragraphs 3-page 2 paragraph 1.
Regarding claim 18, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 17. Qiao further discloses wherein the determination that the AV is present at the traffic intersection is based on: a static position of the AV; a proximity of the AV to the traffic intersection; a velocity of the AV; or any combination thereof (See page 12 paragraphs 2 and 7, and page 13 paragraph 3; when the autonomous vehicle is within a threshold distance of the intersection, the vehicle obtains traffic lane information and performs driving control according to the state information. This process inherently comprises determining that the vehicle is at the intersection based on the threshold distance, i.e. proximity of the AV to the traffic intersection.). 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 system for mapping intersections, including signal to lane association, disclosed by Gigengack, Frankfurth, and Omari to include sending signal to lane association data to an autonomous vehicle based on a determination that the vehicle is approaching the intersection disclosed by Qiao. One of ordinary skill in the art would have been motivated to make this modification in order to provide improved navigation of the intersection for autonomous vehicles, as suggested by as suggested by Qiao at page 1 paragraphs 3-page 2 paragraph 1.
Regarding claim 19, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 16. Gigengack further discloses wherein disseminating the traffic signal-to-lane association to the AV is performed based on: a priori provisioning method; a location-based knowledge provisioning method; a request-response communication mechanism; or any combination thereof (See [0003], a data set containing traffic light/traffic lane allocation is created. See [0014], the data set is stored in the HAD map, and automated vehicles can access the HAD map. This is a request-response communication mechanism.).
Regarding claim 20, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 16. Qiao renders obvious wherein disseminating the traffic signal-to-lane association to the AV is responsive to a determination that the AV is located in a traffic lane at the traffic intersection (See page 3 paragraphs 6-7, when the autonomous vehicle is within a threshold distance of the intersection, i.e. at the intersection, the vehicle determines the identity of the lane it is currently located, i.e. determines that the AV is located in a traffic lane at the intersection. page 2 paragraphs 9-11, the vehicle uses the lane it is in to request traffic light information from the traffic light associated with the lane in a networked device or cloud. Information from the associated light inherently comprises the signal-to-lane association itself.). 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 system for mapping intersections, including signal to lane association, disclosed by Gigengack, Frankfurth, and Omari to include sending signal to lane association data to an autonomous vehicle based on a determination that the vehicle is approaching the intersection disclosed by Qiao. One of ordinary skill in the art would have been motivated to make this modification in order to provide improved navigation of the intersection for autonomous vehicles, as suggested by as suggested by Qiao at page 1 paragraphs 3-page 2 paragraph 1.
Regarding claim 21, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 16. Further disclosure from Frankfurth renders obvious wherein disseminating the traffic signal-to-lane association to the AV is performed by cellular communication interfaces using safety messages (see at least [0034], a safety message comprising position, speed, and direction information from the vehicle is sent to the communication system, the communication from the system to the vehicle comprises the map of the intersection, and communication can occur by LTE-V2X, a form of cellular communication interface). 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 intersection mapping and map dissemination method disclosed by Gigengack, Qiao, and Omari to include the communication by cellular communication interfaces using safety messages of Frankfurth. One of ordinary skill in the art would have been motivated to make this modification to allow the wireless exchange of information between vehicles and infrastructure, as suggested by Frankfurth at [0034].
Regarding claim 22, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 16. Gigengack additionally discloses wherein the traffic signal-to-lane association is embedded in on-board unit (OBU) application-layer messages or OBU subscription services (see at least [0013]-[0014], where updated maps are sent to the vehicles from the server and used for navigation; and [0009], the maps include the signal-to-lane association. The map data being sent to the vehicle and used for navigation indicates that is being stored in an on-board unit or additional storage associated with the on-board unit.).
Regarding claim 24, Gigengack discloses A computer-readable medium storing instructions, the instructions comprising code for (See [0009]-[0013], where a server executes the method for signal-to-lane association. Servers inherently comprise one or more processors and a memory storing instructions for the functionality they implement.) comprising:
determining a traffic signal-to-lane association for a traffic signal at a traffic intersection (See [0003], a data set containing traffic light/traffic lane allocation is created. See Fig. 3 and [0024], the method is used at a traffic intersection. See [0009]-[0013], where a server executes the method for signal-to-lane association.) comprising:
i) processing image data from one or more traffic cameras associated with the traffic intersection to extract vehicle motion data and roadway delineation information, wherein the vehicle motion data represents movement of one or more vehicles through the traffic intersection and the roadway delineation information represents roadway features including lane boundaries (See [0017], vehicle trajectories while the vehicles pass through the traffic intersection are extracted from camera data. Using these observations, the traffic lights are allocated to traffic lanes, i.e. used to determine a traffic signal-to-lane association. See Fig.3 and [0029], the system uses road markings to detect traffic lanes, which is roadway delineation information. The camera records the intersection and is therefore associated with it. Identification of the lanes is used to analyze the vehicle trajectories in part of “method 1”. See [0026], “method 1” is used to produce and update the data set, which comprises the traffic light/lane allocation.);
ii) associating the vehicle motion data with a specific lane based on the roadway delineation information (See [0029], the system determines whether the vehicles are in specific lanes in order to associate their behavior with the lane and the corresponding signal, i.e. associates the vehicle motion data with the specific lane.);
iii) obtaining traffic light information that indicates states of the traffic signal during a time period corresponding to the vehicle motion data (See [0017], the switching state of the traffic light, i.e. the state of the traffic light, is detected. See [0029], the switching state is used in combination with the motion of the vehicle, i.e. the vehicle motion data, to estimate the kind of lane.); and
iv) correlating the associated vehicle motion data extracted from the image data from the one or more traffic cameras with the traffic light information to determine a relationship between vehicle behavior in the specific lane and the states of the traffic signal, (See Fig. 3 and [0029], the switching state is used in combination with the motion of the vehicle, i.e. the vehicle motion data, to estimate the kind of lane. The vehicle motion data is estimated from camera data. The combination of vehicle behavior and switching state information is used to estimate a relationship between the vehicle behavior in the specific lanes and the states of the traffic signal, in this case the car in the right lane turning right and the cars in the three center lanes driving straight ahead. See further [0008], correlation between the vehicle’s trajectory, which comprises starting to drive, i.e. an acceleration pattern, and the time the light’s state changes is used. The time corresponding to a change in the light’s state inherently defines time information on the states of the light themselves, i.e. the states before and after the change.), wherein the correlating comprises:
identifying a first set of vehicle trajectories associated with vehicles that stopped in the specific lane (See [0008], a vehicle in a lane is observed to start driving as the traffic light switching state changes. This means the vehicle was identified as having stopped in the lane before.);
determining that the first set of vehicle trajectories correlates with the traffic signal being in a red light state (See [0008], the vehicle movement is used with the switching state of the traffic light is used in determining the signal to lane allocation. “corresponding movement” indicates that the trajectory while the vehicle is stopped is associated with the red light state of the traffic light and the trajectory when the vehicle starts to drive and turn is associated with a green light state.);
identifying a second set of vehicle trajectories associated with vehicles that accelerated from the specific lane through the traffic intersection (See [0008], the vehicle movement is used with the switching state of the traffic light is used in determining the signal to lane allocation. “corresponding movement” indicates that the trajectory while the vehicle is stopped is associated with the red light state of the traffic light and the trajectory when the vehicle starts to drive and turn is associated with a green light state.);
determining that the second set of vehicle trajectories correlates with the traffic signal being in a green light state (See [0008], the vehicle movement is used with the switching state of the traffic light is used in determining the signal to lane allocation. “corresponding movement” indicates that the trajectory while the vehicle is stopped is associated with the red light state of the traffic light and the trajectory when the vehicle starts to drive and turn is associated with a green light state.). and
determining the traffic signal-to-lane association based on both (i) the correlation between vehicle stopping and red light states and (ii) the correlation between vehicle acceleration and green light states (See [0008], the correlation between stopping and red light states and acceleration and green light states is used for the allocation. See [0009], the data from different observations are used in the traffic light to lane allocation, i.e. signal-to-lane association, including information indicating the traffic light phase or switching phase during which vehicles pass through the intersection.);
disseminating, to an automated vehicle (AV), information describing the traffic signal-to-lane association for the traffic signal at the traffic intersection over a Vehicle-to-Everything (V2X) wireless communication protocol to enable autonomous navigation decisions during approach to the traffic intersection, wherein the AV maneuvers the traffic intersection based on the information describing the traffic signal-to-lane association (See [0003], a data set containing traffic light/traffic lane allocation is created. See [0014], the data set is stored in the HAD map, and automated vehicles access the HAD map. See [0018], the HAD map is sent from the server to the vehicle. Transmission of the HAD map to a vehicle inherently takes place over a V2X protocol because it is communication between a server and a vehicle. See Fig. 2 and [0028], the communication between the vehicle-external communications device associated with the server and the vehicle’s communication device is illustrate. No wired connection between the communication devices is shown. Examiner asserts that the communication protocol is therefore wireless. See [0027], the traffic light/traffic lane allocation is used to optimally navigate in the vicinity of traffic lights. Examiner asserts that optimally navigating an autonomous vehicle using a traffic light/traffic lane allocation comprises navigating the traffic intersection and during approach to the traffic intersection. See specifically [0009], the measurements by different vehicles are transmitted to an external server unit for evaluation to determine the traffic lane to light allocation. The data from the vehicles therefore describes the light/lane allocation. Transmission from vehicle to a server necessarily takes place over a V2X protocol.).
Gigengack does not explicitly disclose a non-transitory computer-readable medium, traffic camera, obtaining traffic light information that indicates states of the traffic signal during a time period corresponding to the vehicle motion data from a road side unit (RSU), or traffic light information obtained from the RSU.
Gigengack renders obvious a non-transitory computer-readable medium (See [0009]-[0013], where a server executes the method for signal-to-lane association. Servers inherently comprise one or more processors and a memory storing instructions for the functionality they implement.).
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 computer-implemented method for associating lanes with traffic signals disclosed by Gigengack to include use of a non-transitory memory, as suggested by Gigengack. One of ordinary skill in the art would have been motivated to make this modification so that the computer would not have to be repeatedly reprogrammed to execute the specified functionality, as suggested by Gigengack at [0009]-[0013].
Frankfurth, in the same field of endeavor and solving a related problem, discloses use of a traffic camera (See the abstract and the application generally, an intersection is mapped based on movement patterns found in sensor data; see at least [0013], the sensors used include a camera; and [0014], the sensors are mounted on a traffic light. A camera mounted on a traffic light is at traffic camera). 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 method for associating lanes with traffic signals disclosed by Gigengack to include the traffic camera of Frankfurth. One of ordinary skill in the art would have been motivated to make this modification to put the camera in a position that would allow data to be continuously gathered from the area of the traffic light of interest, as suggested by Frankfurth at [0032].
Gigengack combined with Frankfurth does not explicitly disclose obtaining traffic light information that indicates states of the traffic signal during a time period corresponding to the vehicle motion data from a road side unit (RSU), or traffic light information obtained from the RSU.
Qiao, in the same field of endeavor and solving a related problem, discloses obtaining traffic light information that indicates states of the traffic signal during a time period corresponding to the vehicle motion data from a road side unit (RSU) (See page 12 paragraph 5, the self-driving vehicle uses the status information to perform driving control. Examiner asserts that the status information of the traffic therefore corresponds to the time that the vehicle is navigation the intersection. See further page 19 paragraph 5-7, the vehicle begins receiving current lighting color, i.e. traffic light information indicating the state of the traffic signal, continuously, specifically every .5 seconds. This means that the traffic light information indicates the state of the light during the time period corresponding to the vehicle’s motion in the vicinity of the intersection.), and traffic light information obtained from the RSU (See page 12 paragraph 5, the self-driving vehicle uses the status information to perform driving control. Examiner asserts that the status information of the traffic therefore corresponds to the time that the vehicle is navigation the intersection. See further page 19 paragraph 5-7, the vehicle begins receiving current lighting color, i.e. traffic light information indicating the state of the traffic signal, continuously, specifically every .5 seconds. This means that the traffic light information indicates the state of the light during the time period corresponding to the vehicle’s motion in the vicinity of the intersection.).
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 method for associating lanes with traffic signals disclosed by Gigengack and Frankfurth to include receiving the traffic light information from an RSU as disclosed by Qiao. One of ordinary skill in the art would have been motivated to make this modification in order to avoid misidentifying the state of the traffic light by using visual data, as suggested by Qiao at page 9 paragraph 6.
Regarding claim 25, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 24. Qiao renders obvious wherein information regarding a motion of the AV is obtained in response to a determination that the AV is present at the traffic intersection (see at least page 3 paragraphs 6-7, when the autonomous vehicle is within a threshold distance of the intersection, i.e. at the intersection, the vehicle determines the identity of the lane it is currently located. See page 12 paragraph 2, this occurs when the vehicle is about to arrive at the intersection, i.e. is in motion. The identity of the lane is therefore information regarding a motion of the AV.). 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 system for mapping intersections, including signal to lane association, disclosed by Gigengack, Frankfurth, and Omari to include sending signal to lane association data to an autonomous vehicle based on a determination that the vehicle is approaching the intersection disclosed by Qiao. One of ordinary skill in the art would have been motivated to make this modification in order to provide improved navigation of the intersection for autonomous vehicles, as suggested by as suggested by Qiao at page 1 paragraphs 3-page 2 paragraph 1.
Regarding claim 26, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 25. Qiao renders obvious wherein the determination that the AV is present at the traffic intersection is based on: a static position of the AV; a proximity of the AV to the traffic intersection; a velocity of the AV; or any combination thereof (See page 12 paragraphs 2 and 7, and page 13 paragraph 3; when the autonomous vehicle is within a threshold distance of the intersection, the vehicle obtains traffic lane information and performs driving control according to the state information. This process inherently comprises determining that the vehicle is at the intersection based on the threshold distance, i.e. proximity of the AV to the traffic intersection.). 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 system for mapping intersections, including signal to lane association, disclosed by Gigengack, Frankfurth, and Omari to include sending signal to lane association data to an autonomous vehicle based on a determination that the vehicle is approaching the intersection disclosed by Qiao. One of ordinary skill in the art would have been motivated to make this modification in order to provide improved navigation of the intersection for autonomous vehicles, as suggested by as suggested by Qiao at page 1 paragraphs 3-page 2 paragraph 1.
Regarding claim 27, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 24. Gigengack further discloses wherein disseminating the traffic signal-to-lane association to the AV is performed based on :a priori provisioning method; a location-based knowledge provisioning method; a request-response communication mechanism; or any combination thereof (See [0003], a data set containing traffic light/traffic lane allocation is created. See [0014], the data set is stored in the HAD map, and automated vehicles can access the HAD map. This is a request-response communication mechanism.).
Regarding claim 28, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 24. Qiao further discloses wherein disseminating the traffic signal-to-lane association to the AV is responsive to a determination that the AV is located in a traffic lane at the traffic intersection (See page 3 paragraphs 6-7, when the autonomous vehicle is within a threshold distance of the intersection, i.e. at the intersection, the vehicle determines the identity of the lane it is currently located, i.e. determines that the AV is located in a traffic lane at the intersection. page 2 paragraphs 9-11, the vehicle uses the lane it is in to request traffic light information from the traffic light associated with the lane in a networked device or cloud. Information from the associated light inherently comprises the signal-to-lane association itself.). 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 system for mapping intersections, including signal to lane association, disclosed by Gigengack, Frankfurth, and Omari to include sending signal to lane association data to an autonomous vehicle based on a determination that the vehicle is approaching the intersection disclosed by Qiao. One of ordinary skill in the art would have been motivated to make this modification in order to provide improved navigation of the intersection for autonomous vehicles, as suggested by as suggested by Qiao at page 1 paragraphs 3-page 2 paragraph 1.
Regarding claim 29, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 24. Further disclosure from Frankfurth renders obvious wherein disseminating the traffic signal-to-lane association to the AV is performed by cellular communication interfaces using safety messages (see at least [0034], a safety message comprising position, speed, and direction information from the vehicle is sent to the communication system, the communication from the system to the vehicle comprises the map of the intersection, and communication can occur by LTE-V2X, a form of cellular communication interface). 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 intersection mapping and map dissemination method disclosed by Gigengack, Qiao, and Omari to include the communication by cellular communication interfaces using safety messages of Frankfurth. One of ordinary skill in the art would have been motivated to make this modification to allow the wireless exchange of information between vehicles and infrastructure, as suggested by Frankfurth at [0034].
Regarding claim 30, Gigengack combined with Frankfurth, Qiao, and Omari renders obvious the limitations of claim 24. Gigengack additionally discloses wherein the traffic signal-to-lane association is embedded in on-board unit (OBU) application-layer messages or OBU subscription services (see at least [0013]-[0014], where updated maps are sent to the vehicles from the server and used for navigation; and [0009], the maps include the signal-to-lane association. The map data being sent to the vehicle and used for navigation indicates that is being stored in an on-board unit or additional storage associated with the on-board unit.).
Response to Arguments
(A) Applicant argues “Rejections Under 35 U.S.C. § 101
The Office Action rejected claims 1-7, 9-22, and 24-30 under 35 U.S.C. § 101 because the claimed invention is allegedly directed to an abstract idea without significantly more. The Office
Action indicated that the § 101 rejections "would be withdrawn if independent claims 1, 9, 16, and 24 were modified to include language similar to ... specifying that a vehicle receives the information or association and necessarily takes at least one specific action based on the received information or association" (Office Action, p. 12).
In view of this guidance, independent claims 1, 9, 16, and 24 have been amended to recite, in relevant part:"disseminating, to an automated vehicle (AV), information describing the traffic
signal-to-lane association for the traffic signal at the traffic intersection over a Vehicle-to- Everything (V2X) wireless communication protocol to enable autonomous navigation decisions during approach to the traffic intersection, wherein the AV maneuvers the traffic intersection based on the information describing the traffic signal-to-lane association."
This amendment directly addresses the stated requirement by specifying that the AV receives the traffic signal-to-lane association information and necessarily takes a specific action, maneuvering the traffic intersection, based on the received information. Applicant respectfully submits that the amended claims now satisfy the Examiner's indicated conditions for withdrawal of the § 101 rejections and are patent-eligible for the reasons set forth below.
The Claims Are Not Directed To a Judicial Exception
The claims require specialized traffic camera hardware, RSU infrastructure, V2X wireless communication protocols, and now explicitly require that an autonomous vehicle physically maneuver through the traffic intersection based on the received information. These elements cannot be performed mentally or with pen and paper. The newly amended limitation requiring the AV to maneuver the intersection based on the disseminated information is fundamentally a physical, real-
world action involving vehicle control systems that is inherently beyond mental processing. Unlike purely abstract data analysis, these claims are inextricably tied to specific technological infrastructure for autonomous vehicle navigation systems and culminate in physical vehicle control, similar to the physical sensors and resulting actions in Thales Visionix, Inc. v. United States, 850 F.3d 1343 (2017).
Accordingly, the amended claims are not directed to a judicial exception at Step 2A, Prong One.
The Claims Integrate the Judicial Exception into a Practical Application
Even if any aspect of the amended claims could be characterized as an abstract idea, the
amended claims integrate any alleged abstract idea into a practical application under Step 2A, Prong Two. In particular, the amended claims now explicitly require that the AV maneuvers the traffic intersection based on the received traffic signal-to-lane association information. This amendment directly satisfies the stated guidance by specifying a concrete, physical action taken by the autonomous vehicle in response to the received information.
The claimed invention is specifically tied to traffic intersection infrastructure with real-world deployment of cameras and RSUs, and autonomous vehicle navigation systems representing practical application to transportation technology. The amended claims provide concrete improvements to technological fields, including autonomous vehicle safety, enabling more accurate traffic signal compliance and safer vehicle navigation through complex intersections. The claims address the concrete technological problem of autonomous vehicles safely navigating complex intersections, an infrastructure challenge with real-world safety implications. The amended features ensure that the disseminated information produces a tangible result: the AV physically maneuvering through the intersection in compliance with the correct traffic signal governing its lane.
The specific combination of traffic cameras, RSU data correlation, V2X communication, and the resulting AV maneuvering creates a technological solution that improves upon existing autonomous vehicle navigation systems. Here, the claims use specific infrastructure components (traffic cameras, RSUs, V2X protocols) to achieve a real-world result: an autonomous vehicle safely navigating an intersection based on accurate traffic signal-to-lane association information.
Accordingly, the claims integrate the judicial exception into a practical application at Step 2A, Prong Two.
The Claims Recite Features that Amount to Significantly More Than the Alleged Judicial Exception
Under Step 2B, the amended claims include numerous technical features that constitute
significantly more than any alleged abstract idea. Specifically, the claims include features that are not well-understood, routine, or conventional in the field, and add unconventional steps that confine the claim to a particular useful application, i.e., improving vehicle navigation of traffic intersections using information that is disseminated over a V2X wireless communication protocol and acted upon by the AV to physically maneuver through the intersection. The claimed correlation analysis requiring "identifying a first set of vehicle trajectories associated with vehicles that stopped in the specific lane; determining that the first set of vehicle trajectories correlates with the traffic signal being in a red light state; identifying a second set of vehicle trajectories associated with vehicles that accelerated from the specific lane through the traffic intersection; determining that the second set of vehicle trajectories correlates with the traffic signal being in a green light state; and determining the traffic signal-to-lane association based on both the correlation between stopping and red light states and the correlation between acceleration and green light states," represents a specific, non- conventional data processing technique that goes well beyond routine data gathering. This sophisticated temporal analysis, combined with V2X dissemination and the resulting AV maneuvering action, creates an unconventional technological solution to the problem of autonomous vehicle navigation at complex intersections.
The newly amended features requiring the AV to maneuver the intersection based on the received information represent an inventive application that transforms any abstract concept into a specific, tangible technological improvement. The ordered combination of (1) infrastructure-based traffic camera processing, (2) RSU-sourced traffic light information, (3) timing correlation analysis,
(4) V2X dissemination, and (5) AV maneuvering based on the received information represents a non-conventional arrangement of components that provides significantly more than any abstract idea. The claimed solution is analogous to BASCOM Global Internet Services v. AT&T Mobility, 827 F.3d 1341 (2016), where a non-conventional and non-generic arrangement of various components was found to qualify as "significantly more."Attorney Docke
Accordingly, Applicant submits that independent claims 1, 9, 16, and 24 are patent-eligible under 35 U.S.C. § 101. The remaining dependent claims are patentable for at least the same reasons as the independent claims on which they are based. Withdrawal of the rejections of the claims under 35 U.S.C. § 101 is respectfully requested.”
As to (A), Examiner does not find the argument persuasive.
Regarding Step 2A Prong One, each independent claim recites several mental processes as explained in the 35 USC 101 section above.
Regarding Step 2A Prong 2, Examiner does not find the argument persuasive. Generally linking the abstract idea to a field of endeavor through specific hardware is not sufficient to show integration into a practical application. Examiner believes that the section specifying navigation of the vehicle specifically mentioned by Applicant, …wherein the AV maneuvers the traffic intersection using based on the information describing the traffic signal-to-lane association, is not sufficient to show integration into a practical application because it is presented at a high level of generality and does not guarantee that at least one specific tangible action (e.g. turn, brake, accelerate, etc.) is taken as a result of the information.
Regarding Step 2B, Examiner does not find the argument persuasive. The limitations specifically mentioned by Applicant are rendered obvious by Gigengack:
identifying a first set of vehicle trajectories associated with vehicles that stopped in the specific lane (See [0008], a vehicle in a lane is observed to start driving as the traffic light switching state changes. This means the vehicle was identified as having stopped in the lane before.);
determining that the first set of vehicle trajectories correlates with the traffic signal being in a red light state (See [0008], the vehicle movement is used with the switching state of the traffic light is used in determining the signal to lane allocation. “corresponding movement” indicates that the trajectory while the vehicle is stopped is associated with the red light state of the traffic light and the trajectory when the vehicle starts to drive and turn is associated with a green light state.);
identifying a second set of vehicle trajectories associated with vehicles that accelerated from the specific lane through the traffic intersection (See [0008], the vehicle movement is used with the switching state of the traffic light is used in determining the signal to lane allocation. “corresponding movement” indicates that the trajectory while the vehicle is stopped is associated with the red light state of the traffic light and the trajectory when the vehicle starts to drive and turn is associated with a green light state.);
determining that the second set of vehicle trajectories correlates with the traffic signal being in a green light state (See [0008], the vehicle movement is used with the switching state of the traffic light is used in determining the signal to lane allocation. “corresponding movement” indicates that the trajectory while the vehicle is stopped is associated with the red light state of the traffic light and the trajectory when the vehicle starts to drive and turn is associated with a green light state.). and
determining the traffic signal-to-lane association based on both (i) the correlation between vehicle stopping and red light states and (ii) the correlation between vehicle acceleration and green light states (See [0008], the correlation between stopping and red light states and acceleration and green light states is used for the allocation. See [0009], the data from different observations are used in the traffic light to lane allocation, i.e. signal-to-lane association, including information indicating the traffic light phase or switching phase during which vehicles pass through the intersection.)
(B) Applicant argues “Rejections Under 35 U.S.C. § 103
The Office Action rejected claims 1-7, 9-22, and 24-30 under 35 USC § 103 as allegedly being obvious over U.S. Patent Publication No. 2020/0126408 to Gigengack et al. (hereinafter "Gigengack") in view of U.S. Patent Publication No. 2020/0211371 to Frankfurth (hereinafter "Frankfurth") and CN Patent No. 112639813 to Qiao (hereinafter "Qiao") and U.S. Patent Publication No. 2021/0166145 to Omari et al. (hereinafter "Omari").
Amended claim 1 recites, in part:
" correlating the associated vehicle motion data extracted from the image data from the one or
more traffic cameras with the traffic light information obtained from the RSU to determine a relationship between vehicle behavior in the specific lane and the states of the traffic signal,
wherein the correlating comprises:
o identifying a first set of vehicle trajectories associated with vehicles that stopped in
the specific lane;
o determining that the first set of vehicle trajectories correlates with the traffic signal
being in a red light state;
o identifying a second set of vehicle trajectories associated with vehicles that
accelerated from the specific lane through the traffic intersection;
o determining that the second set of vehicle trajectories correlates with the traffic signal
being in a green light state; and
o determining the traffic signal-to-lane association based on both (i) the correlation
between vehicle stopping and red light states and (ii) the correlation between vehicle
acceleration and green light states; and
" disseminating, to an automated vehicle (AV), information describing the traffic signal-to- lane association for the traffic signal at the traffic intersection over a Vehicle-to-Everything (V2X) wireless communication protocol to enable autonomous navigation decisions during approach to the traffic intersection, wherein the AV maneuvers the traffic intersection based
on the information describing the traffic signal-to-lane association.
Applicant respectfully submits that the references cited in the Office Action do not render these features of claim 1 unpatentable. On page 18, the Office Action concedes that Gigengack combined with Frankfurth and Qiao does not disclose "wherein the correlating comprises analyzing timing correlations between vehicle stopping patterns and red light states and between vehicleAttor
acceleration patterns and green light states." The Office Action cites Omari as allegedly disclosing these features.
Omari Does Not Teach the Claimed Correlation Methodology
Omari fails to disclose or suggest the specific claimed correlation methodology. The claimed invention addresses the problem of determining which traffic signal governs which lane, i.e., establishing traffic signal-to-lane associations. In contrast, Omari assumes that signal-to-lane associations are already known and uses traffic pattern analysis to infer what traffic maneuvers are permitted (i.e., traffic rules). The cited paragraphs confirm Omari's purpose. In cited 27, Omari states a "computing system may correlate the vehicle trajectories with the traffic-light states, based on which the computing system may learn the traffic rules." In 28, Omari discusses time information, including "the time when a vehicle starts entering an intersection" and "the time when the vehicle starts moving again after the oncoming traffic passes the vehicle." This time information is used to "identify yield relationship[s]" (id.), i.e., determining whether left-turning traffic must yield to oncoming traffic. This is a traffic rule inference, not a signal-to-lane association determination. Further, the example provided in 29 is illustrative:
the traffic patterns may indicate that there are very few right-turns on red from
lane 306 to lane 314 ... between 7 A.M. and 9 A.M. every weekday. The connectivity model, therefore, may be able to infer that vehicles are not
allowed to make right-turns on red from lane 306 to lane 314 between 7
A.M. and 9 A.M. every weekday.
Omari, 29 (emphasis added). Again, Omari determines whether a maneuver is permitted based on learned traffic rules, not which traffic signal controls a particular lane.
In contrast, the claims recite a dual-correlation approach specifically designed to establish which traffic signal governs which lane. The claimed features include:
1. Identifying a first set of vehicle trajectories associated with vehicles that stopped in the
specific lane;
2. Determining that the first set of vehicle trajectories correlates with the traffic signal being in
a red light state;
3. Identifying a second set of vehicle trajectories associated with vehicles that accelerated from
the specific lane through the traffic intersection;
4. Determining that the second set of vehicle trajectories correlates with the traffic signal being
in a green light state; and
5. Determining the traffic signal-to-lane association based on both (i) the correlation between
vehicle stopping and red light states and (ii) the correlation between vehicle acceleration and
green light states.
This dual-correlation approach establishes which traffic signal governs which lane. When vehicles stop in a particular lane during red light states of a particular signal, and accelerate from that lane during green light states of that same signal, the system can reliably conclude that the signal governs that lane. Using both correlations together provides a methodology that avoids false associations. A vehicle might stop for reasons other than a red light (e.g., yielding to pedestrians), but the combination of stopping-during-red and acceleration-during-green patterns establishes the governing relationship with confidence. Omari's disclosure does not perform this dual-correlation to establish signal-to-lane associations. Rather, Omari's system already knows the signal-to-lane relationships and instead analyzes patterns to determine what actions drivers take (or are permitted to take) given those known relationships.
Accordingly, the cited references do not teach or suggest "wherein the correlating comprises: identifying a first set of vehicle trajectories associated with vehicles that stopped in the specific lane; determining that the first set of vehicle trajectories correlates with the traffic signal being in a red light state; identifying a second set of vehicle trajectories associated with vehicles that accelerated from the specific lane through the traffic intersection; determining that the second set of vehicle trajectories correlates with the traffic signal being in a green light state; and determining the traffic signal-to-lane association based on both (i) the correlation between vehicle stopping and red light states and (ii) the correlation between vehicle acceleration and green light states," as claimed.
The Cited References Do Not Teach V2X Dissemination to Enable A V Maneuvering
The cited references fail to disclose "disseminating, to an automated vehicle (AV),
information describing the traffic signal-to-lane association for the traffic signal at the traffic intersection over a Vehicle-to-Everything (V2X) wireless communication protocol to enable autonomous navigation decisions during approach to the traffic intersection, wherein the AV maneuvers the traffic intersection based on the information describing the traffic signal-to-lane association."
None of the cited references disclose or suggest dissemination of information to an AV describing traffic signal-to-lane associations over a V2X wireless protocol or the use of such information describing traffic signal-to-lane associations by an AV to maneuver a traffic intersection. None of the cited references disclose or suggest V2X wireless communication protocols. Gigengack teaches storing traffic light/lane allocation in HAD maps on a server that
vehicles access for navigation, which is a map-based architecture different from V2X dissemination. Frankfurth teaches using traffic cameras mounted on traffic lights to map intersections based on sensor data, but does not disclose disseminating signal-to-lane associations to vehicles over V2X wireless communication. Qiao teaches RSUs sending current traffic light status (red/yellow/green) to vehicles, but not disseminating signal-to-lane association information. Omari teaches encoding lane relationships and governing traffic lights into a map of a region, but does not disclose V2X dissemination of this information.
Accordingly, the cited references do not teach or suggest "disseminating, to an automated
vehicle (AV), information describing the traffic signal-to-lane association for the traffic signal at the traffic intersection over a Vehicle-to-Everything (V2X) wireless communication protocol to enable autonomous navigation decisions during approach to the traffic intersection, wherein the AV maneuvers the traffic intersection based on the information describing the traffic signal-to-lane association."
For at least these reasons, Applicant respectfully submits that claim 1 is patentable over the references cited in the Office Action. Claims 9, 16, and 24 are amended to recite similar features. The dependent claims of claims 1, 9, 16, and 24 incorporate all features of their respective independent claims, and thus are patentable over the references cited in the Office Action for at least the reasons stated above with respect to the independent claims, as well as the additional features recited in those dependent claims.
Although Applicant has not discussed the specific rejections for all dependent claims in this Amendment, Applicant does not necessarily agree with the characterizations of the references cited in the Office Action or the grounds for the rejections made by the Office with respect to these dependent claims.”
Regarding (B), Examiner does not find the argument persuasive.
Regarding the correlation methodology, Gigengack discloses the new limitations specifically mentioned by Applicant:
identifying a first set of vehicle trajectories associated with vehicles that stopped in the specific lane (See [0008], a vehicle in a lane is observed to start driving as the traffic light switching state changes. This means the vehicle was identified as having stopped in the lane before.);
determining that the first set of vehicle trajectories correlates with the traffic signal being in a red light state (See [0008], the vehicle movement is used with the switching state of the traffic light is used in determining the signal to lane allocation. “corresponding movement” indicates that the trajectory while the vehicle is stopped is associated with the red light state of the traffic light and the trajectory when the vehicle starts to drive and turn is associated with a green light state.);
identifying a second set of vehicle trajectories associated with vehicles that accelerated from the specific lane through the traffic intersection (See [0008], the vehicle movement is used with the switching state of the traffic light is used in determining the signal to lane allocation. “corresponding movement” indicates that the trajectory while the vehicle is stopped is associated with the red light state of the traffic light and the trajectory when the vehicle starts to drive and turn is associated with a green light state.);
determining that the second set of vehicle trajectories correlates with the traffic signal being in a green light state (See [0008], the vehicle movement is used with the switching state of the traffic light is used in determining the signal to lane allocation. “corresponding movement” indicates that the trajectory while the vehicle is stopped is associated with the red light state of the traffic light and the trajectory when the vehicle starts to drive and turn is associated with a green light state.). and
determining the traffic signal-to-lane association based on both (i) the correlation between vehicle stopping and red light states and (ii) the correlation between vehicle acceleration and green light states (See [0008], the correlation between stopping and red light states and acceleration and green light states is used for the allocation. See [0009], the data from different observations are used in the traffic light to lane allocation, i.e. signal-to-lane association, including information indicating the traffic light phase or switching phase during which vehicles pass through the intersection.)
Regarding V2X dissemination, Gigengack discloses the limitations specifically mentioned by Applicant. See [0003], a data set containing traffic light/traffic lane allocation is created. See [0014], the data set is stored in the HAD map, and automated vehicles access the HAD map. See [0018], the HAD map is sent from the server to the vehicle. Transmission of the HAD map to a vehicle inherently takes place over a V2X protocol because it is communication between a server and a vehicle. See Fig. 2 and [0028], the communication between the vehicle-external communications device associated with the server and the vehicle’s communication device is illustrate. No wired connection between the communication devices is shown. Examiner asserts that the communication protocol is therefore wireless. See [0027], the traffic light/traffic lane allocation is used to optimally navigate in the vicinity of traffic lights. Examiner asserts that optimally navigating an autonomous vehicle using a traffic light/traffic lane allocation comprises navigating the traffic intersection and during approach to the traffic intersection.
See specifically [0009], the measurements by different vehicles are transmitted to an external server unit for evaluation to determine the traffic lane to light allocation. The data from the vehicles therefore describes the light/lane allocation. Transmission from vehicle to a server necessarily takes place over a V2X protocol.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20210166145 A1 which relates to using traffic patterns to determine traffic rules.
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.
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/AUSTIN ROBERT CHENNAULT/Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
4/20/26
Prosecution Insights