DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/27/2026 has been entered.
Response to Arguments
Applicant’s arguments, see page 8, filed 04/27/2026, with respect to claims 1-20 rejections under 35 USC 112(b) have been fully considered and are persuasive. The 35 USC 112(b) rejections of claims 1-20 have been withdrawn.
Applicant’s arguments, see pages 13-15, with respect to the rejection(s) of claim(s) 1-20 under 35 USC 103 with respect to Zhang et al. (20220105926; hereinafter Zhang, already of record) in view of Xu et al. (20200158530; hereinafter Xu, already of record) have been fully considered and are persuasive because the Applicant’s arguments pertain to newly amended limitations not addressed in the prior Office Action of record. Additionally, the prior Office Action of record utilizes Xu to teach of a corresponding computing device; however, Xu is silent regarding the computing device comprising a radar or a camera as argued upon. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Zhang et al. (20220105926; hereinafter Zhang, already of record) in view of Mortazavi et al. (20180096597; hereinafter Mortazavi).
Applicant's arguments with respect to claims 1-20 rejection under 35 USC 101 have been fully considered but they are not persuasive. Applicant argues that a person could not generate a result using the traffic data of the traffic signal within range of the autonomous vehicle. The Examiner respectfully disagrees. The steps of generating are recited at a high level of generality and merely use a generic computing device to perform the claimed processes, thus the claims still recite a mental process. See 881 F.3d at 1366, 125 USPQ2d at 1652-53.
In regards to Applicant’s arguments that the additional elements integrate the exception into a practical application, the Examiner respectfully disagrees. The underlined claimed limitations in the detailed rejection below, amount to insignificant extra-solution activity (data collection and transmission), applying the abstract idea to generic computing components (aka “apply it”), field of use, which do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are appending well-understood, routine, conventional activities specified at a high level of generality, to the judicial exception.
Additionally, in regards to the newly amended limitations of the use of the result to perform a routing action of the autonomous vehicle, the routing action itself is not positively recited; however, if the routing action was positively recited, it may amount to no more than the equivalent to the words “apply it” because the limitation attempts to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016) and In re Brown, 645 Fed. App'x 1014, 1017 (Fed. Cir. 2016).
A detailed rejection follows below.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-13, 15, and 17-20 are rejected under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Step 1 of the Subject Matter Eligibility Test entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter.
Claim(s) 1-13, 15, and 17-20 are directed towards a method, a computing system, and a non-transitory computer-readable medium. Therefore, claim(s) 1-20 are within at least one of the four statutory categories, i.e., process, machine, manufacture, or composition of matter.
If the claims recite at least one statutory category of invention, the claims require further analysis in Step 2A. Step 2A of the Subject Matter Eligibility Test is a two-prong inquiry. In Prong One, examiners evaluate whether the claims recite a judicial exception of invention.
Claims 1, 17, and 20 recite the following (bolded) abstract limitations (or limitations analogous to):
“A method, comprising:
receiving, by a central computing device comprising a data structure, traffic data from a plurality of traffic signals, each of the plurality of traffic signals comprising or communicatively coupled to a corresponding computing device of a plurality of computing devices, the plurality of computing devices comprising a radar device or a camera, wherein the traffic data comprises real-time radio frequency data or digital imagery data;
storing, by the central computing device, the traffic data in the data structure;
receiving, by the central computing device from an autonomous vehicle, a request for information associated with the traffic data, wherein the request for information comprises a location of the autonomous vehicle;
responsive to receiving the request, identifying, by the central computing device, a traffic signal of the plurality of traffic signals that is within a range of the location of the autonomous vehicle;
retrieving, by the central computing device, the traffic data from the data structure based on identifying the traffic signal that is within the range of the location of the autonomous vehicle;
generating, by the central computing device, a result using the traffic data of the traffic signal of the plurality of traffic signals that is within the range of the location of the autonomous vehicle and the request for information associated with the traffic data, wherein the traffic data of the traffic signal is indicative of traffic conditions of an area associated with the traffic signal; and
sending, by the central computing device, the result to the autonomous vehicle to cause the autonomous vehicle to use the result to perform a routing action.”
Wherein the claimed limitation are functions/processes that can be done entirely manually by a human using pen and paper, that under its broadest reasonable interpretation, cover performance of the limitations in the human mind. For example, a human can receive a request and generate a determination based on the information retrieved in response to said request, based on traffic data and information of an area. Thus, these claims recite an abstract idea without significantly more. Therefore, the claims are directed to an abstract idea without significantly more. The functions described by these limitations are also functions typical of generic computing components, and the functions performed or not performed may be entirely within the realm of computer functions
If the claims recite a judicial exception in step 2A Prong One, the claims require further analysis in step 2A Prong Two. In step 2A Prong Two, examiners evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application
Claims 1, 17, and 20 recite the following (underlined) additional limitations (or limitations analogous to):
“A method, comprising:
receiving, by a central computing device comprising a data structure, traffic data from a plurality of traffic signals, each of the plurality of traffic signals comprising or communicatively coupled to a corresponding computing device of a plurality of computing devices, the plurality of computing devices comprising a radar device or a camera, wherein the traffic data comprises real-time radio frequency data or digital imagery data;
storing, by the central computing device, the traffic data in the data structure;
receiving, by the central computing device from an autonomous vehicle, a request for information associated with the traffic data, wherein the request for information comprises a location of the autonomous vehicle;
responsive to receiving the request, identifying, by the central computing device, a traffic signal of the plurality of traffic signals that is within a range of the location of the autonomous vehicle;
retrieving, by the central computing device, the traffic data from the data structure based on identifying the traffic signal that is within the range of the location of the autonomous vehicle;
generating, by the central computing device, a result using the traffic data of the traffic signal of the plurality of traffic signals that is within the range of the location of the autonomous vehicle and the request for information associated with the traffic data, wherein the traffic data of the traffic signal is indicative of traffic conditions of an area associated with the traffic signal; and
sending, by the central computing device, the result to the autonomous vehicle to cause the autonomous vehicle to use the result to perform a routing action.”
The claimed central computing device and the computing device are additional elements that individually and in combination fail to integrate the judicial exception into a practical application because they merely apply the abstract idea to one or more generic computing components (aka “apply it”; see MPEP 2106.05(f)). The functions of these additional elements are recited at a high-level of generality (e.g. receiving, storing, processing, transmitting data) such that they amount to no more than mere instructions to “apply” the exception using one or more generic components.
In regards to the receiving and sending steps, the claimed limitations amount to insignificant extra-solution activity (data collection and transmission). Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Additionally, the use of the result to perform a routing action of the autonomous vehicle is not positively recited; however, if the routing action was positively recited, it may attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result and is equivalent to the words “apply it”.
If the additional elements do not integrate the exception into a practical application in step 2A Prong Two, then the claims are directed to the recited judicial exception, and require further analysis under Step 2B to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
Regarding claims 1, 17, and 20, additional recitation of “a central computing device”, “the computing device” are recited at such a high level of generality that it amounts to no more than additional elements of instructions to apply an exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Thus, even when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea.
In regards to claims 1, 17 and 20, additional recitation of “receiving” and “sending” of the central computing device amounts to insignificant extra-solution activity (data collection and transmission). The specification demonstrates the well-understood, routine, conventional nature of additional elements as it describes the additional elements as well-understood or routine or conventional (or equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describes the particulars of such additional elements to satisfy 35 U.S.C. §112(a). In addition, the Symantec, TLI, OIP Techs. and buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere receiving or transmitting data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Additionally, claims 1, 17, and 20, additionally recite a radar device or a camera which is merely an object on which the method operates, which does not integrate the exception into a practical application or provide significantly more. See CyberSource v. Retail Decisions, 654 F.3d 1366, 1370, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011). The recited real-time radio frequency data or digital imagery data merely indicates a field of use or technological environment in which to apply a judicial exception in addition to the insignificant extra-solution activity as described above. See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
Thus, even when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea.
Claims 6, 9, 12-13, 15 and 19 further characterizes the previously recited abstract limitations (further characterizing the result/route).
Claim 2-13 and 18-19 further recites receiving, obtaining, retrieving and sending functions, which are recited at a high level of generality and amounts to extra-solution activity. The specification demonstrates the well-understood, routine, conventional nature of additional elements as it describes the additional elements as well-understood or routine or conventional (or equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describes the particulars of such additional elements to satisfy 35 U.S.C. §112(a). In addition, the Symantec, TLI, OIP Techs. and buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere receiving or transmitting data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here). It is noted that use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Claims 12 and 19 further recites a machine learning model, which is merely a generic component for applying the abstract idea, and the training thereof, which is an abstract idea (a process that can be performed by a human).
Regarding claims 3-8 and 10-11, the claims recite an “Application Programming Interface (API)” which amounts to a field of use or technological environment as the limitations merely confine the use of the abstract idea to a particular area as employing generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not add significantly more. See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016); Intellectual Ventures I v. Capital One Bank, 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1640 (Fed. Cir. 2015); Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 120 USPQ2d 1201 (Fed. Cir. 2016). See MPEP 2106.05(h )
Therefore, claim(s) 1-13, 15, and 17-20 are ineligible under 35 USC § 101.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-13, 15, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (20220105926; hereinafter Zhang, already of record) in view of Mortazavi et al. (20180096597; hereinafter Mortazavi).
Regarding claim 1, Zhang teaches a method, comprising:
receiving, by a central computing device comprising a data structure, traffic data from a plurality of traffic signals (Zhang: Fig. 2, “a RSU 212 in a roadside subsystem 112 is configured to acquire perception information related to a detected environment 100” ¶ 75, “a coordinated driving control may also involve traffic infrastructure in a driving environment, such as traffic signal lights 150-3” ¶ 66, “One or more environment perception sources may utilize corresponding types of sensing devices to monitor static and/or dynamic objects in an environment 10 ... traffic facilities related to the traffic passage, such as traffic signal lights and traffic sign lights ... sources may also monitor road surface conditions, road traffic conditions” ¶ 79), each of the plurality of traffic signals comprising or communicatively coupled to a corresponding computing device of a plurality of computing devices,
...
storing, by the central computing device, the traffic data in the data structure (Zhang: “an example device 800 that may be used to implement embodiments of the present disclosure. The device 800 may be used to implement the roadside subsystem 112 or the vehicle-mounted subsystem 132 of FIG. 1 and FIG. 2 ... In the RAM 803, various programs and data required for the operation of the device 800 may also be stored” ¶ 237);
receiving, by the central computing device from an autonomous vehicle, a request for information associated with the traffic data(Zhang: “From the perception message, to the decision-planning message, and then to the control message ... In the interaction with the vehicle-mounted subsystem 132, the specific type of a driving-related message provided may depend on various triggering factors, such as at least one of a time-based trigger, a location-based trigger, and an event-based trigger” ¶ 70), wherein the request for information comprises a location of the autonomous vehicle (Zhang: “triggering one or more predetermined types of driving-related messages based on the location of a transportation means 130. It may be determined whether the transportation means 130 is in a predetermined area (e.g., a traffic intersection), in a specific road section, and/or whether a distance from a reference object” ¶ 72);
responsive to receiving the request, identifying, by the central computing device, a traffic signal of the plurality of traffic signals that is within a range of the location of the autonomous vehicle (Zhang: “One or more environment perception sources may utilize corresponding types of sensing devices to monitor static and/or dynamic objects in an environment 100, such as pedestrians, cyclists, transportation means, objects protruding from the road surface, etc., and may also detect traffic facilities related to the traffic passage, such as traffic signal lights and traffic sign lights” ¶ 79, “Depending on the source and specific content of perception information, the perception message 202 may include indication information related to an environment 100 and/or one or more other aspects of transportation means 130 in the environment 100” ¶ 82);
retrieving, by the central computing device, the traffic data from the data structure based on identifying the traffic signal that is within the range of the location of the autonomous vehicle (Zhang: “information related to a physical condition of a road in the environment 100, for indicating at least one of a road surface physical condition of the road and structured information of the road; information related to a traffic facility in the environment 100, for indicating at least one of a state of a signal light and a traffic sign on the road; information related to a road traffic condition in the environment 100, for indicating at least one of a sign, traffic flow and a traffic event related to the road and/or a lane in the road; and information related to a weather condition in the environment 100” ¶ 82);
generating, by the central computing device, a result using the traffic data of the traffic signal of the plurality of traffic signals that is within the range of the location of the autonomous vehicle and the request for information associated with the traffic data (Zhang: “The obtained perception information is provided to a driving control module 214 ... by analyzing perception information, the driving control module 214 may generate a perception message 202, including an analysis result of the perception information” ¶ 80), wherein the traffic data of the traffic signal is indicative of the traffic conditions of an area associated with the traffic signal (Zhang: “the perception message 202 may include indication information related to an environment 100 and/or one or more other aspects of transportation means 130 in the environment 100 ... information related to a traffic facility in the environment 100, for indicating at least one of a state of a signal light and a traffic sign on the road; information related to a road traffic condition in the environment 100, for indicating at least one of a sign, traffic flow and a traffic event related to the road” ¶ 82); and
sending, by the central computing device, the result to the autonomous vehicle to cause the autonomous vehicle to use the result to perform a routing action (Zhang: “the driving control module 234 in the vehicle-mounted subsystem 132 may perform a driving control of the transportation means 130 based on the perception message 202” ¶ 81, see also ¶ 80, 82).
However, Zhang fails to teach the plurality of computing devices comprising a radar device or a camera, wherein the traffic data comprises real-time radio frequency data or digital imagery data.
In a similar field of endeavor, Mortazavi teaches the plurality of computing devices comprising a radar device or a camera, wherein the traffic data comprises real-time radio frequency data or digital imagery data (Mortazavi: “a camera on a traffic signal may capture still images or video images that provide an indication of the state of the external environment that is viewable by the camera” ¶ 69).
As such, it would have been obvious to one of ordinary skill in the art, at the time of effective filing and with a reasonable expectation for success, to have modified the traffic system of Zhang so that it also includes the element of corresponding computing devices comprising a camera, as taught by Mortazavi, in order to improve traffic flow control (Mortazavi: ¶ 69, 74).
Regarding claim 2, Zhang in view of Mortazavi teaches the method of claim 1, further comprising:
However, Zhang fails to teach prior to receiving the traffic data, obtaining, by the plurality of computing devices, the traffic data based on radar data and camera data associated with the plurality of traffic signals.
In a similar field of endeavor, Mortazavi teaches prior to receiving the traffic data, obtaining, by the plurality of computing devices, the traffic data based on radar data and camera data associated with the plurality of traffic signals (“a camera on a traffic signal may capture still images or video images that provide an indication of the state of the external environment that is viewable by the camera” ¶ 69).
As such, it would have been obvious to one of ordinary skill in the art, at the time of effective filing and with a reasonable expectation for success, to have modified the traffic system of Zhang so that it also includes the element of camera data, as taught by Mortazavi, in order to improve traffic flow control (Mortazavi: ¶ 69, 74).
Regarding claim 3, Zhang in view of Mortazavi teaches the method of claim 1, wherein receiving the request for information associated with the traffic data from the autonomous vehicle comprises receiving an Application Programming Interface (API) request for information associated with the traffic data (Zhang: “each transportation means 130, a communication connection may be established with one of the roadside devices 110 and 120, or each transportation means 130 may have a communication connection with both of the roadside devices 110 and 120” ¶ 56, “A driving-related message may have any format in conformity with a communication technology used between the roadside subsystem 112 and the vehicle-mounted subsystem 132” ¶ 69, see also ¶ 58, 59, 87).
Regarding claim 4, Zhang in view of Mortazavi teaches the method of claim 1, wherein receiving the request for information associated with the traffic data from the autonomous vehicle comprises receiving an Application Programming Interface (API) request for the traffic data (Zhang: “An event-based trigger may include, for example, a request from the vehicle-mounted subsystem 132. The vehicle-mounted subsystem 132 may send a specific type of a driving-related message according to an instruction of the request” ¶ 73, “each transportation means 130, a communication connection may be established with one of the roadside devices 110 and 120, or each transportation means 130 may have a communication connection with both of the roadside devices 110 and 120” ¶ 56, “A driving-related message may have any format in conformity with a communication technology used between the roadside subsystem 112 and the vehicle-mounted subsystem 132” ¶ 69, see also ¶ 58, 59, 87).
Regarding claim 5, Zhang in view of Mortazavi teaches the method of claim 4, wherein sending the result to the autonomous vehicle comprises:
sending, by the central computing device, an API response to the autonomous vehicle, wherein the Application Programming Interface (API) response includes the traffic data (Zhang: “The perception message 202 may be provided to an OBU 232 in the vehicle-mounted subsystem 132 via the RSU 212” ¶ 80, “the perception message 202 may include indication information related to an environment 100 and/or one or more other aspects of transportation means 130 in the environment 100 ...” ¶ 82).
Regarding claim 6, Zhang in view of Mortazavi teaches the method of claim 5, further comprising:
receiving, by the autonomous vehicle, the API response from the central computing device; and
generating, by the autonomous vehicle, a route based on the traffic data in the API response (Zhang: “the driving control module 234 may directly use a received perception message 202 as an input of a decision control of the transportation means 130” ¶ 81, “The perception message, the decision planning message, and/or the control message generated are provided to the transportation means, which achieve a driving control based on the received message” ¶ 48).
Regarding claim 7, Zhang in view of Mortazavi teaches the method of claim 4, wherein the API request for the traffic data is based on the location of the autonomous vehicle (Zhang: “the specific type of a driving-related message provided may depend on various triggering factors, such as at least one of a time-based trigger, a location-based trigger, and an event-based trigger” ¶ 70, “A location-based trigger may be, for example, triggering one or more predetermined types of driving-related messages based on the location of a transportation means 130” ¶ 72).
Regarding claim 8, Zhang in view of Mortazavi teaches the method of claim 1, wherein sending the result to the autonomous vehicle comprises sending an Application Programming Interface (API) response from the central computing device to the autonomous vehicle, wherein the API response includes the result (Zhang: “The driving control module 214 may use various data analysis technologies such as a data fusion technology to process perception information. In some embodiments, by analyzing perception information, the driving control module 214 may generate a perception message 202, including an analysis result of the perception information. The perception message 202 may be provided to an OBU 232 in the vehicle-mounted subsystem 132 via the RSU 212” ¶ 80).
Regarding claim 9, Zhang in view of Mortazavi teaches the method of claim 1, wherein generating the result based on the traffic data and the request for information associated with the traffic data comprises:
determining, based on the traffic data, that an intersection comprising one or more of the plurality of traffic signals is blocked (Zhang: Table 1.1 Element Traffic Events, “Depending on the source and specific content of perception information, the perception message 202 may include indication information related to an environment 100 and/or one or more other aspects of transportation means 130 in the environment 100 ... information related to a road traffic condition in the environment 100, for indicating at least one of a sign, traffic flow and a traffic event related to the road and/or a lane in the road” ¶ 82, see also ¶ 135, 137);
receiving traffic data for each traffic signal in the intersection and traffic data for each traffic signal adjacent to the intersection (Zhang: “Depending on the source and specific content of perception information, the perception message 202 may include indication information related to an environment 100 and/or one or more other aspects of transportation means 130 in the environment 100 ... information related to a road traffic condition in the environment 100, for indicating at least one of a sign, traffic flow and a traffic event related to the road and/or a lane in the road” ¶ 82); and
generating a route based on the traffic data for each traffic signal in the intersection and the traffic data for each traffic signal adjacent to the intersection (Zhang: “the transportation means 130-1 is blocked by other surrounding transportation means 130-2 and 130-3 and cannot leave the predicament through a self-vehicle automatic driving decision” ¶ 124, “the driving control module 214 determines that the transportation means 130-1 cannot bypass the transportation means 130-2 to continue advancing, and the transportation means 130-2 is required to get out of the way for a certain space. Thus, the driving control module 214 further generates another decision planning message and/or another control message based on existing information” ¶ 124);
wherein the result comprises the route (Zhang: “The RSU 212 then provides the generated decision planning message and/or control message to the transportation means” ¶ 124).
Regarding claim 10, Zhang in view of Mortazavi teaches the method of claim 9, wherein the request for information associated with the traffic data comprises an Application Programming Interface (API) request for a route from the autonomous vehicle (Zhang: “a vehicle-mounted subsystem 132 in the transportation means 130-1 may send a takeover request message to a roadside subsystem 112” ¶ 124).
Regarding claim 11, Zhang in view of Mortazavi teaches the method of claim 9, further comprising:
storing, by the central computing device, the route in the data structure (Zhang: “an example device 800 that may be used to implement embodiments of the present disclosure. The device 800 may be used to implement the roadside subsystem 112 or the vehicle-mounted subsystem 132 of FIG. 1 and FIG. 2 ... In the RAM 803, various programs and data required for the operation of the device 800 may also be stored” ¶ 237);
receiving, by the central computing device, an Application Programming Interface (API) request for the route from the autonomous vehicle (Zhang: “a vehicle-mounted subsystem 132 in the transportation means 130-1 may send a takeover request message to a roadside subsystem 112” ¶ 124); and
sending, by the central computing device, the route to the autonomous vehicle (Zhang: The RSU 212 then provides the generated decision planning message and/or control message to the transportation means” ¶ 124)
wherein retrieving the traffic data from the data structure comprises retrieving the route from the data structure (Zhang: “the driving control module 214 may provide a decision planning message 204, so that the transportation means 130-1 may drive according to the decision plan” ¶ 123, “the driving control module 214 determines that the transportation means 130-1 cannot bypass the transportation means 130-2 to continue advancing, and the transportation means 130-2 is required to get out of the way for a certain space. Thus, the driving control module 214 further generates another decision planning message and/or another control message based on existing information” ¶ 124).
Regarding claim 12, Zhang in view of Mortazavi teaches the method of claim 1, wherein generating the result based on the traffic data and the request for information associated with the traffic data comprises:
obtaining, from the traffic data, one or more traffic images corresponding to the plurality of traffic signals (Zhang: “the sensor units in the sensing device 107 may include, but are not limited to: an image sensor (such as a camera) ... An image sensor may collect image information” ¶ 63);
determining, by a machine-learning model (Zhang: “various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, and a digital signal processor (DSP)” ¶ 239), an amount of vehicles in the one or more traffic images (Zhang: “the perception message 202 may include indication information related to an environment 100 and/or one or more other aspects of transportation means 130 in the environment 100 ... for indicating at least one of a sign, traffic flow and a traffic event related to the road and/or a lane in the road” ¶ 82);
obtaining, from the traffic data, a traffic speed corresponding to each traffic signal of the plurality of traffic signals (Zhang: “the perception message 202 may include following information related to one or more aspects: a description related to a target object present in the environment 100, for example, may include at least one of a classification, location information, speed information” ¶ 82);
determining, by the machine-learning model based on the amount of vehicles in the one or more traffic images and the traffic speed, a traffic congestion level (Zhang: “Due to more comprehensive perception information of an environment 100, the roadside subsystem 112 may consider the conditions of all of the transportation means or traffic participants in a certain geographic area, to determine a more reasonable decision plan” ¶ 89); and
generating, by the machine-learning model, a route based on the traffic data and the traffic congestion level (Zhang: “generates the decision planning message 204 based on the perception result ... Due to more comprehensive perception information of an environment 100, the roadside subsystem 112 may consider the conditions of all of the transportation means or traffic participants in a certain geographic area” ¶ 89);
wherein the result comprises the route (Zhang: “Due to more comprehensive perception information of an environment 100, the roadside subsystem 112 may consider the conditions of all of the transportation means or traffic participants in a certain geographic area, to determine a more reasonable decision plan” ¶ 89).
Regarding claim 13, Zhang in view of Mortazavi teaches the method of claim 12, further comprising:
receiving updated traffic data from the plurality of traffic signals (Zhang: “It is discussed above that a perception message 202 and/or a decision planning message 204 may include map information ... map information may indicate at least one of an identification of a map, an update mode of a map, an area of a map to be updated, and location information” ¶ 127);
determining, based on the updated traffic data, an updated traffic congestion level (Zhang: “the map update request message provided from a vehicle-mounted subsystem 132 to a roadside subsystem 112 may be all or part of the above content in Tables 1.1-1.4, or it may include other content” ¶ 128, “generates the decision planning message 204 based on the perception result ... Due to more comprehensive perception information of an environment 100, the roadside subsystem 112 may consider the conditions of all of the transportation means or traffic participants in a certain geographic area” ¶ 89); and
generating an updated route based on the updated traffic data and the updated traffic congestion level (Zhang: “The real-time running information is related to a current running condition of the transportation means 130, and may include, for example, at least one of location information, traveling direction information, traveling route information” ¶ 110, “a perception message received from the outside (e.g., the description related to the target object) as an input of a decision plan and/or a control, to determine how to plan and control a driving, such as controlling the moving direction, the speed and the route of the transportation means 130 to avoid a collision with the stationary target object” ¶ 143);
wherein the result comprises the updated route (Zhang: “The auxiliary planning information may include, for example, at least one of an indication of traveling intention, planned traveling route information, and speed limit information of the transportation means 130” ¶ 110).
Regarding claim 15, Zhang in view of Mortazavi teaches the method of claim 1, wherein the result comprises at least one of an alert, a route (Zhang: “Due to more comprehensive perception information of an environment 100, the roadside subsystem 112 may consider the conditions of all of the transportation means or traffic participants in a certain geographic area, to determine a more reasonable decision plan” ¶ 89), a traffic map, or a travel time.
Regarding claim 17, Zhang teaches a computing system, comprising:
a central computing device comprising a data structure (Zhang: “The external devices 110 and 120 may be any device, node, unit, facility, etc. having computing capabilities. As an example, a remote device may be a general-purpose computer, a server, a mainframe server, a network node such as an edge computing node, a cloud computing device such as a Virtual Machine (VM), and any other device that provides computing power” ¶ 55), a memory (Zhang: “a Read Only Memory (ROM) 802 or loaded from a storage unit 808 to a Random Access Memory (RAM)” ¶ 237), and a processor device coupled to the memory (Zhang: “computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, and a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc.” ¶ 237), the processor device to:
...
In regards to the remainder of claim 17, the claim recites analogous limitations to previously rejected claim 1 and is rejected under the same premise.
In regards to claim(s) 18 and 19, the claim(s) recite analogous limitations to claim(s) 2 and 12, and are therefore rejected under the same premise.
Regarding claim 20, Zhang teaches a non-transitory computer read-able storage medium that includes computer- executable instructions that, when executed, cause one or more processor devices to (Zhang: “The external devices 110 and 120 may be any device, node, unit, facility, etc. having computing capabilities. As an example, a remote device may be a general-purpose computer, a server, a mainframe server, a network node such as an edge computing node, a cloud computing device such as a Virtual Machine (VM), and any other device that provides computing power” ¶ 55, “a Read Only Memory (ROM) 802 or loaded from a storage unit 808 to a Random Access Memory (RAM)” ¶ 237):
...
In regards to the remainder of claim 20, the claim recites analogous limitations to previously rejected claim 1 and is rejected under the same premise.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Martin et al. (20200202711) is in the similar field of endeavor as the claimed invention of adaptive traffic control.
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/C.P./ Examiner, Art Unit 3663
/ABBY J FLYNN/ Supervisory Patent Examiner, Art Unit 3663
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