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 .
Claim Status
Claims 1-5, 8, 10-11, 17-18, and 20 have been amended.
Claims 1-20 are pending.
Response to Arguments
Applicant's arguments filed 09/18/2025, with respect to claims 1-20 rejections under 35 USC 101 have been fully considered but they are not persuasive.
Applicant argues that the claimed invention has been amended to recite generating, by a central computing device, a result based on the traffic data based on the range of the location of the computing device is not an abstract idea because claim 1 as a whole cannot be performed entirely within the human mind; therefore, the claim as a whole is integrates the exception into a practical application.
The Examiner respectfully disagrees. The central computing device and the computing devices are recited at a high level of generality and are merely additional elements to apply an exception. In regards to traffic data being based on the range, this is an additional element as a field of use and/or an insignificant extra-solution activity (see MPEP 2106.05(g) and 2106.05(h)). See also Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016); FairWarning v. Iatric Sys., 839 F.3d 1089, 1094-95, 120 USPQ2d 1293, 1295 (Fed. Cir. 2016).
Applicant’s arguments with respect to claims 3-8, 10, and 11 objections have been fully considered and are persuasive. The objections of claims 3-8, 10, and 11 have been withdrawn.
Applicant’s arguments, with respect to claims 1-20 rejections under 35 USC 102(a)(1), have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Xu et al. (20200158530; hereinafter Xu). Applicant’s arguments pertain to newly amended limitations not addressed in the prior Office Action of record.
In regards to Applicant’s arguments that Zhang et al. (20220105926; hereinafter Zhang, already of record) does not recite receiving data from traffic lights communicatively coupled to a corresponding computing device. Under its broadest reasonable interpretation (BRI), the claimed limitations do not actively recite the plurality traffic signals generating and sending their respective traffic data to a central computing device, as argued by the Applicant. Under BRI, a central computing device receives traffic data from (regarding) a plurality of traffic signals. These traffic signals comprise a corresponding computing device, which is taught by previously recited Zhang.
Additionally, newly amended claim 1 recites:
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;
storing, by the central computing device, the traffic data in the data structure;
receiving, by the central computing device, a request for information associated with the traffic data from a computing device, wherein the request for information comprises a location of the computing device;
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 computing device;
generating, by the central computing device, a result based on the traffic data of the traffic signal of the plurality of traffic signals that is within the range of the location of the computing device 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 computing device.
Wherein it is unclear as to whether the receiving limitation of “a computing device” refers to the previously recited corresponding computing devices comprised within or communicatively coupled to the plurality of traffic signals.
Additionally, it appears that the limitation “each of the plurality of traffic signals comprising or communicatively coupled to a corresponding computing device of a plurality of computing devices” attempts to encompass multiple embodiments, as the claim may read that a corresponding computing device is integrated within a traffic signal or a corresponding computing device may be located outside of a traffic signal, such as within a vehicle passing through the area. This leads to further ambiguity as the identifying limitation recites “a traffic signal of the plurality of traffic signals that is within a range of the location of the computing device”, wherein if the corresponding computing device is located within a traffic signal, then the corresponding computing device may always be within range of its own traffic signal.
A detailed rejection follows below.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Wherein claim 1, 17, and 20 recite the limitation (or limitations analogous to): “... receiving, by the central computing device, a request for information associated with the traffic data from a computing device ...”, wherein it is unclear as to whether “a computing device” refers to the previously recited “corresponding computing device of a plurality of computing devices” or if the limitations attempts to establish a new computing device. Therefore, the limitation addressed above renders the claims indefinite.
In regards to claims 2-16 and 18-19, the claims are dependent on rejected claims 1 and 17, respectively, and are therefore rejected under the same premise.
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 1-20 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) 1-20 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;
storing, by the central computing device, the traffic data in the data structure;
receiving, by the central computing device, a request for information associated with the traffic data from a computing device, wherein the request for information comprises a location of the computing device;
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 computing device;
generating, by the central computing device, a result based on the traffic data of the traffic signal of the plurality of traffic signals that is within the range of the location of the computing device 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 computing device.”
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, identifying, generating, and storing in the mind, a result 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;
storing, by the central computing device, the traffic data in the data structure;
receiving, by the central computing device, a request for information associated with the traffic data from a computing device, wherein the request for information comprises a location of the computing device;
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 computing device;
generating, by the central computing device, a result based on the traffic data of the traffic signal of the plurality of traffic signals that is within the range of the location of the computing device 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 computing device.”
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.
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).
In regards to claims 1, 17 and 20, additional recitation of “identifying” and “within a range of the location of the computing device” also 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).
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 2-14, 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).
Claim 14 further recites the performance of an action, which merely amounts to apply it. The recitation of claim limitations that 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, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015).
Claim 16 further characterizes the computing device, which is merely indicating a field of use or technological environment in which to apply a judicial exception, which cannot integrate the judicial
exception into a practical application or amount to significantly more than the exception itself (see
MPEP 2106.05(h)).
Therefore, claim(s) 1-20 are ineligible under 35 USC § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 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 Xu et al. (20200158530; hereinafter Xu).
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 (see obviousness discussion below pertaining to Xu);
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, a request for information associated with the traffic data from a computing device (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 computing device (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 computing device (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);
generating, by the central computing device, a result based on the traffic data of the traffic signal of the plurality of traffic signals that is within the range of the location of the computing device 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 computing device (Zhang: “the roadside subsystem 112 may also control traffic signal lights 150-3 on the roadside, to jointly implement a communication strategy” ¶ 121).
While Zhang discloses communication technology in paragraph 52 and the system controlling traffic signal lights in paragraph 121, Zhang does not explicitly recite each of the plurality of traffic signals comprising or communicatively coupled to a corresponding computing device of a plurality of computing devices, however, in a similar field of endeavor, Xu teaches the claim limitation of each of the plurality of traffic signals communicatively comprising a corresponding computing device (Xu: “a traffic signal apparatus 30 may be a computing entity, computing device, controller, and/or the like. In various embodiments, the traffic signal apparatus 30 may control a traffic signal 39 ... a traffic signal apparatus 30 may be configured to communicate SPaT information/data to a traffic management system 40, to one or more vehicle apparatuses 20 within the vicinity of the traffic signal 39” ¶ 33, see also ¶ 47). 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 each of the plurality of traffic signals comprising a corresponding computing device, as taught by Xu, in order to improve traffic control flow (Xu: ¶ 37).
Regarding claim 2, Zhang in view of Xu teaches the method of claim 1, further comprising:
prior to receiving the traffic data, obtaining, by the plurality of computing devices (see obviousness discussion below pertaining to Xu), the traffic data based on radar data and camera data associated with the plurality of traffic signals (Zhang: “the sensing device 107 may include, but are not limited to: an image sensor (such as a camera), a laser radar, a millimeter wave radar” ¶ 63, see also ¶ 77, 79).
While Zhang does not explicitly recite the plurality of computing devices, in a similar field of endeavor, Xu teaches the claim limitation of the plurality of computing devices (Xu: “one or more traffic signal apparatuses 30, one or more traffic management apparatuses 40, one or more networks 50” ¶ 27, “a traffic signal apparatus 30 may be a computing entity, computing device, controller, and/or the like. In various embodiments, the traffic signal apparatus 30 may control a traffic signal 39 ... a traffic signal apparatus 30 may be configured to communicate SPaT information/data to a traffic management system 40, to one or more vehicle apparatuses 20 within the vicinity of the traffic signal 39” ¶ 33, see also ¶ 47). 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 plurality of computing devices, as taught by Xu, in order to improve traffic control flow (Xu: ¶ 37).
Regarding claim 3, Zhang in view of Xu teaches the method of claim 1, wherein receiving the request for information associated with the traffic data from the computing device comprises receiving an Application Programming Interface (API) request for information associated with the traffic data from the computing device (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 Xu teaches the method of claim 1, wherein receiving the request for information associated with the traffic data from the computing device comprises receiving an Application Programming Interface (API) request for the traffic data from the computing device (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 Xu teaches the method of claim 4, further comprising:
retrieving, by the central computing device, the traffic data from the data structure (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); and
sending, by the central computing device, an API response to the computing device, 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 Xu teaches the method of claim 5, further comprising:
receiving, by the computing device, the API response from the central computing device; and
generating, by the computing device, 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 Xu teaches the method of claim 4, wherein the API request for the traffic data is based on a location of the computing device (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 Xu teaches the method of claim 1, wherein sending the result to the computing device comprises sending an Application Programming Interface (API) response from the central computing device to the computing device, 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 Xu 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 Xu 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 computing device (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 Xu 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 computing device (Zhang: “a vehicle-mounted subsystem 132 in the transportation means 130-1 may send a takeover request message to a roadside subsystem 112” ¶ 124);
retrieving, by the central computing device, the route from the data structure (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); and
sending, by the central computing device, the route to the computing device (Zhang: The RSU 212 then provides the generated decision planning message and/or control message to the transportation means” ¶ 124).
Regarding claim 12, Zhang in view of Xu 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 Xu 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 14, Zhang in view of Xu teaches the method of claim 1, further comprising:
subsequent to sending the result to the computing device, performing, by the computing device, an action based on the result (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 15, Zhang in view of Xu 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 16, Zhang in view of Xu teaches the method of claim 1, wherein the computing device comprises at least one of an autonomous vehicle (Zhang: “the driving control module 234 may generate a self-vehicle decision plan based on the perception message 202, and control the self-vehicle driving operation based on the generated decision plan” ¶ 81), an emergency services vehicle, or a user device.
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. Goncalves et al. (20200334979) is in the similar field of endeavor as the claimed invention of traffic signal information.
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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/C.P./Examiner, Art Unit 3663
/ABBY J FLYNN/Supervisory Patent Examiner, Art Unit 3663
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