DETAILED ACTION
Response to Amendment
This action is in response to the remark entered on March 23rd, 2026.
Claims 1 – 20 are pending in current application.
Claims 1, 7 and 20 are amended.
Claim 14 is cancelled.
Information Disclosure Statement
The information disclosure statement (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to under 37 CFR 1.83(a).
The drawing in a nonprovisional application must show every feature of the invention specified in the claims. However, conventional features disclosed in the description and claims, where their detailed illustration is not essential for a proper understanding of the invention, should be illustrated in the drawing in the form of a graphical drawing symbol or a labeled representation (e.g., a labeled rectangular box).
In addition, tables that are included in the specification and sequences that are included in sequence listings should not be duplicated in the drawings.
Therefore, the recited claim limitation regarding, “determining….a traffic signal identifier…sending the instructions to a traffic signal…” must be shown or the feature(s) canceled.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) for reference characters not mentioned in the description shall not appear in the drawings.
because they do not include the following reference sign(s) mentioned in the description: In this instant case, the reference characters were not mentioned/existed for “a traffic signal identifier” in the description.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1, 17 and 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claims 1, 17 and 20, applicant’s claim limitation regarding, “instructions include the traffic signal identifier” does not particularly provide the subject matter that is described in the written description, at the time the application filed, convey to skilled in the art that applicant possessed the invention at the time of filing.
In this instant case, upon further review, skilled in the art could not located applicant recited claim limitation within applicant’s written description where the subject matter was not described to convey to skilled in the art at the time of filing. Appropriate further clarification is required.
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.
Claims 1 - 20 are rejected under 35 U.S.C. 112(b) 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.
It is also noted that dependent claims based upon the rejected claims are also rejected based upon the dependency. In this instant case, claims 2 – 16, 18 and 19.
Regarding claims 1, 17 and 20, applicant’s claim limitation regarding, “instructions include the traffic signal identifier” does not distinctly and particularly point out the claim subject matter that ought to be provided particularly and distinctly as to what exactly the instruction referring to as whether directs to signal instruction, identification instruction or traffic instruction that ought to be set forth particularly and distinctly to ascertain metes and bounds regards applicant’s invention. Appropriate further clarification is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3 – 17, 19 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Liu et al (US Pat Pub No. 2018/0261085).
Regarding claims 1, 17 and 20, Liu et al shows a method (See at least Para 0003 for traffic control method), a computing system with a memory and a processor device coupled to memory (See at least figure 10 for computing system 1000 with processor 1002 and memory 1004) and non-transitory computer readable storage medium with computer executable instruction (See at least figure 10 for data storage 1006 with processor 1002 and memory 1004) comprising:
receiving traffic data from a plurality of traffic signals by a central computing device (See at least Para 0005 and 0004 for traffic lights control with coordination control by a master controller);
determining an action for each traffic signal of the plurality of traffic signals to take based on the traffic data by the central computing device (See at least Para 0005 and 0006 for changing traffic patterns in real time by controlling and acted upon green lights in long string as optimization and adaptive control for traffic signals in intersections are controlled/acted based upon real time traffic demand) ;
sending instructions corresponding to the action for each traffic signal of the plurality of traffic signals to take by the central computing device to the pluarlity of traffic signals (See at least Para 0005 and 0006 for changing traffic patterns in real time by controlling and acted upon green lights in long string as optimization and adaptive control for traffic signals in intersections are controlled/acted based upon real time traffic demand);
sending the instructions corresponding to the action for each traffic signal of the plurality of traffic signals to take (See at least Para 0062 for each traffic signal instructions for traffic light L21 – L51 provided with control instruction for 1 represent green light and 0 represent red light; also on Para 0058 for control action computation module sending control action instructions for each traffic signal) comprises:
determining a traffic signal identifier for each traffic signal of the plurality of traffic signals based on the traffic data (See at least Para 0062 for possible control actions for each traffic signals as the traffic data and traffic signal identifier L21 – L51 for each of the traffic signals);
sending the instructions to a traffic signal from among the plurality of traffic signals based on the traffic signal identifier for the traffic signal (See at least Para 0062 for each traffic signal instructions for traffic light L21 – L51 implemented by control action computation 304 and further sending control action instructions for each traffic signal directly by signal control module 306 for each traffic signal on Para 0072),
the instructions include the traffic signal identifier (See at least Para 0062 for possible control actions instructions includes actions for each traffic signal identifier L21 – L51).
Regarding claim 3, Liu et al shows subsequent to sending the instructions by computing devices communicatively coupled to each traffic signal of the plurality of traffic signals ( See at least Para 0072 for signal control module 306 as coupled to each traffic signal as direct communication control), performing the action based on the instructions (See at least Para 0072 for signal control module 306 as coupled to each traffic signal as direct communication control).
Regarding claim 4, Liu et al shows determining an intersection comprise a traffic signals is blocked based on the traffic data ( See at least Para 0040 and 0043 for traffic jam and ultraheavy traffic based on traffic data utilized by traffic control agent; also on Para 0036 and 0037 for traffic signal controlled by control agent using presence data in queue and queue length for intersection);
receiving traffic data for each traffic signal in the intersection (See at least Para 0039 for four way intersection also shown on figure 1) and traffic data for each traffic signal adjacent to the intersection (See at least figure 4 for each node represents an intersection; also on Para 0050 for coordinator system use traffic data of one or more intersection);
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 (See at least figure 4 for each node represents an intersection; also on Para 0050 for coordinator system use traffic data of one or more intersection and recommended candidate routes to the intended destination);
the action for traffic signal of the plurality of traffic signals to take comprises changing light colors of a traffic signals of the plurality of traffic signals based on the route (See at least Para 0051 for route information to be shared with agents to optimize the traffic flow as also discussed on Para 0005 for real time optimization with green wave as changing traffic lights to green based on the route).
Regarding claim 5, Liu et al shows determining the instructions to send to traffic signal of the plurality of traffic signals subsequent to generating the route (See at least figure 4 for each node represents an intersection; also on Para 0050 for coordinator system use traffic data of one or more intersection and recommended candidate routes to the intended destination),
the instructions comprise a command to change the light color of traffic signal and a color for each light to be set (See at least Para 0051 for route information to be shared with agents to optimize the traffic flow as also discussed on Para 0005 for real time optimization with green wave as changing traffic lights to green based on the route).
Regarding claim 6, Liu et al show obtaining traffic data corresponding to the plurality of traffic signals from traffic image (See at least Para 0054 for camera sensor 106 detecting both motorized-user, driver, and non-motorized user, pedestrian walker presented/approaching an intersection with a captured image at intersection for agent 108 for further analyze);
determining an amount of vehicles in the traffic image by a machine learning model (See at least Para 0037 for motorized traffic queue length as an amount for vehicle for q learning model/reinforcement learning; also Para 0043 for traffic rate as number of vehicles; also on Para 0054 for camera sensor 106 detecting both motorized-user, driver, and non-motorized user, pedestrian walker presented/approaching an intersection with a captured image at intersection for agent 108 for further analyze);
determining the intersection comprising a traffic signal is blocked by the machine-learning model based on the amount of vehicles in the traffic image (See at least Para 0043 - 0047 for learning model classify the traffic pattern and learning the traffic pattern with input).
Regarding claim 7, Liu et al shows obtaining traffic image corresponding to the plurality of traffic signals from the traffic data (See at least Para 0054 for camera sensor 106 detecting both motorized-user, driver, and non-motorized user, pedestrian walker presented/approaching an intersection with a captured image at intersection for agent 108 for further analyze);
determining an amount of vehicles in the traffic image by a machine learning model (See at least Para 0037 for motorized traffic queue length as an amount for vehicle for q learning model/reinforcement learning; also Para 0043 for traffic rate as number of vehicles; also on Para 0054 for camera sensor 106 detecting both motorized-user, driver, and non-motorized user, pedestrian walker presented/approaching an intersection with a captured image at intersection for agent 108 for further analyze);
determining a traffic congestion level by the machine-learning model based on the amount of vehicles in the traffic image (See at least Para 0043 - 0047 for learning model classify the traffic pattern and learning the traffic pattern with input, the congestion level with traffic pattern is determined as normal/heavy/ultra heavy traffic and traffic jam as congestion level),
the action is based on the traffic congestion level (See at least Para 0048 for the agent may transmit or provide its traffic data (e.g., traffic data of the intersection) to one or more neighbor agents (e.g., agents that control neighbor intersections). This allows the neighbor agents to incorporate traffic data of this intersection in generating control actions for the traffic signals at the neighbor intersections).
Regarding claim 8, Liu et al determining that the action for traffic signal of the plurality of traffic signals to take is changing light color of traffic signal of the plurality of traffic signals based on the traffic congestion level (See at least Para 0005 for changing traffic patterns in real-time. cameras and sensors are used to detect real-time traffic information, and the central controller uses this information to do real-time optimization. One optimization is a “green wave,” which is a long string of green lights that allows vehicles to travel long distances without encountering a red light; also on at least Para 0008 for generating control actions for traffic signals at an intersection based on q-learning, determining a frequency of change in traffic pattern of the intersection);
generating the instructions to send to traffic signal of the plurality of traffic signals (See at least Para 0005 for changing traffic patterns in real-time. cameras and sensors are used to detect real-time traffic information, and the central controller uses this information to do real-time optimization. One optimization is a “green wave,” which is a long string of green lights that allows vehicles to travel long distances without encountering a red light; also on at least Para 0008 for generating control actions for traffic signals at an intersection based on q-learning, determining a frequency of change in traffic pattern of the intersection),
the instructions comprise a command to change the light color of the traffic signal and a color for each light to be set (See at least Para 0005 for changing traffic patterns in real-time. cameras and sensors are used to detect real-time traffic information, and the central controller uses this information to do real-time optimization. One optimization is a “green wave,” which is a long string of green lights that allows vehicles to travel long distances without encountering a red light; also on at least Para 0008 for generating control actions for traffic signals at an intersection based on q-learning, determining a frequency of change in traffic pattern of the intersection).
Regarding claim 9, Liu et al shows generating a route based on the traffic data and the traffic congestion level (See at least Para 0050 for the coordinator system may use traffic data of one or more intersections to determine improved routes for some or all of the people who have shared their route information. the coordinator system may provide some or all of the collected user information to the agents for use in generating the control actions; also on Para 0051 for using this information, the coordinator system may recommend candidates routes to the intended destinations to the self-driving vehicles. the coordinator system may share the route information with the agents to optimize the traffic flow);
generating the instructions to send to traffic signal of the plurality of traffic signal is based on the route (See at least Para 0050 for the coordinator system may use traffic data of one or more intersections to determine improved routes for some or all of the people who have shared their route information. the coordinator system may provide some or all of the collected user information to the agents for use in generating the control actions; also on Para 0051 for using this information, the coordinator system may recommend candidates routes to the intended destinations to the self-driving vehicles. the coordinator system may share the route information with the agents to optimize the traffic flow).
Regarding claim 10, Liu et al shows receiving updated traffic data from the plurality of traffic signals (See at least Para 0032 for multiple qlearning categories in the control of traffic signals; also on Para 0038 for q-learning, which is a model-free reinforcement learning technique, to generate the control actions for the traffic signals. Q-learning can be used to determine an optimal action-selection policy for any given (finite) Markov decision process (MDP). Q-learning works by learning an action-value function that provides an expected utility of taking a given action (e.g., generating a given control action) in a given state (e.g., given state of the traffic signals) and following the optimal policy thereafter. Examples of policies may be to minimize the length of all queues, both motorized and non-motorized user queues, at the intersection, optimize motorized traffic flow through the intersection, optimize non-motorized traffic flow through the intersection, prioritize traffic flow in a specific direction through the intersection, prioritize public transportation through the intersection, optimize global traffic flow, optimize emission utility, optimize congestion utility);
determining an updated traffic congestion level based on the updated traffic data (also on Para 0038 for optimize congestion utility; Para 0043 for traffic patterns may include, ultralow traffic, low traffic, normal traffic, heavy traffic, ultra heavy traffic, traffic jam, accident, the different traffic patterns may be characterized by traffic rates, such as the number of motorized users (e.g., the number of vehicles) at or coming into n intersection at a particular time slot
);
generating an updated route based on the updated traffic data and the updated traffic congestion level (See at least Para 0051 for the coordinator system may recommend candidates routes to the intended destinations to the self-driving vehicles. Additionally or alternatively, the coordinator system may share the route information with the agents to optimize the traffic flow);
determining the action for traffic signal of the plurality of traffic signals to take is changing light color of traffic signal of the plurality of traffic signals based on the updated route (See at least figure 4 for each node represents an intersection; also on Para 0050 for coordinator system use traffic data of one or more intersection and recommended candidate routes to the intended destination; See at least Para 0051 for route information to be shared with agents to optimize the traffic flow as also discussed on Para 0005 for real time optimization with green wave as changing traffic lights to green based on the route);
generating the instructions to send to traffic signal of the plurality of traffic signals based on the updated route (See at least figure 4 for each node represents an intersection; also on Para 0050 for coordinator system use traffic data of one or more intersection and recommended candidate routes to the intended destination; See at least Para 0051 for route information to be shared with agents to optimize the traffic flow as also discussed on Para 0005 for real time optimization with green wave as changing traffic lights to green based on the route),
the instructions comprise a command to change the light color of the traffic signal and a color for each light to be set (See at least figure 4 for each node represents an intersection; also on Para 0050 for coordinator system use traffic data of one or more intersection and recommended candidate routes to the intended destination; See at least Para 0051 for route information to be shared with agents to optimize the traffic flow as also discussed on Para 0005 for real time optimization with green wave as changing traffic lights to green based on the route).
Regarding claim 11, Liu et al shows obtaining a traffic speed corresponding to each traffic signal of the plurality of traffic signals from the traffic data (See at least Para 0003 for slower vehicle speeds from the increase of the vehicle with traffic congestion at intersection; Para 0006 for vehicle waiting time at intersection as stopped on Para 0039 for agent control action);
determining a traffic congestion level by a machine-learning model based on the traffic speed (See at least Para 0003 for slower vehicle speeds from the increase of the vehicle with traffic congestion at intersection; Para 0006 for vehicle waiting time at intersection as stopped also on Para 0039 for agent control action);
determining the action for each traffic signal of the plurality of traffic signals to take is changing light color of traffic signal of the plurality of traffic signals based on the traffic congestion level (See also at least Para 0095 for operational policy executed for each intersection with traffic pattern cluster with max-min green/red light time upon congestion);
generating the instructions to send to each of the traffic signal of the plurality of traffic signals by the machine-learning model (See also at least Para 0095 for operational policy executed for each intersection with traffic pattern cluster with max-min green/red light time upon congestion implementing q-learning model),
traffic signal the instructions comprise a command to change the light colors of the traffic signal and a color for each light to be set (See also at least Para 0095 for operational policy executed for each intersection with traffic pattern cluster with max-min green/red light time upon congestion implementing q-learning model).
Regarding claim 12, Liu et al shows obtaining one or more traffic images corresponding to the plurality of traffic signals from the traffic data (See at least Para 0054 for camera sensor 106 detecting both motorized-user, driver, and non-motorized user, pedestrian walker presented/approaching an intersection with a captured image at intersection for agent 108 for further analyze);
determining an amount of vehicles in the one or more traffic images by a machine-learning model (See at least Para 0037 for motorized traffic queue length as an amount for vehicle for q learning model/reinforcement learning; also Para 0043 for traffic rate as number of vehicles; also on Para 0054 for camera sensor 106 detecting both motorized-user, driver, and non-motorized user, pedestrian walker presented/approaching an intersection with a captured image at intersection for agent 108 for further analyze );
obtaining a traffic speed corresponding to each traffic signal of the plurality of traffic signals from the traffic data (See at least Para 0003 for slower vehicle speeds from the increase of the vehicle with traffic congestion at intersection; Para 0006 for vehicle waiting time at intersection as stopped on Para 0039 for agent control action);
determining a traffic congestion level by the machine-learning model based on the amount of vehicles in the traffic images and the traffic speed (See at least Para 0003 for slower vehicle speeds from the increase of the vehicle with traffic congestion at intersection; Para 0006 for vehicle waiting time at intersection as stopped on Para 0039 for agent control action; at least Para 0040 and 0043 for traffic jam and ultraheavy traffic based on traffic data utilized by traffic control agent; also on Para 0036 and 0037 for traffic signal controlled by control agent using presence data in queue and queue length for intersection; at least Para 0054 for camera sensor 106 detecting both motorized-user, driver, and non-motorized user, pedestrian walker presented/approaching an intersection with a captured image at intersection for agent 108 for further analyze);
determining that the action for each traffic signal of the plurality of traffic signals to take is changing light colors of traffic signal of the plurality of traffic signals based on the traffic congestion level (See at least Para 0003 for slower vehicle speeds from the increase of the vehicle with traffic congestion at intersection; Para 0006 for vehicle waiting time at intersection as stopped on Para 0039 for agent control action; at least Para 0040 and 0043 for traffic jam and ultraheavy traffic based on traffic data utilized by traffic control agent; also on Para 0036 and 0037 for traffic signal controlled by control agent using presence data in queue and queue length for intersection; at least Para 0054 for camera sensor 106 detecting both motorized-user, driver, and non-motorized user, pedestrian walker presented/approaching an intersection with a captured image at intersection for agent 108 for further analyze);
generating the instructions to send to each of the traffic signal of the plurality of traffic signals by the machine learning model (See at least figure 4 for each node represents an intersection; also on Para 0050 for coordinator system use traffic data of one or more intersection and recommended candidate routes to the intended destination; See at least Para 0051 for route information to be shared with agents to optimize the traffic flow as also discussed on Para 0005 for real time optimization with green wave as changing traffic lights to green based on the route),
traffic signal the instructions comprise a command to change the light colors of the traffic signal and a color for each light to be set (See at least Para 0051 for route information to be shared with agents to optimize the traffic flow as also discussed on Para 0005 for real time optimization with green wave as changing traffic lights to green based on the route; See at least figure 4 for each node represents an intersection; also on Para 0050 for coordinator system use traffic data of one or more intersection and recommended candidate routes to the intended destination; See at least Para 0051 for route information to be shared with agents to optimize the traffic flow as also discussed on Para 0005 for real time optimization with green wave as changing traffic lights to green based on the route).
Regarding claim 13, Liu et al shows obtaining a current date and a current time from the traffic data (See at least Para 0061 for day d and time t for traffic signal at intersection);
determining the action for each traffic signal of the plurality of traffic signals to take by a machine-learning model based on the date and the time (See at least para 0063 for traffic signals at intersection to be controlled with the road intersection network on Para 0060);
the machine-learning model comprises a machine-learning model trained on prior traffic data from the plurality of traffic signals (See at least oara 0059 for control action computation module 304 may apply Q-learning to generate the control actions for the traffic signals that consider both motorized users and non-motorized users at an intersection. As discussed earlier, Q-learning can be used to determine an optimal action-selection policy for any given (finite) Markov decision process (MDP). Q-learning works by learning an action-value function that provides an expected utility of taking a given action (e.g., generating a given control action) in a given state (e.g., given state of the traffic signals) and following the optimal policy).
Regarding claim 15, Liu et al shows at least one of a traffic signal identifier, a date, a time and a traffic congestion level (See at least Para 0061 for state of each traffic signal S in each intersection with date and time, queue length for congestion level).
Regarding claim 16, Liu et al shows at least one of changing a light color of traffic signal of the plurality of traffic signals or setting a timer for changing the light color of traffic signal of the plurality of traffic signals (See also at least Para 0095 for operational policy executed for each intersection with traffic pattern cluster with max-min green/red light time upon congestion).
Regarding claim 19, Liu et al shows a machine-learning model trained on prior traffic data from the plurality of traffic signals (See at least Para 0040 for the agent may incorporate historical traffic data in generating the control actions for the traffic signals at the intersection. The historical traffic data may include traffic statistics at different time periods in a day, traffic statistics in the same time period on different days, etc. The historical traffic data may be data of the same intersection (e.g., the intersection being controlled by the agent). The historical traffic data may be data of another, different intersection. The historical data may be data of multiple intersections. For example, the agent may apply an autoregressive integrated moving average model to calculate estimated instantaneous rewards based on historical traffic data) the machine-learning model to:
receive traffic image corresponding to the plurality of traffic signals (See at least Para 0040 for the agent may incorporate historical traffic data in generating the control actions for the traffic signals at the intersection. The historical traffic data may include traffic statistics at different time periods in a day, traffic statistics in the same time period on different days, etc. The historical traffic data may be data of the same intersection (e.g., the intersection being controlled by the agent). The historical traffic data may be data of another, different intersection. The historical data may be data of multiple intersections. For example, the agent may apply an autoregressive integrated moving average (ARIMA) model to calculate estimated instantaneous rewards based on historical traffic data),
the traffic data comprises the traffic image ( See at least Para 0054 for sensors 106 may be configured to autonomously detect the presence of motorized and non-motorized users at or approaching intersection 100. Sensors 106 may be video cameras that are configured to acquire images of intersection 100 from which motorized user presence and non-motorized user presence may be determined);
determine an amount of vehicles in the traffic image (See at least Para 0060 and 0061 for queue length of the vehicles);
determine a traffic congestion level based on the amount of vehicles in the traffic image (See at least Para 0042 for traffic pattern determined by queue length, traffic pattern including traffic analysis, may specify the number of q-learning categories by specifying the properties that define or characterize the different traffic patterns. examples of traffic patterns may include, without limitation, ultralow traffic, low traffic, normal traffic, heavy traffic, ultra heavy traffic, traffic jam, accident on Para 0043);
the action is based on the traffic congestion level (See at least Para 0049 for the agent may incorporate traffic data of one or more neighbor intersections in generating the control actions for the traffic signals at the intersection).
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 zprior 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 2 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al (US Pat Pub No. 2018/0261085) in view of Gaither et al (US Pat Pub No. 2019/0122547).
Regarding claims 2 and 18, Liu et al further shows obtaining the traffic data based on sensor data and camera data corresponding to the plurality of traffic signals prior to receiving the traffic data (See at least Para 0004 for sensor input for dynamic control for adjusting traffic signal timing and Para 0005 for camera and sensor are used to detect real time traffic information; also on Para 0068 for historical traffic data as prior data),
by computing devices communicatively coupled to each traffic signal of the plurality of traffic signals (See at least Para 0005 for central control uses detected traffic lights information for real time optimization);
perform the action based on the instructions subsequent to sending the instructions subsequent to sending the instructions (See at least Para 0113 if the agent determines that the change in the traffic pattern cluster is a false positive change, decision block 1508 may be followed by block 1502, where the agent continues to monitor the traffic data at the intersection. That is, as the change in the traffic pattern cluster is a false positive, the agent may continue to use the current Q-learning category to generate the control actions for the traffic signals at the intersection), Liu et al states sensor data in various example, in etc., yet does not specify sensor as radar sensor.
Gaither et al further shows radar sensor that is implemented to V2V and V2I infrastructure for traffic signal utilization purpose (See at least Para 0052 for radar unit/sensor included in the roadway infrastructure).
It would have been obvious for one of ordinary skill in the art, at the time of filing, to provide further enhanced radar information of Gaither in V2I infrastructure, for the real time traffic network control of Liu, in order to provide known device for traffic monitoring of Gaither, to the known real time traffic optimization desired by Liu, to yield predictable traffic control discussed by both Liu and Gaither.
Response to Arguments
In response to applicant’s remark that Liu et al does not shows traffic identifier nor the instructions are including the identifiers since Liu et al merely shows a mathematical representation of the traffic light signal yet does not show identifier for the traffic signal are determined nor the instructions including identifiers are sent to traffic signals;
however, it is noted that applicant’s remark does not particularly comprehensive, in this instant case, while the Liu shows a mathematical representation for the traffic signal intersection control, Liu also shows the physical apparatus intersection traffic light implementation upon this mathematical representation.
Applicant’s attention is further directed to Figure 4 of Liu where shows the mathematical representation along with corresponding Figure 5 for physical apparatus signal light in cross road implemented upon this mathematical representation.
In this instant case, each signal light upon four traffic signal directions on a cross road is provided with identifier/ L21 – L51, Figure 5, for the control action instruction issued by control action computation module 304 on Para 0058 - 0059, with respect to each individual identifier correspondingly.
Para 0062 further shows control instructions issued from the control action module 304 with value 1 and 0 represent green and read light for each traffic signal light with identifier L21- L51 for control action issued for each traffic light.
Thus, Liu indeed shows instructions actions for each individual traffic light with the identifier for each traffic light instruction in order to have traffic signal control instruction transmitted for each individual traffic light.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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THIS ACTION IS MADE FINAL. 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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/Ian Jen/Primary Examiner, Art Unit 3657
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