DETAILED ACTION 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. 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. Claim s 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). 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. c ameras 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. c ameras 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. c ameras 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 t he 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 u sing 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 t he 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 u sing 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 q learning 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) ; d etermining 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 ) ; o btaining 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 ) ; d etermining a traffic congestion level by the machine-learning model based on the amount of vehicles in the traffic ima g es 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 se t (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 show s o btaining 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 c ontrol 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 14, Liu et al shows determining a traffic signal identifier for each traffic signal of the plurality of traffic signals based on the traffic data (See at least Para 0061 for state of each traffic signal S in each intersection) ; sending the instructions to a traffic signal from among the plurality of traffic signals based on the traffic signal identifier for the traffic signal, traffic signal the instructions include the traffic signal identifier (See at least Para 0061 for action set of the possible actions as the instruction for each S as traffic signal within traffic network ) . 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 t he 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 . T he 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 t he 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 . T he 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. S ensors 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. e xamples 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 z prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. Claim s 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. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Guan et al, US Pat Pub No. 2021/0144669, machine learning algorithm implementing camera image at intersection with historical data , traffic congestion estimation data, time stamp, traffic condition, traffic light cycle . Leong et al, US Pat Pub No. 2024/0346925, traffic network, traffic signal, adaptive traffic signal control, camera, traffic sensor, traffic flow rate, traffic light sequence, light duration, traffic light cycle, machine learning. Kamiya et al, US Pat Pub No. 10,219,187; US Pat Pub No. 2018/0122233 , traffic light control, traffic flow monitoring using pressure sensor upon intersection, traffic network, traffic signal ID, Ginseber et al, US Pat Pub No. 2012/0274481; US Pat No. 10,083,607 , traffic condition, traffic signal information with location/signal instruction, impending traffic, intersection, routing, Liu et al, US Pat No. 9,972,199 , traffic network, camera, intersection, traffic light control by central controller, reinforcement learning, route recommendation, prior traffic data, congestion data. Lau et al, US Pat Pub No. 2021/0158690, traffic pattern detection, traffic zone, traffic data, historical data, traffic volume, traffic density, IoT, RSU, camera. Vargas et al, US Pat Pub No. 2023/0012196, traffic light, traffic control, historical data, camera, radar. Ferguson et al, US Pat Pub 2015/0379689, traffic signal, camera on vehicle. Silver et al, US Pat Pub No. 2021/0397827, intersection traffic light detected by camera upon autonomous vehicle. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT Ian JEN whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-3274 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT 11AM - 7PM . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abby Lin can be reached at 5712703976 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Ian Jen/ Primary Examiner, Art Unit 3657