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 .
Status of Claims
This communication is in response to application No. 18/812,616 filed on 01 July 2026. Claims 1, 9, 11, 19, and 20 are currently amended. Claims 6, 10, and 16 are cancelled. Claims 2 1-23 are new. Thus, claims 1-5, 7-9, 11-15, and 17-23 are pending in this patent and presented for examination application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/05/2024 and 03/18/2025 is being considered by the examiner.
Response to Arguments
Applicant’s amendments and arguments with respect to the claim interpretation and claims under 35 USC 103 as set forth in the office action of 01 July 2026 have been considered and are NOT PERSUASIVE. Search has been updated as shown below.
Applicant’s arguments, see Arguments/Remarks, filed 01 July 2026, with regard to the rejections of claims 1-5, 7-9, 11-15, and 17-23 under 35 U.S.C. 103 have been fully considered. Applicant’s argument is moot because the argument is directed toward new limitations that have not been previously considered. As such, Applicant’s amendment has necessitated a new ground of rejection set forth in this office action.
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.
Claims 1, 2, 3, 4, 5, 7, 11, 12, 13, 14, 15, , 17, and 20, 21-23 are rejected under 35 U.S.C 103 as being unpatentable over Sofman (WO 2021189027 A1) in view of Dean (WO 2019099465 A1) and Bedadala (US 20210073190 A1).
Regarding claim 1, Sofman discloses wherein the time information indicates a predicted time range of a risk event, and (see at least [fig. 6, 0130]; "The disclosed operations utilize spatiotemporal weather information such as hyperlocal weather forecasts or other types of weather data in order to have accurate predictions of the times and/or locations at which weather conditions (including adverse weather that would be desirable to avoid) will be present along segments of various routes along which the vehicle 100 can travel. ")
wherein the location information indicates a predicted region range of the event; and (see at least [Fig. 6, 0130] ; "In addition, Figure 6 also depicts undesirable weather conditions, represented as blob-like regions 606 on the route 610, impacting a particular distance along the route 610 over a particular time window.
Sofman does not disclose A method comprising: obtaining risk event information comprising time information and location information, storing the risk event information as second dynamic layer data of map data, wherein the map data comprises static layer data, first dynamic layer data, and the second dynamic layer data, wherein the first dynamic layer data comprises one or more of environmental condition information or road status information, and wherein the static layer data comprises one or more of road type or lane quantity, and wherein the risk event information is based on the static layer data and the first dynamic layer data; controlling a first vehicle based on the risk information; and deleting the risk event information upon reaching an end of the predicted time range and without detecting a change to an element affecting the risk event during the predicted time range, wherein the element is the environmental condition information or the road status information.
However, Dean teaches A method comprising: obtaining risk event information comprising time information and location information, (see at least [0017]; "Furthermore, an example risk regressor may further factor in current environmental conditions (e.g., rain, snow, clouds, road conditions, lighting, lighting direction, and the like), and static risk based on lane geometry, traffic conditions, and time of day to compute a fractional risk quantity dynamically for any path segment at any given time.")
storing the risk event information as second dynamic layer data of map data, (see at least Dean [0056]; ""The memory 320 can store data 325 and instructions 330, which the processor 315 can execute to cause the vehicle computing device 310 to perform operations. The data 325 can include map data 327 and cost data 329. The instructions 330 can include cost calculation instructions 332 that, when executed, implement one or more steps, features, or aspects of example methods for determining values for path segments based on risk factors associated with autonomous operation of the vehicle 300 along path segments")
wherein the map data comprises static layer data, first dynamic layer data, and the second dynamic layer data, (see at least [0046]; "the path cost calculation system 260 models scores for each of the cost layers, including the travel time and risk factor cost layers, for path segments (i.e., predetermined lengths of a road or lane of a road) in the geographic region. Events and features extracted and identified from logs and mapping data are provided as input to a statistical model that outputs scores for the cost layers.") The path calculation system can be considered a second dynamic layer, that includes information from static layer data and dynamic layer data.
wherein the first dynamic layer data comprises one or more of environmental condition information or road status information, and (see at least Dean [0047]; "the path cost calculation system 260 takes into account dynamic data, such as real-time traffic and weather, for the geographic region in which the vehicle 200 is operating. ") Dean describes a dynamic layer comprised of environmental conditions such as weather, as well as traffic, which falls into the road status information category.
wherein the static layer data comprises one or more of road type or lane quantity, and (See at least Dean[0041]; "In addition to using the sensor data, the perception system 230 can retrieve map data (e.g., localization maps) that provide detailed information about the surrounding environment of the vehicle. The map data can provide information regarding the identity and location of different paths (e.g., roads, road segments, lanes, lane segments, parking lanes, turning lanes, bicycle lanes, or other portions of a particular path)…Map data can also include the identity and location of buildings, maintenance/service locations for the vehicles, parking areas, traffic signs, traffic lights, traffic control devices, and/or any other features that provide information that could assist the vehicle control system 220 in perceiving and navigating the surrounding environment. ") Dean describes a static later with road types and lane quantity.
wherein the risk event information is based on the static layer data and the first dynamic layer data; (See at least [0013]; " Accordingly, a total path for an AV from a starting point to a destination can be comprised of a sequential set of capability-in-scope lane segments from the starting point to the destination, each having an attributed fractional risk quantity calculated by a risk regressor based on static and dynamic conditions, as described herein.")
controlling a first vehicle based on the risk event information; and (See at least [0018];" In addition, for a given transport request from a requesting user, a route planning system can determine a set of routes between the pick-up location and destination, and the risk regressor can determine an aggregate risk quantity for each of those routes given the current or predicted conditions (e.g., conditions at the time the vehicle traverses a particular path segment), and provide a lowest risk route or other optimal route (e.g., optimized across risk, time, dollar earnings, etc.) as output to a matching engine that pairs the requesting user with an available vehicle.") Dean outlines controlling a vehicle along a path that's based on risk.
wherein the element is the environmental condition information or the road status information. (see at least Dean [0017]; " Furthermore, an example risk regressor may further factor in current environmental conditions (e.g., rain, snow, clouds, road conditions, lighting, lighting direction, and the like), and static risk based on lane geometry, traffic conditions, and time of day to compute a fractional risk quantity dynamically for any path segment at any given time.") The data in the model could be comprised of environmental condition information or road status information.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sofman to incorporate teachings of Dean which teaches storing event information and associating map related information with risk as a static and dynamic layer in order to better anticipate the nature of the risk and how it will affect a chosen route.
Dean and Sofman, in combination, do not explicitly disclose deleting the risk event information upon reaching an end of the predicted time range and without detecting a change to an element affecting the risk event during the predicted time range.
However, Bedadala teaches deleting the risk event information upon reaching an end of the predicted time range and without detecting a change to an element affecting the risk event during the predicted time range, (see at least Bedadala [0006, 0024]; "The storage manager or another component in the information management system can then delete the chunks of these listed archive files… analyzing a component of the decomposed time-series to determine an acceptable range for a time to process archive files to be deleted;") Bedadala teaches a system that gathers data, and after a determined time, deletes the files. The data here could be the risk data comprised of static and dynamic factors, and can be archived based on a change not occuring; creating expired information.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sofman to incorporate teachings of Bedadalala which teaches deleting archived data in order to allow memory space for the processors to continue to operate with applicable information and new routes.
Regarding claim 2, Sofman, Dean, and Bedadala, in combination, disclose the limitations of claim 1 as discussed above, furthermore, Sofman discloses The method of claim 1, further comprising planning a lowest-risk path based on the risk event information. (see at least [0113];" The cost function can consider a plurality of speeds and select whichever speed is associated with the lowest total cost. Specifically, the computing system 112 can calculate a cost from a cost function factoring weather data predicted for a time when the vehicle 100 is predicted to encounter the weather data to determine a plurality of speed and route adjustments with the lowest cost for avoiding the weather.")
Regarding claim 3, Sofman, Dean, and Bedadala, in combination, disclose the limitations of claim 1 as discussed above, furthermore, Sofman discloses The method of claim 2, wherein planning the lowest-risk path comprises: planning a plurality of paths; (see at least [0083]; "As will be discussed in more detail herein, the computing system 112 can at times plan (or replan) a route and then divide the route into one or more potential route segments for the purposes of performing the techniques described herein,")
determining that the predicted region range comprises a location point in the plurality of paths; (see at least [0085]; "Phrased another way, a particular segment of the route can be defined by a patch or patches of weather along the route. Specifically, the two geographical points that define the segment can be a starting point of the weather condition predicted and an ending point of the weather condition predicted on the route such that the segment encompasses all of a specific weather patch")
determining an estimated time at which the first vehicle will travel to the location point;
(see at least [0087]; " Alternatively, the segments can be defined by the expected time of arrival of the vehicle 100 at a point in the route ")
determining, based on the time information and the location information, the predicted time range; (see at least [0087]; "In another embodiment, one or multiple of a distance measurement, or a time measurement, can define the length or duration of the segments on the route.")
determining that the predicted time range comprises the estimated time; (see at least [00914]; " In some examples, the hyperlocal weather forecasts can be queried for a specific time the vehicle 100 is predicted to arrive at a segment.")
determining a traveling risk of at least one of the plurality of paths based on the event; (see a least [0005]; "The method also involves for each potential route segment, evaluating a partial cost function that comprises a summation of a set of segment-weighted cost factors, where at least one segment-weighted cost factor comprises an adverse weather risk factor based on the future weather conditions along the potential route segment. ")
and determining the lowest-risk path in the plurality of paths based on the traveling risk. (see at least [102]; " Based on the speed associated with the lowest cost, for the particular segment of the route the vehicle 100 can adjust the vehicle’s speed and avoid a weather condition predicted for the particular segment. Selecting the speed associated with the lowest cost may also be referred to herein as minimizing the cost or minimizing the cost function with respect to the speed")
Regarding claim 4, Sofman, Dean, and Bedadala, in combination, disclose the limitations of claim 1 as discussed above, furthermore, Sofman discloses The method of claim 1, further comprising sending the risk event information to a second vehicle when: the second vehicle is in the predicted region range; a minimum distance between the second vehicle and the predicted region range is less than a first threshold; or the predicted region range has an intersection with a planned path of the second vehicle. (see at least [0073, 0090, 00145]; "Server computing system 406 may be configured to wirelessly communicate with remote computing system 402 and vehicle 100 via network 404 (or perhaps directly with remote computing system 402 and/or vehicle 100)…Particularly, in situations when a large undesirable weather pattern is predicted to cross the route, it can be meaningful to consider weather patterns that are far away from the vehicle 100 in order to make a significant change to the time of arrival of the vehicle 100 in the segment with the undesirable weather pattern…The computing system 112 can also determine an optimal adjusted route that minimizes the cost function to adjust the route for the at least one segment from the route to the optimal adjusted route. It should be understood that the selected target route can be considered the optimal adjusted route. For optimization of the route, discrete route segments can be searched that avoid weather. The discrete routes can then be weighted by the costs. The computing system 112 can set the lowest cost discrete route as the optimal adjusted route for avoiding the weather condition. The optimal adjusted route is then set as the adjusted route for the vehicle 100 to navigate. Additionally, the computing system can intentionally, and automatically, delay the departure time entirely, to maximize the optimization of the route. [0146] As previously mentioned, the computing system 112 can dynamically update the route as the vehicle 100 travels. Specifically, as the vehicle 100 travels, the route of the vehicle 100 for a particular segment can be repeatedly updated based on the changing cost function. The computing system 112 can evaluate the second cost function while the vehicle is navigating a current segment of the first target route, select, from the plurality of target routes, a second target route associated with the lowest cost, and adjust the route from the first route to the second target route at the end of the current segment. This adjustment to the second target route can happen while the vehicle is travelling and may continue to repeatedly update, such that the route continues to change.")
Regarding claim 5, Sofman, Dean, and Bedadala, in combination, disclose the limitations of claim 1 as discussed above, furthermore, Sofman discloses The method of claim 1, wherein the risk event information further comprises first identification information of the risk event, second identification information of atile to which the risk event belongs, third identification information of a road on which the risk event is located, or risk level information indicating a danger degree of the risk (see at least [0165]; "In some embodiments, the method 700 can also involve identifying, based on the spatiotemporal weather information, one or more geographical adverse weather areas that correspond to one or more selected route segments. For example, one or more of the selected route segments can be route segments whose boundaries correspond to boundaries of a geographical area in which adverse weather is present or is anticipated to be present, such as a rainstorm or a group of multiple adverse weather conditions that may or might not at least partially overlap in their geographical area. As a more particular example, a rainstorm might occupy a first geographical area and thick fog might occupy a second geographical area that at least partially overlaps with the first geographical area. As such, one potential route segment that is one of the selected route segments can be a route segment whose start and end point both fall on, or are inside of, a boundary of a combination of the first and second geographical areas, such that the rainstorm and/or the thick fog are or are predicted to be present throughout the entire route segment. In such embodiments, the target speeds selected for any selected route segment that is within the one or more geographical adverse weather areas can be selected to mitigate adverse weather effects along that/those selected route segments (e.g., slow the vehicle down before a route segment where a rainstorm is present so that the rainstorm is no longer present by the time the vehicle reaches the route segment).")
Regarding claim 7, Sofman, Dean, and Bedadala, in combination, disclose the limitations of claim 1 as discussed above, furthermore, Sofman fails to disclose The method of claim 1, further comprising: determining, based on road topology information in the map data, a road region associated with the predicted region range; and prompting a second vehicle in the road region with the risk event, or controlling a traffic volume of the road region.
However, Mintz teaches The method of claim 1, further comprising:determining, based on road topology information in the map data, a road region associated with the predicted region range; and (see at least [55]; "The potential improvement in traffic flow, which can be obtained from such an approach, depends not just on the efficiency of the method applying the control on trip paths but also on the size and the topology of the networks with further relation to zone to zone trip demand, which determine the potential degrees of freedom on the network to apply predictive control on paths of controlled trips (path controlled trips).")
prompting a second vehicle in the road region with the risk event, or controlling a traffic volume of the road region. (see at least [56]; " Apparatus and method to apply predictive control, which may predictively coordinate paths on the network, should preferably use model predictive control requiring simulation of traffic models to enable controllable traffic predictions. I")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sofman to incorporate the teachings of Mintz which teachings notifying the risk as topology on the map in order to ready the driver or vehicle for the upcoming potential risk as change in speed or comfortability of the ride.
Regarding claim 11, Sofman discloses An apparatus comprising: one or more memories configured to store instructions; and one or more processors coupled to the one or more memories and configured to execute the instructions to cause the apparatus to: (see at least [007]; "A third embodiment describes a system comprising at least one processor and a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations.")
wherein the time information indicates a predicted time range of a risk event, (see at least [fig. , 0130]; "The disclosed operations utilize spatiotemporal weather information such as hyperlocal weather forecasts or other types of weather data in order to have accurate predictions of the times and/or locations at which weather conditions (including adverse weather that would be desirable to avoid) will be present along segments of various routes along which the vehicle 100 can travel. ")
and wherein the location information indicates a predicted region range of the risk event; (see at least [Fig. 6, 0130] ; "In addition, Figure 6 also depicts undesirable weather conditions, represented as blob-like regions 606 on the route 610, impacting a particular distance along the route 610 over a particular time window. ")
Sofman fails to disclose store the risk event information as second dynamic layer data of map, wherein the map data comprises static layer data, first dynamic layer data, and the second dynamic layer data, wherein the first dynamic layer data comprises one or more of environmental condition information or road status information, and wherein the static layer data comprises one or more of road type or lane quantity, and wherein the risk event information is based on the static layer data and the first dynamic layer data;control a first vehicle based on the risk information is based on the static layer data and the first dynamic layer data information;and delete the risk event information upon reaching an end of the predicted time range and without detecting a change to an element affecting the risk event during the predicted time range, wherein the element is the environmental condition information or the road status information.
However, Dean teaches wherein the first dynamic layer data comprises one or more of environmental condition information or road status information, and (see at least Dean [0047]; "the path cost calculation system 260 takes into account dynamic data, such as real-time traffic and weather, for the geographic region in which the vehicle 200 is operating. ") Dean describes a dynamic layer comprised of environmental conditions such as weahter, as well as traffic, which falls into the road status information category.
wherein the static layer data comprises one or more of road type or lane quantity, and (See at least Dean[0041]; "In addition to using the sensor data, the perception system 230 can retrieve map data (e.g., localization maps) that provide detailed information about the surrounding environment of the vehicle. The map data can provide information regarding the identity and location of different paths (e.g., roads, road segments, lanes, lane segments, parking lanes, turning lanes, bicycle lanes, or other portions of a particular path)…Map data can also include the identity and location of buildings, maintenance/service locations for the vehicles, parking areas, traffic signs, traffic lights, traffic control devices, and/or any other features that provide information that could assist the vehicle control system 220 in perceiving and navigating the surrounding environment. ") Dean describes a static later with road types and lane quantity.
wherein the risk event information is based on the static layer data and the first dynamic layer data; (See at least [0013]; " Accordingly, a total path for an AV from a starting point to a destination can be comprised of a sequential set of capability-in-scope lane segments from the starting point to the destination, each having an attributed fractional risk quantity calculated by a risk regressor based on static and dynamic conditions, as described herein.")
control a first vehicle based on the risk event information is based on the static layer data and the first dynamic layer data information;and (See at least [0018];" In addition, for a given transport request from a requesting user, a route planning system can determine a set of routes between the pick-up location and destination, and the risk regressor can determine an aggregate risk quantity for each of those routes given the current or predicted conditions (e.g., conditions at the time the vehicle traverses a particular path segment), and provide a lowest risk route or other optimal route (e.g., optimized across risk, time, dollar earnings, etc.) as output to a matching engine that pairs the requesting user with an available vehicle.") Dean outlines controlling a vehicle along a path that's based on risk.
wherein the element is the environmental condition information or the road status information. (see at least Dean [0017]; " Furthermore, an example risk regressor may further factor in current environmental conditions (e.g., rain, snow, clouds, road conditions, lighting, lighting direction, and the like), and static risk based on lane geometry, traffic conditions, and time of day to compute a fractional risk quantity dynamically for any path segment at any given time.") The data in the model could be comprised of environmental condition information or road status information.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified to incorporate teachings of Dean which teaches storing map data, controlling a vehicle, and associating map related information with risk as a static and dynamic layer in order to better anticipate the nature of the risk and how it will affect a chosen route.
Sofman and Dean, in combination, do not explicitly disclose delete the risk event information upon reaching an end of the predicted time range and without detecting a change to an element affecting the risk event during the predicted time range.
However, Bedadala teaches delete the risk event information upon reaching an end of the predicted time range and without detecting a change to an element affecting the risk event during the predicted time range, (see at least Bedadala [0006, 0024]; "The storage manager or another component in the information management system can then delete the chunks of these listed archive files… analyzing a component of the decomposed time-series to determine an acceptable range for a time to process archive files to be deleted;") Bedadala teaches a system that gathers data, and after a determined time, deletes the files. The data here could be the risk data comprised of static and dynamic factors, and can be archived based on a change not occuring; creating expired information.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sofman to incorporate teachings of Bedadalala which teaches deleting archived data in order to allow memory space for the processors to continue to operate with applicable information and new routes.
Regarding claim 12, Sofman, Dean, and Bedadala, in combination, disclose the limitations of claim 11 as discussed above, furthermore, Sofman discloses wherein the one or more processors are further configured to execute the instructions to cause the apparatus to plan a lowest-riskpath based on the riskevent information. (see at least [0113];" The cost function can consider a plurality of speeds and select whichever speed is associated with the lowest total cost. Specifically, the computing system 112 can calculate a cost from a cost function factoring weather data predicted for a time when the vehicle 100 is predicted to encounter the weather data to determine a plurality of speed and route adjustments with the lowest cost for avoiding the weather.")
Regarding claim 13, Sofman, Dean, and Bedadala, in combination, disclose the limitations of claim 11 as discussed above, furthermore, Sofman discloses wherein the one or more processors are further configured to execute the instructions to cause the apparatus to: plan a plurality of paths; (see at least [0083]; "As will be discussed in more detail herein, the computing system 112 can at times plan (or replan) a route and then divide the route into one or more potential route segments for the purposes of performing the techniques described herein,")
determine that the predicted region range comprises a location point in the plurality of paths; (see at least [0085]; "Phrased another way, a particular segment of the route can be defined by a patch or patches of weather along the route. Specifically, the two geographical points that define the segment can be a starting point of the weather condition predicted and an ending point of the weather condition predicted on the route such that the segment encompasses all of a specific weather patch")
determine an estimated time at which the first vehiclewill travel to the location point; (see at least [0087]; " Alternatively, the segments can be defined by the expected time of arrival of the vehicle 100 at a point in the route ")
determine, based on the time information and the location information, the predicted time range; (see at least [0087]; "In another embodiment, one or multiple of a distance measurement, or a time measurement, can define the length or duration of the segments on the route.")
determine that the predicted time range comprises the estimated time; (see at least [00914]; " In some examples, the hyperlocal weather forecasts can be queried for a specific time the vehicle 100 is predicted to arrive at a segment.")
determine a traveling riskof at least one of the plurality of paths based on the riskevent; and (see a least [0005]; "The method also involves for each potential route segment, evaluating a partial cost function that comprises a summation of a set of segment-weighted cost factors, where at least one segment-weighted cost factor comprises an adverse weather risk factor based on the future weather conditions along the potential route segment. ")
determine a lowest-risk path in the plurality of paths based on the traveling risk. (see at least [102]; " Based on the speed associated with the lowest cost, for the particular segment of the route the vehicle 100 can adjust the vehicle’s speed and avoid a weather condition predicted for the particular segment. Selecting the speed associated with the lowest cost may also be referred to herein as minimizing the cost or minimizing the cost function with respect to the speed")
Regarding claim 14, Sofman, Dean, and Bedadala, in combination, disclose the limitations of claim 11 as discussed above, furthermore, Sofman discloses wherein the one or more processors are further configured to execute the instructions to cause the apparatus to send the risk event information to a vehicle when:a minimum distance between the predicted region range and a planned path of the vehicle is less than a second threshold;a first tile to which the predicted region range belongs is a second tile on which the vehicle is located; orthefirst tile to which the predicted region range belongs is a third tile that the planned path of the vehicle passes through. (see at least [0073, 0090, 00145]; "Server computing system 406 may be configured to wirelessly communicate with remote computing system 402 and vehicle 100 via network 404 (or perhaps directly with remote computing system 402 and/or vehicle 100)…Particularly, in situations when a large undesirable weather pattern is predicted to cross the route, it can be meaningful to consider weather patterns that are far away from the vehicle 100 in order to make a significant change to the time of arrival of the vehicle 100 in the segment with the undesirable weather pattern…The computing system 112 can also determine an optimal adjusted route that minimizes the cost function to adjust the route for the at least one segment from the route to the optimal adjusted route. It should be understood that the selected target route can be considered the optimal adjusted route. For optimization of the route, discrete route segments can be searched that avoid weather. The discrete routes can then be weighted by the costs. The computing system 112 can set the lowest cost discrete route as the optimal adjusted route for avoiding the weather condition. The optimal adjusted route is then set as the adjusted route for the vehicle 100 to navigate. Additionally, the computing system can intentionally, and automatically, delay the departure time entirely, to maximize the optimization of the route. [0146] As previously mentioned, the computing system 112 can dynamically update the route as the vehicle 100 travels. Specifically, as the vehicle 100 travels, the route of the vehicle 100 for a particular segment can be repeatedly updated based on the changing cost function. The computing system 112 can evaluate the second cost function while the vehicle is navigating a current segment of the first target route, select, from the plurality of target routes, a second target route associated with the lowest cost, and adjust the route from the first route to the second target route at the end of the current segment. This adjustment to the second target route can happen while the vehicle is travelling and may continue to repeatedly update, such that the route continues to change.")
Regarding claim 15, Sofman, Dean, and Bedadala, in combination, disclose the limitations of claim 11 as discussed above, furthermore, Sofman discloses wherein the risk event information further comprises risk type information indicating a type of the risk event, warning information indicating content with which a driver or a driving system is prompted based on the risk event, fourth identification information of a dynamic element that affects the risk event, or information about a map element that is affected by the risk event. (see at least [0165]; "In some embodiments, the method 700 can also involve identifying, based on the spatiotemporal weather information, one or more geographical adverse weather areas that correspond to one or more selected route segments. For example, one or more of the selected route segments can be route segments whose boundaries correspond to boundaries of a geographical area in which adverse weather is present or is anticipated to be present, such as a rainstorm or a group of multiple adverse weather conditions that may or might not at least partially overlap in their geographical area. As a more particular example, a rainstorm might occupy a first geographical area and thick fog might occupy a second geographical area that at least partially overlaps with the first geographical area. As such, one potential route segment that is one of the selected route segments can be a route segment whose start and end point both fall on, or are inside of, a boundary of a combination of the first and second geographical areas, such that the rainstorm and/or the thick fog are or are predicted to be present throughout the entire route segment. In such embodiments, the target speeds selected for any selected route segment that is within the one or more geographical adverse weather areas can be selected to mitigate adverse weather effects along that/those selected route segments (e.g., slow the vehicle down before a route segment where a rainstorm is present so that the rainstorm is no longer present by the time the vehicle reaches the route segment).")
Regarding claim 17, Sofman, Dean, and Bedadala, in combination, disclose the limitations of claim 11 as discussed above, furthermore, Sofman fails to wherein the one or more processors are further configured to execute the instructions to cause the apparatus to:determine, based on road topology information in the map data, a road region associated with the predicted region range; and prompt a second vehicle in the road region with the risk event, or control a traffic volume of the road region.
However, Mintz teaches The apparatus of claim 11, wherein the one or more processors are further configured to execute the instructions to cause the apparatus to:determine, based on road topology information in the map data, a road region associated with the predicted region range; and(see at least [55]; "The potential improvement in traffic flow, which can be obtained from such an approach, depends not just on the efficiency of the method applying the control on trip paths but also on the size and the topology of the networks with further relation to zone to zone trip demand, which determine the potential degrees of freedom on the network to apply predictive control on paths of controlled trips (path controlled trips).")
prompt a second vehicle in the road region with the risk event, or control a traffic volume of the road region. (see at least [56]; " Apparatus and method to apply predictive control, which may predictively coordinate paths on the network, should preferably use model predictive control requiring simulation of traffic models to enable controllable traffic predictions. I")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sofman to incorporate the teachings of Mintz which teachings notifying the risk as topology on the map in order to ready the driver or vehicle for the upcoming potential risk as change in speed or comfortability of the ride.
Regarding claim 20, Sofman discloses A non-transitory computer-readable storage medium configured to store program instructions that, when executed by one or more processors of an apparatus, cause the apparatus to: (see at least [0153]; "The program code may be stored on any type of computer readable medium, for example, such as a storage device including a disk or hard drive. The computer readable medium may include non-transitory computer readable medium, for example, such as computer-readable media that stores data for short periods of time like register memory, processor cache and Random Access Memory (RAM).")
wherein the time information indicates a predicted time range of a riskevent, and (see at least [fig. , 0130]; "The disclosed operations utilize spatiotemporal weather information such as hyperlocal weather forecasts or other types of weather data in order to have accurate predictions of the times and/or locations at which weather conditions (including adverse weather that would be desirable to avoid) will be present along segments of various routes along which the vehicle 100 can travel. ")
wherein the location information indicates a predicted region range of the riskevent; and (see at least [Fig. 6, 0130] ; "In addition, Figure 6 also depicts undesirable weather conditions, represented as blob-like regions 606 on the route 610, impacting a particular distance along the route 610 over a particular time window. ")
Sofman fails to disclose obtain risk event information comprising time information and location information, store the risk event information as second dynamic layer data of data, wherein the map data comprises static layer data, first dynamic layer data, and the second dynamic layer data, wherein the first dynamic layer data comprises one or more of environmental condition information or road status information, and wherein the static layer data comprises one or more of road type or lane quantity, and wherein the risk event information is based on the static layer data and the first dynamic layer data;control a first vehicle based on the risk event information; and delete the risk event information upon reaching an end of the predicted time range and without detecting a change to an element affecting the risk event during the predicted time range, wherein the element is the environmental condition information or the road status information. andcontrol a first vehicle based on the risk event information, obtain riskevent information comprising time information and location information, store the riskevent information as map data, wherein the map data comprises static layer data and first dynamic layer data, and wherein the riskevent information is based on the static layer data and the first dynamic layer data.
However, Dean teaches obtain riskevent information comprising time information and location information, (see at least [0017]; "Furthermore, an example risk regressor may further factor in current environmental conditions (e.g., rain, snow, clouds, road conditions, lighting, lighting direction, and the like), and static risk based on lane geometry, traffic conditions, and time of day to compute a fractional risk quantity dynamically for any path segment at any given time.")
store the risk event information as second dynamic layer data of map data, wherein the map data comprises static layer data, first dynamic layer data, and the second dynamic layer data, (see at least Dean [0056, 0046]; "The memory 320 can store data 325 and instructions 330, which the processor 315 can execute to cause the vehicle computing device 310 to perform operations. The data 325 can include map data 327 and cost data 329. The instructions 330 can include cost calculation instructions 332 that, when executed, implement one or more steps, features, or aspects of example methods for determining values for path segments based on risk factors associated with autonomous operation of the vehicle 300 along path segments…the path cost calculation system 260 models scores for each of the cost layers, including the travel time and risk factor cost layers, for path segments (i.e., predetermined lengths of a road or lane of a road) in the geographic region. Events and features extracted and identified from logs and mapping data are provided as input to a statistical model that outputs scores for the cost layers.")
wherein the first dynamic layer data comprises one or more of environmental condition information or road status information, and (see at least Dean [0047]; "the path cost calculation system 260 takes into account dynamic data, such as real-time traffic and weather, for the geographic region in which the vehicle 200 is operating. ") Dean describes a dynamic layer comprised of environmental conditions such as weahter, as well as traffic, which falls into the road status information category.
wherein the static layer data comprises one or more of road type or lane quantity, and (See at least Dean[0041]; "In addition to using the sensor data, the perception system 230 can retrieve map data (e.g., localization maps) that provide detailed information about the surrounding environment of the vehicle. The map data can provide information regarding the identity and location of different paths (e.g., roads, road segments, lanes, lane segments, parking lanes, turning lanes, bicycle lanes, or other portions of a particular path)…Map data can also include the identity and location of buildings, maintenance/service locations for the vehicles, parking areas, traffic signs, traffic lights, traffic control devices, and/or any other features that provide information that could assist the vehicle control system 220 in perceiving and navigating the surrounding environment. ") Dean describes a static later with road types and lane quantity.
wherein the risk event information is based on the static layer data and the first dynamic layer data; (See at least [0013]; " Accordingly, a total path for an AV from a starting point to a destination can be comprised of a sequential set of capability-in-scope lane segments from the starting point to the destination, each having an attributed fractional risk quantity calculated by a risk regressor based on static and dynamic conditions, as described herein.")
control a first vehicle based on the risk event information; and (See at least [0018];" In addition, for a given transport request from a requesting user, a route planning system can determine a set of routes between the pick-up location and destination, and the risk regressor can determine an aggregate risk quantity for each of those routes given the current or predicted conditions (e.g., conditions at the time the vehicle traverses a particular path segment), and provide a lowest risk route or other optimal route (e.g., optimized across risk, time, dollar earnings, etc.) as output to a matching engine that pairs the requesting user with an available vehicle.") Dean outlines controlling a vehicle along a path that's based on risk.
wherein the element is the environmental condition information or the road status information. (see at least Dean [0017]; " Furthermore, an example risk regressor may further factor in current environmental conditions (e.g., rain, snow, clouds, road conditions, lighting, lighting direction, and the like), and static risk based on lane geometry, traffic conditions, and time of day to compute a fractional risk quantity dynamically for any path segment at any given time.") The data in the model could be comprised of environmental condition information or road status information.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified to incorporate teachings of Dean which teaches storing risk event information as map data, associating map related information with risk as a static and dynamic layer in order to better anticipate the nature of the risk and how it will affect a chosen route.
Sofman and Dean, in combination, do not explicitly disclose delete the risk event information upon reaching an end of the predicted time range and without detecting a change to an element affecting the risk event during the predicted time range.
However, Bedadala teaches delete the risk event information upon reaching an end of the predicted time range and without detecting a change to an element affecting the risk event during the predicted time range, (see at least Bedadala [0006, 0024]; "The storage manager or another component in the information management system can then delete the chunks of these listed archive files… analyzing a component of the decomposed time-series to determine an acceptable range for a time to process archive files to be deleted;") Bedadala teaches a system that gathers data, and after a determined time, deletes the files. The data here could be the risk data comprised of static and dynamic factors, and can be archived based on a change not occuring; creating expired information.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sofman to incorporate teachings of Bedadalala which teaches deleting archived data in order to allow memory space for the processors to continue to operate with applicable information and new routes.
Regarding claim 21, Sofman, Dean, and Bedadala, in combination, disclose the limitations of claim 20 as discussed above, furthermore, Sofman discloses wherein the program instructions when executed by the one or more processors further cause the apparatus to plan a lowest-risk path based on the risk event information. (see at least [0006, 0113];" A second embodiment describes an article of manufacture including a non- transitory computer-readable medium having stored thereon instructions that, when executed by a processor in a computing system, causes the computing system to perform operations….The cost function can consider a plurality of speeds and select whichever speed is associated with the lowest total cost. Specifically, the computing system 112 can calculate a cost from a cost function factoring weather data predicted for a time when the vehicle 100 is predicted to encounter the weather data to determine a plurality of speed and route adjustments with the lowest cost for avoiding the weather.")
Regarding claim 22, Sofman, Dean, and Bedadala, in combination, disclose the limitations of claim 20 as discussed above, furthermore, Sofman discloses wherein the program instructions when executed by the one or more processors further cause the apparatus to send the risk event information to a second vehicle when: (see at least Sofman [0100]; "a second vehicle computing system could automatically send gathered weather data to the weather station server. The weather station server can use the gathered weather data from the second vehicle as well as weather forecasts to make weather predictions for the computing system 112 to receive.")
the second vehicle is in the predicted region range; a minimum distance between the second vehicle and the predicted region range is less than a first threshold; or the predicted region range has an intersection with a planned path of the second vehicle. (see at least Sofman [0099]; " Specifically, the computing system 112 can identify that the second vehicle has travelled, or is travelling, in one or more potential route segments ahead of, and within a predetermined distance from, a current location of the vehicle 100. ")
Regarding claim 23, Sofman, Dean, and Bedadala, in combination, disclose the limitations of claim 20 as discussed above, furthermore, Sofman discloses wherein the risk event information further comprises first identification information of the risk event, (see at least Sofman [0094]; " If predicted weather data indicates a risk of severe weather, the computing system 112 can query the farther segments at a higher frequency in order to better predict incoming weather data so as to plan the speed(s) at which the vehicle 100 will travel.")
second identification information of a tile to which the risk event belongs, (see at least Sofman [0095]; "In an example embodiment, the computing system 112 can receive the predicted weather data from a weather station server or other type of server. The weather station server can be a weather station server that is local to a particular geographic area, segment, etc. along or near the route — that is, a weather station server that is dedicated to the particular geographic area, segment, etc. and configured to acquire weather data corresponding to the particular geographic area, segment, etc., and transmit the weather data to one or more vehicle computing systems.")
third identification information of a road on which the risk event is located, or risk level information indicating a danger degree of the risk event. (see at least [0115]; "For example, weather data that indicates a particular weather condition has lower than a particular threshold probability of occurring (e.g., below 40%) can be ignored by the computing system 112.")
Claims 8, 9, 18 and 19 are rejected under 35 U.S.C 103 as being unpatentable over Sofman (WO 2021189027 A1) in view of Dean (WO 2019099465 A1) and Bedadala, and further view of Khasis (US 20170262790 A1).
Regarding claim 8, Sofman, Dean, and Bedadala, in combination, disclose the limitations of claim 1 as discussed above, furthermore, Sofman fails to disclose The method of claim 1, further comprising presenting the risk event information on a map display interface in at least one of the following manners:dynamically displaying a change of the risk event on the map display interface based on the time information and the location information;marking a first predicted region range of at least one first risk event whose risk level at a current time exceeds a threshold in the risk event and providing first description information of the at least one first risk event;marking a second predicted region range of at least one second risk event related to a navigation path in the risk event and providing second description information of the at least one second risk event; or marking a third predicted region range of at least one third risk event at a time selected by a user and providing third description information of the at least one third event
However, Khasis teaches The method of claim 1, further comprising presenting the risk event information on a map display interface in at least one of the following manners: dynamically displaying a change of the risk event on the map display interface based on the time information and the location information;marking a first predicted region range of at least one first risk event whose risk level at a current time exceeds a threshold in the risk event and providing first description information of the at least one first risk event;marking a second predicted region range of at least one second risk event related to a navigation path in the risk event and providing second description information of the at least one second risk event; or marking a third predicted region range of at least one third risk event at a time selected by a user and providing third description information of the at least one third event (see at least [0043]; " In addition to the message being displayed, other traffic data that may be transmitted includes: the conditions that caused the message to display, the estimated length of any traffic restrictions in effect, the name of the road that the segment is a part of, and the location of the segment, such as, e.g., state, city, and/or geographical coordinates.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sofman to incorporate teachings of Khasis which teaches presenting the risk information on a display and its associated information in order to inform the driver or occupants of the anticipated risks and associated estimated time for their convenience.
Regarding claim 9, Sofman, Dean, and Bedadala, in combination, disclose the limitations of claim 1 as discussed above, furthermore, Sofman, Dean and fails to disclose displaying the second dynamic layer data separately or in a superimposed manner with one or more of the static layer data or the first dynamic layer data.
However, Khasis teaches displaying the second dynamic layer data separately or in a superimposed manner with one or more of the static layer data or the first dynamic layer data. (See at least Khasis [0073]; "For example, coordinates may be overlaid with a map to show the positions of vehicles and destination points. The mapping module 114 may communicate with the platform to obtain information and coordinates of real-time, historical and predicted future traffic and weather conditions, existing and new hazards, existing and predicted avoidance zones, custom territory input information, and display the information in a similar manner. ")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sofman to incorporate teachings of Khasis which teaches superimposing dynamic information with the static information to provide a complete view of risks along the vehicle’s path to act accordingly.
Regarding claim 18, Sofman, Dean, and Bedadala, in combination, disclose the limitations of claim 11 as discussed above, furthermore, Sofman fails to disclose a display unit, and the one or more processors are further configured to execute the instructions to cause the apparatus to present the risk event information on a map display interface of the display unit in at least one of the following manners: mark a fourth predicted region range of at least one fourth risk event that satisfies a risk type selected by a user and providing fourth description information of the at least one fourth risk event;mark, using first different colors, first predicted region ranges corresponding to first risk events of different risk levels; ormark, using second different colors, second predicted region ranges corresponding to second risk events of different risk types.
However, Khasis teaches a display unit, and the one or more processors are further configured to execute the instructions to cause the apparatus to present the risk event information on a map display interface of the display unit in at least one of the following manners: mark a fourth predicted region range of at least one fourth risk event that satisfies a risk type selected by a user and providing fourth description information of the at least one fourth risk event;mark, using first different colors, first predicted region ranges corresponding to first risk events of different risk levels; ormark, using second different colors, second predicted region ranges corresponding to second risk events of different risk types. (see at least [0043]; " In addition to the message being displayed, other traffic data that may be transmitted includes: the conditions that caused the message to display, the estimated length of any traffic restrictions in effect, the name of the road that the segment is a part of, and the location of the segment, such as, e.g., state, city, and/or geographical coordinates.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sofman to incorporate teachings of Khasis which teaches presenting the risk information on a display and its associated information in order to inform the driver or occupants of the anticipated risks and associated estimated time for their convenience.
Regarding claim 19, Sofman, Dean, and Bedadala, in combination, disclose the limitations of claim 11 as discussed above, furthermore, Sofman fails to disclose wherein the one or more processors are further configured to execute the instructions to cause the apparatus to display the second dynamic layer data separately or in a superimposed manner with one or more of the static layer data or the first dynamic layer data.
However, Khasis teaches wherein the one or more processors are further configured to execute the instructions to cause the apparatus to display the second dynamic layer data separately or in a superimposed manner with one or more of the static layer data or the first dynamic layer data. (see at least Khasis [0012, 0073]; "the present invention discloses a system and a method for optimizing routes implemented by an optimization server communicatively coupled to a processor and memory of a mobile device of a user or vehicle through a network….For example, coordinates may be overlaid with a map to show the positions of vehicles and destination points. The mapping module 114 may communicate with the platform to obtain information and coordinates of real-time, historical and predicted future traffic and weather conditions, existing and new hazards, existing and predicted avoidance zones, custom territory input information, and display the information in a similar manner. ")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sofman to incorporate teachings of Khasis which teaches superimposing dynamic information with the static information to provide a complete view of risks along the vehicle’s path to act accordingly.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HANA VICTORIA HALL whose telephone number is (571)272-5289. The examiner can normally be reached M-F 9-5.
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/HANA VICTORIA HALL/Examiner, Art Unit 3664
/RACHID BENDIDI/Supervisory Patent Examiner, Art Unit 3664
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