fDETAILED 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 .
This is a Non-final Office Action on the merits. Claims 1, 3-9, 11-17, and 19-20 are currently pending and are addressed below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/01/2026 has been entered.
Response to Arguments
Applicant’s arguments on pages 13-16 of the response, with respect to the rejection(s) of claim(s) 1, 3-9, 11-17, and 19-20 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Lippman.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3-9, 11-17, and 19-20 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 (and claims 9 and 17 by reciting analogous limitations) recites “identifying the risk associated with the road segment comprises correlating fuel consumption data and battery consumption data to the road segment surface water data to identify a damage risk associated with damage to a hybrid electric vehicle (HEV)” and claim 3 (and claims 11 and 19 by reciting analogous limitations) recites “comparing the road segment surface water data to the vehicle speed and the near-vehicle proximity data to identify a damage risk associated with damage to the HEV”. However, the specification does not provide an algorithm for correlating fuel consumption data to road segment water surface data or an algorithm for identifying a damage risk. [0019] defines “correlating” as “cross-referencing or compiling environmental data, vehicle data, and traffic data in a table, graph, etc., to identify a vector (magnitude and direction) representing the relationship between two or more variables or data points”, but it lacks any description of what specific operations to perform on the variables to obtain the vector and how this results in a calculated damage risk. Furthermore, [0063] recites an example of “comparing the road segment surface water data to the vehicle speed and the near-vehicle proximity data (identified via GPS) to identify a risk associated with damage to the HEV”, and [0066] recites an example of identifying a battery use risk by “correlating water data to route data to identify overlaps in the data indicating water is present on the road” and a safety risk “by comparing vehicle speed, drive duration, and driving mode to predefined safety risks”, consolidating the two types of risk, and comparing them to known risks to determine the risk, but neither of these examples mention using fuel consumption data for risk analysis or how to use this analysis to identify a damage risk.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 4-9, and 12-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jia of CN117261901A, published 12/22/2023, hereinafter “Jia”, in view of Lippman of US 20150203107 A1, published 07/23/2015, hereinafter “Lippman”.
Regarding claim 1, Jia teaches:
A computer-implemented method, comprising: receiving, by a processor set, environmental data, vehicle data, and traffic data, wherein the environmental data comprises road segment surface water data, (See at least [0032]: “S101. Obtain environmental information about the vehicle's current location” & [0037-0039]: “…the aforementioned environmental information includes, but is not limited to, one or more of the following: target rainfall information at the vehicle's current location, target road segment information on the road ahead, and wind direction information…The above target road segment information can be used to describe various information such as whether there is water accumulation on the road segment, the location of the water accumulation segment, or the depth of the water accumulation…”)
identifying, by the processor set, a road segment based on the environmental data; (See at least [0056-0057]: “S203. Collect road images of the road ahead and identify the information of the first road segment based on the road images…the vehicle can acquire road images of the road ahead based on a pre-set image sensor. Then, the road image is processed according to the pre-set road segment recognition model to obtain the information of the first road segment.”)
identifying, by the processor set, a risk associated with the road segment based on the environmental data(See at least [0071]: “…Furthermore, when only target road segment information is selected to predict driving conditions, the vehicle only needs to perform the steps of obtaining the first road segment information and the second road segment information to determine the target rainfall information, and predicting driving conditions based on whether the forward road represented by the target road segment information includes waterlogged sections…” & [0112]: “The prediction module is used to predict the driving condition as a water-crossing condition…if the target road section information indicates that the road ahead includes waterlogged sections”)
wherein identifying the risk associated with the road segment comprises correlating fuel consumption data and battery consumption data to the road segment surface water data to identify a damage risk associated with damage to a hybrid electric vehicle (HEV); (See at least [0094-0095]: “In another embodiment, after detecting that the vehicle is in a water-crossing condition and obtaining the remaining power of the vehicle's power battery, the vehicle can also determine the target location from the road it is traveling on. Then, determine the power consumption required for the vehicle to travel from its current location to the target location, and control the vehicle's movement using a wading mode when the remaining power is greater than or equal to the power consumption. Otherwise, when the remaining battery power is less than the power consumption, the vehicle can control the engine to charge the battery until the remaining battery power is greater than or equal to the power consumption. The driving conditions corresponding to the aforementioned target locations are non-water-crossing conditions” & [0098-0099]: “In one embodiment, a correlation between driving range and remaining battery power is typically established within the vehicle. In other words, vehicles are usually equipped with a system that estimates the range the vehicle can continue to travel with its current remaining battery power. Based on this, after determining the target location, the vehicle can calculate the distance between the target location and the current location. Then, based on the above correlation, the required power consumption corresponding to the interval distance is determined. It should be noted that by using the above method to estimate power consumption, when the remaining power is less than the preset power but greater than the power consumption, the vehicle can drive directly in water conditions without stopping to charge the power battery, thus improving vehicle driving efficiency.”)
determining, by the processor set, a driving mode selected from a battery priority mode and a fuel priority mode based on the risk; and (See at least [0117]: “If the remaining battery power is greater than or equal to the preset battery power, the vehicle's wading depth will be identified during driving; if the wading depth is greater than the preset wading depth, the vehicle will be controlled in wading mode” & [n0121]: “The fourth module is used to control the vehicle's movement in a wading mode if the remaining battery power is greater than or equal to the power consumption”. See also [0074] regarding the wading mode being a pure electric drive mode and [n0089-n0090] regarding an original driving mode being an engine driving mode, which is used when the wading depth is less than a preset wading depth.)
instructing, by the processor set, the HEV to enforce the driving mode, wherein the instructing causes the HEV to change the driving mode. (See at least [n0101]: “…during driving, if the vehicle's wading depth is detected to be greater than the preset wading depth, or if the driver's command to activate the wading mode is received, the vehicle will be controlled to drive in the wading mode…”)
Jia does not explicitly teach:
…the vehicle data comprises vehicle speed, and the traffic data comprises near-vehicle proximity data…
identifying, by the processor set, a risk associated with the road segment based on the…vehicle data, and traffic data,
Lippman teaches:
…the vehicle data comprises vehicle speed, and the traffic data comprises near-vehicle proximity data… (See at least [0011]: “Data collectors 110 may include a variety of devices. For example, various controllers in a vehicle may operate as data collectors 110 to provide data 115 via the CAN bus, e.g., data 115 relating to vehicle speed, acceleration, etc.” & [0034]: “Further for example, additional collected data 115 could be used to monitor surrounding traffic, i.e., behavior of one or more other vehicles 101. In combination with precipitation rates and types, other vehicle 101 behavior, e.g., sudden turning acceleration, deceleration, skidding, braking, etc., can be used to determine hydroplane, water pooling and other possible conditions leading to an inconsistent coefficient of friction, i.e., situation where values for a coefficient of friction change significantly on a roadway at a small distance, e.g., foot by foot or yard by yard. In such conditions, as determined by all available data, coefficient of friction calculations may only be useful as a base factor for vehicle 101 control functions, such as maintaining constant speed, acceleration rates and braking rates.”)
identifying, by the processor set, a risk associated with the road segment based on the…vehicle data, and traffic data, (See at least [0020]: “Moreover, vehicle 101 speed can affect detection of a type and rate of precipitation. In one instance, vehicle 101 speed data would be included as a factor in determining a rate of snow fall” & [0035]: “Moreover, in conditions of high precipitation rates, behavior of one or more second vehicles 101 with respect to a roadway lane or lanes can be included as a factor in formulating a control action for a first vehicle 101. For example, where a precipitation condition has been determined and factored into a first vehicle 101 operation, it may also be determined that second vehicles in left and right lanes of a road with three lanes traveling in the same direction, are observed to vary speeds where a constant speed is normally expected. Moreover, it could be determined that vehicles 101 in the center lane have a constant or at least close to constant, consistent rate of travel, than vehicles 101 in surrounding lanes. From this it can be concluded that road conditions, in particular in left and right lanes, have factors causing changes in vehicle 101 control. Likewise, it can be concluded that a vehicle 101 in autonomous mode should be directed to travel in the center lane, and possibly also adding additional following distance from a lead vehicle 101 to compensate for unpredictable yet possible conditions where collected data 115 indicate possible occurrences of water pooling, hydroplane conditions, and, but not limited to, sudden snow covered surfaces.”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Jia’s method with Lippman’s technique of using vehicle data comprising vehicle speed and traffic data comprising near-vehicle proximity data to identify a risk associated with a road segment. Doing so would be obvious “to compensate for unpredictable yet possible conditions where collected data 115 indicate possible occurrences of water pooling, hydroplane conditions, and, but not limited to, sudden snow covered surfaces” (See [0035] of Lippman).
Regarding claim 4, Jia and Lippman in combination teach all the limitations of claim 1 as discussed above.
Jia additionally teaches:
wherein the receiving the environmental data comprises receiving an approximation of surface water present on the road segment. (See at least [0039]: “…The above target road segment information can be used to describe various information such as whether there is water accumulation on the road segment, the location of the water accumulation segment, or the depth of the water accumulation…”)
Regarding claim 5, Jia and Lippman in combination teach all the limitations of claim 4 as discussed above.
Jia additionally teaches:
wherein the identifying the road segment based on the environmental data comprises: identifying a plurality of road segments; and (See at least [0068]: “…the second road segment information can represent all waterlogged road segments along the route from the current location to the destination…”)
categorizing the plurality of road segments based on the approximation of surface water present on the road segment. (See at least [0070]: “…when the first road segment information and/or the second road segment information indicate that the road ahead includes a waterlogged section, the vehicle can determine that the target road segment information indicates that the road ahead includes a waterlogged section. Otherwise, if the first road segment information and the second road segment information indicate that the road ahead does not include waterlogged sections, the vehicle can determine that the target road segment information indicates that the road ahead does not include waterlogged sections.”)
Regarding claim 6, Jia and Lippman in combination teach all the limitations of claim 5 as discussed above.
Jia additionally teaches:
wherein the identifying the risk associated with the road segment comprises identifying a risk associated with a category of each of the plurality of road segments. (See at least [0033]: “S102. If the vehicle's driving condition is predicted to be wading based on environmental information, then obtain the remaining power of the vehicle's power battery” & [0036]: “…the above-mentioned driving conditions can be divided into wading conditions and non-wading conditions. In water-related conditions, the vehicle's engine is considered to be at risk of water ingress. For example, a vehicle may be driving through a low-lying section of road with standing water, or a vehicle may be driving through a section of road with standing water during heavy rain. Therefore, when a vehicle is driving in the above-mentioned scenario, its driving condition can be considered as a water-wading condition.”)
Regarding claim 7, Jia and Lippman in combination teach all the limitations of claim 6 as discussed above.
Jia additionally teaches:
wherein the determining the driving mode based on the risk comprises determining a plurality of driving modes including at least the battery priority mode and the fuel priority mode based on the risk associated with the category of each of the plurality of road segments. (See at least [0082]: “In this embodiment, when the vehicle is detected to be in a water-wading condition, the remaining power of the vehicle's power battery can be obtained first. Then, when the remaining power is greater than or equal to the preset power, it can be considered that the power battery has enough power to support the vehicle to drive in water conditions…” & [0085]: “Therefore, in order to ensure the safety of the engine during driving and to minimize the waste of power battery, if the remaining power is greater than or equal to the preset power, the vehicle can identify the wading depth of the vehicle during driving. Then, when the wading depth is greater than the preset wading depth, the vehicle can use the wading mode to control its movement. Otherwise, when the wading depth is less than or equal to the preset wading depth, the vehicle can still use the original drive mode to control the vehicle's movement.”)
Regarding claim 8, Jia and Lippman in combination teach all the limitations of claim 7 as discussed above.
Jia and Lippman in combination do not explicitly teach:
wherein the plurality of driving modes comprises a first driving mode, a second driving mode, and a third driving mode, and wherein the instructing the HEV to enforce the plurality of driving modes comprises: instructing the HEV to enforce the first driving mode at a first road segment of the plurality of road segments based on a first set of environmental data, vehicle data, and traffic data;
instructing the HEV to enforce the second driving mode at a second road segment of the plurality of road segments based on a second set of environmental data, vehicle data, and traffic data; and
instructing the HEV to enforce the third driving mode at a third road segment of the plurality of road segments based on a third set of environmental data, vehicle data, and traffic data.
However, Jia teaches using either a wading mode or an original driving mode depending on the wading depth (i.e., enforcing different driving modes based on road segment information) (See at least [0082] & [0085] of Jia), and Lippman teaches controlling a vehicle with respect to water pooling conditions determined from data describing the behavior of other vehicles (See at least [0034-0036]). Therefore, the combination of Jia and Lippman render obvious enforcing different driving modes at different road segments depending on the environmental data, vehicle data, and traffic data, which provides the benefit of “to compensate for unpredictable yet possible conditions where collected data 115 indicate possible occurrences of water pooling, hydroplane conditions, and, but not limited to, sudden snow covered surfaces” (See [0035] of Lippman).
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Jia’s method with Lippman’s technique of controlling a vehicle based on the behavior of other vehicles. Doing so would be obvious “to compensate for unpredictable yet possible conditions where collected data 115 indicate possible occurrences of water pooling, hydroplane conditions, and, but not limited to, sudden snow covered surfaces” (See [0035] of Lippman).
Regarding claim 9, Jia teaches:
A computer program product comprising one or more non-transitory computer readable storage media having program instructions collectively stored on the one or more non-transitory computer readable storage media, the program instructions executable to: receive environmental data, vehicle data, and traffic data, wherein the environmental data comprises road segment surface water data, (See at least [0032]: “S101. Obtain environmental information about the vehicle's current location” & [0037]: “…the aforementioned environmental information includes, but is not limited to, one or more of the following: target rainfall information at the vehicle's current location, target road segment information on the road ahead, and wind direction information…”. See also [0044] regarding acquiring road images and [0046] regarding obtaining rainfall information from image and rain sensors of the vehicle, respectively.)
identify a road segment based on the environmental data; (See at least [0056-0057]: “S203. Collect road images of the road ahead and identify the information of the first road segment based on the road images…the vehicle can acquire road images of the road ahead based on a pre-set image sensor. Then, the road image is processed according to the pre-set road segment recognition model to obtain the information of the first road segment.”)
identify a risk associated with the road segment based on the environmental data, (See at least [n0110]: “If the first rainfall information and/or the second rainfall information indicate that the rainfall level is greater than or equal to the preset rainfall level, then the target rainfall information indicates that the rainfall level is greater than or equal to the preset rainfall level; if the first road segment information and/or the second road segment information indicate that the road ahead includes waterlogged sections, then the target road segment information indicates that the road ahead includes waterlogged sections” & [n0112]: “The prediction module is used to predict the driving condition as a water-crossing condition if the target rainfall information indicates that the rainfall level is greater than or equal to the preset rainfall level, or if the target road section information indicates that the road ahead includes waterlogged sections.” See also [0070] regarding determining waterlogged sections.)
wherein identifying the risk associated with the road segment comprises correlating fuel consumption data and battery consumption data to the road segment surface water data to identify a damage risk associated with damage to a hybrid electric vehicle (HEV); (See at least [0094-0095]: “In another embodiment, after detecting that the vehicle is in a water-crossing condition and obtaining the remaining power of the vehicle's power battery, the vehicle can also determine the target location from the road it is traveling on. Then, determine the power consumption required for the vehicle to travel from its current location to the target location, and control the vehicle's movement using a wading mode when the remaining power is greater than or equal to the power consumption. Otherwise, when the remaining battery power is less than the power consumption, the vehicle can control the engine to charge the battery until the remaining battery power is greater than or equal to the power consumption. The driving conditions corresponding to the aforementioned target locations are non-water-crossing conditions” & [0098-0099]: “In one embodiment, a correlation between driving range and remaining battery power is typically established within the vehicle. In other words, vehicles are usually equipped with a system that estimates the range the vehicle can continue to travel with its current remaining battery power. Based on this, after determining the target location, the vehicle can calculate the distance between the target location and the current location. Then, based on the above correlation, the required power consumption corresponding to the interval distance is determined. It should be noted that by using the above method to estimate power consumption, when the remaining power is less than the preset power but greater than the power consumption, the vehicle can drive directly in water conditions without stopping to charge the power battery, thus improving vehicle driving efficiency.”)
determine a driving mode selected from a battery priority mode and a fuel priority mode based on the risk; and (See at least [0117]: “If the remaining battery power is greater than or equal to the preset battery power, the vehicle's wading depth will be identified during driving; if the wading depth is greater than the preset wading depth, the vehicle will be controlled in wading mode” & [n0121]: “The fourth module is used to control the vehicle's movement in a wading mode if the remaining battery power is greater than or equal to the power consumption”. See also [0074] regarding the wading mode being a pure electric drive mode and [n0089-n0090] regarding an original driving mode being an engine driving mode, which is used when the wading depth is less than a preset wading depth.)
instruct the HEV to enforce the driving mode, wherein the instructing causes the HEV to change the driving mode. (See at least [n0101]: “…during driving, if the vehicle's wading depth is detected to be greater than the preset wading depth, or if the driver's command to activate the wading mode is received, the vehicle will be controlled to drive in the wading mode…”)
Jia does not explicitly teach:
…the vehicle data comprises vehicle speed, and the traffic data comprises near-vehicle proximity data…
identify a risk associated with the road segment based on the…vehicle data, and traffic data,
Lippman teaches:
…the vehicle data comprises vehicle speed, and the traffic data comprises near-vehicle proximity data… (See at least [0011]: “Data collectors 110 may include a variety of devices. For example, various controllers in a vehicle may operate as data collectors 110 to provide data 115 via the CAN bus, e.g., data 115 relating to vehicle speed, acceleration, etc.” & [0034]: “Further for example, additional collected data 115 could be used to monitor surrounding traffic, i.e., behavior of one or more other vehicles 101. In combination with precipitation rates and types, other vehicle 101 behavior, e.g., sudden turning acceleration, deceleration, skidding, braking, etc., can be used to determine hydroplane, water pooling and other possible conditions leading to an inconsistent coefficient of friction, i.e., situation where values for a coefficient of friction change significantly on a roadway at a small distance, e.g., foot by foot or yard by yard. In such conditions, as determined by all available data, coefficient of friction calculations may only be useful as a base factor for vehicle 101 control functions, such as maintaining constant speed, acceleration rates and braking rates.”)
identify a risk associated with the road segment based on the…vehicle data, and traffic data, (See at least [0020]: “Moreover, vehicle 101 speed can affect detection of a type and rate of precipitation. In one instance, vehicle 101 speed data would be included as a factor in determining a rate of snow fall” & [0035]: “Moreover, in conditions of high precipitation rates, behavior of one or more second vehicles 101 with respect to a roadway lane or lanes can be included as a factor in formulating a control action for a first vehicle 101. For example, where a precipitation condition has been determined and factored into a first vehicle 101 operation, it may also be determined that second vehicles in left and right lanes of a road with three lanes traveling in the same direction, are observed to vary speeds where a constant speed is normally expected. Moreover, it could be determined that vehicles 101 in the center lane have a constant or at least close to constant, consistent rate of travel, than vehicles 101 in surrounding lanes. From this it can be concluded that road conditions, in particular in left and right lanes, have factors causing changes in vehicle 101 control. Likewise, it can be concluded that a vehicle 101 in autonomous mode should be directed to travel in the center lane, and possibly also adding additional following distance from a lead vehicle 101 to compensate for unpredictable yet possible conditions where collected data 115 indicate possible occurrences of water pooling, hydroplane conditions, and, but not limited to, sudden snow covered surfaces.”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Jia’s method with Lippman’s technique of using vehicle data comprising vehicle speed and traffic data comprising near-vehicle proximity data to identify a risk associated with a road segment. Doing so would be obvious “to compensate for unpredictable yet possible conditions where collected data 115 indicate possible occurrences of water pooling, hydroplane conditions, and, but not limited to, sudden snow covered surfaces” (See [0035] of Lippman).
Regarding claim 12, Jia and Lippman in combination teach all the limitations of claim 9 as discussed above.
Jia additionally teaches:
wherein the receiving the environmental data comprises receiving an approximation of surface water present on the road segment. (See at least [0039]: “…The above target road segment information can be used to describe various information such as whether there is water accumulation on the road segment, the location of the water accumulation segment, or the depth of the water accumulation…”)
Regarding claim 13, Jia and Lippman in combination teach all the limitations of claim 12 as discussed above.
Jia additionally teaches:
wherein the identifying the road segment based on the environmental data comprises: identifying a plurality of road segments; and (See at least [0068]: “…the second road segment information can represent all waterlogged road segments along the route from the current location to the destination…”)
categorizing the plurality of road segments based on the approximation of surface water present on the road segment. (See at least [0070]: “…when the first road segment information and/or the second road segment information indicate that the road ahead includes a waterlogged section, the vehicle can determine that the target road segment information indicates that the road ahead includes a waterlogged section. Otherwise, if the first road segment information and the second road segment information indicate that the road ahead does not include waterlogged sections, the vehicle can determine that the target road segment information indicates that the road ahead does not include waterlogged sections.”)
Regarding claim 14, Jia and Lippman in combination teach all the limitations of claim 13 as discussed above.
Jia additionally teaches:
wherein the identifying the risk associated with the road segment comprises identifying a risk associated with a category of each of the plurality of road segments. (See at least [0033]: “S102. If the vehicle's driving condition is predicted to be wading based on environmental information, then obtain the remaining power of the vehicle's power battery” & [0036]: “…the above-mentioned driving conditions can be divided into wading conditions and non-wading conditions. In water-related conditions, the vehicle's engine is considered to be at risk of water ingress. For example, a vehicle may be driving through a low-lying section of road with standing water, or a vehicle may be driving through a section of road with standing water during heavy rain. Therefore, when a vehicle is driving in the above-mentioned scenario, its driving condition can be considered as a water-wading condition.”)
Regarding claim 15, Jia and Lippman in combination teach all the limitations of claim 14 as discussed above.
Jia additionally teaches:
wherein the determining the driving mode based on the risk comprises determining a plurality of driving modes including at least the battery priority mode and the fuel priority mode based on the risk associated with the category of each of the plurality of road segments. (See at least [0082]: “In this embodiment, when the vehicle is detected to be in a water-wading condition, the remaining power of the vehicle's power battery can be obtained first. Then, when the remaining power is greater than or equal to the preset power, it can be considered that the power battery has enough power to support the vehicle to drive in water conditions…” & [0085]: “Therefore, in order to ensure the safety of the engine during driving and to minimize the waste of power battery, if the remaining power is greater than or equal to the preset power, the vehicle can identify the wading depth of the vehicle during driving. Then, when the wading depth is greater than the preset wading depth, the vehicle can use the wading mode to control its movement. Otherwise, when the wading depth is less than or equal to the preset wading depth, the vehicle can still use the original drive mode to control the vehicle's movement.”)
Regarding claim 16, Jia and Lippman in combination teach all the limitations of claim 15 as discussed above.
Jia and Lippman in combination do not explicitly teach:
wherein the plurality of driving modes comprises a first driving mode, a second driving mode, and a third driving mode, and wherein the instructing the HEV to enforce the plurality of driving modes comprises: instructing the HEV to enforce the first driving mode at a first road segment of the plurality of road segments based on a first set of environmental data, vehicle data, and traffic data;
instructing the HEV to enforce the second driving mode at a second road segment of the plurality of road segments based on a second set of environmental data, vehicle data, and traffic data; and
instructing the HEV to enforce the third driving mode at a third road segment of the plurality of road segments based on a third set of environmental data, vehicle data, and traffic data.
However, Jia teaches using either a wading mode or an original driving mode depending on the wading depth (i.e., enforcing different driving modes based on road segment information) (See at least [0082] & [0085] of Jia), and Lippman teaches controlling a vehicle with respect to water pooling conditions determined from data describing the behavior of other vehicles (See at least [0034-0036]). Therefore, the combination of Jia and Lippman render obvious enforcing different driving modes at different road segments depending on the environmental data, vehicle data, and traffic data, which provides the benefit of “to compensate for unpredictable yet possible conditions where collected data 115 indicate possible occurrences of water pooling, hydroplane conditions, and, but not limited to, sudden snow covered surfaces” (See [0035] of Lippman).
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Jia’s method with Lippman’s technique of controlling a vehicle based on the behavior of other vehicles. Doing so would be obvious “to compensate for unpredictable yet possible conditions where collected data 115 indicate possible occurrences of water pooling, hydroplane conditions, and, but not limited to, sudden snow covered surfaces” (See [0035] of Lippman).
Regarding claim 17, Jia teaches:
A system comprising: a processor set, one or more non-transitory computer readable storage media, and program instructions collectively stored on the one or more non-transitory computer readable storage media, the program instructions executable to: receive environmental data, vehicle data, and traffic data, wherein the environmental data comprises road segment surface water data, (See at least [0032]: “S101. Obtain environmental information about the vehicle's current location” & [0037]: “…the aforementioned environmental information includes, but is not limited to, one or more of the following: target rainfall information at the vehicle's current location, target road segment information on the road ahead, and wind direction information…”. See also [0044] regarding acquiring road images and [0046] regarding obtaining rainfall information from image and rain sensors of the vehicle, respectively.)
identify a road segment based on the environmental data; (See at least [0056-0057]: “S203. Collect road images of the road ahead and identify the information of the first road segment based on the road images…the vehicle can acquire road images of the road ahead based on a pre-set image sensor. Then, the road image is processed according to the pre-set road segment recognition model to obtain the information of the first road segment.”)
identify a risk associated with the road segment based on the environmental data, (See at least [n0110]: “If the first rainfall information and/or the second rainfall information indicate that the rainfall level is greater than or equal to the preset rainfall level, then the target rainfall information indicates that the rainfall level is greater than or equal to the preset rainfall level; if the first road segment information and/or the second road segment information indicate that the road ahead includes waterlogged sections, then the target road segment information indicates that the road ahead includes waterlogged sections” & [n0112]: “The prediction module is used to predict the driving condition as a water-crossing condition if the target rainfall information indicates that the rainfall level is greater than or equal to the preset rainfall level, or if the target road section information indicates that the road ahead includes waterlogged sections.” See also [0070] regarding determining waterlogged sections.)
wherein identifying the risk associated with the road segment comprises correlating fuel consumption data and battery consumption data to the road segment surface water data to identify a damage risk associated with damage to a hybrid electric vehicle (HEV); (See at least [0094-0095]: “In another embodiment, after detecting that the vehicle is in a water-crossing condition and obtaining the remaining power of the vehicle's power battery, the vehicle can also determine the target location from the road it is traveling on. Then, determine the power consumption required for the vehicle to travel from its current location to the target location, and control the vehicle's movement using a wading mode when the remaining power is greater than or equal to the power consumption. Otherwise, when the remaining battery power is less than the power consumption, the vehicle can control the engine to charge the battery until the remaining battery power is greater than or equal to the power consumption. The driving conditions corresponding to the aforementioned target locations are non-water-crossing conditions” & [0098-0099]: “In one embodiment, a correlation between driving range and remaining battery power is typically established within the vehicle. In other words, vehicles are usually equipped with a system that estimates the range the vehicle can continue to travel with its current remaining battery power. Based on this, after determining the target location, the vehicle can calculate the distance between the target location and the current location. Then, based on the above correlation, the required power consumption corresponding to the interval distance is determined. It should be noted that by using the above method to estimate power consumption, when the remaining power is less than the preset power but greater than the power consumption, the vehicle can drive directly in water conditions without stopping to charge the power battery, thus improving vehicle driving efficiency.”)
determine a driving mode selected from a battery priority mode and a fuel priority mode based on the risk; and (See at least [0117]: “If the remaining battery power is greater than or equal to the preset battery power, the vehicle's wading depth will be identified during driving; if the wading depth is greater than the preset wading depth, the vehicle will be controlled in wading mode” & [n0121]: “The fourth module is used to control the vehicle's movement in a wading mode if the remaining battery power is greater than or equal to the power consumption”. See also [0074] regarding the wading mode being a pure electric drive mode and [n0089-n0090] regarding an original driving mode being an engine driving mode, which is used when the wading depth is less than a preset wading depth.)
instruct the HEV to enforce the driving mode, wherein the instructing causes the HEV to change the driving mode. (See at least [n0101]: “…during driving, if the vehicle's wading depth is detected to be greater than the preset wading depth, or if the driver's command to activate the wading mode is received, the vehicle will be controlled to drive in the wading mode…”)
Jia does not explicitly teach:
…the vehicle data comprises vehicle speed, and the traffic data comprises near-vehicle proximity data…
identify a risk associated with the road segment based on the…vehicle data, and traffic data,
Lippman teaches:
…the vehicle data comprises vehicle speed, and the traffic data comprises near-vehicle proximity data… (See at least [0011]: “Data collectors 110 may include a variety of devices. For example, various controllers in a vehicle may operate as data collectors 110 to provide data 115 via the CAN bus, e.g., data 115 relating to vehicle speed, acceleration, etc.” & [0034]: “Further for example, additional collected data 115 could be used to monitor surrounding traffic, i.e., behavior of one or more other vehicles 101. In combination with precipitation rates and types, other vehicle 101 behavior, e.g., sudden turning acceleration, deceleration, skidding, braking, etc., can be used to determine hydroplane, water pooling and other possible conditions leading to an inconsistent coefficient of friction, i.e., situation where values for a coefficient of friction change significantly on a roadway at a small distance, e.g., foot by foot or yard by yard. In such conditions, as determined by all available data, coefficient of friction calculations may only be useful as a base factor for vehicle 101 control functions, such as maintaining constant speed, acceleration rates and braking rates.”)
identify a risk associated with the road segment based on the…vehicle data, and traffic data, (See at least [0020]: “Moreover, vehicle 101 speed can affect detection of a type and rate of precipitation. In one instance, vehicle 101 speed data would be included as a factor in determining a rate of snow fall” & [0035]: “Moreover, in conditions of high precipitation rates, behavior of one or more second vehicles 101 with respect to a roadway lane or lanes can be included as a factor in formulating a control action for a first vehicle 101. For example, where a precipitation condition has been determined and factored into a first vehicle 101 operation, it may also be determined that second vehicles in left and right lanes of a road with three lanes traveling in the same direction, are observed to vary speeds where a constant speed is normally expected. Moreover, it could be determined that vehicles 101 in the center lane have a constant or at least close to constant, consistent rate of travel, than vehicles 101 in surrounding lanes. From this it can be concluded that road conditions, in particular in left and right lanes, have factors causing changes in vehicle 101 control. Likewise, it can be concluded that a vehicle 101 in autonomous mode should be directed to travel in the center lane, and possibly also adding additional following distance from a lead vehicle 101 to compensate for unpredictable yet possible conditions where collected data 115 indicate possible occurrences of water pooling, hydroplane conditions, and, but not limited to, sudden snow covered surfaces.”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Jia’s method with Lippman’s technique of using vehicle data comprising vehicle speed and traffic data comprising near-vehicle proximity data to identify a risk associated with a road segment. Doing so would be obvious “to compensate for unpredictable yet possible conditions where collected data 115 indicate possible occurrences of water pooling, hydroplane conditions, and, but not limited to, sudden snow covered surfaces” (See [0035] of Lippman).
Claim(s) 3, 11, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jia in view of Lippman and further in view of Fields of US 20210309124 A1, filed 02/02/2018, hereinafter “Fields”.
Regarding claim 3, Jia and Lippman in combination teach all the limitations of claim 1 as discussed above.
Jia and Lippman in combination do not explicitly teach:
wherein the identifying the risk associated with the road segment based on the environmental data, vehicle data, and traffic data comprises comparing the road segment surface water data to the vehicle speed and the near-vehicle proximity data to identify a damage risk associated with damage to the HEV.
Fields teaches:
wherein the identifying the risk associated with the road segment based on the environmental data, vehicle data, and traffic data comprises comparing the road segment surface (See at least [0097]: “…to determine whether a particular external driving condition exists, such as an impending collision, the method may compare the received driving environment data with data covering the same weather and road conditions for the same speed at which the vehicle is traveling. The driving environment data may be analyzed to determine whether certain roads and/or intersections are prone to certain types of collision-causing hazards…”. See also [0094] regarding comparing driving environment data, such as objects adjacent to the vehicle and road/weather conditions, with vehicle operation data, such as speed.)
Although Fields does not explicitly teach comparing road segment surface water data to the vehicle speed and the near-vehicle proximity data, only comparing weather conditions, vehicle speed, and data regarding nearby objects, Jia teaches obtaining target road segment information which includes a depth of water on the road. Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to perform the teachings of Fields using the road segment surface water data taught by Jia to “determine whether an external driving condition exists that may be hazardous to the operation of the vehicle and/or a passenger's safety” (See [0094] of Fields).
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Jia and Lippman’s method with Fields’s technique of comparing the road segment surface water data to the vehicle speed and the near-vehicle proximity data to identify a damage risk associated with damage to the HEV. Doing so would be obvious to “determine whether an external driving condition exists that may be hazardous to the operation of the vehicle and/or a passenger's safety” (See [0094] of Fields).
Regarding claim 11, Jia and Lippman in combination teach all the limitations of claim 9 as discussed above.
Jia and Lippman in combination do not explicitly teach:
wherein the identifying the risk associated with the road segment based on the environmental data, vehicle data, and traffic data comprises comparing the road segment surface water data to the vehicle speed and the near-vehicle proximity data to identify a damage risk associated with damage to the HEV.
Fields teaches:
wherein the identifying the risk associated with the road segment based on the environmental data, vehicle data, and traffic data comprises comparing the road segment surface (See at least [0097]: “…to determine whether a particular external driving condition exists, such as an impending collision, the method may compare the received driving environment data with data covering the same weather and road conditions for the same speed at which the vehicle is traveling. The driving environment data may be analyzed to determine whether certain roads and/or intersections are prone to certain types of collision-causing hazards…”. See also [0094] regarding comparing driving environment data, such as objects adjacent to the vehicle and road/weather conditions, with vehicle operation data, such as speed.)
Although Fields does not explicitly teach comparing road segment surface water data to the vehicle speed and the near-vehicle proximity data, only comparing weather conditions, vehicle speed, and data regarding nearby objects, Jia teaches obtaining target road segment information which includes a depth of water on the road. Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to perform the teachings of Fields using the road segment surface water data taught by to “determine whether an external driving condition exists that may be hazardous to the operation of the vehicle and/or a passenger's safety” (See [0094] of Fields).
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Jia and Lippman’s method with Fields’s technique of comparing the road segment surface water data to the vehicle speed and the near-vehicle proximity data to identify a damage risk associated with damage to the HEV to “determine whether an external driving condition exists that may be hazardous to the operation of the vehicle and/or a passenger's safety” (See [0094] of Fields).
Regarding claim 19, Jia and Lippman in combination teach all the limitations of claim 17 as discussed above.
Jia and Lippman in combination do not explicitly teach:
wherein the identifying the risk associated with the road segment based on the environmental data, vehicle data, and traffic data comprises comparing the road segment surface water data to the vehicle speed and the near-vehicle proximity data to identify a damage risk associated with damage to the HEV.
Fields teaches:
wherein the identifying the risk associated with the road segment based on the environmental data, vehicle data, and traffic data comprises comparing the road segment surface (See at least [0097]: “…to determine whether a particular external driving condition exists, such as an impending collision, the method may compare the received driving environment data with data covering the same weather and road conditions for the same speed at which the vehicle is traveling. The driving environment data may be analyzed to determine whether certain roads and/or intersections are prone to certain types of collision-causing hazards…”. See also [0094] regarding comparing driving environment data, such as objects adjacent to the vehicle and road/weather conditions, with vehicle operation data, such as speed.)
Although Fields does not explicitly teach comparing road segment surface water data to the vehicle speed and the near-vehicle proximity data, only comparing weather conditions, vehicle speed, and data regarding nearby objects, Jia teaches obtaining target road segment information which includes a depth of water on the road. Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to perform the teachings of Fields using the road segment surface water data taught by Jia to “determine whether an external driving condition exists that may be hazardous to the operation of the vehicle and/or a passenger's safety” (See [0094] of Fields).
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Jia and Lippman’s method with Fields’s technique of comparing the road segment surface water data to the vehicle speed and the near-vehicle proximity data to identify a damage risk associated with damage to the HEV to “determine whether an external driving condition exists that may be hazardous to the operation of the vehicle and/or a passenger's safety” (See [0094] of Fields).
Regarding claim 20, Jia, Lippman, and Fields in combination each all the limitations of claim 19 as discussed above.
Jia additionally teaches:
wherein the receiving the environmental data comprises receiving an approximation of surface water present on the road segment. (See at least [0039]: “…The above target road segment information can be used to describe various information such as whether there is water accumulation on the road segment, the location of the water accumulation segment, or the depth of the water accumulation…”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20190077406 A1 is directed to estimating a level of water in real time for a vehicle and using the said estimation to perform one or more vehicle control strategies.
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/NIKKI MARIE M MOLINA/Examiner, Art Unit 3662
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