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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/26/2025 has been entered.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Response to Amendment
This action is in response to amendments and remarks filed on 12/26/2025. Claims 1-3 and 5-15 are considered in this office action. Claims 1 and 5 have been amended. Claim 4 has been cancelled. Claims 12-15 have been added. Claims 1-3 and 5-15 are pending examination. The 35 U.S.C. 112(b) rejections of claims 5, 7, 9, and 11 are withdrawn in light of the instant amendments.
Response to Arguments
Applicant presents the following arguments regarding the previous office action:
“…[T]he applied combination of the cited references, alone or in combination, fails to disclose or suggest each and every feature of amended independent claim 1…”
Applicant’s argument A. with respect to the independent claims pertains to newly added claim limitation amendments which have been considered and addressed as detailed below under Claim Rejections.
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 1-3 and 5-9 are rejected under 35 U.S.C. 103 as being unpatentable over Newman et al. (US 20210300352 A1; herein after referred to as Newman) in view of Herman et al. (US 20190080031 A1; herein after referred to as Herman) in further view of Yasui (US 20210300415 A1; herein after referred to as Yasui).
In regards to claim 1, Newman teaches:
A driver assistance apparatus for a vehicle (a collision mitigation system [0096]), the driver assistance apparatus comprising:
one or more processors (a computer or microcontroller [0103]); and
one or more memories communicably coupled to the one or more processors (transient memory such as RAM [0103]), wherein
the one or more memories stores predefined collision-related risk factors for mitigating issues arising from collisions between the vehicle and obstacles around the vehicle (the harm may be calculated by assigning values to various consequences, for example a predicted death may be assigned a numerical value, the expected damage may be estimated as a dollar figure or other value for each vehicle involved in the collision, the total harm may be calculated by multiplying the assigned value for a predicted death/crippling injury/non-crippling injury times the number of deaths/crippling injuries/non-crippling injuries times the probability and adding the vehicle damages at the end [0179]; the system could record in memory the collision details predicted for each avoidance sequence that failed to avoid a collision, and the system can rapidly evaluate the harm caused by each collision [0183]), and
the one or more processors are configured to:
acquire setting data regarding one or more predefined collision-related risk factors (when collision is unavoidable, a processor analyzes the collision and calculates the expected harm of the collision, and the processor then calculates the expected harm for each collision according to each sequence of actions [0111]);
detect the obstacles around the vehicle (sensor means includes external sensors configured to measure information about vehicles around the subject vehicle [0098]);
determine, based on a travel state of the vehicle, whether a collision between the vehicle and any of the detected obstacles is avoidable (the kinetic model determines of the collision is avoidable [0186] and Fig. 11 step 1104);
when the collision is determined to be avoidable, set a driving condition of the vehicle including a target track of the vehicle on a basis associated with the detected obstacles and the one or more predefined collision-related risk factors (processor selects the sequence with the least expected harm, prepares a corresponding strategy [0111]; if a collision is determined to be avoidable, then a collision-avoidance strategy is selected and implemented [0186] and Fig. 11 steps 1108 and 1109);
when the collision is determined to be unavoidable, adding a damage risk value corresponding to the one or more predefined collision-related risk factors, and set the driving condition of the vehicle including the target track (if the collision is not avoidable, the dynamic collision model analyzes the collisions, calculating the expected harm, then a minimum harm strategy is selected [0186] Fig. 11 steps 1105 and 1107);
control the vehicle based on the driving condition of the vehicle to travel along the target track (prepares a strategy including control signals and indirect mitigation steps and implements the strategy [0111]).
However, Newman does not explicitly teach predefined collision-related risk factors “selected from the predefined collision-related risk factors in the one or more memories”, the processors configured to “determine collision risks associated with the detected obstacles, wherein each collision risk is quantified as numerical values, with higher values indicating a greater risk of collision, wherein the numerical values are distributed around the vehicle based on factors including position, size, type, and relative speed of each obstacle; obtain a risk map that aggregates the collision risks associated with the detected obstacles and assigns a maximum risk value to a region overlapping a position of each detected obstacle and progressively smaller values to other regions as a distance from each detected obstacle increases”, determine whether a collision is avoidable “based on the risk map”, when a collision is unavoidable “update the obtained risk map by adding, to the collision risks associated with the detected obstacles,” a damage risk value “to generate an updated risk map”, and setting a driving condition including a target track of the vehicle “on a basis of the risk map.”
Herman teaches a tool for a client device to use to configure computational models for assessing risk. Herman teaches a user can specify weight values for several risk categories (e.g., financial risk, operational risk, environmental risk, social risk, legal risk, regulatory risk, etc.), and threshold values for each of several risk categories (Herman paragraph [0022]).
The tool from Herman in combination with the collision mitigation system in Newman as disclosed above would yield a system:
wherein the one or more processors are configured to: acquire setting data regarding one or more predefined collision-related risk factors (see above, Newman [0179], [0183]) selected from the predefined collision-related risk factors in the one or more memories (weight values and threshold values specified by user for each of several risk categories, Herman [0022]).
The combination of the tool defined by Herman and the system disclosed by Newman would be obvious due to the advantages to assessing risk that Herman provides to the system of Newman. Herman teaches using the weight values and threshold values to improve computational models, improving the collision avoidance system of Newman to cause less damage, both physically and financially. One of ordinary skill in the art would be able to include the technique of user specified risk and threshold values of Herman to improve the risk assessment system of Newman to yield a driver assistance apparatus more capable of reducing damage and harm.
However, the combination of Newman and Herman above does not explicitly teach the processors configured to “determine collision risks associated with the detected obstacles, wherein each collision risk is quantified as numerical values, with higher values indicating a greater risk of collision, wherein the numerical values are distributed around the vehicle based on factors including position, size, type, and relative speed of each obstacle; obtain a risk map that aggregates the collision risks associated with the detected obstacles and assigns a maximum risk value to a region overlapping a position of each detected obstacle and progressively smaller values to other regions as a distance from each detected obstacle increases; determine whether a collision is avoidable “based on the risk map”, when a collision is unavoidable “update the obtained risk map by adding, to the collision risks associated with the detected obstacles,” a damage risk value “to generate an updated risk map”, and setting a driving condition including a target track of the vehicle “on a basis of the risk map.”
From the same field of endeavor of vehicle control based on collision risk, Yasui teaches a processor configured to “determine collision risks associated with the detected obstacles, wherein each collision risk is quantified as numerical values (the risk region calculating part 144 calculates a risk region RA potentially distributed or present around the object recognized by the recognition part 130; the level of risk is treated as a quantitative index value referred to as a “risk potential p” [0061]), with higher values indicating a greater risk of collision (the risk region calculating part 144 increases the risk potential p as it approaches a region close to the preceding vehicle m1 that is one of an object [0065]), wherein the numerical values are distributed around the vehicle (Fig. 8 showing the risk region RA determined by the risk potential p) based on factors including position, size, type, and relative speed of each obstacle (the object recognition device 16 recognizes a position, a type, a speed, or the like, of the object [0033]; the risk region calculating part 144 calculates the risk region RA on the basis of a position of a type of the road marking line, or a position, a speed, an orientation, or the like, of another vehicle therearound [0099]); obtain a risk map that aggregates the collision risks associated with the detected obstacles and assigns a maximum risk value to a region overlapping a position of each detected obstacle and progressively smaller values to other regions as a distance from each detected obstacle increases (Fig. 8 showing risk region RA with the highest risk potential in a region overlapping a position of each detected obstacle with progressively smaller values as the distance from the obstacle increases); determine whether a collision is avoidable “based on the risk map (it would have been obvious to one of ordinary skill to determine whether a collision is avoidable, as taught by the combination of Newman and Herman, based on the risk map that aggregates collision risks taught by Yasui instead of based only on the kinematic model as taught by the combination of Newman and Herman in order to prevent the vehicle from entering a region where the risk of collision is too high (Yasui [0067]))”, setting a driving condition including a target track of the vehicle “on a basis of the risk map (Fig. 11 showing calculating a risk region (S102) which is then input into a rule based and a DNN model (S104) to acquire target trajectories from both models (S106), and after determining if host vehicle will accelerate after speed is equal to or smaller than a predetermined value to select a model target trajectory (S108, S110, S112), controlling the vehicle based on the selected target trajectory (S114))”, and when a collision is unavoidable “update the obtained risk map by adding, to the collision risks associated with the detected obstacles,” a damage risk value “to generate an updated risk map (it would have been obvious to one of ordinary skill in the art to, when a collision is unavoidable, update the risk map taught by Yasui by adding the collision harm value taught by the combination of Newman and Herman to the risk potential p taught by Yasui in order to have the risk map take into account all negative consequences of a collision (Newman [0029])).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to modify the teachings of the combination of Newman and Herman to incorporate the teachings of Yasui with a reasonable expectation of success to have the processors of the driver assistance apparatus taught by the combination of Newman and Herman determine collision risks associated with the detected obstacles and quantified as numerical values which are distributed around the vehicle based on various factors of each obstacle with higher values indicating a greater risk of collision; obtain a risk map aggregating collision risk associated with the detected obstacles and assigns a maximum risk value at the location of each obstacle and progressively smaller risk values to other locations as the distance from each obstacle increases; determine whether a collision is avoidable as taught by the combination of Newman and Herman based on the risk map taught by Yasui, set a target track of the vehicle on a basis of the risk map as taught by Yasui, and when a collision is unavoidable update the obtained risk map taught by Yasui by adding, to the collision risks associated with the detected obstacles, a damage risk value taught by the combination of Newman and Herman to generate an updated risk map.
The motivation for doing so would be to smoothly control driving of a vehicle by generating a target trajectory appropriate for surrounding circumstances (Yasui [0012]), prevent the vehicle from entering a region where the risk of collision is too high (Yasui [0067]), and have the risk map take into account all negative consequences of a collision (Newman [0029]).
In regards to claim 2, the combination of Newman, Herman, and Yasui teaches the claimed invention substantially as claimed as set forth for claim 1 above, and further teaches “wherein the predefined collision-related risk factors include a penal damage risk for a driver who drives the vehicle, a financial damage risk for the driver (Herman, the user may specify types of external data (e.g., news articles, disaster alerts, compliance checks and/or reports, risk data associated with geographical regions, etc.) to be used by computational models, weight values for several risk categories (e.g., financial risk, operational risk, environmental risk, social risk, legal risk, regulatory risk, etc.), threshold values (e.g., a low threshold, a high threshold value) for each of the several risk categories, field definitions, etc. [0022]), a physical damage risk for an opposite party of the collision, and a physical damage risk for the vehicle (Newman, system calculates “harm” of collision, places high value on saving lives, a lower but still high value on preventing injuries, and also a value on any physical damage caused by the collision [0029]).
In regards to claim 3, the combination of Newman, Herman, and Yasui teaches the claimed invention substantially as claimed as set forth for claim 1 above, and further teaches “the collision risks are each set (Yasui, risk region calculating part 144 calculates risk region RA [0061]), for a relevant one of the obstacles (Yasui risk region potentially distributed or present around the object recognized by the recognition part 130 [0061]), to take a maximum value within a range (Yasui, the risk region calculating part 144 increases the risk potential p as it approaches a region close to the preceding vehicle m1 [0065] or lane markings [0063]- [0064]) superposed on a location of the relevant one of the obstacles (risk potential distributed around relevant obstacles [0061], Figs. 6-8), and to take a smaller value as goes farther from the relevant one of the obstacles (risk region calculating part decreases the risk potential p as it approaches a region far from the preceding vehicle m1 [0065]), and on a basis of the one or more predefined collision-related risk factors selected (Herman, the user may specify weight values for several risk categories (e.g., financial risk, operational risk, environmental risk, social risk, legal risk, regulatory risk, etc.), threshold values (e.g., a low threshold, a high threshold value) for each of the several risk categories, field definitions, etc. [0022]), the processors are configured to add a risk value based on the one or more predefined collision-related risk factors (Herman, see equation (2), Rc is the sum of boost (a weight value associated with incident type) times probability times the impact of the occurrence of the incident, all divided by percentage of risk mitigated m [0033]) to the maximum value of the collision risk within the range (Herman, all risk categories summed with other risk categories [0033]) superposed on the location of the relevant one of the obstacles (Herman, risk information associated with individual entities [0037]).
In regards to claim 5, Newman teaches:
A non-transitory computer-readable recording medium (computing means also includes non-transient storage media such as solid-state drives [0103]) containing a program applicable to a driver assistance apparatus for a vehicle (computing means further includes instructions stored on the non-transient media specifying how collisions should be mitigated [0103]) the program, when executed by one or more processors, causing the one or more processors (instructions may be copied to the transient media when the system starts, or at other times, so that the instructions will be instantly available to the processor when needed [0103]) to be configured to:
acquire setting data regarding one or more predefined collision-related risk factors (when collision is unavoidable, a processor analyzes the collision and calculates the expected harm of the collision [0111]) in one or more memories, wherein the one or more memories store the predefined collision-related risk factors for mitigating issues arising from collisions between the vehicle and obstacles around the vehicle (the harm may be calculated by assigning values to various consequences, for example a predicted death may be assigned a numerical value, the expected damage may be estimated as a dollar figure or other value for each vehicle involved in the collision, the total harm may be calculated by multiplying the assigned value for a predicted death/crippling injury/non-crippling injury times the number of deaths/crippling injuries/non-crippling injuries times the probability and adding the vehicle damages at the end [0179]; the system could record in memory the collision details predicted for each avoidance sequence that failed to avoid a collision, and the system can rapidly evaluate the harm caused by each collision [0183])
detect obstacles around the vehicle (sensor means includes external sensors configured to measure information about vehicles around the subject vehicle [0098]);
determine, based on a travel state of the vehicle, whether a collision between the vehicle and any of the detected obstacles is avoidable (the kinetic model determines of the collision is avoidable [0186] and Fig. 11 step 1104);
when the collision is determined to be avoidable, set a driving condition of the vehicle including a target track of the vehicle on a basis associated with the detected obstacles and the one or more predefined collision-related risk factors (processor selects the sequence with the least expected harm, prepares a corresponding strategy [0111]; if a collision is determined to be avoidable, then a collision-avoidance strategy is selected and implemented [0186] and Fig. 11 steps 1108 and 1109);
when the collision is determined to be unavoidable, adding a damage risk value corresponding to the one or more predefined collision-related risk factors, and set the driving condition of the vehicle including the target track (if the collision is not avoidable, the dynamic collision model analyzes the collisions, calculating the expected harm, then a minimum harm strategy is selected [0186] Fig. 11 steps 1105 and 1107);
control the vehicle based on the driving condition of the vehicle to travel along the target track (prepares a strategy including control signals and indirect mitigation steps and implements the strategy [0111]).
However, Newman does not explicitly teach predefined collision-related risk factors “selected from the predefined collision-related risk factors in the one or more memories”, the processors configured to “determine the collision risks associated with the detected obstacles, wherein each collision risk is quantified as numerical values, with higher values indicating a greater risk of collision, wherein the numerical values are distributed around the vehicle based on factors including position, size, type, and relative speed of each obstacle; obtain a risk map that aggregates the collision risks associated with the detected obstacles and assigns a maximum risk value to a region overlapping a position of each detected obstacle and progressively smaller values to other regions as a distance from each detected obstacle increases; and setting a driving condition including a target track of the vehicle “on a basis of the collision risks.”
Herman teaches a tool for a client device to use to configure computational models for assessing risk. Herman teaches a user can specify weight values for several risk categories (e.g., financial risk, operational risk, environmental risk, social risk, legal risk, regulatory risk, etc.), and threshold values for each of several risk categories (Herman paragraph [0022]).
The tool from Herman in combination with the collision mitigation system in Newman as disclosed above would yield a system where the one or more processors are caused to:
acquire setting data regarding one or more predefined collision-related risk factors (see above, Newman [0179], [0183]) selected from the predefined collision-related risk factors in the one or more memories (weight values and threshold values specified by user for each of several risk categories, Herman [0022]).
The combination of the tool defined by Herman and the system disclosed by Newman would be obvious due to the advantages to assessing risk that Herman provides to the system of Newman. Herman teaches using the weight values and threshold values to improve computational models, improving the collision avoidance system of Newman to cause less damage, both physically and financially. One of ordinary skill in the art would be able to include the technique of user specified risk and threshold values of Herman to improve the risk assessment system of Newman to yield a driver assistance apparatus more capable of reducing damage and harm.
However, the combination of Newman and Herman above does not explicitly teach the processors configured to “determine the collision risks associated with the detected obstacles, wherein each collision risk is quantified as numerical values, with higher values indicating a greater risk of collision, wherein the numerical values are distributed around the vehicle based on factors including position, size, type, and relative speed of each obstacle; obtain a risk map that aggregates the collision risks associated with the detected obstacles and assigns a maximum risk value to a region overlapping a position of each detected obstacle and progressively smaller values to other regions as a distance from each detected obstacle increases; and setting a driving condition including a target track of the vehicle “on a basis of the collision risks.”
From the same field of endeavor of vehicle control based on collision risk, Yasui teaches a processor configured to “determine the collision risks associated with the detected obstacles, wherein each collision risk is quantified as numerical values (the risk region calculating part 144 calculates a risk region RA potentially distributed or present around the object recognized by the recognition part 130; the level of risk is treated as a quantitative index value referred to as a “risk potential p” [0061]), with higher values indicating a greater risk of collision (the risk region calculating part 144 increases the risk potential p as it approaches a region close to the preceding vehicle m1 that is one of an object [0065]), wherein the numerical values are distributed around the vehicle (Fig. 8 showing the risk region RA determined by the risk potential p) based on factors including position, size, type, and relative speed of each obstacle (the object recognition device 16 recognizes a position, a type, a speed, or the like, of the object [0033]; the risk region calculating part 144 calculates the risk region RA on the basis of a position of a type of the road marking line, or a position, a speed, an orientation, or the like, of another vehicle therearound [0099]); obtain a risk map that aggregates the collision risks associated with the detected obstacles and assigns a maximum risk value to a region overlapping a position of each detected obstacle and progressively smaller values to other regions as a distance from each detected obstacle increases (Fig. 8 showing risk region RA with the highest risk potential in a region overlapping a position of each detected obstacle with progressively smaller values as the distance from the obstacle increases); determine whether a collision is avoidable “based on the risk map (it would have been obvious to one of ordinary skill to determine whether a collision is avoidable, as taught by the combination of Newman and Herman, based on the risk map that aggregates collision risks taught by Yasui instead of based only on the kinematic model as taught by the combination of Newman and Herman in order to prevent the vehicle from entering a region where the risk of collision is too high (Yasui [0067]))”, setting a driving condition including a target track of the vehicle “on a basis of the risk map (Fig. 11 showing calculating a risk region (S102) which is then input into a rule based and a DNN model (S104) to acquire target trajectories from both models (S106), and after determining if host vehicle will accelerate after speed is equal to or smaller than a predetermined value to select a model target trajectory (S108, S110, S112), controlling the vehicle based on the selected target trajectory (S114))”, and when a collision is unavoidable “update the obtained risk map by adding, to the collision risks associated with the detected obstacles,” a damage risk value “to generate an updated risk map (it would have been obvious to one of ordinary skill in the art to, when a collision is unavoidable, update the risk map taught by Yasui by adding the collision harm value taught by the combination of Newman and Herman to the risk potential p taught by Yasui in order to have the risk map take into account all negative consequences of a collision (Newman [0029])).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to modify the teachings of the combination of Newman and Herman to incorporate the teachings of Yasui with a reasonable expectation of success to have the processors of the driver assistance apparatus taught by the combination of Newman and Herman determine collision risks associated with the detected obstacles and quantified as numerical values which are distributed around the vehicle based on various factors of each obstacle with higher values indicating a greater risk of collision; obtain a risk map aggregating collision risk associated with the detected obstacles and assigns a maximum risk value at the location of each obstacle and progressively smaller risk values to other locations as the distance from each obstacle increases; determine whether a collision is avoidable as taught by the combination of Newman and Herman based on the risk map taught by Yasui, set a target track of the vehicle on a basis of the risk map as taught by Yasui, and when a collision is unavoidable update the obtained risk map taught by Yasui by adding, to the collision risks associated with the detected obstacles, a damage risk value taught by the combination of Newman and Herman to generate an updated risk map.
The motivation for doing so would be to smoothly control driving of a vehicle by generating a target trajectory appropriate for surrounding circumstances (Yasui [0012]), prevent the vehicle from entering a region where the risk of collision is too high (Yasui [0067]), and have the risk map take into account all negative consequences of a collision (Newman [0029]).
In regards to claim 6, the combination of Newman, Herman, and Yasui teaches the claimed invention substantially as claimed as set forth for claim 1 above, and further teaches “wherein the one or more processors are further configured to:
acquire surrounding environment data around the vehicle (Newman, external sensors are configured to measure information about vehicles around the subject vehicle and may acquire data on the road condition and other information that is not directly related to traffic);
estimate a position of an occupant in another vehicle that is one of the obstacles, based on the surrounding environment data (Newman, the occupation of the other cars may be estimated (e.g., as 1.5 per vehicle) and the estimated number of occupants may be modified by a determination of the character of the other vehicle, for example a truck vs. a minivan [0055] and [0179]); and
determine one of the collision risks for the another vehicle based on the position of an occupant in the another vehicle (Newman, the number of fatalities and injuries can be estimated, and the amount of property damage can be estimated [0055]).
However, while Newman teaches estimating the presence and number of occupants in another vehicle, Newman does not explicitly teach estimating a “position” of an occupant in another vehicle. Newman does teach, along with estimating the number of other occupants, determining where on each vehicle the contact will occur, analyzing the collision, and calculating the physical distortions of each vehicle due to the forces of the collision, including frame compression, penetration into the passenger compartment, and the like. With these collision parameters and assumptions, the number of fatalities and injuries is estimated (Newman [0055]). It would have been obvious to one of ordinary skill in the art to use an estimated “position” for each of the estimated occupants of another vehicle in combination with the calculated physical distortions of the vehicle when determining the number of fatalities and injuries as taught by Newman in order to aid in minimizing the amount of harm caused by a potential collision.
In regards to claim 7, the combination of Newman, Herman, and Yasui teaches the claimed invention substantially as claimed as set forth for claim 5 above, and further teaches “wherein the one or more processors are further configured to:
acquire surrounding environment data around the vehicle (Newman, external sensors are configured to measure information about vehicles around the subject vehicle and may acquire data on the road condition and other information that is not directly related to traffic);
estimate a position of an occupant in another vehicle that is one of the obstacles, based on the surrounding environment data (Newman, the occupation of the other cars may be estimated (e.g., as 1.5 per vehicle) and the estimated number of occupants may be modified by a determination of the character of the other vehicle, for example a truck vs. a minivan [0055] and [0179]); and
determine one of the collision risks for the another vehicle based on the position of an occupant in the another vehicle (Newman, the number of fatalities and injuries can be estimated, and the amount of property damage can be estimated [0055]).
However, while Newman teaches estimating the presence and number of occupants in another vehicle, Newman does not explicitly teach estimating a “position” of an occupant in another vehicle. Newman does teach, along with estimating the number of other occupants, determining where on each vehicle the contact will occur, analyzing the collision, and calculating the physical distortions of each vehicle due to the forces of the collision, including frame compression, penetration into the passenger compartment, and the like. With these collision parameters and assumptions, the number of fatalities and injuries is estimated (Newman [0055]). It would have been obvious to one of ordinary skill in the art to use an estimated “position” for each of the estimated occupants of another vehicle in combination with the calculated physical distortions of the vehicle when determining the number of fatalities and injuries as taught by Newman in order to aid in minimizing the amount of harm caused by a potential collision.
In regards to claim 8, the combination of Newman, Herman, and Yasui teaches the claimed invention substantially as claimed as set forth for claim 1 above, and further teaches “wherein obtaining the risk map includes, when the detected obstacles include a first obstacle and a second obstacle:
assigning a first maximum risk value of the maximum risk value to a first region of the region surrounding a first outer periphery of the first obstacle, and progressively smaller values as a distance from the first obstacle increases to a first peripheral region surrounding the first region (Yasui, risk region calculating part 144 calculates risk region RA, risk region potentially distributed or present around the object recognized by the recognition part 130 [0061]; the risk region calculating part 144 increases the risk potential p as it approaches a region close to the preceding vehicle m1 [0065]; risk potential distributed around relevant obstacles [0061], Figs. 6-8; risk region calculating part decreases the risk potential p as it approaches a region far from the preceding vehicle m1 [0065]);
assigning a second maximum risk value of the maximum risk value to a second region of the region surrounding a second outer periphery of the second obstacle, and progressively smaller values as a distance from the second obstacle increases to a second peripheral region surrounding the second region (Yasui, risk region calculating part 144 calculates risk region RA, risk region potentially distributed or present around the object recognized by the recognition part 130 [0061]; the risk region calculating part 144 increases the risk potential p as it approaches a region close to lane markings [0063]- [0064]; risk potential distributed around relevant obstacles [0061], Figs. 6-8; risk region calculating part decreases the risk potential p as it approaches a region far from the preceding vehicle m1 [0065]); and
assigning, based on determining that the first peripheral region and the second peripheral region overlap, a risk value into which risk values at the first peripheral region and the second peripheral region are integrated to an overlap region between the first peripheral region and the second peripheral region (Yasui Fig. 8 shows integrating the risk values for the vehicle with the risk values for the lane markings).
In regards to claim 9, the combination of Newman, Herman, and Yasui teaches the claimed invention substantially as claimed as set forth for claim 5 above, and further teaches “wherein obtaining the risk map includes, when the detected obstacles include a first obstacle and a second obstacle:
assigning a first maximum risk value of the maximum risk value to a first region of the region surrounding a first outer periphery of the first obstacle, and progressively smaller values as a distance from the first obstacle increases to a first peripheral region surrounding the first region (Yasui, risk region calculating part 144 calculates risk region RA, risk region potentially distributed or present around the object recognized by the recognition part 130 [0061]; the risk region calculating part 144 increases the risk potential p as it approaches a region close to the preceding vehicle m1 [0065]; risk potential distributed around relevant obstacles [0061], Figs. 6-8; risk region calculating part decreases the risk potential p as it approaches a region far from the preceding vehicle m1 [0065]);
assigning a second maximum risk value of the maximum risk value to a second region of the region surrounding a second outer periphery of the second obstacle, and progressively smaller values as a distance from the second obstacle increases to a second peripheral region surrounding the second region (Yasui, risk region calculating part 144 calculates risk region RA, risk region potentially distributed or present around the object recognized by the recognition part 130 [0061]; the risk region calculating part 144 increases the risk potential p as it approaches a region close to lane markings [0063]- [0064]; risk potential distributed around relevant obstacles [0061], Figs. 6-8; risk region calculating part decreases the risk potential p as it approaches a region far from the preceding vehicle m1 [0065]); and
assigning, based on determining that the first peripheral region and the second peripheral region overlap, a risk value into which risk values at the first peripheral region and the second peripheral region are integrated to an overlap region between the first peripheral region and the second peripheral region (Yasui Fig. 8 shows integrating the risk values for the vehicle with the risk values for the lane markings).
In regards to claim 12, the combination of Newman, Herman, and Yasui teaches the claimed invention substantially as claimed as set forth for claim 1 above, and further teaches “wherein when one of the predefined collision-related risk factors is set as a physical damage risk for an opposite party of a collision, even within a region overlapping a position of a relevant one of the obstacles, a risk value added, in generating the updated risk map, to a portion of the region where greater physical damage to the opposite party is expected is set higher than a risk value added to another potion of the region where less physical damage to the opposite party is expected (the “harm” of a collision (death, injuries, physical damage, etc.) may be analyzed or quantified according to a valuation scheme (i.e., greater injuries to the opposite party has a higher value than less injuries to the opposite party), Newman [0029]).”
In regards to claim 13, the combination of Newman, Herman, and Yasui teaches the claimed invention substantially as claimed as set forth for claim 1 above, and further teaches “wherein when one of the predefined collision-related risk factors is set as a physical damage risk for the vehicle, even within a region overlapping a position of a relevant one of the obstacles, a risk value added, in generating the updated risk map, is adjusted so as to reduce an expected physical damage to the vehicle (the “harm” of a collision (death, injuries, physical damage, etc.) may be analyzed or quantified according to a valuation scheme (i.e., greater physical damage has a higher value than less physical damage), Newman [0029]).”
In regards to claim 14, the combination of Newman, Herman, and Yasui teaches the claimed invention substantially as claimed as set forth for claim 5 above, and further teaches “wherein when one of the predefined collision-related risk factors is set as a physical damage risk for an opposite party of a collision, even within a region overlapping a position of a relevant one of the obstacles, a risk value added, in generating the updated risk map, to a portion of the region where greater physical damage to the opposite party is expected is set higher than a risk value added to another potion of the region where less physical damage to the opposite party is expected (the “harm” of a collision (death, injuries, physical damage, etc.) may be analyzed or quantified according to a valuation scheme (i.e., greater injuries to the opposite party has a higher value than less injuries to the opposite party), Newman [0029]).”
In regards to claim 15, the combination of Newman, Herman, and Yasui teaches the claimed invention substantially as claimed as set forth for claim 5 above, and further teaches “wherein when one of the predefined collision-related risk factors is set as a physical damage risk for the vehicle, even within a region overlapping a position of a relevant one of the obstacles, a risk value added, in generating the updated risk map, is adjusted so as to reduce an expected physical damage to the vehicle (the “harm” of a collision (death, injuries, physical damage, etc.) may be analyzed or quantified according to a valuation scheme (i.e., greater physical damage has a higher value than less physical damage), Newman [0029]).”
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Newman et al. (US 20210300352 A1; herein after referred to as Newman) in view of Herman et al. (US 20190080031 A1; herein after referred to as Herman), in view of Yasui (US 20210300415 A1; herein after referred to as Yasui), and further in view of Wunderlich (US 20200014759 A1; herein after referred to as Wunderlich).
In regards to claim 10, the combination of Newman, Herman, and Yasui teaches the claimed invention substantially as claimed as set forth for claim 8 above, however the combination of Newman, Herman, and Yasui does not explicitly teach “wherein obtaining the risk map includes generating the risk map in which levels of the collision risk are indicated as contours on a two-dimensional plane.”
From the same field of endeavor of determining a predicted likelihood of collision, Wunderlich teaches “wherein obtaining the risk map includes generating the risk map in which levels of the collision risk are indicated as contours on a two-dimensional plane (creating and maintaining a collective uncertainty contour map to determine a predicted likelihood of collision for a given location represented by the map [0010]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to modify the teachings of the combination of Newman, Herman, and Yasui to incorporate the teachings of Wunderlich with a reasonable expectation of success to have the risk map collision risk levels taught by the combination of Newman, Herman, and Yasui are indicated as contours on a two-dimensional plane as taught by Wunderlich.
The motivation for doing so would be to determine a predicted likelihood of collision for a given location represented by the map (Wunderlich [0010]).
In regards to claim 11, the combination of Newman, Herman, and Yasui teaches the claimed invention substantially as claimed as set forth for claim 9 above, however the combination of Newman, Herman, and Yasui does not explicitly teach “wherein obtaining the risk map includes generating the risk map in which levels of the collision risk are indicated as contours on a two-dimensional plane.”
From the same field of endeavor of determining a predicted likelihood of collision, Wunderlich teaches “wherein obtaining the risk map includes generating the risk map in which levels of the collision risk are indicated as contours on a two-dimensional plane (creating and maintaining a collective uncertainty contour map to determine a predicted likelihood of collision for a given location represented by the map [0010]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to modify the teachings of the combination of Newman, Herman, and Yasui to incorporate the teachings of Wunderlich with a reasonable expectation of success to have the risk map collision risk levels taught by the combination of Newman, Herman, and Yasui are indicated as contours on a two-dimensional plane as taught by Wunderlich.
The motivation for doing so would be to determine a predicted likelihood of collision for a given location represented by the map (Wunderlich [0010]).
Conclusion
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/K.M.F./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665