fNotice 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 .
Response to Arguments
Applicant’s arguments with respect to claims 1-22 have been considered but are found unpersuasive. The examiner believes that Nave does teach the use of determining types and locations of injuries and positions of passengers prior to, during, and after collisions, as well as the use of preventative seat action. The arguments cite that reconstruction and injury estimate do not account to predicting a location within a vehicle after a collision based on a current position. The examiner does believe that Nave cites elements that indicate this. For example, Nave cites that skeletal positioning can be used, as well as the occupants location relative to parts of the vehicle, which seems fairly particular on location of the occupant:
(Page 29, Column 31, Lines 18) (132) The one or more processors may be further programmed to: determine a position and a direction of facing of at least one occupant of the vehicle before, during, and/or after the vehicle collision based upon the internal data; determine occupant skeletal positioning for the at least one occupant before, during, and/or after the vehicle collision based upon the internal data; and/or determine a size of the at least one occupant based upon the internal data.
It also teaches that areas of impact may be considered by considering relative distance of the occupant and their body to particular structural parts of the vehicle:
Page 26, Column 25, Lines 21 to 34, (103) In some embodiments, AM server 206 may be able to determine one or more potential injuries to one or more occupants of the vehicle based upon the scenario model. For example, in the rear-end accident example AM server 206 may determine that there is a 40% chance that driver 115 may have incurred a minor neck injury. In some embodiments, AM server 206 may determine potential injuries have occurred to an occupant based upon the occupant's position relative to vehicle 100 and/or the support structures of vehicle 100. AM server 206 may then transmit the one or more potential injuries to user device 204 for confirmation by user 202. Based upon which injuries that user 202 indicates where incurred, AM server 206 then may update the scenario model.
The teaching of using the present location of the occupants to determine the future location is also provided:
Page 27 Column 28 Line 42 to Page 28 Column 29 Line 20 ((122) Vehicle computer device 110 determines 920 positional information for at least one occupant of vehicle 100. Positional information may include a position of an occupant, a direction of facing of the occupant, a size of the occupant, and/or a skeletal positioning of the occupant. The position of the occupant may include which seat the occupant occupies. The direction of facing of the occupant may include whether the occupant is facing forward, reaching forward, reaching to the side, and/or has his/her head turned. The size of the occupant may determine whether the occupant is an adult or a child. The size of the occupant may also include the occupant's height. The skeletal positioning may include positioning of the occupant's joints, spine, arms, legs, torso, neck face, head, major bones, hands, and/or feet. In some embodiments, the internal sensors 105 constantly transmit sensor data to vehicle computer device 110, which constantly determines 920 the positional information of the occupants. In other embodiments, vehicle computer device 110 transmits the internal sensor data to AM server 415, which determines 920 the positional information and transmits that information to vehicle computer device 110.
(123) In some embodiments, vehicle computer device 110 generates a scenario model of the potential vehicular crash based upon the received external and/or internal sensor data. Scenario models may predict damage to vehicle 100 and injuries that may be experiences by driver 115 and passengers 120 of vehicle 100. In the exemplary embodiment, vehicle computer device 110 accesses a database, such as database 202 (shown in FIG. 2). Database 202 may contain a plurality of crash scenarios and the sensor data associated with these crash scenarios. The scenarios may be based upon information from vehicle crash testing facilities, from past crashes that AM server 415 has analyzed, and/or from other sources that allow vehicle computer device 110 to operate as described herein. Vehicle computer device 110 compares the received sensor data with the different stored crash scenarios to generate a scenario model that is the most likely match for the imminent vehicular crash. In some further embodiments, vehicle computer device 110 may communicate the sensor data to AM server 415, where AM server 415 may generate the scenario model. In the some of these embodiments, vehicle computer device 110 determines one or more potential injuries to one or more occupants of vehicle 100 based upon the positional information and the scenario model. Vehicle computer device 110 may also determine a severity for each potential injury.
Additionally, under an abundance of caution, while the examiner notes that the elements for preventative actions as well as post accident analysis appear to be for separate embodiments, there are sections of the specification which indicate that Nave did consider combining the two embodiments, which at the very least provides a strong rational for combining the two:
Page 28 Column 30 Lines 13 to Page 29 Column 31 Line 8
(128) In other embodiments, vehicle computer device 110 may be able to determine an advantageous position for the at least one occupant. Vehicle computer device 110 may cause a seat to shift or move, such as adjusting the recline angle of the seat, to cause the occupant to change to the advantageous position. Vehicle computer device 110 may also rotate the seat of occupant to cause the occupant to change to the advantageous position or advantageous facing.
(129) In yet other embodiments, vehicle computer device 110, in addition to or alternatively to detecting a potential or actual vehicle collision, may also reconstruct a vehicle collision. For instance, vehicle computer device 110 may use the internal sensor data and/or external sensor data generated and/or collected to reconstruct passenger position within the vehicle, as well as vehicle speed and direction, prior to, during, and after a vehicle collision or impact. The vehicle computer device 110 may include additional, less, or alternate functionality, including that discussed elsewhere herein, to reconstruct vehicle collisions.
(130) In some further embodiments, sensors 105 may detect one or more loose objects in the passenger cabin of vehicle 100. Examples of loose objects include, but are not limited to, mobile electronics, purses and other bags, toys, tissue boxes, trash, and other objects in the vehicle that would move during a vehicular collision. In these embodiments, AM server 415 may include the one or more loose objects in the model scenario and may predict one or more injuries based upon potential trajectories of the one or more loose objects.
(131) For instance, in one embodiment, a computer system for reconstructing a vehicle collision may be provided. The computer system may include one or more processors, sensors, and/or transceivers in communication with at least one memory device. The one or more processors, sensors, and/or transceivers may be programmed or otherwise configured to: (1) receive occupant data from at least one internal sensor, the occupant data being generated or collected before, during, and/or after the vehicle collision; (2) receive external data from the at least one external sensor, the external data being generated or collected before, during, and/or after the vehicle collision; (3) determine positional information for at least one occupant of a vehicle before, during, and/or after the vehicle collision; and/or (4) generate a virtual reconstruction of the vehicle crash, the virtual reconstruction indicating a severity of vehicle damage and a severity of a potential injury to the at least one occupant caused by the vehicle collision. The severity of vehicle damage and a severity of a potential injury to the at least one occupant caused by the vehicle collision may be determined or estimated from processor analysis performed by the one or more processors of (i) the occupant data being generated or collected before, during, and/or after the vehicle collision; (ii) the external data being generated or collected before, during, and/or after the vehicle collision; and/or (iii) the positional information for at least one occupant of a vehicle before, during, and/or after the vehicle collision.
(132) The one or more processors may be further programmed to: determine a position and a direction of facing of at least one occupant of the vehicle before, during, and/or after the vehicle collision based upon the internal data; determine occupant skeletal positioning for the at least one occupant before, during, and/or after the vehicle collision based upon the internal data; and/or determine a size of the at least one occupant based upon the internal data.
Page 30, Column 34, Line 59 to Page 31, Column 34, Line 4 (154) Below are some examples of autonomous or semi-autonomous vehicle-related functionality or technology replacing human driver action when AM server 415 determines that a vehicular crash is imminent. AM server 415 generates a scenario model of the potential vehicular crash and determines at least one potential injury to at least one occupant of vehicle 100 based upon the scenario model. In a first example, AM server 415 may determine that the occupant is facing the wrong direction and that the angle of impact may cause serious injury to the occupant. AM server 415 may sound a noise, such as a horn, to attract the attention of the occupant and entice the occupant to change their direction of facing, such as towards the sounds.
It is noted that the reconstruction can occur before the accident occurs. Additionally, the element in which a horn is used to move the occupant can utilize the scenario models. This is definitely a corrective action taken before the collision occurs, that utilizes the skeletal models. While it is not moving a seat specifically, it meets the other criteria. While the moving of the seat is introduced independent of these elements, the examiner believes that as a protective measure before a crash, it would be obvious to combine with the other embodiment of sounding a horn after the positional analysis to reach the claim limitations.
The examiner also notes that the argument that Nave does not teach predicting a resulting location of the occupant after the collision unpersuasive. The examiner believes that Nave’s skeletal model that tracks the location of the person’s body before and after the collision address this limitation. As shown in the citations it can be performed before a collision occurs. Please refer to the rejections below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 5-9, 12-16, 19, 20, and 21 are rejected under 35 U.S.C. 103 as being unpatentable in light of Hashimoto et al (US Pub 2011/0221247 A1), hereafter known as Hashimoto in light of Nave et al (US Pub 10,106,156 B1 A1), hereafter known as Nave, in light of Breed et al (US Pub 9,102,220 B2), hereafter known as Breed.
For Claim 1, Hashimoto teaches A method, comprising:
Predicting a future occurrence of a collision by a transport; ([0008] Thus, one aspect of the invention relates to an occupant protection device that includes a seat belt that restrains an occupant, an adjusting device that relatively adjusts a positional relationship between the seat belt and the occupant, a collision predicting portion that predicts a collision, and a control portion that controls the adjusting device when the collision is predicted by the collision predicting portion. The control portion first operates the adjusting device at a first speed to restrain the occupant by the seat belt, and then operates the adjusting device to increase the distance between the seat belt and the occupant at a second speed that is slower than the first speed.)
determining a position of the seat of the occupant of a transport and a current driving environment of the transport based on the captured sensor data; ([0030], [0034], [0037-0043], [0047-0056])
determining a prediction of a transport being involved in a collision based on a current driving environment of the vehicle; and ([0008], [0010-0012], [0044-0046], [0063])
modifying the current position of the occupant within the seat of the transport by modifying a configuration of the seat before the collision occurs based on the position of the seat of the transport ([0008], [0010-0012], [0044-0046], [0063]) Hashimoto does not teach predicting a resulting location of an occupant within the transport after the collision occurs based on a current position of the occupant within the transport and;
modifying the current position of the occupant within the seat of the transport by modifying a configuration of the seat before the collision occurs based on the predicted resulting location of the occupant within the transport after the collision.
Nave, however, does teach predicting a resulting location of an occupant within a transport after the collision occurs based on a current position of the occupant within the transport ( Page 22, Column 18, Lines 11 to 47 “In one embodiment, the received sensor data may include sensor data from occupant position sensors that identify a location and/or a position of each occupant within vehicle 100 prior to or at the time of the vehicular crash. The location and/or position of each occupant may be used to determine what forces were applied to an occupant's joints and skeletal structure during the crash and identify any corresponding potential injuries.”
Page 29, Column 31, Lines 40 to 49, (135) FIG. 11 illustrates a flow chart of an exemplary computer-implemented process of estimating an extent of injury to vehicle occupants resulting from a vehicle collision 1100. The method 1100 may include generating and collecting sensor data regarding vehicle occupant positioning prior to, during, and/or after a vehicle collision from one or more occupant position sensors 1102. The one or more occupant position sensors may be in-cabin vehicle-mounted sensors and/or mobile-device (e.g., wearables, smart phone, etc.) sensors.
Page 27 Column 28 Line 42 to Page 28 Column 29 Line 20 ((122) Vehicle computer device 110 determines 920 positional information for at least one occupant of vehicle 100. Positional information may include a position of an occupant, a direction of facing of the occupant, a size of the occupant, and/or a skeletal positioning of the occupant. The position of the occupant may include which seat the occupant occupies. The direction of facing of the occupant may include whether the occupant is facing forward, reaching forward, reaching to the side, and/or has his/her head turned. The size of the occupant may determine whether the occupant is an adult or a child. The size of the occupant may also include the occupant's height. The skeletal positioning may include positioning of the occupant's joints, spine, arms, legs, torso, neck face, head, major bones, hands, and/or feet. In some embodiments, the internal sensors 105 constantly transmit sensor data to vehicle computer device 110, which constantly determines 920 the positional information of the occupants. In other embodiments, vehicle computer device 110 transmits the internal sensor data to AM server 415, which determines 920 the positional information and transmits that information to vehicle computer device 110.
(123) In some embodiments, vehicle computer device 110 generates a scenario model of the potential vehicular crash based upon the received external and/or internal sensor data. Scenario models may predict damage to vehicle 100 and injuries that may be experiences by driver 115 and passengers 120 of vehicle 100. In the exemplary embodiment, vehicle computer device 110 accesses a database, such as database 202 (shown in FIG. 2). Database 202 may contain a plurality of crash scenarios and the sensor data associated with these crash scenarios. The scenarios may be based upon information from vehicle crash testing facilities, from past crashes that AM server 415 has analyzed, and/or from other sources that allow vehicle computer device 110 to operate as described herein. Vehicle computer device 110 compares the received sensor data with the different stored crash scenarios to generate a scenario model that is the most likely match for the imminent vehicular crash. In some further embodiments, vehicle computer device 110 may communicate the sensor data to AM server 415, where AM server 415 may generate the scenario model. In the some of these embodiments, vehicle computer device 110 determines one or more potential injuries to one or more occupants of vehicle 100 based upon the positional information and the scenario model. Vehicle computer device 110 may also determine a severity for each potential injury.
Figure 11)
modifying the current position of the occupant within the seat of the transport by modifying a configuration of the seat before the collision occurs based on the position of the occupant within the seat of the transport and based the predicted resulting injury of the occupant within the transport after the collision. (Page 28 Column 30 Lines 13 to 20, (128) In other embodiments, vehicle computer device 110 may be able to determine an advantageous position for the at least one occupant. Vehicle computer device 110 may cause a seat to shift or move, such as adjusting the recline angle of the seat, to cause the occupant to change to the advantageous position. Vehicle computer device 110 may also rotate the seat of occupant to cause the occupant to change to the advantageous position or advantageous facing.
Page 30, Column 34, Line 59 to Page 31, Column 34, Line 4 (154) Below are some examples of autonomous or semi-autonomous vehicle-related functionality or technology replacing human driver action when AM server 415 determines that a vehicular crash is imminent. AM server 415 generates a scenario model of the potential vehicular crash and determines at least one potential injury to at least one occupant of vehicle 100 based upon the scenario model. In a first example, AM server 415 may determine that the occupant is facing the wrong direction and that the angle of impact may cause serious injury to the occupant. AM server 415 may sound a noise, such as a horn, to attract the attention of the occupant and entice the occupant to change their direction of facing, such as towards the sounds.
Page 27 Column 28 Line 42 to Page 28 Column 29 Line 20 ((122) Vehicle computer device 110 determines 920 positional information for at least one occupant of vehicle 100. Positional information may include a position of an occupant, a direction of facing of the occupant, a size of the occupant, and/or a skeletal positioning of the occupant. The position of the occupant may include which seat the occupant occupies. The direction of facing of the occupant may include whether the occupant is facing forward, reaching forward, reaching to the side, and/or has his/her head turned. The size of the occupant may determine whether the occupant is an adult or a child. The size of the occupant may also include the occupant's height. The skeletal positioning may include positioning of the occupant's joints, spine, arms, legs, torso, neck face, head, major bones, hands, and/or feet. In some embodiments, the internal sensors 105 constantly transmit sensor data to vehicle computer device 110, which constantly determines 920 the positional information of the occupants. In other embodiments, vehicle computer device 110 transmits the internal sensor data to AM server 415, which determines 920 the positional information and transmits that information to vehicle computer device 110.
(123) In some embodiments, vehicle computer device 110 generates a scenario model of the potential vehicular crash based upon the received external and/or internal sensor data. Scenario models may predict damage to vehicle 100 and injuries that may be experiences by driver 115 and passengers 120 of vehicle 100. In the exemplary embodiment, vehicle computer device 110 accesses a database, such as database 202 (shown in FIG. 2). Database 202 may contain a plurality of crash scenarios and the sensor data associated with these crash scenarios. The scenarios may be based upon information from vehicle crash testing facilities, from past crashes that AM server 415 has analyzed, and/or from other sources that allow vehicle computer device 110 to operate as described herein. Vehicle computer device 110 compares the received sensor data with the different stored crash scenarios to generate a scenario model that is the most likely match for the imminent vehicular crash. In some further embodiments, vehicle computer device 110 may communicate the sensor data to AM server 415, where AM server 415 may generate the scenario model. In the some of these embodiments, vehicle computer device 110 determines one or more potential injuries to one or more occupants of vehicle 100 based upon the positional information and the scenario model. Vehicle computer device 110 may also determine a severity for each potential injury.
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Nave such predicting a resulting location of an occupant within the transport after the collision occurs based on a current position of the occupant within the transport and;
modifying the current position of the occupant within the seat of the transport by modifying a configuration of the seat before the collision occurs based on the predicted resulting location of the occupant within the transport after the collision.
It would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Nave in this way because it would allow the system to predict in what way the occupant might collide with the parts of the vehicle and how they might be injured, and allow the system to attempt to put the user into a more advantageous position before the collision. If the user is after the collision expected to be in contact with parts of the vehicle (which would count as their position after the collision) then it would be useful to know how they have hit the vehicle so that injuries can be estimated. In this way severe injuries can be avoided. Nave teaches considering the position of the user for making protective actions (moving the user via sound) as well as moving the seat. It would be obvious to move the seat in response to determining a more optimal location based on the current and future position of the occupant because that would also be expected to be successful at protecting the occupant.
Breed, however, does teach modifying a safety measures of the vehicle based on the position of the occupant within the seat of the transport. (Page 49, Column 23, Line 26-56, Page 47, Column 19 Line 38 to Column 20 Line 6.)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Breed such that the system will be modifying a position of the occupant within the seat of the transport by modifying a position of the seat based on the position of the occupant within the seat of the transport.
It would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in this way because Hashimoto already teaches changing the seat position based on the position of the seat. Using information regarding the position of the user within the seat could either also be used to predict the position of the seat (assuming they are in the seat in a normal position) or could be used to determine that a user is not in the correct seating position, and modifying the position of the seat in certain ways could actually cause more harm than benefit.
For Claim 2, Hashimoto teaches The method of claim 1, wherein the method comprises:
sensing position information of the occupant; and . ([0008], [0044-0046], [0063]) As understood by the specification, an “emergency service node” is a part of an electrically connected system. As such, any electronically connected part that could provide an “emergency service” could meet this limitation.)
transmitting the position information to an emergency service node. . ([0008], [0044-0046], [0063]) As understood by the specification, an “emergency service node” is a part of an electrically connected system. As such, any electronically connected part that could provide an “emergency service” could meet this limitation.)
Hashimoto does not teach sensing the position of the occupant after the collision and transmitting that information to the emergency service node.
Breed, however, does teach sensing the position of the occupant after the collision and transmitting that information to the emergency service node. (Page 83, Column 92, Lines 4 to 24. Page 84, Column 93 Line 55 to Column 94, Line 11. )
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Breed such that the detection can occur also after the accident and send the data to an emergency service node because new ambulances, EMS, or those who wish to assist might find information regarding the current location and position of the occupants of the vehicle valuable and useful when it comes to providing aid.
For Claim 5, Hashimoto teaches The method of claim 1, wherein the predicting of the resulting location comprises:
Hashimoto does not teach predicting the resulting location based on a characteristic of how the occupant is sitting in the seat.
Nave, however, does teach predicting the resulting location based on a characteristic of how the occupant is sitting in the seat. ( Page 22, Column 18, Lines 11 to 47 “In one embodiment, the received sensor data may include sensor data from occupant position sensors that identify a location and/or a position of each occupant within vehicle 100 prior to or at the time of the vehicular crash. The location and/or position of each occupant may be used to determine what forces were applied to an occupant's joints and skeletal structure during the crash and identify any corresponding potential injuries.”
Page 29, Column 31, Lines 40 to 49)
Page 29, Column 31, Lines 40 to 49, (135) FIG. 11 illustrates a flow chart of an exemplary computer-implemented process of estimating an extent of injury to vehicle occupants resulting from a vehicle collision 1100. The method 1100 may include generating and collecting sensor data regarding vehicle occupant positioning prior to, during, and/or after a vehicle collision from one or more occupant position sensors 1102. The one or more occupant position sensors may be in-cabin vehicle-mounted sensors and/or mobile-device (e.g., wearables, smart phone, etc.) sensors.
Figure 11
(Page 28 Column 30 Line 13 to Line 20, “(128) In other embodiments, vehicle computer device 110 may be able to determine an advantageous position for the at least one occupant. Vehicle computer device 110 may cause a seat to shift or move, such as adjusting the recline angle of the seat, to cause the occupant to change to the advantageous position. Vehicle computer device 110 may also rotate the seat of occupant to cause the occupant to change to the advantageous position or advantageous facing.”
Page 30, Column 34, Line 59 to Page 31, Column 34, Line 4 (154) Below are some examples of autonomous or semi-autonomous vehicle-related functionality or technology replacing human driver action when AM server 415 determines that a vehicular crash is imminent. AM server 415 generates a scenario model of the potential vehicular crash and determines at least one potential injury to at least one occupant of vehicle 100 based upon the scenario model. In a first example, AM server 415 may determine that the occupant is facing the wrong direction and that the angle of impact may cause serious injury to the occupant. AM server 415 may sound a noise, such as a horn, to attract the attention of the occupant and entice the occupant to change their direction of facing, such as towards the sounds.)
Page 27 Column 28 Line 42 to Page 28, Column 29 Line 20 (122) Vehicle computer device 110 determines 920 positional information for at least one occupant of vehicle 100. Positional information may include a position of an occupant, a direction of facing of the occupant, a size of the occupant, and/or a skeletal positioning of the occupant. The position of the occupant may include which seat the occupant occupies. The direction of facing of the occupant may include whether the occupant is facing forward, reaching forward, reaching to the side, and/or has his/her head turned. The size of the occupant may determine whether the occupant is an adult or a child. The size of the occupant may also include the occupant's height. The skeletal positioning may include positioning of the occupant's joints, spine, arms, legs, torso, neck face, head, major bones, hands, and/or feet. In some embodiments, the internal sensors 105 constantly transmit sensor data to vehicle computer device 110, which constantly determines 920 the positional information of the occupants. In other embodiments, vehicle computer device 110 transmits the internal sensor data to AM server 415, which determines 920 the positional information and transmits that information to vehicle computer device 110.
(123) In some embodiments, vehicle computer device 110 generates a scenario model of the potential vehicular crash based upon the received external and/or internal sensor data. Scenario models may predict damage to vehicle 100 and injuries that may be experiences by driver 115 and passengers 120 of vehicle 100. In the exemplary embodiment, vehicle computer device 110 accesses a database, such as database 202 (shown in FIG. 2). Database 202 may contain a plurality of crash scenarios and the sensor data associated with these crash scenarios. The scenarios may be based upon information from vehicle crash testing facilities, from past crashes that AM server 415 has analyzed, and/or from other sources that allow vehicle computer device 110 to operate as described herein. Vehicle computer device 110 compares the received sensor data with the different stored crash scenarios to generate a scenario model that is the most likely match for the imminent vehicular crash. In some further embodiments, vehicle computer device 110 may communicate the sensor data to AM server 415, where AM server 415 may generate the scenario model. In the some of these embodiments, vehicle computer device 110 determines one or more potential injuries to one or more occupants of vehicle 100 based upon the positional information and the scenario model. Vehicle computer device 110 may also determine a severity for each potential injury.
Page 29, Column 31 Line 66 to Column 32 Line 25
(138) The method 1100 may include reconstructing occupant skeletal positioning prior to, during, and/or after the vehicle collision 1110. Occupant skeletal positioning may include and/or account for position of occupant joins, spine, arms, legs, torso, neck, face, head, major bones, hands, feet, etc. Occupant skeletal positioning may also include, account for, and/or characterize occupant position as normal or face-forward sitting, reaching forward, reaching to the side or rear, torso or spine twisted, head or neck twisted or turned, size of occupant (adult, child, height, weight), etc.
(139) The method 1100 may include estimating or calculating a likelihood and/or type (or body location) of major injury to one or more occupants based upon (1) impact force and direction of force on the vehicle; (2) vehicle weight distribution; and/or (3) occupant skeletal positioning prior to, during, and/or after the vehicle collision 1112. For instance, the method 1000 may determine or estimate if there was an abnormal stress on joints or bones, such as determine if any broken bones likely resulted from the vehicle collision.
(140) The method 1100 may include if the likelihood of major injury to any occupant is greater than a pre-determined threshold (such as greater than 5, 10, or 20%). If so, the method 1100, may take corrective action 1114. For instance, the method 1100 may request an ambulance and/or notify a hospital via wireless communication or data transmission sent over one or more radio frequency links or communication channels.)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Nave so that the resulting location is based on a characteristic of how the passenger is sitting in the seat because particular poses or positions may lead to common impacts with the vehicle in certain collisions. By being aware of these impacts, the vehicle can take action to prevent injury by ensuring that the passenger is in as safe a position as possible before a collision occurs.
For Claim 6, Hashimoto teaches The method of claim 1, comprising;
Hashimoto does not teach predicting an area of the transport that the occupant will impact during the collision,
wherein the modifying comprises:
modifying the current position of the occupant based on the area of impact.
Nave, however, does teach predicting an locations of the occupant and locations of the structures of the vehicle, and their relative distance to determine injuries during the collision, (Page 22, Column 18, Line 11 to 46
(59) In the exemplary embodiment, AM server 206 generates 310 a scenario model of the vehicular crash based upon the received sensor data. Scenario models may predict damage to vehicle 100 and injuries that may be experiences by driver 115 and passengers 120 of vehicle 100. In one embodiment, the received sensor data may include sensor data from occupant position sensors that identify a location and/or a position of each occupant within vehicle 100 prior to or at the time of the vehicular crash. The location and/or position of each occupant may be used to determine what forces were applied to an occupant's joints and skeletal structure during the crash and identify any corresponding potential injuries. For example, a front row passenger 120 turning around to help a child in the back of vehicle 100 may injure his or her back during the vehicular crash because his or her position at impact would not protect his or her body from injury. The sensor data from the occupant position sensors may be combined with data indicating the position of support structures of vehicle 100 (e.g., frame, windows, seats, steering wheel, etc.) to identify any potential injuries that an occupant may have sustained due to the occupant's position relative to the positions of the support structures. In the exemplary embodiment, AM server 206 accesses a database, such as database 420 (shown in FIG. 4). Database 420 may contain a plurality of crash scenarios and the sensor data associated with these crash scenarios. The scenarios may be based upon information from vehicle crash testing facilities, from past crashes that AM server 206 has analyzed, and/or from other sources that allow AM server 206 to operate as described here. AM server 206 compares the received sensor data with the different stored crash scenarios to generate 310 a scenario model that is the most likely match for the vehicular crash. For example, AM server 206 may determine that vehicle 100 was rear-ended by another vehicle that was going approximately 30 miles an hour while vehicle 100 was stopped.
Page 29, Column 31 Line 66 to Column 32 Line 25
(138) The method 1100 may include reconstructing occupant skeletal positioning prior to, during, and/or after the vehicle collision 1110. Occupant skeletal positioning may include and/or account for position of occupant joins, spine, arms, legs, torso, neck, face, head, major bones, hands, feet, etc. Occupant skeletal positioning may also include, account for, and/or characterize occupant position as normal or face-forward sitting, reaching forward, reaching to the side or rear, torso or spine twisted, head or neck twisted or turned, size of occupant (adult, child, height, weight), etc.
(139) The method 1100 may include estimating or calculating a likelihood and/or type (or body location) of major injury to one or more occupants based upon (1) impact force and direction of force on the vehicle; (2) vehicle weight distribution; and/or (3) occupant skeletal positioning prior to, during, and/or after the vehicle collision 1112. For instance, the method 1000 may determine or estimate if there was an abnormal stress on joints or bones, such as determine if any broken bones likely resulted from the vehicle collision.
(140) The method 1100 may include if the likelihood of major injury to any occupant is greater than a pre-determined threshold (such as greater than 5, 10, or 20%). If so, the method 1100, may take corrective action 1114. For instance, the method 1100 may request an ambulance and/or notify a hospital via wireless communication or data transmission sent over one or more radio frequency links or communication channels.)
wherein the modifying comprises:
modifying the current position of the occupant based on the predicted relative distance of the passenger from the vehicle structures. (Page 28, Column 29 Line 28 to 65
(125) In the exemplary embodiment, vehicle computer device 110 performs 925 at least one action to reduce a severity of a potential injury to at least one occupant prior to impact. Using the scenario model, vehicle computer device 110 may be able to determine an advantageous direction of facing for the at least one occupant. Vehicle computer device 110 may then generate a sound through the audio system of vehicle 100, such a horn or alarm sound. The sound would be generated to cause the at least one occupant to change to the advantageous direction of facing. For example, vehicle computer device 110 may generate a honking sound to cause the passenger to turn around to prevent or reduce potential injuries during the imminent vehicular crash. Additionally or alternatively, vehicle computer device 110 may select and engage one or more autonomous or semi-autonomous vehicle features or systems in an attempt to avoid or mitigate the vehicle collision.
(126) The types of autonomous or semi-autonomous vehicle-related functionality or technology that may be used with the present embodiments to replace human driver actions may include and/or be related to the following types of functionality: (a) fully autonomous (driverless); (b) limited driver control; (c) vehicle-to-vehicle (V2V) wireless communication; (d) vehicle-to-infrastructure (and/or vice versa) wireless communication; (e) automatic or semi-automatic steering; (f) automatic or semi-automatic acceleration; (g) automatic or semi-automatic braking; (h) automatic or semi-automatic blind spot monitoring; (i) automatic or semi-automatic collision warning; (j) adaptive cruise control; (k) automatic or semi-automatic parking/parking assistance; (l) automatic or semi-automatic collision preparation (windows roll up, seat adjusts upright, brakes pre-charge, etc.); (m) driver acuity/alertness monitoring; (n) pedestrian detection; (o) autonomous or semi-autonomous backup systems; (p) road mapping systems; (q) software security and anti-hacking measures; (r) theft prevention/automatic return; (s) automatic or semi-automatic driving without occupants; and/or other functionality.)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Nave such that the system will be predicting an area of the transport that the occupant will impact during the collision,
wherein the modifying comprises:
modifying the current position of the occupant based on the area of impact.
It would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in this way because estimating how the passenger will move, and what surfaces they will impact would help in determining potential injuries, which would be useful in ensuring that the passenger is not seated in particular ways to prevent injuries. It would be obvious to consider the area of impact rather than just relative distance, because if the relative distance becomes zero, then that indicates a high likely hood that an injury is sustained.
For Claim 7, Hashimoto teaches The method of claim 1, further comprising:
Hashimoto does not teach taking, by the transport, an evasive action to avoid the collision only based on the current position, wherein the evasive action comprises one or more of:
moving a seatback of the seat, moving a headrest of the seat, or deploying an airbag sooner than a normal deployment time.
Nave, however, does teach taking, by the transport, an evasive action to avoid the collision only based on the current position, wherein the evasive action comprises one or more of:
moving a seatback of the seat, moving a headrest of the seat, or deploying an airbag sooner than a normal deployment time. (Page 28, Column 29 Line 28 to 65
(125) In the exemplary embodiment, vehicle computer device 110 performs 925 at least one action to reduce a severity of a potential injury to at least one occupant prior to impact. Using the scenario model, vehicle computer device 110 may be able to determine an advantageous direction of facing for the at least one occupant. Vehicle computer device 110 may then generate a sound through the audio system of vehicle 100, such a horn or alarm sound. The sound would be generated to cause the at least one occupant to change to the advantageous direction of facing. For example, vehicle computer device 110 may generate a honking sound to cause the passenger to turn around to prevent or reduce potential injuries during the imminent vehicular crash. Additionally or alternatively, vehicle computer device 110 may select and engage one or more autonomous or semi-autonomous vehicle features or systems in an attempt to avoid or mitigate the vehicle collision.
(126) The types of autonomous or semi-autonomous vehicle-related functionality or technology that may be used with the present embodiments to replace human driver actions may include and/or be related to the following types of functionality: (a) fully autonomous (driverless); (b) limited driver control; (c) vehicle-to-vehicle (V2V) wireless communication; (d) vehicle-to-infrastructure (and/or vice versa) wireless communication; (e) automatic or semi-automatic steering; (f) automatic or semi-automatic acceleration; (g) automatic or semi-automatic braking; (h) automatic or semi-automatic blind spot monitoring; (i) automatic or semi-automatic collision warning; (j) adaptive cruise control; (k) automatic or semi-automatic parking/parking assistance; (l) automatic or semi-automatic collision preparation (windows roll up, seat adjusts upright, brakes pre-charge, etc.); (m) driver acuity/alertness monitoring; (n) pedestrian detection; (o) autonomous or semi-autonomous backup systems; (p) road mapping systems; (q) software security and anti-hacking measures; (r) theft prevention/automatic return; (s) automatic or semi-automatic driving without occupants; and/or other functionality.)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Nave so that evasive action is taken which includes moving a seat or headrest or releasing an airbag earlier because in the case that a collision does occur, it would reduce the likelihood that the passenger becomes injured.
For Claim 8, Hashimoto teaches A system, comprising:
a processor configured to: ([0030] The seat control ECU 12 includes a microcomputer 16 that has a CPU, ROM, RAM, and an input/output interface. The reclining actuator 14 includes a motor 18 and a sensor 20 (adjustment amount detecting portion) for detecting the position (i.e., the reclining angle) of the seat back 44. Incidentally, the sensor 20 detects the reclining angle of the seat back 44 by detecting the rotation speed and rotational position and the like of the motor 18 using a Hall element, for example.)
predict a future occurrence of a collision by a transport; ([0008] Thus, one aspect of the invention relates to an occupant protection device that includes a seat belt that restrains an occupant, an adjusting device that relatively adjusts a positional relationship between the seat belt and the occupant, a collision predicting portion that predicts a collision, and a control portion that controls the adjusting device when the collision is predicted by the collision predicting portion. The control portion first operates the adjusting device at a first speed to restrain the occupant by the seat belt, and then operates the adjusting device to increase the distance between the seat belt and the occupant at a second speed that is slower than the first speed.)
determining a position of the seat of the occupant of a transport and a current driving environment of the transport based on the captured sensor data; ([0030], [0034], [0037-0043], [0047-0056])
determining a prediction of a transport being involved in a collision based on a current driving environment of the vehicle; and ([0008], [0010-0012], [0044-0046], [0063])
modify the current position of the occupant within the seat of the transport by modifying a configuration of the seat before the collision occurs based on the position of the seat of the transport ([0008], [0010-0012], [0044-0046], [0063]) Hashimoto does not teach predict a resulting location of an occupant within the transport after the collision occurs based on a current position of the occupant within the transport and;
modify the current position of the occupant within the seat of the transport by modifying a configuration of the seat before the collision occurs based on the predicted resulting location of the occupant within the transport after the collision.
Nave, however, does teach predict a resulting location of an occupant within a transport after the collision occurs based on a current position of the occupant within the transport ( Page 22, Column 18, Lines 11 to 47 “In one embodiment, the received sensor data may include sensor data from occupant position sensors that identify a location and/or a position of each occupant within vehicle 100 prior to or at the time of the vehicular crash. The location and/or position of each occupant may be used to determine what forces were applied to an occupant's joints and skeletal structure during the crash and identify any corresponding potential injuries.”
Page 29, Column 31, Lines 40 to 49, (135) FIG. 11 illustrates a flow chart of an exemplary computer-implemented process of estimating an extent of injury to vehicle occupants resulting from a vehicle collision 1100. The method 1100 may include generating and collecting sensor data regarding vehicle occupant positioning prior to, during, and/or after a vehicle collision from one or more occupant position sensors 1102. The one or more occupant position sensors may be in-cabin vehicle-mounted sensors and/or mobile-device (e.g., wearables, smart phone, etc.) sensors.
Page 27 Column 28 Line 42 to Page 28 Column 29 Line 20 ((122) Vehicle computer device 110 determines 920 positional information for at least one occupant of vehicle 100. Positional information may include a position of an occupant, a direction of facing of the occupant, a size of the occupant, and/or a skeletal positioning of the occupant. The position of the occupant may include which seat the occupant occupies. The direction of facing of the occupant may include whether the occupant is facing forward, reaching forward, reaching to the side, and/or has his/her head turned. The size of the occupant may determine whether the occupant is an adult or a child. The size of the occupant may also include the occupant's height. The skeletal positioning may include positioning of the occupant's joints, spine, arms, legs, torso, neck face, head, major bones, hands, and/or feet. In some embodiments, the internal sensors 105 constantly transmit sensor data to vehicle computer device 110, which constantly determines 920 the positional information of the occupants. In other embodiments, vehicle computer device 110 transmits the internal sensor data to AM server 415, which determines 920 the positional information and transmits that information to vehicle computer device 110.
(123) In some embodiments, vehicle computer device 110 generates a scenario model of the potential vehicular crash based upon the received external and/or internal sensor data. Scenario models may predict damage to vehicle 100 and injuries that may be experiences by driver 115 and passengers 120 of vehicle 100. In the exemplary embodiment, vehicle computer device 110 accesses a database, such as database 202 (shown in FIG. 2). Database 202 may contain a plurality of crash scenarios and the sensor data associated with these crash scenarios. The scenarios may be based upon information from vehicle crash testing facilities, from past crashes that AM server 415 has analyzed, and/or from other sources that allow vehicle computer device 110 to operate as described herein. Vehicle computer device 110 compares the received sensor data with the different stored crash scenarios to generate a scenario model that is the most likely match for the imminent vehicular crash. In some further embodiments, vehicle computer device 110 may communicate the sensor data to AM server 415, where AM server 415 may generate the scenario model. In the some of these embodiments, vehicle computer device 110 determines one or more potential injuries to one or more occupants of vehicle 100 based upon the positional information and the scenario model. Vehicle computer device 110 may also determine a severity for each potential injury.
Figure 11)
modify the current position of the occupant within the seat of the transport by modifying a configuration of the seat before the collision occurs based on the position of the occupant within the seat of the transport and based the predicted resulting injury of the occupant within the transport after the collision. (Page 28 Column 30 Lines 13 to 20, (128) In other embodiments, vehicle computer device 110 may be able to determine an advantageous position for the at least one occupant. Vehicle computer device 110 may cause a seat to shift or move, such as adjusting the recline angle of the seat, to cause the occupant to change to the advantageous position. Vehicle computer device 110 may also rotate the seat of occupant to cause the occupant to change to the advantageous position or advantageous facing.
Page 30, Column 34, Line 59 to Page 31, Column 34, Line 4 (154) Below are some examples of autonomous or semi-autonomous vehicle-related functionality or technology replacing human driver action when AM server 415 determines that a vehicular crash is imminent. AM server 415 generates a scenario model of the potential vehicular crash and determines at least one potential injury to at least one occupant of vehicle 100 based upon the scenario model. In a first example, AM server 415 may determine that the occupant is facing the wrong direction and that the angle of impact may cause serious injury to the occupant. AM server 415 may sound a noise, such as a horn, to attract the attention of the occupant and entice the occupant to change their direction of facing, such as towards the sounds.
Page 27 Column 28 Line 42 to Page 28 Column 29 Line 20 ((122) Vehicle computer device 110 determines 920 positional information for at least one occupant of vehicle 100. Positional information may include a position of an occupant, a direction of facing of the occupant, a size of the occupant, and/or a skeletal positioning of the occupant. The position of the occupant may include which seat the occupant occupies. The direction of facing of the occupant may include whether the occupant is facing forward, reaching forward, reaching to the side, and/or has his/her head turned. The size of the occupant may determine whether the occupant is an adult or a child. The size of the occupant may also include the occupant's height. The skeletal positioning may include positioning of the occupant's joints, spine, arms, legs, torso, neck face, head, major bones, hands, and/or feet. In some embodiments, the internal sensors 105 constantly transmit sensor data to vehicle computer device 110, which constantly determines 920 the positional information of the occupants. In other embodiments, vehicle computer device 110 transmits the internal sensor data to AM server 415, which determines 920 the positional information and transmits that information to vehicle computer device 110.
(123) In some embodiments, vehicle computer device 110 generates a scenario model of the potential vehicular crash based upon the received external and/or internal sensor data. Scenario models may predict damage to vehicle 100 and injuries that may be experiences by driver 115 and passengers 120 of vehicle 100. In the exemplary embodiment, vehicle computer device 110 accesses a database, such as database 202 (shown in FIG. 2). Database 202 may contain a plurality of crash scenarios and the sensor data associated with these crash scenarios. The scenarios may be based upon information from vehicle crash testing facilities, from past crashes that AM server 415 has analyzed, and/or from other sources that allow vehicle computer device 110 to operate as described herein. Vehicle computer device 110 compares the received sensor data with the different stored crash scenarios to generate a scenario model that is the most likely match for the imminent vehicular crash. In some further embodiments, vehicle computer device 110 may communicate the sensor data to AM server 415, where AM server 415 may generate the scenario model. In the some of these embodiments, vehicle computer device 110 determines one or more potential injuries to one or more occupants of vehicle 100 based upon the positional information and the scenario model. Vehicle computer device 110 may also determine a severity for each potential injury.
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Nave such predict a resulting location of an occupant within the transport after the collision occurs based on a current position of the occupant within the transport and;
modify the current position of the occupant within the seat of the transport by modifying a configuration of the seat before the collision occurs based on the predicted resulting location of the occupant within the transport after the collision.
It would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Nave in this way because it would allow the system to predict in what way the occupant might collide with the parts of the vehicle and how they might be injured, and allow the system to attempt to put the user into a more advantageous position before the collision. If the user is after the collision expected to be in contact with parts of the vehicle (which would count as their position after the collision) then it would be useful to know how they have hit the vehicle so that injuries can be estimated. In this way severe injuries can be avoided. Nave teaches considering the position of the user for making protective actions (moving the user via sound) as well as moving the seat. It would be obvious to move the seat in response to determining a more optimal location based on the current and future position of the occupant because that would also be expected to be successful at protecting the occupant.
Breed, however, does teach modifying a safety measures of the vehicle based on the position of the occupant within the seat of the transport. (Page 49, Column 23, Line 26-56, Page 47, Column 19 Line 38 to Column 20 Line 6.)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Breed such that the system will be modifying a position of the occupant within the seat of the transport by modifying a position of the seat based on the position of the occupant within the seat of the transport.
It would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in this way because Hashimoto already teaches changing the seat position based on the position of the seat. Using information regarding the position of the user within the seat could either also be used to predict the position of the seat (assuming they are in the seat in a normal position) or could be used to determine that a user is not in the correct seating position, and modifying the position of the seat in certain ways could actually cause more harm than benefit.
For Claim 9, Hashimoto teaches The system of claim 8, wherein the processor is configured to;
sense position information of the occupant; and . ([0008], [0044-0046], [0063]) As understood by the specification, an “emergency service node” is a part of an electrically connected system. As such, any electronically connected part that could provide an “emergency service” could meet this limitation.)
transmit the position information to an emergency service node. . ([0008], [0044-0046], [0063]) As understood by the specification, an “emergency service node” is a part of an electrically connected system. As such, any electronically connected part that could provide an “emergency service” could meet this limitation.)
Hashimoto does not teach sensing the position of the occupant after the collision and transmitting that information to the emergency service node.
Breed, however, does teach sensing the position of the occupant after the collision and transmitting that information to the emergency service node. (Page 83, Column 92, Lines 4 to 24. Page 84, Column 93 Line 55 to Column 94, Line 11. )
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Breed such that the detection can occur also after the accident and send the data to an emergency service node because new ambulances, EMS, or those who wish to assist might find information regarding the current location and position of the occupants of the vehicle valuable and useful when it comes to providing aid.
For Claim 12, Hashimoto teaches The system of claim 8, wherein, when the processor predicts the resulting location, the processor is configured to;
Hashimoto does not teach predict the resulting location based on a characteristic of how the occupant is sitting in the seat.
Nave, however, does teach predict the resulting location based on a characteristic of how the occupant is sitting in the seat. ( Page 22, Column 18, Lines 11 to 47 “In one embodiment, the received sensor data may include sensor data from occupant position sensors that identify a location and/or a position of each occupant within vehicle 100 prior to or at the time of the vehicular crash. The location and/or position of each occupant may be used to determine what forces were applied to an occupant's joints and skeletal structure during the crash and identify any corresponding potential injuries.”
Page 29, Column 31, Lines 40 to 49)
Page 29, Column 31, Lines 40 to 49, (135) FIG. 11 illustrates a flow chart of an exemplary computer-implemented process of estimating an extent of injury to vehicle occupants resulting from a vehicle collision 1100. The method 1100 may include generating and collecting sensor data regarding vehicle occupant positioning prior to, during, and/or after a vehicle collision from one or more occupant position sensors 1102. The one or more occupant position sensors may be in-cabin vehicle-mounted sensors and/or mobile-device (e.g., wearables, smart phone, etc.) sensors.
Figure 11
(Page 28 Column 30 Line 13 to Line 20, “(128) In other embodiments, vehicle computer device 110 may be able to determine an advantageous position for the at least one occupant. Vehicle computer device 110 may cause a seat to shift or move, such as adjusting the recline angle of the seat, to cause the occupant to change to the advantageous position. Vehicle computer device 110 may also rotate the seat of occupant to cause the occupant to change to the advantageous position or advantageous facing.”
Page 30, Column 34, Line 59 to Page 31, Column 34, Line 4 (154) Below are some examples of autonomous or semi-autonomous vehicle-related functionality or technology replacing human driver action when AM server 415 determines that a vehicular crash is imminent. AM server 415 generates a scenario model of the potential vehicular crash and determines at least one potential injury to at least one occupant of vehicle 100 based upon the scenario model. In a first example, AM server 415 may determine that the occupant is facing the wrong direction and that the angle of impact may cause serious injury to the occupant. AM server 415 may sound a noise, such as a horn, to attract the attention of the occupant and entice the occupant to change their direction of facing, such as towards the sounds.)
Page 27 Column 28 Line 42 to Page 28, Column 29 Line 20 (122) Vehicle computer device 110 determines 920 positional information for at least one occupant of vehicle 100. Positional information may include a position of an occupant, a direction of facing of the occupant, a size of the occupant, and/or a skeletal positioning of the occupant. The position of the occupant may include which seat the occupant occupies. The direction of facing of the occupant may include whether the occupant is facing forward, reaching forward, reaching to the side, and/or has his/her head turned. The size of the occupant may determine whether the occupant is an adult or a child. The size of the occupant may also include the occupant's height. The skeletal positioning may include positioning of the occupant's joints, spine, arms, legs, torso, neck face, head, major bones, hands, and/or feet. In some embodiments, the internal sensors 105 constantly transmit sensor data to vehicle computer device 110, which constantly determines 920 the positional information of the occupants. In other embodiments, vehicle computer device 110 transmits the internal sensor data to AM server 415, which determines 920 the positional information and transmits that information to vehicle computer device 110.
(123) In some embodiments, vehicle computer device 110 generates a scenario model of the potential vehicular crash based upon the received external and/or internal sensor data. Scenario models may predict damage to vehicle 100 and injuries that may be experiences by driver 115 and passengers 120 of vehicle 100. In the exemplary embodiment, vehicle computer device 110 accesses a database, such as database 202 (shown in FIG. 2). Database 202 may contain a plurality of crash scenarios and the sensor data associated with these crash scenarios. The scenarios may be based upon information from vehicle crash testing facilities, from past crashes that AM server 415 has analyzed, and/or from other sources that allow vehicle computer device 110 to operate as described herein. Vehicle computer device 110 compares the received sensor data with the different stored crash scenarios to generate a scenario model that is the most likely match for the imminent vehicular crash. In some further embodiments, vehicle computer device 110 may communicate the sensor data to AM server 415, where AM server 415 may generate the scenario model. In the some of these embodiments, vehicle computer device 110 determines one or more potential injuries to one or more occupants of vehicle 100 based upon the positional information and the scenario model. Vehicle computer device 110 may also determine a severity for each potential injury.
Page 29, Column 31 Line 66 to Column 32 Line 25
(138) The method 1100 may include reconstructing occupant skeletal positioning prior to, during, and/or after the vehicle collision 1110. Occupant skeletal positioning may include and/or account for position of occupant joins, spine, arms, legs, torso, neck, face, head, major bones, hands, feet, etc. Occupant skeletal positioning may also include, account for, and/or characterize occupant position as normal or face-forward sitting, reaching forward, reaching to the side or rear, torso or spine twisted, head or neck twisted or turned, size of occupant (adult, child, height, weight), etc.
(139) The method 1100 may include estimating or calculating a likelihood and/or type (or body location) of major injury to one or more occupants based upon (1) impact force and direction of force on the vehicle; (2) vehicle weight distribution; and/or (3) occupant skeletal positioning prior to, during, and/or after the vehicle collision 1112. For instance, the method 1000 may determine or estimate if there was an abnormal stress on joints or bones, such as determine if any broken bones likely resulted from the vehicle collision.
(140) The method 1100 may include if the likelihood of major injury to any occupant is greater than a pre-determined threshold (such as greater than 5, 10, or 20%). If so, the method 1100, may take corrective action 1114. For instance, the method 1100 may request an ambulance and/or notify a hospital via wireless communication or data transmission sent over one or more radio frequency links or communication channels.)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Nave so that the resulting location is based on a characteristic of how the passenger is sitting in the seat because particular poses or positions may lead to common impacts with the vehicle in certain collisions. By being aware of these impacts, the vehicle can take action to prevent injury by ensuring that the passenger is in as safe a position as possible before a collision occurs.
For Claim 13, Hashimoto teaches The system of claim 8, wherein the processor is configured to;
Hashimoto does not teach predict an area of the transport that the occupant will impact during the collision,
wherein when the processor modified the current position, the processor is configured to :
modify the current position of the occupant based on the area of impact.
Nave, however, does teach predict an locations of the occupant and locations of the structures of the vehicle, and their relative distance to determine injuries during the collision, (Page 22, Column 18, Line 11 to 46
(59) In the exemplary embodiment, AM server 206 generates 310 a scenario model of the vehicular crash based upon the received sensor data. Scenario models may predict damage to vehicle 100 and injuries that may be experiences by driver 115 and passengers 120 of vehicle 100. In one embodiment, the received sensor data may include sensor data from occupant position sensors that identify a location and/or a position of each occupant within vehicle 100 prior to or at the time of the vehicular crash. The location and/or position of each occupant may be used to determine what forces were applied to an occupant's joints and skeletal structure during the crash and identify any corresponding potential injuries. For example, a front row passenger 120 turning around to help a child in the back of vehicle 100 may injure his or her back during the vehicular crash because his or her position at impact would not protect his or her body from injury. The sensor data from the occupant position sensors may be combined with data indicating the position of support structures of vehicle 100 (e.g., frame, windows, seats, steering wheel, etc.) to identify any potential injuries that an occupant may have sustained due to the occupant's position relative to the positions of the support structures. In the exemplary embodiment, AM server 206 accesses a database, such as database 420 (shown in FIG. 4). Database 420 may contain a plurality of crash scenarios and the sensor data associated with these crash scenarios. The scenarios may be based upon information from vehicle crash testing facilities, from past crashes that AM server 206 has analyzed, and/or from other sources that allow AM server 206 to operate as described here. AM server 206 compares the received sensor data with the different stored crash scenarios to generate 310 a scenario model that is the most likely match for the vehicular crash. For example, AM server 206 may determine that vehicle 100 was rear-ended by another vehicle that was going approximately 30 miles an hour while vehicle 100 was stopped.
Page 29, Column 31 Line 66 to Column 32 Line 25
(138) The method 1100 may include reconstructing occupant skeletal positioning prior to, during, and/or after the vehicle collision 1110. Occupant skeletal positioning may include and/or account for position of occupant joins, spine, arms, legs, torso, neck, face, head, major bones, hands, feet, etc. Occupant skeletal positioning may also include, account for, and/or characterize occupant position as normal or face-forward sitting, reaching forward, reaching to the side or rear, torso or spine twisted, head or neck twisted or turned, size of occupant (adult, child, height, weight), etc.
(139) The method 1100 may include estimating or calculating a likelihood and/or type (or body location) of major injury to one or more occupants based upon (1) impact force and direction of force on the vehicle; (2) vehicle weight distribution; and/or (3) occupant skeletal positioning prior to, during, and/or after the vehicle collision 1112. For instance, the method 1000 may determine or estimate if there was an abnormal stress on joints or bones, such as determine if any broken bones likely resulted from the vehicle collision.
(140) The method 1100 may include if the likelihood of major injury to any occupant is greater than a pre-determined threshold (such as greater than 5, 10, or 20%). If so, the method 1100, may take corrective action 1114. For instance, the method 1100 may request an ambulance and/or notify a hospital via wireless communication or data transmission sent over one or more radio frequency links or communication channels.)
wherein when the processor modified the current position, the processor is configured to :
modify the current position of the occupant based on the predicted relative distance of the passenger from the vehicle structures. (Page 28, Column 29 Line 28 to 65
(125) In the exemplary embodiment, vehicle computer device 110 performs 925 at least one action to reduce a severity of a potential injury to at least one occupant prior to impact. Using the scenario model, vehicle computer device 110 may be able to determine an advantageous direction of facing for the at least one occupant. Vehicle computer device 110 may then generate a sound through the audio system of vehicle 100, such a horn or alarm sound. The sound would be generated to cause the at least one occupant to change to the advantageous direction of facing. For example, vehicle computer device 110 may generate a honking sound to cause the passenger to turn around to prevent or reduce potential injuries during the imminent vehicular crash. Additionally or alternatively, vehicle computer device 110 may select and engage one or more autonomous or semi-autonomous vehicle features or systems in an attempt to avoid or mitigate the vehicle collision.
(126) The types of autonomous or semi-autonomous vehicle-related functionality or technology that may be used with the present embodiments to replace human driver actions may include and/or be related to the following types of functionality: (a) fully autonomous (driverless); (b) limited driver control; (c) vehicle-to-vehicle (V2V) wireless communication; (d) vehicle-to-infrastructure (and/or vice versa) wireless communication; (e) automatic or semi-automatic steering; (f) automatic or semi-automatic acceleration; (g) automatic or semi-automatic braking; (h) automatic or semi-automatic blind spot monitoring; (i) automatic or semi-automatic collision warning; (j) adaptive cruise control; (k) automatic or semi-automatic parking/parking assistance; (l) automatic or semi-automatic collision preparation (windows roll up, seat adjusts upright, brakes pre-charge, etc.); (m) driver acuity/alertness monitoring; (n) pedestrian detection; (o) autonomous or semi-autonomous backup systems; (p) road mapping systems; (q) software security and anti-hacking measures; (r) theft prevention/automatic return; (s) automatic or semi-automatic driving without occupants; and/or other functionality.)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Nave such that the system will be predict an area of the transport that the occupant will impact during the collision,
wherein when the processor modified the current position, the processor is configured to :
modify the current position of the occupant based on the area of impact.
It would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in this way because estimating how the passenger will move, and what surfaces they will impact would help in determining potential injuries, which would be useful in ensuring that the passenger is not seated in particular ways to prevent injuries. It would be obvious to consider the area of impact rather than just relative distance, because if the relative distance becomes zero, then that indicates a high likely hood that an injury is sustained.
For Claim 14, Hashimoto teaches The system of claim 8, wherein the processor is configured to:
Hashimoto does not teach cause the transport to take an evasive action to avoid the collision only based on the current position, wherein the evasive action comprises one or more of:
moving a seatback of the seat, moving a headrest of the seat, or deploying an airbag sooner than a normal deployment time obtain, by a device associated with the occupant, health data; and
include the health data with the position information.
Nave, however, does teach cause the transport to take an evasive action to avoid the collision only based on the current position, wherein the evasive action comprises one or more of:
moving a seatback of the seat, moving a headrest of the seat, or deploying an airbag sooner than a normal deployment time obtain, by a device associated with the occupant, health data; and
include the health data with the position information. (Page 28, Column 29 Line 28 to 65
(125) In the exemplary embodiment, vehicle computer device 110 performs 925 at least one action to reduce a severity of a potential injury to at least one occupant prior to impact. Using the scenario model, vehicle computer device 110 may be able to determine an advantageous direction of facing for the at least one occupant. Vehicle computer device 110 may then generate a sound through the audio system of vehicle 100, such a horn or alarm sound. The sound would be generated to cause the at least one occupant to change to the advantageous direction of facing. For example, vehicle computer device 110 may generate a honking sound to cause the passenger to turn around to prevent or reduce potential injuries during the imminent vehicular crash. Additionally or alternatively, vehicle computer device 110 may select and engage one or more autonomous or semi-autonomous vehicle features or systems in an attempt to avoid or mitigate the vehicle collision.
(126) The types of autonomous or semi-autonomous vehicle-related functionality or technology that may be used with the present embodiments to replace human driver actions may include and/or be related to the following types of functionality: (a) fully autonomous (driverless); (b) limited driver control; (c) vehicle-to-vehicle (V2V) wireless communication; (d) vehicle-to-infrastructure (and/or vice versa) wireless communication; (e) automatic or semi-automatic steering; (f) automatic or semi-automatic acceleration; (g) automatic or semi-automatic braking; (h) automatic or semi-automatic blind spot monitoring; (i) automatic or semi-automatic collision warning; (j) adaptive cruise control; (k) automatic or semi-automatic parking/parking assistance; (l) automatic or semi-automatic collision preparation (windows roll up, seat adjusts upright, brakes pre-charge, etc.); (m) driver acuity/alertness monitoring; (n) pedestrian detection; (o) autonomous or semi-autonomous backup systems; (p) road mapping systems; (q) software security and anti-hacking measures; (r) theft prevention/automatic return; (s) automatic or semi-automatic driving without occupants; and/or other functionality.
Page 26 Column 26 Lines 4 to 12
(108) In some embodiments, mobile device 125 may be able to determine that it is currently in a pocket of user 202. In these embodiments, AM server 206 may be able to determine the exact amounts of force and directions of force that were exerted on user 202 during the vehicular accident. Based upon this information, AM server 206 may be able to more accurately determine the potential injuries received by user 202 and notify emergency personnel of those potential injuries.)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Nave so that evasive action is taken which includes moving a seat or headrest or releasing an airbag earlier because in the case that a collision does occur, it would reduce the likelihood that the passenger becomes injured.
For Claim 15, Hashimoto teaches A non-transitory computer-readable medium comprising instructions that when executed by a processor, cause the processor to perform: ([0030] The seat control ECU 12 includes a microcomputer 16 that has a CPU, ROM, RAM, and an input/output interface. The reclining actuator 14 includes a motor 18 and a sensor 20 (adjustment amount detecting portion) for detecting the position (i.e., the reclining angle) of the seat back 44. Incidentally, the sensor 20 detects the reclining angle of the seat back 44 by detecting the rotation speed and rotational position and the like of the motor 18 using a Hall element, for example.)
Predicting a future occurrence of a collision by a transport; ([0008] Thus, one aspect of the invention relates to an occupant protection device that includes a seat belt that restrains an occupant, an adjusting device that relatively adjusts a positional relationship between the seat belt and the occupant, a collision predicting portion that predicts a collision, and a control portion that controls the adjusting device when the collision is predicted by the collision predicting portion. The control portion first operates the adjusting device at a first speed to restrain the occupant by the seat belt, and then operates the adjusting device to increase the distance between the seat belt and the occupant at a second speed that is slower than the first speed.)
determining a position of the seat of the occupant of a transport and a current driving environment of the transport based on the captured sensor data; ([0030], [0034], [0037-0043], [0047-0056])
determining a prediction of a transport being involved in a collision based on a current driving environment of the vehicle; and ([0008], [0010-0012], [0044-0046], [0063])
modifying the current position of the occupant within the seat of the transport by modifying a configuration of the seat before the collision occurs based on the position of the seat of the transport ([0008], [0010-0012], [0044-0046], [0063]) Hashimoto does not teach predicting a resulting location of an occupant within the transport after the collision occurs based on a current position of the occupant within the transport and;
modifying the current position of the occupant within the seat of the transport by modifying a configuration of the seat before the collision occurs based on the predicted resulting location of the occupant within the transport after the collision.
Nave, however, does teach predicting a resulting location of an occupant within a transport after the collision occurs based on a current position of the occupant within the transport ( Page 22, Column 18, Lines 11 to 47 “In one embodiment, the received sensor data may include sensor data from occupant position sensors that identify a location and/or a position of each occupant within vehicle 100 prior to or at the time of the vehicular crash. The location and/or position of each occupant may be used to determine what forces were applied to an occupant's joints and skeletal structure during the crash and identify any corresponding potential injuries.”
Page 29, Column 31, Lines 40 to 49, (135) FIG. 11 illustrates a flow chart of an exemplary computer-implemented process of estimating an extent of injury to vehicle occupants resulting from a vehicle collision 1100. The method 1100 may include generating and collecting sensor data regarding vehicle occupant positioning prior to, during, and/or after a vehicle collision from one or more occupant position sensors 1102. The one or more occupant position sensors may be in-cabin vehicle-mounted sensors and/or mobile-device (e.g., wearables, smart phone, etc.) sensors.
Page 27 Column 28 Line 42 to Page 28 Column 29 Line 20 ((122) Vehicle computer device 110 determines 920 positional information for at least one occupant of vehicle 100. Positional information may include a position of an occupant, a direction of facing of the occupant, a size of the occupant, and/or a skeletal positioning of the occupant. The position of the occupant may include which seat the occupant occupies. The direction of facing of the occupant may include whether the occupant is facing forward, reaching forward, reaching to the side, and/or has his/her head turned. The size of the occupant may determine whether the occupant is an adult or a child. The size of the occupant may also include the occupant's height. The skeletal positioning may include positioning of the occupant's joints, spine, arms, legs, torso, neck face, head, major bones, hands, and/or feet. In some embodiments, the internal sensors 105 constantly transmit sensor data to vehicle computer device 110, which constantly determines 920 the positional information of the occupants. In other embodiments, vehicle computer device 110 transmits the internal sensor data to AM server 415, which determines 920 the positional information and transmits that information to vehicle computer device 110.
(123) In some embodiments, vehicle computer device 110 generates a scenario model of the potential vehicular crash based upon the received external and/or internal sensor data. Scenario models may predict damage to vehicle 100 and injuries that may be experiences by driver 115 and passengers 120 of vehicle 100. In the exemplary embodiment, vehicle computer device 110 accesses a database, such as database 202 (shown in FIG. 2). Database 202 may contain a plurality of crash scenarios and the sensor data associated with these crash scenarios. The scenarios may be based upon information from vehicle crash testing facilities, from past crashes that AM server 415 has analyzed, and/or from other sources that allow vehicle computer device 110 to operate as described herein. Vehicle computer device 110 compares the received sensor data with the different stored crash scenarios to generate a scenario model that is the most likely match for the imminent vehicular crash. In some further embodiments, vehicle computer device 110 may communicate the sensor data to AM server 415, where AM server 415 may generate the scenario model. In the some of these embodiments, vehicle computer device 110 determines one or more potential injuries to one or more occupants of vehicle 100 based upon the positional information and the scenario model. Vehicle computer device 110 may also determine a severity for each potential injury.
Figure 11)
modifying the current position of the occupant within the seat of the transport by modifying a configuration of the seat before the collision occurs based on the position of the occupant within the seat of the transport and based the predicted resulting injury of the occupant within the transport after the collision. (Page 28 Column 30 Lines 13 to 20, (128) In other embodiments, vehicle computer device 110 may be able to determine an advantageous position for the at least one occupant. Vehicle computer device 110 may cause a seat to shift or move, such as adjusting the recline angle of the seat, to cause the occupant to change to the advantageous position. Vehicle computer device 110 may also rotate the seat of occupant to cause the occupant to change to the advantageous position or advantageous facing.
Page 30, Column 34, Line 59 to Page 31, Column 34, Line 4 (154) Below are some examples of autonomous or semi-autonomous vehicle-related functionality or technology replacing human driver action when AM server 415 determines that a vehicular crash is imminent. AM server 415 generates a scenario model of the potential vehicular crash and determines at least one potential injury to at least one occupant of vehicle 100 based upon the scenario model. In a first example, AM server 415 may determine that the occupant is facing the wrong direction and that the angle of impact may cause serious injury to the occupant. AM server 415 may sound a noise, such as a horn, to attract the attention of the occupant and entice the occupant to change their direction of facing, such as towards the sounds.
Page 27 Column 28 Line 42 to Page 28 Column 29 Line 20 ((122) Vehicle computer device 110 determines 920 positional information for at least one occupant of vehicle 100. Positional information may include a position of an occupant, a direction of facing of the occupant, a size of the occupant, and/or a skeletal positioning of the occupant. The position of the occupant may include which seat the occupant occupies. The direction of facing of the occupant may include whether the occupant is facing forward, reaching forward, reaching to the side, and/or has his/her head turned. The size of the occupant may determine whether the occupant is an adult or a child. The size of the occupant may also include the occupant's height. The skeletal positioning may include positioning of the occupant's joints, spine, arms, legs, torso, neck face, head, major bones, hands, and/or feet. In some embodiments, the internal sensors 105 constantly transmit sensor data to vehicle computer device 110, which constantly determines 920 the positional information of the occupants. In other embodiments, vehicle computer device 110 transmits the internal sensor data to AM server 415, which determines 920 the positional information and transmits that information to vehicle computer device 110.
(123) In some embodiments, vehicle computer device 110 generates a scenario model of the potential vehicular crash based upon the received external and/or internal sensor data. Scenario models may predict damage to vehicle 100 and injuries that may be experiences by driver 115 and passengers 120 of vehicle 100. In the exemplary embodiment, vehicle computer device 110 accesses a database, such as database 202 (shown in FIG. 2). Database 202 may contain a plurality of crash scenarios and the sensor data associated with these crash scenarios. The scenarios may be based upon information from vehicle crash testing facilities, from past crashes that AM server 415 has analyzed, and/or from other sources that allow vehicle computer device 110 to operate as described herein. Vehicle computer device 110 compares the received sensor data with the different stored crash scenarios to generate a scenario model that is the most likely match for the imminent vehicular crash. In some further embodiments, vehicle computer device 110 may communicate the sensor data to AM server 415, where AM server 415 may generate the scenario model. In the some of these embodiments, vehicle computer device 110 determines one or more potential injuries to one or more occupants of vehicle 100 based upon the positional information and the scenario model. Vehicle computer device 110 may also determine a severity for each potential injury.
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Nave such predicting a resulting location of an occupant within the transport after the collision occurs based on a current position of the occupant within the transport and;
modifying the current position of the occupant within the seat of the transport by modifying a configuration of the seat before the collision occurs based on the predicted resulting location of the occupant within the transport after the collision.
It would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Nave in this way because it would allow the system to predict in what way the occupant might collide with the parts of the vehicle and how they might be injured, and allow the system to attempt to put the user into a more advantageous position before the collision. If the user is after the collision expected to be in contact with parts of the vehicle (which would count as their position after the collision) then it would be useful to know how they have hit the vehicle so that injuries can be estimated. In this way severe injuries can be avoided. Nave teaches considering the position of the user for making protective actions (moving the user via sound) as well as moving the seat. It would be obvious to move the seat in response to determining a more optimal location based on the current and future position of the occupant because that would also be expected to be successful at protecting the occupant.
Breed, however, does teach modifying a safety measures of the vehicle based on the position of the occupant within the seat of the transport. (Page 49, Column 23, Line 26-56, Page 47, Column 19 Line 38 to Column 20 Line 6.)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Breed such that the system will be modifying a position of the occupant within the seat of the transport by modifying a position of the seat based on the position of the occupant within the seat of the transport.
It would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in this way because Hashimoto already teaches changing the seat position based on the position of the seat. Using information regarding the position of the user within the seat could either also be used to predict the position of the seat (assuming they are in the seat in a normal position) or could be used to determine that a user is not in the correct seating position, and modifying the position of the seat in certain ways could actually cause more harm than benefit.
For Claim 16, Hashimoto teaches The non-transitory computer-readable medium of claim 15, wherein the instructions cause the processor to perform:
sensing position information of the occupant; and . ([0008], [0044-0046], [0063]) As understood by the specification, an “emergency service node” is a part of an electrically connected system. As such, any electronically connected part that could provide an “emergency service” could meet this limitation.)
transmitting the position information to an emergency service node. . ([0008], [0044-0046], [0063]) As understood by the specification, an “emergency service node” is a part of an electrically connected system. As such, any electronically connected part that could provide an “emergency service” could meet this limitation.)
Hashimoto does not teach sensing the position of the occupant after the collision and transmitting that information to the emergency service node.
Breed, however, does teach sensing the position of the occupant after the collision and transmitting that information to the emergency service node. (Page 83, Column 92, Lines 4 to 24. Page 84, Column 93 Line 55 to Column 94, Line 11. )
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Breed such that the detection can occur also after the accident and send the data to an emergency service node because new ambulances, EMS, or those who wish to assist might find information regarding the current location and position of the occupants of the vehicle valuable and useful when it comes to providing aid.
For Claim 19, Hashimoto teaches The non-transitory computer-readable medium of claim 15, wherein instructions cause the processor to perform :
Hashimoto does not teach predicting an area of the transport that the occupant will impact during the collision,
wherein the modifying comprises:
modifying the current position of the occupant based on the area of impact.
Nave, however, does teach predicting an locations of the occupant and locations of the structures of the vehicle, and their relative distance to determine injuries during the collision, (Page 22, Column 18, Line 11 to 46
(59) In the exemplary embodiment, AM server 206 generates 310 a scenario model of the vehicular crash based upon the received sensor data. Scenario models may predict damage to vehicle 100 and injuries that may be experiences by driver 115 and passengers 120 of vehicle 100. In one embodiment, the received sensor data may include sensor data from occupant position sensors that identify a location and/or a position of each occupant within vehicle 100 prior to or at the time of the vehicular crash. The location and/or position of each occupant may be used to determine what forces were applied to an occupant's joints and skeletal structure during the crash and identify any corresponding potential injuries. For example, a front row passenger 120 turning around to help a child in the back of vehicle 100 may injure his or her back during the vehicular crash because his or her position at impact would not protect his or her body from injury. The sensor data from the occupant position sensors may be combined with data indicating the position of support structures of vehicle 100 (e.g., frame, windows, seats, steering wheel, etc.) to identify any potential injuries that an occupant may have sustained due to the occupant's position relative to the positions of the support structures. In the exemplary embodiment, AM server 206 accesses a database, such as database 420 (shown in FIG. 4). Database 420 may contain a plurality of crash scenarios and the sensor data associated with these crash scenarios. The scenarios may be based upon information from vehicle crash testing facilities, from past crashes that AM server 206 has analyzed, and/or from other sources that allow AM server 206 to operate as described here. AM server 206 compares the received sensor data with the different stored crash scenarios to generate 310 a scenario model that is the most likely match for the vehicular crash. For example, AM server 206 may determine that vehicle 100 was rear-ended by another vehicle that was going approximately 30 miles an hour while vehicle 100 was stopped.
Page 29, Column 31 Line 66 to Column 32 Line 25
(138) The method 1100 may include reconstructing occupant skeletal positioning prior to, during, and/or after the vehicle collision 1110. Occupant skeletal positioning may include and/or account for position of occupant joins, spine, arms, legs, torso, neck, face, head, major bones, hands, feet, etc. Occupant skeletal positioning may also include, account for, and/or characterize occupant position as normal or face-forward sitting, reaching forward, reaching to the side or rear, torso or spine twisted, head or neck twisted or turned, size of occupant (adult, child, height, weight), etc.
(139) The method 1100 may include estimating or calculating a likelihood and/or type (or body location) of major injury to one or more occupants based upon (1) impact force and direction of force on the vehicle; (2) vehicle weight distribution; and/or (3) occupant skeletal positioning prior to, during, and/or after the vehicle collision 1112. For instance, the method 1000 may determine or estimate if there was an abnormal stress on joints or bones, such as determine if any broken bones likely resulted from the vehicle collision.
(140) The method 1100 may include if the likelihood of major injury to any occupant is greater than a pre-determined threshold (such as greater than 5, 10, or 20%). If so, the method 1100, may take corrective action 1114. For instance, the method 1100 may request an ambulance and/or notify a hospital via wireless communication or data transmission sent over one or more radio frequency links or communication channels.)
wherein the modifying comprises:
modifying the current position of the occupant based on the predicted relative distance of the passenger from the vehicle structures. (Page 28, Column 29 Line 28 to 65
(125) In the exemplary embodiment, vehicle computer device 110 performs 925 at least one action to reduce a severity of a potential injury to at least one occupant prior to impact. Using the scenario model, vehicle computer device 110 may be able to determine an advantageous direction of facing for the at least one occupant. Vehicle computer device 110 may then generate a sound through the audio system of vehicle 100, such a horn or alarm sound. The sound would be generated to cause the at least one occupant to change to the advantageous direction of facing. For example, vehicle computer device 110 may generate a honking sound to cause the passenger to turn around to prevent or reduce potential injuries during the imminent vehicular crash. Additionally or alternatively, vehicle computer device 110 may select and engage one or more autonomous or semi-autonomous vehicle features or systems in an attempt to avoid or mitigate the vehicle collision.
(126) The types of autonomous or semi-autonomous vehicle-related functionality or technology that may be used with the present embodiments to replace human driver actions may include and/or be related to the following types of functionality: (a) fully autonomous (driverless); (b) limited driver control; (c) vehicle-to-vehicle (V2V) wireless communication; (d) vehicle-to-infrastructure (and/or vice versa) wireless communication; (e) automatic or semi-automatic steering; (f) automatic or semi-automatic acceleration; (g) automatic or semi-automatic braking; (h) automatic or semi-automatic blind spot monitoring; (i) automatic or semi-automatic collision warning; (j) adaptive cruise control; (k) automatic or semi-automatic parking/parking assistance; (l) automatic or semi-automatic collision preparation (windows roll up, seat adjusts upright, brakes pre-charge, etc.); (m) driver acuity/alertness monitoring; (n) pedestrian detection; (o) autonomous or semi-autonomous backup systems; (p) road mapping systems; (q) software security and anti-hacking measures; (r) theft prevention/automatic return; (s) automatic or semi-automatic driving without occupants; and/or other functionality.)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Nave such that the system will be predicting an area of the transport that the occupant will impact during the collision,
wherein the modifying comprises:
modifying the current position of the occupant based on the area of impact.
It would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in this way because estimating how the passenger will move, and what surfaces they will impact would help in determining potential injuries, which would be useful in ensuring that the passenger is not seated in particular ways to prevent injuries. It would be obvious to consider the area of impact rather than just relative distance, because if the relative distance becomes zero, then that indicates a high likely hood that an injury is sustained.
For Claim 21, Hashimoto teaches The method of claim 1, wherein the predicting of the resulting state comprises;
Hashimoto does not teach predicting an injury to the occupant based on the current position of the occupant
wherein the modifying of the current position comprises:
modifying the configuration of the seat based on the predicted injury.
Nave, however, does teach predicting an injury to the occupant based on the current position of the occupant
wherein the modifying of the current position comprises:
modifying the configuration of the seat based on the predicted injury. (
Page 27 Column 28 Line 63 to Page 28, Column 30, Line 20)
(123) In some embodiments, vehicle computer device 110 generates a scenario model of the potential vehicular crash based upon the received external and/or internal sensor data. Scenario models may predict damage to vehicle 100 and injuries that may be experiences by driver 115 and passengers 120 of vehicle 100. In the exemplary embodiment, vehicle computer device 110 accesses a database, such as database 202 (shown in FIG. 2). Database 202 may contain a plurality of crash scenarios and the sensor data associated with these crash scenarios. The scenarios may be based upon information from vehicle crash testing facilities, from past crashes that AM server 415 has analyzed, and/or from other sources that allow vehicle computer device 110 to operate as described herein. Vehicle computer device 110 compares the received sensor data with the different stored crash scenarios to generate a scenario model that is the most likely match for the imminent vehicular crash. In some further embodiments, vehicle computer device 110 may communicate the sensor data to AM server 415, where AM server 415 may generate the scenario model. In the some of these embodiments, vehicle computer device 110 determines one or more potential injuries to one or more occupants of vehicle 100 based upon the positional information and the scenario model. Vehicle computer device 110 may also determine a severity for each potential injury.
(124) In some embodiments, vehicle computer device 110 generates a plurality of scenario models that may fit the sensor data received. Vehicle computer device 110 may then rank the generated scenarios based upon the likelihood or degree of certainty that the scenario is correct. In some further embodiments, vehicle computer device 110 may compare the degree of certainty to a predetermined threshold.
(125) In the exemplary embodiment, vehicle computer device 110 performs 925 at least one action to reduce a severity of a potential injury to at least one occupant prior to impact. Using the scenario model, vehicle computer device 110 may be able to determine an advantageous direction of facing for the at least one occupant. Vehicle computer device 110 may then generate a sound through the audio system of vehicle 100, such a horn or alarm sound. The sound would be generated to cause the at least one occupant to change to the advantageous direction of facing. For example, vehicle computer device 110 may generate a honking sound to cause the passenger to turn around to prevent or reduce potential injuries during the imminent vehicular crash. Additionally or alternatively, vehicle computer device 110 may select and engage one or more autonomous or semi-autonomous vehicle features or systems in an attempt to avoid or mitigate the vehicle collision.
(126) The types of autonomous or semi-autonomous vehicle-related functionality or technology that may be used with the present embodiments to replace human driver actions may include and/or be related to the following types of functionality: (a) fully autonomous (driverless); (b) limited driver control; (c) vehicle-to-vehicle (V2V) wireless communication; (d) vehicle-to-infrastructure (and/or vice versa) wireless communication; (e) automatic or semi-automatic steering; (f) automatic or semi-automatic acceleration; (g) automatic or semi-automatic braking; (h) automatic or semi-automatic blind spot monitoring; (i) automatic or semi-automatic collision warning; (j) adaptive cruise control; (k) automatic or semi-automatic parking/parking assistance; (l) automatic or semi-automatic collision preparation (windows roll up, seat adjusts upright, brakes pre-charge, etc.); (m) driver acuity/alertness monitoring; (n) pedestrian detection; (o) autonomous or semi-autonomous backup systems; (p) road mapping systems; (q) software security and anti-hacking measures; (r) theft prevention/automatic return; (s) automatic or semi-automatic driving without occupants; and/or other functionality.
(127) For the method discussed directly above, the wireless communication-based autonomous or semi-autonomous vehicle technology or functionality may include and/or be related to: automatic or semi-automatic steering; automatic or semi-automatic acceleration and/or braking; automatic or semi-automatic blind spot monitoring; automatic or semi-automatic collision warning; adaptive cruise control; and/or automatic or semi-automatic parking assistance. Additionally or alternatively, the autonomous or semi-autonomous technology or functionality may include and/or be related to: driver alertness or responsive monitoring; pedestrian detection; artificial intelligence and/or back-up systems; navigation or GPS-related systems; security and/or anti-hacking measures; and/or theft prevention systems.
(128) In other embodiments, vehicle computer device 110 may be able to determine an advantageous position for the at least one occupant. Vehicle computer device 110 may cause a seat to shift or move, such as adjusting the recline angle of the seat, to cause the occupant to change to the advantageous position. Vehicle computer device 110 may also rotate the seat of occupant to cause the occupant to change to the advantageous position or advantageous facing.)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Nave such that the system predicts injuries and then modifies the user’s position based on that prediction because if an upcoming collision is likely to lead to a severe injury, and a different position would not lead to that injury, it would be useful to move the occupant into a safer position. This would be expected to be useful at reducing expected injuries.
For Claim 22, Hashimoto teaches The method of claim 1, wherein the predicting of the occurrence of the collision comprises;
Hashimoto does not teach determining a likelihood of the collision based on a current driving environment of the transport, and
wherein the method further comprises:
predicting a state of the occupant after the collision in response to the likelihood of the collision being above a predefined threshold.
(Figure 11, Page 27 Column 28 Line 34 to Page 28 Column 29 Line 20
(121) Vehicle computer device 110 determines 915 that a potential vehicular crash is imminent based upon the received external sensor data. For example, in the exemplary embodiment, external sensor 105 is an external sensor and may show that another vehicle is about to collide with vehicle 100. Or external sensor 105 may be an impact sensor or any other sensor that allows vehicle computer device 110 to work as described herein.
(122) Vehicle computer device 110 determines 920 positional information for at least one occupant of vehicle 100. Positional information may include a position of an occupant, a direction of facing of the occupant, a size of the occupant, and/or a skeletal positioning of the occupant. The position of the occupant may include which seat the occupant occupies. The direction of facing of the occupant may include whether the occupant is facing forward, reaching forward, reaching to the side, and/or has his/her head turned. The size of the occupant may determine whether the occupant is an adult or a child. The size of the occupant may also include the occupant's height. The skeletal positioning may include positioning of the occupant's joints, spine, arms, legs, torso, neck face, head, major bones, hands, and/or feet. In some embodiments, the internal sensors 105 constantly transmit sensor data to vehicle computer device 110, which constantly determines 920 the positional information of the occupants. In other embodiments, vehicle computer device 110 transmits the internal sensor data to AM server 415, which determines 920 the positional information and transmits that information to vehicle computer device 110.
(123) In some embodiments, vehicle computer device 110 generates a scenario model of the potential vehicular crash based upon the received external and/or internal sensor data. Scenario models may predict damage to vehicle 100 and injuries that may be experiences by driver 115 and passengers 120 of vehicle 100. In the exemplary embodiment, vehicle computer device 110 accesses a database, such as database 202 (shown in FIG. 2). Database 202 may contain a plurality of crash scenarios and the sensor data associated with these crash scenarios. The scenarios may be based upon information from vehicle crash testing facilities, from past crashes that AM server 415 has analyzed, and/or from other sources that allow vehicle computer device 110 to operate as described herein. Vehicle computer device 110 compares the received sensor data with the different stored crash scenarios to generate a scenario model that is the most likely match for the imminent vehicular crash. In some further embodiments, vehicle computer device 110 may communicate the sensor data to AM server 415, where AM server 415 may generate the scenario model. In the some of these embodiments, vehicle computer device 110 determines one or more potential injuries to one or more occupants of vehicle 100 based upon the positional information and the scenario model. Vehicle computer device 110 may also determine a severity for each potential injury.)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto in light of Nave such that the system determines that a vehicle will likely be in a collision, and predict future location of the occupants because it would allow the system to predict what injuries they may sustain, and may assist the vehicle in taking preventative actions to help the occupant.
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hashimoto in light of Nave in light of Breed, in light of Kentley (US Pub 2017/0120803 A1), in light of Lu et al (US Pub 2008/0147277 A1), hereafter known as Lu.
For Claim 3, Hashimoto teaches The method of claim 1, wherein the predicting of the future occurrence of the collision comprises:
identifying the transport being involved in the collision based on a current driving environment. ([0042])
Hashimoto does not teach identifying a probability of the transport being involved in the collision based on a familiarity of a current driving environment to one or more of the transport or the occupant.
Lu, however, does teach that unfamiliar environments for one or more of the transport and the occupant might be an indicate a collision is imminent or more likely. ([0053], [0006-0007])
Kentley, however, does teach determining a probability of the transport being in a collision; (Figures 2A, 2B, 2C, [0083], [0169])
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto’s vehicle control system with Lu’s use of determining unusual circumstances and unfamiliar environments as a possible indicator of a collision and Kentley’s use of determining probabilities for collisions such that identifying a probability of the transport being involved in the collision based on a familiarity of a current driving environment to one or more of the transport or the occupant , because the situation the vehicle in is totally foreign to the driving system or driver, the system or driver may not know the proper action to safely navigation the situation, and may be more likely to crash. This would be especially true for autonomous driving systems, which may not be able to safely create driving plans without having historical data to rely upon.
For Claim 10, Hashimoto teaches The system of claim 8, wherein the processor is configured to;
identify the transport being involved in the collision based on a current driving environment. ([0042])
Hashimoto does not teach identify a probability of the transport being involved in the collision based on a familiarity of a current driving environment to one or more of the transport or the occupant.
Lu, however, does teach that unfamiliar environments for one or more of the transport and the occupant might be an indicate a collision is imminent or more likely. ([0053], [0006-0007])
Kentley, however, does teach determining a probability of the transport being in a collision; (Figures 2A, 2B, 2C, [0083], [0169])
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto’s vehicle control system with Lu’s use of determining unusual circumstances and unfamiliar environments as a possible indicator of a collision and Kentley’s use of determining probabilities for collisions such that identify a probability of the transport being involved in the collision based on a familiarity of a current driving environment to one or more of the transport or the occupant , because the situation the vehicle in is totally foreign to the driving system or driver, the system or driver may not know the proper action to safely navigation the situation, and may be more likely to crash. This would be especially true for autonomous driving systems, which may not be able to safely create driving plans without having historical data to rely upon.
For Claim 17, Hashimoto teaches The non-transitory computer-readable medium of claim 15, wherein the predicting of the future occurrence of the collision comprises:
identifying the transport being involved in the collision based on a current driving environment. ([0042])
Hashimoto does not teach identifying a probability of the transport being involved in the collision based on a familiarity of a current driving environment to one or more of the transport or the occupant.
Lu, however, does teach that unfamiliar environments for one or more of the transport and the occupant might be an indicate a collision is imminent or more likely. ([0053], [0006-0007])
Kentley, however, does teach determining a probability of the transport being in a collision; (Figures 2A, 2B, 2C, [0083], [0169])
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto’s vehicle control system with Lu’s use of determining unusual circumstances and unfamiliar environments as a possible indicator of a collision and Kentley’s use of determining probabilities for collisions such that identifying a probability of the transport being involved in the collision based on a familiarity of a current driving environment to one or more of the transport or the occupant , because the situation the vehicle in is totally foreign to the driving system or driver, the system or driver may not know the proper action to safely navigation the situation, and may be more likely to crash. This would be especially true for autonomous driving systems, which may not be able to safely create driving plans without having historical data to rely upon.
Claims 4, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Hashimoto in light of Breed in light of Nave in light of Kentley et al (US Pub 2017/0120803 A1), hereafter known as Kentley in light of McClellan et al (US Pub 2008/0306996 A1), hereafter known as McClellan.
For Claim 4, Hashimoto teaches The method of claim 1, wherein the predicting of the future occurrence of the collision comprises:
identifying the transport being involved in the collision. ([0042])
Hashimoto does not teach identifying a probability of the transport being involved in the collision based on a comparison of the transport with other transports, with occupants in positions similar to the current position, in similar driving environments.
McClellan, however, does teach wherein the predicted result is based on a comparison of the transport with other transports, with occupants in positions similar to the current position, in similar driving environments. ([0033], [0024-0025])
Kentley, however, does teach determining a probability of the transport being in a collision; (Figures 2A, 2B, 2C, [0083], [0169])
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto’s vehicle control method with McClellan’s use of predicting results of a collision by comparing the collision to other collisions and Kentley’s use of determining probabilities such that identifying a probability of the transport being involved in the collision based on a comparison of the transport with other transports, with occupants in positions similar to the current position, in similar driving environments because it could be a quick and effective method of determining the likely results and chances of an accident if the situation is very similar to a number of earlier accidents. Such information could be valuable for performing cost benefit analysis on potential actions.
For Claim 11, Hashimoto teaches The system of claim 8, wherein the processor is further configured to;
identifying the transport being involved in the collision. ([0042])
Hashimoto does not teach identify a probability of the transport being involved in the collision based on a comparison of the transport with other transports, with occupants in positions similar to the current position, in similar driving environments.
McClellan, however, does teach wherein the predicted result is based on a comparison of the transport with other transports, with occupants in positions similar to the current position, in similar driving environments. ([0033], [0024-0025])
Kentley, however, does teach determining a probability of the transport being in a collision; (Figures 2A, 2B, 2C, [0083], [0169])
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Hashimoto’s vehicle control method with McClellan’s use of predicting results of a collision by comparing the collision to other collisions and Kentley’s use of determining probabilities such that identify a probability of the transport being involved in the collision based on a comparison of the transport with other transports, with occupants in positions similar to the current position, in similar driving environments because it could be a quick and effective method of determining the likely results and chances of an accident if the situation is very similar to a number of earlier accidents. Such information could be valuable for performing cost benefit analysis on potential actions.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pasch et al (US Pub 2021/0407687 A1) relates to estimating injuries during a vehicle crash.
Nagasawa et al (US Pub 2022/0068137 A1) relates to reporting accidents to servers.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRISTAN J GREINER whose telephone number is (571)272-1382. The examiner can normally be reached Mon - Fri 7:30-4:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tran Khoi can be reached on Monday-Thursday. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/T.J.G./Examiner, Art Unit 3664
/KHOI H TRAN/Supervisory Patent Examiner, Art Unit 3656
Prosecution Insights