DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/24/2025 has been entered.
Response to Arguments
Applicant’s arguments, filed 09/24/2025, with respect to claims 1-4, 8-11, and 32-43, have been fully considered but are moot because the arguments do not apply to the current references and current combinations of references being used in the current rejection.
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 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 of this title, 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 1-4, 8, 11, 32-40 and 43 are rejected under 35 U.S.C. 103 as being unpatentable over KONISHI et al. (US 20190177119 A1), hereinafter referenced as KONISHI in view of NAGAI et al. (NAGAI TORU et al., machine translation of Japanese Patent Publication JP2014191145A, published as JP 2016062414 A), hereinafter referenced as NAGAI.
Regarding claim 1, KONISHI explicitly teaches a remote monitoring system (Fig. 1, #10 called a monitoring apparatus. Paragraph [0030]-KONISHI disclose FIG. 1 is a diagram for illustrating a monitoring apparatus. In paragraph [0031]-KONISHI discloses as illustrated in FIG. 1, the elevator monitoring apparatus 10 includes a monitoring camera 2, a determination unit 3, an adjustment unit 4, a recorder 5, and a video transmission device 6. Please also see Fig. 6 and 10) comprising:
at least one memory storing instructions (Fig. 1. Paragraph [0037]-KONISHI discloses the monitoring apparatus 10 includes a memory. The memory includes a read only memory (ROM) and a random access memory (RAM)), and at least one processor (Fig. 1. Paragraph [0037]-KONISHI discloses the monitoring apparatus 10 includes a processor) configured to execute the instructions (Fig. 1. Paragraph [0037]-KONISHI discloses the recorder 5 is formed of the memory. The determination unit 3 and the adjustment unit 4 are formed of the processor and the memory. The determination unit 3 and the adjustment unit 4 are implemented by the processor executing a program stored in the memory. Further, a plurality of processors and a plurality of memories may cooperate with each other to implement the functions of the determination unit 3 and the adjustment unit 4) to:
receive an internal image of an inside object of a mobile object (Fig. 2. Paragraph [0039]-KONISHI discloses first the monitoring camera 2 takes an image of the interior of the elevator car 1 to acquire the car interior image a, and outputs the result as the car interior image data b to the determination unit 3 and the recorder 5 (Step S1) (wherein the monitoring apparatus may be a vehicle or elevator car). Please see paragraph [0122]) through a network (Fig. 1. Paragraph [0036]-KONISHI discloses the video transmission device 6 acquires the car interior image data b from the recorder 5 to transmit the car interior image data b to the outside, for example, a monitoring center. In the video transmission device 6, the image quality and the transmission frequency or the transmission interval to be used when the image is transmitted to the outside can be changed);
determine internal image quality indicating quality of the internal image (Fig. 4. Paragraph [0033]-KONISHI discloses the determination unit 3 receives the car interior image data b from the monitoring camera 2. The determination unit 3 calculates, based on the car interior image data b, the number of passengers in the elevator car 1 and a degree of positional imbalance of the passengers in the elevator car 1 to output the results as a determination result c (wherein the number of people and the positional imbalance are used to determine whether a dangerous phenomena exists). In paragraph [0034]-KONISHI discloses the adjustment unit 4 determines, based on the determination result c, an image quality and a frame rate to be used when the recorder 5 records the car interior image data b, and outputs the results as a recording density d to the recorder 5 (wherein the recording density and frame rate represent an internal image quality)) such that a quality of the important area is higher than a quality of an area other than the important area in the internal image (Fig. 2. Paragraph [0049]-KONISHI discloses the limited recording capacity of the recorder 5 can be effectively utilized by changing the image quality and the frame rate depending on the importance. Each of the image quality and the frame rate may be changed at two stages of the “high level” and the “low level”, or the importance may be ranked (wherein importance is based on whether the number of people or the positional imbalance of people within the car indicate the possibility of a dangerous phenomenon)); and
adjust the quality of the internal image based on the internal image quality (Fig. 1. Paragraph [0063]-KONISHI discloses the image data having high importance is recorded at a high image quality and a high frame rate, and is transmitted at a high image quality and a high transmission frequency. Meanwhile, the image data having low importance is recorded at a low image quality and a low frame rate, and is transmitted at a low image quality and a low transmission frequency. In this manner, the image data having high importance can be ensured to be recorded and transmitted at a high quality. Further, for the image data having low importance, the recording density and the transmission frequency can be suppressed to be low, and thus the amount of data can be reduced as a whole).
KONISHI fails to explicitly teach define an important area containing a person in the internal image based on whether the person is standing in the internal image; predict a risk of occurrence of a passenger falling down inside the mobile object based on the internal image and situation information indicating a situation of the mobile object.
However, NAGAI explicitly teaches predict a risk of occurrence of a passenger falling down (Fig. 1. Paragraph [0011]-NAGAI discloses 1A to 1D are block diagrams showing components of an in-vehicle monitoring device 1. In paragraph [0013]-NAGAI discloses the in-vehicle monitoring apparatus 1 further includes a risk determination means 15 that determines the risk of the passenger falling over based on the passenger's riding state and the traveling state of the vehicle 100. The notification means 13 notifies the safety of the passenger according to the determined degree of risk, and the vehicle control means 14 controls the vehicle 100 according to the determined degree of risk. Further in paragraph [0047]-NAGAI discloses the danger degree determination means 15 includes an information storage unit 110 for storing the boarding state and the traveling state of the vehicle 100 shown in FIG. 2 and an operation control unit 109 for determining the degree of risk of the passenger falling based on the boarding state and the traveling state. Further, the information storage unit 110 stores a program for determining the degree of danger based on the riding state and the traveling state) inside the mobile object (Fig. 2, #100 called a vehicle. Paragraph [0012]-NAGAI discloses the in-vehicle monitoring device 1 is mounted on, for example, a vehicle 100 for public passenger transport for the purpose of transporting a large number of passengers, such as a bus, a train, and a new transportation system. In paragraph [0019]-NAGAI discloses as shown in FIG. 2, the vehicle 100 includes an in-vehicle camera 105. In paragraph [0020]-NAGAI discloses FIG. 4 is a perspective view showing an example of the arrangement of the in-vehicle camera 105 through a part of the vehicle 100. The in-vehicle camera 105 captures images of seats, passengers, baggage, etc. in the vehicle 100 (wherein the in-vehicle camera 105 may be equipped with a fisheye lens and pan head control and may be placed on the ceiling of the vehicle 100 in the front, middle and rear direction of the vehicle 100). Please also read paragraph [0022]) based on the internal image (Paragraph [0032]-NAGAI discloses the riding condition grasping means 11 processes the image obtained from the in-vehicle camera 105 and the information obtained from the in-vehicle sensor 107 by the arithmetic control unit 109, and the necessary information from the information storage unit 110 by the arithmetic control unit 109. Further in paragraph [0042]-NAGAI discloses the boarding state grasping means 11 processes the image and information obtained from the in-vehicle camera 105 and the in-vehicle sensor 107 by the operation control unit 109, reads necessary information from the information storage unit 110, or the information storage unit 110 (wherein the riding state and/or boarding state is determined from the internal image and may include a passenger’s attribute information, location within the vehicle, position and attitude, such as the state of sitting or standing, presence or absence of movement and whether a handrail is being used in a standing position). Please also read paragraph [0022-0024, 0032 and 0071]) and situation information indicating a situation of the mobile object (Fig. 4. Paragraph [0046]-NAGAI discloses the traveling state grasping means 12 processes the information or image obtained from the vehicle sensor 108 or the out-of-vehicle camera 106 by the operation control unit 109 and reads necessary information from the information storage unit 110 or the information storage unit 110. The traveling state grasping means 12 detects the velocity, acceleration, angular velocity, inter-vehicle distance and relative velocity with the preceding vehicle, distance and relative velocity with the obstacle ahead of the host vehicle 100, and distance with the following vehicle (wherein the traveling state also includes, for example, whether vehicle is traveling along areas with high variable acceleration such as a steep slope or curves). Please also see Fig. 5-6 and read paragraph [0051-0053 and 0078-0081]]);
define an important area containing a person in the internal image based on whether the person is standing in the internal image (Fig. 4. Paragraph [0034]-NAGAI discloses when the in-vehicle camera 105 includes the face authentication camera 105 a, the arithmetic control unit 109 can calculate the boarding state by age group to obtain the boarding state of the elderly person who is relatively easy to fall. In paragraph [0056]-NAGAI discloses the tracking of the passenger P may be combined to determine the degree of danger. For example, when the passenger P rides, the danger degree determination unit 15 uses the operation control unit 109 to identify the age and sex of the passenger P based on the image of the face recognition camera 105a, such as an elderly person or a junior person (wherein the risk level is set to high). Then, the danger degree determination unit 15 performs image processing using the arithmetic control unit 109, the in-vehicle camera 105, and the like to perform tracking processing of the passenger P having a high degree of danger, and finally the position where the passenger P's riding condition is determined. When detecting a riding state such as the passenger P standing up or moving on the passage A while the vehicle 100 is traveling, the risk degree determination unit 15 increases the risk degree).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KONISHI of having a remote monitoring system, with the teachings of NAGAI of having define an important area containing a person in the internal image based on whether the person is standing in the internal image; predict a risk of occurrence of a passenger falling down inside the mobile object based on the internal image and situation information indicating a situation of the mobile object.
Wherein having KONISHI’s remote monitoring system having define an important area containing a person in the internal image based on whether the person is standing in the internal image; predict a risk of occurrence of a passenger falling down inside the mobile object based on the internal image and situation information indicating a situation of the mobile object.
The motivation behind the modification would have been to obtain a remote monitoring system that more effectively and efficiently monitors driving and passenger behaviors as well as safety concerns, since both Konishi and NAGAI concern image analysis and monitoring systems. Wherein Konishi’s systems and methods improve the ability to monitor the interior of an area more efficiently by allowing an image to be recorded and transmitted more efficiently, while NAGAI’s systems and methods improves driving technology by monitoring and managing driving and passenger risks. Please see Konishi et al. (US 20190177119 A1), Abstract and Paragraph [0028 and 0122-0125] and NAGAI et al. (NAGAI et al., machine translation of Japanese Patent Publication JP2014191145A, published as JP 2016062414 A), Abstract and Paragraph [0092 and 0095].
Regarding claim 2, KONISHI in view of NAGAI explicitly teach the remote monitoring system according to Claim 1, KONISHI fails to explicitly teach wherein the at least one processor is configured to execute the instructions to predict acceleration of the mobile object in response to the situation information about the mobile object and predict the risk based on a result of the predicted acceleration.
However, NAGAI explicitly teaches wherein the at least one processor (Paragraph [0030]-NAGAI discloses the arithmetic control unit 109 is configured by, for example, an electronic circuit such as a central processing unit (CPU). The information storage unit 110 is configured of, for example, a main storage device to which the CPU can directly access, and stores various programs and information) is configured to execute the instructions to predict acceleration of the mobile object in response to the situation information about the mobile object (Fig. 4. Paragraph [0050]-NAGAI discloses a relatively large acceleration occurs while the vehicle 100 starts moving from a stopped state and increases in speed to shift to constant speed traveling. A relatively large acceleration in the reverse direction occurs during the period from when the vehicle 100 travels at a constant speed to when the vehicle decelerates and stops. Acceleration is also generated when the vehicle is decelerated to secure the distance to the preceding vehicle when traveling at a low speed, or accelerated to narrow the distance to the preceding vehicle. Furthermore, during turning, running on a steep slope, or running on a sharp curve, acceleration may occur not only in the front-rear direction of the vehicle 100 but also in the left-right direction or the up-down direction. In paragraph [0051]-NAGAI discloses the danger degree determination unit 15 executes the program stored in the information storage unit 110 by the arithmetic control unit 109, and the traveling state of the vehicle 100 is based on information such as speed, acceleration, angular velocity, route map, traveling route, etc. It is determined whether the vehicle is in any one of running, stopping, turning, running on a steep slope, or running on a sharp curve, from stop to start) and predict the risk based on a result of the predicted acceleration (Fig. 4. Paragraph [0051]-NAGAI discloses the arithmetic control unit 109 determines the position and posture of the passenger P in the vehicle 100, the boarding state of elderly people over 60, and the traveling state of the vehicle 100. Based on the determination, the degree of risk is determined (wherein multiple risk levels are assessed, which may be based the position/type/density of passengers, the use of hand rails, and whether the vehicle will experience large variation in acceleration/deceleration due to the vehicle being in between start and stop or running on a steep slope or curve). Further in paragraph [0054]-NAGAI discloses the degree of risk can be determined in consideration of other various information. If the inter-vehicle distance with the preceding vehicle and the relative speed exceed the predetermined threshold and there is a risk of collision with the preceding vehicle unless the host vehicle 100 is decelerated, the risk is high. Please also see Fig. 8 and read paragraph [0052-0053 and 0080]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KONISHI in view of NAGAI of having a remote monitoring system, with the teachings of NAGAI of having wherein the at least one processor is configured to execute the instructions to predict acceleration of the mobile object in response to the situation information about the mobile object and predict the risk based on a result of the predicted acceleration.
Wherein having KONISHI’s remote monitoring system having wherein the at least one processor is configured to execute the instructions to predict acceleration of the mobile object in response to the situation information about the mobile object and predict the risk based on a result of the predicted acceleration.
The motivation behind the modification would have been to obtain a remote monitoring system that more effectively and efficiently monitors driving and passenger behaviors as well as safety concerns, since both KONISHI and NAGAI concern image analysis and monitoring systems. Wherein KONISHI’s systems and methods improve the ability to monitor the interior of an area more efficiently by allowing an image to be recorded and transmitted more efficiently, while NAGAI’s systems and methods improves driving technology by monitoring and managing driving and passenger risks. Please see KONISHI et al. (US 20190177119 A1), Abstract and Paragraph [0028 and 0122-0125] and NAGAI et al. (NAGAI et al., machine translation of Japanese Patent Publication JP2014191145A, published as JP 2016062414 A), Abstract and Paragraph [0092 and 0095].
Regarding claim 3, KONISHI in view of NAGAI explicitly teach the remote monitoring system according to Claim 2, KONISHI fails to explicitly teach wherein the at least one processor is configured to execute the instructions to compare an absolute value of the predicted acceleration with a threshold value, and predict that there is a risk of the accident in a case where the absolute value of the predicted acceleration is greater than or equal to the threshold value.
However, NAGAI explicitly teaches wherein the at least one processor (Paragraph [0030]-NAGAI discloses the arithmetic control unit 109 is configured by, for example, an electronic circuit such as a central processing unit (CPU). The information storage unit 110 is configured of, for example, a main storage device to which the CPU can directly access, and stores various programs and information) is configured to execute the instructions to compare an absolute value of the predicted acceleration with a threshold value, and predict that there is a risk of the accident (Fig. 4. Paragraph [0048]-NAGAI discloses FIG. 8 is a table showing an example of determination criteria for determining the risk of a passenger falling (wherein the determination is based on traveling state, riding state and boarding state, riding/boarding state includes information such as passenger position, age, use of hand rail and traveling state includes information such as speed, acceleration, angular velocity, inter-vehicle distance as well as a determination of whether the vehicle is in a state of typically associated with large variation in acceleration/deceleration). In paragraph [0050]-NAGAI discloses a relatively large acceleration occurs while the vehicle 100 starts moving from a stopped state and increases in speed to shift to constant speed traveling. A relatively large acceleration in the reverse direction occurs during the period from when the vehicle 100 travels at a constant speed to when the vehicle decelerates and stops. Acceleration is also generated when the vehicle is decelerated to secure the distance to the preceding vehicle when traveling at a low speed, or accelerated to narrow the distance to the preceding vehicle. Furthermore, during turning, running on a steep slope, or running on a sharp curve, acceleration may occur not only in the front-rear direction of the vehicle 100 but also in the left-right direction or the up-down direction) in a case where the absolute value of the predicted acceleration is greater than or equal to the threshold value (Fig. 4. Paragraph [0051]-NAGAI discloses it is determined whether the vehicle is in any one of running, stopping, turning, running on a steep slope, or running on a sharp curve, from stop to start. In paragraph [0053]-NAGAI discloses when all passengers P standing in the aisle are gripped by a strap or handrail, and the vehicle 100 is turning, the risk is the third highest. When the passenger P standing in the aisle is densely packed and the vehicle 100 is traveling in a steep gradient, the risk is the second highest. When the passenger P standing in the aisle is densely packed, and the vehicle 100 is traveling on a sharp curve, the level of risk is highest 6. Further in paragraph [0054]-NAGAI discloses if the inter-vehicle distance with the preceding vehicle and the relative speed exceed the predetermined threshold and there is a risk of collision with the preceding vehicle unless the host vehicle 100 is decelerated, the risk is high. Please also read paragraph [0078-0081]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KONISHI in view of NAGAI of having a remote monitoring system, with the teachings of NAGAI of having wherein the at least one processor is configured to execute the instructions to compare an absolute value of the predicted acceleration with a threshold value, and predict that there is a risk of the accident in a case where the absolute value of the predicted acceleration is greater than or equal to the threshold value.
Wherein having KONISHI’s remote monitoring system having wherein the at least one processor is configured to execute the instructions to compare an absolute value of the predicted acceleration with a threshold value, and predict that there is a risk of the accident in a case where the absolute value of the predicted acceleration is greater than or equal to the threshold value.
The motivation behind the modification would have been to obtain a remote monitoring system that more effectively and efficiently monitors driving and passenger behaviors as well as safety concerns, since both KONISHI and NAGAI concern image analysis and monitoring systems. Wherein KONISHI’s systems and methods improve the ability to monitor the interior of an area more efficiently by allowing an image to be recorded and transmitted more efficiently, while NAGAI’s systems and methods improves driving technology by monitoring and managing driving and passenger risks. Please see KONISHI et al. (US 20190177119 A1), Abstract and Paragraph [0028 and 0122-0125] and NAGAI et al. (NAGAI et al., machine translation of Japanese Patent Publication JP2014191145A, published as JP 2016062414 A), Abstract and Paragraph [0092 and 0095].
Regarding claim 4, KONISHI in view of NAGAI explicitly teach the remote monitoring system according to Claim 1, KONISHI further teaches wherein in a case where the result of the predicted risk indicates presence of a risk of the accident, the at least one processor is configured to execute the instructions to determine higher quality for the quality of the internal image compared to a case in which the result of the predicted risk indicates no risk of the accident (Fig. 1. Paragraph [0049]-KONISHI discloses the limited recording capacity of the recorder 5 can be effectively utilized by changing the image quality and the frame rate depending on the importance. Each of the image quality and the frame rate may be changed at two stages of the “high level” and the “low level”, or the importance may be ranked (wherein importance is based on whether the number of people or the positional imbalance of people within the car indicates the possibility of a dangerous phenomenon). Further in paragraph [0063]-KONISHI discloses the image data having high importance is recorded at a high image quality and a high frame rate, and is transmitted at a high image quality and a high transmission frequency. The image data having low importance is recorded at a low image quality and a low frame rate, and is transmitted at a low image quality and a low transmission frequency).
Regarding claim 8, KONISHI in view of NAGAI explicitly teach the remote monitoring system according to Claim 1, KONISHI fails to explicitly teach wherein the situation information includes information about a position of the mobile object, and the at least one processor is configured to execute the instructions to predict, based on information about the position of the mobile object, at least one of a situation in which the mobile object stops at a station where a passenger of the mobile object gets on or off the mobile object or a situation in which the mobile object leaves the station, and in a case where a situation in which the mobile object stops at or leaves the station is predicted, predict that there is a risk of occurrence of the accident.
However, NAGAI explicitly teaches wherein the situation information includes information about a position of the mobile object, and the at least one processor (Paragraph [0030]-NAGAI discloses the arithmetic control unit 109 is configured by, for example, an electronic circuit such as a central processing unit (CPU). The information storage unit 110 is configured of, for example, a main storage device to which the CPU can directly access, and stores various programs and information) is configured to execute the instructions to predict, based on information about the position of the mobile object (Paragraph [0047]-NAGAI discloses the danger degree determination means 15 includes an information storage unit 110 for storing the boarding state and the traveling state of the vehicle 100 shown in FIG. 2 and an operation control unit 109 for determining the degree of risk of the passenger falling based on the boarding state and the traveling state. Further, the information storage unit 110 stores a program for determining the degree of danger based on the riding state and the traveling state (wherein the riding state and/or boarding state may include a passenger’s attribute information, location within the vehicle, position and attitude, such as the state of sitting or standing, presence or absence of movement and whether a handrail is being used in a standing position, and the traveling state may include route map, traveling map, direction, velocity, acceleration, angular velocity, inter-vehicle distance, relative velocity with the preceding vehicle, distance and relative velocity with the obstacle ahead of the host vehicle 100, and distance with the following vehicle)), at least one of a situation in which the mobile object stops at a station where a passenger of the mobile object gets on or off the mobile object or a situation in which the mobile object leaves the station, and in a case where a situation in which the mobile object stops at or leaves the station is predicted, predict that there is a risk of occurrence of the accident (Paragraph [0104]-NAGAI discloses when the vehicle 100 stops at the stop and the passenger P is getting on and off, the getting-in state grasping means 11 determines that the getting-in state of the passenger P is moving in step S1. Further, in step S2, the traveling state grasping means 12 grasps that the vehicle 100 is stopped. In this case, in step S4, for example, the degree of danger is determined to be high by the degree-of-risk determination means 15. Then, in step S3, the notification means 13 displays that the passenger P is moving on the monitor 103, or the driver 104 is notified that the passenger P is moving by audio guidance by the speaker 104. As a result, the driver's attention is drawn and the fall of the passenger P is prevented). In paragraph [0106]-NAGAI discloses then, in step S2, when it is determined that the vehicle is stopped by the traveling state grasping means 12 and the riding state of all the passengers P is seated or gripped by a strap or handrail, the danger in step S4. The degree of danger is determined to be low by the degree determination means 15. Here, when there is a passenger P who is not seated and is not gripped by a strap or a handrail, the degree of risk may be determined to be high. In this case, steps S1, S2, S4, and S3 are sequentially repeated as in the case where the passenger P is moving. Please also read paragraph [0050-0053 and 0105]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KONISHI in view of NAGAI of having a remote monitoring system, with the teachings of NAGAI of having wherein the situation information includes information about a position of the mobile object, and the at least one processor is configured to execute the instructions to predict, based on information about the position of the mobile object, at least one of a situation in which the mobile object stops at a station where a passenger of the mobile object gets on or off the mobile object or a situation in which the mobile object leaves the station, and in a case where a situation in which the mobile object stops at or leaves the station is predicted, predict that there is a risk of occurrence of the accident.
Wherein having KONISHI’s remote monitoring system having wherein the situation information includes information about a position of the mobile object, and the at least one processor is configured to execute the instructions to predict, based on information about the position of the mobile object, at least one of a situation in which the mobile object stops at a station where a passenger of the mobile object gets on or off the mobile object or a situation in which the mobile object leaves the station, and in a case where a situation in which the mobile object stops at or leaves the station is predicted, predict that there is a risk of occurrence of the accident.
The motivation behind the modification would have been to obtain a remote monitoring system that more effectively and efficiently monitors driving and passenger behaviors as well as safety concerns, since both KONISHI and NAGAI concern image analysis and monitoring systems. Wherein KONISHI’s systems and methods improve the ability to monitor the interior of an area more efficiently by allowing an image to be recorded and transmitted more efficiently, while NAGAI’s systems and methods improves driving technology by monitoring and managing driving and passenger risks. Please see KONISHI et al. (US 20190177119 A1), Abstract and Paragraph [0028 and 0122-0125] and NAGAI et al. (NAGAI et al., machine translation of Japanese Patent Publication JP2014191145A, published as JP 2016062414 A), Abstract and Paragraph [0092 and 0095].
Regarding claim 11, KONISHI in view of NAGAI explicitly teach the remote monitoring system according to Claim 1, KONISHI fails to explicitly teach wherein the situation information includes a distance between the mobile object and another mobile object present around the mobile object, and the at least one processor is configured to execute the instructions to predict, based on the distance between the mobile object and the another mobile object, a situation in which the mobile object is highly likely to come into contact with the another mobile object, and in a case where a situation is predicted in which the mobile object is likely to come into contact with the another mobile object, and in a case where an absolute value of predicted value of acceleration owing to motion to avoid the contact is greater than or equal to a threshold value, the at least one processor is configured to execute the instructions to predict that there is a risk of occurrence of the accident.
However, NAGAI explicitly teach wherein the situation information includes a distance between the mobile object and another mobile object present around the mobile object (Fig. 4. Paragraph [0046]-NAGAI discloses the traveling state grasping means 12 detects the velocity, acceleration, angular velocity, inter-vehicle distance and relative velocity with the preceding vehicle, distance and relative velocity with the obstacle ahead of the host vehicle 100, and distance with the following vehicle), and the at least one processor (Paragraph [0030]-NAGAI discloses the arithmetic control unit 109 is configured by, for example, an electronic circuit such as a central processing unit (CPU). The information storage unit 110 is configured of, for example, a main storage device to which the CPU can directly access, and stores various programs and information) is configured to execute the instructions to predict, based on the distance between the mobile object and the another mobile object, a situation in which the mobile object is highly likely to come into contact with the another mobile object, and in a case where a situation is predicted in which the mobile object is likely to come into contact with the another mobile object, and in a case where an absolute value of predicted value of acceleration owing to motion to avoid the contact is greater than or equal to a threshold value, the at least one processor is configured to execute the instructions to predict that there is a risk of occurrence of the accident (Fig. 4. Paragraph [0054]-NAGAI discloses the degree of risk can be determined in consideration of other various information. For example, if the inter-vehicle distance with the preceding vehicle and the relative speed exceed the predetermined threshold and there is a risk of collision with the preceding vehicle unless the host vehicle 100 is decelerated, the risk is high. Additionally in paragraph [0080]-NAGAI discloses the risk determination means 15 or the vehicle control means 14 can determine the risk of occurrence of an accident based on the distance between the front and rear vehicles or obstacles and the own vehicle 100 or the relative speed and the riding condition of the passenger P. For example, when a sudden braking or a rear-end collision is predicted based on the distance between the front and rear vehicles or obstacles and the own vehicle 100, the danger level is increased according to the riding state of the passenger P. The warning can be given to the driver and the passenger P in advance, and the fall of the passenger P can be suppressed more reliably. Please also read paragraph [0051-0053]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KONISHI in view of NAGAI of having a remote monitoring system, with the teachings of NAGAI of having wherein the situation information includes a distance between the mobile object and another mobile object present around the mobile object, and the at least one processor is configured to execute the instructions to predict, based on the distance between the mobile object and the another mobile object, a situation in which the mobile object is highly likely to come into contact with the another mobile object, and in a case where a situation is predicted in which the mobile object is likely to come into contact with the another mobile object, and in a case where an absolute value of predicted value of acceleration owing to motion to avoid the contact is greater than or equal to a threshold value, the at least one processor is configured to execute the instructions to predict that there is a risk of occurrence of the accident.
Wherein having KONISHI’s remote monitoring system having wherein the situation information includes a distance between the mobile object and another mobile object present around the mobile object, and the at least one processor is configured to execute the instructions to predict, based on the distance between the mobile object and the another mobile object, a situation in which the mobile object is highly likely to come into contact with the another mobile object, and in a case where a situation is predicted in which the mobile object is likely to come into contact with the another mobile object, and in a case where an absolute value of predicted value of acceleration owing to motion to avoid the contact is greater than or equal to a threshold value, the at least one processor is configured to execute the instructions to predict that there is a risk of occurrence of the accident.
The motivation behind the modification would have been to obtain a remote monitoring system that more effectively and efficiently monitors driving and passenger behaviors as well as safety concerns, since both KONISHI and NAGAI concern image analysis and monitoring systems. Wherein KONISHI’s systems and methods improve the ability to monitor the interior of an area more efficiently by allowing an image to be recorded and transmitted more efficiently, while NAGAI’s systems and methods improves driving technology by monitoring and managing driving and passenger risks. Please see KONISHI et al. (US 20190177119 A1), Abstract and Paragraph [0028 and 0122-0125] and NAGAI et al. (NAGAI et al., machine translation of Japanese Patent Publication JP2014191145A, published as JP 2016062414 A), Abstract and Paragraph [0092 and 0095].
Regarding claim 32, KONISHI in view of NAGAI explicitly teach the remote monitoring system according to Claim 1, KONISHI further teaches wherein the at least one processor is further configured to:
detect an attribute of the person in the internal image (Fig. 1. Paragraph [0045]-KONISHI discloses the monitoring apparatus according to Embodiment 1 of the present invention adjusts the recording density d and the communication frequency e of the car interior image data b acquired by the monitoring camera 2 in accordance with the number of people present in the space to be monitored and the positional imbalance of the people (wherein an attribute of a person is their position)).
KONISHI fails to explicitly teach define the important area containing the person in the internal image based on the attribute of the person and whether the person is standing in the internal image.
However, NAGAI explicitly teach define the important area containing the person in the internal image based on the attribute of the person and whether the person is standing in the internal image (Fig. 4. Paragraph [0056]-NAGAI discloses the tracking of the passenger P may be combined to determine the degree of danger. The danger degree determination unit 15 uses the operation control unit 109 to identify the age and sex of the passenger P based on the image of the face recognition camera 105a, such as an elderly person or a junior person (wherein the falling risk level is set to high). Then, the danger degree determination unit 15 performs image processing using the arithmetic control unit 109, the in-vehicle camera 105, and the like to perform tracking processing of the passenger P having a high degree of danger, and finally the position where the passenger P's riding condition is determined. When detecting a riding state such as the passenger P standing up or moving on the passage A while the vehicle 100 is traveling, the risk degree determination unit 15 increases the risk degree. In paragraph [0059]-NAGAI discloses the notification unit 13 causes at least one of the riding state and the risk degree to be displayed on the monitor 103 as a display device, and performs notification on the safety of the passenger P. In paragraph [0060]-NAGAI discloses elderly people, passengers P or baggage L who are not gripped by a strap or handrail in a state of standing in the aisle may be displayed by being distinguished by marks of different shapes or different colors. Please also see Fig. 5-6 and read paragraph [0070]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KONISHI in view of NAGAI of having a remote monitoring system, with the teachings of NAGAI of having define the important area containing the person in the internal image based on the attribute of the person and whether the person is standing in the internal image.
Wherein having KONISHI’s remote monitoring system having define the important area containing the person in the internal image based on the attribute of the person and whether the person is standing in the internal image.
The motivation behind the modification would have been to obtain a remote monitoring system that more effectively and efficiently monitors driving and passenger behaviors as well as safety concerns, since both KONISHI and NAGAI concern image analysis and monitoring systems. Wherein KONISHI’s systems and methods improve the ability to monitor the interior of an area more efficiently by allowing an image to be recorded and transmitted more efficiently, while NAGAI’s systems and methods improves driving technology by monitoring and managing driving and passenger risks. Please see KONISHI et al. (US 20190177119 A1), Abstract and Paragraph [0028 and 0122-0125] and NAGAI et al. (NAGAI et al., machine translation of Japanese Patent Publication JP2014191145A, published as JP 2016062414 A), Abstract and Paragraph [0092 and 0095].
Regarding claim 33, KONISHI in view of NAGAI explicitly teach the remote monitoring system according to Claim 32, KONISHI fails to explicitly teach wherein the at least one processor is further configured to: in a case where the person is determined to be a vulnerable person requiring special attention, the vulnerable person including at least one of a child, an elderly person, and a person with a disability, predict the risk of the occurrence of the passenger falling down to a first level that is higher than a second level set in a case where the person is not determined to be the vulnerable person.
However, NAGAI explicitly teaches wherein the at least one processor (Paragraph [0030]-NAGAI discloses the arithmetic control unit 109 is configured by, for example, an electronic circuit such as a central processing unit (CPU). The information storage unit 110 is configured of, for example, a main storage device to which the CPU can directly access, and stores various programs and information) is further configured to: in a case where the person is determined to be a vulnerable person requiring special attention, the vulnerable person including at least one of a child, an elderly person, and a person with a disability, predict the risk of the occurrence of the passenger falling down to a first level that is higher than a second level set in a case where the person is not determined to be the vulnerable person (Fig. 1. Paragraph [0048]-NAGAI discloses FIG. 8 is a table showing an example of determination criteria for determining the risk of a passenger falling. The danger degree determination means 15 reads the program stored in the information storage unit 110, the riding condition of the passenger P, and the traveling condition of the vehicle 100 by the arithmetic control unit 109, and the danger of the passenger falling. In paragraph [0056]-NAGAI discloses when the passenger P rides, the danger degree determination unit 15 uses the operation control unit 109 to identify the age and sex of the passenger P based on the image of the face recognition camera 105a, such as an elderly person or a junior person (wherein the falling risk level is set to high for elderly, pregnant and junior persons). The danger degree determination unit 15 performs image processing using the arithmetic control unit 109, the in-vehicle camera 105, and the like to perform tracking processing of the passenger P having a high degree of danger, and finally the position where the passenger P's riding condition is determined. When detecting a riding state such as the passenger P standing up or moving on the passage, while the vehicle 100 is traveling, the risk degree determination unit 15 increases the risk degree).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KONISHI in view of NAGAI of having a remote monitoring system, with the teachings of NAGAI of having wherein the at least one processor is further configured to: in a case where the person is determined to be a vulnerable person requiring special attention, the vulnerable person including at least one of a child, an elderly person, and a person with a disability, predict the risk of the occurrence of the passenger falling down to a first level that is higher than a second level set in a case where the person is not determined to be the vulnerable person.
Wherein having KONISHI’s remote monitoring system having wherein the at least one processor is further configured to: in a case where the person is determined to be a vulnerable person requiring special attention, the vulnerable person including at least one of a child, an elderly person, and a person with a disability, predict the risk of the occurrence of the passenger falling down to a first level that is higher than a second level set in a case where the person is not determined to be the vulnerable person.
The motivation behind the modification would have been to obtain a remote monitoring system that more effectively and efficiently monitors driving and passenger behaviors as well as safety concerns, since both KONISHI and NAGAI concern image analysis and monitoring systems. Wherein KONISHI’s systems and methods improve the ability to monitor the interior of an area more efficiently by allowing an image to be recorded and transmitted more efficiently, while NAGAI’s systems and methods improves driving technology by monitoring and managing driving and passenger risks. Please see KONISHI et al. (US 20190177119 A1), Abstract and Paragraph [0028 and 0122-0125] and NAGAI et al. (NAGAI et al., machine translation of Japanese Patent Publication JP2014191145A, published as JP 2016062414 A), Abstract and Paragraph [0092 and 0095].
Regarding claim 34, KONISHI explicitly teaches a remote monitoring method (Fig. 1. Paragraph [0030]-KONISHI disclose FIG. 1 is a diagram for illustrating a monitoring apparatus. In paragraph [0031]-KONISHI discloses as illustrated in FIG. 1, the elevator monitoring apparatus 10 includes a monitoring camera 2, a determination unit 3, an adjustment unit 4, a recorder 5, and a video transmission device 6. Please also see Fig. 6 and 10) comprising:
receiving an internal image of an inside of a mobile object (Fig. 2. Paragraph [0039]-KONISHI discloses first the monitoring camera 2 takes an image of the interior of the elevator car 1 to acquire the car interior image a, and outputs the result as the car interior image data b to the determination unit 3 and the recorder 5 (Step S1) (wherein the monitoring apparatus may be a vehicle or elevator car). Please see paragraph [0122]) through a network (Fig. 1. Paragraph [0036]-KONISHI discloses the video transmission device 6 acquires the car interior image data b from the recorder 5 to transmit the car interior image data b to the outside, for example, a monitoring center. In the video transmission device 6, the image quality and the transmission frequency or the transmission interval to be used when the image is transmitted to the outside can be changed);
determining internal image quality indicating quality of the internal image (Fig. 4. Paragraph [0033]-KONISHI discloses the determination unit 3 receives the car interior image data b from the monitoring camera 2. The determination unit 3 calculates, based on the car interior image data b, the number of passengers in the elevator car 1 and a degree of positional imbalance of the passengers in the elevator car 1 to output the results as a determination result c (wherein the number of people and the positional imbalance are used to determine whether a dangerous phenomena exists). In paragraph [0034]-KONISHI discloses the adjustment unit 4 determines, based on the determination result c, an image quality and a frame rate to be used when the recorder 5 records the car interior image data b, and outputs the results as a recording density d to the recorder 5 (wherein the recording density and frame rate represent an internal image quality)) such that a quality of the important area is higher than a quality of an area other than the important area in the internal image based on a result of the predicted risk and the important area (Fig. 2. Paragraph [0049]-KONISHI discloses the limited recording capacity of the recorder 5 can be effectively utilized by changing the image quality and the frame rate depending on the importance. Each of the image quality and the frame rate may be changed at two stages of the “high level” and the “low level”, or the importance may be ranked (wherein importance is based on whether the number of people or the positional imbalance of people within the car indicate the possibility of a dangerous phenomenon)); and
adjusting the quality of the internal image based on the internal image quality (Fig. 1. Paragraph [0063]-KONISHI discloses the image data having high importance is recorded at a high image quality and a high frame rate, and is transmitted at a high image quality and a high transmission frequency. Meanwhile, the image data having low importance is recorded at a low image quality and a low frame rate, and is transmitted at a low image quality and a low transmission frequency. In this manner, the image data having high importance can be ensured to be recorded and transmitted at a high quality. Further, for the image data having low importance, the recording density and the transmission frequency can be suppressed to be low, and thus the amount of data can be reduced as a whole).
KONISHI fails to explicitly teach predicting a risk of occurrence of a passenger falling down inside the mobile object; defining an important area containing a person in the internal image based on whether the person is standing in the internal image based on the internal image and situation information indicating a situation of the mobile object.
However, NAGAI explicitly teaches predicting a risk of occurrence of a passenger falling down (Fig. 1. Paragraph [0011]-NAGAI discloses 1A to 1D are block diagrams showing components of an in-vehicle monitoring device 1. In paragraph [0013]-NAGAI discloses the in-vehicle monitoring apparatus 1 further includes a risk determination means 15 that determines the risk of the passenger falling over based on the passenger's riding state and the traveling state of the vehicle 100. The notification means 13 notifies the safety of the passenger according to the determined degree of risk, and the vehicle control means 14 controls the vehicle 100 according to the determined degree of risk. Further in paragraph [0047]-NAGAI discloses the danger degree determination means 15 includes an information storage unit 110 for storing the boarding state and the traveling state of the vehicle 100 shown in FIG. 2 and an operation control unit 109 for determining the degree of risk of the passenger falling based on the boarding state and the traveling state. Further, the information storage unit 110 stores a program for determining the degree of danger based on the riding state and the traveling state) inside the mobile object (Fig. 2, #100 called a vehicle. Paragraph [0012]-NAGAI discloses the in-vehicle monitoring device 1 is mounted on, for example, a vehicle 100 for public passenger transport for the purpose of transporting a large number of passengers, such as a bus, a train, and a new transportation system. In paragraph [0019]-NAGAI discloses as shown in FIG. 2, the vehicle 100 includes an in-vehicle camera 105. In paragraph [0020]-NAGAI discloses FIG. 4 is a perspective view showing an example of the arrangement of the in-vehicle camera 105 through a part of the vehicle 100. The in-vehicle camera 105 captures images of seats, passengers, baggage, etc. in the vehicle 100 (wherein the in-vehicle camera 105 may be equipped with a fisheye lens and pan head control and may be placed on the ceiling of the vehicle 100 in the front, middle and rear direction of the vehicle 100). Please also read paragraph [0022]) based on the internal image (Paragraph [0032]-NAGAI discloses the riding condition grasping means 11 processes the image obtained from the in-vehicle camera 105 and the information obtained from the in-vehicle sensor 107 by the arithmetic control unit 109, and the necessary information from the information storage unit 110 by the arithmetic control unit 109. Further in paragraph [0042]-NAGAI discloses the boarding state grasping means 11 processes the image and information obtained from the in-vehicle camera 105 and the in-vehicle sensor 107 by the operation control unit 109, reads necessary information from the information storage unit 110, or the information storage unit 110 (wherein the riding state and/or boarding state is determined from the internal image and may include a passenger’s attribute information, location within the vehicle, position and attitude, such as the state of sitting or standing, presence or absence of movement and whether a handrail is being used in a standing position). Please also read paragraph [0022-0024, 0032 and 0071]) and situation information indicating a situation of the mobile object (Fig. 4. Paragraph [0046]-NAGAI discloses the traveling state grasping means 12 processes the information or image obtained from the vehicle sensor 108 or the out-of-vehicle camera 106 by the operation control unit 109 and reads necessary information from the information storage unit 110 or the information storage unit 110. The traveling state grasping means 12 detects the velocity, acceleration, angular velocity, inter-vehicle distance and relative velocity with the preceding vehicle, distance and relative velocity with the obstacle ahead of the host vehicle 100, and distance with the following vehicle (wherein the traveling state also includes, for example, whether vehicle is traveling along areas with high variable acceleration such as a steep slope or curves). Please also see Fig. 5-6 and read paragraph [0051-0053 and 0078-0081]]);
defining an important area containing a person in the internal image based on whether the person is standing in the internal image (Fig. 4. Paragraph [0034]-NAGAI discloses when the in-vehicle camera 105 includes the face authentication camera 105 a, the arithmetic control unit 109 can calculate the boarding state by age group to obtain the boarding state of the elderly person who is relatively easy to fall. In paragraph [0056]-NAGAI discloses the tracking of the passenger P may be combined to determine the degree of danger. For example, when the passenger P rides, the danger degree determination unit 15 uses the operation control unit 109 to identify the age and sex of the passenger P based on the image of the face recognition camera 105a, such as an elderly person or a junior person (wherein the risk level is set to high). Then, the danger degree determination unit 15 performs image processing using the arithmetic control unit 109, the in-vehicle camera 105, and the like to perform tracking processing of the passenger P having a high degree of danger, and finally the position where the passenger P's riding condition is determined. When detecting a riding state such as the passenger P standing up or moving on the passage A while the vehicle 100 is traveling, the risk degree determination unit 15 increases the risk degree);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KONISHI of having a remote monitoring method comprising: receiving an internal image of an inside of a mobile object through a network; determining internal image quality indicating quality of the internal image such that a quality of the important area is higher than a quality of an area other than the important area in the internal image based on a result of the predicted risk and the important area; and adjusting the quality of the internal image based on the internal image quality, with the teachings of NAGAI of having predicting a risk of occurrence of a passenger falling down inside the mobile object; defining an important area containing a person in the internal image based on whether the person is standing in the internal image based on the internal image and situation information indicating a situation of the mobile object.
Wherein having KONISHI’s remote monitoring method having predicting a risk of occurrence of a passenger falling down inside the mobile object; defining an important area containing a person in the internal image based on whether the person is standing in the internal image based on the internal image and situation information indicating a situation of the mobile object.
The motivation behind the modification would have been to obtain a remote monitoring method that more effectively and efficiently monitors driving and passenger behaviors as well as safety concerns, since both KONISHI and NAGAI concern image analysis and monitoring systems. Wherein KONISHI’s systems and methods improve the ability to monitor the interior of an area more efficiently by allowing an image to be recorded and transmitted more efficiently, while NAGAI’s systems and methods improves driving technology by monitoring and managing driving and passenger risks. Please see KONISHI et al. (US 20190177119 A1), Abstract and Paragraph [0028 and 0122-0125] and NAGAI et al. (NAGAI et al., machine translation of Japanese Patent Publication JP2014191145A, published as JP 2016062414 A), Abstract and Paragraph [0092 and 0095].
Regarding claim 35, KONISHI in view of NAGAI explicitly teach the remote monitoring method according to Claim 34, KONISHI further teaches further comprising: detecting an attribute of the person in the internal image (Fig. 1. Paragraph [0045]-KONISHI discloses the monitoring apparatus according to Embodiment 1 of the present invention adjusts the recording density d and the communication frequency e of the car interior image data b acquired by the monitoring camera 2 in accordance with the number of people present in the space to be monitored and the positional imbalance of the people (wherein an attribute of a person is their position)); and
KONISHI fails to explicitly teach defining the important area containing the person in the internal image based on the attribute of the person and whether the person is standing in the internal image.
However, NAGAI explicitly teaches defining the important area containing the person in the internal image based on the attribute of the person and whether the person is standing in the internal image (Fig. 4. Paragraph [0056]-NAGAI discloses the tracking of the passenger P may be combined to determine the degree of danger. The danger degree determination unit 15 uses the operation control unit 109 to identify the age and sex of the passenger P based on the image of the face recognition camera 105a, such as an elderly person or a junior person (wherein the falling risk level is set to high). Then, the danger degree determination unit 15 performs image processing using the arithmetic control unit 109, the in-vehicle camera 105, and the like to perform tracking processing of the passenger P having a high degree of danger, and finally the position where the passenger P's riding condition is determined. When detecting a riding state such as the passenger P standing up or moving on the passage A while the vehicle 100 is traveling, the risk degree determination unit 15 increases the risk degree. In paragraph [0059]-NAGAI discloses the notification unit 13 causes at least one of the riding state and the risk degree to be displayed on the monitor 103 as a display device, and performs notification on the safety of the passenger P. In paragraph [0060]-NAGAI discloses elderly people, passengers P or baggage L who are not gripped by a strap or handrail in a state of standing in the aisle may be displayed by being distinguished by marks of different shapes or different colors. Please also see Fig. 5-6 and read paragraph [0070]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KONISHI in view of NAGAI of having a remote monitoring method, with the teachings of NAGAI of having defining the important area containing the person in the internal image based on the attribute of the person and whether the person is standing in the internal image.
Wherein having KONISHI’s remote monitoring method having defining the important area containing the person in the internal image based on the attribute of the person and whether the person is standing in the internal image.
The motivation behind the modification would have been to obtain a remote monitoring method that more effectively and efficiently monitors driving and passenger behaviors as well as safety concerns, since both KONISHI and NAGAI concern image analysis and monitoring systems. Wherein KONISHI’s systems and methods improve the ability to monitor the interior of an area more efficiently by allowing an image to be recorded and transmitted more efficiently, while NAGAI’s systems and methods improves driving technology by monitoring and managing driving and passenger risks. Please see KONISHI et al. (US 20190177119 A1), Abstract and Paragraph [0028 and 0122-0125] and NAGAI et al. (NAGAI et al., machine translation of Japanese Patent Publication JP2014191145A, published as JP 2016062414 A), Abstract and Paragraph [0092 and 0095].
Regarding claim 36, KONISHI in view of NAGAI explicitly teach the remote monitoring method according to Claim 35, KONISHI fails to explicitly teach further comprising: in a case where the person is determined to be a vulnerable person requiring special attention, the vulnerable person including at least one of a child, an elderly person, and a person with a disability, predicting the risk of the occurrence of the passenger falling down to a first level that is higher than a second level set in a case where the person is not determined to be the vulnerable person.
However, NAGAI explicitly teaches further comprising: in a case where the person is determined to be a vulnerable person requiring special attention, the vulnerable person including at least one of a child, an elderly person, and a person with a disability, predicting the risk of the occurrence of the passenger falling down to a first level that is higher than a second level set in a case where the person is not determined to be the vulnerable person (Fig. 8. Paragraph [0048]-NAGAI discloses FIG. 8 is a table showing an example of determination criteria for determining the risk of a passenger falling. The danger degree determination means 15 reads the program stored in the information storage unit 110, the riding condition of the passenger P, and the traveling condition of the vehicle 100 by the arithmetic control unit 109, and the danger of the passenger falling. In paragraph [0056]-NAGAI discloses when the passenger P rides, the danger degree determination unit 15 uses the operation control unit 109 to identify the age and sex of the passenger P based on the image of the face recognition camera 105a, such as an elderly person or a junior person (wherein the falling risk level is set to high for elderly, pregnant and junior persons). The danger degree determination unit 15 performs image processing using the arithmetic control unit 109, the in-vehicle camera 105, and the like to perform tracking processing of the passenger P having a high degree of danger, and finally the position where the passenger P's riding condition is determined. When detecting a riding state such as the passenger P standing up or moving on the passage, while the vehicle 100 is traveling, the risk degree determination unit 15 increases the risk degree).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KONISHI in view of NAGAI of having a remote monitoring method, with the teachings of NAGAI of having further comprising: in a case where the person is determined to be a vulnerable person requiring special attention, the vulnerable person including at least one of a child, an elderly person, and a person with a disability, predicting the risk of the occurrence of the passenger falling down to a first level that is higher than a second level set in a case where the person is not determined to be the vulnerable person.
Wherein having KONISHI’s remote monitoring method having further comprising: in a case where the person is determined to be a vulnerable person requiring special attention, the vulnerable person including at least one of a child, an elderly person, and a person with a disability, predicting the risk of the occurrence of the passenger falling down to a first level that is higher than a second level set in a case where the person is not determined to be the vulnerable person.
The motivation behind the modification would have been to obtain a remote monitoring method that more effectively and efficiently monitors driving and passenger behaviors as well as safety concerns, since both KONISHI and NAGAI concern image analysis and monitoring systems. Wherein KONISHI’s systems and methods improve the ability to monitor the interior of an area more efficiently by allowing an image to be recorded and transmitted more efficiently, while NAGAI’s systems and methods improves driving technology by monitoring and managing driving and passenger risks. Please see KONISHI et al. (US 20190177119 A1), Abstract and Paragraph [0028 and 0122-0125] and NAGAI et al. (NAGAI et al., machine translation of Japanese Patent Publication JP2014191145A, published as JP 2016062414 A), Abstract and Paragraph [0092 and 0095].
Regarding claim 37, KONISHI in view of NAGAI explicitly teach the remote monitoring method according to Claim 34, KONISHI fails to explicitly teach wherein the predicting of the risk includes predicting acceleration of the mobile object in response to the situation information about the mobile object and predicting the risk based on a result of the predicted acceleration.
However, NAGAI explicitly teaches wherein the predicting of the risk (Paragraph [0047]-NAGAI discloses the danger degree determination means 15 includes an information storage unit 110 for storing the boarding state and the traveling state of the vehicle 100 shown in FIG. 2 and an operation control unit 109 for determining the degree of risk of the passenger falling based on the boarding state and the traveling state. Further, the information storage unit 110 stores a program for determining the degree of danger based on the riding state and the traveling state (wherein boarding/riding state information includes, for example, sex, age, position within vehicle, position such as sitting or standing and whether gripping a handrail, and traveling state includes, for example, route map, traveling map, direction, velocity, acceleration, angular velocity, inter-vehicle distance, relative velocity with the preceding vehicle, distance and relative velocity with the obstacle ahead of the host vehicle 100, distance with the following vehicle and whether vehicle is traveling along areas with high variable acceleration such as a steep slope or curves)) includes predicting acceleration of the mobile object in response to the situation information about the mobile object (Paragraph [0050]-NAGAI discloses a relatively large acceleration occurs while the vehicle 100 starts moving from a stopped state and increases in speed to shift to constant speed traveling. In addition, a relatively large acceleration in the reverse direction occurs during the period from when the vehicle 100 travels at a constant speed to when the vehicle decelerates and stops. In addition, acceleration is also generated when the vehicle is decelerated to secure the distance to the preceding vehicle when traveling at a low speed, or accelerated to narrow the distance to the preceding vehicle. Furthermore, during turning, running on a steep slope, or running on a sharp curve, acceleration may occur not only in the front-rear direction of the vehicle 100 but also in the left-right direction or the up-down direction) and predicting the risk based on a result of the predicted acceleration (Paragraph [0051]-NAGAI discloses it is determined that the vehicle is in any one of running, stopping, turning, running on a steep slope, or running on a sharp curve, from stop to start. Further, the arithmetic control unit 109 reads out necessary information from the information storage unit 110, and determines the position and posture of the passenger P in the vehicle 100, the boarding rate of elderly people over 60, and the traveling state of the vehicle 100. Based on the determination, the degree of risk is determined and stored in the information storage unit 110. Further in paragraph [0054]-NAGAI discloses the degree of risk can be determined in consideration of other various information. For example, if the inter-vehicle distance with the preceding vehicle and the relative speed exceed the predetermined threshold and there is a risk of collision with the preceding vehicle unless the host vehicle 100 is decelerated, the risk is high. Please also read paragraph [0052-0053 and 0080]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KONISHI in view of NAGAI of having a remote monitoring method, with the teachings of NAGAI of having wherein the predicting of the risk includes predicting acceleration of the mobile object in response to the situation information about the mobile object and predicting the risk based on a result of the predicted acceleration.
Wherein having KONISHI’s remote monitoring method having wherein the predicting of the risk includes predicting acceleration of the mobile object in response to the situation information about the mobile object and predicting the risk based on a result of the predicted acceleration.
The motivation behind the modification would have been to obtain a remote monitoring method that more effectively and efficiently monitors driving and passenger behaviors as well as safety concerns, since both KONISHI and NAGAI concern image analysis and monitoring systems. Wherein KONISHI’s systems and methods improve the ability to monitor the interior of an area more efficiently by allowing an image to be recorded and transmitted more efficiently, while NAGAI’s systems and methods improves driving technology by monitoring and managing driving and passenger risks. Please see KONISHI et al. (US 20190177119 A1), Abstract and Paragraph [0028 and 0122-0125] and NAGAI et al. (NAGAI et al., machine translation of Japanese Patent Publication JP2014191145A, published as JP 2016062414 A), Abstract and Paragraph [0092 and 0095].
Regarding claim 38, KONISHI in view of NAGAI explicitly teach the remote monitoring method according to Claim 37, KONISHI fails to explicitly teach wherein the predicting of the risk includes comparing an absolute value of the predicted acceleration with a threshold value, and predicting that there is a risk of the accident in a case where the absolute value of the predicted acceleration is greater than or equal to the threshold value.
However, NAGAI explicitly teaches wherein the predicting of the risk includes comparing an absolute value of the predicted acceleration with a threshold value (Fig. 4. Paragraph [0048]-NAGAI discloses FIG. 8 is a table showing an example of determination criteria for determining the risk of a passenger falling (wherein the determination is based on traveling state, riding state and boarding state, riding/boarding state includes information such as passenger position, age, use of hand rail and traveling state includes information such as speed, acceleration, angular velocity, inter-vehicle distance as well as a determination of whether the vehicle is in a state of typically associated with large variation in acceleration/deceleration). In paragraph [0050]-NAGAI discloses a relatively large acceleration occurs while the vehicle 100 starts moving from a stopped state and increases in speed to shift to constant speed traveling. A relatively large acceleration in the reverse direction occurs during the period from when the vehicle 100 travels at a constant speed to when the vehicle decelerates and stops. Acceleration is also generated when the vehicle is decelerated to secure the distance to the preceding vehicle when traveling at a low speed, or accelerated to narrow the distance to the preceding vehicle. Furthermore, during turning, running on a steep slope, or running on a sharp curve, acceleration may occur not only in the front-rear direction of the vehicle 100 but also in the left-right direction or the up-down direction), and predicting that there is a risk of the accident in a case where the absolute value of the predicted acceleration is greater than or equal to the threshold value (Fig. 4. Paragraph [0051]-NAGAI discloses it is determined whether the vehicle is in any one of running, stopping, turning, running on a steep slope, or running on a sharp curve, from stop to start. In paragraph [0053]-NAGAI discloses when all passengers P standing in the aisle are gripped by a strap or handrail, and the vehicle 100 is turning, the risk is the third highest. When the passenger P standing in the aisle is densely packed and the vehicle 100 is traveling in a steep gradient, the risk is the second highest. When the passenger P standing in the aisle is densely packed, and the vehicle 100 is traveling on a sharp curve, the level of risk is highest 6. Further in paragraph [0054]-NAGAI discloses if the inter-vehicle distance with the preceding vehicle and the relative speed exceed the predetermined threshold and there is a risk of collision with the preceding vehicle unless the host vehicle 100 is decelerated, the risk is high. Please also read paragraph [0078-0081]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KONISHI in view of NAGAI of having a remote monitoring method, with the teachings of NAGAI of having wherein the predicting of the risk includes comparing an absolute value of the predicted acceleration with a threshold value, and predicting that there is a risk of the accident in a case where the absolute value of the predicted acceleration is greater than or equal to the threshold value.
Wherein having KONISHI’s remote monitoring method having wherein the predicting of the risk includes comparing an absolute value of the predicted acceleration with a threshold value, and predicting that there is a risk of the accident in a case where the absolute value of the predicted acceleration is greater than or equal to the threshold value.
The motivation behind the modification would have been to obtain a remote monitoring method that more effectively and efficiently monitors driving and passenger behaviors as well as safety concerns, since both KONISHI and NAGAI concern image analysis and monitoring systems. Wherein KONISHI’s systems and methods improve the ability to monitor the interior of an area more efficiently by allowing an image to be recorded and transmitted more efficiently, while NAGAI’s systems and methods improves driving technology by monitoring and managing driving and passenger risks. Please see KONISHI et al. (US 20190177119 A1), Abstract and Paragraph [0028 and 0122-0125] and NAGAI et al. (NAGAI et al., machine translation of Japanese Patent Publication JP2014191145A, published as JP 2016062414 A), Abstract and Paragraph [0092 and 0095].
Regarding claim 39, KONISHI in view of NAGAI explicitly teach the remote monitoring method according to Claim 34, KONISHI further teaches wherein the determining the internal image quality includes, in a case where the result of the predicted risk indicates presence of a risk of the accident, determining higher quality for the quality of the internal image compared to a case in which the result of the predicted risk indicates no risk of the accident (Fig. 1. Paragraph [0049]-KONISHI discloses the limited recording capacity of the recorder 5 can be effectively utilized by changing the image quality and the frame rate depending on the importance. Each of the image quality and the frame rate may be changed at two stages of the “high level” and the “low level”, or the importance may be ranked (wherein importance is based on whether the number of people or the positional imbalance of people within the car indicate the possibility of a dangerous phenomenon). Further in paragraph [0063]-KONISHI discloses the image data having high importance is recorded at a high image quality and a high frame rate, and is transmitted at a high image quality and a high transmission frequency. The image data having low importance is recorded at a low image quality and a low frame rate, and is transmitted at a low image quality and a low transmission frequency).
Regarding claim 40, KONISHI in view of NAGAI explicitly teach the remote monitoring method according to Claim 34, KONISHI fails to explicitly teach wherein the situation information includes information about a position of the mobile object, and the remote monitoring method further comprises predicting, based on information about the position of the mobile object, at least one of a situation in which the mobile object stops at a station where a passenger of the mobile object gets on or off the mobile object or a situation in which the mobile object leaves the station, and in a case where a situation in which the mobile object stops at or leaves the station is predicted, predicting that there is a risk of occurrence of the accident.
However, NAGAI explicitly teaches wherein the situation information includes information about a position of the mobile object (Paragraph [0047]-NAGAI discloses the danger degree determination means 15 includes an information storage unit 110 for storing the boarding state and the traveling state of the vehicle 100 shown in FIG. 2 and an operation control unit 109 for determining the degree of risk of the passenger falling based on the boarding state and the traveling state. Further, the information storage unit 110 stores a program for determining the degree of danger based on the riding state and the traveling state (wherein the riding state and/or boarding state may include a passenger’s attribute information, location within the vehicle, position and attitude, such as the state of sitting or standing, presence or absence of movement and whether a handrail is being used in a standing position and traveling state may include route map, traveling map, direction, velocity, acceleration, angular velocity, inter-vehicle distance, relative velocity with the preceding vehicle, distance and relative velocity with the obstacle ahead of the host vehicle 100, and distance with the following vehicle)), and the remote monitoring method further comprises predicting, based on information about the position of the mobile object, at least one of a situation in which the mobile object stops at a station where a passenger of the mobile object gets on or off the mobile object or a situation in which the mobile object leaves the station, and in a case where a situation in which the mobile object stops at or leaves the station is predicted, predicting that there is a risk of occurrence of the accident (Paragraph [0104]-NAGAI discloses when the vehicle 100 stops at the stop and the passenger P is getting on and off, the getting-in state grasping means 11 determines that the getting-in state of the passenger P is moving in step S1. Further, in step S2, the traveling state grasping means 12 grasps that the vehicle 100 is stopped. In this case, in step S4, for example, the degree of danger is determined to be high by the degree-of-risk determination means 15. Then, in step S3, the notification means 13 displays that the passenger P is moving on the monitor 103, or the driver 104 is notified that the passenger P is moving by audio guidance by the speaker 104. As a result, the driver's attention is drawn and the fall of the passenger P is prevented). In paragraph [0106]-NAGAI discloses then, in step S2, when it is determined that the vehicle is stopped by the traveling state grasping means 12 and the riding state of all the passengers P is seated or gripped by a strap or handrail, the danger in step S4. The degree of danger is determined to be low by the degree determination means 15. Here, when there is a passenger P who is not seated and is not gripped by a strap or a handrail, the degree of risk may be determined to be high. In this case, steps S1, S2, S4, and S3 are sequentially repeated as in the case where the passenger P is moving. Please also read paragraph [0050-0053 and 0105]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KONISHI in view of NAGAI of having a remote monitoring method, with the teachings of NAGAI of having wherein the situation information includes information about a position of the mobile object, and the remote monitoring method further comprises predicting, based on information about the position of the mobile object, at least one of a situation in which the mobile object stops at a station where a passenger of the mobile object gets on or off the mobile object or a situation in which the mobile object leaves the station, and in a case where a situation in which the mobile object stops at or leaves the station is predicted, predicting that there is a risk of occurrence of the accident.
Wherein having KONISHI’s remote monitoring method having wherein the situation information includes information about a position of the mobile object, and the remote monitoring method further comprises predicting, based on information about the position of the mobile object, at least one of a situation in which the mobile object stops at a station where a passenger of the mobile object gets on or off the mobile object or a situation in which the mobile object leaves the station, and in a case where a situation in which the mobile object stops at or leaves the station is predicted, predicting that there is a risk of occurrence of the accident.
The motivation behind the modification would have been to obtain a remote monitoring method that more effectively and efficiently monitors driving and passenger behaviors as well as safety concerns, since both KONISHI and NAGAI concern image analysis and monitoring systems. Wherein KONISHI’s systems and methods improve the ability to monitor the interior of an area more efficiently by allowing an image to be recorded and transmitted more efficiently, while NAGAI’s systems and methods improves driving technology by monitoring and managing driving and passenger risks. Please see KONISHI et al. (US 20190177119 A1), Abstract and Paragraph [0028 and 0122-0125] and NAGAI et al. (NAGAI et al., machine translation of Japanese Patent Publication JP2014191145A, published as JP 2016062414 A), Abstract and Paragraph [0092 and 0095].
Regarding claim 43, KONISHI in view of NAGAI explicitly teach The remote monitoring method according to Claim 34, KONISHI fails to explicitly teach wherein the situation information includes a distance between the mobile object and another mobile object present around the mobile object, and the remote monitoring method further comprises predicting, based on the distance between the mobile object and the another mobile object, a situation in which the mobile object is highly likely to come into contact with the another mobile object, and in a case where a situation is predicted in which the mobile object is likely to come into contact with the another mobile object, and in a case where an absolute value of predicted value of acceleration owing to motion to avoid the contact is greater than or equal to a threshold value, predicting that there is a risk of occurrence of the accident.
However, NAGAI explicitly teaches wherein the situation information includes a distance between the mobile object and another mobile object present around the mobile object (Fig. 4. Paragraph [0046]-NAGAI discloses the traveling state grasping means 12 detects the velocity, acceleration, angular velocity, inter-vehicle distance and relative velocity with the preceding vehicle, distance and relative velocity with the obstacle ahead of the host vehicle 100, and distance with the following vehicle), and the remote monitoring method further comprises predicting, based on the distance between the mobile object and the another mobile object, a situation in which the mobile object is highly likely to come into contact with the another mobile object, and in a case where a situation is predicted in which the mobile object is likely to come into contact with the another mobile object, and in a case where an absolute value of predicted value of acceleration owing to motion to avoid the contact is greater than or equal to a threshold value, predicting that there is a risk of occurrence of the accident (Fig. 4. Paragraph [0054]-NAGAI discloses the degree of risk can be determined in consideration of other various information. For example, if the inter-vehicle distance with the preceding vehicle and the relative speed exceed the predetermined threshold and there is a risk of collision with the preceding vehicle unless the host vehicle 100 is decelerated, the risk is high. Additionally in paragraph [0080]-NAGAI discloses the risk determination means 15 or the vehicle control means 14 can determine the risk of occurrence of an accident based on the distance between the front and rear vehicles or obstacles and the own vehicle 100 or the relative speed and the riding condition of the passenger P. For example, when a sudden braking or a rear-end collision is predicted based on the distance between the front and rear vehicles or obstacles and the own vehicle 100, the danger level is increased according to the riding state of the passenger P. The warning can be given to the driver and the passenger P in advance, and the fall of the passenger P can be suppressed more reliably. Please also read paragraph [0051-0053 and 0078-0079]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KONISHI in view of NAGAI of having a remote monitoring method, with the teachings of NAGAI of having wherein the situation information includes a distance between the mobile object and another mobile object present around the mobile object, and the remote monitoring method further comprises predicting, based on the distance between the mobile object and the another mobile object, a situation in which the mobile object is highly likely to come into contact with the another mobile object, and in a case where a situation is predicted in which the mobile object is likely to come into contact with the another mobile object, and in a case where an absolute value of predicted value of acceleration owing to motion to avoid the contact is greater than or equal to a threshold value, predicting that there is a risk of occurrence of the accident.
Wherein having KONISHI’s remote monitoring method having wherein the situation information includes a distance between the mobile object and another mobile object present around the mobile object, and the remote monitoring method further comprises predicting, based on the distance between the mobile object and the another mobile object, a situation in which the mobile object is highly likely to come into contact with the another mobile object, and in a case where a situation is predicted in which the mobile object is likely to come into contact with the another mobile object, and in a case where an absolute value of predicted value of acceleration owing to motion to avoid the contact is greater than or equal to a threshold value, predicting that there is a risk of occurrence of the accident.
The motivation behind the modification would have been to obtain a remote monitoring method that more effectively and efficiently monitors driving and passenger behaviors as well as safety concerns, since both KONISHI and NAGAI concern image analysis and monitoring systems. Wherein KONISHI’s systems and methods improve the ability to monitor the interior of an area more efficiently by allowing an image to be recorded and transmitted more efficiently, while NAGAI’s systems and methods improves driving technology by monitoring and managing driving and passenger risks. Please see KONISHI et al. (US 20190177119 A1), Abstract and Paragraph [0028 and 0122-0125] and NAGAI et al. (NAGAI et al., machine translation of Japanese Patent Publication JP2014191145A).
Claims 9-10 and 41-42 are rejected under 35 U.S.C. 103 as being unpatentable over KONISHI et al. (US 20190177119 A1), hereinafter referenced as KONISHI in view of NAGAI et al. (NAGAI et al., machine translation of Japanese Patent Publication JP2014191145A, published as JP 2016062414 A), hereinafter referenced as NAGAI and in further view of MUDALIGE et al. (US 20170113665 A1), hereinafter referenced as MUDALIGE.
Regarding claim 9, KONISHI in view of NAGAI explicitly teach the remote monitoring system according to Claim 1, KONISHI in view of NAGAI fail to explicitly teach wherein the situation information includes information about a position of the mobile object and route information for the mobile object, and the at least one processor is configured to execute the instructions to predict, based on information about the position of the mobile object and the route information for the mobile object, a situation in which the mobile object turns right or left at an intersection, and in a case where a situation in which the mobile object turns right or left at the intersection is predicted, predict that there is a risk of occurrence of the accident.
However, MUDALIGE explicitly teaches wherein the situation information includes information about a position of the mobile object and route information for the mobile object (Fig. 1, #12, #80, called a vehicle and host vehicle, respectively. Paragraph [0040]-MUDALIGE discloses FIG. 1 is a simple illustration of a vehicle system 10 that includes a vehicle 12 having a map database 14, a navigation system 16, an operation controller 18, a warning device 20, sensors/detectors 32 and a vehicle controller 22. The map database 14 stores map information at any level of detail that is available, including specific information about intersections, such as the number of lanes, the lane travel patterns, etc. The map database 14 operates in association with the navigation system 16 to display the various maps and other information that is available, and allow a user to input, plan and display a route), and the at least one processor is configured to execute the instructions to predict (Fig. 1, #18 called a controller. Paragraph [0040]-MUDALIGE discloses the controller 18 is intended to represent all of the separate modules, controllers, processors, electronic control units, etc. that are necessary to perform and run the various algorithms and processes discussed herein), based on information about the position of the mobile object and the route information for the mobile object, a situation in which the mobile object turns right or left at an intersection, and in a case where a situation in which the mobile object turns right or left at the intersection is predicted, predict that there is a risk of occurrence of the accident (Fig. 3. Paragraph [0048]-MUDALIGE discloses the collision assessment algorithms assess the threats of a collision in the assessment region 118 by analyzing the expected position and predicted path of the host vehicle 80, the expected position and predicted path of the opposite direction remote vehicle 84, and the expected position and predicted path of the lateral direction remote vehicle 86, which are determined by sensor information and dynamics of the velocity, acceleration and predicted path of the host vehicle 80 and the remote vehicles 84 and 86. Given the current location, predicted path and speed of the host vehicle 80, and the current location, predicted path and speed of the remote vehicles 84 and/or 86, a collision zone 146 is defined that is the area where the host vehicle 80 could collide with the remote vehicle 84 or 86 when the host vehicle 80 is turning right in the assessment region 118, a collision zone 148 is defined that is the area where the other direction remote vehicle 84 could collide with the host vehicle 80 when the remote vehicle 84 is turning left in the assessment region 118).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KONISHI in view of NAGAI of having a remote monitoring system, with the teachings of MUDALIGE of having wherein the situation information includes information about a position of the mobile object and route information for the mobile object, and the at least one processor is configured to execute the instructions to predict, based on information about the position of the mobile object and the route information for the mobile object, a situation in which the mobile object turns right or left at an intersection, and in a case where a situation in which the mobile object turns right or left at the intersection is predicted, predict that there is a risk of occurrence of the accident.
Wherein having KONISHI’s remote monitoring system having wherein the situation information includes information about a position of the mobile object and route information for the mobile object, and the at least one processor is configured to execute the instructions to predict, based on information about the position of the mobile object and the route information for the mobile object, a situation in which the mobile object turns right or left at an intersection, and in a case where a situation in which the mobile object turns right or left at the intersection is predicted, predict that there is a risk of occurrence of the accident.
The motivation behind the modification would have been to obtain a remote monitoring system that more effectively and efficiently monitors driving and passenger behaviors as well as safety concerns, since both KONISHI and MUDALIGE concern image analysis and monitoring systems. Wherein KONISHI’s systems and methods improve the ability to monitor the interior of an area more efficiently by allowing an image to be recorded and transmitted more efficiently, while MUDALIGE provides vehicle monitoring methods and systems for improving collision threat assessments at intersections that equally applies to countries with differing roadways. Please see KONISHI et al. (US 20190177119 A1), Abstract and Paragraph [0028 and 0122-0125] and MUDALIGE et al. (US 20170113665 A1), Paragraph [0038-0039 and 0064].
Regarding claim 10, KONISHI in view of NAGAI explicitly teach the remote monitoring system according to Claim 1, KONISHI in view of NAGAI fail to explicitly teach wherein the situation information includes information indicating a status of lights of a traffic signal present in a direction in which the mobile object is traveling, and the at least one processor is configured to execute the instructions to predict, based on information indicating the status of the lights of the traffic signal, at least one of a situation in which the mobile object stops at the traffic signal or a situation in which the mobile object accelerates, and in a case where a situation is predicted in which the mobile object stops or accelerates at the traffic signal, and in a case where an absolute value of predicted value of acceleration associated with deceleration or acceleration is greater than or equal to a threshold value, the at least one processor is configured to execute the instructions to predict that there is a risk of occurrence of the accident.
However, MUDALIGE explicitly teaches wherein the situation information includes information indicating a status of lights of a traffic signal present in a direction in which the mobile object Fig. 1, #12, #80, called a vehicle and host vehicle, respectively. Paragraph [0040]) is traveling (Fig. 2. Paragraph [0058]-MUDALIGE discloses FIG. 12 is a flow chart diagram 194 showing a process for determining whether the host vehicle 80 can enter the intersection 42, where the collision assessment algorithm starts at box 196. It is noted that the algorithm associated with the flow diagram 194 is repeatedly performed until the host vehicle 80 enters the intersection 42. At decision diamond 198 the algorithm determines whether the vehicle 80 is allowed to enter the intersection 42, by, for example, determining whether a signal light is red, etc., using the available resources, such as camera data provided by cameras on the vehicle 80, lidar sensors, V2X communications from infrastructure including the signal light, etc.), and the at least one processor is configured to execute the instructions to predict (Fig. 1, #18 called a controller. Paragraph [0040]-MUDALIGE discloses the controller 18 is intended to represent all of the separate modules, controllers, processors, electronic control units, etc. that are necessary to perform and run the various algorithms and processes discussed herein), based on information indicating the status of the lights of the traffic signal, at least one of a situation in which the mobile object stops at the traffic signal or a situation in which the mobile object accelerates, and in a case where a situation is predicted in which the mobile object stops or accelerates at the traffic signal, and in a case where an absolute value of predicted value of acceleration associated with deceleration or acceleration is greater than or equal to a threshold value, the at least one processor is configured to execute the instructions to predict that there is a risk of occurrence of the accident (Fig. 12. Paragraph [0048]-MUDALIGE discloses the collision assessment algorithms assess the threats of a collision in the assessment region 118 by analyzing the expected position and predicted path of the host vehicle 80, the expected position and predicted path of the opposite direction remote vehicle 84, and the expected position and predicted path of the lateral direction remote vehicle 86, which are determined by sensor information and dynamics of the velocity, acceleration and predicted path of the host vehicle 80 and the remote vehicles 84 and 86. In paragraph [0058]-MUDALIGE discloses If the host vehicle 80 is allowed to enter the intersection 42 at the decision diamond 198, then the algorithm assesses the immediate collisions threats from other vehicles in the intersection 42 at box 200, such as threats coming from cross-traffic on the left side. It is noted that assessing the immediate collision threats from other vehicles from the left side is for those roadway and countries where vehicles travel on the right. Please also read paragraph [0049-0050, 0060 and 0071]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KONISHI in view of NAGAI of having a remote monitoring system, with the teachings of MUDALIGE of having wherein the situation information includes information indicating a status of lights of a traffic signal present in a direction in which the mobile object is traveling, and the at least one processor is configured to execute the instructions to predict, based on information indicating the status of the lights of the traffic signal, at least one of a situation in which the mobile object stops at the traffic signal or a situation in which the mobile object accelerates, and in a case where a situation is predicted in which the mobile object stops or accelerates at the traffic signal, and in a case where an absolute value of predicted value of acceleration associated with deceleration or acceleration is greater than or equal to a threshold value, the at least one processor is configured to execute the instructions to predict that there is a risk of occurrence of the accident.
Wherein having KONISHI’s remote monitoring system having wherein the situation information includes information indicating a status of lights of a traffic signal present in a direction in which the mobile object is traveling, and the at least one processor is configured to execute the instructions to predict, based on information indicating the status of the lights of the traffic signal, at least one of a situation in which the mobile object stops at the traffic signal or a situation in which the mobile object accelerates, and in a case where a situation is predicted in which the mobile object stops or accelerates at the traffic signal, and in a case where an absolute value of predicted value of acceleration associated with deceleration or acceleration is greater than or equal to a threshold value, the at least one processor is configured to execute the instructions to predict that there is a risk of occurrence of the accident.
The motivation behind the modification would have been to obtain a remote monitoring system that more effectively and efficiently monitors driving and passenger behaviors as well as safety concerns, since both KONISHI and MUDALIGE concern image analysis and monitoring systems. Wherein KONISHI’s systems and methods improve the ability to monitor the interior of an area more efficiently by allowing an image to be recorded and transmitted more efficiently, while MUDALIGE provides vehicle monitoring methods and systems for improving collision threat assessments at intersections that equally applies to countries with differing roadways. Please see KONISHI et al. (US 20190177119 A1), Abstract and Paragraph [0028 and 0122-0125] and MUDALIGE et al. (US 20170113665 A1), Paragraph [0038-0039 and 0064].
Regarding claim 41, KONISHI in view of NAGAI explicitly teach the remote monitoring method according to Claim 34, KONISHI in view of NAGAI fail to explicitly teach wherein the situation information includes information about a position of the mobile object and route information for the mobile object, and the remote monitoring method further comprises predicting, based on information about the position of the mobile object and the route information for the mobile object, a situation in which the mobile object turns right or left at an intersection, and in a case where a situation in which the mobile object turns right or left at the intersection is predicted, predicting that there is a risk of occurrence of the accident.
However, MUDALIGE explicitly teaches wherein the situation information includes information about a position of the mobile object and route information for the mobile object (Fig. 1, #12, #80, called a vehicle and host vehicle, respectively. Paragraph [0040]-MUDALIGE discloses FIG. 1 is a simple illustration of a vehicle system 10 that includes a vehicle 12 having a map database 14, a navigation system 16, an operation controller 18, a warning device 20, sensors/detectors 32 and a vehicle controller 22. The map database 14 stores map information at any level of detail that is available, including specific information about intersections, such as the number of lanes, the lane travel patterns, etc. The map database 14 operates in association with the navigation system 16 to display the various maps and other information that is available, and allow a user to input, plan and display a route), and the remote monitoring method further comprises predicting, based on information about the position of the mobile object and the route information for the mobile object, a situation in which the mobile object turns right or left at an intersection, and in a case where a situation in which the mobile object turns right or left at the intersection is predicted, predicting that there is a risk of occurrence of the accident (Fig. 3. Paragraph [0048]-MUDALIGE discloses the collision assessment algorithms assess the threats of a collision in the assessment region 118 by analyzing the expected position and predicted path of the host vehicle 80, the expected position and predicted path of the opposite direction remote vehicle 84, and the expected position and predicted path of the lateral direction remote vehicle 86, which are determined by sensor information and dynamics of the velocity, acceleration and predicted path of the host vehicle 80 and the remote vehicles 84 and 86. Given the current location, predicted path and speed of the host vehicle 80, and the current location, predicted path and speed of the remote vehicles 84 and/or 86, a collision zone 146 is defined that is the area where the host vehicle 80 could collide with the remote vehicle 84 or 86 when the host vehicle 80 is turning right in the assessment region 118, a collision zone 148 is defined that is the area where the other direction remote vehicle 84 could collide with the host vehicle 80 when the remote vehicle 84 is turning left in the assessment region 118).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KONISHI in view of NAGAI of having a remote monitoring method, with the teachings of MUDALIGE of having wherein the situation information includes information about a position of the mobile object and route information for the mobile object, and the remote monitoring method further comprises predicting, based on information about the position of the mobile object and the route information for the mobile object, a situation in which the mobile object turns right or left at an intersection, and in a case where a situation in which the mobile object turns right or left at the intersection is predicted, predicting that there is a risk of occurrence of the accident.
Wherein having KONISHI’s remote monitoring method having wherein the situation information includes information about a position of the mobile object and route information for the mobile object, and the remote monitoring method further comprises predicting, based on information about the position of the mobile object and the route information for the mobile object, a situation in which the mobile object turns right or left at an intersection, and in a case where a situation in which the mobile object turns right or left at the intersection is predicted, predicting that there is a risk of occurrence of the accident.
The motivation behind the modification would have been to obtain a remote monitoring method that more effectively and efficiently monitors driving and passenger behaviors as well as safety concerns, since both KONISHI and MUDALIGE concern image analysis and monitoring systems. Wherein KONISHI’s systems and methods improve the ability to monitor the interior of an area more efficiently by allowing an image to be recorded and transmitted more efficiently, while MUDALIGE provides vehicle monitoring methods and systems for improving collision threat assessments at intersections that equally applies to countries with differing roadways. Please see KONISHI et al. (US 20190177119 A1), Abstract and Paragraph [0028 and 0122-0125] and MUDALIGE et al. (US 20170113665 A1), Paragraph [0038-0039 and 0064].
Regarding claim 42, KONISHI in view of NAGAI explicitly teach the remote monitoring method according to Claim 34, KONISHI in view of NAGAI fail to explicitly teach wherein the situation information includes information indicating a status of lights of a traffic signal present in a direction in which the mobile object is traveling, and the remote monitoring method further comprises predicting, based on information indicating the status of the lights of the traffic signal, at least one of a situation in which the mobile object stops at the traffic signal or a situation in which the mobile object accelerates, and in a case where a situation is predicted in which the mobile object stops or accelerates at the traffic signal, and in a case where an absolute value of predicted value of acceleration associated with deceleration or acceleration is greater than or equal to a threshold value, predicting that there is a risk of occurrence of the accident.
However, MUDALIGE explicitly teaches the remote monitoring method according to Claim 34, wherein the situation information includes information indicating a status of lights of a traffic signal present in a direction in which the mobile object (Fig. 1, #12, #80, called a vehicle and host vehicle, respectively. Paragraph [0040]) is traveling (Fig. 2. Paragraph [0058]-MUDALIGE discloses FIG. 12 is a flow chart diagram 194 showing a process for determining whether the host vehicle 80 can enter the intersection 42, where the collision assessment algorithm starts at box 196. It is noted that the algorithm associated with the flow diagram 194 is repeatedly performed until the host vehicle 80 enters the intersection 42. At decision diamond 198 the algorithm determines whether the vehicle 80 is allowed to enter the intersection 42, by, for example, determining whether a signal light is red, etc., using the available resources, such as camera data provided by cameras on the vehicle 80, lidar sensors, V2X communications from infrastructure including the signal light, etc.), and the remote monitoring method further comprises predicting, based on information indicating the status of the lights of the traffic signal, at least one of a situation in which the mobile object stops at the traffic signal or a situation in which the mobile object accelerates, and in a case where a situation is predicted in which the mobile object stops or accelerates at the traffic signal, and in a case where an absolute value of predicted value of acceleration associated with deceleration or acceleration is greater than or equal to a threshold value, predicting that there is a risk of occurrence of the accident (Fig. 12. Paragraph [0048]-MUDALIGE discloses the collision assessment algorithms assess the threats of a collision in the assessment region 118 by analyzing the expected position and predicted path of the host vehicle 80, the expected position and predicted path of the opposite direction remote vehicle 84, and the expected position and predicted path of the lateral direction remote vehicle 86, which are determined by sensor information and dynamics of the velocity, acceleration and predicted path of the host vehicle 80 and the remote vehicles 84 and 86. In paragraph [0058]-MUDALIGE discloses if the host vehicle 80 is allowed to enter the intersection 42 at the decision diamond 198, then the algorithm assesses the immediate collisions threats from other vehicles in the intersection 42 at box 200, such as threats coming from cross-traffic on the left side. It is noted that assessing the immediate collision threats from other vehicles from the left side is for those roadway and countries where vehicles travel on the right. Please also read paragraph [0049-0050, 0060 and 0071]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KONISHI in view of NAGAI of having a remote monitoring method, with the teachings of MUDALIGE of having wherein the situation information includes information about a position of the mobile object and route information for the mobile object, and the remote monitoring method further comprises predicting, based on information about the position of the mobile object and the route information for the mobile object, a situation in which the mobile object turns right or left at an intersection, and in a case where a situation in which the mobile object turns right or left at the intersection is predicted, predicting that there is a risk of occurrence of the accident.
Wherein having KONISHI’s remote monitoring method having wherein the situation information includes information about a position of the mobile object and route information for the mobile object, and the remote monitoring method further comprises predicting, based on information about the position of the mobile object and the route information for the mobile object, a situation in which the mobile object turns right or left at an intersection, and in a case where a situation in which the mobile object turns right or left at the intersection is predicted, predicting that there is a risk of occurrence of the accident.
The motivation behind the modification would have been to obtain a remote monitoring method that more effectively and efficiently monitors driving and passenger behaviors as well as safety concerns, since both KONISHI and MUDALIGE concern image analysis and monitoring systems. Wherein KONISHI’s systems and methods improve the ability to monitor the interior of an area more efficiently by allowing an image to be recorded and transmitted more efficiently, while MUDALIGE provides vehicle monitoring methods and systems for improving collision threat assessments at intersections that equally applies to countries with differing roadways. Please see KONISHI et al. (US 20190177119 A1), Abstract and Paragraph [0028 and 0122-0125] and MUDALIGE et al. (US 20170113665 A1), Paragraph [0038-0039 and 0064].
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant`s disclosure.
UNO (WO 2020003748 A1)- This vehicle control method which is executed by a computer (50, 100, 200) and controls the traveling of a vehicle (ADV) comprises: by at least one processor (51, 61, 261), acquiring attribute information on each passenger (P) boarding the vehicle (S101); determining the risk of falling for each individual passenger on the basis of the attribute information (S102); tracking high-risk persons (Pr) with a high risk of falling in a cabin (C), and recognizing the states of the individual high-risk persons in the cabin (S111); and limiting the traveling of the vehicle on the basis of the state of each high-risk person (S112)................... Please see Fig. 2. Abstract.
HITOTSUMATSU et al. (US 20190394626 A1)- A vehicle communication device for use in a vehicle includes a data acquisition unit for acquiring captured images as travel data; a data generator for generating transmission data from the travel data for transmission to a data center; and a data transmitter transmitting the transmission data generated by the data generator to the data center. When there is a plurality of different travel data types, the data generator can increase the priority of any of the travel data types based on a data center necessity level, and prioritize the transmission of the transmission data generated from prioritized travel data................... Please see Fig. 1, 3-6. Abstract.
Balakrishnan et al. (US 20200312063 A1)- Personal safety concerns for users of vehicles can be indicated, identified, communicated, analyzed, or acted on to make the users and other participants in the technology aware of the safety concerns and to reduce the risks to the users associated with the safety concerns. Personal safety concerns can be recognized based on safety concern triggers. Once recognized, the personal safety concerns can be reported to the users and other participants in the technology by safety alerts. The safety alert can prompt one or more telematics devices at the vehicle to capture, store, or transmit telematics data, including, for example, audio, image, or video data or combinations of them. The captured telematics data can be used to verify the safety alert and the safety concern and present the captured data to a third party participant to enable the third party participant to determine an appropriate response or action.................. Please see Fig. 1 and 7-8. Abstract.
WESTOVER et al. (US 20180257655 A1)- Methods, systems, and apparatus for predicting the braking or acceleration of a vehicle. The pedal change prediction system includes a braking sensor for providing braking data or an acceleration sensor pedal for providing acceleration data. The pedal change prediction system includes an electronic control unit that is configured to determine a rate of depression of the brake pedal or the acceleration pedal that is associated with a first braking force or a first acceleration force. The electronic control unit is configured to predict a triggering event that is either a braking event or an acceleration event. The electronic control unit predicts the triggering event based on the rate of depression of the brake pedal or the acceleration pedal and causes the vehicle to apply a second braking force or a second acceleration force................... Please see Fig. 2-5. Abstract.
KIM et al. (US 20210146957 A1)- A method for controlling an autonomous driving operation comprising at least one processor includes determining a predicted driving condition of a vehicle; determining a predicted driving operation of the vehicle based on the predicted driving condition; determining necessity of wearing of a seat belt of a passenger, based on an image of the passenger captured by an interior vision sensor and the predicted driving operation; requesting the passenger to wear the seat belt and determining wearing of a seat belt of the passenger based on the necessity of wearing of a seat belt of the passenger; and controlling a driving operation of the vehicle based on a result obtained by determining the wearing of a seat belt of the passenger. The method of the present disclosure may be performed based on a deep neural network generated through machine learning and an Internet of Things (IoT) environment using a 5G network.................. Please see Fig. 11. Abstract.
Morimoto (US 20150281652 A1)- An in-vehicle monitoring system including monitoring
cameras and emergency call devices, a display unit, and an information control device, and monitoring inside of a vehicle by using images taken by the monitoring cameras, wherein a table, in which IP addresses of the emergency call devices and IP addresses of the monitoring cameras are stored in association with each other in a one-to-one relationship or a one-to-many relationship, is set in the information control device, and upon receiving a call signal from an operated emergency call device, the information control device specifies an IP address of a monitoring camera corresponding to the operated emergency call device from the IP addresses of the monitoring cameras based on the table, selects imaging data from the specified monitoring camera from pieces of imaging data from the monitoring cameras, and transmits it to the display unit................ Please see Fig. 1-2, 7, 8, 11. Abstract.
Yagi et al. (US 20110057783 A1)-A normal image quality coding unit generates normal quality compressed moving image data by compressing moving image data generated by capturing an image around a vehicle with a normal image quality. A high image quality coding unit generates high-quality compressed moving image data by compressing the moving image data with an image quality higher than the normal image quality. The normal-quality compressed moving image data is recorded in a normal image quality data storage unit. A trigger detection unit detects an abnormal condition which possibly happens to the vehicle currently travelling. A high image quality data storage unit records therein the high-quality compressed moving image data based on a timing by which the abnormal condition is detected by the trigger detection unit................. Please see Fig. 1A-B, 4-9, 11. Abstract.
KIM et al. (US 20190325667 A1)- A method for collecting vehicle-related data is disclosed. The method includes: obtaining navigation streaming video data related to a vehicle operation and vehicle operation information from a first terminal handling vehicle operation information; encoding in real time the navigation streaming video data, which is received from the first terminal via a wired or wireless network, by an encoder; and synchronizing and storing the real-time encoded navigation streaming video data and vehicle operation information based on time information.................. Please see Fig. 4-5. Abstract.
Any inquiry concerning this communication or earlier communications from the examiner
should be directed to Aaron Bonansinga whose telephone number is (703) 756-5380 The examiner can normally be reached on Monday-Friday, 9:00 a.m. - 6:00 p.m. ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s
supervisor, Chineyere Wills-Burns can be reached by phone at (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/AARON TIMOTHY BONANSINGA/Examiner, Art Unit 2673 /CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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