DETAILED ACTION
Response to Amendment
This office action regarding application number 18/915,851, filed October 15, 2024, is in response to the applicants arguments and amendments filed May 6, 2026. Claims 1-9 have been amended. Claims 1-9 are currently pending and are addressed below.
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 .
Response to Arguments
The applicants arguments and amendments to the application have overcome some of the objections and rejections previously set forth in the Non-Final action mailed February 17, 2026. Applicants amendments to the claims have rendered the previous interpretation under 35 USC 112(f) moot through the removal of the interpreted language, therefore the interpretation is withdrawn. Applicants amendments to claims 1 and 6 in combination with the applicants arguments have been deemed sufficient to overcome the previous 35 USC 101 rejections through the recitation of complex calculation processes that are performed in real time and could not be performed mentally by a person of ordinary skill in the art, therefore the rejections are withdrawn. Applicants amendments to claims 1 and 6 have been deemed sufficient to overcome the previous 35 USC 103 rejections through the inclusion of “perform Real Time Kinematic (RTK) calculations to provide a centi-meter level accuracy” therefore the rejections are withdrawn. However as this changes the scope of the claims, new art rejections have been made based on the changes in scope. Additionally the applicants arguments have been fully considered but are not fully persuasive for the reasons seen below.
Applicant’s arguments with respect to claim(s) 1 and 6, specifically with regards to the newly amended “perform Real Time Kinematic (RTK) calculations to provide a centi-meter level accuracy” have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
On pages 13 the applicant argues “Manohar discloses functions related to position information reception, communication, and display. However, Manohar does not disclose … nor does it disclose a specific computational structure for determining hazards based on distance calculation using velocity vectors and displacement vectors. Oba discloses a configuration for displaying position, area, and hazard information on a screen. However, Oba does not disclose receiving RTCM correction data from a plurality of reference stations and performing RTK computation for precise positioning, nor does it disclose a vector-based distance computation and hazard determination structure based on real-time exchange of precise position data between moving objects,” the examiner respectfully disagrees.
MPEP 2142-2144 discusses the requirements for a case of obviousness using 35 USC 103 and provides examples of such cases. MPEP 2111 discusses Broadest Reasonable Interpretation and the interpretation of claims.
As discussed in the rejections below Manohar teaches a processing a plurality of received data pieces to monitor the surroundings of the device (Paragraph [0087], "The SOBE also interfaces with on board sensors that can watch the road and driving conditions such as cameras, range sensors, vibration sensors, microphones, or any other sensor that allows of such monitoring. A SOBE will also monitor the immediate surroundings and create a map of all the static and moving objects."); Manohar further teaches determining hazards based on a distance calculation using velocity and displacement (Paragraph [0191], "BSMs contain, among other information, the location, heading, speed, and future path of the vehicle. Other connected vehicles can tune in to these messages and use them to create a map of vehicles present in their surroundings. Knowing where the surrounding vehicles are, a vehicle, whether it is autonomous or not, will have information useful to maintain a high level of safety."); the system is using received data in order to determine a map of devices in the surroundings and can determine a hazard condition based on the localization which includes velocity and displacement (Paragraph [0153], "For a pedestrian crossing, sensors will monitor the pedestrian and other vulnerable road users (e.g., cyclists) crossing at the intersection and the areas in the vicinity of the intersection. The data from these sensors may be segmented as representing conditions with respective different virtual zones to help in detection and localization. The zones can be chosen to correspond to respective critical areas where dangerous situations may be expected, such as sidewalks, entrances of walkways, and incoming approaches 405, 406, 407, 408 of the roads to the intersection.”). Oba is not specifically relied upon for these limitations, Oba teaches displaying determined precision positioning data on a mobile device, such as the data determined above by Manohar.
Therefore the combination of Manohar, Oba, and Diggelen teaches a specific computational structure for determining hazards based on distance calculation using velocity vectors and displacement vectors, and the rejections under 35 USC 103 are maintained.
On pages 14-15 the applicant argues “Moreover, the claimed invention is characterized not merely by distance calculation, but by hazard determination in dynamic movement situations using velocity vectors and displacement vectors. However, neither Manohar nor Oba teaches or suggests a hazard determination structure based on such vector-based computations. This is not a simple functional addition but a fundamental change in both the computational method and decision criteria, which is beyond what a person skilled in the art would readily derive. In particular, the present invention is based on a motion-aware hazard determination mechanism reflecting real-time movement behavior through velocity vectors and displacement vectors, which is not disclosed or suggested in the prior art. Furthermore, the claimed invention provides an organically integrated processing flow of precision position data acquisition through RTK calculations -- real-time exchange of precision position data between multiple moving objects -- distance calculation using velocity vectors and displacement vectors -- hazard determination and generation of danger notification information and alert signals. In contrast, Manohar and Oba merely disclose isolated functional elements and do not provide any teaching or motivation for combining them into such an integrated processing structure.”, the examiner respectfully disagrees.
MPEP 2142-2144 discusses the requirements for a case of obviousness using 35 USC 103 and provides examples of such cases. MPEP 2111 discusses Broadest Reasonable Interpretation and the interpretation of claims.
As discussed in the rejections below Manohar teaches a processing a plurality of received data pieces to monitor the surroundings of the device (Paragraph [0087], "The SOBE also interfaces with on board sensors that can watch the road and driving conditions such as cameras, range sensors, vibration sensors, microphones, or any other sensor that allows of such monitoring. A SOBE will also monitor the immediate surroundings and create a map of all the static and moving objects."); Manohar further teaches determining hazards based on a distance calculation using velocity and displacement (Paragraph [0191], "BSMs contain, among other information, the location, heading, speed, and future path of the vehicle. Other connected vehicles can tune in to these messages and use them to create a map of vehicles present in their surroundings. Knowing where the surrounding vehicles are, a vehicle, whether it is autonomous or not, will have information useful to maintain a high level of safety."); the system is using received data in order to determine a map of devices in the surroundings and can determine a hazard condition based on the localization which includes velocity and displacement (Paragraph [0153], "For a pedestrian crossing, sensors will monitor the pedestrian and other vulnerable road users (e.g., cyclists) crossing at the intersection and the areas in the vicinity of the intersection. The data from these sensors may be segmented as representing conditions with respective different virtual zones to help in detection and localization. The zones can be chosen to correspond to respective critical areas where dangerous situations may be expected, such as sidewalks, entrances of walkways, and incoming approaches 405, 406, 407, 408 of the roads to the intersection.”).
Manohar further teaches using these determined localized positions in order to alert connected entities of hazardous situations (Paragraph [0038], “Based on a prediction of a dangerous situation, an alert is sent from the infrastructure devices at the intersection to all connected entities in the vicinity of the intersection. Every entity that receives an alert, processes the data in the alert and performs alert filtering. Alert filtering is a process of discarding or disregarding alerts that are not beneficial to the entity. If an alert is considered beneficial (i.e., is not disregarded as a result of the filtering), such as an alert of an impending collision, the entity either automatically reacts to the alert (such as by applying brakes), or a notification is presented to the driver or both.”).
The processes of Manohar specifically recite the use of velocity and displacement in the determination of these localized positions and the hazardous situation determination (Paragraph [0009], “a memory storing instructions executable by the processor to generate updated position correction information based on the first position correction information and on the information representing the parameter of motion”) (Paragraph [0058], “Messages sent by an OPE can include kinematic information associated with the vulnerable road user including, but not limited to, time of day, 3D position, heading, velocity, and acceleration.”).
Oba is not specifically relied upon for these limitations, Oba teaches displaying determined precision positioning data on a mobile device, such as the data determined above by Manohar.
Therefore the combination of Manohar, Oba, and Diggelen teaches a specific computational structure for determining hazards based on distance calculation using velocity vectors and displacement vectors, and the rejections under 35 USC 103 are maintained.
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, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 1-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Manohar (US-20210065551) in view of Oba (US-20220161813) and further in view of Diggelen (US-20240421474).
Regarding claim 1, Manohar teaches an apparatus for identifying a position of a moving object based on global navigation satellite system (GNSS) (Paragraph [0081], "A location receiver 202 (such as a GPS receiver) that provides localization data (e.g., coordinates of the location of the ground transportation entity).")
and notifying of dangerous accidents, the apparatus comprising (Paragraph [0007], "a memory storing instructions executable by the processor to generate and send safety message information")
a memory to store a set of instruction and a processor to execute the set of instruction to (Paragraph [0007], “(b) a processor, and (c) a memory storing instructions executable by the processor to generate and send safety message information”)
receive current position information data of a mobile device by receiving each GNSS position signal from a plurality of GNSS satellites (Paragraph [0081], "A location receiver 202 (such as a GPS receiver) that provides localization data (e.g., coordinates of the location of the ground transportation entity).")
receive position correction data of the mobile device in the form of an RTCM stream through a transport of RTCM via internet protocol (NTRIP) (Paragraph [0010], "The source external to the first ground transportation entity includes a RSE or an external service configured to transmit RTCM correction messages over the Internet.") (Paragraph [0261], "On receiving these RTCM correction messages, the ESOBE 2508 on vehicle 2506 corrected its own position and also stored the RTCM correction data for later usage.")
the position correction data being repeatedly generated by a plurality of reference stations for each GPS satellite (Paragraph [0261], "On receiving these RTCM correction messages, the ESOBE 2508 on vehicle 2506 corrected its own position and also stored the RTCM correction data for later usage. While the particular RSE was in range, the ESOBE continued to correct its position using received correction messages and to update these messages for later usage.")
acquire precision position data of the mobile device by applying the position correction data to the current position information data (Paragraph [0009], "an equipment for use on board a first ground transportation entity has (a) a receiver for first position correction information sent from a source external to the first ground transportation entity, (b) a receiver for information representing a parameter of position or motion of the first ground transportation entity, (c) a processor, and (d) a memory storing instructions executable by the processor to generate updated position correction information based on the first position correction information and on the information representing the parameter of motion, and send a position correction message to another ground transportation entity based on the updated position correction information.")
calculate distances between the mobile device and the other mobile devices, the work section, and the danger area using velocity vectors and displacement vectors (Paragraph [0087], "The SOBE also interfaces with on board sensors that can watch the road and driving conditions such as cameras, range sensors, vibration sensors, microphones, or any other sensor that allows of such monitoring. A SOBE will also monitor the immediate surroundings and create a map of all the static and moving objects.") (Paragraph [0153], "For a pedestrian crossing, sensors will monitor the pedestrian and other vulnerable road users (e.g., cyclists) crossing at the intersection and the areas in the vicinity of the intersection. The data from these sensors may be segmented as representing conditions with respective different virtual zones to help in detection and localization. The zones can be chosen to correspond to respective critical areas where dangerous situations may be expected, such as sidewalks, entrances of walkways, and incoming approaches 405, 406, 407, 408 of the roads to the intersection.”) (Paragraph [0191], "BSMs contain, among other information, the location, heading, speed, and future path of the vehicle. Other connected vehicles can tune in to these messages and use them to create a map of vehicles present in their surroundings. Knowing where the surrounding vehicles are, a vehicle, whether it is autonomous or not, will have information useful to maintain a high level of safety.")
and generate danger notification information and alert signals when at least one of the distances falls within a pre-set danger range (Paragraph [0038], “Based on a prediction of a dangerous situation, an alert is sent from the infrastructure devices at the intersection to all connected entities in the vicinity of the intersection. Every entity that receives an alert, processes the data in the alert and performs alert filtering. Alert filtering is a process of discarding or disregarding alerts that are not beneficial to the entity. If an alert is considered beneficial (i.e., is not disregarded as a result of the filtering), such as an alert of an impending collision, the entity either automatically reacts to the alert (such as by applying brakes), or a notification is presented to the driver or both.”).
However while Manohar teaches determining position data of the mobile device and another mobile device within a predetermined range, a work section and a danger area on a diagram (Paragraph [0087], "The SOBE also interfaces with on board sensors that can watch the road and driving conditions such as cameras, range sensors, vibration sensors, microphones, or any other sensor that allows of such monitoring. A SOBE will also monitor the immediate surroundings and create a map of all the static and moving objects.") (Paragraph [0153], "For a pedestrian crossing, sensors will monitor the pedestrian and other vulnerable road users (e.g., cyclists) crossing at the intersection and the areas in the vicinity of the intersection. The data from these sensors may be segmented as representing conditions with respective different virtual zones to help in detection and localization. The zones can be chosen to correspond to respective critical areas where dangerous situations may be expected, such as sidewalks, entrances of walkways, and incoming approaches 405, 406, 407, 408 of the roads to the intersection.”) (Paragraph [0191], "BSMs contain, among other information, the location, heading, speed, and future path of the vehicle. Other connected vehicles can tune in to these messages and use them to create a map of vehicles present in their surroundings. Knowing where the surrounding vehicles are, a vehicle, whether it is autonomous or not, will have information useful to maintain a high level of safety.").
Manohar does not explicitly teach displaying this information on a screen of a mobile device.
Oba teaches generating various types of display data for a vehicle that performs control to switch between automated driving and manual driving including
display the precision position data of the mobile device (Paragraph [0193], “estimating the position and posture of the user's automobile and the like. In addition, as necessary, the current-position estimating section 132 generates a local map (hereinafter, referred to as a map for current position estimation) used for estimating the current position”) (Paragraph [0227], “on the basis of positional information of the user's automobile and the acquired LDM update information, the display of the driving zone display keeps being updated. As a result, the driving zone display is scroll-displayed in association with the driving as if each zone comes toward the user's automobile”) (Paragraph [0235], “In this embodiment, all the zones in the driving zone display are divided into three zones as depicted … The immediate zone that approaches along with the driving provides a visually intuitive effect equivalent to representation on a map as if the vehicle moves on it at a constant speed. Accordingly, this gives an advantage that the driver can start a preparation for a right return to driving as an event approaches, and can intuitively recognize a point where a return is to be started, accurately to some extent,” here the display includes an immediate zone representing a map on which the vehicles own position moves)
real time precision position data received from other mobile devices (Paragraph [0198], “For example, the situation recognizing section 153 performs a process of recognizing the situation of the user's automobile, the situation around the user's automobile, the situation of the driver of the user's automobile, and the like. In addition, the situation recognizing section 153 generates a local map (hereinafter, referred to as a map for situation recognition) to be used for recognition of the situation around the user's automobile, as necessary. The map for situation recognition is an occupancy grip map (Occupancy Grid Map), for example.“) (Paragraph [0200], “For example, the condition around the user's automobile to be recognition targets include the types and positions of surrounding stationary objects; the types, positions, and motions of surrounding moving objects (e.g., speed, acceleration, moving direction, etc.)”)
a work section (Paragraph [0225], “a display of driving zones on a driving route is started. Other than being displayed on the instrument panel, this driving zone display is displayed also on a tablet or the like on which the driver performs a secondary task, for example, next to a work window.”)
and a danger area (Paragraph [0176], “For example, the output control section 105 generates output signals including at least one of visual information (e.g., image data) and auditory information (e.g., sound data), and supplies them to the output section 106, to thereby control output of visual information and auditory information from the output section 106. … regarding a danger such as collision, contact, or entrance into a danger zone, and supplies output signals including the generated sound data to the output section 106.”)
on a diagram that is pre-stored and activated on a screen of the mobile device (Paragraph [0046], “FIG. 17 depicts diagrams illustrating examples of a display of driving zones on a driving route displayed on a screen of tablet terminal equipment (hereinafter, simply denoted as a “tablet”).”).
Manohar and Oba are analogous art as they are both generally related to systems for monitoring the position and surroundings of road users.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include display the acquired precision position data of the mobile device, a position of another mobile device position within a predetermined range, a work section, and a danger area, on a diagram that is pre-stored and activated on a screen of the mobile device of Oba in the system for identifying a position of a moving object using GNSS data and notifying of dangerous situations of Manohar with a reasonable expectation of success in order to present relevant information to a driver using a display so that a driver can appropriately prepare for the current or upcoming driving situation (Paragraph [0235], “The immediate zone that approaches along with the driving provides a visually intuitive effect equivalent to representation on a map as if the vehicle moves on it at a constant speed. Accordingly, this gives an advantage that the driver can start a preparation for a right return to driving as an event approaches, and can intuitively recognize a point where a return is to be started, accurately to some extent. That is, the purpose of the display of this zone is to provide a user with start determination information regarding a right returning point of a driver rightly.”).
However the combination does not explicitly teach perform Real Time Kinematic (RTK) calculations to provide a centimeter level accuracy.
Diggelen teaches a pair of mobile devices can establish a wireless communication connection with one device acting as a base station and the other being a rover for real-time kinematic (RTK) positioning including
perform Real Time Kinematic (RTK) calculations to provide a centimeter level accuracy (Paragraph [0020], “The application then reports, in real time, when accurate measurements have been produced by the devices (e.g., centimeter-level accuracy has been achieved), which occurs after RTK integer ambiguity resolution has been successfully completed,” here the system is using current position information and received position correction information to perform real time kinematic calculations that result in centimeter level accuracy, while this correction data is not received from RTCM, the same methodology can reasonably be applied to correction data received via RTCM such as the correction data taught by Manohar).
Manohar, Oba, and Diggelen are analogous art as they are both generally related to systems for improving the positioning of mobile devices.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include perform Real Time Kinematic (RTK) calculations to provide a centimeter level accuracy of Diggelen in the system for identifying a position of a moving object using GNSS data and notifying of dangerous situations of Manohar and Oba with a reasonable expectation of success in order to improve the accuracy of device position estimation using RTK techniques (Paragraph [0003], “Example embodiments relate to techniques for centimeter-accurate localization using asymmetric antennas. For instance, a pair of smartphones or another type of mobile computing devices with asymmetric antennas can be aligned in orientation to cancel phase-center errors when performing disclosed techniques to achieve centimeter-accurate location measurements.”) (Paragraph [0048], “. By factoring the measurement or correction data from the second mobile computing device, the first mobile computing device can improve the accuracy of its position estimation.”).
Regarding claim 2, the combination of Manohar, Oba, and Diggelen teaches the system as discussed above in claim 1, Manohar further teaches wherein the mobile device is a terminal attached to a work vehicle or carried by a worker (Paragraph [0076-0077], “The onboard equipment typically may be original equipment for a ground transportation entity … A communication unit 203 that enables the sending and receiving, or both, of data to and from nearby vehicles, pedestrians, cyclists, or other ground transportation entities, and infrastructure, and combinations of them.”).
Regarding claim 3, the combination of Manohar, Oba, and Diggelen teaches the system as discussed above in claim 1, Manohar further teaches wherein the processor is to receive the real time precision position data from the other mobile devices (Paragraph [0033], “Typically, cooperative entities are continuously broadcasting their state data. Connected entities in the vicinity of a broadcasting entity are able to receive these broadcasts and can process and act on the received data. If, for example, a vulnerable road user has a wearable device that can receive broadcasts from an entity, say an approaching truck, the wearable device can process the received data and let the vulnerable user know when it is safe to cross the road.”).
Regarding claim 4, the combination of Manohar, Oba, and Diggelen teaches the system as discussed above in claim 1, Manohar further teaches wherein the processor is to measure a distance between the precision position data of the mobile device and positions of the other mobile devices (Paragraph [0072], “The sensors may include, but are not limited to, cameras, radars, lidars, ultrasonic detectors or any other hardware that can sense or infer from sensed data the distance to, speed, heading, location, or combinations of them, among other things, of a ground transportation entity. Sensor fusion is performed using aggregations or combinations of data from two or more sensors 101.”)
a distance between the precision position data of the mobile device and the work section (Paragraph [0042], “The ground transportation entities using a ground transportation network move with a variety of speeds and may reach a given intersection at different speeds and times of the day. If the speed and distance of an entity from the intersection is known, dividing the distance by the speed (both expressed in the same unit system) will give the time of arrival at the intersection.”)
and a distance between the precision position data of the mobile device and the danger area (Paragraph [0160], “The RSE then applies current data captured from the sensors to the AI model to cause it to predict intent and behavior, to determine when a dangerous situation is imminent, and to trigger corresponding alerts that are distributed (e.g., broadcast) to the vehicles and other ground transportation entities and to the vulnerable road users and drivers as early warnings in time to enable the vulnerable road users and drivers to undertake collision avoidance steps.”).
Regarding claim 5, the combination of Manohar, Oba, and Diggelen teaches the system as discussed above in claim 1, Manohar further teaches wherein the processor is to generate and output the danger notification information on the screen of the mobile device (Paragraph [0038], “Based on a prediction of a dangerous situation, an alert is sent from the infrastructure devices at the intersection to all connected entities in the vicinity of the intersection.”)
when a distance between the precision position data of the mobile device and positions of the other mobile devices, a distance between the precision position data of the mobile device and the work section, or a distance between the precision position data of the mobile device and the danger area is included in the pre-set danger range (Paragraph [0044], “In other words, in addition to detecting information about ground transportation entities directly from the sensor data, the system uses artificial intelligence and machine learning to process vast amounts of sensor data to learn the patterns of motion, behaviors, and intentions of ground transportation entities, for example, at intersections of ground transportation networks, on approaches to such intersections, and at crosswalks of ground transportation networks. Based on the direct use of current sensor data and on the results of applying the artificial intelligence and machine learning to the current sensor data, the system produces early warnings such as alerts of dangerous situations and therefore aids collision avoidance. With respect to early warnings in the form of instructions or commands, the command or instruction could be directed to a specific autonomous or human-driven entity to control the vehicle directly. For example, the instruction or command could slow down or stop an entity being driven by a malevolent person who has been determined to be about to run a red light for the purpose of trying to hurt people.”) (Paragraph [0090], “It will then apply the fused data to an artificial intelligence model that is not only able to predict the next action or reaction of the driver or user of the vehicle or other ground transportation entity or vulnerable road user, but also be able to predict the intent and future trajectories and associated near-miss or collision risks due to other vehicles, ground transportation entities and vulnerable road users nearby.”).
Regarding claim 6, Manohar teaches a method for identifying a position of a moving object based on Global Navigation Satellite System (GNSS) (Paragraph [0081], "A location receiver 202 (such as a GPS receiver) that provides localization data (e.g., coordinates of the location of the ground transportation entity).")
and notifying of dangerous accidents, the method comprising (Paragraph [0007], "a memory storing instructions executable by the processor to generate and send safety message information")
receiving, using a processor, current position information data of a mobile device by receiving each GNSS position signal from a plurality of GNSS satellites (Paragraph [0081], "A location receiver 202 (such as a GPS receiver) that provides localization data (e.g., coordinates of the location of the ground transportation entity).") (Paragraph [0007], “(b) a processor, and (c) a memory storing instructions executable by the processor to generate and send safety message information”)
receiving, using a processor, position correction data of the mobile device (Paragraph [0261], "On receiving these RTCM correction messages, the ESOBE 2508 on vehicle 2506 corrected its own position and also stored the RTCM correction data for later usage.")
in the form of an RTCM stream through a Network Transport of RTCM via Internet Protocol (NTRIP), the position correction data being repeatedly generated by a plurality of reference stations for each GPS satellite (Paragraph [0010], "The source external to the first ground transportation entity includes a RSE or an external service configured to transmit RTCM correction messages over the Internet.") (Paragraph [0009], "an equipment for use on board a first ground transportation entity has (a) a receiver for first position correction information sent from a source external to the first ground transportation entity, (b) a receiver for information representing a parameter of position or motion of the first ground transportation entity, (c) a processor, and (d) a memory storing instructions executable by the processor to generate updated position correction information based on the first position correction information and on the information representing the parameter of motion, and send a position correction message to another ground transportation entity based on the updated position correction information.")
acquiring, using a processor, precision position data of the mobile device by applying the position correction data to the current position information data (Paragraph [0009], "an equipment for use on board a first ground transportation entity has (a) a receiver for first position correction information sent from a source external to the first ground transportation entity, (b) a receiver for information representing a parameter of position or motion of the first ground transportation entity, (c) a processor, and (d) a memory storing instructions executable by the processor to generate updated position correction information based on the first position correction information and on the information representing the parameter of motion, and send a position correction message to another ground transportation entity based on the updated position correction information.")
and displaying, using a processor (Paragraph [0090], "If the risk of collision is higher than a certain threshold, then the warning is displayed to the driver of the host vehicle")
calculating, using the processor, distances between the mobile device and the other mobile devices, the work section, and the danger area using velocity vectors and displacement vectors (Paragraph [0087], "The SOBE also interfaces with on board sensors that can watch the road and driving conditions such as cameras, range sensors, vibration sensors, microphones, or any other sensor that allows of such monitoring. A SOBE will also monitor the immediate surroundings and create a map of all the static and moving objects.") (Paragraph [0153], "For a pedestrian crossing, sensors will monitor the pedestrian and other vulnerable road users (e.g., cyclists) crossing at the intersection and the areas in the vicinity of the intersection. The data from these sensors may be segmented as representing conditions with respective different virtual zones to help in detection and localization. The zones can be chosen to correspond to respective critical areas where dangerous situations may be expected, such as sidewalks, entrances of walkways, and incoming approaches 405, 406, 407, 408 of the roads to the intersection.”) (Paragraph [0191], "BSMs contain, among other information, the location, heading, speed, and future path of the vehicle. Other connected vehicles can tune in to these messages and use them to create a map of vehicles present in their surroundings. Knowing where the surrounding vehicles are, a vehicle, whether it is autonomous or not, will have information useful to maintain a high level of safety.")
and generating, using the processor, danger notification information and alert signals when at least one of the distances falls within a pre-set danger range (Paragraph [0038], “Based on a prediction of a dangerous situation, an alert is sent from the infrastructure devices at the intersection to all connected entities in the vicinity of the intersection. Every entity that receives an alert, processes the data in the alert and performs alert filtering. Alert filtering is a process of discarding or disregarding alerts that are not beneficial to the entity. If an alert is considered beneficial (i.e., is not disregarded as a result of the filtering), such as an alert of an impending collision, the entity either automatically reacts to the alert (such as by applying brakes), or a notification is presented to the driver or both.”).
However while Manohar teaches determining position data of the mobile device and another mobile device within a predetermined range, a work section and a danger area on a diagram (Paragraph [0087], "The SOBE also interfaces with on board sensors that can watch the road and driving conditions such as cameras, range sensors, vibration sensors, microphones, or any other sensor that allows of such monitoring. A SOBE will also monitor the immediate surroundings and create a map of all the static and moving objects.") (Paragraph [0153], "For a pedestrian crossing, sensors will monitor the pedestrian and other vulnerable road users (e.g., cyclists) crossing at the intersection and the areas in the vicinity of the intersection. The data from these sensors may be segmented as representing conditions with respective different virtual zones to help in detection and localization. The zones can be chosen to correspond to respective critical areas where dangerous situations may be expected, such as sidewalks, entrances of walkways, and incoming approaches 405, 406, 407, 408 of the roads to the intersection.”) (Paragraph [0191], "BSMs contain, among other information, the location, heading, speed, and future path of the vehicle. Other connected vehicles can tune in to these messages and use them to create a map of vehicles present in their surroundings. Knowing where the surrounding vehicles are, a vehicle, whether it is autonomous or not, will have information useful to maintain a high level of safety.").
Manohar does not explicitly teach displaying this information on a screen of a mobile device.
Oba teaches generating various types of display data for a vehicle that performs control to switch between automated driving and manual driving including
displaying, using a processor, the precision position data of the mobile device (Paragraph [0193], “estimating the position and posture of the user's automobile and the like. In addition, as necessary, the current-position estimating section 132 generates a local map (hereinafter, referred to as a map for current position estimation) used for estimating the current position”) (Paragraph [0227], “on the basis of positional information of the user's automobile and the acquired LDM update information, the display of the driving zone display keeps being updated. As a result, the driving zone display is scroll-displayed in association with the driving as if each zone comes toward the user's automobile”) (Paragraph [0235], “In this embodiment, all the zones in the driving zone display are divided into three zones as depicted … The immediate zone that approaches along with the driving provides a visually intuitive effect equivalent to representation on a map as if the vehicle moves on it at a constant speed. Accordingly, this gives an advantage that the driver can start a preparation for a right return to driving as an event approaches, and can intuitively recognize a point where a return is to be started, accurately to some extent,” here the display includes an immediate zone representing a map on which the vehicles own position moves)
real time precision position data received from other mobile devices (Paragraph [0198], “For example, the situation recognizing section 153 performs a process of recognizing the situation of the user's automobile, the situation around the user's automobile, the situation of the driver of the user's automobile, and the like. In addition, the situation recognizing section 153 generates a local map (hereinafter, referred to as a map for situation recognition) to be used for recognition of the situation around the user's automobile, as necessary. The map for situation recognition is an occupancy grip map (Occupancy Grid Map), for example.“) (Paragraph [0200], “For example, the condition around the user's automobile to be recognition targets include the types and positions of surrounding stationary objects; the types, positions, and motions of surrounding moving objects (e.g., speed, acceleration, moving direction, etc.)”)
a work section (Paragraph [0225], “a display of driving zones on a driving route is started. Other than being displayed on the instrument panel, this driving zone display is displayed also on a tablet or the like on which the driver performs a secondary task, for example, next to a work window.”)
and a danger area (Paragraph [0176], “For example, the output control section 105 generates output signals including at least one of visual information (e.g., image data) and auditory information (e.g., sound data), and supplies them to the output section 106, to thereby control output of visual information and auditory information from the output section 106. … regarding a danger such as collision, contact, or entrance into a danger zone, and supplies output signals including the generated sound data to the output section 106.”)
on a diagram that is pre-stored and activated on a screen of the mobile device (Paragraph [0046], “FIG. 17 depicts diagrams illustrating examples of a display of driving zones on a driving route displayed on a screen of tablet terminal equipment (hereinafter, simply denoted as a “tablet”).”).
Manohar and Oba are analogous art as they are both generally related to systems for monitoring the position and surroundings of road users.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include display the acquired precision position data of the mobile device, a position of another mobile device position within a predetermined range, a work section, and a danger area, on a diagram that is pre-stored and activated on a screen of the mobile device of Oba in the system for identifying a position of a moving object using GNSS data and notifying of dangerous situations of Manohar with a reasonable expectation of success in order to present relevant information to a driver using a display so that a driver can appropriately prepare for the current or upcoming driving situation (Paragraph [0235], “The immediate zone that approaches along with the driving provides a visually intuitive effect equivalent to representation on a map as if the vehicle moves on it at a constant speed. Accordingly, this gives an advantage that the driver can start a preparation for a right return to driving as an event approaches, and can intuitively recognize a point where a return is to be started, accurately to some extent. That is, the purpose of the display of this zone is to provide a user with start determination information regarding a right returning point of a driver rightly.”).
However the combination does not explicitly teach performing Real Time Kinematic (RTK) calculations to provide a centimeter level accuracy.
Diggelen teaches a pair of mobile devices can establish a wireless communication connection with one device acting as a base station and the other being a rover for real-time kinematic (RTK) positioning including
performing Real Time Kinematic (RTK) calculations to provide a centimeter level accuracy (Paragraph [0020], “The application then reports, in real time, when accurate measurements have been produced by the devices (e.g., centimeter-level accuracy has been achieved), which occurs after RTK integer ambiguity resolution has been successfully completed,” here the system is using current position information and received position correction information to perform real time kinematic calculations that result in centimeter level accuracy, while this correction data is not received from RTCM, the same methodology can reasonably be applied to correction data received via RTCM such as the correction data taught by Manohar).
Manohar, Oba, and Diggelen are analogous art as they are both generally related to systems for improving the positioning of mobile devices.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include performing Real Time Kinematic (RTK) calculations to provide a centimeter level accuracy of Diggelen in the system for identifying a position of a moving object using GNSS data and notifying of dangerous situations of Manohar and Oba with a reasonable expectation of success in order to improve the accuracy of device position estimation using RTK techniques (Paragraph [0003], “Example embodiments relate to techniques for centimeter-accurate localization using asymmetric antennas. For instance, a pair of smartphones or another type of mobile computing devices with asymmetric antennas can be aligned in orientation to cancel phase-center errors when performing disclosed techniques to achieve centimeter-accurate location measurements.”) (Paragraph [0048], “. By factoring the measurement or correction data from the second mobile computing device, the first mobile computing device can improve the accuracy of its position estimation.”).
Regarding claim 7, claim 7 is similar in scope to claim 3, and is therefore rejected under similar rationale.
Regarding claim 8, claim 8 is similar in scope to claim 4, and is therefore rejected under similar rationale.
Regarding claim 9, claim 9 is similar in scope to claim 5, and is therefore rejected under similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kishimoto (US-12169243) teaches positioning processor calculates a current position of the reference station based on GNSS observation data and GNSS augmentation data obtained from the augmentation information included in the received GNSS signals. Bobye (US-10969228) a modified RTK procedure to calculate an associated post update correction that is applied to the aircraft INS position in order to produce an accurate relative position of the aircraft. Dill (US-20180120445) teaches a method may be executed by a base station or mobile device to improve accuracy of a global positioning system (GPS)-based position or “geoposition” of the mobile device.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CHRISTOPHER GEORGE FEES/Primary Examiner, Art Unit 3662
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