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 .
DETAILED ACTION
Response to Arguments
Regarding objections.
Applicant argues:
This response amends the title to recite "METHOD, APPARATUS, AND SYSTEM FOR DISPLAYING ROAD INFORMATION". The title as amended is clearly indicative of the invention to which the claims are directed. Withdrawal of the objection is respectfully requested.
Examiner replies that:
Withdrawn.
Regarding 35 USC § 102/103.
Applicant argues:
It can be seen that, in Toyota, the collection of the sensor data is performed, for example, every 0.1 second, and all the sensor data collected periodically are used to maintain an up-to- date view of the surrounding environment. Contrary to amended claim 1, the collected sensor data that are used to provide the surrounding environment is not obtained within preset duration before a first moment at which the predicted position of the target object is obtained.
Further, in Toyota, when predicting the potential hazards, the timing of predicting the potential hazards is vague, which is generally described as "current or projected/forecasted future trajectories." Unlike amended claim 1, Toyota does not indicate that the prediction is conducted at a particular moment. Thus, Toyota does not mention the feature of obtaining a predicted position of the target object at a first moment based on the obtained sensing information of the target object as recited in amended claim 1.
Moreover, Toyota at most describes how to avoid potential hazard based on the AR system 180's warning and graphical elements provided from the engagement module 230.
[…]
However, Toyota does not disclose using a combination of the sensor data which form the "up-to-date view" particularly collected before the timing of predicted the potential hazards, and the predicted potential hazards to provide the driver the prompt information of the target object.
Thus, Toyota fails to disclose that the display apparatus displays prompt information of the target object at the first moment based on together the predicted position and the measurement position. Needless to say, Toyota fails to disclose further details that the prompt information of the target object is displayed based on an average value of the predicted position and the measurement position obtained through fusion and correction, as required in amended claim 1.
Accordingly, Toyota does not disclose the distinguishing features of amended claim 1, and fails to anticipate amended claim 1.
Examiner replies that:
Applicant has amended the claims to change the scope since the previous action. The amendment(s) necessitate new ground(s) of rejection and are rejected in detail under the § 102/103 headings below.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 5, 8-11, 13, 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Toyoda U.S. Patent/PG Publication 20180322783 in view of Seder U.S. Patent/PG Publication 20100253918.
Regarding claim 1:
(Currently Amended) A display method applied to a display apparatus including a hardware processor, the display method comprising: (Toyoda [0023] Referring to FIG. 1, an example of a vehicle 100 is illustrated. As used herein, a “vehicle” is any form of motorized transport. In one or more implementations, the vehicle 100 is an automobile. While arrangements will be described herein with respect to automobiles, it will be understood that embodiments are not limited to automobiles. In some implementations, the vehicle 100 may be any other form of motorized transport that, for example, benefits from the functionality discussed herein.).
obtaining sensing information of a target object (Toyoda [0039] At 310, the monitoring module 220 collects data from at least one sensor of the vehicle 100.).
obtaining a predicted position of the target object at a first moment based on the obtained sensing information of the target object (Toyoda [0044] More particularly, the potential hazards are likelihoods of risk from detected objects and circumstances (e.g., associations between detected objects and/or contextual circumstances) in the surrounding environment. That is, the potential hazards represent risks (e.g., collisions) to the vehicle 100 in the surrounding environment. In general, the potential hazards can be classified as discrete hazards and contextual hazards. The discrete hazards are plain openly visible risks to the vehicle 100 that include, for example, detected objects with intersecting current or projected/forecasted future trajectories that are predicted to intercept or closely miss the vehicle 100, and other similar direct risks. ).
obtaining a measurement position of the target object within preset duration before the first moment (Toyoda [0039] At 310, the monitoring module 220 collects data from at least one sensor of the vehicle 100. In one embodiment, the monitoring module 220 collects data from sensors of the sensor system 120 including lidar 124, radar 123, and/or other sensors on at least a semi-continuous basis. That is, for example, the monitoring module 220 collects the sensor data every x seconds (e.g., 0.1 s) to maintain an up-to-date view of the surrounding environment and the driver. In general, the monitoring module 220 is operable to collect data from whichever sensors are available within the vehicle 100 and/or collect data from third party sources (e.g., weather sources, traffic sources, etc.) through, for example, a communications system of the vehicle 100. In either case, the monitoring module 220 generally collects data associated with several different classes of information. [0040] For example, the monitoring module 220 can collect environmental information, contextual information, and driver state information. The environmental information is information about the surroundings of the vehicle 100 including information about objects (e.g., locations and trajectories), obstacles, terrain, surfaces, and so on.) where the system collects data every x seconds, which is a preset duration.
and enabling, based on the predicted position and the measurement position , the[[a]] display apparatus to display prompt information of the target object at the first moment (Toyoda [0051] In further aspects, the engagement module 230 renders graphical elements as animations of the potential hazards as though the potential hazards are occurring when, in fact, the potential hazards are not occurring. Thus, the portrayal or imitation of the potential hazard by the engagement module 230 through the AR system 180 acts as a warning to the driver about the potential hazard. Moreover, when the engagement module 230 renders the graphical elements in familiar forms such as with shapes of persons including children, families, bouncing balls, animals (e.g., dogs, cats, etc.), etc., the graphical elements induce a heightened sense of responsibility within the driver by relating the potential hazard to the driver. That is, when the driver is aware of a particular nature of the potential hazard such as a child running into the roadway, the driver generally becomes more aware and cautious of the potential hazard since the graphical elements facilitate relating the potential hazard to the driver. Thus, the driver may be self-motivated to engage the driving tasks to avoid the potential hazards.).
Toyoda does not teach fusion and correction. In a related field of endeavor, Seder teaches:
obtaining a measurement position of the target object within preset duration before the first moment (Seder [0131] Algorithms described herein are typically executed during preset loop cycles such that each algorithm is executed at least once each loop cycle. Algorithms stored in the non-volatile memory devices are executed by one of the central processing units and are operable to monitor inputs from the sensing devices and execute control and diagnostic routines to control operation of a respective device, using preset calibrations. Loop cycles are typically executed at regular intervals, for example each 3, 6.25, 15, 25 and 100 milliseconds during ongoing vehicle operation. Alternatively, algorithms may be executed in response to occurrence of an event.) where the preset duration is the loop cycle.
and enabling, based on an average value of the predicted position and the measurement position obtained through fusion and correction, (Seder [0025] FIG. 18 schematically depicts an exemplary bank of Kalman filters operating to estimate position and velocity of a group objects, in accordance with the present disclosure) where a Kalman filter is an algorithm that performs these functions (See Wikipedia Kalman Filter https://web.archive.org/web/20180701033626/https://en.wikipedia.org/wiki/Kalman_filter)
Therefore, it would have been obvious before the effective filing date of the claimed invention to use fusion and correction as taught by Seder. The motivation for doing so would have been that a Kalman filter is a well-known algorithm to provide more accurate and more reliable data for tracking purposes. Therefore it would have been obvious to combine Seder with Toyoda to obtain the invention.
Regarding claim 2:
(Currently Amended) The method according to claim 1, has all of its limitations taught by Toyoda in view of Seder. Toyoda further teaches wherein the enabling, based on the predicted position and the measurement position, the[[a]] display apparatus to display the prompt information of the target object at the first moment comprises:
when based on the measurement position of the target object being obtained before the first moment, enabling the display apparatus to display the prompt information of the target object at the first moment and at a display position, wherein the display position is related to the predicted position and the measurement position (Toyoda [0062] As a further example, FIG. 6 illustrates a view 600 of a narrow alley with two blind corners 610 and 620 caused by the buildings. Accordingly, the engagement system 170 identifies the blind corners 610 and 620 and renders an outline of a person 630 walking from behind the blind corner 610. Moreover, the engagement system 170 can also illustrate a cartoonish collision symbol 640 to further emphasize the possibility of a collision with a person or object crossing the roadway from the blind corner 610. In this way, the engagement system 170 uses information about detected potential hazards in the surrounding environment to render graphics with the AR system 180 and motivate the driver to engage driving tasks.).
Regarding claim 3:
The method according to claim 1, has all of its limitations taught by Toyoda in view of Seder. Toyoda further teaches wherein the enabling, based on the predicted position and the measurement position, the[[a]] display apparatus to display the prompt information of the target object at the first moment comprises:
when based on the measurement position of the target object being not obtained before the first moment, enabling the display apparatus to display the prompt information of the target object at the first moment and at a display position, wherein the display position is related to the predicted position (Toyoda [0059] As a further example, the view 400 includes a line of parked vehicles 440 on the roadway. The parked vehicles 440 represent a potential hazard to the vehicle 100 because the parked vehicles 440 can obstruct a view of the driver of the vehicle 100, especially in circumstances where one or more of the parked vehicles 440 is a van or truck and/or when a shorter person such as a child moves from between the vehicles 440. Thus, the system 170 animates a person 450 (e.g., a child) repetitively moving from between the vehicles 440 with an arrow pointing in a direction of movement. In this way, the system 170 directs attention of the driver to zones of potential hazards around the vehicle 100. In further aspects, the engagement system 170 can also render graphical elements to inform the driver of how to control the vehicle 100. That is, the engagement module 230 can render graphics indicating to the driver that the vehicle 100 should slow down, merge away from the cars 440, and so on.).
Regarding claim 5:
(Currently Amended) The method according to claim 1, has all of its limitations taught by Toyoda in view of Seder. Toyoda further teaches wherein the enabling, based on the predicted position and the measurement position, the[[a]] display apparatus to display the prompt information of the target object at the first moment comprises:
when based on a plurality of measurement positions of the target object are being obtained before the first moment, enabling the display apparatus to display the prompt information of the target object at the first moment and at the display position, wherein the display position is related to the predicted position and a last obtained measurement position in the plurality of measurement positions (Toyoda [0033] In one embodiment, the database 240 stores the sensor data along with, for example, metadata that characterizes various aspects of the sensor data. For example, the metadata can include location coordinates (e.g., longitude and latitude), relative map coordinates or tile identifiers, time/date stamps from when the separate sensor data was generated, and so on.)(Toyoda [0039] At 310, the monitoring module 220 collects data from at least one sensor of the vehicle 100. In one embodiment, the monitoring module 220 collects data from sensors of the sensor system 120 including lidar 124, radar 123, and/or other sensors on at least a semi-continuous basis. That is, for example, the monitoring module 220 collects the sensor data every x seconds (e.g., 0.1 s) to maintain an up-to-date view of the surrounding environment and the driver. In general, the monitoring module 220 is operable to collect data from whichever sensors are available within the vehicle 100 and/or collect data from third party sources (e.g., weather sources, traffic sources, etc.) through, for example, a communications system of the vehicle 100. In either case, the monitoring module 220 generally collects data associated with several different classes of information.)(Toyoda [0071] As noted above, the vehicle 100 can include the sensor system 120. The sensor system 120 can include one or more sensors. “Sensor” means any device, component and/or system that can detect, and/or sense something. The one or more sensors can be configured to detect, and/or sense in real-time. As used herein, the term “real-time” means a level of processing responsiveness that a user or system senses as sufficiently immediate for a particular process or determination to be made, or that enables the processor to keep up with some external process.) since a real-time device is continuously using the last obtained measurement.
Regarding claim 8:
(Original) The method according to claim 1, has all of its limitations taught by Toyoda in view of Seder. Toyoda further teaches wherein the target object comprises one or more of a vehicle, a person, an obstacle, and a traffic sign (Toyoda [0068] Examples of static obstacles include trees, buildings, curbs, fences, railings, medians, utility poles, statues, monuments, signs, benches, furniture, mailboxes, large rocks, hills.)(Toyoda [0086] The autonomous driving module(s) 160 can determine the location of obstacles, obstacles, or other environmental features including traffic signs, trees, shrubs, neighboring vehicles, pedestrians, etc.)(Toyoda [0061] Additionally, the engagement system 170 identifies a person 530 walking a dog 540 in a cavalier manner by, for example, not leashing the dog 540 and/or by using a reel-style leash that permits the dog 540 to abruptly run from the person 530. In either case, the engagement system 170 animates a moving arrow 550 indicating that the dog 540 and/or the person 530 may abruptly enter the roadway in front of the vehicle 100.).
Regarding claim 9:
The claim is a parallel version of claim 1. As such it is rejected under the same teachings.
Regarding claim 10:
The claim is a parallel version of claim 2. As such it is rejected under the same teachings.
Regarding claim 11:
The claim is a parallel version of claim 3. As such it is rejected under the same teachings.
Regarding claim 13:
The claim is a parallel version of claim 5. As such it is rejected under the same teachings.
Regarding claim 16:
The claim is a parallel version of claim 8. As such it is rejected under the same teachings.
Regarding claim 17:
The claim is a parallel version of claim 1. As such it is rejected under the same teachings.
Regarding claim 18:
The claim is a parallel version of claim 2. As such it is rejected under the same teachings.
Regarding claim 19:
The claim is a parallel version of claim 3. As such it is rejected under the same teachings.
Claim(s) 4, 7, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Toyoda U.S. Patent/PG Publication 20180322783 in view of Seder U.S. Patent/PG Publication 20100253918 and Paradie U.S. Patent/PG Publication 20060031015.
Regarding claim 4:
(Currently Amended) The method according to claim 1, has all of its limitations taught by Toyoda in view of Seder. Toyoda further teaches wherein the enabling, based on the predicted position and the measurement position, the[[a]] display apparatus to display the prompt information of the target object at the first moment comprises:
when based on a plurality of measurement positions of the target object are being obtained before the first moment, (Toyoda [0033] In one embodiment, the database 240 stores the sensor data along with, for example, metadata that characterizes various aspects of the sensor data. For example, the metadata can include location coordinates (e.g., longitude and latitude), relative map coordinates or tile identifiers, time/date stamps from when the separate sensor data was generated, and so on.)(Toyoda [0039] At 310, the monitoring module 220 collects data from at least one sensor of the vehicle 100. In one embodiment, the monitoring module 220 collects data from sensors of the sensor system 120 including lidar 124, radar 123, and/or other sensors on at least a semi-continuous basis. That is, for example, the monitoring module 220 collects the sensor data every x seconds (e.g., 0.1 s) to maintain an up-to-date view of the surrounding environment and the driver. In general, the monitoring module 220 is operable to collect data from whichever sensors are available within the vehicle 100 and/or collect data from third party sources (e.g., weather sources, traffic sources, etc.) through, for example, a communications system of the vehicle 100. In either case, the monitoring module 220 generally collects data associated with several different classes of information.)
enabling the display apparatus to display the prompt information of the target object at the first moment and at the display position, wherein the display position is related to the predicted position and an (Toyoda [0051] In further aspects, the engagement module 230 renders graphical elements as animations of the potential hazards as though the potential hazards are occurring when, in fact, the potential hazards are not occurring. Thus, the portrayal or imitation of the potential hazard by the engagement module 230 through the AR system 180 acts as a warning to the driver about the potential hazard. Moreover, when the engagement module 230 renders the graphical elements in familiar forms such as with shapes of persons including children, families, bouncing balls, animals (e.g., dogs, cats, etc.), etc., the graphical elements induce a heightened sense of responsibility within the driver by relating the potential hazard to the driver. That is, when the driver is aware of a particular nature of the potential hazard such as a child running into the roadway, the driver generally becomes more aware and cautious of the potential hazard since the graphical elements facilitate relating the potential hazard to the driver. Thus, the driver may be self-motivated to engage the driving tasks to avoid the potential hazards.).
Toyoda does not teach averaging values. In a related field of endeavor, Paradie teaches:
when based on a plurality of measurement positions of the target object are being obtained before the first moment, enabling the display apparatus to display the prompt information of the target object at the first moment and at the display position, wherein the display position is related to the predicted position and an average value of the plurality of measurement positions (Paradie [0040] Establishing the position of the object may be performed on an image-by-image basis with each new image corresponding to a different object position and, thus, to a different establishment of collision and safe zones. However, since commercially-available sensing systems tend to have significant standard deviations, particularly with respect to bearing data, it is preferred to base the position of the observed object on a number of observations. For example, in one preferred embodiment, the position is determined as an average of image data. Referring to FIG. 4, the x-coordinate value for the estimated object location 411 equals a moving average of observed n number of x-coordinate values 412, and the y-coordinate value for the estimated object location 411 equals a moving average of observed n number of y-coordinate values. Accordingly, the average will change, or has the potential to change, but not drastically, with every new observed value. Alternatively, rather than basing the position on a simple moving average of observed images, the x center may be based on a variance-weighted average of observed x values and the y center may be based on a variance-weighted average of observed y values. The variance may be, for example, estimated .sigma..sub.x.sup.2 and .sigma..sub.y.sup.2 for each observation. It is also possible to estimate an object location with respect to the time of the first image, or, alternately, to the time of the most recent image, rather than the time between the first and the most recent image. Still other approaches for establishing the observed object's position will be obvious to those of skill in the art in light of this disclosure.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to average values as taught by Paradie. The motivation for doing so would have been to improve bearing data accuracy (Paradie [0040]). Therefore it would have been obvious to combine Paradie with Toyoda to obtain the invention.
Regarding claim 7:
(Currently Amended) The method according to claim 2, has all of its limitations taught by Toyoda in view of Seder. Toyoda further teaches wherein the display position is further related to an associated with a plurality of adjacent moments before the first moment (Toyoda [0033] In one embodiment, the database 240 stores the sensor data along with, for example, metadata that characterizes various aspects of the sensor data. For example, the metadata can include location coordinates (e.g., longitude and latitude), relative map coordinates or tile identifiers, time/date stamps from when the separate sensor data was generated, and so on.)(Toyoda [0039] At 310, the monitoring module 220 collects data from at least one sensor of the vehicle 100. In one embodiment, the monitoring module 220 collects data from sensors of the sensor system 120 including lidar 124, radar 123, and/or other sensors on at least a semi-continuous basis. That is, for example, the monitoring module 220 collects the sensor data every x seconds (e.g., 0.1 s) to maintain an up-to-date view of the surrounding environment and the driver. In general, the monitoring module 220 is operable to collect data from whichever sensors are available within the vehicle 100 and/or collect data from third party sources (e.g., weather sources, traffic sources, etc.) through, for example, a communications system of the vehicle 100. In either case, the monitoring module 220 generally collects data associated with several different classes of information.)
Toyoda does not teach averaging values. In a related field of endeavor, Paradie teaches:
wherein the display position is further related to an average value of display positions corresponding to associated with a plurality of adjacent moments before the first moment (Paradie [0040] Establishing the position of the object may be performed on an image-by-image basis with each new image corresponding to a different object position and, thus, to a different establishment of collision and safe zones. However, since commercially-available sensing systems tend to have significant standard deviations, particularly with respect to bearing data, it is preferred to base the position of the observed object on a number of observations. For example, in one preferred embodiment, the position is determined as an average of image data. Referring to FIG. 4, the x-coordinate value for the estimated object location 411 equals a moving average of observed n number of x-coordinate values 412, and the y-coordinate value for the estimated object location 411 equals a moving average of observed n number of y-coordinate values. Accordingly, the average will change, or has the potential to change, but not drastically, with every new observed value. Alternatively, rather than basing the position on a simple moving average of observed images, the x center may be based on a variance-weighted average of observed x values and the y center may be based on a variance-weighted average of observed y values. The variance may be, for example, estimated .sigma..sub.x.sup.2 and .sigma..sub.y.sup.2 for each observation. It is also possible to estimate an object location with respect to the time of the first image, or, alternately, to the time of the most recent image, rather than the time between the first and the most recent image. Still other approaches for establishing the observed object's position will be obvious to those of skill in the art in light of this disclosure.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to average values as taught by Paradie. The motivation for doing so would have been to improve bearing data accuracy (Paradie [0040]). Therefore it would have been obvious to combine Paradie with Toyoda to obtain the invention.
Regarding claim 12:
The claim is a parallel version of claim 4. As such it is rejected under the same teachings.
Regarding claim 15:
The claim is a parallel version of claim 7. As such it is rejected under the same teachings.
Regarding claim 20:
The claim is a parallel version of claim 4. As such it is rejected under the same teachings.
Claim(s) 6, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Toyoda U.S. Patent/PG Publication 20180322783 in view of Seder U.S. Patent/PG Publication 20100253918 and SDOBNIKOV U.S. Patent/PG Publication 20210185232.
Regarding claim 6:
(Currently Amended) The method according to claim 2, has all of its limitations taught by Toyoda in view of Seder. Toyoda does not teach error correction. In a related field of endeavor, Sdobnikov teaches:
wherein the display position is further related to a preset correction value, and the preset correction value is used to reduce an error generated when upon a vehicle wobbles in a driving process (Sdobnikov [0018] The second object set is solved by the fact that in the method of shaking compensation for automotive augmented reality systems, according to which the compensations, associated with the motion of a vehicle and surrounding object, are described considering time latencies and prediction, according to the invention, the recognition results from front facing camera are transferred into the prediction module with corresponding frequency and latency in relation to the moment of light entering onto a matrix of the front facing camera, and the gyro sensor and accelerometer transfer data into prediction module and into positioning module, vehicle sensors transfer data with various frequencies and latencies into prediction module and into positioning module, vehicle position and rotation are calculated by means of positioning module, as well as their relative displacement for the time moment, remoted from current moment by cumulative time of module operation, and transfer them into prediction module, where based on the data received, the positions of static and dynamic objects are predicted separately, the data from gyro sensor and accelerometer enter into the vehicle shaking compensation module, where the prediction of shaking low-frequency is made over the period of operation of rendering and data display modules, and the rest of the shaking are given with the predicted portion into the module of data rendering for visualization on projection display, while calculations are made in the data rendering module, and the part of or all the portions are added, which were integrated over the period of operation of shaking compensation module, and correction of the image being formed to compensate the displacement of driver's eyes is applied after all corrections in the rendering module, the final result for the driver is visualized on projection display.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to user error correction as taught by Sdobnikov. The motivation for doing so would have been to improve perception and reduce distraction (Sdobnikov [0011]). Therefore it would have been obvious to combine Sdobnikov with Toyoda to obtain the invention.
Regarding claim 14:
The claim is a parallel version of claim 6. As such it is rejected under the same teachings.
Conclusion
For the prior art referenced and the prior art considered pertinent to Applicant’s disclosure but not relied upon, see PTO-892 “Notice of References Cited”.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON PRINGLE-PARKER whose telephone number is (571) 272-5690 and e-mail is jason.pringle-parker@uspto.gov. The examiner can normally be reached on 8:30am-5:00pm est Monday-Friday. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, King Poon can be reached on (571) 270-0728. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JASON A PRINGLE-PARKER/
Primary Examiner, Art Unit 2617
Prosecution Insights