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 .
This action is in response to the amendments filed on 01/29/2026, in which claims 1-20 are pending and addressed below.
Response to Amendment
Applicant has amended the title of the invention to overcome the objection to the title. Accordingly, the objection to the title of the invention has been withdrawn.
Applicant has amended the claims to remove generic placeholders and recite sufficient structure. Accordingly, the claims are no longer subject to interpretation under 35 U.S.C. 112(f).
Applicant has amended the claims to overcome the 35 U.S.C. 112(b) rejections. Accordingly, the 35 U.S.C. 112(b) rejections have been withdrawn.
Response to Arguments
Applicant's arguments filed 01/29/2026 have been fully considered but they are not persuasive.
With respect to the 35 U.S.C. 103 rejections:
Applicant argues on page 12 of the remarks that Magzimof and Haynes would not have rendered obvious the independent claims because “neither reference makes a selection of a specific analysis process based on a difference between an allowable delay and a transfer delay where the selection includes determining whether the specific analysis process is executable within a time period corresponding to the difference.”
In response to applicant’s arguments, the examiner respectfully disagrees that Magzimof in view of Haynes fail to disclose all elements of the amended independent claims. Haynes teaches switching between a normal data processing flow and an alternative data processing flow to reduce reaction time and latency (Haynes [0089], Fig. 3B). The alternative data processing flow reduces reaction time and latency by omitting processing steps otherwise included in the normal data processing flow (Haynes [0091]). Haynes further teaches processing can be delayed or prevented for data not relevant to unexpected behavior so the computing system can make the more critical determination regarding the unexpected behavior (Haynes [0087]). Therefore, Haynes teaches “selection of a specific analysis process based on a difference between an allowable delay and a transfer delay where the selection includes determining whether the specific analysis process is executable within a time period corresponding to the difference” because Hayne teaches selecting reduced processing of irrelevant data in response to unexpected behavior so a critical determination can be made in reduced timing.
Applicant’s arguments have been fully considered and have been found not persuasive.
Claim Interpretation
Claims 3-4 recite “the specific analysis process not performed on the sensor data,” and claim 3 further recites “a second layer including an analysis result of the specific analysis process not performed on the sensor data.” The instant application defines the process not performed on the sensor data as “an analysis process to be performed on data other than the sensor data (e.g., dynamic information, hereinafter referred to as “non-sensor data”)” ([0051]). The instant application further defines the second layer as “including the movement prediction, etc., of the dynamic object” ([0051]). Therefore, “the specific analysis process not performed on the sensor data” is interpreted as a process to predict movement of a dynamic object where the dynamic information is data that is a result of received sensor data, as evidenced by instant application [0030] and [0051].
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.
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.
Claims 1-6, 9-13, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Magzimof et al., U.S. Patent Application Publication No. 2020/0324761 A1 (hereinafter Magzimof), in view of Haynes et al., U.S. Patent Application Publication No. 2021/0255622 A1 (hereinafter Haynes).
Regarding claim 1, Magzimof discloses an in-vehicle device installed in a vehicle having an automated driving function (Magzimof Fig. 1), comprising:
processing circuitry configured to (see at least Magzimof [0061]: “This apparatus may be specially constructed for the purposes, e.g., a specific computer, or it may comprise a computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs), or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus.”)
estimate, as an allowable delay, a time until the vehicle reaches a dynamic object (see at least Magzimof [0026]: “The kinematic computational unit (KCU) 108 generates predictions about a time until the vehicle 102 will collide with an object absent some remedial action, based on the current vehicle state, object states, and sensed environmental conditions.”; under broadest reasonable interpretation an allowable delay includes a time until collision);
estimate, as a transfer delay, a time from when the in-vehicle device receives data from outside of the vehicle to when the in-vehicle device transfers the data to an execution processor for executing the automated driving function, based on load states of information processing and information transfer in the vehicle (see at least Magzimof [0040]: “Further still, the speed limit may adjust dependent on current network conditions to account for varying latency that may be introduced in the time it takes for video or other sensor data to reach the teleoperator and/or the time it takes for teleoperation commands to be received an executed by the vehicle 102.”; under broadest reasonable interpretation a transfer delay includes a latency);
and generate driving support information by executing the specific analysis process (see at least Magzimof [0030]: “The SCU 110 may be designed to limit the speed in a manner that enables the vehicle to be decelerated in time to avoid a collision in response to an emergency braking signal from a teleoperator.”; [0018]: “For example, the drive-by-wire system 107 may receive steering control signals, braking control signals, acceleration control signals, or other vehicle control signals to control operation of the vehicle 102 when being teleoperated. The drive-by-wire system 107 may furthermore provide sensor data to the remote support server 101 to enable the remote support server 101 to generate the control signals in response to the sensed information (either based on human teleoperator controls or from an artificial intelligence agent).”),
wherein the data received from the outside includes information regarding the dynamic object (see at least Magzimof [0038]: “In an embodiment, the system safety 105 may use the time profile of the position of a mobile obstacle mi relative to the vehicle 102 to determine its velocity vi relative to the ground, heading angle θi and steering angle φi. The safety system 105 may further use the information on dynamics of mobile obstacles during simulations to compute the position and heading of a mobile obstacle”),
and the driving support information is transferred to the execution processor for executing the automated driving function (see at least Magzimof [0018]: “The drive-by-wire system 107 receives control signals from the remote support server 101 and controls operation of the vehicle 102 in response to the control signals to enable teleoperation of the vehicle 102. For example, the drive-by-wire system 107 may receive steering control signals, braking control signals, acceleration control signals, or other vehicle control signals to control operation of the vehicle 102 when being teleoperated.”).
Magzimof fails to expressly disclose selecting a specific analysis process from a plurality of analysis processes for analyzing the data received from the outside based on a difference between the allowable delay and the transfer delay. However, Haynes teaches
select a specific analysis process from among a plurality of analysis processes for analyzing the data received from the outside based on a difference between the allowable delay and the transfer delay (see at least Haynes [0084]: “Executing a motion plan from the first compute cycle 302 can reduce the reaction time of the autonomous vehicle to the unexpected behavior of the actor as the motion plan generated during the first compute cycle 302 can be made available sooner than the first motion plan 330 generated in the second compute cycle 304 (e.g., according to a normal data processing flow of the second compute cycle).”)
including determining whether the specific analysis process is executable within a time period corresponding to the difference (see at least Haynes [0087]: “By delaying and/or preventing processing of other portions of the second sensor data 308 that do not correspond with the unexpected area and/or path, the speed can be increased at which the computing system can make the more critical determination regarding the unexpected behavior. Thus, the reaction time of the autonomous vehicle can be improved.”; [0091]: “The alternative data processing flow can have reduced latency and/or reaction time by omitting or skipping processing by one or more models and/or systems as compared with the normal data processing flow…Thus, latency associated with the alternative data processing flow can be less than a latency associated with the primary data processing flow.”; Haynes teaches determining the process is executable within a time period corresponding to the difference because in response to a surprise movement of an actor, the data processing is limited so the reaction time is decreased compared to the normal data processing);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the system disclosed by Magzimof with the process selection taught by Haynes with reasonable expectation of success. Haynes is directed towards the related field of detecting unexpected movements of an actor with respect to an autonomous vehicle. Therefore, one of ordinary skill in the art would be motivated to modify Magzimof with Haynes to improve reaction time (see at least Haynes [0021]: “In such instances, if the actor traverses an unexpected path that could lead to a dangerous condition (e.g., that a path that intersects a path of the autonomous vehicle), then the autonomous computing system can predict the actor's trajectory with a different and/or more simple approach (e.g., a simple ballistic trajectory) and take corrective action based on this trajectory. Further, the autonomous vehicle's reaction time can be reduced for such unexpected actions by prioritizing processing of sensor data for processing to determine whether an actor that appears to be starting to act in an unexpected way, continues to do so.”).
Regarding claim 2, Magzimof in view of Haynes teach all elements of the in-vehicle device according to claim 1, as explained above. Magzimof further teaches
wherein the data received from the outside further includes sensor data (see at least Magzimof [0013]: “Furthermore, other sensor data or video streams may be provided from the vehicle 102 or external sensor arrays 104 to the remote support server 101.”),
the information regarding the dynamic object includes position information and simple attribute information of the dynamic object (see at least Magzimof [0038]: “In an embodiment, the system safety 105 may use the time profile of the position of a mobile obstacle mi relative to the vehicle 102 to determine its velocity vi relative to the ground, heading angle θi and steering angle φi. The safety system 105 may further use the information on dynamics of mobile obstacles during simulations to compute the position and heading of a mobile obstacle {xi(t), yi(t), θi(t)}, which in conjunction with the known geometric size of a mobile obstacle may be used by the safety system 105 to determine bounding boxes around each mobile obstacle.”),
Haynes further teaches
and the processing circuitry is configured to generate the driving support information that is hierarchized so as to include, as hierarchical layers, a result of the specific analysis process, and the position information and the simple attribute information (see at least Haynes [0034]-[0035]: “Thus, the computing system can be configured to control the autonomous vehicle based on the second motion plan from the earlier, first compute cycle instead of the second motion plan (from the first compute cycle) to more rapidly address the actor's unexpected behavior (e.g., entering the unexpected area or following the unexpected path). In some implementations, the portion(s) of the second sensor data including and/or corresponding with the unexpected area(s) and/or path(s) can be prioritized for processing to more quickly determine whether the actor has followed the unexpected path and/or entered the unexpected area. For example, a portion of the second sensor data that corresponds with the unexpected area and/or path can be processed without processing all of the second sensor data from the second compute cycle. By delaying and/or preventing processing of other portions of the second sensor data that do not correspond with the unexpected area and/or path, the speed can be increased at which the computing system can make the more critical determination regarding the unexpected behavior. Thus, the reaction time of the autonomous vehicle can be improved.”).
Regarding claim 3, Magzimof in view of Haynes teach all elements of the in-vehicle device according to claim 2, as explained above. Haynes further teaches wherein the driving support information includes:
a first layer including an analysis result of the specific analysis process performed on the sensor data being a processing target (see at least Haynes [0030]: “For a second compute cycle that is later than the first compute cycle, the computing system can obtain sensor data (e.g., second sensor data) and determine based on the second sensor data and the failsafe region data, that the actor has followed the unexpected path or entered the unexpected area.”; [0103]: “Thus, the unexpected area 546 can be defined based on a variety of characteristics of the pedestrian 540 or other actor, such as location, attentive state (e.g., distracted, focused, etc.), and motion forecast data (e.g., describing speed, heading, and the like).”; instant application [0051] defines the first layer as including the “detailed attribute” of the dynamic object; instant application [0037] defines a “detailed attribute” as including whether a person is distracted);
and a second layer including an analysis result of the specific analysis process not performed on the sensor data (see at least Haynes [0025]: “For a first compute cycle, the computing system can obtain motion forecast data with respect to an actor relative to an autonomous vehicle.”; instant application [0051] defines the second layer as including the movement prediction of the dynamic object).
Regarding claim 4, Magzimof in view of Haynes teach all elements of the in-vehicle device according to claim 3, as explained above. Haynes further teaches
wherein the specific analysis process not performed on the sensor data has, as a processing target, at least one of the analysis result of the specific analysis process performed on the sensor data, and the information regarding the dynamic object (see at least Haynes [0032]: “In some implementations, the computing system can be configured to determine a motion plan for the autonomous vehicle in the first compute cycle in response to the unexpected path or unexpected area being identified when the failsafe region data is determined. In the second compute cycle, if the computing system determines that the actor has, in fact, entered the unexpected area or followed the unexpected path, the computing system can execute the motion plan from the earlier, first compute cycle (as opposed to a motion plan generated during the second compute cycle).”).
Regarding claim 5, Magzimof in view of Haynes teach all elements of the in-vehicle device according to claim 1, as explained above. Magzimof further teaches wherein the processing circuitry is configured to:
calculate the difference by subtracting the transfer delay from the allowable delay, determine whether or not the difference is larger than a predetermined value that is equal to or larger than 0 (see at least Magzimof [0028]: “The safety computational unit (SCU) 110 restricts the speed of the vehicle 102 using the VSCPI 109 in such a manner that the estimated time to collision computed by the KCU 108 exceeds the sum of the estimated remote operator reaction time and the braking time as computed using an appropriate method.”; Magzimof discloses subtracting the transfer delay (remote operator reacting time) from the allowable delay (estimated time to collision)),
Haynes further teaches
select the specific analysis process when the difference is larger than the predetermined value (see at least Haynes [0089]-[0091]: “A normal data processing flow is represented by the solid arrows, and an alternative data processing flow having reduced latency and/or reaction time can be represented, in part, by dashed arrows 351, 353… The alternative data processing flow can have reduced latency and/or reaction time by omitting or skipping processing by one or more models and/or systems as compared with the normal data processing flow.”; [0033]: “The first motion plan can be determined in a normal operating flow of the autonomous computing system (e.g., that does not account for the foreseeable yet unexpected behavior of the actor). For instance, the first motion plan can include continuing in a current lane of the autonomous vehicle under the assumption that the pedestrian will not step out into the crosswalk or that the vehicle will not pull out in front of the autonomous vehicle.”; the difference is larger than a predetermined value of 0 when the allowable delay exceeds the transfer delay; Haynes discloses selecting the normal data processing flow when the processing latency (i.e., transfer delay) does not need to be reduced because the processing latency (i.e., transfer delay) does not exceed a collision time to an actor (i.e., allowable delay)),
and not select the specific analysis process when the difference is equal to or smaller than the predetermined value (see at least Haynes [0091]: “The alternative data processing flow can have reduced latency and/or reaction time by omitting or skipping processing by one or more models and/or systems as compared with the normal data processing flow.”; [0021]: “Further, the autonomous vehicle's reaction time can be reduced for such unexpected actions by prioritizing processing of sensor data for processing to determine whether an actor that appears to be starting to act in an unexpected way, continues to do so.”; the difference is smaller than a predetermined value of 0 when the transfer delay exceeds the allowable delay; Haynes discloses selecting the alternative data processing flow when the processing latency (i.e., transfer delay) needs to be reduced to avoid an actor (i.e., allowable delay)).
Regarding claim 6, Magzimof in view of Haynes teach all elements of the in-vehicle device according to claim 5, as explained above. Haynes further teaches
wherein when the difference is equal to or smaller than the predetermined value, the information regarding the dynamic object is transferred to the execution processor together with information indicating that the transfer delay is equal to or larger than the allowable delay (see at least Haynes [0091]: “The alternative data processing flow can have reduced latency and/or reaction time by omitting or skipping processing by one or more models and/or systems as compared with the normal data processing flow. For example, data received from the perception system 352 (e.g., the 3D location system 358) can be processed and/or transmitted (as represented by the dashed arrow 351, 353) to the plan validation system 372. The second motion plan 332 (e.g., as described above with reference to FIG. 3A) can be determined based on the data 351 (e.g., failsafe region data) received from the perception system 352. The second motion plan 332 can describe a deviation and/or reaction to the unexpected action. The second motion plan 332 can replace the first motion plan 330 for input to the plan validation system 372.”; [0021]: “Further, the autonomous vehicle's reaction time can be reduced for such unexpected actions by prioritizing processing of sensor data for processing to determine whether an actor that appears to be starting to act in an unexpected way, continues to do so.”; [0085]: “Thus, determining the deviation 324 for controlling the movement of the autonomous vehicle, in the second compute cycle 304, can include obtaining a second motion plan 332 that is determined during the first compute cycle 302 (e.g., by the motion planning system 318 and/or surprise movement planning system 322) in response to determining that the actor has followed the unexpected path or entered the unexpected area.”; under broadest reasonable interpretation “information indicating that the transfer delay is equal to or larger than the allowable delay” includes information that the first motion plan is replaced by the second motion plan, indicating the alternative data processing flow is executed, which occurs when the transfer delay exceeds the allowable delay).
Regarding claim 9, Magzimof in view of Haynes teach all elements of the in-vehicle device according to claim 1, as explained above. Haynes further teaches an in-vehicle system installed in a vehicle having an automated driving function (Haynes Fig. 1), comprising:
an execution processor configured to execute the automated driving function (see at least Haynes [0064]: “The autonomy computing system 130 can communicate with the one or more vehicle control systems 135 to operate the vehicle 105 according to the motion plan.”;
a communication circuitry configured to acquire data including information regarding a dynamic object (see at least Haynes [0023]: “The computing system can receive sensor data from one or more sensors that are coupled to or otherwise included within the autonomous vehicle…The sensor data can include raw sensor data and/or data that has been processed or manipulated in some manner before being provided to other systems within the autonomy computing system.”; [0058]: “The vehicle 105 can include a communications system 120 configured to allow the vehicle computing system 100 (and its computing device(s)) to communicate with other computing devices.”);
and the in-vehicle device according to claim 1 (see rejection to claim 1 above).
Regarding claim 10, Magzimof in view of Haynes teach all elements of the in-vehicle system according to claim 9, as explained above. Magzimof further teaches
wherein the communication circuitry is configured to transmit the driving support information generated by the in- vehicle device to another vehicle together with information on a position and a traveling direction of the vehicle (see at least Magzimof [0026]: “For example, in an embodiment, the KCU 108 receives various kinematic parameters (e.g., position, speed, acceleration, vehicle trajectory, etc.) of the vehicle, a depth map representing distances to detected objects in a vicinity of the vehicle 102, and time information from a system clock running with a certain degree of granularity. Based on the received information, the KCU 108 updates an estimate of the time until a collision of the vehicle 102 following the current trajectory with an obstacle, provided that a set of assumptions on the behavior of the environment is valid. These estimates may be made available to other components of the vehicle safety system 105 or may be made available to other vehicles 102 in the vicinity.”).
Regarding claim 11, Magzimof in view of Haynes teach all elements of the in-vehicle system according to claim 10, as explained above. Magzimof further teaches wherein the processing circuitry is configured to:
estimate a communication time of the driving support information to be transmitted from the communication circuitry (see at least Magzimof [0042]: “The reaction time may be determined based on, for example… the control message transmission time Δtn2”; under broadest reasonable interpretation communication time includes control message transmission time),
and select the specific analysis process from among the plurality of analysis processes based on a difference between the allowable delay and a sum of the transfer delay and the communication time (see at least Magzimof [0028]: “The safety computational unit (SCU) 110 restricts the speed of the vehicle 102 using the VSCPI 109 in such a manner that the estimated time to collision computed by the KCU 108 exceeds the sum of the estimated remote operator reaction time and the braking time as computed using an appropriate method.”; Magzimof [0042] discloses the reaction time includes control message transmission time).
Regarding claim 12, this claim recites a method performed by the in-vehicle device of claim 1. The combination of Magzimof in view of Haynes also teaches a method performed by the device of claim 1, as outlined in the rejection to claim 1 above. Therefore, claim 12 is rejected for the same rationale as claim 1.
Regarding claim 13, this claim recites a medium embodying the in-vehicle device of claim 1. The combination of Magzimof in view of Haynes also teaches a medium (Magzimof [0061]) embodying the device of claim 1, as outlined in the rejection to claim 1 above. Therefore, claim 13 is rejected for the same rationale as claim 1.
Regarding claim 16, Magzimof in view of Haynes teach all elements of the in-vehicle device according to claim 1, as explained above. Haynes further teaches
wherein the plurality of analysis processes includes a first process for analyzing sensor data included in the data received from the outside to identify a detailed attribute of the dynamic object (see at least Haynes [0030]: “For a second compute cycle that is later than the first compute cycle, the computing system can obtain sensor data (e.g., second sensor data) and determine based on the second sensor data and the failsafe region data, that the actor has followed the unexpected path or entered the unexpected area.”; [0103]: “Thus, the unexpected area 546 can be defined based on a variety of characteristics of the pedestrian 540 or other actor, such as location, attentive state (e.g., distracted, focused, etc.), and motion forecast data (e.g., describing speed, heading, and the like).”; instant application [0037] defines a “detailed attribute” as including whether a person is distracted),
and a second process for analyzing the information regarding the dynamic object without using the sensor data to predict a movement of the dynamic object (see at least Haynes [0025]: “For a first compute cycle, the computing system can obtain motion forecast data with respect to an actor relative to an autonomous vehicle.”; [0066]: “The prediction system 160 can be configured to predict a motion of the object(s) within the surrounding environment of the vehicle 105. For instance, the prediction system 160 can generate prediction data 175 associated with such object(s). The prediction data 175 can be indicative of one or more predicted future locations of each respective object. For example, the prediction system 160 can determine a predicted motion trajectory along which a respective object is predicted to travel over time. A predicted motion trajectory can be indicative of a path that the object is predicted to traverse and an associated timing with which the object is predicted to travel along the path. The predicted path can include and/or be made up of a plurality of way points. In some implementations, the prediction data 175 can be indicative of the speed and/or acceleration at which the respective object is predicted to travel along its associated predicted motion trajectory. In some implementations, the prediction data 175 can include a predicted object intention (e.g., a right turn) based on physical attributes of the object. The prediction system 160 can output the prediction data 175 (e.g., indicative of one or more of the predicted motion trajectories) to the motion planning system 165.”).
Regarding claim 18, Magzimof in view of Haynes teach all elements of the in-vehicle device according to claim 5, as explained above. Haynes further teaches
wherein the processing circuitry is configured to transfer the information regarding the dynamic object to the execution processor without executing the specific analysis process when the difference is equal to or smaller than 0 (see at least Haynes [0091]: “The alternative data processing flow can have reduced latency and/or reaction time by omitting or skipping processing by one or more models and/or systems as compared with the normal data processing flow.”; [0021]: “Further, the autonomous vehicle's reaction time can be reduced for such unexpected actions by prioritizing processing of sensor data for processing to determine whether an actor that appears to be starting to act in an unexpected way, continues to do so.”; the difference is smaller than a predetermined value of 0 when the transfer delay exceeds the allowable delay; Haynes discloses selecting the alternative data processing flow when the processing latency (i.e., transfer delay) needs to be reduced to avoid an actor (i.e., allowable delay)).
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Magzimof in view of Haynes, and further in view of Zhu et al., U.S. Patent No. 11733693 B2 (hereinafter Zhu).
Regarding claim 7, Magzimof in view of Haynes teach all elements of the in-vehicle device according to claim 5, as explained above. Magzimof in view of Haynes fail to expressly disclose storing a processing time table for recording a processing time corresponding to an amount of data to be processed for each analysis process. However, Zhu teaches
a memory having, stored therein, a processing time table in which, for each of the plurality of analysis processes, a processing time corresponding to an amount of data to be processed is recorded (see at least Zhu Col. 8, lines 40-52: “In the present embodiment, after the server determines the indication information, the indication information may be distributed to the vehicle in a form of a task table, where the task table includes various contents of the indication information. In a possible implementation, the indication information may include at least one of the following information: task attribution: a vehicle series, a vehicle type, and a software version at a vehicle end that performs a specified task. Task execution cycle: setting start and end time of a task, and the task will be automatically voided when the end time is reached”; under broadest reasonable interpretation a processing time table includes a task table and a processing time includes a task execution cycle time),
wherein when the difference is larger than the predetermined value, the processing circuitry is configured to specify a processing time for the data with reference to the processing time table by using the amount of the data, and thereafter determine whether or not the processing time is equal to or less than the difference (This limitation is taught through the combination of Haynes and Zhu. Haynes discloses determining a normal data processing flow when the difference is larger than the predetermined value because the processing latency does not need to be reduced to avoid a collision with an actor (Haynes [0089]-[0091]). Haynes fails to expressly disclose specifying a processing time for the data with reference to the processing time table. However, Zhu discloses determining a processing time for task execution and task selection using a task table (see at least Zhu Col. 8, lines 40-52: “In the present embodiment, after the server determines the indication information, the indication information may be distributed to the vehicle in a form of a task table, where the task table includes various contents of the indication information. In a possible implementation, the indication information may include at least one of the following information: task attribution: a vehicle series, a vehicle type, and a software version at a vehicle end that performs a specified task. Task execution cycle: setting start and end time of a task, and the task will be automatically voided when the end time is reached”; Col. 10, lines 8-14: “Task selection: selecting a data acquisition task to be executed by a qualified vehicle, where the data acquisition task may be a task indicated by the above indication information, for example, one acquisition condition may correspond to one data acquisition task. The data acquisition task may create a configuration in advance for selection and use when creating a task table.”). Therefore, the combination of Haynes and Zhu teach the entirety of this limitation.),
thereby selecting the specific analysis process (see at least Zhu Col. 10, lines 8-14: “Task selection: selecting a data acquisition task to be executed by a qualified vehicle, where the data acquisition task may be a task indicated by the above indication information, for example, one acquisition condition may correspond to one data acquisition task. The data acquisition task may create a configuration in advance for selection and use when creating a task table.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the system disclosed by Magzimof in view of Haynes with the processing time table taught by Zhu with reasonable expectation of success. Zhu is directed towards the related field of a data acquisition method for autonomous driving. Therefore, one of ordinary skill in the art would be motivated to modify Magzimof in view of Haynes with Zhu to improve data acquisition efficiency (see at least Zhu Col. 2, lines 40-43: “According to technologies of the present disclosure, needed driving scene data can be acquired flexibly and efficiently, thereby effectively improving efficiency of the data acquisition.”).
Regarding claim 8, Magzimof in view of Haynes and Zhu teach all elements of the in-vehicle device according to claim 7, as explained above. Zhu further teaches
wherein the processing time table further includes an acquisition time required for acquiring sensor data being a processing target regarding an analysis process having the sensor data as the processing target among the plurality of analysis processes (see at least Zhu Col. 8, lines 40-61: “In the present embodiment, after the server determines the indication information, the indication information may be distributed to the vehicle in a form of a task table, where the task table includes various contents of the indication information. In a possible implementation, the indication information may include at least one of the following information…Acquisition content: including basic data such as time, status, a software version, and a hardware parameter, data of sensor, such as a cameras, ultrasonic radar, and millimeter wave radar, vehicle body information such as a gear position, wheel speed, and lamp status, and automatic driving module data, state machine data, system log data, etc.”),
and when the difference is larger than the predetermined value, the processing circuitry is configured to determine whether or not a sum of the processing time and the acquisition time, which are specified with reference to the processing time table, is equal to or smaller than the difference, thereby selecting the specific analysis process (This limitation is taught through the combination of Haynes and Zhu. Haynes discloses selecting a normal data processing flow when the difference is larger than the predetermined value because the processing latency does not need to be reduced to avoid a collision with an actor, compared to an alternative data processing flow which needs to remove processing time of the acquired data (Haynes [0089]-[0091], Fig. 3B). Haynes fails to expressly disclose the processing time and acquisition time specified with reference to the processing time table. However, Zhu discloses the processing time and acquisition time specified with reference to the processing time table (see at least Zhu Col. 8, lines 40-61: “In the present embodiment, after the server determines the indication information, the indication information may be distributed to the vehicle in a form of a task table, where the task table includes various contents of the indication information. In a possible implementation, the indication information may include at least one of the following information: task attribution: a vehicle series, a vehicle type, and a software version at a vehicle end that performs a specified task. Task execution cycle: setting start and end time of a task, and the task will be automatically voided when the end time is reached… Acquisition content: including basic data such as time, status, a software version, and a hardware parameter, data of sensor, such as a cameras, ultrasonic radar, and millimeter wave radar, vehicle body information such as a gear position, wheel speed, and lamp status, and automatic driving module data, state machine data, system log data, etc.”). Therefore, the combination of Haynes and Zhu teach the entirety of this limitation.).
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Magzimof in view of Haynes, and further in view of Ucar et al., U.S. Patent Application Publication No. 2021/0074154 A1 (hereinafter Ucar).
Regarding claim 14, Magzimof in view of Haynes teach all elements of the in-vehicle device according to claim 1, as explained above. Magzimof in view of Haynes fail to expressly disclose hierarchized driving support information with the result of the specific analysis process and the dynamic object information as different hierarchical layers. However, Ucar teaches
wherein generating the driving support information includes generating the driving support information hierarchized to include a result of the specific analysis process and the information regarding the dynamic object as different hierarchical layers (see at least Ucar [0076]: “Then, the AI manager 204 generates hierarchical AI data that describes one or more of the following: real-life traffic information; real-time traffic information; and predicted future traffic information. For example, the hierarchical AI data describes: (1) current locations, speeds, headings, etc., of various vehicles present in the roadway environment; and (2) predicted locations, speeds, headings, etc., of the various vehicles in a future time window.”; under broadest reasonable interpretation predicted information is a result of a specific analysis process; Ucar Fig. 5B shows various levels of hierarchical AI data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the system disclosed by Magzimof in view of Haynes with the hierarchized information taught by Ucar with reasonable expectation of success. Ucar is directed towards the related field of anomaly detection in a roadway environment. Therefore, one of ordinary skill in the art would be motivated to modify Magzimof in view of Haynes with Ucar to minimize the effect of an anomaly (see at least Ucar [0017]-[0018]: “Entities that are near the anomaly (e.g., behind or ahead of the anomaly) may need to be directed with proper control strategies so that the effect of the anomaly is minimized. The determination of which entities are affected by the anomaly and which control strategies are to be provided to the different entities is a challenging task. Described herein are embodiments of an anomaly managing system installed in a server and an anomaly managing client installed in a vehicle. The anomaly managing system and the anomaly managing client may cooperate with one another to manage an anomaly that occurs in a roadway environment. The anomaly managing system and the anomaly managing client may cooperate with one another to manage entities that are affected by the anomaly (referred to as “anomaly-affected entities” hereinafter). As a result, an effect of the anomaly in the roadway environment can be minimized.”).
Regarding claim 15, Magzimof in view of Haynes and Ucar teach all elements of the in-vehicle device according to claim 14, as explained above. Ucar further teaches
wherein the processing circuitry is configured to transfer the driving support information hierarchized to include the result of the specific analysis process and the information regarding the dynamic object as the different hierarchical layers to the execution processor (see at least Ucar [0106]-[0108]: “At step 413, the anomaly manager 206 determines whether the hierarchical AI data is sufficient to determine anomaly severity indices…At step 417, the anomaly manager 206 generates a set of control strategies to manage the group of anomaly-affected entities within the influence region based on the set of anomaly severity indices.”; [0076]: “Then, the AI manager 204 generates hierarchical AI data that describes one or more of the following: real-life traffic information; real-time traffic information; and predicted future traffic information. For example, the hierarchical AI data describes: (1) current locations, speeds, headings, etc., of various vehicles present in the roadway environment; and (2) predicted locations, speeds, headings, etc., of the various vehicles in a future time window.”; under broadest reasonable interpretation predicted information is a result of a specific analysis process; Ucar Fig. 5B shows various levels of hierarchical AI data).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Magzimof in view of Haynes, and further in view of Zhang et al., U.S. Patent Application Publication No. 2023/0080319 A1 (hereinafter Zhang).
Regarding claim 17, Magzimof in view of Haynes teach all elements of the in-vehicle device according to claim 16, as explained above. Magzimof in view of Haynes fail to expressly disclose determining whether a pedestrian is using a smartphone while walking or ignoring a traffic signal. However, Zhang teaches
wherein the detailed attribute identified by the first process includes determining whether a pedestrian is using a smartphone while walking or ignoring a traffic signal (see at least Zhang [0033]: “Beside the vehicles 103, the system 100 can provide apply the process of FIG. 2 to other modes of transport, such as walking, bicycles (e.g., detecting incidents such as pedestrian walking on bike lane(s) slowly thereby clogging the bike lane). By way of example, the system 100 can apply the process of FIG. 2 to indoor/outdoor pedestrian trajectory data (e.g., cross walkways, malls, etc.) to update pedestrian indoor/outdoor map data, report incidents (e.g., inattentive pedestrians staring at smartphones thereby clogging the walkways), etc.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the system disclosed by Magzimof in view of Haynes with Zhang with reasonable expectation of success. Zhang is directed towards the related field of aggregating a vehicle route based on high-resolution sampling. Therefore, one of ordinary skill in the art would be motivated to modify Magzimof in view of Haynes with Zhang to reduce computation while maintaining navigation accuracy and efficiency (see at least Zhang [0001]-[0002]: “Accordingly, mapping service providers face significant technical challenges to reduce computation in the existing probability-based route-builder methods while maintaining navigation accuracy and efficiency. Therefore, there is a need for an approach for aggregating an incident route based on high-resolution sampling to reduce computation, such as high-density sampling (e.g., selecting a portion of the vehicle trajectory points near road link connecting locations that have high density of vehicle location data points), high-probability aggregation (e.g., reducing a number of traveled link options at a sampled high-density point based on respective probabilities), stopping criteria (e.g., for discarding inaccurate trajectory points), etc.”).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Magzimof in view of Haynes, and further in view of Eshima, U.S. Patent Application Publication No. 2021/0181743 A1.
Regarding claim 19, Magzimof in view of Haynes teach all elements of the in-vehicle device according to claim 1, as explained above. Magzimof in view of Haynes fail to expressly disclose executing multiple specific analysis processes in a multitasking manner when the difference allows for a sum of processing times of the multiple specific analysis processes. However, Eshima teaches
wherein the processing circuitry is configured to execute multiple specific analysis processes in a multitasking manner when the difference allows for a sum of processing times of the multiple specific analysis processes (see at least Eshima [0084]: “In this case, for example, the preparation time determination unit 34 determines the total time of the individual preparation required time that is longest among the individual preparation required times of the departure preparation items that can be executed concurrently in parallel and the individual preparation required times of the departure preparation items that cannot be executed simultaneously with the other departure preparation items as the departure preparation required time.”; [0104]: “For example, in the case where the departure preparation items can be executed in parallel when the autonomous vehicle 2 is in the power-on state, the automatic parking system determines the longest individual preparation required time as the departure preparation required time such that extension of the departure preparation required time is suppressed and the departure preparation required time can be determined appropriately.”; [0014]: “With the configuration above, even when there is the plurality of departure preparation items, the automatic parking system can set the on-control time based on the total time of the individual preparation required times of the departure preparation items.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the system disclosed by Magzimof in view of Haynes with Eshima with reasonable expectation of success. Eshima is directed towards the related field of automatic parking systems. Therefore, one of ordinary skill in the art would be motivated to modify Magzimof in view of Haynes with Eshima to appropriately determine a required preparation time (see at least Eshima [0012]: “For example, in the case where the departure preparation items can be executed in parallel when the autonomous vehicle is in the power-on state, the automatic parking system determines the longest individual preparation required time as the departure preparation required time such that extension of the departure preparation required time is suppressed and the departure preparation required time can be determined appropriately.”).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Magzimof in view of Haynes, and further in view of Hsieh, U.S. Patent Application Publication No. 2020/0282962 A1.
Regarding claim 20, Magzimof in view of Haynes teach all elements of the in-vehicle device according to claim 1, as explained above. Magzimof in view of Haynes fail to expressly disclose calculating the allowable delay by dividing a distance between the vehicle and the dynamic object by a speed of the vehicle. However, Hsieh teaches
wherein the processing circuitry is configured to calculate the allowable delay by dividing a distance between the vehicle and the dynamic object by a speed of the vehicle (see at least Hsieh [0031]: “For example, the processing unit 32 divides the relative position of the vehicle 3 and the front vehicle 4 by a relative speed of the vehicle 3 and the front vehicle 4, to obtain a front relative time to collision (TTC) between the vehicle 3 and the front vehicle 4. When the front relative value is greater than a threshold (for example, a front relative time to collision is greater than 3 seconds), the processing unit 32 remains in the original vehicle control mode, so that the original vehicle controller 11 generates a control signal according to a change of a position of the accelerator pedal 10 which the driver depresses, to control power output of the driving unit 12. When the front relative value is less than a threshold (for example, a front relative time to collision is less than 2.5 seconds), the processing unit 32 switches to the automatic emergency brake mode, to perform processing according to the surrounding information V of the vehicle to generate an analog pedal signal I and an analog braking signal B.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the system disclosed by Magzimof in view of Haynes with Hsieh with reasonable expectation of success. Hsieh is directed towards the related field of a driver assistance device. Therefore, one of ordinary skill in the art would be motivated to modify Magzimof in view of Haynes with Hsieh to maintain a safe distance to another vehicle (see at least Hsieh [0037]: “When the front relative value is greater than a threshold (for example, a front relative time to collision is greater than 5 seconds), the processing unit 32 continues to use the analog pedal signal I to control the driving unit 12 to operate at the predetermined vehicle speed. When the front relative value is less than a threshold (for example, a front relative time to collision is less than 3 seconds), the processing unit 32 generates an analog pedal adjustment signal and transmits the analog pedal adjustment signal to the original vehicle controller 11 via the output unit 33, to control power output of the driving unit 12 to be reduced (such as, control the accelerator opening degree of the driving unit 12 to decrease from 50% to 10%) to reduce the vehicle speed, so that a safety distance remains between the vehicle 3 and the front vehicle 4.”).
Conclusion
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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/ELIZABETH J SLOWIK/ Examiner, Art Unit 3662
/ANISS CHAD/ Supervisory Patent Examiner, Art Unit 3662
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