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 .
Response to Amendment
This correspondence is in response to amendments filed on May 7, 2026. Claims 1-4, 6-11, and 13-20 are amended. Claims 5 and 12 are filed as originally presented. The claim objections and 101 rejection are obviated by amendment and have been withdrawn accordingly. Examiner withdraws 112b rejections which were obviated as a result of amendment. An explanation regarding those 112b rejections which are maintained are addressed in response to arguments below. Additionally, an Examiner’s response to arguments regarding the prior art rejections have been included below.
Response to Arguments
Applicant argues that amendments made to the claims obviate all 112b rejections set forth in the previous rejection (Remarks Page 7). However, no such amendment was made in line 8 of claim 1 regarding “…the action being at or above the second threshold…” which was previously rejected. Applicant made appropriate correction to line 7 of amended claim 1 which obviated the rejection previously set forth regarding “…the action being below a second threshold…” but failed to acknowledge Examiner’s remark regarding the similar 112b rejection of line 8. Accordingly the rejection of claim 1 regarding this limitation and the similar limitation of claims 8 and 15 have been maintained and should be addressed accordingly.
In addition to this rejection of claim 1, the 112b rejection regarding claim 9 has been maintained. Applicant failed to correct claim 9 to specify which threshold is being referred to in line 4. A similar correction to claims 2 and 16 were made to recite “…at least one vehicle operation threshold…” but no such correction was included in the resubmission of claim 9. As such, Examiner maintains this rejection of claim 9 and requests appropriate corrections upon resubmission of the claims.
Applicant argues that the cited art is not believed to determine a query via a vehicle related to the action and generating a first response and sending the query to a server (Remarks Page 8). Applicant further argues that the notion of analyzing a roadway sign and determining a query based on sensor information from the vehicle and a vehicle action which were enacted based on the road sign, and using vehicle sensor information to determine the query and then using a context from the query and the original query to process another query based on another threshold is beyond the scope of all the references cited (Remarks Page 8). Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Applicant provided no such evidence regarding the teachings which Examiner believes reads on the claim language. Thus, there is insubstantial argument which would render the content of the references to be outside the scope of the claims which have been filed. Thus, argument has been considered, but given that Applicant provides only general allegations of patentability, the arguments are NOT PERSUASIVE.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “…responsive to the action being at or above a second threshold…” in line 8. The way the claim is currently written, the action would be attributed to a value which may be compared to a threshold. However, throughout Applicant’s specification, the actions are attributed to general qualitative responses the vehicle must take in order to comply with traffic signs and traffic patterns such as slowing down or changing lanes (see Examples in [0057], [0060], [0114], [0133], and [0138] among others). In paragraph [0144] of Applicant’s disclosure, the description discloses that a level of action is compared to the second threshold value. As such, Examiner will interpret the claim to read “…responsive to the level of action being at or above a second threshold…” such that a level value is compared to the threshold. A similar correction was made to line 7 of claim 1 which obviated part of the previous rejection regarding the current limitation in question.
Claims 8 and 15 are rejected as having similar limitations.
Claims 2-7, 9-14, and 16-20 are rejected as being dependent on claims 1, 8, and 15 respectively.
Claim 9 recites the limitation “…compare the at least one parameter to at least one threshold to identify the action…” in line 4. Given that a first and a second threshold have already been defined for the action and level of action respectively, it is unclear if this at least one threshold is one of the first or second thresholds or a different threshold entirely. Given that this threshold is specific to the parameter and there is no clear indication from the specification that it is the same as either previously defined threshold, Examiner will interpret the claim to instead read “…comparing the at least one parameter to at least one vehicle operation threshold…” such that this threshold is unique to the vehicle operation parameter itself and reflects the corresponding language of amended claims 2 and 16.
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 1-4, 7-11, and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Oniwa et al. (US 2019/0311207 A1; hereinafter “Oniwa”) in view of Nölle (US 2024/0069543 A1).
Regarding claim 1, Oniwa teaches a method (“The present invention relates to a vehicle control device, a vehicle control method, and a storage medium” [0002].) comprising:
analyzing a sign proximate a road that a vehicle is traveling on to determine an action and a level of action to be performed by the vehicle (“The control state changer 142 performs control for changing (shifting) the control state of driving control on the basis of a first condition based on the travel situation of the own vehicle M recognized by the travel situation recognizer 132 and a second condition based on the traffic signs recognized by the traffic sign recognizer 134” [0067]. Thus, there is a traffic sign recognizer which analyzes a sign proximate a road that a vehicle is travelling on to determine the control state, i.e., an action, to be performed by the vehicle and an associated level of the action to be performed based on the results of the travel situation recognizer and the traffic sign recognizer. See more details about the levels of actions in the paragraphs which follow in “Functions of Control State Changer” section.);
determining a query based on sensor information obtained from one or more vehicle sensors and related to the action when the action is above a first threshold (“The control state changer 142 changes the control state to one matching the first and second conditions and performs the driving control in the changed control state” [0067]. Thus, there is a control state determined based on a first and second condition, i.e., query related to the driving action to be performed. The first threshold will be considered as the numeric value of the control state, wherein those control states rated above the first control state, i.e., basic conditions, will be considered as exceeding the first threshold. As described in [0061] and [0063], the first and second condition which the query is based on are a result of sensor information obtained from one or more vehicle sensors.);
responsive to the level of the action being below a second threshold, processing the query based on the sensor information to generate a first response comprising contextual information generated by a first AI model (For the second threshold, the second condition determines a speed threshold based on the detected speed limit (see [0067]). For each second condition shown in Fig. 5, the control state operation is suppressed, i.e., a first response is generated, when the speed of the driving is below the speed limit threshold (see [0070] as an example for the second control state with control degree 2). It is best understood that this suppression is meant to continue driving at the current control state level. As described in [0048], there is a first AI model which provides the contextual information for the first and second condition for determining the first response and thus the first response comprises said contextual information generated by the first AI model.);
responsive to the action being at or above the second threshold,query (In converse to the above limitation, for each second condition shown in Fig. 5, the control state operation is performed, i.e., a second response is received to perform the control in the selected control state, when the speed of the driving is at or above the speed limit threshold (see [0070] as the same example for the second control state with control degree 2). It is best understood that the performing the action in the control state without suppression would be the determined driving control which is associated with the control state.); and
autonomously performing the action, by the vehicle, based on at least one of the first response or the second response (“Automated driving is, for example, performing driving control by controlling one or both of the steering or acceleration/deceleration of a vehicle. Control states of the driving control that can be performed by the automatically driven vehicle include a first control state and a second control state in which the automation rate is higher than the first control state or a lower level of task is required of an occupant than the first control state. The control states may also include a third control state in which the automation rate is higher than the second control state or a lower level of task is required of the occupant than the second control state” [0032]. Thus, in each control state there is an automated driving for performing driving control based on the level of control determined. Driving control is issued by the automated driving control device 100 shown in Fig. 2.).
However, Oniwa does not explicitly teach …responsive to the action being at or above the second threshold, sending the query and the generated contextual information to a server;
receiving a second response to the sent query…
Nölle, pertinent to the problem at hand, teaches …responsive to the action being at or above the second threshold, sending the query and the generated contextual information to a server (“In response to the trigger event, at operation 304, the vehicle 102 communicates to the server 178 to request for remote guidance by sending a request. The remote guidance request may include various information entries. For instance, the remote guidance request may include type/category of the trigger event as detected via vehicle sensors 184. The remote guidance request may further include information associated with the trigger event such as the current location of the vehicle 102, the weather and temperature data. The remote guidance request may further include data reflecting the current condition of the vehicle such as vehicle make/model, suspension setting (e.g., height), fuel level (e.g., battery state of charge), tire pressure, motor/engine operating condition (e.g., temperature), vehicle occupancy data (e.g., number of occupant, presence of children) or the like that may be used to determine if certain maneuvers are available” [0029]. Thus, in response to a trigger event, i.e., the action being at or above the second threshold as exemplified by Oniwa in the rejection above, there is a request sent to the server including the query information and pertinent contextual information generated for determining a control action.);
receiving a second response to the sent query (“At operation 316, responsive to determining that one or more alternative trajectories are available, the ADC 316 operates the vehicle 102 to perform maneuvers corresponding to the selected alternative trajectory while being monitored by the operator associated with the server 178. The server 178 may continuously send updated trajectories and commands in remote guidance while the vehicle 102 traverses the selected trajectory until the ADC 182 and/or the operator determines the vehicle 102 has successfully overcome the situation associated with the trigger event” [0035]. Thus, a response to the associated query is received in the form of trajectories which determine alternative trajectories and maneuvers while the vehicle overcomes the trigger event.)…
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the second response of performing a control state change as taught by Oniwa to include the server response to trigger events as taught by Nölle with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification because the server is able to provide better processing power and is configured to better analyze the sensor data to provide autonomous driving instruction without having to involve the human operator (Nölle, [0030]).
Regarding claim 2, Oniwa as modified by Nölle teaches the method of claim 1,
with Oniwa further teaching wherein the determining the level of the action to be performed by the vehicle comprises:
analyzing at least one parameter associated with an operation of the vehicle (“The vehicle sensors 40 include a vehicle speed sensor that detects the speed of the own vehicle M, an acceleration sensor that detects the acceleration thereof, a yaw rate sensor that detects an angular speed thereof about the vertical axis, an orientation sensor that detects the orientation of the own vehicle M, or the like” [0041]. Thus, there is a vehicle speed sensor which detects the speed of the vehicle, i.e., a parameter associated with an operation of the vehicle.); and
comparing the at least one parameter to at least one vehicle operation threshold to identify the action (“For example, when a first variable sign MK5 is recognized by the traffic sign recognizer 134, the control state changer 142 determines whether or not there is a deviation equal to or greater than a threshold value (for example, ±20 km/h) between a speed limit (60 km/h) indicated by the variable sign MK5 and the speed VM of the own vehicle M and notifies the occupant through the HMI controller 180 to urge the occupant to change the set speed if there is such a deviation” [0087]. Thus, the speed of the vehicle, i.e., parameter, is compared to at least one speed threshold value, to determine the action to be taken in response to the detected speed limit.).
Regarding claim 3, Oniwa as modified by Nölle teaches the method of claim 1,
with Oniwa further teaching wherein the query related to the action to be performed is determined by the first AI model deployed on the vehicle, the first AI model analyzes data associated with the sign and contextual information to generate the query (“The first controller 120 includes, for example, a recognizer 130 and a behavior plan generator 140. For example, the first controller 120 realizes a function based on artificial intelligence (AI) and a function based on a previously given model in parallel. For example, the function of “recognizing an intersection” is realized by performing recognition of an intersection through deep learning or the like and recognition based on previously given conditions (presence of a signal, a road sign, or the like for which pattern matching is possible) in parallel and evaluating both comprehensively through scoring” [0048]. Thus, there is a first AI model deployed in parallel with previously given/recognized models to determine data related to the sign and driving context which comprise the query for first and second conditions.).
Regarding claim 4, Oniwa as modified by Nölle teaches the method of claim 1,
with Nölle further teaching wherein the processing of the query is performed by a second AI model on the server, the second AI model generating the second response by analyzing the query in combination with external data (“In response to receiving the remote guidance request, at operation 306, the server 178 assigns an operator to help provide remote guidance to the requesting vehicle 102. In one example, the operator may be a human being (e.g., technician). Additionally or alternatively, the operator may be a computer program (e.g., artificial intelligence) configured to analyze and resolve more difficult situations than the ADC 182 is configured to handle” [0030]. Thus, the operator which is assigned to respond to the remote guidance request may be a computer program, i.e., artificial intelligence which will be the second AI model on the server. As noted in the rejection of claim 1, the server request includes a combination of external data with the query related to driving data of the vehicle.).
Regarding claim 7, Oniwa as modified by Nölle teaches the method of claim 1, comprising:
with Oniwa further teaching detecting, by the server, a road condition greater than a road condition threshold in an area past the sign (In the example of [0070], the control state changer determines a branch/merge point, i.e., road condition in an area past the speed limiting sign, to be an interchange, i.e., greater than a threshold for classifications of branch/merge points.); and
wherein the second response causes the vehicle to respond, based on the road condition (Based on the detection of the interchange, i.e., the branch/merge point greater than the predetermined threshold for classifying the branch/merge point, causes the control state changer to perform the operation, i.e., second response.).
Regarding claim 8, Oniwa teaches a system for an adaptive road sign interpretation and vehicle response system (Vehicle control system 1 of Fig. 1.) comprising:
a memory (“a storage device configured to store a program”; [0100]); and
at least one processor (“a hardware processor”; [0101]),
wherein the memory and the at least one processor are communicatively coupled (“wherein the hardware processor is configured to execute the program stored in the storage device”; [0102], thus since the hardware processor executes the program stored in the storage device, the processor and memory are communicatively coupled to share the program information.), wherein the at least one processor is configured to:
analyze a sign proximate a road that a vehicle travels on to determine an action and a level of action to be performed by the vehicle (“The control state changer 142 performs control for changing (shifting) the control state of driving control on the basis of a first condition based on the travel situation of the own vehicle M recognized by the travel situation recognizer 132 and a second condition based on the traffic signs recognized by the traffic sign recognizer 134” [0067]. Thus, there is a traffic sign recognizer which analyzes a sign proximate a road that a vehicle is travelling on to determine the control state, i.e., an action, to be performed by the vehicle and an associated level of the action to be performed based on the results of the travel situation recognizer and the traffic sign recognizer. See more details about the levels of actions in the paragraphs which follow in “Functions of Control State Changer” section.);
determine a query based on sensor information obtained from one or more vehicle sensors and related to the action to be performed when the action is above a first threshold (“The control state changer 142 changes the control state to one matching the first and second conditions and performs the driving control in the changed control state” [0067]. Thus, there is a control state determined based on a first and second condition, i.e., query related to the driving action to be performed. The first threshold will be considered as the numeric value of the control state, wherein those control states rated above the first control state, i.e., basic conditions, will be considered as exceeding the first threshold. As described in [0061] and [0063], the first and second condition which the query is based on are a result of sensor information obtained from one or more vehicle sensors.);
responsive to the level of the action being below a second threshold, process the query based on the sensor information to generate a first response comprising contextual information generated by a first AI model (For the second threshold, the second condition determines a speed threshold based on the detected speed limit (see [0067]). For each second condition shown in Fig. 5, the control state operation is suppressed, i.e., a first response is generated, when the speed of the driving is below the speed limit threshold (see [0070] as an example for the second control state with control degree 2). It is best understood that this suppression is meant to continue driving at the current control state level. As described in [0048], there is a first AI model which provides the contextual information for the first and second condition for determining the first response and thus the first response comprises said contextual information generated by the first AI model.);
responsive to the action being at or above the second threshold,(In converse to the above limitation, for each second condition shown in Fig. 5, the control state operation is performed, i.e., a second response is received to perform the control in the selected control state, when the speed of the driving is at or above the speed limit threshold (see [0070] as the same example for the second control state with control degree 2). It is best understood that the performing the action in the control state without suppression would be the determined driving control which is associated with the control state.); and
autonomously perform the action, by the vehicle, based on at least one of the first response or the second response (“Automated driving is, for example, performing driving control by controlling one or both of the steering or acceleration/deceleration of a vehicle. Control states of the driving control that can be performed by the automatically driven vehicle include a first control state and a second control state in which the automation rate is higher than the first control state or a lower level of task is required of an occupant than the first control state. The control states may also include a third control state in which the automation rate is higher than the second control state or a lower level of task is required of the occupant than the second control state” [0032]. Thus, in each control state there is an automated driving for performing driving control based on the level of control determined. Driving control is issued by the automated driving control device 100 shown in Fig. 2.).
However, Oniwa does not explicitly teach …responsive to the action that is at or above the second threshold, send the query and the generated contextual information to a server;
receive a second response to the sent query…
Nölle, pertinent to the problem at hand, teaches …responsive to the action that is at or above the second threshold, send the query to a server (“In response to the trigger event, at operation 304, the vehicle 102 communicates to the server 178 to request for remote guidance by sending a request. The remote guidance request may include various information entries. For instance, the remote guidance request may include type/category of the trigger event as detected via vehicle sensors 184. The remote guidance request may further include information associated with the trigger event such as the current location of the vehicle 102, the weather and temperature data. The remote guidance request may further include data reflecting the current condition of the vehicle such as vehicle make/model, suspension setting (e.g., height), fuel level (e.g., battery state of charge), tire pressure, motor/engine operating condition (e.g., temperature), vehicle occupancy data (e.g., number of occupant, presence of children) or the like that may be used to determine if certain maneuvers are available” [0029]. Thus, in response to a trigger event, i.e., the action being at or above the second threshold as exemplified by Oniwa in the rejection above, there is a request sent to the server including the query information and pertinent contextual information generated for determining a control action.);
receive a second response from the server to the query (“At operation 316, responsive to determining that one or more alternative trajectories are available, the ADC 316 operates the vehicle 102 to perform maneuvers corresponding to the selected alternative trajectory while being monitored by the operator associated with the server 178. The server 178 may continuously send updated trajectories and commands in remote guidance while the vehicle 102 traverses the selected trajectory until the ADC 182 and/or the operator determines the vehicle 102 has successfully overcome the situation associated with the trigger event” [0035]. Thus, a response to the associated query is received in the form of trajectories which determine alternative trajectories and maneuvers while the vehicle overcomes the trigger event.)…
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the second response of performing a control state change as taught by Oniwa to include the server response to trigger events as taught by Nölle with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification because the server is able to provide better processing power and is configured to better analyze the sensor data to provide autonomous driving instruction without having to involve the human operator (Nölle, [0030]).
Regarding claim 9, Oniwa as modified by Nölle teaches the system of claim 8,
with Oniwa further teaching wherein the determination of the level of the action related to the vehicle causes the at least one processor to:
analyze at least one parameter associated with an operation of the vehicle (“The vehicle sensors 40 include a vehicle speed sensor that detects the speed of the own vehicle M, an acceleration sensor that detects the acceleration thereof, a yaw rate sensor that detects an angular speed thereof about the vertical axis, an orientation sensor that detects the orientation of the own vehicle M, or the like” [0041]. Thus, there is a vehicle speed sensor which detects the speed of the vehicle, i.e., a parameter associated with an operation of the vehicle.); and
compare the at least one parameter to at least one threshold to identify the action (“For example, when a first variable sign MK5 is recognized by the traffic sign recognizer 134, the control state changer 142 determines whether or not there is a deviation equal to or greater than a threshold value (for example, ±20 km/h) between a speed limit (60 km/h) indicated by the variable sign MK5 and the speed VM of the own vehicle M and notifies the occupant through the HMI controller 180 to urge the occupant to change the set speed if there is such a deviation” [0087]. Thus, the speed of the vehicle, i.e., parameter, is compared to at least one speed threshold value, to determine the action to be taken in response to the detected speed limit.).
Regarding claim 10, Oniwa as modified by Nölle teaches the system of claim 8,
with Oniwa further teaching wherein the query related to the action to be performed is determined by the first AI model deployed on the vehicle, the first AI model analyzes data associated with the sign and contextual information to generate the query (“The first controller 120 includes, for example, a recognizer 130 and a behavior plan generator 140. For example, the first controller 120 realizes a function based on artificial intelligence (AI) and a function based on a previously given model in parallel. For example, the function of “recognizing an intersection” is realized by performing recognition of an intersection through deep learning or the like and recognition based on previously given conditions (presence of a signal, a road sign, or the like for which pattern matching is possible) in parallel and evaluating both comprehensively through scoring” [0048]. Thus, there is a first AI model deployed in parallel with previously given/recognized models to determine data related to the sign and driving context which comprise the query for first and second conditions.).
Regarding claim 11, Oniwa as modified by Nölle teaches the system of claim 8,
with Nölle further teaching wherein the query being processed is performed by a second AI model on the server, the second AI model generates the second response by an analysis of the query in combination with external data (“In response to receiving the remote guidance request, at operation 306, the server 178 assigns an operator to help provide remote guidance to the requesting vehicle 102. In one example, the operator may be a human being (e.g., technician). Additionally or alternatively, the operator may be a computer program (e.g., artificial intelligence) configured to analyze and resolve more difficult situations than the ADC 182 is configured to handle” [0030]. Thus, the operator which is assigned to respond to the remote guidance request may be a computer program, i.e., artificial intelligence which will be the second AI model on the server. As noted in the rejection of claim 1, the server request includes a combination of external data with the query related to driving data of the vehicle.).
Regarding claim 14, Oniwa as modified by Nölle teaches the system of claim 8,
with Oniwa further teaching wherein the at least one processor is configured to:
detect, by the server, a road condition greater than a road condition threshold in an area past the sign (In the example of [0070], the control state changer determines a branch/merge point, i.e., road condition in an area past the speed limiting sign, to be an interchange, i.e., greater than a threshold for classifications of branch/merge points.); and
wherein the second response causes the vehicle to respond, based on the road condition (Based on the detection of the interchange, i.e., the branch/merge point greater than the predetermined threshold for classifying the branch/merge point, causes the control state changer to perform the operation, i.e., second response.).
Regarding claim 15, Oniwa teaches a computer program product stored on a non-transitory computer readable medium that when executed by a processor (As described in [0097], the driving control device 100 of figure 11 further includes a storage device 100-5 (a non-transitory computer readable medium) which stores a program 100-5a (computer program product) which is to be executed by the CPU 100-2 (processor).) is configured to perform:
analyzing a sign proximate a road that a vehicle is traveling on to determine an action and a level of action to be performed by the vehicle (“The control state changer 142 performs control for changing (shifting) the control state of driving control on the basis of a first condition based on the travel situation of the own vehicle M recognized by the travel situation recognizer 132 and a second condition based on the traffic signs recognized by the traffic sign recognizer 134” [0067]. Thus, there is a traffic sign recognizer which analyzes a sign proximate a road that a vehicle is travelling on to determine the control state, i.e., an action, to be performed by the vehicle and an associated level of the action to be performed based on the results of the travel situation recognizer and the traffic sign recognizer. See more details about the levels of actions in the paragraphs which follow in “Functions of Control State Changer” section.);
determining a query based on sensor information obtained from one or more vehicle sensors and related to the action when the action is above a first threshold (“The control state changer 142 changes the control state to one matching the first and second conditions and performs the driving control in the changed control state” [0067]. Thus, there is a control state determined based on a first and second condition, i.e., query related to the driving action to be performed. The first threshold will be considered as the numeric value of the control state, wherein those control states rated above the first control state, i.e., basic conditions, will be considered as exceeding the first threshold. As described in [0061] and [0063], the first and second condition which the query is based on are a result of sensor information obtained from one or more vehicle sensors.);
responsive to the level of the action being below a second threshold, processing the query based on the sensor information to generate a first response comprising contextual information generated by a first AI model (For the second threshold, the second condition determines a speed threshold based on the detected speed limit (see [0067]). For each second condition shown in Fig. 5, the control state operation is suppressed, i.e., a first response is generated, when the speed of the driving is below the speed limit threshold (see [0070] as an example for the second control state with control degree 2). It is best understood that this suppression is meant to continue driving at the current control state level. As described in [0048], there is a first AI model which provides the contextual information for the first and second condition for determining the first response and thus the first response comprises said contextual information generated by the first AI model.);
responsive to the action being at or above the second threshold,query (In converse to the above limitation, for each second condition shown in Fig. 5, the control state operation is performed, i.e., a second response is received to perform the control in the selected control state, when the speed of the driving is at or above the speed limit threshold (see [0070] as the same example for the second control state with control degree 2). It is best understood that the performing the action in the control state without suppression would be the determined driving control which is associated with the control state.); and
autonomously performing the action, by the vehicle, based on at least one of the first response or the second response (“Automated driving is, for example, performing driving control by controlling one or both of the steering or acceleration/deceleration of a vehicle. Control states of the driving control that can be performed by the automatically driven vehicle include a first control state and a second control state in which the automation rate is higher than the first control state or a lower level of task is required of an occupant than the first control state. The control states may also include a third control state in which the automation rate is higher than the second control state or a lower level of task is required of the occupant than the second control state” [0032]. Thus, in each control state there is an automated driving for performing driving control based on the level of control determined. Driving control is issued by the automated driving control device 100 shown in Fig. 2.).
However, Oniwa does not explicitly teach …responsive to the action being at or above the second threshold, sending the query and the generated contextual information to a server;
receiving a second response to the sent query…
Nölle, pertinent to the problem at hand, teaches …responsive to the action being at or above the second threshold, sending the query and the generated contextual information to a server (“In response to the trigger event, at operation 304, the vehicle 102 communicates to the server 178 to request for remote guidance by sending a request. The remote guidance request may include various information entries. For instance, the remote guidance request may include type/category of the trigger event as detected via vehicle sensors 184. The remote guidance request may further include information associated with the trigger event such as the current location of the vehicle 102, the weather and temperature data. The remote guidance request may further include data reflecting the current condition of the vehicle such as vehicle make/model, suspension setting (e.g., height), fuel level (e.g., battery state of charge), tire pressure, motor/engine operating condition (e.g., temperature), vehicle occupancy data (e.g., number of occupant, presence of children) or the like that may be used to determine if certain maneuvers are available” [0029]. Thus, in response to a trigger event, i.e., the action being at or above the second threshold as exemplified by Oniwa in the rejection above, there is a request sent to the server including the query information and pertinent contextual information generated for determining a control action.);
receiving a second response to the sent query (“At operation 316, responsive to determining that one or more alternative trajectories are available, the ADC 316 operates the vehicle 102 to perform maneuvers corresponding to the selected alternative trajectory while being monitored by the operator associated with the server 178. The server 178 may continuously send updated trajectories and commands in remote guidance while the vehicle 102 traverses the selected trajectory until the ADC 182 and/or the operator determines the vehicle 102 has successfully overcome the situation associated with the trigger event” [0035]. Thus, a response to the associated query is received in the form of trajectories which determine alternative trajectories and maneuvers while the vehicle overcomes the trigger event.)…
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the second response of performing a control state change as taught by Oniwa to include the server response to trigger events as taught by Nölle with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification because the server is able to provide better processing power and is configured to better analyze the sensor data to provide autonomous driving instruction without having to involve the human operator (Nölle, [0030]).
Regarding claim 16, Oniwa as modified by Nölle teaches the computer program product stored on a non-transitory computer-readable medium of claim 15,
with Oniwa further teaching …analyzing at least one parameter associated with an operation of the vehicle (“The vehicle sensors 40 include a vehicle speed sensor that detects the speed of the own vehicle M, an acceleration sensor that detects the acceleration thereof, a yaw rate sensor that detects an angular speed thereof about the vertical axis, an orientation sensor that detects the orientation of the own vehicle M, or the like” [0041]. Thus, there is a vehicle speed sensor which detects the speed of the vehicle, i.e., a parameter associated with an operation of the vehicle.); and
comparing the at least one parameter to at least one vehicle operation threshold to identify the action (“For example, when a first variable sign MK5 is recognized by the traffic sign recognizer 134, the control state changer 142 determines whether or not there is a deviation equal to or greater than a threshold value (for example, ±20 km/h) between a speed limit (60 km/h) indicated by the variable sign MK5 and the speed VM of the own vehicle M and notifies the occupant through the HMI controller 180 to urge the occupant to change the set speed if there is such a deviation” [0087]. Thus, the speed of the vehicle, i.e., parameter, is compared to at least one speed threshold value, to determine the action to be taken in response to the detected speed limit.).
Regarding claim 17, Oniwa as modified by Nölle teaches the computer program product stored on a non-transitory computer readable medium of claim 15,
with Oniwa further teaching wherein the query related to the action to be performed is determined by the first AI model deployed on the vehicle, the first AI model analyzes data associated with the sign and contextual information to generate the query (“The first controller 120 includes, for example, a recognizer 130 and a behavior plan generator 140. For example, the first controller 120 realizes a function based on artificial intelligence (AI) and a function based on a previously given model in parallel. For example, the function of “recognizing an intersection” is realized by performing recognition of an intersection through deep learning or the like and recognition based on previously given conditions (presence of a signal, a road sign, or the like for which pattern matching is possible) in parallel and evaluating both comprehensively through scoring” [0048]. Thus, there is a first AI model deployed in parallel with previously given/recognized models to determine data related to the sign and driving context which comprise the query for first and second conditions.).
Regarding claim 18, Oniwa as modified by Nölle teaches the computer program product stored on a non-transitory computer readable storage medium of claim 15,
with Nölle further teaching wherein the processing of the query is performed by a second AI model on the server, the second AI model generating the second response by analyzing the query in combination with external data (“In response to receiving the remote guidance request, at operation 306, the server 178 assigns an operator to help provide remote guidance to the requesting vehicle 102. In one example, the operator may be a human being (e.g., technician). Additionally or alternatively, the operator may be a computer program (e.g., artificial intelligence) configured to analyze and resolve more difficult situations than the ADC 182 is configured to handle” [0030]. Thus, the operator which is assigned to respond to the remote guidance request may be a computer program, i.e., artificial intelligence which will be the second AI model on the server. As noted in the rejection of claim 1, the server request includes a combination of external data with the query related to driving data of the vehicle.).
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Oniwa in view of Nölle and further in view of Lindholm et al. (US 2018/0342155 A1; hereinafter “Lindholm”).
Regarding claim 5, Oniwa as modified by Nölle teaches the method of claim 1.
Oniwa as modified by Nölle does not explicitly teach …enhancing the first response with area-specific information when an area where the vehicle is maneuvering is a new area.
Lindholm, pertinent to the problem at hand, teaches …enhancing the first response with area-specific information when an area where the vehicle is maneuvering is a new area (“ Additionally, the system 100 can determine road type without the assistance of communicating with infrastructure and without using map and/or GPS information… For example, GPS and/or map information may be limited when there are new roads not mapped via GPS, when communication of GPS and/or map information requires a wireless connection, and the like” [0047]. Thus, when the vehicle is travelling on new roads which are not mapped, i.e., maneuvering in a new area, the first response can be enhanced by road types, i.e., area-specific information, which are determined without the assistance of external knowledge or infrastructure.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the responses of Oniwa to include area-specific information in new areas as taught by Lindholm with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification because by determining the road type without the use of GPS or map information, there is a reduced need for and reduced cost of additional infrastructure to supplement areas which do not support GPS and mapping, in addition to reducing processing power required for GPS and mapping (Lindholm, [0047]).
Regarding claim 12, Oniwa as modified by Nölle teaches the system of claim 8.
Oniwa as modified by Nölle does not explicitly teach …wherein the at least one processor is configured to:
enhance the first response with area-specific information when an area where the vehicle is maneuvers is a new area.
Lindholm, pertinent to the problem at hand, teaches …wherein the at least one processor is configured to:
enhance the first response with area-specific information when an area where the vehicle is maneuvers is a new area (“ Additionally, the system 100 can determine road type without the assistance of communicating with infrastructure and without using map and/or GPS information… For example, GPS and/or map information may be limited when there are new roads not mapped via GPS, when communication of GPS and/or map information requires a wireless connection, and the like” [0047]. Thus, when the vehicle is travelling on new roads which are not mapped, i.e., maneuvering in a new area, the first response can be enhanced by road types, i.e., area-specific information, which are determined without the assistance of external knowledge or infrastructure.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the responses of Oniwa to include area-specific information in new areas as taught by Lindholm with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification because by determining the road type without the use of GPS or map information, there is a reduced need for and reduced cost of additional infrastructure to supplement areas which do not support GPS and mapping, in addition to reducing processing power required for GPS and mapping (Lindholm, [0047]).
Regarding claim 19, Oniwa as modified by Nölle teaches the computer program product of claim 15 stored on a non-transitory computer readable medium.
Oniwa as modified by Nölle does not explicitly teach …wherein the processor is further configured to perform:
enhancing the first response with area-specific information when an area where the vehicle is maneuvering is a new area.
Lindholm, pertinent to the problem at hand teaches …wherein the processor is further configured to perform:
enhancing the first response with area-specific information when an area where the vehicle is maneuvering is a new area (“ Additionally, the system 100 can determine road type without the assistance of communicating with infrastructure and without using map and/or GPS information… For example, GPS and/or map information may be limited when there are new roads not mapped via GPS, when communication of GPS and/or map information requires a wireless connection, and the like” [0047]. Thus, when the vehicle is travelling on new roads which are not mapped, i.e., maneuvering in a new area, the first response can be enhanced by road types, i.e., area-specific information, which are determined without the assistance of external knowledge or infrastructure.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the responses of Oniwa to include area-specific information in new areas as taught by Lindholm with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification because by determining the road type without the use of GPS or map information, there is a reduced need for and reduced cost of additional infrastructure to supplement areas which do not support GPS and mapping, in addition to reducing processing power required for GPS and mapping (Lindholm, [0047]).
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Oniwa in view of Nölle and further in view of Breed et al. (US 2015/0197248 A1; hereinafter “Breed”).
Regarding claim 6, Oniwa as modified by Nölle teaches the method of claim 1…
However, Oniwa as modified by Nölle does not explicitly teach …receiving data related to a visibility of the sign; and
raising the level of the action when the visibility is below a visibility threshold.
Breed, pertinent to the problem at hand, teaches …receiving data related to a visibility of the sign (“As such, the monitoring system 190 may monitor weather conditions around the road, visibility for operators of the vehicles on the road, traffic on the road, accidents on the road, emergency situations of vehicles on the road and/or the speed of vehicles travelling on the road and a distance between adjacent vehicles” [0581]. Thus, there is data received which is related to the visibility of the road, i.e., the road signs.); and
raising the level of the action when the visibility is below a visibility threshold (“The control system 192 may be coupled to or integrated with a map database containing a predetermined speed limit for the road under normal travel conditions, and thus would determine a change in this predetermined speed limit based on the monitored conditions. The control system 192 may be managed by a highway authority or other local authorities” [0581]. Thus, the predetermined speed limit may be changed, i.e., the level of the action is raised, when the visibility is poor, i.e., below a threshold. Furthering the use of a visibility threshold, “Under the RtZF.RTM. plan, it is recommended that the speed of the host vehicle be limited such that vehicle can come to a complete stop in one half or less of the visibility distance. This will permit the laser radar system to observe and identify threatening objects that are beyond the visibility distance, apply the brakes to the vehicle if necessary causing the vehicle to stop prior to an impact, providing an added degree of safety to the host vehicle” [0532]. Thus, speed is limited to one half or less of the visibility distance.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the control states of Oniwa to include the visibility thresholds for speed as taught by Breed with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification because by further limiting speed based on visibility thresholds, the vehicle will be able to maintain a safe stopping distance and avoid impact with obstacles which are detected beyond the visibility distance (Breed, [0532]).
Regarding claim 13, Oniwa as modified by Nölle teaches the system of claim 8…
However, Oniwa as modified by Nölle does not explicitly teach …wherein the at least one processor is configured to:
receive data related to a visibility of the sign; and
raise the level of the action when the visibility is below a visibility threshold.
Breed, pertinent to the problem at hand teaches …wherein the at least one processor is configured to:
receive data related to a visibility of the sign (“As such, the monitoring system 190 may monitor weather conditions around the road, visibility for operators of the vehicles on the road, traffic on the road, accidents on the road, emergency situations of vehicles on the road and/or the speed of vehicles travelling on the road and a distance between adjacent vehicles” [0581]. Thus, there is data received which is related to the visibility of the road, i.e., the road signs.); and
raise the level of the action when the visibility is below a visibility threshold (“The control system 192 may be coupled to or integrated with a map database containing a predetermined speed limit for the road under normal travel conditions, and thus would determine a change in this predetermined speed limit based on the monitored conditions. The control system 192 may be managed by a highway authority or other local authorities” [0581]. Thus, the predetermined speed limit may be changed, i.e., the level of the action is raised, when the visibility is poor, i.e., below a threshold. Furthering the use of a visibility threshold, “Under the RtZF.RTM. plan, it is recommended that the speed of the host vehicle be limited such that vehicle can come to a complete stop in one half or less of the visibility distance. This will permit the laser radar system to observe and identify threatening objects that are beyond the visibility distance, apply the brakes to the vehicle if necessary causing the vehicle to stop prior to an impact, providing an added degree of safety to the host vehicle” [0532]. Thus, speed is limited to one half or less of the visibility distance.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the control states of Oniwa to include the visibility thresholds for speed as taught by Breed with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification because by further limiting speed based on visibility thresholds, the vehicle will be able to maintain a safe stopping distance and avoid impact with obstacles which are detected beyond the visibility distance (Breed, [0532]).
Regarding claim 20, Oniwa as modified by Nölle teaches the computer program product of claim 15 stored on a non-transitory computer readable medium…
However, Oniwa as modified by Nölle does not explicitly teach …wherein the processor is further configured to perform:
receiving data related to a visibility of the sign; and
raising the level of the action when the visibility is below a visibility threshold.
Breed, pertinent to the problem at hand, teaches …wherein the processor is further configured to perform:
receiving data related to a visibility of the sign (“As such, the monitoring system 190 may monitor weather conditions around the road, visibility for operators of the vehicles on the road, traffic on the road, accidents on the road, emergency situations of vehicles on the road and/or the speed of vehicles travelling on the road and a distance between adjacent vehicles” [0581]. Thus, there is data received which is related to the visibility of the road, i.e., the road signs.); and
raising the level of the action when the visibility is below a visibility threshold (“The control system 192 may be coupled to or integrated with a map database containing a predetermined speed limit for the road under normal travel conditions, and thus would determine a change in this predetermined speed limit based on the monitored conditions. The control system 192 may be managed by a highway authority or other local authorities” [0581]. Thus, the predetermined speed limit may be changed, i.e., the level of the action is raised, when the visibility is poor, i.e., below a threshold. Furthering the use of a visibility threshold, “Under the RtZF.RTM. plan, it is recommended that the speed of the host vehicle be limited such that vehicle can come to a complete stop in one half or less of the visibility distance. This will permit the laser radar system to observe and identify threatening objects that are beyond the visibility distance, apply the brakes to the vehicle if necessary causing the vehicle to stop prior to an impact, providing an added degree of safety to the host vehicle” [0532]. Thus, speed is limited to one half or less of the visibility distance.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the control states of Oniwa to include the visibility thresholds for speed as taught by Breed with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification because by further limiting speed based on visibility thresholds, the vehicle will be able to maintain a safe stopping distance and avoid impact with obstacles which are detected beyond the visibility distance (Breed, [0532]).
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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/S.L.M./ Examiner, Art Unit 3656
/WADE MILES/ Supervisory Patent Examiner, Art Unit 3656
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