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 .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 9, 14, 17, and 20 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claims contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor at the time the application was filed, had possession of the claimed invention.
Regarding claims 9 and 20, applicant recites, mutatis mutandis, “determining a confidence level associated with the estimated potential lateral drift; and
wherein adjusting operation of the first vehicle is based at least in part on the determined confidence level.” (emphasis added).
However, this “confidence level” never appears in the specification of the present case or the parent case. Yet, applicant claims that the present case is a continuation of the parent case. This is false. The “confidence level” therefore lacks written description and the claim is rejected under 35 USC 112(a).
Examiner’s suggestion to help applicant overcome the 112(a) rejection: applicant can overcome the 112(a) rejection of claim 9 by cancelling the claim. Applicant can overcome the 112(a) rejection of claim 20 by deleting all of the claim limitations that mention the “confidence level”.
Regarding claim 14, applicant recites, “detecting a change in road surface type along a projected path of the first vehicle; and
wherein estimating the potential lateral drift comprises adjusting the estimated potential lateral drift based on the detected change in road surface type.” (emphasis added).
However, this “change in road surface type” never appears in the specification of the present case or the parent case. Yet, applicant claims that the present case is a continuation of the parent case. This is false. The “change in road surface type” therefore lacks written description and the claim is rejected under 35 USC 112(a).
Examiner’s suggestion to help applicant overcome the 112(a) rejection: applicant can overcome the 112(a) rejection by cancelling the claim.
Regarding claim 17, applicant recites, “determining a mass distribution of the first vehicle; and
wherein estimating the potential lateral drift comprises estimating the potential lateral drift based at least in part on the determined mass distribution of the first vehicle.” (emphasis added).
However, this “mass distribution” never appears in the specification of the present case or the parent case. Yet, applicant claims that the present case is a continuation of the parent case. This is false. The “mass distribution” therefore lacks written description and the claim is rejected under 35 USC 112(a).
Examiner’s suggestion to help applicant overcome the 112(a) rejection: applicant can overcome the 112(a) rejection by cancelling the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 6-7, 9-14, and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claimed invention is directed to the concept of collecting sensor data, estimating a likelihood of an interaction between two vehicles, and estimating a potential lateral drift of a vehicle. This judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Regarding claims 1 and 18, applicant recites A system comprising:
a first vehicle;
a sensor system coupled to the first vehicle; and
a computing device coupled to the first vehicle and configured to:
receive, from the sensor system, sensor data representing an environment of the first vehicle;
detect, based on the sensor data, a second vehicle navigating near the first vehicle;
determine an increased likelihood of an interaction between the first vehicle and the second vehicle;
determine, using the sensor data, a plurality of parameters related to at least one of the first vehicle, the second vehicle, and the environment;
estimate, responsive to determining the increased likelihood of the interaction, a potential lateral drift for at least one of the first vehicle or the second vehicle during the interaction based on at least some of the plurality of parameters; and
adjust operation of the first vehicle based on the estimated potential lateral drift.
Claim 1 is a series of steps and therefore is directed to a process, which satisfies step 1 of the Section 101 analysis. Claim 18 is a system which performs those steps, and therefore is directed to an apparatus, which also satisfies step 1 of the Section 101 analysis. Under the two-prong inquiry, the claim is eligible at revised step 2A unless: Prong One: the claim recites a judicial exception; and Prong Two: the exception is not integrated into a practical application of the exception.
The above claim steps are directed to the concept of collecting sensor data, estimating a likelihood of an interaction between two vehicles, and estimating a potential lateral drift of a vehicle, which is an abstract idea that can be performed by a user mentally or manually and falls within the Mental Processes grouping. (Prong one: YES, recites an abstract idea).
Other than reciting the use of a first vehicle, a second vehicle, a sensor system, and a computing device, nothing in the claim elements precludes the steps from being performed entirely by a human. The use of one or more computing devices is insufficient to amount to significantly more than the judicial exception and does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Furthermore, the generically recited step of “adjusting operation” of the first vehicle may simply refer to outputting an alert, which would merely amount to outputting the judicial exception, which is insignificant extra-solution activity that does not integrate the judicial exception into a practical application (Prong Two: NO, does not recite additional elements that integrate the abstract idea into a practical application similar to that shown in MPEP 2106.05).
Under step 2B, the claimed invention does not recite additional elements that are indicative of an inventive concept. The additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The sensor system and computing device are described in at least paragraphs [0024] and [0052] of applicant’s specification as merely general purpose computer components. Furthermore, the recited vehicles just amount to an environment for these generic computer components. Therefore these additional limitations are no more than mere instructions to apply the exception using generic computer components. The recitation of generic processors/computers does not take the above limitations out of the mental processes grouping.
Moreover, the implementation of the abstract idea on generic computers and/or generic computer components does not add significantly more, similar to how the recitation of the computer in Alice amounted to mere instructions to apply the abstract idea on a generic computer. The claims merely invoke the additional elements as tools that are being used in their ordinary capacity. Further, the courts have found that simply limiting the use of the abstract idea to a particular environment does not add significantly more. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation.
Examiner’s suggestion to help applicant overcome the 101 rejections: applicant can overcome the 101 rejections by amending the claims as follows:
In claim 1, “adjusting operation of the first vehicle based on the estimated potential lateral drift.” should be “adjusting a driving operation of the first vehicle based on the estimated potential lateral drift.”
In claim 18, “adjust operation of the first vehicle based on the estimated potential lateral drift.” should be “adjust a driving operation of the first vehicle based on the estimated potential lateral drift.”
In claim 20, “adjusting operation of the first vehicle based on the estimated potential lateral drift” should be “adjusting a driving operation of the first vehicle based on the estimated potential lateral drift”
The above amendments would be adequate to overcome the 101 rejections.
Regarding claim 2, applicant recites The method of claim 1, further comprising: determining a relative speed between the first vehicle and the second vehicle; and wherein estimating the potential lateral drift comprises estimating the potential lateral drift based at least in part on the determined relative speed.
However, a user can mentally or manually determine the value of the parameter and estimate the potential lateral drift based off of it.
Regarding claim 3, applicant recites The method of claim 1, wherein the plurality of parameters includes at least one of: a vehicle type, a vehicle size, a trailer size, a wind speed, a wind direction, a road condition, or a road grade.
However, a user can mentally or manually determine the value of the parameters and estimate the potential lateral drift based off of them.
Regarding claim 4, applicant recites The method of claim 1, further comprising:
detecting a third vehicle navigating near the first vehicle and the second vehicle; and
wherein estimating the potential lateral drift comprises estimating the potential lateral drift based at least in part on a position of the third vehicle relative to the first vehicle and the second vehicle.
However, a user can mentally or manually detect the third vehicle, determine the value of the parameter pertaining to it, and estimate the potential lateral drift based off of it.
Regarding claim 6, applicant recites The method of claim 1, wherein estimating the potential lateral drift comprises: inputting at least some of the plurality of parameters into a machine learning model trained on historical data of vehicle interactions; and receiving an output from the machine learning model indicating the estimated potential lateral drift.
However, a user can mentally or manually determine the value of the lateral drift based off of historical data.
Regarding claim 7, applicant recites The method of claim 1, further comprising: detecting an object in the environment of the first vehicle; and wherein estimating the potential lateral drift comprises estimating the potential lateral drift based at least in part on a position of the detected object relative to the first vehicle and the second vehicle.
However, a user can mentally or manually determine the object, detect the value of the parameter associated with the object, and estimate the potential lateral drift based off of it.
Regarding claim 9, applicant recites The method of claim 1, further comprising:
determining a confidence level associated with the estimated potential lateral drift; and
wherein adjusting operation of the first vehicle is based at least in part on the determined confidence level.
However, a user can mentally or manually determine a confidence value of the determined potential lateral drift. Moreover, the operation adjustment could just refer to outputting data, which is insignificant extra-solution activity that does not integrate the judicial exception into a practical application.
Regarding claim 10, applicant recites The method of claim 1, further comprising: monitoring behavior of the second vehicle for a predetermined time period prior to estimating the potential lateral drift; and wherein estimating the potential lateral drift is based at least in part on the monitored behavior of the second vehicle.
However, a user can mentally or manually determine the value of a parameter for a predetermined time before estimating potential lateral drift and can then estimate the potential lateral drift based off of it.
Regarding claim 11, applicant recites The method of claim 1, wherein the interaction between the first vehicle and the second vehicle comprises at least one of: a passing maneuver, a merging maneuver, or a lane change maneuver.
However, the type of interaction that could be observed does not change that the user can predict the likelihood of the interaction and potential drift associated with it mentally or manually.
Regarding claim 12, applicant recites The method of claim 1, wherein estimating the potential lateral drift comprises: comparing the plurality of parameters to historical data of previous interactions between vehicles; and determining the potential lateral drift based on lateral movements observed in the historical data for similar parameter combinations.
However, a user can mentally or manually estimate a potential lateral drift based off of historical data.
Regarding claim 13, applicant recites The method of claim 1, further comprising: determining a current weather condition in the environment of the first vehicle; and wherein estimating the potential lateral drift comprises estimating the potential lateral drift based at least in part on the determined current weather condition.
However, a user can mentally or manually determine the weather and estimate the potential lateral drift based off of it.
Regarding claim 14, applicant recites The method of claim 1, further comprising: detecting a change in road surface type along a projected path of the first vehicle; and wherein estimating the potential lateral drift comprises adjusting the estimated potential lateral drift based on the detected change in road surface type.
However, a user can mentally or manually determine the parameter and estimate the potential lateral drift based off of it.
Regarding claim 16, applicant recites The method of claim 1, further comprising: detecting a curvature of the road ahead of the first vehicle; and wherein estimating the potential lateral drift comprises estimating the potential lateral drift based at least in part on the detected road curvature.
However, a user can mentally or manually determine the value of the parameter and estimate the potential lateral drift based off of it.
Regarding claim 17, applicant recites The method of claim 1, further comprising: determining a mass distribution of the first vehicle; and wherein estimating the potential lateral drift comprises estimating the potential lateral drift based at least in part on the determined mass distribution of the first vehicle.
However, a user can mentally or manually determine the value of the parameter and estimate the potential lateral drift based off of it.
Regarding claim 19, applicant recites The system of claim 18, wherein the computing device is further configured to: determine a current weather condition in the environment of the first vehicle; determine a road surface condition based on the sensor data; and estimate the potential lateral drift based at least in part on the determined current weather condition and the road surface condition.
However, a user can mentally or manually determine the parameters and estimate the potential lateral drift based off of them.
Regarding claim 20, applicant recites A non-transitory computer-readable medium storing instructions that, when executed by a computing device, cause the computing device to perform operations comprising:
receiving sensor data representing an environment of a first vehicle;
detecting, based on the sensor data, a second vehicle navigating near the first vehicle;
determining an increased likelihood of an interaction between the first vehicle and the second vehicle;
determining, using the sensor data, a plurality of parameters related to at least one of the first vehicle, the second vehicle, and the environment;
estimating, responsive to determining the increased likelihood of the interaction, a potential lateral drift for at least one of the first vehicle or the second vehicle during the interaction based on at least some of the plurality of parameters;
determining a confidence level associated with the estimated potential lateral drift;
and adjusting operation of the first vehicle based on the estimated potential lateral drift and the determined confidence level.
Claim 20 recites a non-transitory computer-readable medium which is executed to perform a series of steps, and therefore is directed to an apparatus, which satisfies step 1 of the Section 101 analysis. Under the two-prong inquiry, the claim is eligible at revised step 2A unless: Prong One: the claim recites a judicial exception; and Prong Two: the exception is not integrated into a practical application of the exception.
The above claim steps are directed to the concept of collecting sensor data, estimating a likelihood of an interaction between two vehicles, estimating a potential lateral drift of a vehicle, and determining a confidence in that estimated potential lateral drift which is an abstract idea that can be performed by a user mentally or manually and falls within the Mental Processes grouping. (Prong one: YES, recites an abstract idea).
Other than reciting the use of a first vehicle, a second vehicle, a non-transitory computer-readable medium, and a computing device, nothing in the claim elements precludes the steps from being performed entirely by a human. The use of one or more computing devices is insufficient to amount to significantly more than the judicial exception and does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Furthermore, the generically recited step of “adjusting operation” of the first vehicle may simply refer to outputting an alert, which would merely amount to outputting the judicial exception, which is insignificant extra-solution activity that does not integrate the judicial exception into a practical application (Prong Two: NO, does not recite additional elements that integrate the abstract idea into a practical application similar to that shown in MPEP 2106.05).
Under step 2B, the claimed invention does not recite additional elements that are indicative of an inventive concept. The additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The non-transitory computer-readable medium and computing device are described in at least paragraph [0052] of applicant’s specification as merely general purpose computer components. Furthermore, the recited vehicles just amount to an environment for these generic computer components. Therefore these additional limitations are no more than mere instructions to apply the exception using generic computer components. The recitation of generic processors/computers does not take the above limitations out of the mental processes grouping.
Moreover, the implementation of the abstract idea on generic computers and/or generic computer components does not add significantly more, similar to how the recitation of the computer in Alice amounted to mere instructions to apply the abstract idea on a generic computer. The claims merely invoke the additional elements as tools that are being used in their ordinary capacity. Further, the courts have found that simply limiting the use of the abstract idea to a particular environment does not add significantly more. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation.
Examiner’s suggestion to help applicant overcome the 101 rejections: applicant can overcome the 101 rejections by amending the claims as follows:
In claim 1, “adjusting operation of the first vehicle based on the estimated potential lateral drift.” should be “adjusting a driving operation of the first vehicle based on the estimated potential lateral drift.”
In claim 18, “adjust operation of the first vehicle based on the estimated potential lateral drift.” should be “adjust a driving operation of the first vehicle based on the estimated potential lateral drift.”
In claim 20, “adjusting operation of the first vehicle based on the estimated potential lateral drift” should be “adjusting a driving operation of the first vehicle based on the estimated potential lateral drift”
The above amendments would be adequate to overcome the 101 rejections.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 6-9, 11-12, 18, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Seshadri et al. (US 20220017077 A1), hereinafter referred to as Seshadri.
Regarding claims 1 and 18, Seshadri teaches A system (See at least Fig. 1 in Seshadri: Seshadri discloses a block diagram of the collision avoidance system 100 [See at least Seshadri, 0016]) comprising:
a first vehicle (See at least Fig. 2 in Seshadri: Seshadri discloses an example vehicle system 200 that executes the collision avoidance system of FIG. 1 [See at least Seshadri, 0032]. Also see at least Fig. 3 in Seshadri: Seshadri discloses a driving environment 300 for a vehicle that includes the collision avoidance system of FIG. 1 [See at least Seshadri, 0047]. Seshadri further discloses collision avoidance application 140 included in primary vehicle 310 [See at least Seshadri, 0048]);
a sensor system coupled to the first vehicle (See at least Fig. 1 in Seshadri: Seshadri discloses Sensor(s) 120 [See at least Seshadri, 0025]); and
a computing device coupled to the first vehicle and configured to (See at least Fig. 1 in Seshadri: Seshadri discloses Processing unit 112 executes collision avoidance application 140 [See at least Seshadri, 0025]. Also see at least Fig. 5 in Seshadri: Seshadri discloses a flowchart of method steps for identifying a collision risk [See at least Seshadri, 0065]. Seshadri further discloses that this flowchart of Fig. 5 does represent collision avoidance application 140 [See at least Seshadri, 0066-0069]):
receive, from the sensor system, sensor data representing an environment of the first vehicle (See at least Fig. 5 in Seshadri: Seshadri discloses step 501, where collision avoidance application 140 acquires sensor data [See at least Seshadri, 0066]);
detect, based on the sensor data, a second vehicle navigating near the first vehicle (See at least Fig. 5 in Seshadri: Seshadri discloses that At step 503, collision avoidance application 140 generates lane change data values from the acquired sensor data [See at least Seshadri, 0067]. Seshadri further discloses that In some embodiments, driver information module 210 and/or contextual information module 230 may process the acquired sensor data to generate one or more lane change data values that are associated with one or more vehicles attempting to occupy an empty lane position [See at least Seshadri, 0067]. Seshadri further discloses that collision avoidance application 140 may generate lane change data value associated with one or more vehicles (e.g., the primary vehicle and one or more secondary vehicles) that are attempting to occupy the empty lane position [See at least Seshadri, 0067]);
determine an increased likelihood of an interaction between the first vehicle and the second vehicle (See at least Fig. 5 in Seshadri: Seshadri discloses that At step 505, collision avoidance application 140 compares the set of lane change values in order to determine a collision risk [See at least Seshadri, 0068]. Seshadri further discloses that collision avoidance application 140 assesses the set of lane change data values (e.g., eye gaze direction, head position, driver attentiveness, vehicle parameters, etc.) and generates a collision risk value (e.g., low, medium, high) based on at least a portion of the lane change data values included in the set [See at least Seshadri, 0068]);
determine, using the sensor data, a plurality of parameters related to at least one of the first vehicle, the second vehicle, and the environment (Seshadri further discloses that driver information module 210 and/or contextual information module 230 may process the acquired sensor data to generate one or more lane change data values that are associated with one or more vehicles attempting to occupy an empty lane position [See at least Seshadri, 0067]. Seshadri further discloses that collision avoidance application 140 may generate lane change data value associated with one or more vehicles (e.g., the primary vehicle and one or more secondary vehicles) that are attempting to occupy the empty lane position [See at least Seshadri, 0067]);
estimate, responsive to determining the increased likelihood of the interaction, a potential lateral drift for at least one of the first vehicle or the second vehicle during the interaction (See at least Fig. 5 in Seshadri: Seshadri discloses that At step 505, collision avoidance application 140 compares the set of lane change values in order to determine a collision risk [See at least Seshadri, 0068]. Also see at least Fig. 3 in Seshadri: Seshadri further discloses that integration module 240 may aggregate the lane change data values and process the set of lane change data values in order to determine the likelihood of collision when the primary driver attempts to occupy the empty lane position 330 [See at least Seshadri, 0068]. Seshadri further discloses that integration module 240 generates a collision risk value that indicates the likelihood that a driver is intending to move into the empty lane position 330, as well as the likelihood that the driver will attempt the move without seeing other vehicles that are also attempting to occupy the empty lane position [See at least Seshadri, 0068]. This position 330 may be regarded as an estimation of potential lateral drift) based on at least some of the plurality of parameters (Seshadri discloses that collision avoidance application 140 assesses the set of lane change data values (e.g., eye gaze direction, head position, driver attentiveness, vehicle parameters, etc.) and generates a collision risk value (e.g., low, medium, high) based on at least a portion of the lane change data values included in the set [See at least Seshadri, 0068]); and
adjust operation of the first vehicle based on the estimated potential lateral drift (See at least Fig. 5 in Seshadri: Seshadri discloses that At step 507, collision avoidance application 140 causes the generation of an output signal based on the determined collision risk [See at least Seshadri, 0069]. It will be appreciated that this is based on the potential lateral drift position 330 of Fig. 3 and [Seshadri, 0068]).
Examiner’s suggestion to help applicant overcome the prior art of record: in order to help applicant overcome the prior art of record, examiner has included a whole section called “Suggested Amendments to Help Applicant Overcome the Prior Art of Record” in the second to last section of this office action. Examiner recommends that applicant take a look at that section.
Regarding claim 2, Seshadri discloses The method of claim 1, further comprising:
determining a relative speed between the first vehicle and the second vehicle (Seshadri discloses a primary vehicle could capture image data of a nearby vehicle, while the nearby vehicle could broadcast a V2V signal broadcasting its speed [See at least Seshadri, 0041]. Seshadri further discloses that In such instances, contextual information module 230 could generate one or more data values from the acquired sensor data and connected vehicle data, where the data values (e.g., head position of the nearby driver, speed of the nearby vehicle) are associated with the nearby vehicle and indicates intended navigation of the nearby vehicle [See at least Seshadri, 0041]); and
wherein estimating the potential lateral drift comprises estimating the potential lateral drift (See at least Fig. 3 in Seshadri: Seshadri discloses that integration module 240 generates a collision risk value that indicates the likelihood that a driver is intending to move into the empty lane position 330, as well as the likelihood that the driver will attempt the move without seeing other vehicles that are also attempting to occupy the empty lane position [See at least Seshadri, 0068]. This position 330 may be regarded as an estimation of potential lateral drift) based at least in part on the determined relative speed (Seshadri discloses that In such instances, contextual information module 230 could generate one or more data values from the acquired sensor data and connected vehicle data, where the data values (e.g., head position of the nearby driver, speed of the nearby vehicle) are associated with the nearby vehicle and indicates intended navigation of the nearby vehicle [See at least Seshadri, 0041]).
Regarding claim 3, Seshadri discloses The method of claim 1, wherein the plurality of parameters includes at least one of:
a vehicle type, a vehicle size, a trailer size, a wind speed, a wind direction, a road condition (Seshadri discloses that data values may include contextual information, such as presence of empty lane positions (“empty spots”) within the driving environment [See at least Seshadri, 0017]. The existence of such an empty lane position is a road condition), or a road grade.
Regarding claim 4, Seshadri discloses The method of claim 1, further comprising:
detecting a third vehicle navigating near the first vehicle and the second vehicle (See at least Fig. 3 in Seshadri: Seshadri discloses Collision avoidance application 140 analyzes sensor data to determine data values associated with one or more components of primary vehicle 310, adjacent vehicle 340, and/or secondary vehicle 320 [See at least Seshadri, 0048]. Adjacent vehicle 340 is applicant’s “third vehicle”); and
wherein estimating the potential lateral drift comprises estimating the potential lateral drift based at least in part on a position of the third vehicle relative to the first vehicle and the second vehicle (See at least Fig. 3 in Seshadri: Seshadri discloses collision avoidance application 140 may analyze sensor data associated with adjacent vehicle 340 in order to compute the size of empty lane position 330 and determine whether primary vehicle 310 can safely occupy the empty lane position 330 [See at least Seshadri, 0050]. Seshadri further discloses that Similarly, collision avoidance application 140 could determine that, due to adjacent vehicle 340, empty lane position 330 is the only available position in lane 302(2) [See at least Seshadri, 0050]. Position 330 may be regarded as an estimation of potential lateral drift).
Regarding claim 6, Seshadri discloses The method of claim 1, wherein estimating the potential lateral drift comprises:
inputting at least some of the plurality of parameters into a machine learning model trained on historical data of vehicle interactions (Seshadri discloses that contextual information module 230 may retrieve historical data (e.g., previous image data, calculations that were computed by remote devices, etc.) [See at least Seshadri, 0039]. Seshadri further discloses that For example, contextual information module 230 could compare the current position of the empty lane position relative to previous positions to determine whether the empty lane position remains available [See at least Seshadri, 0039]. This is, broadly, machine-learning; the system learned the old value and is now comparing the new value to it to determine if the spot is still available); and
receiving an output from the machine learning model indicating the estimated potential lateral drift (Seshadri discloses that contextual information module 230 may retrieve historical data (e.g., previous image data, calculations that were computed by remote devices, etc.) [See at least Seshadri, 0039]. Seshadri further discloses that For example, contextual information module 230 could compare the current position of the empty lane position relative to previous positions to determine whether the empty lane position remains available [See at least Seshadri, 0039]. This is, broadly, machine-learning; the system learned the old value and is now comparing the new value to it to determine if the spot is still available).
Regarding claim 7, Seshadri discloses The method of claim 1, further comprising:
detecting an object in the environment of the first vehicle (See at least Fig. 3 in Seshadri: Seshadri discloses Collision avoidance application 140 analyzes sensor data to determine data values associated with one or more components of primary vehicle 310, adjacent vehicle 340, and/or secondary vehicle 320 [See at least Seshadri, 0048]. Adjacent vehicle 340 is applicant’s “object”); and
wherein estimating the potential lateral drift comprises estimating the potential lateral drift based at least in part on a position of the detected object relative to the first vehicle and the second vehicle (See at least Fig. 3 in Seshadri: Seshadri discloses collision avoidance application 140 may analyze sensor data associated with adjacent vehicle 340 in order to compute the size of empty lane position 330 and determine whether primary vehicle 310 can safely occupy the empty lane position 330 [See at least Seshadri, 0050]. Seshadri further discloses that Similarly, collision avoidance application 140 could determine that, due to adjacent vehicle 340, empty lane position 330 is the only available position in lane 302(2) [See at least Seshadri, 0050]. Position 330 may be regarded as an estimation of potential lateral drift).
Regarding claim 8, Seshadri discloses The method of claim 1, wherein adjusting operation of the first vehicle comprises at least one of:
adjusting a speed of the first vehicle (See at least Fig. 5 in Seshadri: Seshadri discloses that At step 507, collision avoidance application 140 causes the generation of an output signal based on the determined collision risk [See at least Seshadri, 0069]. Seshadri further discloses that output module 250 generates an output signal that modifies a driving parameter (e.g., slows speed of the vehicle) in order to lower the collision risk [See at least Seshadri, 0069]), adjusting a lateral position of the first vehicle within a lane, or initiating a lane change maneuver.
Regarding claim 9, Seshadri discloses The method of claim 1, further comprising:
determining a confidence level associated with the estimated potential lateral drift (See at least Fig. 5 in Seshadri: Seshadri discloses that At step 505, collision avoidance application 140 compares the set of lane change values in order to determine a collision risk [See at least Seshadri, 0068]. Seshadri further discloses that collision avoidance application 140 assesses the set of lane change data values (e.g., eye gaze direction, head position, driver attentiveness, vehicle parameters, etc.) and generates a collision risk value (e.g., low, medium, high) based on at least a portion of the lane change data values included in the set [See at least Seshadri, 0068]); and
wherein adjusting operation of the first vehicle is based at least in part on the determined confidence level (See at least Fig. 5 in Seshadri: Seshadri discloses that At step 507, collision avoidance application 140 causes the generation of an output signal based on the determined collision risk [See at least Seshadri, 0069]).
Regarding claim 11, Seshadri discloses The method of claim 1, wherein the interaction between the first vehicle and the second vehicle comprises at least one of:
a passing maneuver, a merging maneuver, or a lane change maneuver (Seshadri discloses that collision avoidance application 140 may generate lane change data value associated with one or more vehicles (e.g., the primary vehicle and one or more secondary vehicles) that are attempting to occupy the empty lane position [See at least Seshadri, 0067]).
Regarding claim 12, Seshadri discloses The method of claim 1, wherein estimating the potential lateral drift comprises:
comparing the plurality of parameters to historical data of previous interactions between vehicles (Seshadri discloses that contextual information module 230 may retrieve historical data (e.g., previous image data, calculations that were computed by remote devices, etc.) [See at least Seshadri, 0039]. Seshadri further discloses that For example, contextual information module 230 could compare the current position of the empty lane position relative to previous positions to determine whether the empty lane position remains available [See at least Seshadri, 0039]. This is, broadly, machine-learning; the system learned the old value and is now comparing the new value to it to determine if the spot is still available); and
determining the potential lateral drift based on lateral movements observed in the historical data for similar parameter combinations (Seshadri discloses that contextual information module 230 may retrieve historical data (e.g., previous image data, calculations that were computed by remote devices, etc.) [See at least Seshadri, 0039]. Seshadri further discloses that For example, contextual information module 230 could compare the current position of the empty lane position relative to previous positions to determine whether the empty lane position remains available [See at least Seshadri, 0039]. This is, broadly, machine-learning; the system learned the old value and is now comparing the new value to it to determine if the spot is still available).
Regarding claim 20, Seshadri discloses A non-transitory computer-readable medium storing instructions that, when executed by a computing device, cause the computing device to perform operations (See at least Fig. 1 in Seshadri: Seshadri discloses Processing unit 112 executes collision avoidance application 140 [See at least Seshadri, 0025]. Also see at least Fig. 5 in Seshadri: Seshadri discloses a flowchart of method steps for identifying a collision risk [See at least Seshadri, 0065]. Seshadri further discloses that this flowchart of Fig. 5 does represent collision avoidance application 140 [See at least Seshadri, 0066-0069]) comprising:
receiving sensor data representing an environment of a first vehicle (See at least Fig. 5 in Seshadri: Seshadri discloses step 501, where collision avoidance application 140 acquires sensor data [See at least Seshadri, 0066]);
detecting, based on the sensor data, a second vehicle navigating near the first vehicle (See at least Fig. 5 in Seshadri: Seshadri discloses that At step 503, collision avoidance application 140 generates lane change data values from the acquired sensor data [See at least Seshadri, 0067]. Seshadri further discloses that In some embodiments, driver information module 210 and/or contextual information module 230 may process the acquired sensor data to generate one or more lane change data values that are associated with one or more vehicles attempting to occupy an empty lane position [See at least Seshadri, 0067]. Seshadri further discloses that collision avoidance application 140 may generate lane change data value associated with one or more vehicles (e.g., the primary vehicle and one or more secondary vehicles) that are attempting to occupy the empty lane position [See at least Seshadri, 0067]);
determining an increased likelihood of an interaction between the first vehicle and the second vehicle (See at least Fig. 5 in Seshadri: Seshadri discloses that At step 505, collision avoidance application 140 compares the set of lane change values in order to determine a collision risk [See at least Seshadri, 0068]. Seshadri further discloses that collision avoidance application 140 assesses the set of lane change data values (e.g., eye gaze direction, head position, driver attentiveness, vehicle parameters, etc.) and generates a collision risk value (e.g., low, medium, high) based on at least a portion of the lane change data values included in the set [See at least Seshadri, 0068]);
determining, using the sensor data, a plurality of parameters related to at least one of the first vehicle, the second vehicle, and the environment (Seshadri further discloses that driver information module 210 and/or contextual information module 230 may process the acquired sensor data to generate one or more lane change data values that are associated with one or more vehicles attempting to occupy an empty lane position [See at least Seshadri, 0067]. Seshadri further discloses that collision avoidance application 140 may generate lane change data value associated with one or more vehicles (e.g., the primary vehicle and one or more secondary vehicles) that are attempting to occupy the empty lane position [See at least Seshadri, 0067]);
estimating, responsive to determining the increased likelihood of the interaction, a potential lateral drift for at least one of the first vehicle or the second vehicle during the interaction (See at least Fig. 5 in Seshadri: Seshadri discloses that At step 505, collision avoidance application 140 compares the set of lane change values in order to determine a collision risk [See at least Seshadri, 0068]. Also see at least Fig. 3 in Seshadri: Seshadri further discloses that integration module 240 may aggregate the lane change data values and process the set of lane change data values in order to determine the likelihood of collision when the primary driver attempts to occupy the empty lane position 330 [See at least Seshadri, 0068]. Seshadri further discloses that integration module 240 generates a collision risk value that indicates the likelihood that a driver is intending to move into the empty lane position 330, as well as the likelihood that the driver will attempt the move without seeing other vehicles that are also attempting to occupy the empty lane position [See at least Seshadri, 0068]. This position 330 may be regarded as an estimation of potential lateral drift) based on at least some of the plurality of parameters (Seshadri discloses that collision avoidance application 140 assesses the set of lane change data values (e.g., eye gaze direction, head position, driver attentiveness, vehicle parameters, etc.) and generates a collision risk value (e.g., low, medium, high) based on at least a portion of the lane change data values included in the set [See at least Seshadri, 0068]);
determining a confidence level associated with the estimated potential lateral drift (See at least Fig. 5 in Seshadri: Seshadri discloses that At step 505, collision avoidance application 140 compares the set of lane change values in order to determine a collision risk [See at least Seshadri, 0068]. Seshadri further discloses that collision avoidance application 140 assesses the set of lane change data values (e.g., eye gaze direction, head position, driver attentiveness, vehicle parameters, etc.) and generates a collision risk value (e.g., low, medium, high) based on at least a portion of the lane change data values included in the set [See at least Seshadri, 0068]); and
adjusting operation of the first vehicle based on the estimated potential lateral drift (See at least Fig. 5 in Seshadri: Seshadri discloses that At step 507, collision avoidance application 140 causes the generation of an output signal based on the determined collision risk [See at least Seshadri, 0069]. It will be appreciated that this is based on the potential lateral drift position 330 of Fig. 3 and [Seshadri, 0068]) and the determined confidence level (See at least Fig. 5 in Seshadri: Seshadri discloses that At step 507, collision avoidance application 140 causes the generation of an output signal based on the determined collision risk [See at least Seshadri, 0069]).
Examiner’s suggestion to help applicant overcome the prior art of record: in order to help applicant overcome the prior art of record, examiner has included a whole section called “Suggested Amendments to Help Applicant Overcome the Prior Art of Record” in the second to last section of this office action. Examiner recommends that applicant take a look at that section.
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.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Seshadri et al. (US 20220017077 A1) in view of Zhou et al. (US 20250128713 A1), hereinafter referred to as Zhou.
Regarding claim 5, Seshadri discloses The method of claim 1.
However, Seshadri does not explicitly teach the method further comprising:
determining a lateral distance between the first vehicle and the second vehicle; and
wherein adjusting operation of the first vehicle comprises adjusting a lateral position of the first vehicle within a lane based on the determined lateral distance and the estimated potential lateral drift.
However, Zhou does teach a method further comprising:
determining a lateral distance between the first vehicle and the second vehicle (Zhou discloses changing a steering angle of the vehicle 100, so that the autonomous driving vehicle follows a given trajectory and/or maintains a safe lateral and longitudinal distance from an object (for example, a car in an adjacent lane on the road) near the autonomous driving vehicle [See at least Zhou, 0075]); and
wherein adjusting operation of the first vehicle comprises adjusting a lateral position of the first vehicle within a lane based on the determined lateral distance and the estimated potential lateral drift (Zhou discloses changing a steering angle of the vehicle 100, so that the autonomous driving vehicle follows a given trajectory and/or maintains a safe lateral and longitudinal distance from an object (for example, a car in an adjacent lane on the road) near the autonomous driving vehicle [See at least Zhou, 0075]). Both Zhou and Seshadri teach methods for controlling positions of autonomous vehicles based on lateral proximity to vehicles in other lanes. However, only Zhou explicitly teach where the autonomous vehicle may monitor the lateral distance to another vehicle in another lane and control its steering to maintain a particular lateral distance to that other vehicle in that other lane.
It would have been obvious to anyone of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle control method of Seshadri to also monitor the lateral distance to another vehicle in another lane and control its steering to maintain a particular lateral distance to that other vehicle in that other lane, as in Zhou. Doing so improves safety by reducing the likelihood of collision between the vehicles.
Regarding claim 15, Seshadri discloses The method of claim 1.
However, Seshadri does not explicitly teach the method wherein adjusting operation of the first vehicle comprises:
determining a safety envelope around the first vehicle based on the estimated potential lateral drift; and
controlling the first vehicle to maintain the determined safety envelope during the interaction with the second vehicle.
However, Zhou does teach a method wherein adjusting operation of the first vehicle comprises:
determining a safety envelope around the first vehicle based on the estimated potential lateral drift (Zhou discloses changing a steering angle of the vehicle 100, so that the autonomous driving vehicle follows a given trajectory and/or maintains a safe lateral and longitudinal distance from an object (for example, a car in an adjacent lane on the road) near the autonomous driving vehicle [See at least Zhou, 0075]); and
controlling the first vehicle to maintain the determined safety envelope during the interaction with the second vehicle (Zhou discloses changing a steering angle of the vehicle 100, so that the autonomous driving vehicle follows a given trajectory and/or maintains a safe lateral and longitudinal distance from an object (for example, a car in an adjacent lane on the road) near the autonomous driving vehicle [See at least Zhou, 0075]). Both Zhou and Seshadri teach methods for controlling positions of autonomous vehicles based on lateral proximity to vehicles in other lanes. However, only Zhou explicitly teach where the autonomous vehicle may monitor the lateral distance to another vehicle in another lane and control its steering to maintain a particular lateral distance to that other vehicle in that other lane.
It would have been obvious to anyone of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle control method of Seshadri to also monitor the lateral distance to another vehicle in another lane and control its steering to maintain a particular lateral distance to that other vehicle in that other lane, as in Zhou. Doing so improves safety by reducing the likelihood of collision between the vehicles.
Claims 10 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Seshadri et al. (US 20220017077 A1) in view of Morriello et al. (US 20250326398 A1), hereinafter referred to as Morriello.
Regarding claim 10, Seshadri discloses The method of claim 1.
However Seshadri does not explicitly teach the method further comprising:
monitoring behavior of the second vehicle for a predetermined time period prior to estimating the potential lateral drift; and
wherein estimating the potential lateral drift is based at least in part on the monitored behavior of the second vehicle.
However, Morriello does teach a method further comprising:
monitoring behavior of the second vehicle for a predetermined time period prior to estimating the potential lateral drift (Morriello teaches that functions operate directly on the scenario ground truth 310 to extract, respectively, a time-varying lateral distance signal (measuring a lateral distance between the ego agent and the identified other agent), and a time-varying safe lateral distance signal for the ego agent and the identified other agent [See at least Morriello, 0155]. Also see at least Fig. 6D in Morriello: Morriello teaches that The safe distance rule is breached at time t3 when the lateral distance between the ego vehicle 602 and the other vehicle 604 becomes unsafe [See at least Morriello, 0184]); and
wherein estimating the potential lateral drift is based at least in part on the monitored behavior of the second vehicle (See at least Fig. 6D in Morriello: Morriello teaches that The safe distance rule is breached at time t3 when the lateral distance between the ego vehicle 602 and the other vehicle 604 becomes unsafe [See at least Morriello, 0184]). Both Morriello and Seshadri teach methods for monitoring lateral positions of other vehicles in other lanes. However, only Morriello explicitly teaches where the distance between the ego vehicle and the other vehicle may be measured over time before it is determined that the other vehicle is poised to violate a particular lateral position by drifting with respect to the ego vehicle.
It would have been obvious to anyone of ordinary skill prior to the effective filing date of the claimed invention to modify the ego vehicle lateral position monitoring system of Seshadri so that the distance between the ego vehicle and the other vehicle may be measured over time before it is determined that the other vehicle is poised to violate a particular lateral position by drifting with respect to the ego vehicle, as in Morriello. Doing so improves safety by using data over time to ascertain and protect the ego vehicle from the other vehicle’s dangerous behavior.
Regarding claim 13, Seshadri discloses The method of claim 1.
However, Seshadri does not explicitly teach the method further comprising:
determining a current weather condition in the environment of the first vehicle; and
wherein estimating the potential lateral drift comprises estimating the potential lateral drift based at least in part on the determined current weather condition.
However, Morriello does teach a method further comprising:
determining a current weather condition in the environment of the first vehicle (Morriello teaches that The physical context may comprise a static road layout and a given set of environmental conditions (e.g. weather) [See at least Morriello, 0088]); and
wherein estimating the potential lateral drift comprises estimating the potential lateral drift (Morriello teaches that functions operate directly on the scenario ground truth 310 to extract, respectively, a time-varying lateral distance signal (measuring a lateral distance between the ego agent and the identified other agent), and a time-varying safe lateral distance signal for the ego agent and the identified other agent [See at least Morriello, 0155]. Also see at least Fig. 6D in Morriello: Morriello teaches that The safe distance rule is breached at time t3 when the lateral distance between the ego vehicle 602 and the other vehicle 604 becomes unsafe [See at least Morriello, 0184]) based at least in part on the determined current weather condition (Morriello teaches that The safe lateral distance signal could depend on various factors, such as the speed of the ego agent and the speed of the other agent (captured in the traces 212), and environmental conditions (e.g. weather) [See at least Morriello, 0155]). Both Morriello and Seshadri teach methods for monitoring when other vehicles in other lanes violate lateral distance thresholds, indicating that they are dangerously approaching the ego vehicle. However, only Morriello explicitly teaches where the threshold for this violation may be varied based on weather conditions.
It would have been obvious to anyone of ordinary skill prior to the effective filing date of the claimed invention to modify the other vehicle lateral position monitoring system of Seshadri so that the threshold for this violation may be varied based on weather conditions, as in Morriello. Doing so improves safety by accounting for this dangerous surrounding condition.
Claims 14, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Seshadri et al. (US 20220017077 A1) in view of Wu et al. (US 20230331259 A1), hereinafter referred to as Wu.
Regarding claim 14, Seshadri discloses The method of claim 1.
However, Seshadri does not explicitly teach the method further comprising:
detecting a change in road surface type along a projected path of the first vehicle; and
wherein estimating the potential lateral drift comprises adjusting the estimated potential lateral drift based on the detected change in road surface type.
However, Wu does teach the method further comprising:
detecting a change in road surface type along a projected path of the first vehicle (Wu teaches that the intelligent vehicle 100 or a computing device such as the computer system 112, the computer vision system 140, and the memory 114 in FIG. 1 associated with the intelligent vehicle 100 may predict a behavior of the identified object based on the characteristics of the identified object and a status (for example, traffic, rain, and ice on a road) of the ambient environment [See at least Wu, 0101]. Ice on the road is a change in the surface of the road; the road was not built with ice on it); and
wherein estimating the potential lateral drift comprises adjusting the estimated potential lateral drift (Wu teaches that In addition to providing an instruction for adjusting the speed of the intelligent vehicle, the computing device may further provide an instruction for modifying a steering angle of the intelligent vehicle 100, so that the intelligent vehicle 100 follows a given track and/or maintains safe lateral and longitudinal distances from an object (for example, a car in a neighboring lane on a road) near the intelligent vehicle 100 [See at least Wu, 0101]) based on the detected change in road surface type (Wu teaches that the intelligent vehicle 100 or a computing device such as the computer system 112, the computer vision system 140, and the memory 114 in FIG. 1 associated with the intelligent vehicle 100 may predict a behavior of the identified object based on the characteristics of the identified object and a status (for example, traffic, rain, and ice on a road) of the ambient environment [See at least Wu, 0101]). Both Wu and Seshadri teach methods for controlling a position of an ego vehicle with respect to a detected neighboring vehicle. However, only Wu explicitly teaches where the lateral drift of the ego vehicle may be controlled to maintain a safe lateral distance from the neighboring vehicle while accounting for rain and ice on the road.
It would have been obvious to anyone of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the ego vehicle control method of Seshadri so that the lateral drift of the ego vehicle may be controlled to maintain a safe lateral distance from the neighboring vehicle while accounting for rain and ice on the road, as in Wu. Doing so improves safety by accounting for these dangerous road conditions.
Regarding claim 16, Seshadri discloses The method of claim 1.
However, Seshadri does not explicitly teach the method further comprising:
detecting a curvature of the road ahead of the first vehicle; and
wherein estimating the potential lateral drift comprises estimating the potential lateral drift based at least in part on the detected road curvature.
However, Wu does explicitly teach a method further comprising:
detecting a curvature of the road ahead of the first vehicle (Wu teaches that In the process, another factor may be considered to determine the speed of the intelligent vehicle 100, for example, a lateral location of the intelligent vehicle 100 on a driving road, a curvature of the road, and proximity between the intelligent vehicle 100 and each of static and dynamic objects [See at least Wu, 0101]); and
wherein estimating the potential lateral drift comprises estimating the potential lateral drift (Wu teaches that In addition to providing an instruction for adjusting the speed of the intelligent vehicle, the computing device may further provide an instruction for modifying a steering angle of the intelligent vehicle 100, so that the intelligent vehicle 100 follows a given track and/or maintains safe lateral and longitudinal distances from an object (for example, a car in a neighboring lane on a road) near the intelligent vehicle 100 [See at least Wu, 0101]) based at least in part on the detected road curvature (Wu teaches that In the process, another factor may be considered to determine the speed of the intelligent vehicle 100, for example, a lateral location of the intelligent vehicle 100 on a driving road, a curvature of the road, and proximity between the intelligent vehicle 100 and each of static and dynamic objects [See at least Wu, 0101]). Both Wu and Seshadri teach methods for controlling a position of an ego vehicle with respect to a detected neighboring vehicle. However, only Wu explicitly teaches where the lateral drift of the ego vehicle may be controlled to maintain a safe lateral distance from the neighboring vehicle while accounting for road curvature.
It would have been obvious to anyone of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the ego vehicle control method of Seshadri so that the lateral drift of the ego vehicle may be controlled to maintain a safe lateral distance from the neighboring vehicle while accounting for road curvature, as in Wu. Doing so improves safety by accounting for this potentially dangerous variable.
Regarding claim 19, Seshadri discloses The system of claim 18.
However, Seshadri does not explicitly teach the system wherein the computing device is further configured to:
determine a current weather condition in the environment of the first vehicle;
determine a road surface condition based on the sensor data; and
estimate the potential lateral drift based at least in part on the determined current weather condition and the road surface condition.
However, Wu does teach a system wherein the computing device is further configured to:
determine a current weather condition in the environment of the first vehicle (Wu teaches that the intelligent vehicle 100 or a computing device such as the computer system 112, the computer vision system 140, and the memory 114 in FIG. 1 associated with the intelligent vehicle 100 may predict a behavior of the identified object based on the characteristics of the identified object and a status (for example, traffic, rain, and ice on a road) of the ambient environment [See at least Wu, 0101]);
determine a road surface condition based on the sensor data (Wu teaches that the intelligent vehicle 100 or a computing device such as the computer system 112, the computer vision system 140, and the memory 114 in FIG. 1 associated with the intelligent vehicle 100 may predict a behavior of the identified object based on the characteristics of the identified object and a status (for example, traffic, rain, and ice on a road) of the ambient environment [See at least Wu, 0101]. Ice on the road is a change in the surface of the road; the road was not built with ice on it); and
estimate the potential lateral drift (Wu teaches that In addition to providing an instruction for adjusting the speed of the intelligent vehicle, the computing device may further provide an instruction for modifying a steering angle of the intelligent vehicle 100, so that the intelligent vehicle 100 follows a given track and/or maintains safe lateral and longitudinal distances from an object (for example, a car in a neighboring lane on a road) near the intelligent vehicle 100 [See at least Wu, 0101]) based at least in part on the determined current weather condition and the road surface condition (Wu teaches that the intelligent vehicle 100 or a computing device such as the computer system 112, the computer vision system 140, and the memory 114 in FIG. 1 associated with the intelligent vehicle 100 may predict a behavior of the identified object based on the characteristics of the identified object and a status (for example, traffic, rain, and ice on a road) of the ambient environment [See at least Wu, 0101]). Both Wu and Seshadri teach methods for controlling a position of an ego vehicle with respect to a detected neighboring vehicle. However, only Wu explicitly teaches where the lateral drift of the ego vehicle may be controlled to maintain a safe lateral distance from the neighboring vehicle while accounting for rain and ice on the road.
It would have been obvious to anyone of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the ego vehicle control method of Seshadri so that the lateral drift of the ego vehicle may be controlled to maintain a safe lateral distance from the neighboring vehicle while accounting for rain and ice on the road, as in Wu. Doing so improves safety by accounting for these dangerous road conditions.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Seshadri et al. (US 20220017077 A1) in view of Takenaka et al. (US 20160375948 A1), hereinafter referred to as Takenaka.
Regarding claim 17, Seshadri discloses The method of claim 1.
However, Seshadri does not explicitly teach the method further comprising:
determining a mass distribution of the first vehicle; and
wherein estimating the potential lateral drift comprises estimating the potential lateral drift based at least in part on the determined mass distribution of the first vehicle.
However, Takenaka does teach a method further comprising:
determining a mass distribution of the first vehicle (Takenaka teaches that the control device 60 is configured to carry out the control processing established on the basis of the two-mass-point model [See at least Takenaka, 0185]); and
wherein estimating the potential lateral drift comprises estimating the potential lateral drift (Takenaka teaches the control device 60 controls the posture (inclination angle) in the roll direction of the vehicle body 2 by controlling the inverted pendulum mass point lateral movement amount Pb_diff_y through the steering of the front wheel 3f [See at least Takenaka, 0185]. This predicted, and subsequently executed, steering correction does amount to a potential lateral drift because it ultimately does result in laterally displacing the vehicle) based at least in part on the determined mass distribution of the first vehicle (Takenaka teaches that the control device 60 is configured to carry out the control processing established on the basis of the two-mass-point model [See at least Takenaka, 0185]). Both Takenaka and Seshadri teach methods for controlling lateral movement of vehicles. However, only Takenaka explicitly teaches where the potential lateral drift indicative of where the vehicle may go laterally is based on a mass distribution model of the vehicle.
It would have been obvious to anyone of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the lateral movement control method of Seshadri so that the potential lateral drift indicative of where the vehicle may go laterally is based on a mass distribution model of the vehicle, as in Takenaka. Doing so improves safety by ensuring that any lateral controls of the vehicle consider the mass distribution of the vehicle, which is an important vehicle characteristic.
Suggested Amendments to Help Applicant Overcome the Prior Art of Record
Applicant can overcome the prior art of record for by way of the following suggested amendments:
1. (Currently Amended) A method comprising:
receiving, at a computing device coupled to a first vehicle, sensor data representing an environment of the first vehicle;
based on the sensor data, detecting a second vehicle navigating near the first vehicle;
determining an increased likelihood of an interaction between the first vehicle and the second vehicle;
determining, by the computing device and using the sensor data, a plurality of parameters related to at least one of the first vehicle, the second vehicle, and the environment, the plurality of parameters including an oscillation of a trailer of the second vehicle prior to a pass maneuver between the first vehicle and the second vehicle;
responsive to determining the increased likelihood of the interaction, estimating a potential lateral drift for at least one of the first vehicle or the second vehicle during the interaction based [[on]] at least in part on the oscillation of the trailer of the second vehicle prior to the pass maneuver between the first vehicle and the second vehicle; and
adjusting a driving operation of the first vehicle based on the estimated potential lateral drift.
18. (Currently Amended) A system comprising:
a first vehicle;
a sensor system coupled to the first vehicle; and
a computing device coupled to the first vehicle and configured to:
receive, from the sensor system, sensor data representing an environment of the first vehicle;
detect, based on the sensor data, a second vehicle navigating near the first vehicle;
determine an increased likelihood of an interaction between the first vehicle and the second vehicle;
determine, using the sensor data, a plurality of parameters related to at least one of the first vehicle, the second vehicle, and the environment, the plurality of parameters including an oscillation of a trailer of the second vehicle prior to a pass maneuver between the first vehicle and the second vehicle;
estimate, responsive to determining the increased likelihood of the interaction, a potential lateral drift for at least one of the first vehicle or the second vehicle during the interaction based [[on]] at least in part on the oscillation of the trailer of the second vehicle prior to the pass maneuver between the first vehicle and the second vehicle; and
adjust a driving operation of the first vehicle based on the estimated potential lateral drift.
20. (Currently Amended) A non-transitory computer-readable medium storing instructions that, when executed by a computing device, cause the computing device to perform operations comprising:
receiving sensor data representing an environment of a first vehicle;
detecting, based on the sensor data, a second vehicle navigating near the first vehicle;
determining an increased likelihood of an interaction between the first vehicle and the second vehicle;
determining, using the sensor data, a plurality of parameters related to at least one of the first vehicle, the second vehicle, and the environment, the plurality of parameters including an oscillation of a trailer of the second vehicle prior to a pass maneuver between the first vehicle and the second vehicle;
estimating, responsive to determining the increased likelihood of the interaction, a potential lateral drift for at least one of the first vehicle or the second vehicle during the interaction based [[on]] at least in part on the oscillation of the trailer of the second vehicle prior to the pass maneuver between the first vehicle and the second vehicle;
adjusting a driving operation of the first vehicle based on the estimated potential lateral drift
The above proposed amendments have basis in at least paragraph [00133] of applicant’s specification.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAEEM T ALAM whose telephone number is (571)272-5901. The examiner can normally be reached M-F, 9am-5pm.
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/NAEEM TASLIM ALAM/Examiner, Art Unit 3668
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