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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/20/2025 has been entered.
Response to Amendment
This action is in response to amendments and remarks filed on 11/20/2025. The examiner notes the following adjustments to the claims by the applicant:
Claims 1, 9 and 10 are amended;
No claims are newly cancelled or added.
Therefore, Claims 1, 3-7 and 9-21 are pending examination, in which Claims 1, 9 and 10 are independent claims.
In light of the instant amendments and arguments:
Regarding the rejection of Claims 1, 3-7 and 9-21 under 35 U.S.C. § 101, the applicant’s arguments have been considered but not found persuasive. The rejection is maintained.
Further examination resulted in a new rejection of Claims 1, 3-7 and 9-21 under 35 U.S.C. § 103, as detailed below.
THIS ACTION IS MADE FINAL. Necessitated by amendment.
Response to Arguments
Applicant presents the following arguments regarding the previous office action:
The 35 U.S.C. § 101 rejection, the applicant has amended each independent claim to include the additional underlined limitations: ”receiving a signal input by the unmanned vehicle containing occupancy state information of a reference start position on a guide line”;
To overcome the 35 U.S.C. § 103 rejection, the applicant has amended each independent claim to include the additional underlined limitations: "dynamically generating a target start point sequence on the basis of the reference start position and the initial position, wherein each target start point in the target start point sequence is a new position point obtained by calculating based on the reference start position and the initial position";
“unmanned vehicles often rely on a predefined reference start position for initiating movement. However, when this position is occupied, the vehicle cannot start normally, requiring manual reselection of a start position and significantly reducing operational efficiency. Existing path planning systems, such as those described in Derendarz, Kubota, and Boxmeyer, do not adequately address the dynamic generation of alternative start points based on real-time road width information and iterative condition checking… In the present patent application, it is clear that the method for starting the unmanned vehicle integrates a set of specific steps into an overall claimed process which, when viewed as a whole, has a very clear practical application of enabling reliable and efficient autonomous vehicle starting in complex environments.”;
“Derendarz is fundamentally reliant on a pre-recorded library of trajectories. It focuses exclusively on the selection process, i.e., choosing the most appropriate option from an existing, finite set of pre-defined possibilities based on inputs of additional information. According to Derendarz, if environmental changes render all target positions corresponding to the pre-stored trajectories unusable (e.g., all spaces in a home garage and the area in front of it are occupied), Derendarz may fail to find a solution, as its functionality is bounded by its pre-defined set of options. The subject matter of claim 1 differs from Derendarz in several substantial aspects. Claim 1 does not involve selecting from a set of pre-stored options.”;
“Kubota focuses on providing route guidance based on road topology (e.g., lane count, lane marking type, path point identifiers like P22). These path points are derived from map data or pre-set reference points and are used for lane- level navigation. However, these path points are statically defined road feature points; they are not new positions dynamically calculated algorithmically based on a "reference start position" and an "initial position".”;
“Boxmeyer focuses on longitudinal control and collision avoidance. Boxmeyer obtains the status of other vehicles through wireless communication and calculates control commands by combining the host vehicle's motion parameters. Boxmeyer does not involve concepts related to route planning, such as the "start points" or the "generation of a sequence of position points””.
Applicant's arguments A. through F. appear to be directed to the instantly amended subject matter. Accordingly, they have been addressed in the rejections below.
Claim Rejections - 35 USC § 101
Claims 1, 3-7 and 9-21 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
As described in MPEP § 2106, the analyses as to whether a claim qualifies as eligible subject matter under 35 U.S.C. § 101 includes the following determinations:
(1) Whether the claim is to a statutory category, i.e. to a process, machine, manufacture or composition of matter ("Step 1")- see MPEP §§ 2106, subsection III, and 2106.03.
(2) If the claim is to a statutory category, whether the claim recites any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity, or mental processes) ("Step 2A, Prong One") - see MPEP §§ 2106, subsection III, and 2106.04.
(3) If the claim recites a judicial exception, whether the claim recites additional elements that integrate the judicial exception into a practical application ("Step 2A, Prong Two") - see MPEP §§ 2106, subsection III, and 2106.04.
(4) If the claim does not recite additional elements that integrate the judicial exception into a practical application, whether the claim recites additional elements that amount to significantly more than the judicial exception ("Step 2B") – see MPEP §§ 2106, subsection III, and 2106.05.
Step 1: Claims 1, 3-7 and 11-21 are a method, Claim 9 is an electronic device, and Claim 10 is non-volatile computer-readable medium. Thus, each independent claim, on its face, is directed to one of the four statutory categories of 35 U.S.C. §101 (MPEP 2106.03).
Claim 1 is considered a representative independent claim. The examiner has determined, the following analysis is applicable to each independent claim. With regard to Claim 1:
A method for starting an unmanned vehicle, applied to the unmanned vehicle wherein the method is executable by a processor, the processor being in communication with a memory configured to store executable instructions and being configured to execute the executable instructions to implement the method, and comprises: in response to completion of a task of the unmanned vehicle at a current node, receiving a signal input by the unmanned vehicle containing occupancy state information of a reference start position on a guide line,
wherein the guide line is a preset driving route of the unmanned vehicle from the current node to a next node, the reference start position is a position on the guide line reached by the unmanned vehicle from a current position, and the occupancy state information is an occupancy state or a non-occupancy state; in response to a determination that the occupancy state information is the occupancy state, projecting the current position of the unmanned vehicle to the guide line to determine an initial position of the current position on the guide line; determining a reference start position of the reference start position on the guide line; and dynamically generating a target start point sequence on the basis of the reference start position and the initial position, wherein each target start point in the target start point sequence is a new position point obtained by calculating based on the reference start position and the initial position, wherein the dynamically generating a target start point sequence on the basis of the reference start position and the initial position comprises: for the reference start position, performing the following processing steps: obtaining position information corresponding to the reference start position, wherein the position information comprises left road width corresponding to the guide line and right road width corresponding to the guide line; on the basis of the left road width and the reference start position, generating at least one left alternative start point through a preset threshold; on the basis of the right road width and the reference start position, generating at least one right alternative start point through the preset threshold; merging at least one left target start point and the at least one right alternative start point to obtain an alternative start point group; determining whether the difference between the reference start position and the initial position meets a preset condition; in response to condition meeting, generating an alternative start position on the basis of the reference start position and the initial position; and determining the alternative start position as the reference start position, and performing the processing steps again.
Step 2A, Prong 1:
Regarding Prong 1 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
The examiner submits that the foregoing bolded limitations constitute a combination of “mental process” (i.e., concepts performed in the human mind, such an observation, evaluation, judgment and opinion) and “mathematical concepts”. But for the additional underlined elements, the claim limitations pertain to planning a navigational route, which can practically be performed in the human mind (with, or without, the use of a physical aids such as pen and paper), in combination with mathematical calculations.
Specifically, Claim 1 recites the general idea of planning movement of an autonomous vehicle from its current position (no longer on a preset navigational route or guide line) to another position, termed the starting position, before proceeding to a destination, while taking into account the surroundings (i.e., road with) and identifying alternative start positions, in case the preferred start position, directly on the navigational route, is occupied. A comparable situation is carried out mentally by a driver when identifying a parallel parking spot and planning the maneuvers necessary to successfully park the vehicle (i.e., observing or gathering data about the surroundings, evaluating the parking options and judging that a safe path exists to move the vehicle into an open spot). The driver looks for an unoccupied space amongst the occupied ones [“receiving a signal…containing occupancy state information of a reference start position on a guide line … and the occupancy state information is an occupancy state or a non-occupancy state”], visualizes an S-shaped curve/route – or “guide line” - needed to parallel park the vehicle [“the guide line is a preset driving route of the unmanned vehicle from the current node to a next node”], and judges the driving steps necessary to place their vehicle adjacent the parked vehicle in the parking spot in front of the empty parking [“dynamically generating a target start point sequence on the basis of the reference start position and the initial position”], including judging all specific positions and distances involved in maneuvering the vehicle [“projecting the current position of the unmanned vehicle to the guide line to determine an initial position of the current position on the guide line… the reference start position is a position on the guide line reached by the unmanned vehicle from a current position….determining a reference start position of the reference start position on the guide line”]. The parallel parking process includes taking into account road boundaries (such as the curb and road dividing line) and parked vehicles to identify different potential starting positions associated with different parking spots [“…position information comprises…road width corresponding to the guide line…generating at least one…alternative start point through a preset threshold… merging at least one left…and the at least one right alternative start point to obtain an alternative start point group”]; and judging the vehicle maneuvers required for parking in different open spots, behind different sized vehicles, taking into account different factors (such as how big the car is in front of a spot, and how close they parked to the curb) to identify, for example, the parking spot that will be easiest to get into, and which of a variety of paths to take to successfully park the vehicle [“generating…alternative start point through the preset threshold; merging…left target start point and…right alternative start point to obtain an alternative start point group; determining whether the difference between the reference start position and the initial position meets a preset condition…generating an alternative start position on the basis of the reference start position and the initial position; and determining the alternative start position as the reference start position”]. And repeating this mental process for each open spot until a preferred parking spot is identified [“and performing the processing steps again.”]. These steps represent the carrying out of a mental process, supplemented by mathematical concepts [i.e., “wherein each target start point in the target start point sequence is a new position point obtained by calculating based on the reference start position and the initial position”].
Thus, the claim recites a simple process, which under its broadest reasonable interpretation, recites the combination of mathematical concepts and mental processes utilizing observation, evaluation and judgement abilities of a human being, and thus amounts to an abstract idea (See MPEP § 2106.04(a)(2), subsection III).
Step 2A, Prong 2:
Regarding Prong 2 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer or processor to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The examiner submits that the foregoing underlined additional limitations do not integrate the above-noted abstract idea into a practical application. Simply adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use (i.e., “unmanned vehicles”; or referring to signals received from a sensor: “receiving a signal input by the unmanned vehicle”) does not integrate a judicial exception into a “practical application”. In addition, the use of a “processor….in communication with a memory configured to store executable instructions and being configured to execute the executable instructions to implement the method” merely invokes the use of a generic-computer to implement an abstract idea. The courts have indicated that additional elements such as merely using a computer to implement an abstract idea [MPEP 2106.05(f)] does not integrate a judicial exception into a “practical application”:
Courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
On the other hand, courts have held computer-implemented processes to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic. DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1257-59, 113 USPQ2d 1097, 1105-07 (Fed. Cir. 2014).
Thus, the additional elements does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: The examiner further submits that the aforementioned additional elements in Claim 1 are not sufficient to amount to significantly more than the judicial exception for the same reason discussed above for Step 2A, Prong 2. Specifically, the additional element of an ”unmanned vehicle” and “receiving a signal input by the unmanned vehicle” generally links the judicial exception to a particular technological environment or field of use (i.e., autonomous vehicles) [see MPEP 2106.05(h)]. Additionally, the additional elements of “starting the unmanned vehicle” and “completing a task of the unmanned vehicle” are well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception [see MPEP 2106.05(d) and 2106.07(a)III]. Moreover, the use of generic computer components [“processor….in communication with a memory configured to store executable instructions and being configured to execute the executable instructions to implement the method”] falls under the category of “merely using a computer to implement an abstract idea” [MPEP 2106.05(f)], and thus, does not provide an inventive concept in Step 2B. Hence, the claim is not patent eligible.
Dependent: Claims 3-7 and 11-21 do not recite any further limitations that cause the claims to be patent eligible. Rather, the dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. For example, with regard to Claims 3-7 and 11-21, the claimed invention is directed to additional abstract ideas associated with “methods of organizing human activities”:
• judging starting position alternatives (Claim 3);
• estimating or judging distances associated alternative starting positions (Claims 4 and 11);
• deciding on a starting position (Claim 5 and 12-14);
• determine occupancy of different starting positions (Claim 6);
• determine final travel route and distances (Claims 7 and 15);
• method implementation using a generic computer (Claims 16-21);
Therefore, Claims 1, 3-7 and 9-21 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 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 1, 3-7 and 9-21 are rejected under 35 U.S.C. §103 as being unpatentable over the combination of Derendarz et al. (US 10,889,323 B2, henceforth Derendarz), Kubota et al. (US 10,160,452 B2, henceforth Kubota), and Boxmeyer (US 5,369,591 A).
Regarding Claim 1, Derendarz recites the limitations: a method for starting an unmanned vehicle {Abstract and Figs. 2-4, involving moving an autonomous vehicle from a starting position, such as 23 in Fig. 3, to an end/target position, 41 in Fig. 3, which corresponds to starting the process of parking a vehicle in a garage, for which getting the vehicle aligned properly in front of the garage 22, Fig. 3, can be considered the “start” and parking with the garage the “target” or destination, wherein alternative trajectories must be considered to account for the fact that a preferred garage bay, for example on parked at previously, may be occupied}, applied to the unmanned vehicle, wherein the method is executable by a processor, the processor being in communication with a memory configured to store executable instructions and being configured to execute the executable instructions to implement the method {“a device 1 for carrying out an automated drive of a vehicle 50. The device 1 comprises an environmental detection device 2 for detecting an environment of the vehicle 50. The device 1 also comprises a control unit 3, a memory 4, and a display and operating device 5.”, Col. 6, Lns. 14-19 and Fig. 1}, and comprises: in response to completion of a task {arrival of vehicle 50 at property 21, Fig. 3} of the unmanned vehicle at a current node {current position 23 or decision point 36, Fig. 3}, receiving a signal input by the unmanned vehicle containing {“availability of the target position of the selected trajectory is carried out by an environmental detection device of the vehicle and/or via a query to a server. Such an environmental detection device can be, for example, a camera, a radar, an ultrasonic sensor, a top-view camera or a LIDAR.”, Col. 5, Lns. 62-67} occupancy state information {occupancy of garage: “depending on a current occupancy status of the target position of a provided trajectory, an alternative target position should be selected”, Col. 3, Lns. 31-41} of a reference start position {vehicle positioned directly in front of garage bay to start the parking process, Fig. 3} on a guide line {branch trajectories 71-72 of trajectories 31-32, Fig. 3}, wherein the guide line is a preset driving route of the unmanned vehicle {“carrying out an automatic drive of a vehicle along a provided trajectory which provides at least one stored trajectory for a current position of the vehicle, selects one of the provided trajectories, and carries out an automatic drive of the vehicle”, Abstract} from the current node to a next node {23 or 36 relative to front of garage bay, Fig. 3}, the reference start position is a position on the guide line reached by the unmanned vehicle {vehicle positioned directly in front of garage bay to start the parking process, Fig. 3} from a current position {current location 23, Fig. 3}, and the occupancy state information is an occupancy state or a non-occupancy state {“depending on a current occupancy status of the target position of a provided trajectory, an alternative target position should be selected”, Col. 3, Lns. 31-41, corresponding to vehicle 50 taking trajectory 32 to target position 42, where the garage space is unoccupied, Fig. 3}; in response to a determination that the occupancy state information is the occupancy state {“the target position of the selected trajectory is (again) occupied, then an alternative will be selected from the provided trajectories, so that the method operations at 102 to 104 are performed again.”, Col. 7, Lns. 5-9}, projecting the current position of the unmanned vehicle to the guide line to determine an initial position of the current position on the guide line {movement of vehicle from current location 23 to master decision point 36 is along master trajectory 34 in Fig. 3, wherein the control unit 3 and environmental detection unit 2 calculate the current position: “calculated current position 23”, Col. 7, Lns. 27-30 – which one skilled in the art will appreciate, involves constant calculation of the distance between the vehicle’s current location and decision point 36, to enable to vehicle to determine it has reached point 36}; determining a reference start position of the reference start position on the guide line {using the aforementioned sensing and controls, the distance to the front of the garage along either trajectories 31 or 32, can correspond to the total start distance to prepare the vehicle to be parked in the garage after arriving at the property 21, Fig. 3, wherein “The trajectories 30, 31, 32, 33 are, for example, stored in a memory.”, Col. 7, Lns. 15-16, which means the distance/length of each trajectory branch 70-73 is known, and known relative to landmarks identified by the environmental detection unit 2, such as the vehicle reaching the garage opening before entrance; the use of landmarks by combination of control unit 3 and environmental detection unit 2 during motion is reinforced by the ability of the system to avoid running into objects in the garage: “it is provided that the at least one piece of additional information is a specified minimum distance of the vehicle to an obstacle at a particular target position of the provided trajectories. For example, it can be provided that the minimum distance to an obstacle in a garage on the right-hand and on the left-hand side of the vehicle should be at least one meter.”, Col. 4, Lns. 2-8}; and dynamically generating a target start point sequence on the basis of the reference start position and the initial position {moving the vehicle from current location 23 to the front of a garage bay, Fig. 3, under guidance system provided by control unit 3 and environmental detection device 2, Fig. 1; “A method for carrying out an automatic drive of a vehicle along a provided trajectory which provides at least one stored trajectory for a current position of the vehicle, selects one of the provided trajectories, and carries out an automatic drive of the vehicle.”, Abstract}, wherein each target start point in the target start point sequence is a new position point obtained by calculating based on the reference start position and the initial position {determination of alternative trajectories described in Col. 7, Lns. 10-19, and calculations necessary to determine the trajectory are carried out by control unit 3 and environmental detection unit 2, Fig. 1: “The control unit then retrieves from the memory the trajectories 30, 31, 32, 33 stored for the calculated current position 23, or for the environment 24 respectively, and makes these available for selection”, Col. 7, Lns. 27-30 – which one skilled in the art will appreciate, involves constant calculation of the distance between the vehicle’s current location and decision point 36, to enable to vehicle to determine it has reached point 36}, wherein the dynamically generating a target start point sequence on the basis of the reference start position and the initial position comprises: for the reference start position, performing the following processing steps: obtaining position information corresponding to the reference start position, wherein the position information corresponds to the guide line {trajectories 31-32 are known: “The trajectories 30, 31, 32, 33 are, for example, stored in a memory.”, Col. 7, Lns. 15-16, relative to identifying the current position 23 to enable determination of the trajectory from 23 to decision point 36: “calculated current position 23”, Col. 7, Lns. 27-30}; and generating an alternative start position on the basis of the reference start position and the initial position {as evident in Fig. 3, trajectories 30-33 all have differing length; “four different trajectories 30, 31, 32, 33 have been learned in previous training journeys, each of which lead to different target positions 40, 41, 42, 43. The trajectories 30, 31, 32, 33 are, for example, stored in a memory. The trajectories 30, 31, 32, 33 all have the same master trajectory 34 and can therefore be combined to form a trajectory tree 35, which is sub-divided into a plurality of branch trajectories 70, 71, 72, 73”, Col. 7, Lns. 10-19}; and determining the alternative start position as the reference start position, and performing the processing steps again {based on occupancy evaluation, a processing step must be repeated to determine which trajectory will be implemented: “in a method operation at 105 it can be continuously monitored whether the target position of the selected trajectory is still available or is (again) occupied. If the target position is available, the automated drive along the trajectory is continued. If, on the other hand, the target position of the selected trajectory is (again) occupied, then an alternative will be selected from the provided trajectories, so that the method operations at 102 to 104 are performed again.”, Col. 7, Lns. 1-9}.
Derendarz does not appear to explicitly recite the limitations: wherein the position information comprises left road width corresponding to the guide line and right road width corresponding to the guide line; on the basis of the left road width and the reference start position, generating at least one left alternative start point through a preset threshold; on the basis of the right road width and the reference start position, generating at least one right alternative start point through the preset threshold; merging at least one left target start point and the at least one right alternative start point to obtain an alternative start point group; determining whether the difference between the reference start position and the initial position meets a preset condition.
However, Kubota explicitly recites the limitations: wherein the position information comprises left road width corresponding to the guide line and right road width corresponding to the guide line {“The lane mark recognition part 162 recognizes the lane marks (lane marks 504a to 504d in FIG. 3, etc.) based on the camera information le (surrounding image lea) from the camera 130, and outputs information Ilm (hereinafter also referred to as "lane mark information Ilm") regarding the lane marks. The lane mark recognition part 162 may be constituted as a part of the surrounding object recognition part 160.”, Col. 9, Lns. 38-45 and Fig. 7}; use of preset thresholds {with respect to guidance control method in Fig. 4, the route calculations include conditions S14 and S21}; and determining whether the difference between the reference start position and the initial position meets a preset condition {with regard to Fig. 2, route guidance apparatus 12 includes multiple position and distance calculations corresponding to 110-116}.
Derendarz and Kubota are analogous art because they both deal with vehicle navigation assistance.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Derendarz and Kubota before them, to modify the teachings Derendarz to include the teachings of Kubota to identify positional changes of an AV relative to a starting or target location, and repeat necessary distance calculations to ensure the AV moves precisely to the correct location.
The combination of Derendarz and Kubota does not appear to explicitly disclose: on the basis of the left road width and the reference start position, generating at least one left alternative start point through a preset threshold; on the basis of the right road width and the reference start position, generating at least one right alternative start point through the preset threshold; merging at least one left target start point and the at least one right alternative start point to obtain an alternative start point group.
However, Boxmeyer explicitly recites limitations: on the basis of the left road width and the reference start position, generating at least one left alternative start point through a preset threshold; on the basis of the right road width and the reference start position, generating at least one right alternative start point through the preset threshold; merging at least one left target start point and the at least one right alternative start point to obtain an alternative start point group {these steps amount to sub-dividing a lane in the width direction to accommodate differing vehicle positions, these transverse demarcations are referred to as sublanes: “Each highway lane is, additionally, divided into sublanes by sublane boundaries 2 interior to the lane, the sublane boundaries being parallel to the direction of vehicle travel”, Col. 8, Lns. 57-61 and Fig. 2}.
The combination of Derendarz and Kubota along with Boxmeyer are analogous art because they deal with some level of automated vehicle control.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Derendarz, Kubota and Boxmeyer before them, to modify the teachings of the combination of Derendarz and Kubota to include the teachings of Boxmeyer to identify unoccupied, lateral lane positions that a vehicle may move freely towards.
Regarding Claim 3, the combination of Derendarz, Kubota and Boxmeyer discloses all the limitations of Claim 1, as discussed supra. In addition, Derendarz explicitly recites the limitation: wherein the generating a target start point sequence on the basis of the reference start position and the initial position further comprises: arranging all alternative start points in the obtained alternative start point group according to a preset arrangement instruction to generate a target start point sequence {building upon the discussion of Claim 1, alternate start points – towards the target of parking the vehicle in double garage 22, mislabeled as 21 in Fig. 3 - are represented by the positions along branch trajectories 71-72 at the entrance to each garage door opening, Fig. 3; additionally, the system in Fig. 1 generates slightly different “starting points” for garage entry when an obstructed object is near the wall the garage: “it can be provided that the minimum distance to an obstacle in a garage on the right-hand and on the left-hand side of the vehicle should be at least one meter. If, for example, the environment detecting device then detects a bicycle positioned against the left-hand garage wall, so that it is not possible to park the vehicle in the garage while maintaining the minimum distance of one meter, then the provided trajectory for the left-hand garage is not selected.”, Col. 4, Lns. 2-14}.
Regarding Claim 4, the combination of Derendarz, Kubota and Boxmeyer discloses all the limitations of Claim 1, as discussed supra. In addition, Derendarz explicitly recites the limitation: wherein the generating an alternative start position on the basis of the reference start position and the initial position comprises: generating an initial start position on the basis of the reference start position and the initial position {as evident in Fig. 3, trajectories 30-33 all have differing length; “four different trajectories 30, 31, 32, 33 have been learned in previous training journeys, each of which lead to different target positions 40, 41, 42, 43. The trajectories 30, 31, 32, 33 are, for example, stored in a memory. The trajectories 30, 31, 32, 33 all have the same master trajectory 34 and can therefore be combined to form a trajectory tree 35, which is sub-divided into a plurality of branch trajectories 70, 71, 72, 73”, Col. 7, Lns. 10-19}; and
determining the sum of the initial start position and the initial position as an alternative start position {alternate start points – towards the target of parking the vehicle in double garage 22, mislabeled as 21 in Fig. 3 - are represented by the positions along branch trajectories 71-72 at the entrance to each garage door opening, Fig. 3, wherein the combined length of the two corresponding trajectories are 31 and 32; additionally, the system in Fig. 1 generates slightly different “starting points” for garage entry when an obstructed object is near the wall the garage: “it can be provided that the minimum distance to an obstacle in a garage on the right-hand and on the left-hand side of the vehicle should be at least one meter. If, for example, the environment detecting device then detects a bicycle positioned against the left-hand garage wall, so that it is not possible to park the vehicle in the garage while maintaining the minimum distance of one meter, then the provided trajectory for the left-hand garage is not selected.”, Col. 4, Lns. 2-14}.
Regarding Claim 5, the combination of Derendarz, Kubota and Boxmeyer discloses all the limitations of Claim 1, as discussed supra. In addition, Derendarz explicitly recites the limitation: wherein the method further comprises: selecting a target start point from the target start point sequence as the target start position {building upon the discussion of Claim 1, Fig. 3 can be interpreted as a first vehicle parking in garage bay 25 and the arrive vehicle identifying the occupancy and taking trajectory 32 to garage bay 26; the exact “lateral” start point for entry into bay 26 will depend on objects near the wall of the garage as described in Col. 4, Lns. 2-14}.
Regarding Claim 6, the combination of Derendarz, Kubota and Boxmeyer discloses all the limitations of Claim 5, as discussed supra. In addition, Derendarz explicitly recites the limitation: wherein the selecting a target start point from the target start point sequence as the target start position comprises: for each target start point in the target start point sequence, performing the following steps: determining the occupancy state information of the target start point; and in response to a determination that the occupancy state information is a non-occupancy state {occupancy of garage: “depending on a current occupancy status of the target position of a provided trajectory, an alternative target position should be selected”, Col. 3, Lns. 31-41}, determining the target start point as the target start position {with respect to Fig. 3, once garage bay 25 is identified as occupied, trajectory 32 is chose rather than trajectory 31, thus changing the parking starting point from in front of bay 25 to bay 26}.
Regarding Claim 7, the combination of Derendarz, Kubota and Boxmeyer discloses all the limitations of Claim 5, as discussed supra. In addition, Derendarz explicitly recites the limitation: generating a driving track according to the current position and the target start position {trajectory 32, Fig. 3}; controlling the unmanned vehicle to drive according to the driving track {driving vehicle 50 into garage bay 26, comparable to the vehicle in garage bay 25, Fig. 3}; and in response to the distance between the unmanned vehicle and the target start position meeting a first target condition and the driving direction of the unmanned vehicle meeting a second target condition, generating the target start point sequence again {in Col. 4, Lns. 2-14, the description of the adaptive ability to deal with objects extending from the garage wall means that the “lateral” starting point of the vehicle as it enters the garage will inevitably vary depending on dimensions and location – left wall, right wall or both - of the objects}.
Regarding Claim 9, Derendarz discloses an electronic device {“a device 1 for carrying out an automated drive of a vehicle 50. The device 1 comprises an environmental detection device 2 for detecting an environment of the vehicle 50. The device 1 also comprises a control unit 3, a memory 4, and a display and operating device 5.”, Col. 6, Lns. 14-19 and Fig. 1}, comprising: at least one processor; a storage apparatus, having at least one program stored thereon {Fig. 1}; and a radar, configured to monitor objects {“Such an environmental detection device can be, for example, a camera, a radar, an ultrasonic sensor, a top-view camera or a LIDAR.”, Col. 5, Lns. 65-67}; when executed by the at least one processor, the at least one program enables the at least one processor to implement a method for starting an unmanned vehicle {Abstract and Figs. 2-4, involving moving an autonomous vehicle from a starting position, such as 23 in Fig. 3, to an end/target position, 41 in Fig. 3, which corresponds to starting the process of parking a vehicle in a garage, for which getting the vehicle aligned properly in front of the garage 22, Fig. 3, can be considered the “start” and parking with the garage the “target” or destination, wherein alternative trajectories must be considered to account for the fact that a preferred garage bay, for example on parked at previously, may be occupied}, and comprising: in response to completion of a task {arrival of vehicle 50 at property 21, Fig. 3} of the unmanned vehicle at a current node {current position 23 or decision point 36, Fig. 3}, receiving a signal input by the unmanned vehicle containing {“availability of the target position of the selected trajectory is carried out by an environmental detection device of the vehicle and/or via a query to a server. Such an environmental detection device can be, for example, a camera, a radar, an ultrasonic sensor, a top-view camera or a LIDAR.”, Col. 5, Lns. 62-67} occupancy state information {“depending on a current occupancy status of the target position of a provided trajectory, an alternative target position should be selected”, Col. 3, Lns. 31-41} of a reference start position {decision point 36, Fig. 3} on a guide line {branch trajectories 70-73 of trajectories 30-33, Fig. 3}, wherein the guide line is a preset driving route of the unmanned vehicle {“carrying out an automatic drive of a vehicle along a provided trajectory which provides at least one stored trajectory for a current position of the vehicle, selects one of the provided trajectories, and carries out an automatic drive of the vehicle”, Abstract} from the current node to a next node {23 to 36, or 36 to one of 40-43, Fig. 3}, the reference start position is a position on the guide line reached by the unmanned vehicle from a current position {decision point 36 relative to current location 23, Fig. 3}, and the occupancy state information is an occupancy state or a non-occupancy state {“depending on a current occupancy status of the target position of a provided trajectory, an alternative target position should be selected”, Col. 3, Lns. 31-41, corresponding to vehicle 50 taking trajectory 32 to target position 42, where the garage space is unoccupied, Fig. 3}; in response to a determination that the occupancy state information is the occupancy state {“the target position of the selected trajectory is (again) occupied, then an alternative will be selected from the provided trajectories, so that the method operations at 102 to 104 are performed again.”, Col. 7, Lns. 5-9}, projecting the current position of the unmanned vehicle to the guide line to determine an initial position of the current position on the guide line {movement of vehicle from current location 23 to master decision point 36 is along master trajectory 34 in Fig. 3, wherein the control unit 3 and environmental detection unit 2 calculate the current position: “calculated current position 23”, Col. 7, Lns. 27-30 – which one skilled in the art will appreciate, involves constant calculation of the distance between the vehicle’s current location and decision point 36, to enable to vehicle to determine it has reached point 36}; determining a reference start position of the reference start position on the guide line {“The trajectories 30, 31, 32, 33 are, for example, stored in a memory.”, Col. 7, Lns. 15-16, which means the distance/length of each trajectory branch 70-73 is known}; and dynamically generating a target start point sequence on the basis of the reference start position and the initial position {Fig. 3 represents vehicle motion sequences beginning from current position 23 to one of the target positions 40-42, in which the sequence is divided into two distinct segments with distinct travel lengths, the first from current location 23 to decision point 36, and the second from decision point 36 to a target location , based on the decision tree in Fig. 2 and the control unit 3 and environmental detection device 2 in Fig. 1; “A method for carrying out an automatic drive of a vehicle along a provided trajectory which provides at least one stored trajectory for a current position of the vehicle, selects one of the provided trajectories, and carries out an automatic drive of the vehicle.”, Abstract}, wherein each target start point in the target start point sequence is a new position point obtained by calculating based on the reference start position and the initial position {determination of alternative trajectories described in Col. 7, Lns. 10-19, and calculations necessary to determine the trajectory are carried out by control unit 3 and environmental detection unit 2, Fig. 1: “The control unit then retrieves from the memory the trajectories 30, 31, 32, 33 stored for the calculated current position 23, or for the environment 24 respectively, and makes these available for selection”, Col. 7, Lns. 27-30 – which one skilled in the art will appreciate, involves constant calculation of the distance between the vehicle’s current location and decision point 36, to enable to vehicle to determine it has reached point 36}, wherein the dynamically generating a target start point sequence on the basis of the reference start position and the initial position {moving the vehicle from current location 23 to the front of a garage bay, Fig. 3, under guidance system provided by control unit 3 and environmental detection device 2, Fig. 1; “A method for carrying out an automatic drive of a vehicle along a provided trajectory which provides at least one stored trajectory for a current position of the vehicle, selects one of the provided trajectories, and carries out an automatic drive of the vehicle.”, Abstract} comprises: for the reference start position, performing the following processing steps: obtaining position information corresponding to the reference start position, wherein the position information corresponds to the guide line {trajectories 31-32 are known: “The trajectories 30, 31, 32, 33 are, for example, stored in a memory.”, Col. 7, Lns. 15-16, relative to identifying the current position 23 to enable determination of the trajectory from 23 to decision point 36: “calculated current position 23”, Col. 7, Lns. 27-30}; and generating an alternative start position on the basis of the reference start position and the initial position {as evident in Fig. 3, trajectories 30-33 all have differing length; “four different trajectories 30, 31, 32, 33 have been learned in previous training journeys, each of which lead to different target positions 40, 41, 42, 43. The trajectories 30, 31, 32, 33 are, for example, stored in a memory. The trajectories 30, 31, 32, 33 all have the same master trajectory 34 and can therefore be combined to form a trajectory tree 35, which is sub-divided into a plurality of branch trajectories 70, 71, 72, 73”, Col. 7, Lns. 10-19}; and determining the alternative start position as the reference start position, and performing the processing steps again {based on occupancy evaluation, a processing step must be repeated to determine which trajectory will be implemented: “in a method operation at 105 it can be continuously monitored whether the target position of the selected trajectory is still available or is (again) occupied. If the target position is available, the automated drive along the trajectory is continued. If, on the other hand, the target position of the selected trajectory is (again) occupied, then an alternative will be selected from the provided trajectories, so that the method operations at 102 to 104 are performed again.”, Col. 7, Lns. 1-9}.
Derendarz does not appear to explicitly recite the limitations: wherein the position information comprises left road width corresponding to the guide line and right road width corresponding to the guide line; on the basis of the left road width and the reference start position, generating at least one left alternative start point through a preset threshold; on the basis of the right road width and the reference start position, generating at least one right alternative start point through the preset threshold; merging at least one left target start point and the at least one right alternative start point to obtain an alternative start point group; determining whether the difference between the reference start position and the initial position meets a preset condition.
However, Kubota explicitly recites the limitations: wherein the position information comprises left road width corresponding to the guide line and right road width corresponding to the guide line {“The lane mark recognition part 162 recognizes the lane marks (lane marks 504a to 504d in FIG. 3, etc.) based on the camera information le (surrounding image lea) from the camera 130, and outputs information Ilm (hereinafter also referred to as "lane mark information Ilm") regarding the lane marks. The lane mark recognition part 162 may be constituted as a part of the surrounding object recognition part 160.”, Col. 9, Lns. 38-45 and Fig. 7}; use of preset thresholds {with respect to guidance control method in Fig. 4, the route calculations include conditions S14 and S21}; and determining whether the difference between the reference start position and the initial position meets a preset condition {with regard to Fig. 2, route guidance apparatus 12 includes multiple position and distance calculations corresponding to 110-116}.
The combination of Derendarz and Kubota does not appear to explicitly disclose: on the basis of the left road width and the reference start position, generating at least one left alternative start point through a preset threshold; on the basis of the right road width and the reference start position, generating at least one right alternative start point through the preset threshold; merging at least one left target start point and the at least one right alternative start point to obtain an alternative start point group.
However, Boxmeyer explicitly recites limitations: on the basis of the left road width and the reference start position, generating at least one left alternative start point through a preset threshold; on the basis of the right road width and the reference start position, generating at least one right alternative start point through the preset threshold; merging at least one left target start point and the at least one right alternative start point to obtain an alternative start point group {these steps amount to sub-dividing a lane in the width direction to accommodate differing vehicle positions, these transverse demarcations are referred to as sublanes: “Each highway lane is, additionally, divided into sublanes by sublane boundaries 2 interior to the lane, the sublane boundaries being parallel to the direction of vehicle travel”, Col. 8, Lns. 57-61 and Fig. 2}.
Regarding Claim 10, Derendarz discloses a non-volatile computer-readable medium, storing a computer program thereon {“a device 1 for carrying out an automated drive of a vehicle 50. The device 1 comprises an environmental detection device 2 for detecting an environment of the vehicle 50. The device 1 also comprises a control unit 3, a memory 4, and a display and operating device 5.”, Col. 6, Lns. 14-19 and Fig. 1}, wherein a method for starting an unmanned vehicle {Abstract and Figs. 2-4, involving moving an autonomous vehicle from a starting position, such as 23 in Fig. 3, to an end/target position, 41 in Fig. 3, which corresponds to starting the process of parking a vehicle in a garage, for which getting the vehicle aligned properly in front of the garage 22, Fig. 3, can be considered the “start” and parking with the garage the “target” or destination, wherein alternative trajectories must be considered to account for the fact that a preferred garage bay, for example on parked at previously, may be occupied} is implemented when the program is executed by a processor, the method for starting an unmanned vehicle, comprising: in response to completion of a task {arrival of vehicle 50 at property 21, Fig. 3} of the unmanned vehicle at a current node {current position 23 or decision point 36, Fig. 3}, receiving a signal input by the unmanned vehicle containing {“availability of the target position of the selected trajectory is carried out by an environmental detection device of the vehicle and/or via a query to a server. Such an environmental detection device can be, for example, a camera, a radar, an ultrasonic sensor, a top-view camera or a LIDAR.”, Col. 5, Lns. 62-67} occupancy state information {“depending on a current occupancy status of the target position of a provided trajectory, an alternative target position should be selected”, Col. 3, Lns. 31-41} of a reference start position {decision point 36, Fig. 3} on a guide line {branch trajectories 70-73 of trajectories 30-33, Fig. 3}, wherein the guide line is a preset driving route of the unmanned vehicle {“carrying out an automatic drive of a vehicle along a provided trajectory which provides at least one stored trajectory for a current position of the vehicle, selects one of the provided trajectories, and carries out an automatic drive of the vehicle”, Abstract} from the current node to a next node {23 to 36, or 36 to one of 40-43, Fig. 3}, the reference start position is a position on the guide line reached by the unmanned vehicle from a current position {decision point 36 relative to current location 23, Fig. 3}, and the occupancy state information is an occupancy state or a non-occupancy state {“depending on a current occupancy status of the target position of a provided trajectory, an alternative target position should be selected”, Col. 3, Lns. 31-41, corresponding to vehicle 50 taking trajectory 32 to target position 42, where the garage space is unoccupied, Fig. 3}; in response to a determination that the occupancy state information is the occupancy state {“the target position of the selected trajectory is (again) occupied, then an alternative will be selected from the provided trajectories, so that the method operations at 102 to 104 are performed again.”, Col. 7, Lns. 5-9}, projecting the current position of the unmanned vehicle to the guide line to determine an initial position of the current position on the guide line {movement of vehicle from current location 23 to master decision point 36 is along master trajectory 34 in Fig. 3, wherein the control unit 3 and environmental detection unit 2 calculate the current position: “calculated current position 23”, Col. 7, Lns. 27-30 – which one skilled in the art will appreciate, involves constant calculation of the distance between the vehicle’s current location and decision point 36, to enable to vehicle to determine it has reached point 36}; determining a reference start position of the reference start position on the guide line {“The trajectories 30, 31, 32, 33 are, for example, stored in a memory.”, Col. 7, Lns. 15-16, which means the distance/length of each trajectory branch 70-73 is known}; and dynamically generating a target start point sequence on the basis of the reference start position and the initial position {Fig. 3 represents vehicle motion sequences beginning from current position 23 to one of the target positions 40-42, in which the sequence is divided into two distinct segments with distinct travel lengths, the first from current location 23 to decision point 36, and the second from decision point 36 to a target location , based on the decision tree in Fig. 2 and the control unit 3 and environmental detection device 2 in Fig. 1; “A method for carrying out an automatic drive of a vehicle along a provided trajectory which provides at least one stored trajectory for a current position of the vehicle, selects one of the provided trajectories, and carries out an automatic drive of the vehicle.”, Abstract}, wherein each target start point in the target start point sequence is a new position point obtained by calculating based on the reference start position and the initial position {determination of alternative trajectories described in Col. 7, Lns. 10-19, and calculations necessary to determine the trajectory are carried out by control unit 3 and environmental detection unit 2, Fig. 1: “The control unit then retrieves from the memory the trajectories 30, 31, 32, 33 stored for the calculated current position 23, or for the environment 24 respectively, and makes these available for selection”, Col. 7, Lns. 27-30 – which one skilled in the art will appreciate, involves constant calculation of the distance between the vehicle’s current location and decision point 36, to enable to vehicle to determine it has reached point 36}, wherein the dynamically generating a target start point sequence on the basis of the reference start position and the initial position {moving the vehicle from current location 23 to the front of a garage bay, Fig. 3, under guidance system provided by control unit 3 and environmental detection device 2, Fig. 1; “A method for carrying out an automatic drive of a vehicle along a provided trajectory which provides at least one stored trajectory for a current position of the vehicle, selects one of the provided trajectories, and carries out an automatic drive of the vehicle.”, Abstract} comprises: for the reference start position, performing the following processing steps: obtaining position information corresponding to the reference start position, wherein the position information corresponds to the guide line {trajectories 31-32 are known: “The trajectories 30, 31, 32, 33 are, for example, stored in a memory.”, Col. 7, Lns. 15-16, relative to identifying the current position 23 to enable determination of the trajectory from 23 to decision point 36: “calculated current position 23”, Col. 7, Lns. 27-30}; and generating an alternative start position on the basis of the reference start position and the initial position {as evident in Fig. 3, trajectories 30-33 all have differing length; “four different trajectories 30, 31, 32, 33 have been learned in previous training journeys, each of which lead to different target positions 40, 41, 42, 43. The trajectories 30, 31, 32, 33 are, for example, stored in a memory. The trajectories 30, 31, 32, 33 all have the same master trajectory 34 and can therefore be combined to form a trajectory tree 35, which is sub-divided into a plurality of branch trajectories 70, 71, 72, 73”, Col. 7, Lns. 10-19}; and determining the alternative start position as the reference start position, and performing the processing steps again {based on occupancy evaluation, a processing step must be repeated to determine which trajectory will be implemented: “in a method operation at 105 it can be continuously monitored whether the target position of the selected trajectory is still available or is (again) occupied. If the target position is available, the automated drive along the trajectory is continued. If, on the other hand, the target position of the selected trajectory is (again) occupied, then an alternative will be selected from the provided trajectories, so that the method operations at 102 to 104 are performed again.”, Col. 7, Lns. 1-9}.
Derendarz does not appear to explicitly recite the limitations: wherein the position information comprises left road width corresponding to the guide line and right road width corresponding to the guide line; on the basis of the left road width and the reference start position, generating at least one left alternative start point through a preset threshold; on the basis of the right road width and the reference start position, generating at least one right alternative start point through the preset threshold; merging at least one left target start point and the at least one right alternative start point to obtain an alternative start point group; determining whether the difference between the reference start position and the initial position meets a preset condition.
However, Kubota explicitly recites the limitations: wherein the position information comprises left road width corresponding to the guide line and right road width corresponding to the guide line {“The lane mark recognition part 162 recognizes the lane marks (lane marks 504a to 504d in FIG. 3, etc.) based on the camera information le (surrounding image lea) from the camera 130, and outputs information Ilm (hereinafter also referred to as "lane mark information Ilm") regarding the lane marks. The lane mark recognition part 162 may be constituted as a part of the surrounding object recognition part 160.”, Col. 9, Lns. 38-45 and Fig. 7}; use of preset thresholds {with respect to guidance control method in Fig. 4, the route calculations include conditions S14 and S21}; and determining whether the difference between the reference start position and the initial position meets a preset condition {with regard to Fig. 2, route guidance apparatus 12 includes multiple position and distance calculations corresponding to 110-116}.
The combination of Derendarz and Kubota does not appear to explicitly disclose: on the basis of the left road width and the reference start position, generating at least one left alternative start point through a preset threshold; on the basis of the right road width and the reference start position, generating at least one right alternative start point through the preset threshold; merging at least one left target start point and the at least one right alternative start point to obtain an alternative start point group.
However, Boxmeyer explicitly recites limitations: on the basis of the left road width and the reference start position, generating at least one left alternative start point through a preset threshold; on the basis of the right road width and the reference start position, generating at least one right alternative start point through the preset threshold; merging at least one left target start point and the at least one right alternative start point to obtain an alternative start point group {these steps amount to sub-dividing a lane in the width direction to accommodate differing vehicle positions, these transverse demarcations are referred to as sublanes: “Each highway lane is, additionally, divided into sublanes by sublane boundaries 2 interior to the lane, the sublane boundaries being parallel to the direction of vehicle travel”, Col. 8, Lns. 57-61 and Fig. 2}.
Regarding Claim 11, the combination of Derendarz, Kubota and Boxmeyer discloses all the limitations of Claim 3, as discussed supra. In addition, Derendarz explicitly recites the limitations: wherein the generating an alternative start position on the basis of the reference start position and the initial position comprises: generating an initial start position on the basis of the reference start position and the initial position {as evident in Fig. 3, trajectories 30-33 all have differing length; “four different trajectories 30, 31, 32, 33 have been learned in previous training journeys, each of which lead to different target positions 40, 41, 42, 43. The trajectories 30, 31, 32, 33 are, for example, stored in a memory. The trajectories 30, 31, 32, 33 all have the same master trajectory 34 and can therefore be combined to form a trajectory tree 35, which is sub-divided into a plurality of branch trajectories 70, 71, 72, 73”, Col. 7, Lns. 10-19}; and determining the sum of the initial start position and the initial position as an alternative start position {alternate start points – towards the target of parking the vehicle in double garage 22, mislabeled as 21 in Fig. 3 - are represented by the positions along branch trajectories 71-72 at the entrance to each garage door opening, Fig. 3, wherein the combined length of the two corresponding trajectories are 31 and 32; additionally, the system in Fig. 1 generates slightly different “starting points” for garage entry when an obstructed object is near the wall the garage: “it can be provided that the minimum distance to an obstacle in a garage on the right-hand and on the left-hand side of the vehicle should be at least one meter. If, for example, the environment detecting device then detects a bicycle positioned against the left-hand garage wall, so that it is not possible to park the vehicle in the garage while maintaining the minimum distance of one meter, then the provided trajectory for the left-hand garage is not selected.”, Col. 4, Lns. 2-14}.
Regarding Claim 12, the combination of Derendarz, Kubota and Boxmeyer discloses all the limitations of Claim 1, as discussed supra. In addition, Derendarz explicitly recites the limitations: the method further comprises: selecting a target start point from the target start point sequence as the target start {building upon the discussion of Claim 1, Fig. 3 can be interpreted as a first vehicle parking in garage bay 25 and the arrive vehicle identifying the occupancy and taking trajectory 32 to garage bay 26; the exact “lateral” start point for entry into bay 26 will depend on objects near the wall of the garage as described in Col. 4, Lns. 2-14}.
Regarding Claim 13, the combination of Derendarz, Kubota and Boxmeyer discloses all the limitations of Claim 3, as discussed supra. In addition, Derendarz explicitly recites the limitations: the method further comprises: selecting a target start point from the target start point sequence as the target start {building upon the discussion of Claim 1, Fig. 3 can be interpreted as a first vehicle parking in garage bay 25 and the arrive vehicle identifying the occupancy and taking trajectory 32 to garage bay 26; the exact “lateral” start point for entry into bay 26 will depend on objects near the wall of the garage as described in Col. 4, Lns. 2-14}.
Regarding Claim 14, the combination of Derendarz, Kubota and Boxmeyer discloses all the limitations of Claim 4, as discussed supra. In addition, Derendarz explicitly recites the limitations: the method further comprises: selecting a target start point from the target start point sequence as the target start {building upon the discussion of Claim 1, Fig. 3 can be interpreted as a first vehicle parking in garage bay 25 and the arrive vehicle identifying the occupancy and taking trajectory 32 to garage bay 26; the exact “lateral” start point for entry into bay 26 will depend on objects near the wall of the garage as described in Col. 4, Lns. 2-14}.
Regarding Claim 15, the combination of Derendarz, Kubota and Boxmeyer discloses all the limitations of Claim 6, as discussed supra. In addition, Derendarz explicitly recites the limitations: the method further comprises: generating a driving track according to the current position and the target start position {trajectory 32, Fig. 3}; controlling the unmanned vehicle to drive according to the driving track {driving vehicle 50 into garage bay 26, comparable to the vehicle in garage bay 25, Fig. 3}; and in response to the distance between the unmanned vehicle and the target start position meeting a first target condition and the driving direction of the unmanned vehicle meeting a second target condition, generating the target start point sequence again {in Col. 4, Lns. 2-14, the description of the adaptive ability to deal with objects extending from the garage wall means that the “lateral” starting point of the vehicle as it enters the garage will inevitably vary depending on dimensions and location – left wall, right wall or both - of the objects}.
Regarding Claim 16, the combination of Derendarz, Kubota and Boxmeyer discloses all the limitations of Claim 1, as discussed supra. In addition, Derendarz explicitly recites the limitations: the method further comprising an electronic device {Fig. 1}, comprising: at least one processor; a storage apparatus, having at least one program stored thereon {“a device 1 for carrying out an automated drive of a vehicle 50. The device 1 comprises an environmental detection device 2 for detecting an environment of the vehicle 50. The device 1 also comprises a control unit 3, a memory 4, and a display and operating device 5.”, Col. 6, Lns. 14-19 and Fig. 1}; and a radar {“Such an environmental detection device can be, for example, a camera, a radar, an ultrasonic sensor, a top-view camera or a LIDAR.”, Col. 5, Lns. 65-67}, configured to monitor objects; when executed by the at least one processor {environmental detection unit 2, Fig. 1}, the at least one program enables the at least one processor to implement the method for starting the unmanned vehicle {“In the memory 4, trajectories for an environment of the vehicle 50 that were learned, for example, in a training drive are stored. The environment detection device 2 detects a current environment of the vehicle 50 and provides collected environmental data to the control unit 3. From this data, the control unit 3 determines a current position of the vehicle and retrieves the trajectories stored in the memory 4 for a subsequent journey and makes them available for selection”, Col. 6, Lns. 28-35}.
Regarding Claim 17, the combination of Derendarz, Kubota and Boxmeyer discloses all the limitations of Claim 3, as discussed supra. In addition, Derendarz explicitly recites the limitations: the method further comprising an electronic device {Fig. 1}, comprising: at least one processor; a storage apparatus, having at least one program stored thereon {“a device 1 for carrying out an automated drive of a vehicle 50. The device 1 comprises an environmental detection device 2 for detecting an environment of the vehicle 50. The device 1 also comprises a control unit 3, a memory 4, and a display and operating device 5.”, Col. 6, Lns. 14-19 and Fig. 1}; and a radar {“Such an environmental detection device can be, for example, a camera, a radar, an ultrasonic sensor, a top-view camera or a LIDAR.”, Col. 5, Lns. 65-67}, configured to monitor objects; when executed by the at least one processor {environmental detection unit 2, Fig. 1}, the at least one program enables the at least one processor to implement the method for starting the unmanned vehicle {“In the memory 4, trajectories for an environment of the vehicle 50 that were learned, for example, in a training drive are stored. The environment detection device 2 detects a current environment of the vehicle 50 and provides collected environmental data to the control unit 3. From this data, the control unit 3 determines a current position of the vehicle and retrieves the trajectories stored in the memory 4 for a subsequent journey and makes them available for selection”, Col. 6, Lns. 28-35}.
Regarding Claim 18, the combination of Derendarz, Kubota and Boxmeyer discloses all the limitations of Claim 4, as discussed supra. In addition, Derendarz explicitly recites the limitations: the method further comprising an electronic device {Fig. 1}, comprising: at least one processor; a storage apparatus, having at least one program stored thereon {“a device 1 for carrying out an automated drive of a vehicle 50. The device 1 comprises an environmental detection device 2 for detecting an environment of the vehicle 50. The device 1 also comprises a control unit 3, a memory 4, and a display and operating device 5.”, Col. 6, Lns. 14-19 and Fig. 1}; and a radar {“Such an environmental detection device can be, for example, a camera, a radar, an ultrasonic sensor, a top-view camera or a LIDAR.”, Col. 5, Lns. 65-67}, configured to monitor objects; when executed by the at least one processor {environmental detection unit 2, Fig. 1}, the at least one program enables the at least one processor to implement the method for starting the unmanned vehicle {“In the memory 4, trajectories for an environment of the vehicle 50 that were learned, for example, in a training drive are stored. The environment detection device 2 detects a current environment of the vehicle 50 and provides collected environmental data to the control unit 3. From this data, the control unit 3 determines a current position of the vehicle and retrieves the trajectories stored in the memory 4 for a subsequent journey and makes them available for selection”, Col. 6, Lns. 28-35}.
Regarding Claim 19, the combination of Derendarz, Kubota and Boxmeyer discloses all the limitations of Claim 1, as discussed supra. In addition, Derendarz explicitly recites the limitations: the method further comprising a non-volatile computer-readable medium, storing a computer program thereon {“a device 1 for carrying out an automated drive of a vehicle 50. The device 1 comprises an environmental detection device 2 for detecting an environment of the vehicle 50. The device 1 also comprises a control unit 3, a memory 4, and a display and operating device 5.”, Col. 6, Lns. 14-19 and Fig. 1}, wherein the method is implemented when the program is executed by a processor {“In the memory 4, trajectories for an environment of the vehicle 50 that were learned, for example, in a training drive are stored. The environment detection device 2 detects a current environment of the vehicle 50 and provides collected environmental data to the control unit 3. From this data, the control unit 3 determines a current position of the vehicle and retrieves the trajectories stored in the memory 4 for a subsequent journey and makes them available for selection”, Col. 6, Lns. 28-35}.
Regarding Claim 20, the combination of Derendarz, Kubota and Boxmeyer discloses all the limitations of Claim 3, as discussed supra. In addition, Derendarz explicitly recites the limitations: the method further comprising a non-volatile computer-readable medium, storing a computer program thereon {“a device 1 for carrying out an automated drive of a vehicle 50. The device 1 comprises an environmental detection device 2 for detecting an environment of the vehicle 50. The device 1 also comprises a control unit 3, a memory 4, and a display and operating device 5.”, Col. 6, Lns. 14-19 and Fig. 1}, wherein the method is implemented when the program is executed by a processor {“In the memory 4, trajectories for an environment of the vehicle 50 that were learned, for example, in a training drive are stored. The environment detection device 2 detects a current environment of the vehicle 50 and provides collected environmental data to the control unit 3. From this data, the control unit 3 determines a current position of the vehicle and retrieves the trajectories stored in the memory 4 for a subsequent journey and makes them available for selection”, Col. 6, Lns. 28-35}.
Regarding Claim 21, the combination of Derendarz, Kubota and Boxmeyer discloses all the limitations of Claim 4, as discussed supra. In addition, Derendarz explicitly recites the limitations: the method further comprising a non-volatile computer-readable medium, storing a computer program thereon {“a device 1 for carrying out an automated drive of a vehicle 50. The device 1 comprises an environmental detection device 2 for detecting an environment of the vehicle 50. The device 1 also comprises a control unit 3, a memory 4, and a display and operating device 5.”, Col. 6, Lns. 14-19 and Fig. 1}, wherein the method is implemented when the program is executed by a processor {“In the memory 4, trajectories for an environment of the vehicle 50 that were learned, for example, in a training drive are stored. The environment detection device 2 detects a current environment of the vehicle 50 and provides collected environmental data to the control unit 3. From this data, the control unit 3 determines a current position of the vehicle and retrieves the trajectories stored in the memory 4 for a subsequent journey and makes them available for selection”, Col. 6, Lns. 28-35}.
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 11,180,165 B2 – Use of a target selection engine {“FIG. 2C shows examples of selected initial target locations for the agent. For convenience, the selection process will be described as being performed by a system of one or more computers located in one or more locations. For example, a target selection engine, e.g., the initial target location selection engine 130 of FIG. 1d, appropriately programmed in accordance with this specification, can perform the process to select the initial target locations.”, Col. 9, Lns. 62-67} to identify potential locations for vehicle movements, along the width dimension of a road, to modify the future trajectory or positioning of the vehicle relative to the width of the road {“A planning system of the vehicle can use the likely future trajectories for the agent to make planning decisions to plan a future trajectory of the vehicle, e.g., by generating or modifying the future trajectory to avoid collisions with any of the likely future trajectories of the agent.”, Col. 2, Lns. 16-21}.
US 11,772,635 B2 – Three-point maneuver to park a vehicle in a narrow space {Fig. 7}.
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/R.E.G./Examiner, Art Unit 3665
/CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665