DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is a Non-Final rejection on the merits of this application. Claims 1-17 are currently pending, as discussed below.
Examiner Notes that the fundamentals of the rejections are based on the broadest reasonable interpretation of the claim language. Applicant is kindly invited to consider the reference as a whole. References are to be interpreted as by one of ordinary skill in the art rather than as by a novice. See MPEP 2141. Therefore, the relevant inquiry when interpreting a reference is not what the reference expressly discloses on its face but what the reference would teach or suggest to one of ordinary skill in the art.
Priority
Acknowledgement is made that the present application is a national stage entry of PCT/EP2022/061611 filed on 04/29/2022.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 10/22/2024 is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, the recited limitation “travel deviation time indicating a possible time adjustment in the travel duration” is indefinite because the term “possible time adjustment” is unclear and does not specify whether a time adjustment will occur or not; and further is “travel deviation time” is maximum, minimum, range or a probabilistic value, is it symmetric or asymmetric; rendering the metes and bounds of claim limitation unclear.
Regarding claim 1, the recited limitation “flexibility metric(s)” is indefinite and unclear to the Examiner: (i) flexible relative to what, under what operating conditions, and what thresholds? and (ii) flexibility us a subjective term. Hence, this limitation renders the claim to be indefinite.
The dependent claims that dependent upon independent claims are also rejected under 112 second paragraph by the fact that they are dependent upon the rejected independent claims.
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.
Claim 1-15 and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
101 Analysis – Step 1 – YES
Claim 1 is directed to a method. Therefore, Claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I 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.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A method for routing an autonomous vehicle from a start location to a target destination location via a set of road segments, the method comprising:
obtaining for each respective road segment in the set of road segments, a travel duration time and a travel deviation time indicating a possible time adjustment in the travel duration, when adjusting a speed of the autonomous vehicle in the respective road segment,
obtaining a target time of arrival at the target destination location, and
using the travel duration time, determining at least two candidate routes comprising a respective subset of the set of road segments, wherein each of the at least two candidate routes indicates how the autonomous vehicle shall travel from the start location, via the respective subset of the set of road segments, to arrive at the target destination location within a time interval of the target time of arrival,
determining a flexibility metric for each of the at least two candidate routes, the flexibility metric being determined based on the travel deviation time of each of the respective road segments in the respective candidate route,
based on the determined flexibility metrics, selecting a route from the at least two candidate routes to be used by the autonomous vehicle to travel to the target destination location.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” and/or “mathematical concepts” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind and/or mathematical calculations. For example, the bolded limitation can be performed by a human, for example, a person plans a trip from his/her home to an airport using a paper map and identifying roads connecting the two points; he/she notes that a highway usually takes about 30 mins but may take longer during rush hour and a local road usually takes 40 minutes but allows speeding up or slowing down depending on traffic and mentally associates each road segment with an estimated time and possibly delay (corresponds to “obtaining for each respective road segments…a possible time adjustment… in the respective road segment”); he/she decides that he/she needs to arrive at the airport at 9:00 am in order to drop off/pick up a person based on scheduled flight information (corresponds to “obtaining a target time...destination location”); he/she then traces at least two different paths on the paper map to assess whether each route fits the desired arrival time window (e.g., route A using mostly highway arriving between 8:30-8:45 am, route B using surface streets only arriving 8:35-8:55 am; corresponds to “using the travel duration…within a time interval of the target time of arrival”); he/she may rate each route (e.g., route A is risky because it’s a single highway with few exits and would causes long delay if there’s an accident, route B is flexible because there are plurality of alternate surface streets for detouring, corresponds to “determining a flexibility metric…respective candidate route”); he/she selects a route based on his/her mental evaluation of the route options (corresponds to “based on the determined flexibility metrics…target destination location”).
Examiner would also note MPEP 2106.04(a)(2)(III): The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same). Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. Here, the determination is a form of making evaluation and judgement based on observation (driver behavior).
Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II 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 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.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A method for routing an autonomous vehicle from a start location to a target destination location via a set of road segments, the method comprising:
obtaining for each respective road segment in the set of road segments, a travel duration time and a travel deviation time indicating a possible time adjustment in the travel duration, when adjusting a speed of the autonomous vehicle in the respective road segment,
obtaining a target time of arrival at the target destination location, and
using the travel duration time, determining at least two candidate routes comprising a respective subset of the set of road segments, wherein each of the at least two candidate routes indicates how the autonomous vehicle shall travel from the start location, via the respective subset of the set of road segments, to arrive at the target destination location within a time interval of the target time of arrival,
determining a flexibility metric for each of the at least two candidate routes, the flexibility metric being determined based on the travel deviation time of each of the respective road segments in the respective candidate route,
based on the determined flexibility metrics, selecting a route from the at least two candidate routes to be used by the autonomous vehicle to travel to the target destination location.
The claim does not recite any additional elements that integrate the abstract idea into practical application because no vehicle control signals are generated, no sensors, actuators, motion planning subsystem are recited and no improvement to autonomous vehicle technology is described. The recitation of autonomous vehicle is merely indicating a field of use.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impost any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 1 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the examiner submits that the recitation of autonomous vehicle is generic and conventional and no inventive improvement to the vehicle, software or control system is described.
As explained, the additional elements are recited at a high level of generality to simply implement the abstract idea and are not themselves being technologically improved. See, e.g., MPEP §2106.05; Alice Corp. v. CLS Bank, 573 U.S., 208,223 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention”). Electric Power Group, LLC v, Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) (Selecting information for collection, analysis and display constitute insignificant extra-solution activity). Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016)( Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components). Hence, the claims are not patent eligible.
Dependent Claims
Dependent claims 2-15, and 17 do not recite any further limitations that causes the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial except and/or additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-15 and 17 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
As such, claims 1-15 and 17 are rejected under 35 USC § 101 as being drawn to an abstract idea without significant more, and thus are ineligible.
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding claim 16, the claim(s) does not fall within at least one of the four categories of patent eligible subject matter because claim 16 is directed toward a computer program which is software per se. Therefore, claim 16 is not within at least one of the four statutory categories (see MPEP 2106.03, software expressed as code or a set of instructions detached from any medium is an idea without physical embodiment. See Microsoft Corp. v. AT&T Corp., 550 U.S. 437, 449, 82 USPQ2d 1400, 1407 (2007); see also Benson, 409 U.S. 67, 17S USPQ2d 675 (An "idea" is not patent eligible). Thus, claim 16 does not fall within any statutory category.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-14 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Kelly et al. (US 2019/0346275 A1 hereinafter Kelly) in view of Quint et al. (US 2022/0136839 A1 hereinafter Quint).
Regarding Claim 1, Kelly teaches A method for routing an autonomous vehicle from a start location to a target destination location via a set of road segments (see at least Fig. 4-9 Abstract [0308-0340]: Basic journey data comprises a start location for the global journey and a destination location for the global journey. Figures 6, 7 and 9 shows directed graph comprises nodes and interconnecting edges forming a basic representation of the road networks. ) , the method comprising:
obtaining for each respective road segment in the set of road segments, a travel duration time and a travel deviation time indicating a possible time adjustment in the travel duration, when adjusting a speed of the autonomous vehicle in the respective road segment, (see at least Fig. 7-37 [0321-0540]: the legality objective is indicative of an attitude toward speed limits that may comprise an absolute speed value indicative of a desired maximum speed relative to the speed limit or a relative speed value indicative of a desired maximum percentage of the speed limit. Each different combination of possible route action and possible speed action is treated as a possible action. A plurality of probabilistic states are determined wherein each probabilistic state comprises a probabilistic progress metric for the vehicle when it arrives at the next node. The time that it might take to travel down a road from one node to the next. Heuristic algorithm is used to assess the value of time metric and/or efficiency metric of each of probabilistic states. The value of the time metric could be assessed by consider a best and worst case time to arrive at the end of the journey if the vehicle were to travel at the speed limit, at the maximum/minimum speed limit allowable, or some speed at particular tolerance above either values on each of the remaining edges of the journey. That is, speed limits defines the narrow range within which the speed of a vehicle may be adjusted on each road segment (i.e. edge) which directly limits what can be obtained when determining a travel duration time and a travel deviation time for that segment because the speed adjustment is bounded by minimum and maximum limits, the travel deviation time merely reflects a small, constrained adjustment to the travel duration.)
obtaining a target time of arrival at the target destination location, (see at least Fig. 7-11 [0321-0340]: The punctuality objective is indicative of the desired arrival time at the destination location. The punctuality objective may comprises a specific desired arrival time at the destination or a range/window of desired arrival times at the destination.) and
using the travel duration time, determining at least two candidate routes comprising a respective subset of the set of road segments, wherein each of the at least two candidate routes indicates how the autonomous vehicle shall travel from the start location, via the respective subset of the set of road segments, to arrive at the target destination location within a time interval of the target time of arrival, (see at least Fig. 7-37 [0321-0540]: Fig. 7 & 9 shows directed graph representing the potential routes from the start location A to the destination location B. A sliding window strategy is applied to route planning and probabilistic progress metrics per node and edge on the directed graph. The siding window optimization produces segment level travel durations by computing a target speed profile for each segment (constrained by vehicle capabilities, traffic, and environmental conditions) prescribing traversal behavior for the autonomous vehicle along each segment. The system explicitly considers multiple candidate routes as shown in at least fig. 7 &9 each comprising a sequence of road segments connecting the start location to the destination and each candidate route is formed from a subset of road networks. The use of probabilistic progress metrics allows estimation of earliest and latest arrival time for each segment and for the route as a whole. By summing these segment level durations, the system can evaluate which candidate routes are likely to satisfy a targe arrival time interval, effectively guiding route selection to meet temporal constraint.)
determining a flexibility metric for each of the at least two candidate routes, the flexibility metric being determined based on the travel deviation time of each of the respective road segments in the respective candidate route, and based on the determined flexibility metrics, selecting a route from the at least two candidate routes to be used by the autonomous vehicle to travel to the target destination location. (see at least Fig. 7-37 [0321-0540]: The system evaluates multiple candidate routes wherein each roue consists a sequence of road segments/edges between nodes. Segment level travel durations are determined using a sliding window optimization strategy which computes target speed profile for each segment. The sliding window approach divides each candidate route into discrete segment/windows, compute travel time per segment based on allowable vehicle speed and acceleration, and updates the segment speed profile dynamically as the vehicle progresses. The probabilistic progress metric are assigned for each segment (node and edge) representing best case travel time assuming maximum allowable speed, and worst case travel time assuming minimum allowable speed/speed limits. These metrics provide a range of expected travel duration per segment which defines segment level travel deviation times. The per segment travel deviation time (from min/max or probabilistic range) are summed or averaged along each candidate route, producing a route level flexibility metric that quantifies how much the autonomous vehicle may adjust its travel duration along the route by adapting speed per segment. Since each candidate route has a distinct sequence of segments with associated travel deviation times, each route receives its own flexibility metric, allowing the system to rank/select routes based on temporal adjustability.)
it may be alleged that Kelly does not explicitly teach the flexibility metric being determined based on the travel deviation time of each of the respective road segments in the respective candidate route,
Quint is directed to route planning for arriving a target destination by the desired target arrival time, Quint teaches determining a flexibility metric for each of the at least two candidate routes, the flexibility metric being determined based on the travel deviation time of each of the respective road segments in the respective candidate route, and based on the determined flexibility metrics, selecting a route from the at least two candidate routes to be used by the autonomous vehicle to travel to the target destination location. (see at least Fig. 1 [0021-0031]: Each of the one or more routes presented by the routing module 118 is associated with a timeline. The timeline incorporates an expected time of travel between the origin, the destination, and/or including a trip to the intermediate destination based on a total time to travel each of the segments included in the route at an anticipated travel speed. The anticipated travel speed may be based on a combination of the legal speed limit for each of the segments, anticipated or actual traffic delays, and one or more additional factors. A route may be selected from the one or more routes provided by the routing module 118 in order to reach the destination by the desired arrival time. Also, using the timeline, it may be determined whether there is time and/or how much time is potentially available to spend at the intermediate destination.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Kelly’s vehicle route guidance system to incorporate the technique of determining a flexibility metric for each of the at least two candidate routes, the flexibility metric being determined based on the travel deviation time of each of the respective road segments in the respective candidate route, and based on the determined flexibility metrics, selecting a route from the at least two candidate routes to be used by the autonomous vehicle to travel to the target destination location as taught by Quint with reasonable of expectation of success and doing so would increase operational flexibility without sacrificing timeliness.
Regarding claim 2, the combination of Kelly in view of Quint teaches The method of Claim 1, further comprising Kelly further teaches obtaining, for each respective road segment in the set of road segments, at least one road segment characteristic, and wherein obtaining the travel deviation time for each respective road segment in the set of road segments, comprises determining the travel deviation time based on the at least one road segment characteristics. (see at least [0381-0410]: The road on which the vehicle would travel if a left turn is made may have different attributes affecting potential speeds (for example, a different speed limit, weather conditions, traffic conditions, etc) to the road on which the vehicle would travel if a right turn is made. The probabilistic progress metric are assigned for each segment (node and edge) representing best case travel time assuming maximum allowable speed, and worst case travel time assuming minimum allowable speed/speed limits. These metrics provide a range of expected travel duration per segment which defines segment level travel deviation times.)
Regarding claim 3, the combination of Kelly in view of Quint teaches The method of Claim 2, wherein each of the at least one road segment characteristics comprises any one or more out of:
Kelly further teaches weather conditions for at least one time period in the road segment, distance of the road segment, a driving profile of the road segment, a maximum speed, a minimum speed, a confidence level of travel duration, information of obstacles in the road segment, an amount of traffic and/or traffic variation of the road segment, and a road type of the road segment. (see at least Fig. 6 [0318]: one or more (preferably all) of the interconnecting edges may have associated attributes, such as the traveling distance represented by the interconnecting edges, a speed limit, a gradient of the road, a type of the road (for example, single lane, duel carriageway, three-lane highway/motorway, etc.) any road laws particular to the stretch of road represented by the edge (for example, one-way street), an indication of the road surface represented by the interconnecting edge, an indication of the curvature of road represented by the interconnecting edge (for example, smallest radius of curvature on the road, etc) etc.)
Regarding claim 4, the combination of Kelly in view of Quint teaches The method of Claim 2, Kelly further teaches wherein determining the travel deviation time based on the at least one road characteristics comprises mapping the at least one road characteristics to a predetermined travel deviation time. (see at least Fig. 7-37 [0321-0540]: the legality objective is indicative of an attitude toward speed limits that may comprise an absolute speed value indicative of a desired maximum speed relative to the speed limit or a relative speed value indicative of a desired maximum percentage of the speed limit. Each different combination of possible route action and possible speed action is treated as a possible action. A plurality of probabilistic states are determined wherein each probabilistic state comprises a probabilistic progress metric for the vehicle when it arrives at the next node. The time that it might take to travel down a road from one node to the next. Heuristic algorithm is used to assess the value of time metric and/or efficiency metric of each of probabilistic states. The value of the time metric could be assessed by consider a best and worst case time to arrive at the end of the journey if the vehicle were to travel at the speed limit, at the maximum/minimum speed limit allowable, or some speed at particular tolerance above either values on each of the remaining edges of the journey. That is, speed limits defines the narrow range within which the speed of a vehicle may be adjusted on each road segment (i.e. edge) which directly limits what can be obtained when determining a travel duration time and a travel deviation time for that segment because the speed adjustment is bounded by minimum and maximum limits, the travel deviation time merely reflects a small, constrained adjustment to the travel duration.)
Regarding claim 5, the combination of Kelly in view of Quint teaches The method of Claim 1, Kelly further teaches wherein determining the flexibility metric comprises estimating a time spent performing one or more traffic actions when traveling each of the respective road segments. (see at least [0318-0340]: One or more (preferably all) of the nodes may have associated attributes, such as its geographical location on the road network (for example, GPS coordinates), its significance on the road network (for example, a junction, intersection, etc), average vehicle waiting times at the node, the altitude of the geographical location represented by the node, etc. One or more (preferably all) of the interconnecting edges may have associated attributes, such as the travelling distance represented by the interconnecting edge, a speed limit, a gradient of the road, a type of road (for example, single lane, duel-carriageway, three-lane highway/motorway, etc), any road laws particular to the stretch of road represented by the edge (for example, one-way street), an indication of the road surface represented by the interconnecting edge, an indication of the curvature of road represented by the interconnecting edge (for example, smallest radius of curvature on the road, etc) etc.)
Regarding claim 6, the combination of Kelly in view of Quint teaches The method of Claim 5, Kelly further teaches wherein estimating the time spent performing the one or more traffic actions comprises estimating any one or more of: time spent in traffic jams, (see at least [0318-0340]: One or more (preferably all) of the nodes may have associated attributes, such as its geographical location on the road network (for example, GPS coordinates), its significance on the road network (for example, a junction, intersection, etc), average vehicle waiting times at the node, the altitude of the geographical location represented by the node, etc.) time spent in tolls, time spent re-fuelling and/or re-charging the autonomous vehicle, time spent avoiding pedestrians, and time spent avoiding wildlife encounters.
Regarding claim 7, the combination of Kelly in view of Quint teaches The method of Claim 1, further comprising:
Kelly further teaches triggering the autonomous vehicle to travel the selected route. (see at least [0451, 0481]: if the vehicle is an autonomous vehicle, the action data may be output to a vehicle controller for use in autonomous control of the vehicle. Action data based on the selected recommended action is output to the autonomous vehicle for the next interconnecting edge of the local journey until the vehicle arrives at the end location.)
Regarding claim 8, the combination of Kelly in view of Quint teaches The method of Claim 1,
Kelly further teaches wherein the flexibility metric is a total or average deviation times for the road segments in the respective at least two candidate routes. (see at least Fig. 7-37 [0321-0540]: The system evaluates multiple candidate routes wherein each roue consists a sequence of road segments/edges between nodes. Segment level travel durations are determined using a sliding window optimization strategy which computes target speed profile for each segment. The sliding window approach divides each candidate route into discrete segment/windows, compute travel time per segment based on allowable vehicle speed and acceleration, and updates the segment speed profile dynamically as the vehicle progresses. The probabilistic progress metric are assigned for each segment (node and edge) representing best case travel time assuming maximum allowable speed, and worst case travel time assuming minimum allowable speed/speed limits. These metrics provide a range of expected travel duration per segment which defines segment level travel deviation times. The per segment travel deviation time (from min/max or probabilistic range) are summed or averaged along each candidate route, producing a route level flexibility metric that quantifies how much the autonomous vehicle may adjust its travel duration along the route by adapting speed per segment. Since each candidate route has a distinct sequence of segments with associated travel deviation times, each route receives its own flexibility metric, allowing the system to rank/select routes based on temporal adjustability.)
Regarding claim 9, the combination of Kelly in view of Quint teaches The method of Claim 1,
Kelly further teaches wherein the time interval is a time interval before or after the target time of arrival. (see at least Fig. 8 & 11 [0291]: desired punctuality such as an arrival time or arrival time window.)
Regarding claim 10, the combination of Kelly in view of Quint teaches The method of Claim 1,
Kelly further teaches wherein the target time of arrival is determined based on one or more predefined time slots and/or opening hours associated with the target destination location. (see at least [0326]: The punctuality objective is indicative of the desired arrival time at the destination location. The punctuality objective may comprise a specific desired arrival time at the destination (for example 11:38, or 17:00, or 22:21, etc), or a range (or window) of desired arrival times at the destination (for example 11:30 to 11:50, or 14:55 to 16:10, etc)).
Regarding claim 11, the combination of Kelly in view of Quint teaches The method of Claim 1,
Kelly further teaches wherein the target time of arrival is selected by a user. (see at least [0291-0292]: As well as designating a start location and a destination location for the journey, a user (such as a driver or other occupant in the vehicle) can define other desired objectives for the journey such as desired punctuality (such as an arrival time, or arrival time window)
Regarding claim 12, the combination of Kelly in view of Quint teaches The method of Claim 1,
Kelly further teaches wherein determining the at least two candidate routes comprises selecting road segments to be part of the respective candidate route by initiating a route search from the start location to the target destination. (see at least Fig. 5-6 [0320]: one or more potential routes may be determined based on the basic journey data (e.g. start location, destination location) and the directed graph and optionally also traffic data and weather data using any suitable techniques.)
Regarding claim 13, the combination of Kelly in view of Quint teaches The method of Claim 1,
Kelly further teaches A control unit configured to perform the method. (see at least Fig. 1)
Regarding claim 14, the combination of Kelly in view of Quint teaches The method of Claim 13,
Kelly further teaches An autonomous vehicle configured to travel from a start location via a set of road segments, to arrive at a target destination location within a time interval of a target time of arrival, and wherein the autonomous vehicle comprises the control unit according to claim 13. (see at least [0424, 0451, 0481, 0530]: The recommended actions for a journey guidance policy (for example, the ‘global’ journey policy) are actions that will be recommended to the driver (for example, audibly and/or visually) and/or the vehicle (for example, for autonomous vehicles) during the journey. On the journey, when the vehicle arrives at a node represented in the directed graph of routing options, the recommended action may be used by the route guidance module 110 to suggest a navigation action (for example, ‘turn left’ or ‘continue’, etc) and/or a target speed (for example, drive at 35 mph, etc). Therefore, the recommended actions are actions that will help guide the vehicle on a journey.)
Regarding claim 16, the combination of Kelly in view of Quint teaches The method of Claim 1,
Kelly further teaches A computer program comprising program code for performing the steps of claim 1 when said program is run on a computer. (see at least [0070, 0144, 0162, 0166]: a computer program configured to perform the method of any one of the first to seventh aspects when executed on a processor of an electronic device)
Regarding claim 17, the combination of Kelly in view of Quint teaches The method of Claim 1,
Kelly further teaches A non-transitory computer program medium carrying a computer program code for performing the steps of claim 1 when said program code is run on a computer. (see at least [0070, 0144, 0162, 0166]: a computer program configured to perform the method of any one of the first to seventh aspects when executed on a processor of an electronic device)
Claim(s) 15 is rejected under 35 U.S.C. 103 as being unpatentable over Kelly in view of Quint and Bier et al. (US 2017/0192437 A1 hereinafter Bier).
Regarding claim 15, the combination of Kelly in view of Quint teaches The control unit according claim 13, the combination of Kelly in view of Quint does not explicitly teach A control station configured to route the autonomous vehicle from a start location to a target destination location via a set of road segments, to arrive at the target destination location within a time interval of a target time of arrival, wherein the control station comprises the control unit according to claim 13, and wherein the control station is communicatively coupled with an autonomous vehicle.
Bier is directed to system and method for autonomous vehicle fleet routing, Bier teaches A control station configured to route the autonomous vehicle from a start location to a target destination location via a set of road segments, to arrive at the target destination location within a time interval of a target time of arrival, wherein the control station comprises the control unit according to claim 13, and wherein the control station is communicatively coupled with an autonomous vehicle. (see at least Fig. 1-1A, 5-6 [0031-0044, 0072-0079, 0106-0107]: The routing coordinator functions to provide routing assistance to one or more of the plurality of autonomous vehicle. The routing coordinator is preferably a remote server or distributed computing system connected to the autonomous vehicle via an internet connection. Some examples of routing goals may involve trip duration (either per trip, or average trip duration across some set of vehicles and/or times), physics or laws or company policies (e.g., adjusting routes chosen by users that end in lakes or the middle of intersections, refusing to take routes on highways, etc.), distance, velocity (e.g., max., min., average), source/destination (e.g., it may be optimal for vehicles to start/end up in a certain place such as in a pre-approved parking space or charging station), intended arrival time (e.g., when a user wants to arrive at a destination), duty cycle (e.g., how often a car is on an active trip vs. idle), energy consumption (e.g., gasoline or electrical energy), maintenance cost (e.g., estimated wear and tear), money earned (e.g., for vehicles used for ridesharing), person-distance (e.g., the number of people moved multiplied by the distance moved), occupancy percentage, higher confidence of arrival time, user-defined routes or waypoints, fuel status (e.g., how charged a battery is, how much gas is in the tank), passenger satisfaction (e.g., meeting goals set by or set for a passenger) or comfort goals, environmental impact, passenger safety, pedestrian safety, toll cost, etc.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Kelly and Quint to provide a remote control station for routing autonomous vehicles as taught by Bier with reasonable expectation of success to ensure autonomous vehicle’s operational safety and scalability.
Conclusion
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/DANA F ARTIMEZ/ Examiner, Art Unit 3667
/FARIS S ALMATRAHI/ Supervisory Patent Examiner, Art Unit 3667