DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Regarding the previous 35 USC 112(b) rejections, the previous 35 USC 112(b) rejections are withdrawn in light of the present claim amendments.
Regarding the previous 35 USC 101 rejections, Applicant’s arguments have been fully considered but are not persuasive. Applicants argue that the claim 1 recites details to tailor complexity values to a specific automated vehicle with varying levels of capability and complexity. This enables, for example, detailed understanding and adjustment of the road complexity values to specific vehicles and reflects a real-time movement over time that addresses technical problems related to how complex road segments are for automated vehicles. Applicants argue that Applicant submits that the instant claim set improves the technological field of automated vehicle roadway assessment. The tailoring of complexity values to a specific automated vehicle’s sensor suite and ML model ensures that roadway evaluations reflect the vehicle’s unique perception and decision-making capabilities, directly solving the technical problem of mismatched generic assessments that can lead to unsafe routing or operation in diverse automated vehicle fleets. Examiner respectfully disagrees. The limitations of determining complexity values based on an associated behavior and based on modifiers including information indicative of a sensor suite or model with the automated vehicle may be interpreted as a mental process of mapping the values for road segments based on modifiers associated with collected weather or traffic data and associated with components of a vehicle. The claims appear to merely claim that the concept is performed on a generic computer and merely using a computer as a tool to perform the concept. The claim language does not appear to improve computer capabilities, and an improvement in the abstract idea itself is not an improvement in technology (see at least MPEP 2106.05(a)). As similarly discussed in the previous office action, MPEP 2106.05(d), and the cases cited therein, indicate that mere collection or receipt of data over a network, receiving or transmitting data over a network, storing and retrieving information in memory and that mere displaying of data is a well understood, routine, and conventional activity. Accordingly, the previous 35 USC 101 rejection is maintained.
For purposes of compact prosecution, the independent claims may be amended to incorporate performing, by the one or more processors, automated driving based on the complexity values in order to overcome the instant 35 USC 101 rejection because the instant specification appears to provide support at the following: [0065]: information 412 may reflect the current route the automated vehicle is traversing…complexity information 422 may then be provided to the automated vehicle 402, [0066]: automated vehicle 402 may include a processor system 404 which is used for automated driving. Additionally, the system 404 may output information to a display included in the vehicle 402. In the illustrated example, the system 404 has determined to adjust its current route and will be turning left ahead, and [0067]: While adjusting route may be informed by complexity information 422, the processor system 404 may additionally incorporate the information 422 into its automated driving platform. For example, the information 422 may be input into a machine learning model usable to perform automated driving.
Regarding the previous 35 USC 103 rejection, Applicant’s arguments with respect to claims have been considered but are moot because the new ground of rejection does not rely on the combination of references applied in the prior rejection of record for any teaching or matter specifically challenged in the arguments. A new rejection is made in view of US 11441916 (“Konrardy ‘916”).
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(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
A claim that recites an abstract idea, a law of nature, or a natural phenomenon is directed to a judicial exception. Abstract ideas include the following groupings of subject matter, when recited as such in a claim limitation: (a) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; (b) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and (c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See 2019 PEG.
Even when a judicial element is recited in the claim, an additional claim element(s) that integrates the judicial exception into a practical application of that exception renders the claim eligible under §101. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The following examples are indicative that an additional element or combination of elements may integrate the judicial exception into a practical application:
the additional element(s) reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
the additional element(s) that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
the additional element(s) implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
the additional element(s) effects a transformation or reduction of a particular article to a different state or thing; and
the additional element(s) applies or uses 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 more than a drafting effort designed to monopolize the exception.
Examples in which the judicial exception has not been integrated into a practical application include:
the additional element(s) merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
the additional element(s) adds insignificant extra-solution activity to the judicial exception; and
the additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
See 2019 PEG.
101 Analysis – Step 1
Claims 1, 8, 15 are directed to a method, a system, a non-transitory computer storage media. Therefore, the claims are 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 following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim(s) 8 includes limitations that recite an abstract idea (the underlined portions are the “additional limitation” while the bolded portions continue to represent the abstract idea)) and will be used as a representative claims for the remainder of the 101 rejection.
Claim 8 recites:
a geographic information system (GIS) database storing data associated with geographic regions, the data reflecting characteristics of portions of roadways in the geographic regions and being stored as a spatial database; and
one or more processors and non-transitory computer storage media storing instructions that when executed by the one or more processors, cause the processors to perform operations comprising:
obtaining, via the GIS database, information defining a roadway on which an automated vehicle is to navigate;
determining primary behaviors associated with a plurality of roadway segments which form the roadway, wherein the roadway is segmented into the roadway segments based on adjustments of primary behavior which the automated vehicle will perform;
determining individual complexity values of a plurality of complexity values for individual roadway segments of the plurality of roadway segments, wherein the individual complexity values are based on the associated primary behavior and a plurality of complexity modifiers, wherein a first complexity modifier reflects real-time weather or traffic conditions associated with a roadway segment,
wherein a second complexity modified is based on information indicative of a sensor suite and/or machine learning model associated with the automated vehicle, and wherein the individual complexity values are tailored according to the automated vehicle; and
causing presentation of a user interface, wherein the user interface includes: a roadway complexity value determined based on the complexity values.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, the limitation(s) in the context of this claim encompasses collecting information regarding a roadway, determining possible maneuvers associated with roadway segments for a vehicle, identify attributes that may affect navigation difficulty for the segment, determining a value for each segment, determine an overall road value based on the value for each segment.
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 idea 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 limitation” while the bolded portions continue to represent the abstract idea):
Claim 8 recites:
a geographic information system (GIS) database storing data associated with geographic regions, the data reflecting characteristics of portions of roadways in the geographic regions and being stored as a spatial database; and
one or more processors and non-transitory computer storage media storing instructions that when executed by the one or more processors, cause the processors to perform operations comprising:
obtaining, via the GIS database, information defining a roadway on which an automated vehicle is to navigate;
determining primary behaviors associated with a plurality of roadway segments which form the roadway, wherein the roadway is segmented into the roadway segments based on adjustments of primary behavior which the automated vehicle will perform;
determining individual complexity values of a plurality of complexity values for individual roadway segments of the plurality of roadway segments, wherein the individual complexity values are based on the associated primary behavior and a plurality of complexity modifiers, wherein a first complexity modifier reflects real-time weather or traffic conditions associated with a roadway segment,
wherein a second complexity modified is based on information indicative of a sensor suite and/or machine learning model associated with the automated vehicle, and wherein the individual complexity values are tailored according to the automated vehicle; and
causing presentation of a user interface, wherein the user interface includes: a roadway complexity value determined based on the complexity values.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations, the examiner submits that these limitations are generic computer components or insignificant extra-solution activities that merely use a computer to perform the process. The additional elements are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. 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. The additional limitation steps are recited at a high level of generality (i.e. as a general means of gathering data, storing data, transmitting signals), and amounts to mere data gathering (real-time weather or traffic conditions) and storing and transmitting do not add a meaningful limitation to the process (MPEP 2106.05(g) v. Consulting and updating an activity log, Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754), which are forms of insignificant extra-solution activities. 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 drafting effort designed to monopolize the exception (MPEP 2106.05). The additional limitations merely describe how to generally apply the otherwise mental judgements in a generic or general purpose automated vehicle environment. The additional limitations are recited at a high level of generality and merely automates the steps. Accordingly additional limitation(s) do/does not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim does 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 additional elements amount to nothing more than applying the exception using generic computer components. Generally applying an exception using a generic computer component cannot provide an inventive concept.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations do not provide any indication that the additional elements are anything other than a conventional computer within a vehicle. Also, MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, INC., 788 F.3d 1359, 1363 (Fed. Cir. 2015), and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 indicate that mere collection or receipt of data over a network, receiving or transmitting data over a network, and storing and retrieving information in memory are a well-understood, routine, and conventional functions when claimed in a merely generic manner (as it is here). Further, the Federal Circuit in Trading Techs. Int’l v. IBGLLC, 921 F.3d1084,1093(Fed. Cir.2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function.
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim(s) is/are not patent eligible.
Dependent claims 2-7, 9-14, 16-20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of 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. The additional elements are recited at a high level of generality and merely automate the steps. The additional limitations are recited at a high level of generality and amounts to mere data gathering, which is a form of insignificant extra-solution activity and post-solution insignificant activity of outputting by a user interface; the additional limitations are well-understood, routine, and conventional activity because the specification does not provide any indication that the additional elements are anything other than a conventional computer components. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. Further, MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, INC., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner. Further, mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Therefore, dependent claims2 -7, 9-14, 16-20 are not patent eligible under the same rationale as provided for in the rejection of the independent claim.
Therefore, claim(s) 1-20 is/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.
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, 3, 4, 6, 8, 10, 11, 13, 15, 17, 18, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20180245937 (“Moore”) in view of US 20220092970 (“Radakovic”), US 11441916 (“Konrardy ‘916”), and US 20250027780 (“Filimonov”).
As per claim(s) 1, 15, Moore discloses a method implemented by a system comprising one or more processors, the method comprising:
obtaining information defining a roadway on which a vehicle is to navigate (see at least [0022]: Map data store 102 also stores map properties such as navigation rules for the map data, which can include road properties that describe characteristics of road segments, such as speed limits, road directionality (e.g., one-way or two-way), traffic history, traffic conditions, addresses on the road segment, length of the road segment, and type of the road segment (e.g., surface street, residential, highway, toll). The map properties can also include properties about road intersections, such as turn restrictions, light timing information, traffic throughput, and connecting road segments),
the information reflecting characteristics of portions of the roadway including real-time, geographical and/or behavior information (see at least [0022]: map data store 102 also stores map properties such as navigation rules for the map data, which can include road properties that describe characteristics of road segments, such as speed limits, road directionality (e.g., one-way or two-way), traffic history, traffic conditions, addresses on the road segment, length of the road segment, and type of the road segment (e.g., surface street, residential, highway, toll). The map properties can also include properties about road intersections, such as turn restrictions, light timing information, traffic throughput, and connecting road segments);
determining primary behaviors associated with a plurality of roadway segments which form the roadway (see at least [0031]: Complexity scoring engine 204 analyzes one or more route segments of a navigation route…complexity of a route segment increases with the rate of routing maneuvers required along the segment. A routing maneuver may in various embodiments include a turn, roundabout entrance or exit, freeway entrance or exit, or avoidance of a road hazard; or, generally, any required deviation from continuing on a road segment in as close to a forward manner as the road allows),
wherein the roadway is segmented into the roadway segments based on adjustments of primary behavior which the automated vehicle will perform (see at least [0031]: complexity scoring engine 204 may analyze two route segments—in the first route segment, the navigation route proceeds forward along a road for 1000 meters, turns right, and continues for another 500 meters. In the second route segment, the navigation route proceeds forward for 100 meters, turns left, continues for 300 meters, enters a roundabout, exits the roundabout at the second right turn, continues forward for 400 meters, and enters a freeway via a freeway on-ramp, [0032]: scoring algorithm used by complexity scoring engine 204 in one embodiment includes assigning a weight to each type of routing maneuver, and determining the total cost of all maneuvers over the analyzed route segment);
determining individual complexity values of a plurality of complexity values for individual roadway segments of the plurality of roadway segments (see at least [0031]: Complexity scoring engine 204 determines a complexity score for both route segments, scoring the second route segment as more complex than the first route segment, [0032]: scoring algorithm used by complexity scoring engine 204 in one embodiment includes assigning a weight to each type of routing maneuver, and determining the total cost of all maneuvers over the analyzed route segment),
wherein the individual complexity values are based on the associated primary behavior and a plurality of complexity modifiers (see at least [0032]: scoring algorithm used by complexity scoring engine 204 in one embodiment includes assigning a weight to each type of routing maneuver, and determining the total cost of all maneuvers over the analyzed route segment, [0033]: scoring algorithm used by complexity scoring engine 204 includes assigning a weight to the maneuver based on real-time or historical traffic conditions. For instance, the scoring engine 204 may increase the complexity of certain maneuvers when the scoring engine 204 estimates, based on real-time and/or historical traffic data, high-volume traffic. Other factors may affect complexity score in various embodiments, such as a speed limit differential between back-to-back maneuvers, weather conditions, construction or other road conditions, amount of daylight, etc), and
wherein a first complexity modifier reflects real-time weather or traffic conditions associated with a roadway segment (see at least [0033]: scoring algorithm used by complexity scoring engine 204 includes assigning a weight to the maneuver based on real-time or historical traffic conditions. For instance, the scoring engine 204 may increase the complexity of certain maneuvers when the scoring engine 204 estimates, based on real-time and/or historical traffic data, high-volume traffic. Other factors may affect complexity score in various embodiments, such as a speed limit differential between back-to-back maneuvers, weather conditions, construction or other road conditions, amount of daylight, etc); and
causing presentation of a user interface (see at least [0015]: an initial segment of a navigation route can be displayed with an indication of a first maneuver and an alternative maneuver, [0034]: If the complexity score is higher than a threshold level, route preview module 206 determines an amount of the navigation route to display on the driver's device),
Moore does not explicitly disclose an automated vehicle.
However, Radakovic teaches an automated vehicle is to navigate (see at least [0084]: HD mapping data enable highly automated vehicles to precisely localize themselves on the road, and to determine road attributes (e.g., learned speed limit values) to at high accuracy levels. In some embodiments, the HD mapping data also comprises temporal information (e.g., timestamps) relating to the service request, [0106]: geographic database 111 can be a master geographic database stored in a format that facilitates updating, maintenance, and development. For example, the master geographic database or data in the master geographic database can be in an Oracle spatial format or other spatial format, such as for development or production purposes).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Moore by incorporating the teachings of Radakovic with a reasonable expectation of success in order to enable highly automated vehicles to precisely localize themselves on a road.
Moore does not explicitly disclose wherein a second complexity modified is based on information of a sensor suite and/or machine learning model associated with the automated vehicle, and wherein the individual complexity values are tailored according to the automated vehicle.
However, Konrardy ‘916 teaches wherein a second complexity modified is based on information of a sensor suite and/or machine learning model associated with the automated vehicle, and wherein the individual complexity values are tailored according to the automated vehicle (see at least abstract: autonomous and semi-autonomous vehicle routing…roadway suitability for autonomous operation is scored, column 7 lines 11-15: (4) determining an autonomous route from the starting location to the target location using only road segments suitable for fully autonomous vehicle travel, column 38 lines 53-55: Blocks 508-516 may be repeated in a loop until all road segments (or all road segment selected for analysis) have been analyzed and scored, column 39 lines 24-36: block 510…determine one or more risk levels associated with the road segment. Machine learning techniques (e.g., support vectors, neural networks, random forests, naïve Bayesian classifiers, etc.) may be used to identify or estimate the magnitude of salient risk factors associated with autonomous operation feature use on the road segment. Such risk factors may include time of day, weather conditions, traffic conditions, speed, type of vehicle, types of sensors used by the vehicle, types of autonomous operation features in use, versions of autonomous operation features, interactions between autonomous operation features, autonomous operation feature settings or configurations, driver behavior, or other similar factors that may be derived from the data, column 40 lines 59-66: electronic map may include map tiles indicating only road segments for which one or more autonomous operation features (e.g., a set of particular autonomous operation features, particular types of autonomous operation features, or particular levels of autonomous operation features) may be safely used (i.e., road segments meeting a minimum score threshold for safe use of the relevant autonomous operation features), column 41 lines 32-37: Relevant map data associated with autonomous operation scores of road segments may then be accessed (block 608), such as from an autonomous operation suitability map database, and the map data may then be used to identify road segments within the relevant map data meeting the minimum requirements).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Moore by incorporating the teachings of Konrardy ‘916 with a reasonable expectation of success in order to ensure safe operation of autonomous or semi-autonomous vehicle (see at least Konrardy ‘916 column 3 lines 62-65: Ensuring safe operation of such autonomous or semi-autonomous vehicles is of the utmost importance because the automated systems of these vehicles may not function properly in all environments).
Moore does not explicitly disclose wherein the user interface includes: a roadway complexity value determined based on the complexity values.
However, Filimonov teaches wherein the user interface includes: a roadway complexity value determined based on the complexity values (see at least [0053]: overall stress index of the one or more routes may be displayed on a display of the head unit of the vehicle).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Moore by incorporating the teachings of Filimonov with a reasonable expectation of success in order to reduce risk of accidents and improve road safety.
As per claim(s) 3, 10, 17, Moore discloses wherein an individual primary behavior of the primary behaviors includes an intersection, a merge, or straight travel (see at least [0031]: complexity of a route segment increases with the rate of routing maneuvers required along the segment. A routing maneuver may in various embodiments include a turn, roundabout entrance or exit, freeway entrance or exit, or avoidance of a road hazard; or, generally, any required deviation from continuing on a road segment in as close to a forward manner as the road allows).
As per claim(s) 4, 11, 18, Moore discloses wherein the complexity modifiers for an individual roadway segment of the roadway segments further include speed associated with the individual roadway segment or density of vehicles on the individual roadway segment (see at least [0033]: factors may affect complexity score in various embodiments, such as a speed limit differential between back-to-back maneuvers).
As per claim(s) 6, 13, 20, Moore discloses wherein the user interface further includes a graphical depiction of the roadway (see at least [0027]: routing engine 202 displays a map of the area around the driver's vehicle and recommends a navigation route…the routing engine 202 may highlight the recommended navigation route on the displayed map of the area by coloring road graphical elements that comprise the representation of the navigation route in a manner distinct from the coloration of other roads graphical elements of the displayed map).
Moore does not explicitly disclose wherein individual roadway segments of the plurality of roadway segments are assigned colors selected based on the individual complexity values.
However, Filimonov teaches wherein individual roadway segments of the plurality of roadway segments are assigned colors selected based on the individual complexity values (see at least [0053]: overall stress index of the one or more routes may be displayed on a display of the head unit of the vehicle, [0059]: stress indexes along different routes, with dark grey-colored road segments indicating relatively high stress and light grey-colored road segments indicating relatively low stress).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Moore by incorporating the teachings of Filimonov with a reasonable expectation of success in order to reduce risk of accidents and improve road safety.
As per claim(s) 8, Moore discloses a system comprising:
database storing data associated with geographic regions, the data reflecting characteristics of portions of roadways in the geographic regions (see at least [0022]: Map data store 102 also stores map properties such as navigation rules for the map data, which can include road properties that describe characteristics of road segments, such as speed limits, road directionality (e.g., one-way or two-way), traffic history, traffic conditions, addresses on the road segment, length of the road segment, and type of the road segment (e.g., surface street, residential, highway, toll). The map properties can also include properties about road intersections, such as turn restrictions, light timing information, traffic throughput, and connecting road segments);
and one or more processors and non-transitory computer storage media storing instructions that when executed by the one or more processors (see at least [0046]: one or more processors…memory), cause the processors to perform operations comprising:
obtaining, via the database, information defining a roadway on which a vehicle is to navigate (see at least [0022]: Map data store 102 also stores map properties such as navigation rules for the map data, which can include road properties that describe characteristics of road segments, such as speed limits, road directionality (e.g., one-way or two-way), traffic history, traffic conditions, addresses on the road segment, length of the road segment, and type of the road segment (e.g., surface street, residential, highway, toll). The map properties can also include properties about road intersections, such as turn restrictions, light timing information, traffic throughput, and connecting road segments),
determining primary behaviors associated with a plurality of roadway segments which form the roadway (see at least [0031]: complexity of a route segment increases with the rate of routing maneuvers required along the segment. A routing maneuver may in various embodiments include a turn, roundabout entrance or exit, freeway entrance or exit, or avoidance of a road hazard; or, generally, any required deviation from continuing on a road segment in as close to a forward manner as the road allows),
wherein the roadway is segmented into the roadway segments based on adjustments of primary behavior which the automated vehicle will perform (see at least [0031]: complexity scoring engine 204 may analyze two route segments—in the first route segment, the navigation route proceeds forward along a road for 1000 meters, turns right, and continues for another 500 meters. In the second route segment, the navigation route proceeds forward for 100 meters, turns left, continues for 300 meters, enters a roundabout, exits the roundabout at the second right turn, continues forward for 400 meters, and enters a freeway via a freeway on-ramp, [0032]: scoring algorithm used by complexity scoring engine 204 in one embodiment includes assigning a weight to each type of routing maneuver, and determining the total cost of all maneuvers over the analyzed route segment);
determining individual complexity values of a plurality of complexity values for individual roadway segments of the plurality of roadway segments (see at least [0031]: Complexity scoring engine 204 determines a complexity score for both route segments, scoring the second route segment as more complex than the first route segment),
wherein the individual complexity values are based on the associated primary behavior and a plurality of complexity modifiers (see at least [0032]: scoring algorithm used by complexity scoring engine 204 in one embodiment includes assigning a weight to each type of routing maneuver, and determining the total cost of all maneuvers over the analyzed route segment, [0033]: scoring algorithm used by complexity scoring engine 204 includes assigning a weight to the maneuver based on real-time or historical traffic conditions. For instance, the scoring engine 204 may increase the complexity of certain maneuvers when the scoring engine 204 estimates, based on real-time and/or historical traffic data, high-volume traffic. Other factors may affect complexity score in various embodiments, such as a speed limit differential between back-to-back maneuvers, weather conditions, construction or other road conditions, amount of daylight, etc),
wherein a first complexity modifier reflects real-time weather or traffic conditions associated with a roadway segment (see at least [0033]: scoring algorithm used by complexity scoring engine 204 includes assigning a weight to the maneuver based on real-time or historical traffic conditions. For instance, the scoring engine 204 may increase the complexity of certain maneuvers when the scoring engine 204 estimates, based on real-time and/or historical traffic data, high-volume traffic. Other factors may affect complexity score in various embodiments, such as a speed limit differential between back-to-back maneuvers, weather conditions, construction or other road conditions, amount of daylight, etc), and
causing presentation of a user interface (see at least [0015]: an initial segment of a navigation route can be displayed with an indication of a first maneuver and an alternative maneuver, [0034]: If the complexity score is higher than a threshold level, route preview module 206 determines an amount of the navigation route to display on the driver's device).
Moore does not explicitly disclose an automated vehicle; a geographic information system (GIS) database with the data being stored as a spatial database.
However, Radakovic teaches an automated vehicle (see at least [0084]: HD mapping data enable highly automated vehicles to precisely localize themselves on the road, and to determine road attributes (e.g., learned speed limit values) to at high accuracy levels. In some embodiments, the HD mapping data also comprises temporal information (e.g., timestamps) relating to the service request), and
a geographic information system (GIS) database storing data associated with geographic regions, the data being stored as a spatial database (see at least [0106]: geographic database 111 can be a master geographic database stored in a format that facilitates updating, maintenance, and development. For example, the master geographic database or data in the master geographic database can be in an Oracle spatial format or other spatial format, such as for development or production purposes).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Moore by incorporating the teachings of Radakovic with a reasonable expectation of success in order to enable highly automated vehicles to precisely localize themselves on a road.
Moore does not explicitly disclose wherein a second complexity modified is based on information of a sensor suite and/or machine learning model associated with the automated vehicle, and wherein the individual complexity values are tailored according to the automated vehicle.
However, Konrardy ‘916 teaches wherein a second complexity modified is based on information of a sensor suite and/or machine learning model associated with the automated vehicle, and wherein the individual complexity values are tailored according to the automated vehicle (see at least abstract: autonomous and semi-autonomous vehicle routing…roadway suitability for autonomous operation is scored, column 7 lines 11-15: (4) determining an autonomous route from the starting location to the target location using only road segments suitable for fully autonomous vehicle travel, column 38 lines 53-55: Blocks 508-516 may be repeated in a loop until all road segments (or all road segment selected for analysis) have been analyzed and scored, column 39 lines 24-36: block 510…determine one or more risk levels associated with the road segment. Machine learning techniques (e.g., support vectors, neural networks, random forests, naïve Bayesian classifiers, etc.) may be used to identify or estimate the magnitude of salient risk factors associated with autonomous operation feature use on the road segment. Such risk factors may include time of day, weather conditions, traffic conditions, speed, type of vehicle, types of sensors used by the vehicle, types of autonomous operation features in use, versions of autonomous operation features, interactions between autonomous operation features, autonomous operation feature settings or configurations, driver behavior, or other similar factors that may be derived from the data, column 40 lines 59-66: electronic map may include map tiles indicating only road segments for which one or more autonomous operation features (e.g., a set of particular autonomous operation features, particular types of autonomous operation features, or particular levels of autonomous operation features) may be safely used (i.e., road segments meeting a minimum score threshold for safe use of the relevant autonomous operation features), column 41 lines 32-37: Relevant map data associated with autonomous operation scores of road segments may then be accessed (block 608), such as from an autonomous operation suitability map database, and the map data may then be used to identify road segments within the relevant map data meeting the minimum requirements).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Moore by incorporating the teachings of Konrardy ‘916 with a reasonable expectation of success in order to ensure safe operation of autonomous or semi-autonomous vehicle (see at least Konrardy ‘916 column 3 lines 62-65: Ensuring safe operation of such autonomous or semi-autonomous vehicles is of the utmost importance because the automated systems of these vehicles may not function properly in all environments).
Moore does not explicitly disclose wherein the user interface includes: a roadway complexity value determined based on the complexity values.
However, Filimonov teaches wherein the user interface includes: a roadway complexity value determined based on the complexity values (see at least [0053]: overall stress index of the one or more routes may be displayed on a display of the head unit of the vehicle).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Moore by incorporating the teachings of Filimonov with a reasonable expectation of success in order to reduce risk of accidents and improve road safety.
Claim(s) 2, 9, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Moore in view of Radakovic, Konrardy ‘916, and Filimonov, and further in view of US 20220402521 (“Hetang”).
As per claim(s) 2, 9, 16, Moore discloses wherein the roadway reflects a roadway under development (see at least [0033]: construction) but does not explicitly disclose wherein the primary behaviors are specified via user input or determined based on an analysis of an underlying model of the roadway under development.
However, Hetang teaches wherein the primary behaviors are specified via user input or determined based on an analysis of an underlying model of the roadway under development (see at least [0063]: in the context of a construction zone, the roadgraph solver can also automatically deform lanes in view of a construction zone placed within the scene, [0066]: 1) each scene synthesizer models its corresponding distribution of a specific subset of scene types (e.g., “lane shift due to a construction zone along road edge,” “small construction zone inside an intersection,”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Moore by incorporating the teachings of Hetang with a reasonable expectation of success in order to provide autonomous path generation with path optimization.
Claim(s) 7, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Moore in view of Radakovic, Konrardy ‘916, and Filimonov, and further in view of US 11763391 (“Slusar”).
As per claim(s) 7, 14, Moore discloses wherein the information is received from a vehicle traversing the roadway (see at least [0015]: an initial segment of a navigation route can be displayed with an indication of a first maneuver and an alternative maneuver, [0022]: map data store, [0047]: transportation management system 100 can communicate with one or more computing devices, and one or more servers, [0048]: transportation management system 100 can be configured to receive sensor data (e.g., such as GPS data) from one or more location tracking devices via the network link).
Moore does not explicitly disclose an automated vehicle, and wherein the method further comprises: providing the complexity values to the automated vehicle, wherein the automated vehicle is configured to adjust a route based on the complexity values.
However, Radakovic teaches an automated vehicle (see at least [0084]: HD mapping data enable highly automated vehicles to precisely localize themselves on the road, and to determine road attributes (e.g., learned speed limit values) to at high accuracy levels. In some embodiments, the HD mapping data also comprises temporal information (e.g., timestamps) relating to the service request).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Moore by incorporating the teachings of Radakovic with a reasonable expectation of success in order to enable highly automated vehicles to precisely localize themselves on a road.
However, Filimonov teaches wherein the method further comprises: providing the complexity values to the vehicle, wherein the vehicle is configured to provide a route based on the complexity values (see at least [0031]: Exemplary road representation 100 may represent a display of a navigation application running on a vehicle head unit positioned in the vehicle, the head unit having a processor with memory storing instructions for carrying out the operations described herein and/or holding data in a format as described herein, or portions thereof. In another example, the system may communicate with a server system having a processor with memory instructions storing instructions for carrying out the operations described herein and/or holding data in a format as described herein, or portions thereof, [0053]: overall stress index of the one or more routes may be displayed on a display of the head unit of the vehicle).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Moore by incorporating the teachings of Filimonov with a reasonable expectation of success in order to reduce risk of accidents and improve road safety.
However, Slusar teaches wherein the method further comprises: providing the complexity values to the vehicle, wherein the method further comprises: providing the complexity values to the vehicle, wherein the vehicle is configured to adjust a route based on the complexity values (see at least column 2 lines 18-26: a personal navigation device, mobile device, and/or personal computing device may communicate, directly or indirectly, with a server (or other device) to transmit and receive a risk score(s), a risk map(s), and/or received information, column 19 lines 54-56: particular risk object, road segment, or route may be updated and assigned the new modified risk score, column 38 lines 25-26: guide a vehicle (autonomous or semi-autonomous) to prepare for risk or alert a driver of risk).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Moore by incorporating the teachings of Slusar with a reasonable expectation of success in order to provide a safer route to travel.
Allowable Subject Matter
Claim(s) 5, 12, 19 would be allowable if the rejection(s) under 35 U.S.C. 101, set forth in this Office action, are overcome and to include all of the limitations of the base claim and any intervening claims.
Moore discloses assigning a weight to each type of routing maneuver, determining a total cost of all maneuvers over an analyzed route segment, wherein the cost may be adjusted to account for the total length of the route segment being analyzed (see at least [0022], [0036]); Radakovic teaches a road segment length (see at least [0040]); Konrardy ‘916 teaches an optimization criteria for an optimal route may include distance and teaches lengthy road construction that may include changing travel patterns with little notice to drivers (see at least column 5 line 55, column 27 line 41), and Filimonov teaches segments of constant length (see at least [0052]). However, the prior art does not explicitly disclose wherein determining the roadway complexity value comprises: identifying weighting factors associated with the complexity values, wherein each weighting factor is associated with a particular range of complexity values; aggregating, for each weighting factor, lengths of roadway segments associated with the weighting factor; and determining determinants for each weighting factor based on the aggregated lengths, wherein the roadway complexity value is determined based on the determinants and a non-linear function.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20210293572 (“Konrardy ‘572) (see at least [0245]: (1) receive map data including indications of a plurality of road segments; (2) receive operating data from a plurality of vehicles having at least one autonomous operation feature, wherein the operating data includes location data associated with operation of the plurality of vehicles; (3) identify a road segment to analyze from the plurality of road segments; (4) associate a subset of the operating data with the identified road segment based upon the location data; (5) determine one or more risk levels associated with the road segment based upon the associated subset of the operating data (such as by inputting the subset of operating data into a machine learning program trained to identify risk levels associated with road segments using operating data); (6) determine one or more suitability scores for the road segment based upon the one or more risk levels, each suitability score indicating a category of suitability for autonomous or semi-autonomous vehicle operation (such as by inputting the risk levels into a machine learning program trained to determine suitability scores for road segments using risk levels); and/or (7) store, in a database, the one or more suitability scores, each suitability score further stored with an indication of the associated road segment to facilitate roadway suitability for autonomous or semi-autonomous vehicle operation analysis).
US 12594960 (“Tam”) (see at least column 7 lines 32-43: processor(s) 28 can also use information from the environmental sensors 16B to identify, the type of road (e.g., type of lanes and lane segments, urban or highway), difficulty of traversal of lane(s) and lane segment(s), density of traffic, the level of the density, etc. In the illustrated embodiment, the processor(s) 28 is programmed to anticipate information regarding upcoming conditions near the vehicle 10 vicinity based on one or more of the real-time information received from the on-board satellite navigation device NAV, the crowdsourced information and the predetermined information (stored in the computer readable medium)).
US 20220126864 (“Moustafa”) (see at least [0618]: determining a safety score for a vehicle at 8720…safety score can be used to represent the relative safety of an autonomous vehicle and may be used to determine the score limit of the roads that a car can drive on autonomously. Similar to the road safety score, the vehicle safety score may be a single score calculated by weighting important safety elements of the vehicle. Examples of criteria to be considered for the vehicle safety score can include: the type of sensors on the vehicle (e.g., LIDAR, cameras, GPS, ultrasound, radar, hyperspectral sensors, and inertial measurement units), the number of each sensor, the quality of the sensors, the quality of the driving algorithms implemented by the vehicle, the amount of road mapping data available, etc.).
US 20190078899 (“Iagnemma”) (see at least claim 23: in accordance with a determination that the level of performance of at least one sensor of the one or more sensors is below a predetermined threshold value, determining that the autonomous vehicle cannot travel the segment of the trajectory).
US 20210316755 (“Liu”) (see at least abstract: calculating a zone failure risk score for each of predetermined zones based on a sensor failure risk score associated with each of sensors mounted on the ADV. The predetermined zones being defined based on a sensor layout of the sensors. A sensor capability coverage of the ADV is determined based on the zone failure risk score associated with each of the predetermined zones. A drivable area of the ADV is determined based on the sensor capability coverage in view of map data associated with a current location of the ADV. A trajectory is planned based on the drivable area to autonomously drive the ADV to navigate a driving environment surrounding the ADV).
US 20210116907 (“Altman”) (see at least [0105]: vehicle may comprise a navigation route generator 192, able to generate or modify a navigation route for said vehicle (e.g., in in conjunction with a conventional route guidance application or navigation application), by taking into account at least: an estimated level of wireless communication availability at different route segments; or, by taking into account at least: (i) an estimated level of wireless communication availability at different route segments, and also (ii) one or more constraints regarding safety requirements for passengers of said vehicle; or, by generating or modifying a navigation route for said vehicle to include road-segments having wireless communication availability that is above a pre-defined threshold value; or, configured to increase safety of travel of said vehicle which is a tele-operated vehicle, by generating or modifying a navigation route for said vehicle to include road-segments in which tele-operation of said vehicle is estimated to be successful beyond a—pre-defined threshold level; or, configured to perform other route modification or route determination operations in view of safety considerations).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGELINA M SHUDY whose telephone number is (571)272-6757. The examiner can normally be reached M - F 10am - 6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fadey Jabr can be reached at 571-272-1516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
Angelina Shudy
Primary Examiner
Art Unit 3668
/Angelina M Shudy/Primary Examiner, Art Unit 3668
Prosecution Insights