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 Remarks
Claim Rejections – 35 U.S.C. 101
Applicant’s amendments have been fully considered but they are not persuasive.
Applicant argues (pg. 7-8) that similar to Diamond v. Diehr, the amendments of claim 1 limits the judicial exception to the practical application by providing a vehicle route and navigation to a user based on a likelihood of an event in which a vehicle will damage a roadside object, thereby decreasing a likelihood in which the user's vehicle attribute to damaging the roadside object while the vehicle traverses a road network.
Examiner respectfully disagrees. Regarding the amendments, “generate a vehicle route based on the output data” is a mental process, “output the vehicle route on a user interface for vehicle navigation” is insignificant extra solution activity, and “causing the machine learning model to generate the output data as a function of the input data” is a mental process with a high-level recitation of using a machine learning model to generate the output data. Taking claim 1 as a whole, those amendments do not sufficiently limit the practical use of the output data because there are insufficient details regarding the inner workings of the neural network. Examiner suggests including claim language regarding the characteristics of the neural network, such as the layers, weights, connections, structure/architecture, activation functions, etc. See rejection below for more details.
The foregoing applies to all independent claims and their dependent claims.
Claim Rejections – 35 U.S.C. 103
Applicant’s prior art arguments have been fully considered and they are (not) persuasive.
Applicant argues (pgs. 9-10) that the cited references do not teach the newly amended limitations that further clarify that the system generates a reason for the cause of the collision and a severity of damage inflicted on the vehicle involved in the collision.
Examiner respectfully disagrees. Roberts teaches that the server can determine the possible cause of a dangerous situation, such as a collision of the transport/vehicle, as shown in the following: Roberts [¶ 0101]: “In one embodiment, the solutions can also be utilized to determine that sounds from a transport are atypical and transmit data related to the sounds as well as a possible source location to a server wherein the server can determine possible causes and avoid a potentially dangerous situation.”
Furthermore, Roberts teaches that the server can access files that indicate the extent/assessment of the damage of the transport/vehicle and can store it in the blockchain, which may be an output of the algorithm, as shown in the following. Roberts [¶ 0092]: “The server accesses one or more media files to access the damage to the transport and stores the damage assessment onto a blockchain.”
The other amendments that are made but not specifically argued for by the Applicant in their Remarks (i.e. the last three limitations of claim 1) are addressed by the cited references. See rejection below for details.
The foregoing applies to all independent claims and their dependent 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.
Claims 1-16, 19-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 19-22 are method claims. Claims 1-16 are machine/system/product claims. Therefore, claims 1-16, 19-22 are directed to either a process, machine, manufacture or composition of matter.
With respect to claim 1:
Step 2A – Prong 1:
…
…
… the historical data indicating first attributes associated with surroundings of the first roadside objects, second attributes associated with the first vehicles, and route maneuver information associated with the first vehicles; (mental process – a person can recognize that the historical data indicates first attributes associated with surroundings of the first roadside objects, second attributes associated with the first vehicles, and route maneuver information associated with the first vehicles.)
…
wherein the input data indicate a second roadside object and contextual information associated with the second roadside object, and wherein the output data indicates a likelihood of an event in which one or more second vehicles will damage the second roadside object, a reason for a cause of the event, and a severity of damage inflicted on the second roadside object. (mental process – a person can recognize that the input data indicate a second roadside object and contextual information associated with the second roadside object, and wherein the output data indicates a likelihood of an event in which one or more second vehicles will damage the second roadside object, a reason for a cause of the event, and a severity of damage inflicted on the second roadside object.)
… generate the output data as the function of the input data; (mental process – a person can manually generate output data as a function of the input data with the assistance of a pen/paper.)
generate a vehicle route based on the output data; (mental process – a person can manually g. generate a vehicle route based on the output data with the assistance of a pen/paper.)
…
Step 2A – Prong 2: This judicial exception is not integrated into a practical application.
An apparatus comprising at least one processor and at least one non-transitory memory including computer program code instructions, the computer program code instructions configured to, when executed, cause the apparatus to: … (mere instructions to apply the exception using a generic computer component – processor, non-transitory memory, and computer program code instructions apply exception.)
… receive historical data indicating events in which first vehicles damaged first roadside objects, … (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
…
using the historical data, train a machine learning model to generate output data as a function of input data, (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: High level recitation of training the machine learning engine to generate output data as a function of input data.);
…
cause the machine learning model to … (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: High level recitation of training the machine learning engine to generate output data as a function of input data.);
…
and output the vehicle route on a user interface for vehicle navigation. (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
An apparatus comprising at least one processor and at least one non-transitory memory including computer program code instructions, the computer program code instructions configured to, when executed, cause the apparatus to: … (mere instructions to apply the exception using a generic computer component – processor, non-transitory memory, and computer program code instructions apply exception.)
… receive historical data indicating events in which first vehicles damaged first roadside objects, … (MPEP 2106.05(d)(II) indicate that merely “Receiving or transmitting data over a network, e.g., using the Internet to gather data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim – the historical data is merely received by the apparatus). Thereby, a conclusion that the claimed distribute step is well-understood, routine, conventional activity is supported under Berkheimer.)
…
using the historical data, train a machine learning model to generate output data as a function of input data, (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: High level recitation of training the machine learning engine to generate output data as a function of input data.);
…
cause the machine learning model to … (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: High level recitation of training the machine learning engine to generate output data as a function of input data.);
…
and output the vehicle route on a user interface for vehicle navigation. (MPEP 2106.05(d)(II) indicate that merely “Receiving or transmitting data over a network, e.g., using the Internet to gather data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim – the vehicle route is merely transmitted to a user interface). Thereby, a conclusion that the claimed distribute step is well-understood, routine, conventional activity is supported under Berkheimer.)
With respect to claim 2:
Step 2A – Prong 1:
The apparatus of claim 1, wherein the first roadside objects and the second roadside object are sign posts, traffic light posts, streetlight posts, or a combination thereof. (mental process – a person can recognize that the first roadside objects and the second roadside object are sign posts, traffic light posts, streetlight posts, or a combination thereof.)
With respect to claim 3:
Step 2A – Prong 1:
The apparatus of claim 1, wherein the first attributes indicate: (i) road attributes associated with roads having the first roadside objects within peripherals of the roads; (ii) proximity of the first roadside objects relative to the roads; or (iii) a combination thereof. (mental process – a person can recognize that the first attributes indicate: (i) road attributes associated with roads having the first roadside objects within peripherals of the roads; (ii) proximity of the first roadside objects relative to the roads; or (iii) a combination thereof.)
With respect to claim 4:
Step 2A – Prong 1:
The apparatus of claim 3, wherein the road attributes indicate, for each of the roads: (i) a type of road; (ii) a curvature of said road; (iii) a classification of said road; (iv) traffic rules associated with said road; (v) a number of lanes with said road; (vi) dimensions of said road; (vii) one or more points-of-interest (POIs) associated with said road; or (viii) a combination thereof. (mental process – a person can recognize that the road attributes indicate, for each of the roads: (i) a type of road; (ii) a curvature of said road; (iii) a classification of said road; (iv) traffic rules associated with said road; (v) a number of lanes with said road; (vi) dimensions of said road; (vii) one or more points-of-interest (POIs) associated with said road; or (viii) a combination thereof.)
With respect to claim 5:
Step 2A – Prong 1:
The apparatus of claim 1, wherein the second attributes indicate, for each of the first vehicles: (i) a type of vehicle; (ii) a classification of said vehicle; (iii) dimensions of said vehicle; (iv) dimensions of one or more loads transported by said vehicle; or (v) a combination thereof. (mental process – a person can recognize that the second attributes indicate, for each of the first vehicles: (i) a type of vehicle; (ii) a classification of said vehicle; (iii) dimensions of said vehicle; (iv) dimensions of one or more loads transported by said vehicle; or (v) a combination thereof.)
With respect to claim 6:
Step 2A – Prong 1:
The apparatus of claim 1, wherein the route maneuver information indicate: (i) patterns of routes traversed by the first vehicles; (ii) maneuvers executed by the first vehicles at locations including the first roadside objects; or (iii) a combination thereof. (mental process – a person can recognize that the route maneuver information indicate: (i) patterns of routes traversed by the first vehicles; (ii) maneuvers executed by the first vehicles at locations including the first roadside objects; or (iii) a combination thereof.)
With respect to claim 7:
Step 2A – Prong 1:
The apparatus of claim 1, wherein the historical data further indicate driver attribute data associated with drivers of the first vehicles, the driver attribute data indicating: (i) patterns at which the drivers maneuver the first vehicles; (ii) a first number of occurrences in which the drivers maneuvered the first vehicles to disobey traffic rules; (iii) a second number of occurrences in which the drivers were involved in vehicle-related accidents; or (iv) a combination thereof. (mental process – a person can recognize that the historical data further indicate driver attribute data associated with drivers of the first vehicles, the driver attribute data indicating: (i) patterns at which the drivers maneuver the first vehicles; (ii) a first number of occurrences in which the drivers maneuvered the first vehicles to disobey traffic rules; (iii) a second number of occurrences in which the drivers were involved in vehicle-related accidents; or (iv) a combination thereof.)
With respect to claim 8:
Step 2A – Prong 1:
The apparatus of claim 1, wherein the historical data further indicate roadside object attribute data associated with the first roadside objects, the roadside object attribute data indicating: (i) a type of roadside object; (ii) dimensions of the first roadside objects; or (iii) a combination thereof. (mental process – a person can recognize that the historical data further indicate roadside object attribute data associated with the first roadside objects, the roadside object attribute data indicating: (i) a type of roadside object; (ii) dimensions of the first roadside objects; or (iii) a combination thereof.)
With respect to claim 9:
Step 2A – Prong 1:
The apparatus of claim 1, wherein the route maneuver information is first route maneuver information, and wherein the contextual information indicate: (i) road attributes associated with a road proximate to the second roadside object; (iii) vehicle attributes associated with the one or more second vehicles; (iv) second route maneuver information associated with the one or more second vehicles; or (v) a combination thereof. (mental process – a person can recognize that the route maneuver information is first route maneuver information, and wherein the contextual information indicate: (i) road attributes associated with a road proximate to the second roadside object; (iii) vehicle attributes associated with the one or more second vehicles; (iv) second route maneuver information associated with the one or more second vehicles; or (v) a combination thereof.)
With respect to claim 10:
Step 2A – Prong 1:
…
…
… to generate output data as a function of the input data, wherein the output data indicate a likelihood of an event in which one or more first vehicles will damage the first roadside object, a reason for a cause of the event, and a severity of damage inflicted on the first roadside object; (mental process – a person can manually generate output data, wherein the output data indicate a likelihood of an event in which one or more first vehicles will damage the first roadside object, a reason for a cause of the event, and a severity of damage inflicted on the second roadside object, as a function of the input data with the assistance of a pen/paper.)
… generate the output data as a function of the input data by using historical data indicating events in which second vehicles damaged second roadside objects, (mental process – a person can manually generate the output data as a function of the input data by using historical data indicating events in which second vehicles damaged second roadside objects, with the assistance of a pen/paper.)
and wherein the historical data indicate first attributes associated with surroundings of the second roadside objects, second attributes associated with the second vehicles, and route maneuver information associated with the second vehicles. (mental process – a person can recognize that the historical data indicate first attributes associated with surroundings of the second roadside objects, second attributes associated with the second vehicles, and route maneuver information associated with the second vehicles.)
generate a vehicle route based on the output data; (mental process – a person can manually generate a vehicle route based on the output data with the assistance of a pen/paper.)
…
Step 2A – Prong 2: This judicial exception is not integrated into a practical application.
A non-transitory computer-readable storage medium having computer program code instructions stored therein, the computer program code instructions, when executed by at least one processor, cause the at least one processor to: (mere instructions to apply the exception using a generic computer component – non-transitory computer-readable storage medium, computer code apply exception.)
receive input data indicating a first roadside object and contextual information associated with the first roadside object; (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g))
cause a machine learning model … (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: High level recitation of training the machine learning engine to generate output data as a function of input data.);
wherein the machine learning model is trained to … (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: High level recitation of training the machine learning engine to generate output data as a function of input data.);
…
…
and output the vehicle route on a user interface for vehicle navigation. (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
A non-transitory computer-readable storage medium having computer program code instructions stored therein, the computer program code instructions, when executed by at least one processor, cause the at least one processor to: (mere instructions to apply the exception using a generic computer component – non-transitory computer-readable storage medium, computer code apply exception.)
receive input data indicating a first roadside object and contextual information associated with the first roadside object; (MPEP 2106.05(d)(II) indicate that merely “Receiving or transmitting data over a network, e.g., using the Internet to gather data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim – the input data is merely received). Thereby, a conclusion that the claimed distribute step is well-understood, routine, conventional activity is supported under Berkheimer.)
cause a machine learning model … (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: High level recitation of training the machine learning engine to generate output data as a function of input data.);
wherein the machine learning model is trained to … (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: High level recitation of training the machine learning engine to generate output data as a function of input data.);
…
…
and output the vehicle route on a user interface for vehicle navigation. (MPEP 2106.05(d)(II) indicate that merely “Receiving or transmitting data over a network, e.g., using the Internet to gather data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim – the vehicle route is merely transmitted to a user interface). Thereby, a conclusion that the claimed distribute step is well-understood, routine, conventional activity is supported under Berkheimer.)
Claims 11-16 are rejected on the same grounds under 35 U.S.C. 101 as claims 2-7 as they are substantially similar, respectively. Mutatis mutandis.
Claim 19 is substantially similar to claim 10, but has the following additional elements:
With respect to claim 19:
Step 2A – Prong 1:
A method of providing a map layer of roadside events, the method comprising: (mental process – a person can manually provide a map layer of roadside events with the assistance of a pen/paper.)
and updating the map layer to include a datapoint indicating the output data at a location of the first roadside object. (mental process – a person can manually update the map layer to include a datapoint indicating the output data at a location of the first roadside object with the assistance of a pen/paper.)
With respect to claim 20:
Step 2A – Prong 1:
The method of claim 19, wherein the map layer includes one or more other datapoints indicating one or more other likelihoods in which the one or more first vehicles will damage one or more third roadside objects. (mental process – a person can recognize that the map layer includes one or more other datapoints indicating one or more other likelihoods in which the one or more first vehicles will damage one or more third roadside objects.)
With respect to claim 21:
Step 2A – Prong 1:
The method of claim 19, wherein the first roadside object and the second roadside objects are sign posts, traffic posts, light posts, or a combination thereof. (mental process – a person can recognize that the first roadside object and the second roadside objects are sign posts, traffic posts, light posts, or a combination thereof.)
With respect to claim 22:
Step 2A – Prong 1:
The method of claim 19, further comprising causing a user interface associated with the one or more first vehicles to present the map layer. (mental process – a person can manually present the map layer.)
Claim Rejections – 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-16, 22 are rejected under 35 U.S.C. 103 as being unpatentable over Chi-Johnston et al. (US20230339459A1) hereinafter known as Chi-Johnston in view of Roberts et al. (US 20230048359 A1) hereinafter known as Roberts in view of Shin et al. (“Road Dynamic Object Mapping System Based on Edge-Fog-Cloud Computing”) hereinafter known as Shin.
Regarding independent claim 1, Chi-Johnston teaches:
…
using the historical data, train a machine learning model to generate output data as a function of input data, wherein the input data indicate a second roadside object and contextual information associated with the second roadside object, and wherein the output data indicates a likelihood of an event in which one or more second vehicles will damage the second roadside object, … (Chi-Johnston [¶ 0042]: “The data center 150 illustrated in FIG. 1 can use AV driving data (e.g., historical driving data) generated by sensor systems” Chi-Johnston teaches historical data from sensor systems. Chi-Johnston [¶ 0074]: “In some instances, the simulation safety score is based on a continuous scale between two numbers, for example, 0 and 1 (i.e., a fractional value). Also, the continuous scale may correlate to a continuous likelihood of the collision between the AV and the simulated object in simulation.” Chi-Johnston teaches the likelihood of a collision between the autonomous vehicle and the object.)
…
…
…
Chi-Johnston does not explicitly teach:
An apparatus comprising at least one processor and at least one non-transitory memory including computer program code instructions, the computer program code instructions configured to, when executed, cause the apparatus to: receive historical data indicating events in which first vehicles damaged first roadside objects, the historical data indicating first attributes associated with surroundings of the first roadside objects, second attributes associated with the first vehicles, and route maneuver information associated with the first vehicles;
… a reason for a cause of the event, and a severity of damage inflicted on the second roadside object;
cause the machine learning model to generate the output data as the function of the input data;
generate a vehicle route based on the output data;
However, Roberts teaches:
An apparatus comprising at least one processor and at least one non-transitory memory including computer program code instructions, the computer program code instructions configured to, when executed, cause the apparatus to: receive historical data indicating events in which first vehicles damaged first roadside objects, the historical data indicating first attributes associated with surroundings of the first roadside objects, second attributes associated with the first vehicles, and route maneuver information associated with the first vehicles; (Roberts [¶ 0005]: “non-transitory computer readable medium comprising instructions, that when read by a processor” Roberts teaches non-transitory memory and processor. Roberts [¶ 0061]: “As the level of risk rises, more detailed data is accumulated, such as all possible proximate data of the surrounding area of the transport … Historical data may also be obtained by remote servers such as previous collisions involving the one or more occupants.” Roberts teaches that historical data of the collision may contain information regarding the surroundings of the vehicle/transport. Roberts [¶ 0061]: “This data may include speed limits for the areas, as well as historical traffic patterns” Roberts teaches that the data includes traffic patterns, or movement of the vehicle, which can be considered route maneuver information.)
… a reason for a cause of the event, and a severity of damage inflicted on the second roadside object; (Roberts [¶ 0101]: “In one embodiment, the solutions can also be utilized to determine that sounds from a transport are atypical and transmit data related to the sounds as well as a possible source location to a server wherein the server can determine possible causes and avoid a potentially dangerous situation.” Roberts teaches that the server can determine the possible cause of a dangerous situation, such as in a collision of the transport/vehicle. Roberts [¶ 0092]: “The server accesses one or more media files to access the damage to the transport and stores the damage assessment onto a blockchain.” Roberts teaches that the server can access files that indicate the extent/assessment of the damage of the transport/vehicle and can store it in the blockchain, which may be an output of the algorithm.)
cause the machine learning model to generate the output data as the function of the input data; (Roberts [¶ 0051]: “The current solution determines a level of risk of an environment of a transport, using sensors on the transport”. Roberts teaches that the solution is the output data based on the input data of the sensors. The solution/output can determine a level of risk in an environment of a transport/vehicle.)
generate a vehicle route based on the output data; (Roberts [¶ 0087]: “In one embodiment, the solutions can also be utilized to determine an amount of energy needed by a transport to provide another transport with needed energy via transport to transport energy transfer based on one or more conditions such as weather, traffic, road conditions, car conditions, and occupants and/or goods in another transport, and instruct the transport to route to another transport and provide the energy.” Roberts teaches that the solution/output can also be used to instruct the transport/vehicle to take a route in the direction of another transport/vehicle.)
Chi-Johnston and Roberts are in the same field of endeavor as the present invention, as the references are directed to using machine learning models to avoid collisions of vehicles and roadside objects. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine calculating a likelihood of a collision using the model as taught in Chi-Johnston with feeding historical data detailing the vehicle/traffic surroundings as taught in Roberts. Roberts provides this additional functionality. As such, it would have been obvious to one of ordinary skill in the art to modify the teachings of Chi-Johnston to include teachings of Roberts because the combination would allow for the calculation of the likelihood of a crash given the surroundings of the vehicle of the road, including various roadside objects. This has the potential benefit of more accurately determining whether a collision is a risk for various types of roadside objects, such as traffic lights or hazard signs.
Chi-Johnston and Roberts do not explicitly teach:
and output the vehicle route on a user interface for vehicle navigation.
However, Shin teaches:
and output the vehicle route on a user interface for vehicle navigation. (Shin [Page 19, Figure 11]: Shin teaches that a map, which has vehicle routes on it, can be dynamically displayed on the edge device application, which may be an application of a vehicle.)
Shin is in the same field as the present invention, since it is directed to displaying the map layer to the user of the vehicle. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine calculation of the likelihood of a crash given the surroundings of the vehicle of the road as taught in Chi-Johnston as modified by Roberts with displaying this map to the user as taught in Shin. Shin provides this additional functionality. As such, it would have been obvious to one of ordinary skill in the art to modify the teachings of Chi-Johnston as modified by Roberts to include teachings of Shin because the combination would allow for the user to see the locations of the vehicles/objects on the road that otherwise may not be as visible on the road. This has the potential benefit of providing, to the user, foresight into the conditions of the road to increase the likelihood of avoiding a potential collision.
Regarding dependent claim 2, Chi-Johnston and Roberts teach:
The apparatus of claim 1,
Chi-Johnston teaches:
wherein the first roadside objects and the second roadside object are sign posts, traffic light posts, streetlight posts, or a combination thereof. (Chi-Johnston [¶ 0029]: “objects sharing the road with the AV 102 (e.g., pedestrians, bicycles, vehicles, ambulances, buses, cable cars, trains, traffic lights, lanes, road markings, etc.)” Chi-Johnston teaches that the objects along the side, close to the autonomous vehicle, can include a traffic light.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 3, Chi-Johnston and Roberts teach:
The apparatus of claim 1,
Roberts teaches:
wherein the first attributes indicate: (i) road attributes associated with roads having the first roadside objects within peripherals of the roads; (ii) proximity of the first roadside objects relative to the roads; or (iii) a combination thereof. (Roberts [¶ 0061]: “As the level of risk rises, more detailed data is accumulated, such as all possible proximate data of the surrounding area of the transport” Roberts teaches that the data includes the attributes of the surrounding area of the transport, which includes the road and other peripherals.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 4, Chi-Johnston and Roberts teach:
The apparatus of claim 3,
Chi-Johnston teaches:
wherein the road attributes indicate, for each of the roads: (i) a type of road; (ii) a curvature of said road; (iii) a classification of said road; (iv) traffic rules associated with said road; (v) a number of lanes with said road; (vi) dimensions of said road; (vii) one or more points-of-interest (POIs) associated with said road; or (viii) a combination thereof. (Chi-Johnston [¶ 0077]: “the potential trajectories of the simulated object can be predicted based on, for example, speed, acceleration, deceleration, and semantic expressions of intent (e.g., turning signals) as well as environmental features relating to the road (e.g. static and dynamic object(s) perceived by AV, such as vehicles and pedestrians; weather conditions; road conditions (e.g., ice patches, flooding, slipperiness, etc.); lighting conditions, etc.)” Chi-Johnston teaches the data pertaining to the type of the road, such as the environmental features relating to the road.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 5, Chi-Johnston and Roberts teach:
The apparatus of claim 1,
Roberts teaches:
wherein the second attributes indicate, for each of the first vehicles: (i) a type of vehicle; (ii) a classification of said vehicle; (iii) dimensions of said vehicle; (iv) dimensions of one or more loads transported by said vehicle; or (v) a combination thereof. (Roberts [¶ 0154]: “a particular transport/vehicle 525 is engaged in transactions (e.g., vehicle service, dealer transactions, delivery/pickup, transportation services, etc.) … transaction module 520 may record information, such as assets, parties, credits, service descriptions, date, time, location, results, notifications, unexpected events” Roberts teaches that the transaction module may record information regarding the delivery/pickup of the transactions, which is the load that the vehicle is carrying.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 6, Chi-Johnston and Roberts teach:
The apparatus of claim 1,
Roberts teaches:
wherein the route maneuver information indicate: (i) patterns of routes traversed by the first vehicles; (ii) maneuvers executed by the first vehicles at locations including the first roadside objects; or (iii) a combination thereof. (Roberts [¶ 0091]: “the solutions can also be utilized to perform a normally dangerous maneuver in a safe manner, such as when the system determines that an exit is upcoming and when the transport is seemingly not prepared to exit” Roberts teaches that the vehicles can perform dangerous maneuvers when there are roadside objects, such as exit signs.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 7, Chi-Johnston and Roberts teach:
The apparatus of claim 1,
Chi-Johnston teaches:
wherein the historical data further indicate driver attribute data associated with drivers of the first vehicles, the driver attribute data indicating: (i) patterns at which the drivers maneuver the first vehicles; (ii) a first number of occurrences in which the drivers maneuvered the first vehicles to disobey traffic rules; (iii) a second number of occurrences in which the drivers were involved in vehicle-related accidents; or (iv) a combination thereof. (Chi-Johnston [¶ 0079]: “method 400 includes comparing the simulation safety score against a number of collisions observed in the driving data” Chi-Johnston teaches the number of collisions, or accidents, that the vehicles had in the driving data.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 8, Chi-Johnston and Roberts teach:
The apparatus of claim 1,
Chi-Johnston teaches:
wherein the historical data further indicate roadside object attribute data associated with the first roadside objects, the roadside object attribute data indicating: (i) a type of roadside object; (ii) dimensions of the first roadside objects; or (iii) a combination thereof. (Chi-Johnston [¶ 0026]: “The perception stack 112 can detect and classify objects and determine their current locations, speeds, directions, and the like.” Chi-Johnston teaches the classification or typing of the roadside object.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 9, Chi-Johnston and Roberts teach:
The apparatus of claim 1,
Chi-Johnston teaches:
wherein the route maneuver information is first route maneuver information, and wherein the contextual information indicate: (i) road attributes associated with a road proximate to the second roadside object; (iii) vehicle attributes associated with the one or more second vehicles; (iv) second route maneuver information associated with the one or more second vehicles; or (v) a combination thereof. (Chi-Johnston [¶ 0029]: “the planning stack 118 can receive the location, speed, and direction of the AV 102, geospatial data, data regarding objects sharing the road with the AV 102 (e.g., pedestrians, bicycles, vehicles, ambulances, buses, cable cars, trains, traffic lights, lanes, road markings, etc.) or certain events occurring during a trip (e.g., emergency vehicle blaring a siren, intersections, occluded areas, street closures for construction or street repairs, double-parked cars, etc.), traffic rules and other safety standards or practices for the road” Chi-Johnston teaches road attributes, such as street closures, construction, street repairs.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 10, Roberts teaches:
A non-transitory computer-readable storage medium having computer program code instructions stored therein, the computer program code instructions, when executed by at least one processor, cause the at least one processor to: receive input data indicating a first roadside object and contextual information associated with the first roadside object; (Roberts [¶ 0005]: “non-transitory computer readable medium comprising instructions, that when read by a processor” Roberts teaches non-transitory memory and processor. Roberts [¶ 0061]: “As the level of risk rises, more detailed data is accumulated, such as all possible proximate data of the surrounding area of the transport … Historical data may also be obtained by remote servers such as previous collisions involving the one or more occupants.” Roberts teaches that historical data of the collision may contain information regarding the surroundings of the vehicle/transport. Roberts [¶ 0061]: “This data may include speed limits for the areas, as well as historical traffic patterns” Roberts teaches that the data includes traffic patterns, or movement of the vehicle, which can be considered route maneuver information.)
… a reason for a cause of the event, and a severity of damage inflicted on the first roadside object, … (Roberts [¶ 0101]: “In one embodiment, the solutions can also be utilized to determine that sounds from a transport are atypical and transmit data related to the sounds as well as a possible source location to a server wherein the server can determine possible causes and avoid a potentially dangerous situation.” Roberts teaches that the server can determine the possible cause of a dangerous situation, such as in a collision of the transport/vehicle. Roberts [¶ 0092]: “The server accesses one or more media files to access the damage to the transport and stores the damage assessment onto a blockchain.” Roberts teaches that the server can access files that indicate the extent/assessment of the damage of the transport/vehicle and can store it in the blockchain, which may be an output of the algorithm.)
generate a vehicle route based on the output data; (Roberts [¶ 0087]: “In one embodiment, the solutions can also be utilized to determine an amount of energy needed by a transport to provide another transport with needed energy via transport to transport energy transfer based on one or more conditions such as weather, traffic, road conditions, car conditions, and occupants and/or goods in another transport, and instruct the transport to route to another transport and provide the energy.” Roberts teaches that the solution/output can also be used to instruct the transport/vehicle to take a route in the direction of another transport/vehicle.)
Chi-Johnston teaches:
cause a machine learning model to generate output data as a function of the input data, wherein the output data indicate a likelihood of an event in which one or more first vehicles will damage the first roadside object, … wherein the machine learning model is trained to generate the output data as a function of the input data by using historical data indicating events in which second vehicles damaged second roadside objects, and wherein the historical data indicate first attributes associated with surroundings of the second roadside objects, second attributes associated with the second vehicles, and route maneuver information associated with the second vehicles. (Chi-Johnston [¶ 0042]: “The data center 150 illustrated in FIG. 1 can use AV driving data (e.g., historical driving data) generated by sensor systems” Chi-Johnston teaches historical data from sensor systems. Chi-Johnston [¶ 0074]: “In some instances, the simulation safety score is based on a continuous scale between two numbers, for example, 0 and 1 (i.e., a fractional value). Also, the continuous scale may correlate to a continuous likelihood of the collision between the AV and the simulated object in simulation.” Chi-Johnston teaches the likelihood of a collision between the autonomous vehicle and the object.)
Shin teaches:
and output the vehicle route on a user interface for vehicle navigation. (Shin [Page 19, Figure 11]: Shin teaches that a map, which has vehicle routes on it, can be dynamically displayed on the edge device application, which may be an application of a vehicle.)
The reasons to combine are substantially similar to those of claim 1.
Claims 11-16 are rejected on the same grounds under 35 U.S.C. 103 as claims 2-7 as they are substantially similar, respectively. Mutatis mutandis.
Regarding dependent claim 22, Chi-Johnston, Roberts, and Choi teach:
The method of claim 19,
Shin teaches:
further comprising causing a user interface associated with the one or more first vehicles to present the map layer. (Shin [Page 2, Paragraph 2]: “A grid-based information mapping method is proposed to display dynamic objects collected from a heterogeneous vehicle into a single map. … A high-definition (HD) map is a highly accurate digital map that provides information such as lane-level information, traffic lights, and signs so that drivers can drive safely” Shin teaches that the map that detects the vehicle movement amongst roadside objects on a road is displayed to a user to facilitate safety.)
The reasons to combine are substantially similar to those of claim 1.
Claims 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Chi-Johnston in view of Roberts in view of Shin in view of Choi (US20240001959A1) hereinafter known as Choi.
Claim 19 is substantially similar to claim 10, with the following additional elements:
Regarding dependent claim 19, Choi teaches:
A method of providing a map layer of roadside events, the method comprising: (Choi [¶ 0049]: “The autonomous vehicle 202 may further receive, based on the route, map data representing a portion of the environment along the route. For example, the autonomous vehicle 202 may receive map data with lattice layer based on the start position and the end position.” Choi teaches that there is map data that gives the datapoints of start and end locations.)
and updating the map layer to include a datapoint indicating the output data at a location of the first roadside object. (Choi [¶ 0049]: “The autonomous vehicle 202 may further receive, based on the route, map data representing a portion of the environment along the route. For example, the autonomous vehicle 202 may receive map data with lattice layer based on the start position and the end position.” Choi teaches that there is map data that gives the datapoints of start and end locations.)
Choi is in the same field as the present invention, since it is directed to providing a map of the vehicle and objects on the road and determining the likelihood of a collision. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine calculation of the likelihood of a crash given the surroundings of the vehicle of the road as taught in Chi-Johnston as modified by Roberts as modified by Shin with implementing the positions of the vehicles and objects in map data as taught in Choi. Choi provides this additional functionality. As such, it would have been obvious to one of ordinary skill in the art to modify the teachings of Chi-Johnston as modified by Roberts as modified by Shin to include teachings of Choi because the combination would allow for more precise geospatial data to keep track of locations of vehicles/objects. This has the potential benefit of more accurately avoiding collisions, as the calculation of these more exact coordinates/locations can provide greater mathematical precision in the collision-avoidance likelihood calculation.
Regarding dependent claim 20, Chi-Johnston, Roberts, and Choi teach:
The method of claim 19,
Choi teaches:
wherein the map layer includes one or more other datapoints indicating one or more other likelihoods in which the one or more first vehicles will damage one or more third roadside objects. (Choi [¶ 0071]: “The collision probability represents a probability of a collision between the autonomous vehicle 402 and the object 408. The collision probability may be determined based on bounding box information of the autonomous vehicle 402. Choi teaches that the map layer includes the probability of collision between the autonomous vehicle and the roadside object.)
The reasons to combine are substantially similar to those of claim 19.
Regarding dependent claim 21, Chi-Johnston, Roberts, and Choi teach:
The method of claim 19,
Choi teaches:
wherein the first roadside object and the second roadside objects are sign posts, traffic posts, light posts, or a combination thereof. (Choi [¶ 0068]: “For example, the guidance system of the autonomous vehicle 402 may identify one or more dynamic and/or static objects, such as a vehicle with a protruding object, a traffic cone, a hazard road sign, fencing, a double-parked vehicle, or the like.” Choi teaches that the objects may include a traffic cone or hazard road signs.)
The reasons to combine are substantially similar to those of claim 19.
Conclusion
THIS ACTION IS MADE FINAL. 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 KYU HYUNG HAN whose telephone number is (703) 756-5529. The examiner can normally be reached on MF 9-5.
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/Kyu Hyung Han/
Examiner
Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123