Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is final.
This action is in response to the amendments filed on Nov 04th, 2025.
Claims 1-20 are pending and have been considered. Claims 1, 9, and 18 are independent claims.
Claims 1, 4, 6, 9, and 18 have been amended. No claims have been canceled.
Claims 1-2, 8-10, 16, 18-19 1 9-10 18 are rejected under 35 U.S.C. 103 as being unpatentable over Yershov et al (US 20200257931 A1) in view Jin et al The calibration and validation of cellular automaton traffic flow model with empirical and experimental data Publication: IET Intelligent Transport Systems, Volume 12, Issue 5, 2018 https://doi.org/10.1049/iet-its.2016.0275 in further view of Benjafaar et al, Cellular Automata for Traffic Flow Modeling, 1997, in further view of Lin et al (US-20190051181-A1) , in further view of Tava et al (US 20190011276).
Claims 7, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Yershov et al (US 20200257931) in view Jin et al The calibration and validation of cellular automaton traffic flow model with empirical and experimental data Publication: IET Intelligent Transport Systems, Volume 12, Issue 5, 2018 https://doi.org/10.1049/iet-its.2016.0275 in further view of Benjafaar et al, Cellular Automata for Traffic Flow Modeling, 1997, in further view of Lin et al (US-20190051181-A1) , view of Tava et al (US 20190011276) in further view of Nasseri et al, 2020 Autonomous Vehicle Technology Report, https://www.wevolver.com/article/2020.autonomous.vehicle.technology.report
Claims 3, 11 20, are rejected under 35 U.S.C. 103 as being unpatentable over Yershov et al (US 20200257931) in view Jin et al The calibration and validation of cellular automaton traffic flow model with empirical and experimental data Publication: IET Intelligent Transport Systems, Volume 12, Issue 5, 2018 https://doi.org/10.1049/iet-its.2016.0275 in further view of Benjafaar et al, Cellular Automata for Traffic Flow Modeling, 1997, in further view of Lin et al (US-20190051181-A1) , in further view of Tava et al (US 20190011276) in further view of Li, X. Ying and et al (henceforth referred to as LiYing), "GRIP: Graph-based Interaction-aware Trajectory Prediction," 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 2019, pp. 3960-3966, doi: 10.1109/ITSC.2019.8917228.
Claims 4, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Yershov et al (US 20200257931) in view Jin et al The calibration and validation of cellular automaton traffic flow model with empirical and experimental data Publication: IET Intelligent Transport Systems, Volume 12, Issue 5, 2018 https://doi.org/10.1049/iet-its.2016.0275 in further view of Benjafaar et al, Cellular Automata for Traffic Flow Modeling, 1997, in further view of Lin et al (US-20190051181-A1) , in further view of Tava et al (US 20190011276). in further view of Zhao H. T. et al (Cellular automata model for traffic flow at intersections in internet of vehicles, Physica A: Statistical Mechanics and its Applications, Volume 494, 15 March 2018, Pages 40-51)
Claims 5, 13 are rejected under 35 U.S.C. 103 as being unpatentable over Yershov et al (US 20200257931) in view Jin et al The calibration and validation of cellular automaton traffic flow model with empirical and experimental data Publication: IET Intelligent Transport Systems, Volume 12, Issue 5, 2018 https://doi.org/10.1049/iet-its.2016.0275 in further view of Benjafaar et al, Cellular Automata for Traffic Flow Modeling, 1997, in further view of Lin et al (US-20190051181-A1) , in further view of Tava et al (US 20190011276) in view of Zhao H. T. et al (Cellular automata model for traffic flow at intersections in internet of vehicles, Physica A: Statistical Mechanics and its Applications, Volume 494, 15 March 2018, Pages 40-51), in further view of Muhammad, T. et al., Simulation Study of Autonomous Vehicles’ Effect on Traffic Flow Characteristics including Autonomous Buses, Journal of Advanced Transportation, 2020, 4318652, 17 pages, 2020. https://doi.org/10.1155/2020/4318652
Claims 6,14 are rejected under 35 U.S.C. 103 as being unpatentable over Yershov et al (US 20200257931) in view Jin et al The calibration and validation of cellular automaton traffic flow model with empirical and experimental data Publication: IET Intelligent Transport Systems, Volume 12, Issue 5, 2018 https://doi.org/10.1049/iet-its.2016.0275 in further view of Benjafaar et al, Cellular Automata for Traffic Flow Modeling, 1997, in further view of Lin et al (US-20190051181-A1) , in further view of Tava et al (US 20190011276) in view of Zhao H. T. et al (Cellular automata model for traffic flow at intersections in internet of vehicles, Physica A: Statistical Mechanics and its Applications, Volume 494, 15 March 2018, Pages 40-51), in further view of Muhammad, T. et al., Simulation Study of Autonomous Vehicles’ Effect on Traffic Flow Characteristics including Autonomous Buses, Journal of Advanced Transportation, 2020, 4318652, 17 pages, 2020. https://doi.org/10.1155/2020/4318652 in further view of SUMO "Simulation of Urban MObility" (SUMO) -open source, highly portable, microscopic and continuous traffic simulation package. Below from the ‘Changes in the 2020 releases (versions 1.5.0, 1.6.0, 1.7.0 and 1.8.0) Version 1.8.0 (02.12.2020) ‘ which are before the priority date. https://sumo.dlr.de/docs/ChangeLog/Changes_in_2020_releases.html#enhancements_3
Claims 17 are rejected under 35 U.S.C. 103 as being unpatentable over Yershov et al (US 20200257931) in view Jin et al The calibration and validation of cellular automaton traffic flow model with empirical and experimental data Publication: IET Intelligent Transport Systems, Volume 12, Issue 5, 2018 https://doi.org/10.1049/iet-its.2016.0275 in further view of Benjafaar et al, Cellular Automata for Traffic Flow Modeling, 1997, in further view of Lin et al (US-20190051181-A1) , in further view of Tava et al (US 20190011276) in further view of Dosovitskiy, A. et al "CARLA: An open urban driving simulator." In Conference on robot learning, pp. 1-16. PMLR, 2017, https://proceedings.mlr.press/v78/dosovitskiy17a/dosovitskiy17a.pdf
Response to Amendments and Arguments
In the amendment filed on Nov 4th , 2025, applicant amended claim 1, 4, 6, 9, and 18 to include new limitations. The amendments have been fully considered.
Regarding Claim rejections under 35 USC 101, the arguments have been considered but have not been found persuasive.
Applicant makes the remark that the closest Examiner comes to articulating an abstract idea is "determine a graph-derived CA model for navigating in traffic and determine if a vehicle needs it." which Applicant considers “an over-generalization of the claim that ignores crucial limitation” and that when properly construed, the claims do not merely recite an abstract idea.
First, each and every single limitation is analyzed and pointed out when it recites an abstract idea. Second, MPEP guidance is clear to consider the limitations together as a single abstract idea, see for example MPEP 2106.04 paragraph below:
“In other claims, multiple abstract ideas, which may fall in the same or different groupings, or multiple laws of nature may be recited. In these cases, examiners should not parse the claim. For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A Prong One to make the analysis clear on the record. However, if possible, the examiner should consider the limitations together as a single abstract idea for Step 2A Prong Two and Step 2B (if necessary) rather than as a plurality of separate abstract ideas to be analyzed individually.
Third, limitations that were additional elements were identified as such. However, these were not sufficient for integrating the abstract idea into a practical application or provide sufficiently more.
Examiner does not dispute the specification describes specific technical challenges and that a solution is found. What the Examiner points out is that the improvement in the claims is directed to the judicial exception.
Examiner’s analysis does not suggest the entire method can be carried out in one’s head. Instead it shows that certain method steps can be performed in the mind, those determine an abstract idea, and, that other additional elements – which clearly are not mental, are insufficient to integrate into a practical application (in the way required by MPEP) or provide significantly more. Applicant refers in the remarks to “controlling a vehicle”, but Examiner points out that claims stop short from claiming vehicle control. The only thing that claimed was sending rules to a vehicle, wherein the vehicle uses the rules to navigate in traffic. Controlling the steering, break or acceleration etc would integrate into the practical application. Nothing is stated to be controlled in currently recited claims.
Applicant states claims involve analyzing potentially large sets of vehicle trajectory data (which could be thousands of GPS readings or sensor data points), The Specification has no information about how GPS is used, and nothing is recited in the claims. Large data beyond the ability of a human to practically perform in the mind would exclude a mental process, but there is no language in the claim that would indicate such a scale; this of course may remain an abstract idea of reciting mathematical concepts – note that graphs are a mathematical concept.
Regarding the remark that many recited steps involve more than just observing and judging … For instance, Applicant remarks "determine that a first autonomous vehicle is not trained for the traffic environment and flow conditions" might appear to be an observation, but it actually requires comparing the vehicle's behavior against the modeled environment's demands (e.g., noticing the AV's performance metrics deviate from what the simulation predicts that a well-tuned vehicle should do in that scenario)…
The claims are examined in broadest reasonable interpretation. Also in view of the specification, but that does not mean one can bring the specification into the claim.
The 35 USC 101 rejection of all claims is maintained.
Regarding the rejection under U.S.C. § 103 -
Regarding independent claim rejection and the fact that Applicant disagrees that ‘graph node associated with a first infrastructure feature’ can be interpreted in BRI as a graph nodes in Yershov, where cells are divided by a grid/mesh - including an unstructured mesh- when cells can be generated ‘by dividing a driving environment’.
Though not explicitly reciting the word ‘features’, the word ‘environment’ is interpreted to extend beyond the ‘road’ and have some characteristics/features, and an ‘unstructured mesh’ suggests not a rigid equally spaced cell structure, but potentially something that can be determined by environment …or features of the environment. The Examiner does appreciate a difference between the two which is clearer in the specification, and, while it remains at the opinion that the claim as currently recited can still be interpreted to be covered by Yershov’s disclosure, considers that an amended recitation of the claim may differentiate it beyond what can be interpreted to Yershov’s diosclosure.
Regarding Applicant’s argument that: “ the recited ‘graph node area’ is a defined spatial region. A node in Yershov has no area, it's effectively a point or abstract marker along a roadway.” Examiner disagrees and draws attention to Yershov [0118] …. An example of such a discretized representation 1100 is shown in FIG. 11. In this example, the discretized representation 1100 includes a grid map with multiple individual cells 1105 (also referred to as grid cells) that each represents a unit area (or volume) of the environment. Yershov clearly discloses a cell being associated with an area.
Regarding ‘a connecting link type’ the Applicant’s argument is not persuasive. In broadest reasonable interpretation based on the claim language, an edge connecting cells is a connecting link. The fact that the specification gives other examples can not be used as one can not bring the specification into the claim, only what is recited in the claim is analyzed.
The fact that Jin and Benjafaar do not disclose things Yershov do only emphasizes the need of the combination; since Yershov discloses the aspects those don’t need to be repeated in the other art in combination.
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 1-20 are rejected under 35 USC 101 because the claimed invention is not directed to patent eligible subject matter. The claimed matter is directed to a judicial exception, i.e., an abstract idea, not integrated into a practical application, and without significantly more.
Per Step 1 of the multi-step eligibility analysis, claims 1-8 are directed to a computer implemented method, claims 9-17 are directed to a system, and claims 18-20 are directed to a non-transitory computer-readable storage medium.
Thus, on its face, each independent claim and the associated dependent claims are directed to a statutory category of invention.
[INDEPENDENT CLAIMS]
Independent claim 9 (which is representative of claims 1, 18) is rejected under 35 U.S.C. 101 because the claim is directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application, and without significantly more.
Per Step 2A.1. The limitations of the independent claim 9 (which is representative of claims 1, 18) recite an abstract idea, shown in bold in the following:
[A] A system, comprising: a processor; and a memory for storing executable instructions, the processor programmed to execute the executable instructions to:
[B] convert a continuous space road map to a graph node network:
[C] generate, based on the graph node network, a first graph node associated with a first infrastructure feature and a first area; and
[D] generate, based on the graph node network a second graph node associated with a second infrastructure feature and a second area;
[E] determine a first graph node area associated with the first graph node;
[F] determine a second graph node area associated with the second graph node;
[G] determine a connecting link type that connects the first graph node to the second graph node; and
[H] determine cellular automata (CA) parameters based on the first graph node area, the second graph node area, and historic vehicular trajectory data;
[I] determine traffic environment and flow conditions, wherein the traffic environment and flow conditions include traffic information, the CA parameters, and driver behavior data;
[J] determine that a first autonomous vehicle is not trained for the traffic environment and flow conditions; and
[K] send, based on the first autonomous vehicle not being trained for the traffic environment and flow conditions a rule set to the first autonomous vehicle for navigating the traffic environment and flow conditions
[L] wherein the fist autonomous vehicle uses the rule set to navigate the traffic environment and flow conditions.
Independent claim 9 (which is representative of claims 1, 18) recites: convert a map to a graph network ([A]); extracting/generating a graph with nodes characteristic of map elements characteristics ([C], [D]); determine areas associated with nodes ([E], [F]), and connections types between nodes [G]; determine parameters of a computational model using CA [H] determine (decide) if an autonomous vehicle is trained or not [J], which, based on the claim language and in view of the application specification, represents a process aimed at: “determine a graph-derived CA model for navigating in traffic and determine if a vehicle needs it”
Each limitation that recites an abstract idea is in bold below, analyzed in detail with examples on how a person can perform the mental process in the mind.:
[B] convert a continuous space road map to a graph node network: The reason this is a mental process is because humans can perform this in the mind or using a pen and paper. For example a human can look, from driver seat or from a vantage point, e.g. a bridge, observe that the freeway between two points in the map, use evaluation and judgement and decision-making to -mentally or with pen - mark on a map and then copy on blank paper only the allocated region with features marked n zones with rectangles; further next to it draw a simplified representation of nodes interconnected by edges, where nodes correspond to various zone (rectangles) on the road, and the edges connect adjacent nodes., which converted the road map into a “graph node network” Interconnecting adjacent nodes (corresponding to adjacent zones on the rode) gives the edges. The limitation does recite anything that would prevent a person to practically perform that in the mind. Furthermore a graph is a mathematical concept.
[C] generate, based on the graph node network, a first graph node associated with a first infrastructure feature and a first area; and [D] generate, based on the graph node network a second graph node associated with a second infrastructure feature and a second area; Similarly a mental process of observation, evaluation, judgment and decision-making. For example a human could identify the infrastructure feature such as the entrance on the freeway, allocated a rectangular area to the segment of the road it forms, and -mentally or on paper – and associate a node of the graph to it, marking with a symbol indicating correspondence, e.g. area being A11 and node being N11. Similarly, a second infrastructure area, e.g. the zone between the traffic light at entrance to freeway on the right lane, and the place where the lane merges into lane on its left, made as a rectangle marked A12 and a node on the graph, along the the direction of the ‘network’ arbitrarily chosen ‘to the right’ similar as in Fig.2B create the second node marked N12. There is no ‘complexity’ or anything of other nature that would prevent this to be practically performed in the mind. Furthermore a graph is a mathematical concept.
[E] determine a first graph node area associated with the first graph node; [F] determine a second graph node area associated with the second graph node; These limitations also recites a mental process. To cite directly from the specification [0079]… This step may include evaluating a relative area proximate to the first node, based on user input, or an area or bounding dimensions that define a node size. A person can do this evaluation or a relative area in the mind also can make the association with a graph node in the mind. Nothing prevents a person to perform the above steps in the mind: it observes/evaluates proximity of an area and makes an association which can be marked. Furthermore a graph is a mathematical concept.
[G] determine a connecting link type that connects the first graph node to the second graph node; and
According to the specification “[0080] identifying the first node and the second node and determining the relative proximity to one another.” . In broadest reasonable interpretation and in view of the specification a person can perform in the mind the observation, evaluation, judgement in identifying two nodes and relative proximity between them. Furthermore a graph is a mathematical concept.
[H] determine cellular automata (CA) parameters based on the first graph node area, the second graph node area, and historic vehicular trajectory data;
As indicated in past Office Actions, the specification does not provide any details on how to do it. The Applicant refers to Nagel- Schreckenberg model and publications in IDS. To quote from Nagel- Schreckenberg publication “A cellular automaton model for freeway traffic” p 224 bottom, For an analytical treatment of the circular traffic, one chooses as starting point the case vmax = 1”. For speeds larger than 1 an additional parameter for the current speed is needed which gives serious difficulties to analytical treatments”…. This makes of course a difference to the parallel updating (which can simply be seen by simulating the two different updates) but the results should be qualitatively similar and the randomization parameter p plays a particular simple role for random sequential update. With the use of the more familiar spin variables a; plus /minus 1, with +1 for occupied and -1 for empty sites, the transition probability W(-a;, -a;+i la;,«;+i) from («;, a;+i) to (-a;, -a;+i) (I.e. a car moves from I to I + I) simply reads
PNG
media_image1.png
48
477
media_image1.png
Greyscale
In another paper form IDS, Jin et al “Calibration and validation of Cellular Automata…” [Abstract[ We use a simple CA model, which only has two important parameters to be calibrated” They also teach “”This makes some usual techniques for the calibration of car-following models unavailable for the calibration of CA models, including the use of vehicle trajectories”… [end of section 2.1] in this paper, we will always set p as a constant value: p=0.1, and the focus is on AD and r. Only dealing with two important parameters will make the work easy to control”]
In broadest reasonable interpretation, in view of the 1) lack of any details in the specification of what are these parameters representing, and 2) according to the Applicant in the Arguments pointing to Nagel-Schreckenberg model, the Examiner interprets the CA parameters to be transition probability of moving forward and transition probability of changing lane, and those can be determined from observing the traffic for a duration, say 1h, counting the number of vehicles that did not change lane in a specific zone and how many did in fact change lane – assume for even more realistic case that the count to change to the left and to the right are done .. These histograms can be used to make a judgement and a decision on what probability for transition to assign, and are interpreted here as the CA parameters which can be determined in the mind. Furthermore a graph is a mathematical concept. Should it be found this is not reciting an abstract idea, the Examiner points out that the Applicant has made the statement that this limitation is a WURC.
[J] determine that a first autonomous vehicle is not trained for the traffic environment and flow conditions
This limitation recites an abstract idea since the determination that a vehicle is not trained for a traffic environment and flow conditions can be practically performed in the mind. For example, a driver in the traffic can immediately determine a vehicle is not trained for the traffic environment by observing the sub-optimal behavior and the fact a vehicle’s behavior more impedes that traffic flow, such as for example slowing down when road ahead is empty, accelerating and changing lane to the left when the left lane is crowded and vehicle in front on the left is very close, etc. Each of us driving on a road can determine cars that behavior erratically, most often than not due to aggressive drivers which from their action may advance in traffic at the price of slowing down a column of vehicles in the next lane when cutting in front of a vehicle that has to abruptly press the breaks. There is nothing stopping a person to perform the limitation in the mind.
Regarding page 10 line 4, “Similarly, in the present application, the claims recite steps that include complex calculation involving large amount of data that is complex.” The examiner used broadest reasonable interpretation of the claims, there was no limitation to complex calculations (or no recitation of a calculation which would have been a mathematical concept. Applicant is reminded that no explanation of calculation of parameters was made, not even clarifying which parameters were calculated.
Regarding the examples 37 and 39 these are not found analogous. Example 37 was in fact found to recite a judicial exception, so Applicant’s argument “In both these examples, the complexity and volume of data precluded the steps to be performed within a human mind or with a pen and paper” does not apply to Example 37. As for Example 39, humans are not expected to do in the mind operations such as “contrast reduction of digital facial images”, which are clear what they are. and indeed involve complex calculations, though not recited in the claim, thus not mathematical concepts.
In contrast, all limitations related to the graph .or determine a vehicle is untrained are not expected to involve any complexity of operations or calculations, and in terms of determining CA parameters, as already explained, the interpretation adopted, in lack of even mentioning what parameters are determined, is that these relate to a statistical observation which would determine a probability, and that can be performed in the mind, for example 70 out of 100 vehicles changing lanes to the left one would assign a probability of 0.35 (35/100) to changing to the left lane action.
Overall, as a whole, the claim recites an abstract idea which is “determine a graph-derived CA model for navigating in traffic and determine if a vehicle needs it”
This is an abstract idea that describes a mental process, as exemplified above, and a human can perform in in the mind in the mind, by observing traffic flow characteristics, and behavior of vehicles of the road, and associating those with parameters including the transitions in an ordered graph; also observing and making a judgement on behavior of vehicles. Thus the claim recites an abstract idea.
This is a combination that, under its broadest reasonable interpretation (and in view of the specification that indicates how obtaining the model data involves identification of elements), covers performance of limitations expressing observation, evaluation, judgement in discretizing a map, generating the graph associated with a map, associating probabilities for vehicle actions to nodes and determine changes that occur based on assigned probabilities, mentally or manually. Nothing in the claim elements precludes the steps from being practically performed mentally or manually by a human. These are Mental Processes – Concepts Performed in the Human Mind (MPEP § 2106.04(a)(2), subsection III).
Accordingly, claim 9 (which is representative of claims 1, 18) recites an abstract idea.
Per Step 2A.2. The identified abstract idea is not integrated into a practical application.
The additional elements of processor and memory to execute instructions [A] constitutes mere instructions to apply an exception (MPEP § 2106.05(f)); data gathering and manipulation 2106.05(g)), Insignificant Extra-Solution Activity; while obtaining information about traffic and driver behavior [I], sending a rule to a vehicle [K] fail to amount to more than the judicial exception itself.
To determine if it is integrated into a practical application the additional limitations are examined. The additional limitations are, in bold:
I] determine traffic environment and flow conditions, wherein the traffic environment and flow conditions include traffic information, the CA parameters, and driver behavior data;
With the exception of the determination of the CA parameters, which was already analyzed, the determination of traffic information and driver behavior data is considered data gathering
[K] send, based on the first autonomous vehicle not being trained for the traffic environment and flow conditions a rule set to the first autonomous vehicle for navigating the traffic environment and flow conditions
This is interpreted as a process for outputting data, which is simply ‘apply it’, applying the exception .
wherein the first autonomous vehicle uses the rule set to navigate the traffic environment and flow conditions.
This is interpreted as an insignificant extra (post) activity. As recited it moves the claim towards being directed to a practical application, yet stops short from indicating an actual control of the car based on the rule.
The additional elements in these limitations, individually or in combination, and considering the claim as a whole, do not integrate the judicial exception into a practical application. Using the rule to navigate is close but not sufficient to have the claim directed to a modification, improvement of traffic coordination or how car is controlled to improved navigation
Per Step 2B. Independent claim 9 (which is representative also of claim 1, 18) does not contribute an inventive concept.
The additional elements, when considered individually, are limitations that the courts have found not enough to qualify as “significantly more” than the judicial exception; and amount to no more than a recitation of the words "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer. Further, the insignificant extra-solution data gathering, data manipulation, and data transmission activities are also Well-Understood, Routine and Conventional (WURC) (see MPEP § 2106.05(d)(ll)). The above Insignificant Extra Solution Activities do not add a meaningful limitation to the process of modeling with CA and determining if a vehicle needs it.
Should it be found that limitation reciting determining CA parameters is not reciting a mental process or mathematical concept it should be noted it is a WURC activity based on Applicant;s statement.
When considered as a whole, as an ordered combination, the additional elements in the claim only amount to instructions to apply the abstract idea on a computer. Moreover, as noted above, there is nothing about the computing environment or the additional steps that is significant or meaningful to the underlying judicial exception because the identified abstract idea (“determine a graph-derived CA model for navigating in traffic and determine if a vehicle needs it”) could have been reasonably performed when provided with the relevant data and/or information. The claim as a whole does not amount to significantly more than the judicial exception itself.
Therefore, it is concluded that independent claims 1, 9, 18 are deemed ineligible.
[DEPENDENT CLAIMS]
Claim 10 (dependent on claim 9), representative of claims 2 (dependent on claim 1) and claim 19 (dependent on claim 18), further recites: wherein the continuous space road map comprises:
[A] a first plurality of graph nodes having at least one vehicle agent operating within defined boundaries of each node of the first plurality of graph nodes; and
[B] a second plurality of graph nodes having no vehicle agent operating within defined boundaries of any node of the second plurality of graph nodes.
The additional elements in this dependent claim amount to no more than mere instructions to apply an exception (MPEP 2106.05(f)). Both when considered individually and in combination, these additional elements recited by the claim only further elaborate on the abstract idea identified in the independent claims - the claim continues to recite the identified abstract idea: “determine a graph-derived CA model for navigating in traffic and determine if a vehicle needs it“. These additional elements do not do not impose any meaningful limits on practicing the abstract idea, and do not integrate the abstract idea into a practical application.
Moreover, when considered as a whole, as an ordered combination, the dependent claim elaborates on the identified abstract idea. It does not practically or significantly alter how the identified abstract idea would be performed. There is no inventive concept - the claim as a whole does not amount to significantly more than the judicial exception itself.
Therefore, claim 2, 10, 19 are deemed ineligible.
Claim 11 (dependent on claim 10), representative of claim 3 (dependent on claim 2) and claim 20 (dependent on claim 19), further recites:
[A] wherein the processor is further programmed to model the vehicle agent driving action by executing the instructions to:
[B] compute a set of probabilities of the vehicle agent driving actions for the first plurality of graph nodes; and
[C] omit computation of the set of probabilities for the vehicle agent driving actions for the second plurality of graph nodes.
The claim elements reciting computing probabilities for actions for a first set of graph nodes ([B]) - and not computing for a second set ([C]) covers performance of limitations expressing observation, evaluation, judgement mentally or manually (distinguishing between two graph node sets, computation of probabilities) . For example a person could observe traffic for a period of time, and over say 100 vehicles passing by a bifurcation and determine how many changed lanes before, how many stayed in the lane and slowed down etc, determining thus a probability by counting cases. Nothing in the claim elements precludes the steps from being practically performed mentally or manually by a human. These are Mental Processes, (MPEP § 2106.04(a)(2), subsection III); furthermore, one should note, calculations using probabilities, i.e. mathematical calculations, are recited which are Mathematical Concepts (MPEP § 2106.04(a)(2), subsection I). Thus, claim elements recite another abstract idea.
The additional elements in this dependent claim ([A]) amount to no more than mere instructions to apply an exception (MPEP 2106.05(f)). When considered individually and in combination, these additional elements recited by the claim only further elaborate on the abstract idea identified in the independent claims - the claim continues to recite the identified abstract idea: “determine a graph-derived CA model for navigating in traffic and determine if a vehicle needs it”. These additional elements do not do not impose any meaningful limits on practicing the abstract idea, are not directed to any specific improvement of the claim, and do not integrate the abstract idea into a practical application.
Moreover, when considered as a whole, as an ordered combination, the dependent claim elaborates on the previously identified abstract idea (“determine a graph-derived CA model for navigating in traffic and determine if a vehicle needs it”). It does not practically or significantly alter how the identified abstract idea would be performed. There is no inventive concept - the claim as a whole does not amount to significantly more than the exception itself.
Therefore, claims 3, 11, 20 are deemed ineligible.
Claim 12 (dependent on claim 10), representative of claim 4 (dependent on claim 2), further recites:
[A] wherein the processor is further programmed to model the vehicle agent by executing the executable instructions to:
[B] determine a behavioral rule based on a link type, and further based on a rule of a behavioral rule set; and
[C] assign, based on the rule. a key performance indicator (KPI) associated with the vehicle agent driving action.
The claim elements reciting determining a rule based on a condition ([B]), and accordingly assign an indicator ([C]) covers performance of limitations expressing observation, evaluation, judgement mentally or manually (distinguishing between two graph node sets, computations) . For example a person can analyze a link type, make an evaluation based on the rule, make a judgment assigning a KPI.i Nothing in the claim elements precludes the steps from being practically performed mentally or manually by a human. These are Mental Processes, (MPEP § 2106.04(a)(2), subsection III). Thus, claim elements recite another abstract idea.
The additional elements in this dependent claim ([A]) amount to no more than mere instructions to apply an exception (MPEP 2106.05(f)). When considered individually and in combination, these additional elements recited by the claim only further elaborate on the abstract idea identified in the independent claims - the claim continues to recite the identified abstract idea: “determine a graph-derived CA model for navigating in traffic and determine if a vehicle needs it”. These additional elements do not do not impose any meaningful limits on practicing the abstract idea, are not directed to any specific improvement of the claim, and do not integrate the abstract idea into a practical application.
Moreover, when considered as a whole, as an ordered combination, the dependent claim elaborates on the previously identified abstract idea (“determine a graph-derived CA model for navigating in traffic and determine if a vehicle needs it”). It does not practically or significantly alter how the identified abstract idea would be performed. There is no inventive concept - the claim as a whole does not amount to significantly more than the exception itself.
Therefore, claims 4, 12 are deemed ineligible.
Claim 13 (dependent on claim 12), representative of claim 5 (dependent on claim 4), further recites:
wherein the behavioral rule set is user selectable to include weighted modeling rules associated with a driving action of a set of driving actions comprising: merging; aggressive merging moving left aggressive moving left; moving right; aggressive moving right; overtaking; aggressive overtaking: undertaking; aggressive undertaking; drifting right; drifting left; cruising; cruising left; and cruising right.
The additional elements in this dependent claim amount to no more than mere instructions to apply an exception (MPEP 2106.05(f)). Both when considered individually and in combination, these additional elements recited by the claim only further elaborate on the abstract idea identified in the independent claims - the claim continues to recite the identified abstract idea: “determine a graph-derived CA model for navigating in traffic and determine if a vehicle needs it”. These additional elements do not do not impose any meaningful limits on practicing the abstract idea, and do not integrate the abstract idea into a practical application.
Moreover, when considered as a whole, as an ordered combination, the dependent claim elaborates on the previously identified abstract idea (“determine a graph-derived CA model for navigating in traffic and determine if a vehicle needs it”). It does not practically or significantly alter how the identified abstract idea would be performed. There is no inventive concept - the claim as a whole does not amount to significantly more than the judicial exception itself.
Therefore, claims 5, 13 are deemed ineligible.
Claim 14 (dependent on claim 13), representative of claim 6 (dependent on claim 5), further recites:
[A] wherein the processor is further programmed to discretizing the continuous space road map by executing the executable instructions to:
[B] receive a user selection indicative of a selectable behavioral rule on a behavioral rules list;
[C] receive a user input comprising a probability indicator associated with the selectable behavioral rule;
[D] generate a model for the vehicle agent driving action based on the probability indicator and the selectable behavioral rule; and
[E] output the KPI associated with the vehicle agent driving action using the model.
The claim elements reciting generating a model for driving based on a probability and a rule ([D]) covers performance of limitations expressing observation, evaluation, judgement mentally or manually. A person can create such a model in the mind or by writing down on paper, based on the set of behavior rules and probability indicators Nothing in the claim elements precludes the steps from being practically performed mentally or manually by a human. These are Mental Processes, (MPEP § 2106.04(a)(2), subsection III). and, therefore, claim elements recite another abstract idea.
The additional elements in this dependent claim ([A] -[C], [E])) amount to no more than mere instructions to apply an exception (MPEP 2106.05(f)). When considered individually and in combination, these additional elements recited by the claim only further elaborate on the abstract idea identified in the independent claims - the claim continues to recite the identified abstract idea: “determine a graph-derived CA model for navigating in traffic and determine if a vehicle needs it”. These additional elements do not do not impose any meaningful limits on practicing the abstract idea, are not directed to any specific improvement of the claim, and do not integrate the abstract idea into a practical application.
Moreover, when considered as a whole, as an ordered combination, the dependent claim elaborates on the previously identified abstract idea (“determine a graph-derived CA model for navigating in traffic and determine if a vehicle needs it”). It does not practically or significantly alter how the identified abstract idea would be performed. There is no inventive concept - the claim as a whole does not amount to significantly more than the exception itself.
Therefore, claims 6, 14 are deemed ineligible
Claim 15 (dependent on claim 9), representative of claim 7 (dependent on claim 1), further recites:
wherein a plurality of vehicle agents executes a driving model instruction set that mimics driving behavior of a connected autonomous vehicle (CAV).
The additional elements in this dependent claim amount to no more than linking the use of a judicial exception to a field of use (MPEP § 2106.05(h)). When considered individually and in combination, these additional elements recited by the claim only further elaborate on the abstract idea identified in the independent claims - the claim continues to recite the identified abstract idea: “determine a graph-derived CA model for navigating in traffic and determine if a vehicle needs it”. These additional elements do not do not impose any meaningful limits on practicing the abstract idea, and do not integrate the abstract idea into a practical application.
Moreover, when considered as a whole, as an ordered combination, the dependent claim elaborates on the previously identified abstract idea (“determine a graph-derived CA model for navigating in traffic and determine if a vehicle needs it”). It does not practically or significantly alter how the identified abstract idea would be performed. There is no inventive concept - the claim as a whole does not amount to significantly more than the judicial exception itself.
Therefore, claims 7, 15 are deemed ineligible.
Claim 16 (dependent claim 9) representative of claim 8 (depending on claim 1) further recites
wherein the first infrastructure feature and the second infrastructure feature comprises one of: a roadway travel direction; a freeway; a side road; a toll road; an intersection; and a number of turning lanes.
The additional elements in this dependent claim amount to no more than mere instructions to apply an exception (MPEP 2106.05(f)). Both when considered individually and in combination, these additional elements recited by the claim only further elaborate on the abstract idea identified in the independent claims - the claim continues to recite the identified abstract idea: “determine a graph-derived CA model for navigating in traffic and determine if a vehicle needs it”. These additional elements do not do not impose any meaningful limits on practicing the abstract idea, and do not integrate the abstract idea into a practical application.
Moreover, when considered as a whole, as an ordered combination, the dependent claim elaborates on the previously identified abstract idea (“determine a graph-derived CA model for navigating in traffic and determine if a vehicle needs it”). It does not practically or significantly alter how the identified abstract idea would be performed. There is no inventive concept - the claim as a whole does not amount to significantly more than the judicial exception itself.
Therefore, claims 8, 16 are deemed ineligible.
Claim 17 (dependent on claim 9), further recites:
wherein the plurality of vehicle agents executes a driving model instruction set that mimics driving behavior of a connected human- driven vehicle.
The additional elements in this dependent claim amount to no more than linking the use of a judicial exception to a field of use (MPEP § 2106.05(h)). When considered individually and in combination, these additional elements recited by the claim only further elaborate on the abstract idea identified in the independent claims - the claim continues to recite the identified abstract idea: “determine a graph-derived CA model for navigating in traffic and determine if a vehicle needs it”. These additional elements do not do not impose any meaningful limits on practicing the abstract idea, and do t integrate the abstract idea into a practical application.
Moreover, when considered as a whole, as an ordered combination, the dependent claim elaborates on the previously identified abstract idea (“determine a graph-derived CA model for navigating in traffic and determine if a vehicle needs it”). It does not practically or significantly alter how the identified abstract idea would be performed. There is no inventive concept - the claim as a whole does not amount to significantly more than the judicial exception itself.
Therefore, claim 17 is deemed ineligible.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
i. Determining the scope and contents of the prior art.
ii. Ascertaining the differences between the prior art and the claims at issue.
iii. Resolving the level of ordinary skill in the pertinent art.
iv. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 8-10, 16, 18-19 1 9-10 18 are rejected under 35 U.S.C. 103 as being unpatentable over Yershov et al (US 20200257931 A1) in view Jin et al The calibration and validation of cellular automaton traffic flow model with empirical and experimental data Publication: IET Intelligent Transport Systems, Volume 12, Issue 5, 2018 https://doi.org/10.1049/iet-its.2016.0275 in further view of Benjafaar et al, Cellular Automata for Traffic Flow Modeling, 1997, in further view of Lin et al (US-20190051181-A1) , in further view of Tava et al (US 20190011276).
Claims with similar limitations are grouped, and the rejection analysis is performed on the system claim; the other claims of the group are rejected under the same rationale.
Independent claim 9 is directed to a system and recites all of the limitations of independent method claim 1 and non-transitory computer storage readable medium claim 18 , along with additional limitations. Claims 1, 18 are rejected under the same rationale set forth for claim 9.
Regarding claim 9 (representative for claims 1, 18) Yershov discloses: a system, comprising: a processor; and a memory for storing executable instructions, the processor programmed to execute the executable instructions to:{see at least [Abstract] generating, using one or more processing devices of a vehicle operating in an environment, a discretized representation of the environment, including a plurality of cell... generating a prediction of occupancy of at least one cell in the discretized representation based on the updated particle density function, and operating the vehicle, using a controller circuit of the vehicle, based at least in part on the prediction. [0007] a vehicle that includes one or more computer processors; and one or more non-transitory storage media for storing instructions. The instructions, when executed by one or more processors}
convert a continuous space road map to a graph node network: {see at least Fig. 8; [0119] Occupancy grid maps divide the environment of an autonomous vehicle into a collection of individual grid cells, and the probabilities of occupancy of individual grid cells are computed. In some implementations, the cells are generated by dividing a map (or a driving environment) based on a Cartesian grid, a polar coordinate system, a structured mesh, a block structured mesh, or an unstructured mesh. In some implementations, the cells can be described by a graph, where each cell corresponds to a node and two adjacent cells are characterized by an edge on the graph.}. Convert a continuous road map to a graph node network interpreted as dividing the map into grid cells, cells corresponding to nodes, and described by a graph (the graph node network) .
generate, based on the graph node network, a first graph node associated with a first infrastructure feature and a first area; and generate, based on the graph node network, a second graph node associated with a second infrastructure feature and a second area; { see at least [0119] Occupancy grid maps divide the environment of an autonomous vehicle into a collection of individual grid cell… the cells are generated by dividing a map (or a driving environment) based on a Cartesian grid…In some implementations, the cells can be described by a graph, where each cell corresponds to a node and two adjacent cells are characterized by an edge on the graph.}. Generate a first graph node and second graph modes interpreted as nodes on the graph that describes the cells; infrastructure with features and area interpreted as driving environment.
determine, a first graph node area associated with the first graph node; determine, a second graph node area associated with the second graph node; {see at least [0101] … The route 702 is typically defined by one or more segments; [0105] In an embodiment, the directed graph 800 has nodes 806a-d representing different locations between the start point 802 and the end point 804 that could be occupied by an AV 100. …the nodes 806a-d represent segments of roads. For example, a segment is a distance to be traveled over at least a portion of a street, road, highway, driveway, or other physical area appropriate for automobile travel.; [0119] the cells can be described by a graph, where each cell corresponds to a node; [0105] In some examples, e.g., when the start point 802 and the end point 804 represent different locations on the same road, the nodes 806a-d represent different positions on that road; Fig., 8, (806a)-(806b)} A graph node area is interpreted as a segment of the road. First graph node area and second raph node area are the segments of the road corresponding to nodes 806a and 806b.
determine a connecting link type that connects the first graph node to the second graph node; and { see at least [0119] the cells can be described by a graph, where each cell corresponds to a node and two adjacent cells are characterized by an edge on the graph.} Connecting link type that connects the first graph node to the second graph node is the edge that connects nodes.
wherein the first autonomous vehicle uses the rule set to navigate the traffic environment and flow conditions { [0103] In an embodiment, the inputs to the planning module 404 includes database data … the database data 714 includes rules used in planning. Rules are specified using a formal language, e.g., using Boolean logic. In any given situation encountered by the AV 100, at least some of the rules will apply to the situation. A rule applies to a given situation if the rule has conditions that are met based on information available to the AV 100, e.g., information about the surrounding environment.}
Yershov does not disclose, however Jin discloses:
determine cellular automata (CA) parameters based on the first graph node area, the second graph node area, and historic vehicular trajectory data;
Jin 2018 {[Abstract] For traffic flow models, calibration and validation are essential. Cellular Automaton (CA) models are a special class of models, describing the movement of vehicles in discretized space and time. … We use a simple CA model, which only has two important parameters to be calibrated. … Three different sites are used as cases to show the methodology, for which different types of data (video trajectories or GPS data) are available. }
In view of the specification, the first and second graph node area, correspond to a first and second infrastructure feature and a second area, which broadest reasonable interpretation the locations are interpreted a locations on the trajectory for which historic data is available. Also, in broadest reasonable interpretation and in view of the specification which recites “[0089] The CAV modeling system 107 may calibrate cellular automata (CA) parameters as shown in block 520 by receiving vehicular trajectory historic data 525, generating the CA parameters based on the vehicular trajectory historic data 525” the examiner interprets “determine cellular automata (CA) parameters based on the first graph node area, the second graph node area, and historic vehicular trajectory data” as determining CA parameters using available video trajectories.
In addition, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Yershov to include the elements of Jin. One would have been motivated to do so, in order to have advantages of a computational method (CA) that well matches and is rapid in determining evaluating cell occupancy. Furthermore, the Supreme Court has supported that combining well known prior art elements, in a well-known manner, to obtain predictable results is sufficient to determine an invention obvious over such combination (see KSR International Co. v. Teleflex Inc. (KSR), 550 U.S.,82 USPQ2d 1385 (2007) & MPEP 2143). In the instant case, Yershov evidently discloses the generating a graph-based model for a vehicle in traffic. Jin is merely relied upon to illustrate the functionality of a specific model (CA), in the same or similar context. As best understood by Examiner, since both the modeling of the traffic and a specific model (CA) are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Yershov, as well as Jin would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that the results of the combination would be predictable.
Accordingly, the claimed subject matter would have been obvious over Yershov in view of Jin.
Yershov, Jin does not disclose, however Benhafaar discloses:
determine traffic environment and flow conditions, wherein the traffic environment and flow conditions include traffic information, the CA parameters, and driver behavior data; { see at least [page 6 2nd paragraph] an automated highway system would operate very much like a deterministic cellular automaton with rules specifying the total number of vehicles allowed in the system and the set of allowable vehicle behaviors [3rd paragraph] For each density level, various traffic flow measures were obtained (e.g., throughput, average speed, speed variance, traffic periodicity, etc.). [page 4, bottom] The state of the system at an iteration is determined by the distribution of vehicles among the cells and the speed of each vehicle in each cell. }
Traffic information interpreted as various traffic flow measures Driver behavior data interpreted as allowable vehicle behaviors. Distribution of vehicles and speed of each vehicle as CA parameters.
In addition, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Yershov, Jin to include the elements of Benjafaar. One would have been motivated to do so, in order to have advantages of being able to run a model tuned using obtained data about traffic, car and other parameters. Furthermore, the Supreme Court has supported that combining well known prior art elements, in a well-known manner, to obtain predictable results is sufficient to determine an invention obvious over such combination (see KSR International Co. v. Teleflex Inc. (KSR), 550 U.S.,82 USPQ2d 1385 (2007) & MPEP 2143). In the instant case, Yershov, Jin evidently discloses the generating a graph-based model for a vehicle in traffic. Benjafaar is merely relied upon to illustrate the types of traffic data needed to tune the model, in the same or similar context. As best understood by Examiner, since both the modeling of the traffic and obtaining data to tune the model are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Yershov, Jin as well as Benjafaar would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that the results of the combination would be predictable.
Accordingly, the claimed subject matter would have been obvious over Yershov, Jin in view of Benjafaar.
Yershov, Jin, Benjafaar does not disclose, however Lin discloses:
determine that a first autonomous vehicle is not trained for the traffic environment and flow conditions; and {[0006] When the second vehicle needs to obtain a traffic service, the second vehicle may send, to the management center, a request message used to request a target traffic service. After receiving the request message, the management center determines which vehicles are related to the target traffic service. .[0041] The traffic service in this embodiment of the present invention may be understood as a service, such as a path planning service or an anti-collision service, provided by the transport system for the vehicle. A transport system includes a plurality of vehicles. A service may be related to a plurality of vehicles.” Determine is not trained for the traffic environment and flow conditions interpreted as vehicle needs to obtain a traffic service and manage center determine vehicles related to traffic service.
In addition, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Yershov, Jin, Benjafaar to include the elements of Lin. One would have been motivated to do so, in order to have advantages of using the obtained vehicle guidance for vehicles that need it. Furthermore, the Supreme Court has supported that combining well known prior art elements, in a well-known manner, to obtain predictable results is sufficient to determine an invention obvious over such combination (see KSR International Co. v. Teleflex Inc. (KSR), 550 U.S.,82 USPQ2d 1385 (2007) & MPEP 2143). In the instant case, Yershov, Jin, Benjafaar evidently discloses the generating a graph-based model for a vehicle in traffic. Lin is merely relied upon to determine which vehicle could use the model results, in the same or similar context. As best understood by Examiner, since both the modeling of the traffic and determining which vehicle can benefit from model are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Yershov, Jin, Benjafaar as well as Lin would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that the results of the combination would be predictable.
Accordingly, the claimed subject matter would have been obvious over Yershov, Jin, Benjafaar in view of Lin.
Yershov, Jin, Benjafaar, Lin do not disclose, however Tava discloses:
send, based on determining that the first autonomous vehicle is not trained for the traffic environment and flow conditions, a rule set to the first autonomous vehicle for navigating the traffic environment and flow conditions {see at least [0028] In a further configuration of the method for navigating a vehicle, the navigation unit in the vehicle sends to the management unit a request for the management unit to send traffic rules stored on the management unit and referring to vehicle information of the vehicle, whereupon the management unit sends traffic rules referring to vehicle information of the vehicle to the navigation unit and the computation module of the navigation unit computes the navigation information by taking into consideration the received traffic rules referring to vehicle information of the vehicle. This keeps down the data traffic between navigation unit and management unit as far as possible, since only single roads affected by traffic rules referring to vehicle information of the vehicle, and the applicable traffic rules, need to be sent to the navigation appliance. [0029] In a further preferred configuration of the method for navigating a vehicle, the navigation unit sends the identification number of the vehicle together with the request. The management unit identifies the traffic rules referring to vehicle information of the vehicle on the basis of the vehicle identification number and then sends said traffic rules to the navigation unit or takes them into consideration to compute navigation information by means of the computation module and subsequently sends said navigation information to the navigation unit. } “send a rule set to the first autonomous vehicle for navigating the traffic environment and flow conditions” is interpreted as send rules to the navigation unit (of the vehicle) to compute navigation (in traffic)’ based on the determination the vehicle is not trained in traffic in broadest reasonable interpretation based on the request to send traffic rules”
In addition, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Yershov, Jin, Benjafaar, Lin to include the elements of Tava. One would have been motivated to do so, in order to have advantages of actually using the navigation in the identified vehicle that needed it. Furthermore, the Supreme Court has supported that combining well known prior art elements, in a well-known manner, to obtain predictable results is sufficient to determine an invention obvious over such combination (see KSR International Co. v. Teleflex Inc. (KSR), 550 U.S.,82 USPQ2d 1385 (2007) & MPEP 2143). In the instant case, Yershov, Jin, Benjafaar, Lin evidently discloses the generating a graph-based model for a vehicle in traffic and determining if a vehicle needed it. Tava is merely relied upon to sending the model to the identified vehicle that is determined to need it based on the request, in the same or similar context. As best understood by Examiner, since both the modeling of the traffic and determining which vehicle can benefit from model, and sending it to that vehicle are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Yershov, Jin, Benjafaar, Lin as well as Tava would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that the results of the combination would be predictable.
Accordingly, the claimed subject matter would have been obvious over Yershov, Jin, Benjafaar, Lin in view of Tava.
Regarding claim 2, 10 19, Yershov, Jin, Benjafaar, Lin, Tava discloses the limitations of claims 1, 9, 18. Yershov further discloses:
wherein the continuous space road map comprises: a first plurality of graph nodes having at least one vehicle agent operating within defined boundaries of each node of the first plurality of graph nodes; and a second plurality of graph nodes having no vehicle agent operating within defined boundaries of any node of the second plurality of graph nodes. {see at least [0119] In some implementations, the cells can be described by a graph, where each cell corresponds to a node... Each grid cell can be considered to be in one of two states—occupied or free.}. Having a vehicle agent and having no vehicle operating within the defined boundaries of each node is interpreted as the cells being in two states, occupied or free.
Accordingly, the claimed subject matter would have been obvious over Yershov, Jin, Benjafaar, Lin, Tava.
Regarding claims 8, 16, Yershov, Jin, Benjafaar, Lin, Tava discloses the limitations of claims 1, 9, Yershov further discloses
wherein the first infrastructure feature and the second infrastructure feature comprises one of:a roadway travel direction; a freeway; a side road; a toll road; an intersection; and a number of turning lanes. { see at least [0054] As used herein, a “road” is a physical area that can be traversed by a vehicle, and may correspond to a named thoroughfare (e.g., city street, interstate freeway, etc.) or may correspond to an unnamed thoroughfare (e.g., a driveway in a house or office building, a section of a parking lot, a section of a vacant lot, a dirt path in a rural area, etc.).} The infrastructure with features was associated with driving environment. Thus, the infrastructure set of features is interpreted to include physical areas that can be traversed by a vehicle and may correspond to enumerated thoroughfare, such as freeway; a side rode is interpreted as a dirt path in a rural area (could also be a driveway, for example).
Accordingly, the claimed subject matter would have been obvious over Yershov, Jin, Benjafaar, Lin, Tava.
Claims 7, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Yershov et al (US 20200257931) in view Jin et al The calibration and validation of cellular automaton traffic flow model with empirical and experimental data Publication: IET Intelligent Transport Systems, Volume 12, Issue 5, 2018 https://doi.org/10.1049/iet-its.2016.0275 in further view of Benjafaar et al, Cellular Automata for Traffic Flow Modeling, 1997, in further view of Lin et al (US-20190051181-A1) , view of Tava et al (US 20190011276) in further view of Nasseri et al, 2020 Autonomous Vehicle Technology Report, https://www.wevolver.com/article/2020.autonomous.vehicle.technology.report
Regarding claims 7, 15 - Yershov, Jin, Benjafaar, Lin, Tava disclose the limitations of claims 1, 9. Yershov, Jin, Benjafaar, Lin, Tava does not disclose, however Nasseri discloses:
wherein the plurality of vehicle agents executes a driving model instruction set that mimics driving behavior of a connected autonomous vehicle (CAV). {See at least Section Communication and Connectivity “A way for inter-vehicle coordination to impact the driving environment is through cooperative maneuvering. One application getting much attention is ‘platooning.’ When autonomous / semi-autonomous vehicles platoon they move in a train-like manner, keeping only small distances between vehicles, to reduce fuel consumption and achieve efficient transport}. Plurality of vehicle agents executes a driving model instruction that mimics driving behavior of a connected vehicle is interpreted as autonomous vehicles moving in a train-like manner (i.e. almost identically following each other)
In addition, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Yershov, Jin, Benjafaar, Lin, Tava to include the elements of Nasseri. One would have been motivated to do so, in order to have advantages of a richer model that takes in consideration how connected cars mirroring cars efficient in traffic influence the traffic simulations. Furthermore, the Supreme Court has supported that combining well known prior art elements, in a well-known manner, to obtain predictable results is sufficient to determine an invention obvious over such combination (see KSR International Co. v. Teleflex Inc. (KSR), 550 U.S.,82 USPQ2d 1385 (2007) & MPEP 2143). In the instant case, Yershov, Jin, Benjafaar, Lin, Tava evidently discloses modeling and simulation of vehicle traffic with a probabilistic graph. Nasseri is merely relied upon to enhance the driving model with a specific instruction set, in the same or similar context. As best understood by Examiner, since both the modeling and simulation of the traffic and enhancing a model for a vehicle in traffic are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Yershov, Jin, Benjafaar, Lin, Tava, as well as Nasser would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that the results of the combination would be predictable.
Accordingly, the claimed subject matter would have been obvious over Yershov, Jin, Benjafaar, Lin, Tava in further view of Nasseri.
Claims 3, 11 20, are rejected under 35 U.S.C. 103 as being unpatentable over Yershov et al (US 20200257931) in view Jin et al The calibration and validation of cellular automaton traffic flow model with empirical and experimental data Publication: IET Intelligent Transport Systems, Volume 12, Issue 5, 2018 https://doi.org/10.1049/iet-its.2016.0275 in further view of Benjafaar et al, Cellular Automata for Traffic Flow Modeling, 1997, in further view of Lin et al (US-20190051181-A1) , in further view of Tava et al (US 20190011276) in further view of Li, X. Ying and et al (henceforth referred to as LiYing), "GRIP: Graph-based Interaction-aware Trajectory Prediction," 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 2019, pp. 3960-3966, doi: 10.1109/ITSC.2019.8917228.
Regarding claims 3, 11 20, Yershov, Jin, Benjafaar, Lin, Tava discloses the limitations of claims 2,10,19. Yershov, Jin, Benjafaar, Lin, Tava does not disclose, however LiYing discloses:
compute a set of probabilities of the vehicle agent driving actions for the first plurality of graph nodes; and omit computation of the set of probabilities for the vehicle agent driving actions for the second plurality of graph nodes.{See Section 3: “formulate the trajectory prediction problem as one which estimates the future positions of all objects in a scene based on their trajectory histories” Section IV “we process a traffic scene within 180 feet (± 90 feet). All objects within this region will be observed and predicted in the future. While constructing the graph”. Compute a set of probabilities is interpreted as estimates in the prediction problem, omit computation foe second plurality of graph interpreted as the prediction is made for all objects that are observed – implicitly if no object is observed than no prediction calculation is made.
In addition, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Yershov, Jin, Benjafaar, Lin, Tava to include the elements of LiYing. One would have been motivated to do so, in order to have advantages of reduced computation, not performing computations for graph nodes which correspond to areas without vehicles.. Furthermore, the Supreme Court has supported that combining well known prior art elements, in a well-known manner, to obtain predictable results is sufficient to determine an invention obvious over such combination (see KSR International Co. v. Teleflex Inc. (KSR), 550 U.S.,82 USPQ2d 1385 (2007) & MPEP 2143). In the instant case, Yershov, Jin, Benjafaar, Lin, Tava evidently discloses modeling and simulation of vehicle traffic with a probabilistic graph. LiYing is merely relied upon to specify that computations only take place in relevant graph nodes, associated with vehicle occupancy. As best understood by Examiner, since both the modeling and simulation of the traffic and optimizing computation in executing the model are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Yershov, Jin, Benjafaar, Lin, Tava, as well as LiYing would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that the results of the combination would be predictable.
Accordingly, the claimed subject matter would have been obvious over Yershov, Jin, Benjafaar, Lin, Tava in further view of LiYing.
Claims 4, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Yershov et al (US 20200257931) in view Jin et al The calibration and validation of cellular automaton traffic flow model with empirical and experimental data Publication: IET Intelligent Transport Systems, Volume 12, Issue 5, 2018 https://doi.org/10.1049/iet-its.2016.0275 in further view of Benjafaar et al, Cellular Automata for Traffic Flow Modeling, 1997, in further view of Lin et al (US-20190051181-A1) , in further view of Tava et al (US 20190011276). in further view of Zhao H. T. et al (Cellular automata model for traffic flow at intersections in internet of vehicles, Physica A: Statistical Mechanics and its Applications, Volume 494, 15 March 2018, Pages 40-51)
Regarding claims 4, 12 Yershov, Jin, Benjafaar, Lin, Tava discloses the limitations of claims 2,10. Yershov, Jin, Benjafaar, Lin, Tava does not disclose, however Zhao discloses: wherein the processor is further programmed to model the vehicle agent by executing the executable instructions to:
determine a behavioral rule based on a link type, and further based on a rule of a behavioral rule set; and { See at least page 50- Conclusions ”The model takes into account the speed effect of the front vehicle and the influence of the brake light, and also adds the deceleration rule considering the preceding vehicle speed, speed rules based on traffic conditions and other evolution rules to accurately reflect the actual operation of the traffic flow.} behavioral tule based on a link type interpreted as one speed rule depending on traffic condition or deceleration rile considering the preceding vehicle speed. Based on a rule of a behavioral set interpreted as a deceleration rule or speed rule of the set of comprising speed and deceleration rules.
assign, based on the rule a key performance indicator (KPI) associated with the vehicle agent driving action. {See at least page 50- Conclusions ”The model takes into account the speed effect of the front vehicle and the influence of the brake light, and also adds the deceleration rule considering the preceding vehicle speed, speed rules based on traffic conditions and other evolution rules to accurately reflect the actual operation of the traffic flow. Moreover, through the numerical simulation, the relationship between the traffic flow parameters under different environments is obtained. The simulation results show that: (1) The space–time diagram reproduces the traffic congestion and dissipation at the intersection. In IoV, the vehicle’s queue length is shorter, congestion dissipates faster, traffic flow runs more smoothly.}
In addition, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Yershov, Jin, Benjafaar, Lin, Tava to include the elements of Zhao. One would have been motivated to do so, in order to have advantages of increased modelling accuracy for behaviors that are characteristic to conditions of different types of driving areas and obtain overall measures of quality of traffic for those conditions. Furthermore, the Supreme Court has supported that combining well known prior art elements, in a well-known manner, to obtain predictable results is sufficient to determine an invention obvious over such combination (see KSR International Co. v. Teleflex Inc. (KSR), 550 U.S.,82 USPQ2d 1385 (2007) & MPEP 2143). In the instant case, ). In the instant case, Yershov, Jin, Benjafaar, Lin, Tava evidently discloses modeling and simulation of vehicle traffic with a probabilistic graph. Zhao is merely relied upon to specify the behavior rules on graph nodes, associated with type of driving areas. As best understood by Examiner, since both the modeling and simulation of the traffic and including characteristics of driving roads in the models are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Yershov, Jin, Benjafaar, Lin, Tava, as well as Zhao would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that the results of the combination would be predictable.
Accordingly, the claimed subject matter would have been obvious over Yershov, Jin, Benjafaar, Lin, Tava Li in further view of Zhao.
Claims 5, 13 are rejected under 35 U.S.C. 103 as being unpatentable over Yershov et al (US 20200257931) in view Jin et al The calibration and validation of cellular automaton traffic flow model with empirical and experimental data Publication: IET Intelligent Transport Systems, Volume 12, Issue 5, 2018 https://doi.org/10.1049/iet-its.2016.0275 in further view of Benjafaar et al, Cellular Automata for Traffic Flow Modeling, 1997, in further view of Lin et al (US-20190051181-A1) , in further view of Tava et al (US 20190011276) in view of Zhao H. T. et al (Cellular automata model for traffic flow at intersections in internet of vehicles, Physica A: Statistical Mechanics and its Applications, Volume 494, 15 March 2018, Pages 40-51), in further view of Muhammad, T. et al., Simulation Study of Autonomous Vehicles’ Effect on Traffic Flow Characteristics including Autonomous Buses, Journal of Advanced Transportation, 2020, 4318652, 17 pages, 2020. https://doi.org/10.1155/2020/4318652
Regarding claims 5, 13 Yershov, Jin, Benjafaar, Lin, Tava, Zhao discloses the limitations of claims 4,12. Yershov, Jin, Benjafaar, Lin, Tava, Zhao does not disclose, however Muhammad discloses:
wherein the behavioral rule set is user selectable to include weighted modeling rules associated with a driving action of a set of driving actions comprising: merging; aggressive merging moving left aggressive moving left; moving right; aggressive moving right; overtaking; aggressive overtaking: undertaking; aggressive undertaking; drifting right; drifting left; cruising; cruising left; and cruising right. {see at least Page 3 Section 2.1. Model. In this new developed CA model, we simulate the behavior of different types of vehicles. All the parameters of the model are described in Table 1. We have four types of vehicles such as the autonomous car (AC), manual car (MC),autonomous bus (AB), and manual bus (MB). Regarding AC, the lengths of these cars do not exceed 5 meters and their size is equal to the size of each cell in the model. The main differences between AC and MC are a delay for re-action and policy for a lane change. AC can follow both aggressive lane change (ALC) and polite lane change (PLC)}
In addition, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Yershov, Jin, Benjafaar, Lin, Tava Zhao to include the elements of Muhammad. One would have been motivated to do so, in order to have the advantages of increased model fidelity, including absolutely essential elements of traffic situations, such as overtaking, cruising, etc. Furthermore, the Supreme Court has supported that combining well known prior art elements, in a well-known manner, to obtain predictable results is sufficient to determine an invention obvious over such combination (see KSR International Co. v. Teleflex Inc. (KSR), 550 U.S.,82 USPQ2d 1385 (2007) & MPEP 2143). In the instant case, Yershov, Jin, Benjafaar, Lin, Tava, Zhao evidently discloses a probabilistic model for vehicle traffic. Muhammad is merely relied upon to illustrate the functionality of certain driving behaviors, in the same or similar context. As best understood by Examiner, since both the modeling and simulation of the traffic and illustration of a number of behaviors of cars in traffic are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Yershov, Jin, Benjafaar, Lin, Tava, Zhao as well as Muhammad would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that the results of the combination would be predictable.
Accordingly, the claimed subject matter would have been obvious over Yershov, Jin, Benjafaar, Lin, Tava, Zhao/Muhammad.
Claims 6,14 are rejected under 35 U.S.C. 103 as being unpatentable over Yershov et al (US 20200257931) in view Jin et al The calibration and validation of cellular automaton traffic flow model with empirical and experimental data Publication: IET Intelligent Transport Systems, Volume 12, Issue 5, 2018 https://doi.org/10.1049/iet-its.2016.0275 in further view of Benjafaar et al, Cellular Automata for Traffic Flow Modeling, 1997, in further view of Lin et al (US-20190051181-A1) , in further view of Tava et al (US 20190011276) in view of Zhao H. T. et al (Cellular automata model for traffic flow at intersections in internet of vehicles, Physica A: Statistical Mechanics and its Applications, Volume 494, 15 March 2018, Pages 40-51), in further view of Muhammad, T. et al., Simulation Study of Autonomous Vehicles’ Effect on Traffic Flow Characteristics including Autonomous Buses, Journal of Advanced Transportation, 2020, 4318652, 17 pages, 2020. https://doi.org/10.1155/2020/4318652 in further view of SUMO "Simulation of Urban MObility" (SUMO) -open source, highly portable, microscopic and continuous traffic simulation package. Below from the ‘Changes in the 2020 releases (versions 1.5.0, 1.6.0, 1.7.0 and 1.8.0) Version 1.8.0 (02.12.2020) ‘ which are before the priority date. https://sumo.dlr.de/docs/ChangeLog/Changes_in_2020_releases.html#enhancements_3
Re Claims 6,14, Yershov, Jin, Benjafaar, Lin, Tava, Zhao, Muhammad disclose the limitations of claims 5,13. Zhao further discloses
output the KPI associated with the vehicle agent driving action using the model. {Page 45, Section 4, Numerical simulation and analysis: “It can be found that the vehicle’s red light queue length is shorter at the same time in IoV, and congestion flow dissipates faster”.} output the KPI associated with vehicle agent driving using the model is interpreted as the result from simulation of the vehicle model which indicates the queue length is shorter.}
Yershov, Jin, Benjafaar, Lin, Tava, Zhao, Muhammad does not disclose, however SUMO discloses
receive a user selection indicative of a selectable behavioral rule on a behavioral rules list;
{Fixed invalid right-of-way rules in left-hand network. Issue #6496; Fixed unsafe intersection rules for double connection with internal junction. Issue #7622; Fixed building of unsafe right-of-way rules at traffic light junctions where the highest priority road makes a turn. Issue #7764 Fixed invalid right-of-way at traffic light junctions with right-turn-on-red rules. Issue #6068}
receive a user input comprising a probability indicator associated with the selectable behavioral rule;
{ routeSampler.py: Now supports option --weighted. This causes routes to be sampled according to their probability. The probability can either be specified explicitly using route attribute 'probability' or implicitly if the same sequence of edges appears multiple times in the route input. Issue #7501}
generate a model for the vehicle agent driving action based on the probability indicator and the selectable behavioral rule; and
{see at least Documentation: "Simulation of Urban MObility" (SUMO) is an open source, highly portable, microscopic and continuous traffic simulation package designed to handle large networks; When using the sublane model, vehicles will now consider the travel speed on lanes beyond their current neighboring lanes for tactical lane changing. Issue #7620; routeSampler.py: Now supports option --weighted. This causes routes to be sampled according to their probability. The probability can either be specified explicitly using route attribute 'probability' or implicitly if the same sequence of edges appears multiple times in the route input. Issue #7501; Fixed building of unsafe right-of-way rules at traffic light junctions where the highest priority road makes a turn. Issue #7764}
In addition, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Yershov, Jin, Benjafaar, Lin, Tava, Zhao<Muhammad to include the elements of SUMO. One would have been motivated to do so, in order to have the advantages of powerful simulator, with a rich set of selectable options by the user . Furthermore, the Supreme Court has supported that combining well known prior art elements, in a well-known manner, to obtain predictable results is sufficient to determine an invention obvious over such combination (see KSR International Co. v. Teleflex Inc. (KSR), 550 U.S.,82 USPQ2d 1385 (2007) & MPEP 2143). In the instant case, Yershov, Jin, Benjafaar, Lin, Tava, Zhao, Muhammad evidently discloses a probabilistic model for vehicle traffic. SUMO is merely relied upon to illustrate the the user-friendly interfaces and capability to easily program and run the simulation, in the same or similar context As best understood by Examiner, since both the modeling and simulation of the traffic and implementation of a customizable, user-friendly interface for the simulator are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Yershov, Li, Zhao, Muhammad as well as SUMO would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that the results of the combination would be predictable.
Accordingly, the claimed subject matter would have been obvious over Yershov, Jin, Benjafaar, Lin, Tava Zhao, Muhammad/Sumo.
Claims 17 are rejected under 35 U.S.C. 103 as being unpatentable over Yershov et al (US 20200257931) in view Jin et al The calibration and validation of cellular automaton traffic flow model with empirical and experimental data Publication: IET Intelligent Transport Systems, Volume 12, Issue 5, 2018 https://doi.org/10.1049/iet-its.2016.0275 in further view of Benjafaar et al, Cellular Automata for Traffic Flow Modeling, 1997, in further view of Lin et al (US-20190051181-A1) , in further view of Tava et al (US 20190011276) in further view of Dosovitskiy, A. et al "CARLA: An open urban driving simulator." In Conference on robot learning, pp. 1-16. PMLR, 2017, https://proceedings.mlr.press/v78/dosovitskiy17a/dosovitskiy17a.pdf
Regarding claim 17. Yershov, Jin, Benjafaar, Lin, Tava discloses the limitations of claim 9. Yershov, Jin, Benjafaar, Lin, Tava does not disclose, however Dosovitskiy discloses:
wherein the plurality of vehicle agents executes a driving model instruction set that mimics driving behavior of a connected human- driven vehicle.{see at least Page 5, Section 3.2 Imitation learning. Our second method is conditional imitation learning, a form of imitation learning that uses highlevel commands in addition to perceptual input [4]. This method utilizes a dataset of driving traces recorded by human drivers in the training town. The dataset D = {<oi , ci , ai>} consists of tuples, each of which contains an observation oi , a command ci , and an action ai . The commands are provided by drivers during data collection and indicate their intentions, akin to turn signals. We use a set of four commands: follow the lane (default), drive straight at the next intersection, turn left at the next intersection, and turn right at the next intersection. The observations are images from a forward-facing camera.} Mimics driving behavior of a connected human driven vehicle interpreted as imitation of drivers, connected vehicle interpreted as receiving data from the vehicle driven by human.
In addition, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Yershov, Jin, Benjafaar, Lin, Tava to include the elements of Dosovitskiy. One would have been motivated to do so, in order to have advantages of including in vehicle action models the driving behaviors of human drivers, which bring realism into traffic simulations and also can provide good examples of handing road situations. Furthermore, the Supreme Court has supported that combining well known prior art elements, in a well-known manner, to obtain predictable results is sufficient to determine an invention obvious over such combination (see KSR International Co. v. Teleflex Inc. (KSR), 550 U.S.,82 USPQ2d 1385 (2007) & MPEP 2143). In the instant case, ). In the instant case, Yershov, Jin, Benjafaar, Lin, Tava evidently discloses modeling and simulation of vehicle traffic. Dosovitskiy is merely relied upon to provide models that incorporate human driving behaviors. As best understood by Examiner, since both the modeling and simulation of the traffic and including behavior of human drivers in models are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Yershov, Jin, Benjafaar, Lin, Tava as well as Dosovitskiy would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that the results of the combination would be predictable.
Accordingly, the claimed subject matter would have been obvious over Yershov, Jin, Benjafaar, Lin, Tava in further view of Dosovitskiy.
The prior art made of record and not relied upon which, however, is considered pertinent to applicant's disclosure:
Marchetti US 20210004012 A1
[0050] The operating modes of the vehicle 102 can be stored in a memory onboard the vehicle 102. For example, the operating modes can be defined by an operating mode data structure (e.g., rule, list, table, etc.) that indicates one or more operating parameters for the vehicle 102, while in the particular operating mode. For example, an operating mode data structure can indicate that the vehicle 102 is to autonomously plan its motion when in the fully autonomous operating mode. The vehicle computing system 110 can access the memory when implementing an operating mode.
B P. A. Lopez et al., "Microscopic Traffic Simulation using SUMO," 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 2018, pp. 2575-2582, doi: 10.1109/ITSC.2018.8569938.
Abstract: Microscopic traffic simulation is an invaluable tool for traffic research. In recent years, both the scope of research and the capabilities of the tools have been extended considerably. This article presents the latest developments concerning intermodal traffic solutions, simulator coupling and model development and validation on the example of the open source traffic simulator SUMO.
McKay et al, Automating Army Convoys, RAND,, Feb 5, 2020
https://www.rand.org/pubs/research_reports/RR2406.html
Abstract: This report examines how the U.S. Army can move ahead with the development and integration of automated driving technology for its convoy operations in the near future. Robotic ground vehicles are quickly maturing in the commercial sphere and could potentially save lives and increase efficiency if utilized in Army convoys. However, it may be many years until fully unmanned convoy vehicles are able to operate in rough terrain or manage adversarial attacks. In response, the authors of this report examine different employment concepts of automated trucks in Army convoys that appear viable in the next one to five years and would still reduce soldier casualties. The authors investigate technical and tactical benefits and risks of these concepts. A bridging option, the minimally manned employment concept, leading to the eventual use of a mix of manned and unmanned trucks in a convoy, is developed in this report to address the current technical and tactical risks of concepts requiring use of unmanned, automated trucks in Army convoys.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADRIAN STOICA whose telephone number is (571) 272-3428. The examiner can normally be reached Monday to Friday, 9 a.m. -5 p.m. PT.
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, Ryan Pitaro can be reached on (571) 272-4071. 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.
/A.S./Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188