DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is a Non-Final rejection on the merits of this application. Claims 1-12 and 14 are currently pending, as discussed below.
Examiner Notes that the fundamentals of the rejections are based on the broadest reasonable interpretation of the claim language. Applicant is kindly invited to consider the reference as a whole. References are to be interpreted as by one of ordinary skill in the art rather than as by a novice. See MPEP 2141. Therefore, the relevant inquiry when interpreting a reference is not what the reference expressly discloses on its face but what the reference would teach or suggest to one of ordinary skill in the art.
Priority
Acknowledgment is made that the present application is a national stage entry of PCT/DE2022/200042 filed on 03/16/2022 which claims foreign priority to patent application DE10 2021 204 067.5 filed on 04/23/2021.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 12/16/2024, 02/13/2025 and 07/18/2025 are being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-12 and 14 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding Claim 1, Applicant has apparently not described, in the specification, in sufficient details, by what algorithm(s) or by what steps/procedure, he/she determined, any and all of the “calculating collision probabilities based on the trajectory…”. The published specification ([0024]) discloses that “A check is then carried out in order to determine whether the movement predictions of two or more objects overlap and therefore there is a risk of collision…”, that is the specification suggests a qualitative assessment of collision risk rather than a quantitative calculation of collision probabilities (as claimed) since the term “collision probabilities” implies a numerical or statistical probability value, which is not described or explained in sufficient details in the specification. The specification further does not sufficiently describe how such probabilities are derived from the predicted trajectory(ies), nor does it provide or reference any mathematical models, probability functions or algorithm(s) that one person having ordinary skilled in the art to understand and calculate the probabilities based on predicted trajectories as claims. See the 2019 35 U.S.C. 112 Compliance Federal Register Notice (Federal Register, Vol. 84, No. 4, Monday, January 7, 2019, pages 57 to 63). See also http://ptoweb.uspto.gov/patents/exTrain/documents/2019-112-guidance-initiative.pptx . Quoting the FR Notice at pages 61 and 62, "The Federal Circuit emphasized that ‘‘[t]he written description requirement is not met if the specification merely describes a ‘desired result.’ ’’ Vasudevan, 782 F.3d at 682 (quoting Ariad, 598 F.3d at 1349). . . . When examining computer-implemented, software-related claims, examiners should determine whether the specification discloses the computer and the algorithm(s) that achieve the claimed function in sufficient detail that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. An algorithm is defined, for example, as 'a finite sequence of steps for solving a logical or mathematical problem or performing a task.' Microsoft Computer Dictionary (5th ed., 2002). Applicant may 'express that algorithm in any understandable terms including as a mathematical formula, in prose, or as a flow chart, or in any other manner that provides sufficient structure.' Finisar, 523 F.3d at 1340 (internal citation omitted). It is not enough that one skilled in the art could theoretically write a program to achieve the claimed function, rather the specification itself must explain how the claimed function is achieved to demonstrate that the applicant had possession of it. See, e.g., Vasudevan, 782 F.3d at 682–83. If the specification does not provide a disclosure of the computer and algorithm(s) in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention that achieves the claimed result, a rejection under 35 U.S.C. 112(a) for lack of written description must be made. See MPEP § 2161.01, subsection I." Accordingly, the Examiner believes that Applicant has not demonstrated to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The claims 2-12 and 14 are dependent upon claim 1 are also rejected under 112 first paragraph by the fact that they are dependent upon the rejected claimed 1.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1-12 and 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
101 Analysis – Step 1 – YES
Claim 1 is directed to method for creating a map with collision probabilities for an area. Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A method for creating a map with collision probabilities for an area, the method comprising:
detecting one or more vehicles driving in the area;
determining movement data of the one or more vehicles;
predicting, for each vehicle among the one or more vehicles, a trajectory based on the movement data of the vehicle;
calculating collision probabilities based on the trajectory; and
storing the collision probabilities in the map.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind (observing a surrounding and forming a judgement based on the observation). For example, “determining…”, “predicting…”, and “calculating…” steps in the context of this claim illustrate a process of a person observing a scene and making a judgement based on the observation either mentally or using pen and paper (consider a scenario where a person standing at a busy intersection (or someone who lives on a high rise building observing the intersection from his/her window) and observes vehicles traveling in the environment, noting that the vehicles’ speed and direction, predicting where each vehicle is headed and based on predictions to assess whether any two vehicles may collide with each other, and then this person notes down the dangerous spot on a paper map).
Examiner would also note MPEP 2106.04(a)(2)(III): The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same). Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. Here, the determination is a form of making evaluation and judgement based on observation (driver behavior). Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A method for creating a map with collision probabilities for an area, the method comprising:
detecting one or more vehicles driving in the area;
determining movement data of the one or more vehicles;
predicting, for each vehicle among the one or more vehicles, a trajectory based on the movement data of the vehicle;
calculating collision probabilities based on the trajectory; and
storing the collision probabilities in the map.
For the following reason(s), the examiner submits that the above identified limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitation of “detecting…”, the examiner submits that this limitation is recited at a high level of generality (i.e. as a general means of acquiring data) and amounts to mere data gathering which is a form of insignificant extra-solution activity; and the limitation of “storing…map” amounts to merely post solution activity which is a form of insignificant extra-solution activity.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impost any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 1 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations of “detecting…” and “storing…” discussed above are insignificant extra-solutions activities. There is no indication that the claimed method improves the functioning of a computer or another technology. The steps are recited at a high level of generality, and the claim lacks detail about how the vehicle detection, trajectory prediction or probability calculation are performed beyond generic computer functions. The specification does not indicate any of the computer or sensors used are anything other than conventional sensors and computer.
As explained, the additional elements are recited at a high level of generality to simply implement the abstract idea and are not themselves being technologically improved. See, e.g., MPEP §2106.05; Alice Corp. v. CLS Bank, 573 U.S., 208,223 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention”). Electric Power Group, LLC v, Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) (Selecting information for collection, analysis and display constitute insignificant extra-solution activity). Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016)( Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components). Hence, the claims are not patent eligible.
Dependent Claims
Dependent claims 2-12, and 14 do not recite any further limitations that causes the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial except and/or additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-12 and 14 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
As such, claims 1-12 and 14 are rejected under 35 USC § 101 as being drawn to an abstract idea without significant more, and thus are 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 for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-12 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sahim et al. (WO 2019/230683 A1 hereinafter Sahim) in view of Van Heukelom et al. (US 2020/0174481 A1 hereinafter Van Heukelom).
Regarding claim 1, Sahim teaches A method for creating a map with collision probabilities for an area (see at least Abstract), the method comprising:
detecting one or more vehicles driving in the area; (see at least Fig. 1-14B [0017-0182]: The roadside devices 30 images a plurality of vehicles 20 existing in the vicinity of the intersection 103 (that includes an area where roads 101 and 102 intersect and a surrounding area), and identifies all the vehicles in the intersection 103 from the video signals obtained by the imaging. The detection unit 31 specifies the number and position of the vehicles 20 existing in the intersection 103 from the video signal in the vicinity of the intersection 103 imaged by the sensor 31a and outputs it to the control unit which outputs the position information the danger map creation unit 32. As shown in the Figures (e.g. 11), there are five vehicles 20a-20e in the vicinity of the intersection 103. Each of the plurality of vehicles 20 transmits information indicating the degree of danger of the traveling state of the host vehicle to the roadside device 30 in addition to the prediction result shown in the first embodiment. The prediction result is information including the future travel route of the host vehicle, and the information indicating the degree of danger indicating the current travel state of the vehicle.)
determining movement data of the one or more vehicles; (see at least Fig. 1-14B [0017-0182]: the roadside devices 30 images a plurality of vehicles 20 existing in the vicinity of the intersection 103 (that includes an area where roads 101 and 102 intersect and a surrounding area), and identifies all the vehicles in the intersection 103 from the video signals obtained by the imaging. The detection unit 31 specifies the number and position of the vehicles 20 existing in the intersection 103 from the video signal in the vicinity of the intersection 103 imaged by the sensor 31a and outputs it to the control unit which outputs the position information the danger map creation unit 32.)
predicting, for each vehicle among the one or more vehicles, a trajectory based on the movement data of the vehicle; (see at least Fig. 1-14B [0017-0182]: the danger map creation unit 32 uses the prediction result for map information indicating the situation around the roadside device 30 acquired by the map information acquisition unit, and the current vehicle positions 20a to 20c and the predicted route are superimposed and displayed (e.g., the predicted route is indicated by a dashed arrow as shown in Fig. 7). The predicted route of the vehicle 20b and the vehicle 20c intersects each other at time t2 that indicates a risk of collision between the vehicles. Therefore, the danger map creation unit 32 displays an area including a point where the predicted routes intersects as a danger area R1. )
calculating collision probabilities based on the trajectory; (see at least Fig. 1-14B [0017-0182]: The danger map creation unit creates a danger map for each vehicle using the detection results detected by the sensor 31 an the prediction results and determination results obtained from each of the plurality of vehicles 20. As shown in Fig. 13, the danger map creation unit first creates an overall danger map near the intersection 103. For example, the danger map creation unit uses the prediction result for map information indicating the situation around the roadside device 30 acquired by the map information acquisition 31c and the current vehicle positions 20a-20e and the predicted routes (indicated by dashed arrows) are superimposed and displayed. The danger region R1 is displayed and further shows dangerous area R2 and a caution area R3. The danger map creations an overall danger map in which the level of danger can be recognized from the predicted route and the degree of danger for each of the plurality of vehicles 20.) and
storing the collision probabilities in the map. (see at least Fig. 1-14B [0017-0182]: the danger map creation unit 32 of the roadside machine 30 creates a danger map indicating a dangerous area in the vicinity of the intersection 103 from the prediction result of the driving behavior of the vehicle 20 acquired from each of the plurality of vehicles 20. Specifically, the danger map creation unit 32 determines the possibility of a collision of the plurality of the vehicles 20 from the prediction results, and creates a danger map based on the result of determining the possibility of the collision. The control unit 33 may further store the danger map in the storage unit 35. The danger map creation unit 32 creates an overall danger map near the intersection 103 such that it is possible to know a dangerous area in the vicinity of the intersection 103.)
It may be alleged that Sahim does not explicitly teach calculating collision probabilities based on the trajectory;
Van Heukelom is directed to probabilistic risk assessment for trajectory evaluation, Van Heukelom teaches calculating collision probabilities based on the trajectory; (see at least Fig. 1-5 [0011-0064]: The sensor data captured by the vehicle representing objects in the environment can be used to generate a discretized probability distribution representing possible locations of the object in the environment over time. In some examples, individual discretized probability distributions can be generated for individual objects in the environment and aggregated to generate an aggregated discretized probability distributions representing aggregated prediction probabilities of a plurality of objects in an environment. A planning system of the vehicle can generate a plurality of trajectories for the vehicle to follow in the environment. In some cases, for a single trajectory, the operations can include determining a region associated with the vehicle along the trajectory at a future time (e.g., 0.5 second, 1 second, 2 seconds, etc. in the future) based on a velocity, acceleration, etc. of the vehicle. The operations can include determining an overlap between the region associated with the vehicle and a portion of a discretized probability distribution corresponding to the particular future time. In some examples, a region probability can be determined by summing, integrating, or otherwise aggregating the individual probabilities of the discretized probability distribution corresponding to the region of the vehicle (also referred to as an overlapping region or an overlap).)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Sahim’s information processing system that creates a danger map indicating a danger area to incorporate the technique of calculating collision probabilities based on the trajectory as taught by Van Heukelom with reasonable expectation of success because considering predicted locations of objects in an environment may allow the vehicle (such as an autonomous vehicle) to generate more accurate and/or safer trajectories for the vehicle to traverse the environment (Van Heukelom [0023]).
Regarding claim 2, the combination of Sahim in view of Van Heukelom teaches The method as claimed in claim 1,
Sahim further teaches wherein the determining the movement data comprises repeatedly determining the movement data during a journey of the vehicle through the area, and wherein the predicting comprises the trajectory based on the journey. (see at least Fig. 1-14B [0017-0182]: The determination unit 22 predicts an action next to the host vehicle using the acquired detection result and host vehicle information (S12). Specifically, the driving behavior prediction unit 22b predicts the driving behavior of the own vehicle corresponding to the input information by inputting the detection result and the own vehicle information to the driving behavior prediction NN constructed by the learning unit 22a. As shown in FIGS. 5A to 5C, the driving action prediction result includes time (t), x-coordinate, and y-coordinate. Time 0 means the current time. Times 1, 2, 3,... Mean future times with time 0 as a reference. As the x coordinate and y coordinate at time 0, for example, the GPS positioning result acquired by the position information acquisition unit 21c is used. Note that the x coordinate and y coordinate at time 0 are not limited to the GPS positioning result (global coordinate system). For example, the position after time 1 is determined based on the x coordinate and y coordinate at time 0 as a reference (zero). The control unit 23 transmits the prediction result of the driving behavior of the host vehicle predicted by the determination unit 22 to the roadside device 30 via the communication unit 24.)
Regarding claim 3, the combination of Sahim in view of Van Heukelom teaches The method as claimed in claim 2,
Sahim further teaches wherein determining the movement data comprises determining the moment data at predetermined time intervals. (see at least Fig. 1-14B [0017-0182]: Referring to FIG. 4 again, the control unit 23 transmits the prediction result of the driving behavior of the host vehicle predicted by the determination unit 22 to the roadside device 30 via the communication unit 24 (S13). Each of the plurality of vehicles 20 repeats the operations of steps S11 to S13 at predetermined time intervals.)
Regarding claim 4, the combination of Sahim in view of Van Heukelom teaches The method as claimed in claim 3,
Sahim does not explicitly teach wherein the trajectory comprises a plurality of trajectories with respective associated probabilities.
Van Heukelom is directed to probabilistic risk assessment for trajectory evaluation, Van Heukelom teaches wherein the trajectory comprises a plurality of trajectories with respective associated probabilities. (see at least Fig. 1-5 [0011-0064]: The possible locations of the object in the future based on covariance matrices can be represented as Gaussian distributions that can be discretized into a cells or portions of the environment proximate to the object or proximate to the vehicle. In some examples, individual discretized probability distributions can be generated for individual objects in the environment and aggregated to generate an aggregated discretized probability distributions representing aggregated prediction probabilities of a plurality of objects in an environment.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Sahim’s information processing system that creates a danger map indicating a danger area to incorporate the technique of associating a plurality of trajectories with respective probabilities as taught by Van Heukelom with reasonable expectation of success because considering predicted locations of objects in an environment may allow the vehicle (such as an autonomous vehicle) to generate more accurate and/or safer trajectories for the vehicle to traverse the environment (Van Heukelom [0023]).
Regarding claim 5, the combination of Sahim in view of Van Heukelom teaches The method as claimed in claim 4,
Sahim further teaches wherein determining the movement data comprises determining the movement data by one or more road-side environment sensors. (see at least Fig. 1-14B [0017-0182]: the roadside devices 30 images a plurality of vehicles 20 existing in the vicinity of the intersection 103 (that includes an area where roads 101 and 102 intersect and a surrounding area), and identifies all the vehicles in the intersection 103 from the video signals obtained by the imaging. The detection unit 31 specifies the number and position of the vehicles 20 existing in the intersection 103 from the video signal in the vicinity of the intersection 103 imaged by the sensor 31a and outputs it to the control unit which outputs the position information the danger map creation unit 32.)
Regarding claim 6, the combination of Sahim in view of Van Heukelom teaches The method as claimed in claim 5,
Sahim further teaches wherein determining the movement data comprises determining the movement data based on the information received via radio from the one or more vehicles. (see at least Fig. 1-14B [0017-0182]: Referring to FIG. 4 again, the control unit 23 transmits the prediction result of the driving behavior of the host vehicle predicted by the determination unit 22 to the roadside device 30 via the communication unit 24 (S13). Each of the plurality of vehicles 20 repeats the operations of steps S11 to S13 at predetermined time intervals. The communication unit 24 is a communication interface that communicates with the communication unit 34 of the roadside device 30. Communication between the communication unit 24 and the communication unit 34 is performed wirelessly. Communication between the communication unit 24 and the communication unit 34, that is, communication between the vehicle 20 and the roadside device 30, is performed by, for example, V2X (Vehicle to X) communication including VI (Vehicle to Infrastructure) communication and VP communication (Vehicle to Pedestrian). )
Regarding claim 7, the combination of Sahim in view of Van Heukelom teaches The method as claimed in claim 6,
Sahim further teaches wherein the area comprises an intersection, a junction, a bend or T-junction. (see at least Fig. 1-14B)
Regarding claim 8, the combination of Sahim in view of Van Heukelom teaches The method as claimed in claim 7, further comprising
Sahim does not explicitly teach normalizing the collision probabilities to a reference value.
Van Heukelom is directed to probabilistic risk assessment for trajectory evaluation, Van Heukelom teaches normalizing the collision probabilities to a reference value. (see at least Fig. 1-5 [0011-0064]: In some examples, individual discretized probability distributions can be generated for individual objects in the environment and aggregated to generate an aggregated discretized probability distributions representing aggregated prediction probabilities of a plurality of objects in an environment. For example, discretized probability distributions for objects can be aligned and individual prediction probabilities can be summed to represent summed or aggregated prediction probabilities. Further, in some cases, a resulting discretized probability distribution can be normalized so that prediction probabilities across the discretized probability distribution collectively add up to 1 (where a probability of 1 represents a certainty that an event will occur). Thus, a single discretized probability distribution can represent prediction probabilities associated with a plurality of objects in the environment.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Sahim’s information processing system that creates a danger map indicating a danger area to incorporate the technique of normalizing the collision probabilities to a reference value as taught by Van Heukelom with reasonable expectation of success because considering predicted locations of objects in an environment may allow the vehicle (such as an autonomous vehicle) to generate more accurate and/or safer trajectories for the vehicle to traverse the environment (Van Heukelom [0023]).
Regarding claim 9, the combination of Sahim in view of Van Heukelom teaches The method as claimed in claim 8, wherein the map comprises predefined subdivision of the area.
Sahim does not explicitly teach wherein the map comprises predefined subdivision of the area.
Van Heukelom is directed to probabilistic risk assessment for trajectory evaluation, Van Heukelom teaches wherein the map comprises predefined subdivision of the area. (see at least Fig. 1-5 [0011-0064, 0088-0093]: Further, in some cases, a resulting discretized probability distribution can be normalized so that prediction probabilities across the discretized probability distribution collectively add up to 1 (where a probability of 1 represents a certainty that an event will occur). Thus, a single discretized probability distribution can represent prediction probabilities associated with a plurality of objects in the environment. The probability map merging component 732 can merge two or more probability maps corresponding to a same predicted time. Merging can include summing or otherwise aggregating probabilities associated with corresponding cells to determine a merged probability.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Sahim’s information processing system that creates a danger map indicating a danger area to incorporate the technique of merging plurality of predefined subdivision of the area probability maps as taught by Van Heukelom with reasonable expectation of success because considering predicted locations of objects in an environment may allow the vehicle (such as an autonomous vehicle) to generate more accurate and/or safer trajectories for the vehicle to traverse the environment (Van Heukelom [0023]).
Regarding claim 10, the combination of Sahim in view of Van Heukelom teaches The method as claimed in claim 9,
Sahim does not explicitly teach wherein the predicting comprises predicting the trajectory based on a prediction uncertainty and/or error limits of the movement data.
Van Heukelom is directed to probabilistic risk assessment for trajectory evaluation, Van Heukelom teaches wherein the predicting comprises predicting the trajectory based on a prediction uncertainty and/or error limits of the movement data. (see at least Fig. 1-5 [0011-0064, 0088-0093]: The sensor data captured by the vehicle representing objects in the environment can be used to generate a discretized probability distribution representing possible locations of the object in the environment over time. For example, a prediction system can determine a covariance matrix associated with an uncertainty of an object at an initial state or time. The covariance matrix can include a variance with respect to a longitudinal and/or lateral position in the environment. A location of the object can be evaluated over time to determine possible locations of the object based on the object classification, position, speed, acceleration, sensor uncertainty, and the like. As the object location is evaluated over time (e.g., in the future), the covariance matrix can be evaluated as well to determine a covariance matrix associated with position(s) of the object in the future.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Sahim’s information processing system that creates a danger map indicating a danger area to incorporate the technique of predicting the trajectory based on a prediction uncertainty and/or error limits of the movement data as taught by Van Heukelom with reasonable expectation of success because considering predicted locations of objects in an environment may allow the vehicle (such as an autonomous vehicle) to generate more accurate and/or safer trajectories for the vehicle to traverse the environment (Van Heukelom [0023]).
Regarding claim 11, the combination of Sahim in view of Van Heukelom teaches The method as claimed in claim 10,
Sahim further teaches wherein the storing comprises storing the collision probability of a vehicle pairing including the vehicle. (see at least Fig. 1-14B [0017-0182]: each of the plurality of vehicles 20 displays the danger map on the display unit 25 provided in the vehicle 20 (S33). Specifically, the control unit 23 displays a danger map in which the host vehicle route is superimposed on the danger map received from the roadside device 30 on the display unit 25.)
Regarding claim 12, the combination of Sahim in view of Van Heukelom teaches The method as claimed in claim 11,
Sahim does not explicitly teach wherein the storing comprises storing the collision probability from a predefined time window.
Van Heukelom is directed to probabilistic risk assessment for trajectory evaluation, Van Heukelom teaches wherein the storing comprises storing the collision probability from a predefined time window. (see at least Fig. 1-14B [0017-0182]: A prediction system associated with the vehicle can generate one or more discretized probability distributions or heat maps including prediction probabilities associated with possible locations of the objects in the environment. A discretized probability distribution can be generated to represent any point or period of time in the future, such as 1 second, 2 seconds, 5 seconds, etc. in the future. Further, such a discretized probability distribution can represent prediction probabilities associated with a single object or can represent aggregated prediction probabilities associated with a plurality of objects. The planning system can determine an overlap between a region associated with the vehicle along the trajectory (e.g., at a future time) and a portion of a discretized probability distribution corresponding to the respective future time. Prediction probabilities associated with the overlap can be summed or otherwise aggregated to determine a region probability associated with the overlap, where a region probability is related to (e.g., proportional) to a collision risk for the vehicle. Region probabilities can be determined for a plurality of future times along the trajectory and the region probabilities can be summed or aggregated to determine a trajectory probability.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Sahim’s information processing system that creates a danger map indicating a danger area to incorporate the technique of storing the collision probability from a predefined time window as taught by Van Heukelom with reasonable expectation of success because considering predicted locations of objects in an environment may allow the vehicle (such as an autonomous vehicle) to generate more accurate and/or safer trajectories for the vehicle to traverse the environment (Van Heukelom [0023]).
Regarding claim 14, the combination of Sahim in view of Van Heukelom teaches The method as claimed in claim 12, further comprising
Sahim further teaches determining one or more near-collision events based on the trajectory. (see at least Fig. 1-14B [0017-0182]: FIG. 13 is a diagram illustrating an example of the overall danger map M11 near the intersection 103 created by the roadside device 30. The predicted route is indicated by a dashed arrow. Since the predicted routes of the vehicle 20b and the vehicle 20c intersect each other, the danger region R1 is displayed and further shows a dangerous area R2 and a caution area R3. The dangerous area R2 is an area that may collide with the vehicle 20d. FIG. 13 shows that the higher the dot hatch density in the region, the higher the danger level. For example, the dangerous areas R1 and R2 may be displayed in red, and the caution area R3 may be displayed in yellow.)
Conclusion
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/DANA F ARTIMEZ/ Examiner, Art Unit 3667
/FARIS S ALMATRAHI/ Supervisory Patent Examiner, Art Unit 3667