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 .
The Status of Claims
Claims 1-17 are presented for examination.
Claims 1-17 are rejected.
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 1 is directed to “processor-implemented method…”, claim 9 is directed to “A system…”, and claim 17 is directed to “One or more non-transitory machine-readable information storage mediums…”. Therefore, claims 1, 9, and 17 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the 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. The other analogous claims 9, 17 are rejected for the same reasons as the representative claim 1 as discussed here. Claim 1 recites:
“A processor-implemented method, comprising the steps of: receiving, via one or more hardware processors, one or more road contextual parameters to create a 360-degree scene perception of road surroundings of a host vehicle, wherein the 360-degree scene perception of road surroundings of the host vehicle comprises one or more actors present on a road as the host vehicle navigates on the road; estimating, via the one or more hardware processors, one or more 3-dimensional (3-D) scene semantics of each of the one or more actors present on the road as the host vehicle navigates, using the 360-degree scene perception of road surroundings, using a localizing technique; detecting, via the one or more hardware processors, one or more priority actors those lead to probable collisions, out of the one or more actors present on the road as the host vehicle navigates, based on the one or more 3-dimensional (3-D) scene semantics, using a path estimation and tracking technique; deciding, via the one or more hardware processors, to generate a symbiotic warning signal, to one or more priority actors, based on the one or more 3-dimensional (3-D) scene semantics and the 360-degree scene perception of road surroundings, using a symbiotic warning trained model; and generating, via the one or more hardware processors, the symbiotic warning signal, to one or more priority actors, based on the decision to generate, using one or more output means.”
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “create…estimate…”, and “detect…” all the various data in the context of this claim encompasses a person looking at data collected (received, determined, estimates, detected, identified, and analyzed etc.) and forming a simple judgement (determination, analysis, comparison, and judgement etc.) either mentally or using a pen and paper. Accordingly, the claim recites at least one abstract idea. The Examiner notes that under 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).
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 processor-implemented method, comprising the steps of: receiving, via one or more hardware processors, one or more road contextual parameters to create a 360-degree scene perception of road surroundings of a host vehicle, wherein the 360-degree scene perception of road surroundings of the host vehicle comprises one or more actors present on a road as the host vehicle navigates on the road; estimating, via the one or more hardware processors, one or more 3-dimensional (3-D) scene semantics of each of the one or more actors present on the road as the host vehicle navigates, using the 360-degree scene perception of road surroundings, using a localizing technique; detecting, via the one or more hardware processors, one or more priority actors those lead to probable collisions, out of the one or more actors present on the road as the host vehicle navigates, based on the one or more 3-dimensional (3-D) scene semantics, using a path estimation and tracking technique; deciding, via the one or more hardware processors, to generate a symbiotic warning signal, to one or more priority actors, based on the one or more 3-dimensional (3-D) scene semantics and the 360-degree scene perception of road surroundings, using a symbiotic warning trained model; and generating, via the one or more hardware processors, the symbiotic warning signal, to one or more priority actors, based on the decision to generate, using one or more output means.”
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations above, the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (processor) to perform the process. In particular, the “One or more non-transitory machine-readable information storage mediums…one or more hardware processors…” steps from / using sensor system(s) are recited at a high level of generality (i.e. as a general means of a processor; and a computer readable media, and other steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The “receiving, via one or more hardware processors, one or more road contextual parameters to create a 360-degree scene perception of road surroundings of a host vehicle…using the 360-degree scene perception of road surroundings, using a localizing technique…” steps are also recited at a high level of generality and amounts to mere post solution action, which is a form of insignificant extra-solution activity. Lastly, claims 1, 9, and 17 further recite “generate a symbiotic warning signal, to one or more priority actors, based on the one or more 3-dimensional (3-D) scene semantics and the 360-degree scene perception of road surroundings…” and “…using a symbiotic warning trained model; and generating, via the one or more hardware processors, the symbiotic warning signal, to one or more priority actors, based on the decision to generate, using one or more output means.” merely describes how to generally “apply” the otherwise mental judgements in a generic or general purpose vehicle control environment. See Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. at 223 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). The device(s) and processor(s) are recited at a high level of generality and merely automates the steps. In order to expedite prosecution, Examiner also notes that the mere recitation of “generating, via the one or more hardware processors, the symbiotic warning signal, to one or more priority actors” in claim 1 and “generate the symbiotic warning signal, to one or more priority actors, based on the decision to generate, using one or more output means” in claim 9, and “generating the symbiotic warning signal, to one or more priority actors, based on the decision to generate, using one or more output means” in claim 17, are not significant enough to integrate the judicial exception into a practical application since the claims do not include a positive recitation of “controlling the host vehicle to avoid collision with the one or more priority actors …” (if supported by the specification, such limitation is an example of a significant enough limitation to integrate the judicial exception into a practical application).
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 impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 19 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause the processor to perform the steps amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations discussed above are insignificant extra-solution activities.
The additional limitations of “…mapping (i) the one or more predicted moves and (ii) a current move, of each of the one or more actors of interest and the host vehicle…” steps are well-understood, routine and conventional activities because the background recites that the sensors are all conventional sensors, and the specification does not provide any indication that the processor is anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. The additional limitation of “…receiving a training dataset comprising a plurality of training samples… assigning a training symbiotic warning signal of a plurality of symbiotic warning signal…wherein the symbiotic warning signal is generated through horn, headlights, a vehicle-vehicle to communication, and a combination thereof, based on type of the priority actor and a driving environment scenario…wherein the one or more road contextual parameters are associated with road modelling and the one or more road contextual parameters are received from one or more of: one or more 360-degree Lidars, one or more front corner radars, one or more rear corner radars, one or more cameras, one or more ultrasonic sensors, and one or more geographical road information devices, or a combination thereof, installed in the host vehicle…” is a well-understood, routine, and conventional activity because the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere performance which in the instant application is warning to an operator is a well understood, routine, and conventional function. Hence, the claim is not patent eligible.
Dependent claim(s) 2-8, 10-16 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-8, 10-16 do are not patent eligible under the same rationale as provided for in the rejection of claims 1, 9, and 17.
Therefore, claim(s) 1-17 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 6-11, and 14-17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Takaki (US Pub. No.: 2021/0166564 A1: hereinafter “Takaki”).
Consider claims 1, 9, and 17:
Takaki teaches one or more non-transitory machine-readable information storage mediums (Figs. 2-3 elements 110-230, Steps 300-340), a system (Figs. 1-2 elements 100-170), and a processor-implemented method (See Takaki, e.g., “…providing a warning from a subject vehicle to surrounding objects about a collision hazard…identifying the surrounding objects of a subject vehicle according to sensor data about a surrounding environment of the subject vehicle…determining a collision probability indicating a likelihood of collision between a first object and a second object of the surrounding objects…in response to the collision probability satisfying a collision threshold, communicating, by the subject vehicle, an alert to at least one of the surrounding objects about the collision hazard associated with the surrounding objects colliding...”, of Abstract, ¶ [0004]-¶ [0008], ¶ [0049]-¶ [0060], Figs. 1-2 elements 100-170, Figs. 3, 5 Steps 300-530, Fig. 6A-B elements 1000-670, and Fig. 9 elements 100-950, and Fig. 10 elements 100-1040), comprising the steps of: receiving (e.g., the detection module 220 identifies surrounding objects of the subject vehicle 100 according to the sensor data 250 of Fig. 3 step 310), via one or more hardware processors (Fig. 1 element 110, “…the collision warning system 170 includes a processor 110…”), one or more road contextual parameters (Fig. 2 element 250, “…the sensor data 250 includes information that embodies observations of the surrounding environment of the vehicle 100…”) to create a 360-degree scene perception of road surroundings of a host vehicle (See Takaki, e.g., “…control the processor 110 to acquire data inputs from one or more sensors (e.g., the sensor system 120) of the vehicle 100 that form the sensor data 250…the sensor data 250 includes information that embodies observations of the surrounding environment of the vehicle 100. The observations of the surrounding environment…include surrounding lanes, vehicles, objects, obstacles, etc. that may be present in the lanes...”, of ¶ [0004]-¶ [0008], ¶ [0031]-¶ [0036], ¶ [0049]-¶ [0060], Figs. 1-2 elements 100-170, Figs. 3, 5 Steps 300-530, Fig. 6A-B elements 1000-670, and Fig. 9 elements 100-950, and Fig. 10 elements 100-1040), wherein the 360-degree scene perception of road surroundings of the host vehicle comprises one or more actors (e.g., vehicles, objects, obstacles etc.) present on a road as the host vehicle navigates on the road (See Takaki, e.g., “…the surrounding objects can include various types of objects such as vehicular (e.g., automobiles, trucks, motorcycles, etc.), non-vehicular (e.g., pedestrians, animals, bicycles, etc.), and even inanimate objects (e.g., road debris, potholes, etc.). Whichever objects makeup the detected surrounding objects, the collision warning system 170 generally functions to determine the potential for a collision and provide the alerts when at least one of the surrounding objects is a vehicular or non-vehicular object...”, of ¶ [0031]-¶ [0037], ¶ [0049]-¶ [0060], Figs. 1-2 elements 100-170, Figs. 3, 5 Steps 300-530, Fig. 6A-B elements 1000-670, and Fig. 9 elements 100-950, and Fig. 10 elements 100-1040); estimating (e.g., “…identifies surrounding objects of the subject vehicle 100 according to the sensor data 250…” of Fig. 3 steps 310-340), via the one or more hardware processors, one or more 3-dimensional (3-D) scene semantics of each of the one or more actors present on the road as the host vehicle navigates (See Takaki, e.g., “…detect the surrounding objects and determine characteristics of the objects from the sensor data 250…include at least a current position relative to the vehicle 100, and a velocity (i.e., speed and direction)...”, of ¶ [0031]-¶ [0039], ¶ [0049]-¶ [0060], Figs. 1-2 elements 100-170, Figs. 3, 5 Steps 300-530, Fig. 6A-B elements 1000-670, and Fig. 9 elements 100-950, and Fig. 10 elements 100-1040), using the 360-degree scene perception of road surroundings, using a localizing technique (See Takaki, e.g., “…At 310, the detection module 220 identifies surrounding objects of the subject vehicle 100 according to the sensor data 250…the detection module 220, in one or more implementations, iteratively acquires the sensor data 250 from one or more sensors of the sensor system 120…observations of a surrounding environment of the subject vehicle 100…analyzes the sensor data 250 using one or more detection/identification routines that generally function to detect the presence of objects, classify/identify a type of the objects (e.g., vehicle, pedestrian, etc.), and localize the objects relative to the subject vehicle 100...”, of ¶ [0004]-¶ [0008], ¶ [0031]-¶ [0039], ¶ [0049]-¶ [0060], Figs. 1-2 elements 100-170, Figs. 3, 5 Steps 300-530, Fig. 6A-B elements 1000-670, and Fig. 9 elements 100-950, and Fig. 10 elements 100-1040); detecting, via the one or more hardware processors, one or more priority actors those lead to probable collisions (e.g., “…At 320, the detection module 220 uses the previously derived information from the sensor data 250 to determine a collision probability for at least two of the objects…”, of Fig. 3 steps 310-340), out of the one or more actors present on the road as the host vehicle navigates, based on the one or more 3-dimensional (3-D) scene semantics, using a path estimation and tracking technique (See Takaki, e.g., “…the detection module 220 may further determine more complex trajectories that are, for example, extrapolated from multiple prior observations (e.g., over two or more prior time steps). In any case, the detection module 220 generally uses the position and velocity information about the separate surrounding objects to predict future positions of the objects from which the module 220 can generate a determination about the likelihood of collision between the objects...”, of ¶ [0035]-¶ [0043], ¶ [0049]-¶ [0060], Figs. 1-2 elements 100-170, Figs. 3, 5 Steps 300-530, Fig. 6A-B elements 1000-670, and Fig. 9 elements 100-950, and Fig. 10 elements 100-1040); deciding, via the one or more hardware processors, to generate a symbiotic warning signal, to one or more priority actors (e.g., “…At 330, the warning module 230 determines whether the collision probability satisfies a collision threshold…the warning module 230 communicates the alert when a collision hazard is deemed “likely,” and subsequently may adapt the threshold according to the noted factors (e.g., when safer operation is desired because of weather, school zones, etc.)…”, of Fig. 3 steps 310-340), based on the one or more 3-dimensional (3-D) scene semantics and the 360-degree scene perception of road surroundings (See Takaki, e.g., “…determine whether the collision probability satisfies a collision threshold...define the collision threshold, which the warning module 230 applies to determine when to provide an alert. The collision threshold is, for example, a limit for the collision probability that when satisfied, indicates a collision is sufficiently likely such that the warning module 230 is to warn the surrounding objects…”, of ¶ [0035]-¶ [0045], ¶ [0049]-¶ [0060], ¶ [0074], ¶ [0103], Figs. 1-2 elements 100-170, Figs. 3, 5 Steps 300-530, Fig. 6A-B elements 1000-670, and Fig. 9 elements 100-950, and Fig. 10 elements 100-1040), using a symbiotic warning trained model (See Takaki, e.g., “…the detection module 220 analyzes the sensor data 250 using one or more detection/identification routines that generally function to detect the presence of objects, classify/identify a type of the objects (e.g., vehicle, pedestrian, etc.), and localize the objects relative to the subject vehicle 100... one or more of the modules described herein can include artificial or computational intelligence elements, e.g., neural network, fuzzy logic, or other machine learning algorithms…one or more of the modules can be distributed among a plurality of the modules…”, of ¶ [0035]-¶ [0044], ¶ [0049]-¶ [0060], ¶ [0074], ¶ [0103], Figs. 1-2 elements 100-170, Figs. 3, 5 Steps 300-530, Fig. 6A-B elements 1000-670, and Fig. 9 elements 100-950, and Fig. 10 elements 100-1040); and generating, via the one or more hardware processors, the symbiotic warning signal, to one or more priority actors (e.g., “…At 340, the warning module 230 communicates an alert to at least one of the surrounding objects about the collision hazard associated with the surrounding objects colliding…”, of Fig. 3 steps 310-340), based on the decision to generate, using one or more output means (See Takaki, e.g., “…The warning module 230 communicates an alert in response to determining the collision probability satisfies the collision threshold…the warning module 230 communicates the alert by using exterior lights of the vehicle 100…the warning module 230 can actuate various conspicuity lamps on the vehicle 100 to communicate the alert to the surrounding objects…”, of ¶ [0035]-¶ [0044], ¶ [0046]-¶ [0047], ¶ [0049]-¶ [0060], ¶ [0074], ¶ [0103], Figs. 1-2 elements 100-170, Figs. 3, 5 Steps 300-530, Fig. 6A-B elements 1000-670, and Fig. 9 elements 100-950, and Fig. 10 elements 100-1040).
Consider claims 2, 10:
Takaki teaches everything claimed as implemented above in the rejection of claims 1, 9. In addition, Takaki teaches wherein detecting one or more priority actors those lead to probable collisions, out of the one or more actors present on the road as the host vehicle navigates (e.g., “…At 320, the detection module 220 uses the previously derived information from the sensor data 250 to determine a collision probability for at least two of the objects…”, of Fig. 3 steps 310-340), based on the one or more 3-dimensional (3-D) scene semantics, using a path estimation and tracking technique (See Takaki, e.g., “…the detection module 220 may further determine more complex trajectories that are, for example, extrapolated from multiple prior observations (e.g., over two or more prior time steps). In any case, the detection module 220 generally uses the position and velocity information about the separate surrounding objects to predict future positions of the objects from which the module 220 can generate a determination about the likelihood of collision between the objects...”, of ¶ [0035]-¶ [0044], ¶ [0046]-¶ [0047], ¶ [0049]-¶ [0060], ¶ [0074], ¶ [0103], Figs. 1-2 elements 100-170, Figs. 3, 5 Steps 300-530, Fig. 6A-B elements 1000-670, and Fig. 9 elements 100-950, and Fig. 10 elements 100-1040), comprises: detecting one or more actors of interest, from the one or more actors present on the road as the host vehicle navigates, using the path estimation and tracking technique (See Takaki, e.g., “…the detection module 220 generally uses the position and velocity information about the separate surrounding objects to predict future positions of the objects from which the module 220 can generate a determination about the likelihood of collision between the objects...”, of ¶ [0035]-¶ [0044], ¶ [0046]-¶ [0047], ¶ [0049]-¶ [0060], ¶ [0074], ¶ [0103], Figs. 1-2 elements 100-170, Figs. 3, 5 Steps 300-530, Fig. 6A-B elements 1000-670, and Fig. 9 elements 100-950, and Fig. 10 elements 100-1040); determining one or more predicted moves of (i) each of the one or more actors of interest and (ii) the host vehicle, using the path estimation and tracking technique; and mapping (i) the one or more predicted moves and (ii) a current move, of each of the one or more actors of interest and the host vehicle, to detect the one or more priority actors those may lead to probable collisions (See Takaki, e.g., “…the detection module 220 predicting future positions, determining blind spots, and so on. That is, for example, the detection module 220 may predict movements of the object according to a particular type/class. By way of example, the detection module 220 may apply intuition according to the type, including maintaining particular lane assignments (e.g., bike lanes for bicycles, etc.), predicting speeds, or potential speed categories (e.g., average speeds of pedestrians), and so on...”, of ¶ [0035]-¶ [0044], ¶ [0046]-¶ [0047], ¶ [0049]-¶ [0060], ¶ [0074], ¶ [0103], Figs. 1-2 elements 100-170, Figs. 3, 5 Steps 300-530, Fig. 6A-B elements 1000-670, and Fig. 9 elements 100-950, and Fig. 10 elements 100-1040).
Consider claims 3, 11:
Takaki teaches everything claimed as implemented above in the rejection of claims 1, 9. In addition, Takaki teaches wherein deciding to generate the symbiotic warning signal of the one or more symbiotic warning signals, to one or more priority actors based on the one or more 3-dimensional (3-D) scene semantics and the 360-degree scene perception of road surroundings, using the symbiotic warning trained model, comprising: determining a current state of the host vehicle and the one or more priority actors, using the 360-degree scene perception of road surroundings (See Takaki, e.g., “…the detection module 220 predicting future positions, determining blind spots, and so on. That is, for example, the detection module 220 may predict movements of the object according to a particular type/class. By way of example, the detection module 220 may apply intuition according to the type, including maintaining particular lane assignments (e.g., bike lanes for bicycles, etc.), predicting speeds, or potential speed categories (e.g., average speeds of pedestrians), and so on...”, of ¶ [0035]-¶ [0044], ¶ [0046]-¶ [0047], ¶ [0049]-¶ [0060], ¶ [0072]-¶ [0075], ¶ [0103], Figs. 1-2 elements 100-170, Figs. 3, 5 Steps 300-530, Fig. 6A-B elements 1000-670, and Fig. 9 elements 100-950, and Fig. 10 elements 100-1040), wherein the state is associated with current dynamics of the each of the one or more priority actors on the road (e.g., “…At 340, the warning module 230 communicates an alert to at least one of the surrounding objects about the collision hazard associated with the surrounding objects colliding…”, of Fig. 3 steps 310-340); determining a current action for the host vehicle and the one or more priority actors, based on the current state of the host vehicle and the one or more priority actors, using the one or more 3-dimensional (3-D) scene semantics and the 360-degree scene perception of road surroundings (See Takaki, e.g., “…communicates an alert to at least one of the surrounding objects about the collision hazard associated with the surrounding objects colliding…activates lights of the subject vehicle 100 to visibly communicate the alert to the at least one of the surrounding objects…activates the lamps/lights of the vehicle 100 in different ways to communicate the alert…identifies which object/vehicle is unaware of the hazard…determines which lamps of the vehicle 100 are visible to the object and activates the associated lights to communicate the alert…selects between front and rear hazard lights…activates the front hazard lights, and when the object is to the rear of the vehicle 100…activates the rear hazard lights...”, of ¶ [0035]-¶ [0044], ¶ [0046]-¶ [0047], ¶ [0049]-¶ [0060], ¶ [0072]-¶ [0075], ¶ [0103], Figs. 1-2 elements 100-170, Figs. 3, 5 Steps 300-530, Fig. 6A-B elements 1000-670, and Fig. 9 elements 100-950, and Fig. 10 elements 100-1040), wherein the action describes one or more possible moves that each of the one or more priority actors and the host vehicle; and passing (i) the current state of the host vehicle and the one or more priority actors, (ii) the current action for the host vehicle and the one or more priority actors, to the symbiotic warning trained model, to decide whether to generate the symbiotic warning signal, to one or more priority actors those lead to probable collisions (See Takaki, e.g., “…communicates an alert to at least one of the surrounding objects about the collision hazard associated with the surrounding objects colliding…activates lights of the subject vehicle 100 to visibly communicate the alert to the at least one of the surrounding objects…activates the lamps/lights of the vehicle 100 in different ways to communicate the alert…identifies which object/vehicle is unaware of the hazard…determines which lamps of the vehicle 100 are visible to the object and activates the associated lights to communicate the alert…selects between front and rear hazard lights…activates the front hazard lights, and when the object is to the rear of the vehicle 100…activates the rear hazard lights...”, of ¶ [0035]-¶ [0044], ¶ [0046]-¶ [0047], ¶ [0049]-¶ [0060], ¶ [0072]-¶ [0075], ¶ [0103], Figs. 1-2 elements 100-170, Figs. 3, 5 Steps 300-530, Fig. 6A-B elements 1000-670, and Fig. 9 elements 100-950, and Fig. 10 elements 100-1040).
Consider claims 6, 14:
Takaki teaches everything claimed as implemented above in the rejection of claims 1, 9. In addition, Takaki teaches wherein the one or more actors present on the road comprises one or more motorized and non-motorized vehicles, pedestrians, and animals present on the road surrounding the host vehicle (See Takaki, e.g., “…the surrounding objects can include various types of objects such as vehicular (e.g., automobiles, trucks, motorcycles, etc.), non-vehicular (e.g., pedestrians, animals, bicycles, etc.), and even inanimate objects (e.g., road debris, potholes, etc.). Whichever objects makeup the detected surrounding objects, the collision warning system 170 generally functions to determine the potential for a collision and provide the alerts when at least one of the surrounding objects is a vehicular or non-vehicular object...”, of ¶ [0031]-¶ [0037], ¶ [0049]-¶ [0060], Figs. 1-2 elements 100-170, Figs. 3, 5 Steps 300-530, Fig. 6A-B elements 1000-670, and Fig. 9 elements 100-950, and Fig. 10 elements 100-1040).
Consider claims 7, 15:
Takaki teaches everything claimed as implemented above in the rejection of claims 1, 9. In addition, Takaki teaches wherein the symbiotic warning signal is generated through horn, headlights, a vehicle-vehicle to communication, and a combination thereof, based on type of the priority actor and a driving environment scenario (See Takaki, e.g., “…The warning module 230 communicates an alert in response to determining the collision probability satisfies the collision threshold…the warning module 230 communicates the alert by using exterior lights of the vehicle 100…the warning module 230 can actuate various conspicuity lamps on the vehicle 100 to communicate the alert to the surrounding objects…communicate the alert, such as vehicle-to-vehicle (V2V) systems, vehicle-to-infrastructure (V2I)…”, of ¶ [0035]-¶ [0044], ¶ [0046]-¶ [0047], ¶ [0049]-¶ [0060], ¶ [0074], ¶ [0103], Figs. 1-2 elements 100-170, Figs. 3, 5 Steps 300-530, Fig. 6A-B elements 1000-670, and Fig. 9 elements 100-950, and Fig. 10 elements 100-1040).
Consider claims 8, 16:
Takaki teaches everything claimed as implemented above in the rejection of claims 1, 9. In addition, Takaki teaches wherein the one or more road contextual parameters are associated with road modelling and the one or more road contextual parameters are received from one or more of: one or more 360-degree Lidars, one or more front corner radars, one or more rear corner radars, one or more cameras, one or more ultrasonic sensors, and one or more geographical road information devices, or a combination thereof, installed in the host vehicle (See Takaki, e.g., “…the sensor data 250 may include varying forms of observations about the surrounding environment that the detection module 220 derives from a single type of sensor (e.g., a radar sensor) or that the detection module 220 derives from fusing sensor data from multiple sources (e.g., mono-camera, stereo camera, LiDAR, radar, ultrasonic, etc.)…”, of ¶ [0035]-¶ [0044], ¶ [0046]-¶ [0047], ¶ [0049]-¶ [0060], ¶ [0074], ¶ [0103], Figs. 1-2 elements 100-170, Figs. 3, 5 Steps 300-530, Fig. 6A-B elements 1000-670, and Fig. 9 elements 100-950, and Fig. 10 elements 100-1040).
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 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) 4-5, 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Takaki in view of Huber et al. (US Pub. No.: 2020/0398743 A1: hereinafter “Huber”).
Consider claims 4, 12:
Takaki teaches everything claimed as implemented above in the rejection of claims 3, 9. In addition, Takaki teaches “…the detection module 220 provides the particular type with a further granularity to specify the type as a specific class within the separate noted categories…To achieve the classification of type/class, the detection module 220 may implement one or more machine learning algorithms (e.g., convolutional neural networks) that process the sensor data 250 (e.g., images) and generate the classifications. In further aspects still, the detection module 220 may determine actual dimensions of the objects and define the objects according to the dimensions and type…the detection module 220 implements machine learning algorithms such as convolutional neural networks to identify/detect objects from the sensor data 250. Moreover, the detection module 220 may implement further routines to perform the localization such as simultaneous localization and mapping (SLAM) routines. In any case, the detection module 220 uses the sensor data to acquire awareness about the surrounding environment including aspects relating to the surrounding objects.”, as taught in ¶ [0035]-¶ [0045], ¶ [0049]-¶ [0060], ¶ [0074], ¶ [0103], and exhibited in Figs. 1-2 elements 100-170, Figs. 3, 5 Steps 300-530, Fig. 6A-B elements 1000-670, and Fig. 9 elements 100-950, and Fig. 10 elements 100-1040. However, Takaki does not explicitly teach the symbiotic warning trained model is obtained by: receiving a training dataset comprising a plurality of training samples, wherein the training dataset is associated with one or more driving environment scenarios and comprises one or more training sub-datasets, wherein each training sub-dataset is associated with each driving environment scenario and comprises one or more training samples of the plurality of training samples, and wherein each of the plurality of training samples comprises (i) a training 360-degree scene perception of road surroundings of a training host vehicle and (ii) one or more training 3-dimensional (3-D) scene semantics of the training host vehicle, associated with a plurality of training actors; processing each of the plurality of training samples present in each training sub-dataset to obtain a plurality of processed training samples from the plurality of training samples, wherein each processed training sample comprises a state and an action of each training priority actor and the training host vehicle, from (i) the training 360-degree scene perception of road surroundings and (ii) one or more training 3-dimensional (3-D) scene semantics; assigning a training symbiotic warning signal of a plurality of symbiotic warning signals, for each processed training sample, based on the state and the action of each training priority actor and the training host vehicle; and training a deep reinforcement learning neural network model, with each processed training sample at a time using an associated training symbiotic warning signal assigned, until the plurality of processed training samples is completed, to obtain the symbiotic warning trained model.
In an analogous field of endeavor, Huber teaches the symbiotic warning trained model is obtained (See Huber, e.g., “…the machine learning unit 109 may instruct the visual notification operating unit 106 to output a visual warning to the potentially unsafe pedestrian, using a spotlight or a laser projection system…output an audio notification signal from the audio speaker or buzzer system 120 as an optimal notification option…the machine learning unit 109 selects to output a notification to the pedestrian mobile device 140 using the communication unit 104…the machine learning unit 109 selects to output no notifications, both a visual and an audio notification, or any other combination of notification modalities…”, of Abstract, ¶ [0045]-¶ [0047], ¶ [0069]-¶ [0070], ¶ [0081]-¶ [0093], Figs. 3-4 steps 300-434, Figs. 5A-C elements 210-522) by: receiving a training dataset comprising a plurality of training samples (See Huber, e.g., “…at block 416, the input data may be processed using a machine learning model. In at least one of the various embodiments, a confidence value may be generated and associated with the predicted output data…the machine learning model may be arranged to re-train if a number of detected notification errors (e.g., false positive, label conflicts, or the like) exceeds a defined threshold…machine learning unit 109 may determine if generated machine learning model's confidence value exceeds the predefined confidence threshold. If the generated confidence value is below the threshold (decision block 416, “No” branch), the disclosed training process returns to block 402. However, if the machine learning model's confidence value reaches the defined threshold (decision block 416, “Yes” branch), the training process stops at block 418…”, of ¶ [0045]-¶ [0047], ¶ [0069]-¶ [0070], ¶ [0081]-¶ [0093], Figs. 3-4 steps 300-434, Figs. 5A-C elements 210-522), wherein the training dataset is associated with one or more driving environment scenarios and comprises one or more training sub-datasets (See Huber, e.g., “…bicyclist 508a could be approaching the vehicle 200 from a side. The information acquiring unit 112 may calculate a projected position 508b of the fourth bicyclist…notification controller 114 may ask again the machine learning unit 109 to select a particular notification mode…the machine learning unit 109 selects another type of visual notification in a form of the danger zone image 509 projected by spotlight digital projector 204…”, of ¶ [0045]-¶ [0047], ¶ [0069]-¶ [0070], ¶ [0081]-¶ [0093], Figs. 3-4 steps 300-434, Figs. 5A-C elements 210-522), wherein each training sub-dataset is associated with each driving environment scenario and comprises one or more training samples of the plurality of training samples (See Huber, e.g., “…notification controller 114 may ask the machine learning unit 109 to select a particular notification mode…selects an audible notification 518 rendered by the audio speaker or buzzer system 120…”, of ¶ [0045]-¶ [0047], ¶ [0069]-¶ [0070], ¶ [0081]-¶ [0093], Figs. 3-4 steps 300-434, Figs. 5A-C elements 210-522), and wherein each of the plurality of training samples comprises (i) a training 360-degree scene perception of road surroundings of a training host vehicle and (ii) one or more training 3-dimensional (3-D) scene semantics of the training host vehicle, associated with a plurality of training actors (See Huber, e.g., “…the machine learning unit 109 selects another type of visual notification in a form of the danger zone image 509 projected by spotlight digital projector 204…notification controller 114 may ask again the machine learning unit 109 to select a particular notification mode…the machine learning unit 109 selects another type of visual notification in a form of the danger zone image 509 projected by spotlight digital projector 204…”, of ¶ [0045]-¶ [0047], ¶ [0069]-¶ [0070], ¶ [0081]-¶ [0093], Figs. 3-4 steps 300-434, Figs. 5A-C elements 210-522); processing each of the plurality of training samples present in each training sub-dataset to obtain a plurality of processed training samples from the plurality of training samples (See Huber, e.g., “…notification controller 114 may ask again the machine learning unit 109 to select a particular notification mode…the machine learning unit 109 selects another type of visual notification in a form of the danger zone image 509 projected by spotlight digital projector 204…”, of ¶ [0045]-¶ [0047], ¶ [0069]-¶ [0070], ¶ [0081]-¶ [0093], Figs. 3-4 steps 300-434, Figs. 5A-C elements 210-522), wherein each processed training sample comprises a state and an action of each training priority actor and the training host vehicle, from (i) the training 360-degree scene perception of road surroundings and (ii) one or more training 3-dimensional (3-D) scene semantics (See Huber, e.g., “…the information acquiring unit 112 predicts a potential path of travel of the vehicle 200 and the first potentially unsafe pedestrian 520a and may calculate a projected position 520b of the first potentially unsafe pedestrian. In this case, the information acquiring unit 112 may determine that the first potentially unsafe pedestrian 520a is likely to enter the danger zone 510. Accordingly, notification controller 114 may ask the machine learning unit 109 to select a particular notification mode…”, of ¶ [0045]-¶ [0047], ¶ [0069]-¶ [0070], ¶ [0081]-¶ [0093], Figs. 3-4 steps 300-434, Figs. 5A-C elements 210-522); assigning a training symbiotic warning signal of a plurality of symbiotic warning signals, for each processed training sample, based on the state and the action of each training priority actor and the training host vehicle (See Huber, e.g., “…the information acquiring unit 112 may determine that the first potentially unsafe pedestrian 520a is likely to enter the danger zone 510. Accordingly, notification controller 114 may ask the machine learning unit 109 to select a particular notification mode…”, of ¶ [0045]-¶ [0047], ¶ [0069]-¶ [0070], ¶ [0081]-¶ [0093], Figs. 3-4 steps 300-434, Figs. 5A-C elements 210-522); and training a deep reinforcement learning neural network model, with each processed training sample at a time using an associated training symbiotic warning signal assigned, until the plurality of processed training samples is completed, to obtain the symbiotic warning trained model (See Huber, e.g., “…after detecting the second potentially unsafe pedestrian 522a and acquiring corresponding pedestrian parameters, the information acquiring unit 112 predicts a potential path of travel of the vehicle 200 and the second potentially unsafe pedestrian 522a and may determine that the second potentially unsafe pedestrian 522a is also likely to enter the danger zone 510. In this case, the machine learning unit 109 selects similar visual notification 220b but this time the color is orange…”, of ¶ [0045]-¶ [0047], ¶ [0069]-¶ [0070], ¶ [0081]-¶ [0093], Figs. 3-4 steps 300-434, Figs. 5A-C elements 210-522).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine “…providing a warning from a subject vehicle to surrounding objects about a collision hazard…identifying the surrounding objects of a subject vehicle according to sensor data about a surrounding environment of the subject vehicle…determining a collision probability indicating a likelihood of collision between a first object and a second object of the surrounding objects…in response to the collision probability satisfying a collision threshold, communicating, by the subject vehicle, an alert to at least one of the surrounding objects about the collision hazard associated with the surrounding objects colliding…”, as disclosed in Takaki with “the symbiotic warning trained model is obtained by: receiving a training dataset comprising a plurality of training samples, wherein the training dataset is associated with one or more driving environment scenarios and comprises one or more training sub-datasets, wherein each training sub-dataset is associated with each driving environment scenario and comprises one or more training samples of the plurality of training samples, and wherein each of the plurality of training samples comprises (i) a training 360-degree scene perception of road surroundings of a training host vehicle and (ii) one or more training 3-dimensional (3-D) scene semantics of the training host vehicle, associated with a plurality of training actors; processing each of the plurality of training samples present in each training sub-dataset to obtain a plurality of processed training samples from the plurality of training samples, wherein each processed training sample comprises a state and an action of each training priority actor and the training host vehicle, from (i) the training 360-degree scene perception of road surroundings and (ii) one or more training 3-dimensional (3-D) scene semantics; assigning a training symbiotic warning signal of a plurality of symbiotic warning signals, for each processed training sample, based on the state and the action of each training priority actor and the training host vehicle; and training a deep reinforcement learning neural network model, with each processed training sample at a time using an associated training symbiotic warning signal assigned, until the plurality of processed training samples is completed, to obtain the symbiotic warning trained model”, as taught in Huber with a reasonable expectation of success to yield a system, method for robustly, seamlessly, and efficiently utilizing Vehicle to Pedestrian (V2P) communication to improve pedestrian safety.
Consider claims 5, 13:
Takaki teaches everything claimed as implemented above in the rejection of claims 3, 12. In addition, Takaki teaches “…the detection module 220 provides the particular type with a further granularity to specify the type as a specific class within the separate noted categories…To achieve the classification of type/class, the detection module 220 may implement one or more machine learning algorithms (e.g., convolutional neural networks) that process the sensor data 250 (e.g., images) and generate the classifications. In further aspects still, the detection module 220 may determine actual dimensions of the objects and define the objects according to the dimensions and type…the detection module 220 implements machine learning algorithms such as convolutional neural networks to identify/detect objects from the sensor data 250. Moreover, the detection module 220 may implement further routines to perform the localization such as simultaneous localization and mapping (SLAM) routines. In any case, the detection module 220 uses the sensor data to acquire awareness about the surrounding environment including aspects relating to the surrounding objects.”, as taught in ¶ [0035]-¶ [0045], ¶ [0049]-¶ [0060], ¶ [0074], ¶ [0103], and exhibited in Figs. 1-2 elements 100-170, Figs. 3, 5 Steps 300-530, Fig. 6A-B elements 1000-670, and Fig. 9 elements 100-950, and Fig. 10 elements 100-1040. However, Takaki does not explicitly teach wherein training the deep reinforcement learning neural network model, with each processed training sample at a time using the associated training symbiotic warning signal assigned, until the plurality of processed training samples is completed, to obtain the symbiotic warning trained model, comprises: passing the state of a training host vehicle, of each processed training sample, to a reinforcement learning (RL) agent of the deep reinforcement learning neural network model; obtaining (i) a predicted action of each training priority actor and the training host vehicle, for a given state of the training host vehicle, and (ii) a predicted symbiotic warning signal, from each processed training sample, from the RL agent; comparing (i) the predicted action of each training priority actor and the training host vehicle with the from each processed training sample with the action of each training priority actor and the training host vehicle, and (ii) the predicted symbiotic warning signal with the corresponding training symbiotic warning signal assigned, for each processed training sample, to provide a reward for the RL agent based on the comparison; and performing the training of the reinforcement learning (RL) agent with a successive processed training sample, until the plurality of processed training samples is completed, to maximize the reward for the RL agent.
In an analogous field of endeavor, Huber teaches wherein training the deep reinforcement learning neural network model, with each processed training sample at a time using the associated training symbiotic warning signal assigned, until the plurality of processed training samples is completed, to obtain the symbiotic warning trained model (See Huber, e.g., “…the machine learning unit 109 may instruct the visual notification operating unit 106 to output a visual warning to the potentially unsafe pedestrian, using a spotlight or a laser projection system…output an audio notification signal from the audio speaker or buzzer system 120 as an optimal notification option…the machine learning unit 109 selects to output a notification to the pedestrian mobile device 140 using the communication unit 104…the machine learning unit 109 selects to output no notifications, both a visual and an audio notification, or any other combination of notification modalities…”, of Abstract, ¶ [0045]-¶ [0047], ¶ [0069]-¶ [0070], ¶ [0081]-¶ [0093], Figs. 3-4 steps 300-434, Figs. 5A-C elements 210-522), comprises: passing the state of a training host vehicle, of each processed training sample, to a reinforcement learning (RL) agent of the deep reinforcement learning neural network model (See Huber, e.g., “…the machine learning model may be arranged to re-train if a number of detected notification errors (e.g., false positive, label conflicts, or the like) exceeds a defined threshold…machine learning unit 109 may determine if generated machine learning model's confidence value exceeds the predefined confidence threshold. If the generated confidence value is below the threshold (decision block 416, “No” branch), the disclosed training process returns to block 402. However, if the machine learning model's confidence value reaches the defined threshold (decision block 416, “Yes” branch), the training process stops at block 418…”, of ¶ [0045]-¶ [0047], ¶ [0069]-¶ [0070], ¶ [0081]-¶ [0093], Figs. 3-4 steps 300-434, Figs. 5A-C elements 210-522); obtaining (i) a predicted action of each training priority actor and the training host vehicle, for a given state of the training host vehicle, and (ii) a predicted symbiotic warning signal, from each processed training sample, from the RL agent (See Huber, e.g., “…bicyclist 508a could be approaching the vehicle 200 from a side. The information acquiring unit 112 may calculate a projected position 508b of the fourth bicyclist…notification controller 114 may ask again the machine learning unit 109 to select a particular notification mode…the machine learning unit 109 selects another type of visual notification in a form of the danger zone image 509 projected by spotlight digital projector 204…”, of ¶ [0045]-¶ [0047], ¶ [0069]-¶ [0070], ¶ [0081]-¶ [0093], Figs. 3-4 steps 300-434, Figs. 5A-C elements 210-522); comparing (i) the predicted action of each training priority actor and the training host vehicle with the from each processed training sample with the action of each training priority actor and the training host vehicle (See Huber, e.g., “…the information acquiring unit 112 predicts a potential path of travel of the vehicle 200 and the first potentially unsafe pedestrian 520a and may calculate a projected position 520b of the first potentially unsafe pedestrian. In this case, the information acquiring unit 112 may determine that the first potentially unsafe pedestrian 520a is likely to enter the danger zone 510. Accordingly, notification controller 114 may ask the machine learning unit 109 to select a particular notification mode…”, of ¶ [0045]-¶ [0047], ¶ [0069]-¶ [0070], ¶ [0081]-¶ [0093], Figs. 3-4 steps 300-434, Figs. 5A-C elements 210-522), and (ii) the predicted symbiotic warning signal with the corresponding training symbiotic warning signal assigned, for each processed training sample, to provide a reward for the RL agent based on the comparison (See Huber, e.g., “…the information acquiring unit 112 may determine that the first potentially unsafe pedestrian 520a is likely to enter the danger zone 510. Accordingly, notification controller 114 may ask the machine learning unit 109 to select a particular notification mode…”, of ¶ [0045]-¶ [0047], ¶ [0069]-¶ [0070], ¶ [0081]-¶ [0093], Figs. 3-4 steps 300-434, Figs. 5A-C elements 210-522); and performing the training of the reinforcement learning (RL) agent with a successive processed training sample, until the plurality of processed training samples is completed, to maximize the reward for the RL agent (See Huber, e.g., “…after detecting the second potentially unsafe pedestrian 522a and acquiring corresponding pedestrian parameters, the information acquiring unit 112 predicts a potential path of travel of the vehicle 200 and the second potentially unsafe pedestrian 522a and may determine that the second potentially unsafe pedestrian 522a is also likely to enter the danger zone 510. In this case, the machine learning unit 109 selects similar visual notification 220b but this time the color is orange…”, of ¶ [0045]-¶ [0047], ¶ [0069]-¶ [0070], ¶ [0081]-¶ [0093], Figs. 3-4 steps 300-434, Figs. 5A-C elements 210-522).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine “…providing a warning from a subject vehicle to surrounding objects about a collision hazard…identifying the surrounding objects of a subject vehicle according to sensor data about a surrounding environment of the subject vehicle…determining a collision probability indicating a likelihood of collision between a first object and a second object of the surrounding objects…in response to the collision probability satisfying a collision threshold, communicating, by the subject vehicle, an alert to at least one of the surrounding objects about the collision hazard associated with the surrounding objects colliding…”, as disclosed in Takaki with “wherein training the deep reinforcement learning neural network model, with each processed training sample at a time using the associated training symbiotic warning signal assigned, until the plurality of processed training samples is completed, to obtain the symbiotic warning trained model, comprises: passing the state of a training host vehicle, of each processed training sample, to a reinforcement learning (RL) agent of the deep reinforcement learning neural network model; obtaining (i) a predicted action of each training priority actor and the training host vehicle, for a given state of the training host vehicle, and (ii) a predicted symbiotic warning signal, from each processed training sample, from the RL agent; comparing (i) the predicted action of each training priority actor and the training host vehicle with the from each processed training sample with the action of each training priority actor and the training host vehicle, and (ii) the predicted symbiotic warning signal with the corresponding training symbiotic warning signal assigned, for each processed training sample, to provide a reward for the RL agent based on the comparison; and performing the training of the reinforcement learning (RL) agent with a successive processed training sample, until the plurality of processed training samples is completed, to maximize the reward for the RL agent”, as taught in Huber with a reasonable expectation of success to yield a system, method for robustly, seamlessly, and efficiently utilizing Vehicle to Pedestrian (V2P)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kannon et al. (US Pub. No.: 2017/0178512 A1) teaches “A vehicle proximity warning system, installed in a primary vehicle having a front, a rear, a front bumper, a rear bumper, a horn, a front windshield, and a rear window, and an associated smartphone. Sensor bars are mounted near the front and rear bumpers for determining a proximity distance of a secondary vehicle as it approaches, each sensor bar having an accelerometer and a camera for detecting a collision with the secondary vehicle and acquiring images thereof that are wirelessly shared with the smartphone. Indicator bars are located in the front windshield and rear window and each have indicator LEDs that are successively illuminated as the proximity distance decreases, to provide a visual indication that can be seen by the secondary vehicle. When the proximity distance falls below a predetermined danger threshold, the system sounds the horn, flashes the indicator LEDs, and activates the camera.”
Bonilla (US Pub. No.: 2009/0256698 A1) teaches “A brake light warning system provides a warning to nearby motorists whose vehicles are within a warning area adjacent a user vehicle. The warning system may provide a visual warning to these motorists by illuminating one or more brake lights of the user vehicle. A user alert device may also be activated to alert the user to a nearby vehicle that is within the warning area. The warning area may be fixed or may bet set based on the speed of the user vehicle to warn nearby motorists that they are too close to the user vehicle. The warning system may provide a warning via a brake light without activating the braking system of the user vehicle. The warning system may utilize components of the user vehicle to detect vehicle speed, nearby vehicles, provide warnings, provide alerts, or a combination thereof.”
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/BABAR SARWAR/Primary Examiner, Art Unit 3667