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. Notice to Applicant The following is a Non-Final, first Office Action responsive to Applicant’s communication of 1/17/23 , in which applicant filed the application. Claims 1- 7 are pending in the instant application and have been rejected below. Priority Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Claim Interpretation The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “unit” throughout claim 5 – data collection unit point cloud creation unit probability calculation unit threshold calculation unit curve surface creation unit. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. Examiner interprets these “units” to correspond to “a computer-readable storage medium storing program instructions executed by a processor” as supported by [0059-0060] of the publication of the application. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. 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- 7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without reciting significantly more. Step One - First, pursuant to step 1 in MPEP 2106.03 , the claim 1 is directed to a method which is a statutory category. Step 2A, Prong One - MPEP 2106.04 - The claim 1 recites– A time threshold model creation method based on an autonomous braking system, comprising: obtaining braking data about a target vehicle, the braking data comprising a braking time, a speed of the target vehicle, and a relative speed between the target vehicle and a target obstacle; creating a three-dimensional space in accordance with the braking time, the speed of the target vehicle, and the relative speed, and obtaining a point cloud of the braking data in the three-dimensional space; dividing a two-dimensional plane defined by the speed of the target vehicle and the relative speed into a plurality of statistical regions, and calculating a probability in each statistical region in accordance with the point cloud, so as to obtain a fitted probability distribution curve of the braking time in each statistical region; calculating a time threshold in each statistical region through a percentage partitioning algorithm in accordance with the fitted probability distribution curve; and obtaining a target curve surface of the time thresholds in accordance with the distribution of the time threshold in each statistical region in the three-dimensional space, wherein the calculating the probability in each statistical region in accordance with the point cloud so as to obtain the fitted probability distribution curve of the braking time in each statistical region comprises, with respect to all data points in the point cloud, calculating a probability of each data point in each statistical region so as to obtain the fitted probability distribution curve of the braking time, an x-axis value of the fitted probability distribution curve is the braking time, and a y-axis value of the fitted probability distribution curve is a proportion of the quantity of data points on a target time threshold to all data points in a target region, wherein the calculating the time threshold in each statistical region through the percentage partitioning algorithm in accordance with the fitted probability distribution curve comprises: selecting the target region; obtaining a percentage of data points whose braking time is greater than a target braking time in the target region in all data points in the target region in accordance with the fitted probability distribution curve; determining whether the percentage is greater than or equal to a predetermined percentage, and when the percentage is greater than or equal to the predetermined percentage, taking the target braking time as the time threshold in the target region; and obtaining the time threshold in each statistical region as the target region, wherein the obtaining the target curve surface of the time thresholds in accordance with the distribution of the time threshold in each statistical region in the three-dimensional space comprises: taking coordinates of a center of the target region as coordinates of the time threshold in the target region on the two-dimensional plane; forming a target three-dimensional point in accordance with the center of the target region and the time threshold; and obtaining the target curve surface of the time thresholds through a fitting algorithm in accordance with the target three-dimensional points . As drafted, this is, under its broadest reasonable interpretation, directed to the Abstract idea groupings of “ mathematical relationships ” as here we have a series of explicit math – a braking time, speed of target vehicle, relative speed between target vehicle and target vehicle, three-dimensional space is a plot/graph with three axes (See e.g. Applicant’s FIG. 7), calculating probability in different statistical regions of the 3-D space/graph; obtain a target curve surface of a calculating time threshold using a percentage partitioning algorithm, calculating probabilities, additional calculations, taking coordinates of a center of a target region, and using a fitting algorithm . At this time, the claim is viewed as a series of mathematical relationships. Step 2A, Prong Two - MPEP 2106.04 - This judicial exception is not integrated into a practical application. At this time, no additional elements or even a computer is recited . Examiner recommends as initial step here reciting a computer performs each step. The preamble does state there is “autonomous braking system,” but it is not required by the claim, as instead this is what the “time threshold” method is modeling ; thus the data is viewed as representing data from “autonomous braking”. The first step states “obtaining braking data about a target vehicle,” but there is no recitation of how this occurs. Most likely, it is intended to be a computer here that collects the data, as in [0058] as published. It also appears the intention is a computer performs each step, but even if “by a computer” added to each step, this would be viewed as mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Step 2B in MPEP 2106.05 - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, even if the claim is interpreted/ amended to include a computer, it is considered MPEP 2106.05(f) ( Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235 ) and MPEP 2106.05h (field of use). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. The claim is not patent eligible. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Claim s 2 -4 further narrow the abstract idea by having additional mathematical representations . Claims 5-7 recite similar limitations and are rejected for the same reasons as claim 1 above. Claims 5-7 explicitly have a computer storing program instructions to execute methods. As explained above in Step 2a, prong two and step 2B, just adding “by a computer” with memory storing executable instructions, is considered “apply it [abstract idea] on a computer” (MPEP 2106.05f). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. For more information on 101 rejections, see MPEP 2106. Possible suggestions – Examiner suggests considering actively reciting controlling/braking a vehicle, perhaps from [0057] as published “As a result, it is able to accurately calculate the time for braking or decelerating the vehicle through the created time threshold model, thereby to provide data for the subsequent braking or decelerating.” Perhaps [0030] can be inspiration for a claim amendment also (e.g. “An object of the present disclosure is to provide a time threshold model creation method based on an autonomous braking system, so as to calculate a time for braking (or decelerating) a vehicle in a more appropriate manner, thereby to provide data for controlling the braking of the vehicle.”). Other portions of the specification may also be helpful. Claim Rejections - 35 USC § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. Claims 1 - 7 are rejected under 35 U.S.C. 103 as being unpatentable over Okbuo (US 2017/0129466) , in view of Chen “ A Robust Active Safety Enhancement Strategy With Learning Mechanism in Vehicular Networks ,” 2020, IEEE Transactions on Intelligent Transportation Systems, Vol. 21, No. 12, pages 5160-5176, and Beglerovic , “ Testing of Autonomous Vehicles Using Surrogate Models and Stochastic Optimization ,” 2017 IEEE 20 th International Conference on Intelligent Transportation Systems, pages 1-6 . Concerning claim 1, Okubo discloses: A time threshold model creation method based on an autonomous braking system ( Okubo see par 20 – vehicle collision prevention apparatus in FIG. 1 includes collision likelihood calculation unit, braking processing determination unit 105 and brake apparatus control unit 107) , comprising: obtaining braking data about a target vehicle, the braking data comprising a braking time, a speed of the target vehicle ( Okubo – see par 26 - The stopping time prediction unit 102D calculates a predicted stopping time required for the relative velocity between the host vehicle and the frontward obstruction to reach zero on the basis of the relative velocity between the host vehicle and the frontward obstruction and the deceleration of the host vehicle . see par 27 - the deceleration correction unit 102C does not have to be used, and instead, the stopping time prediction unit 102D may calculate the stopping time by means of a simple algebra operation using the predetermined value of the deceleration of the host vehicle. see par 32 - the braking processing determination unit 105 monitors the numerical value indicating the likelihood of a front end collision , calculated by the front end collision likelihood calculation unit 102, and activates a brake apparatus, not shown in the drawing, by outputting a brake force instruction value to the brake apparatus control unit 107 to prevent a collision between the host vehicle and the frontward obstruction ) and a relative speed between the target vehicle and a target obstacle ( Okubo – see par 26 - The stopping time prediction unit 102D calculates a predicted stopping time required for the relative velocity between the host vehicle and the frontward obstruction to reach zero on the basis of the relative velocity between the host vehicle and the frontward obstruction and the deceleration of the host vehicle ) . Okubo discloses having a collision likelihood evaluation unit 102e that can predict stopping time and include a probability distribution for magnitude of difference between “distance between host vehicle and frontward obstruction” and “distance traveled by the host vehicle following the elapse of the predicted stopping time” that is corrected in consideration of direction, size, shape, and type” of obstruction (See par 28). Chen and Beglerovic disclose: creating a three-dimensional space in accordance with the braking time, the speed of the target vehicle, and the relative speed, and obtaining a point cloud of the braking data in the three-dimensional space ( Chen page 5169, Col. 1, ; Section A, 1 st paragraph – denote LV as leading vehicle and FV as following vehicle; Col. 1, Section A, 3 rd paragraph - Fig.10(b) shows a “space” of data - the trend of relative safety distance in case 2. Since LV drives in an accelerated state, the relative safety distance becomes zero only when the initial speed of LV (lead vehicle) is greater than that of the FV (following vehicle). see also Beglero v ic – see page 2 , col. 1, 1 st paragraph - we propose an iterative approach that guides the testing towards faulty behavior regions in the parameter space as shown in Fig.1 (on page 1) . In order to identify the faulty behavior regions, appropriate cost functions, which can be minimized by various optimization methods, must be defined. As we are interested in the region around the worst behavior, and we want to avoid false positives in the form of local minima, global optimization methods are needed ; see page 2, col. 2, 3 rd paragraph ); Okubo, Chen, and Beglerovic disclose: dividing a two-dimensional plane defined by the speed of the target vehicle and the relative speed into a plurality of statistical regions ( Okubo – see par 27 - the deceleration correction unit 102C does not have to be used, and instead, the stopping time prediction unit 102D may calculate the stopping time by means of a simple algebra operation using the predetermined value of the deceleration of the host vehicle. see also Beglerovic – See page 2, Col. 2, last paragraph – find parameters where evaluation criteria not satisfied; limit search for specific set of parameters and avoid exploring regions of search space not of interest ; See page 3, Col. 1, 1 st paragraph – selecting appropriate cost function to guide testing towards regions where behavior is not satisfactory and where evaluation criterion is not satisfied) , and calculating a probability in each statistical region in accordance with the point cloud, so as to obtain a fitted probability distribution curve of the braking time in each statistical region ( Okubo – see par 28 - The collision likelihood evaluation unit 102E evaluates the likelihood of a collision on the basis of the distance between the host vehicle and the frontward obstruction and the predicted stopping time calculated by the stopping time prediction unit 102D. In the evaluation, the distance between the host vehicle and the frontward obstruction may simply be compared with a distance traveled by the host vehicle following the elapse of the predicted stopping time. Alternatively, a probability distribution may be assigned in accordance with the magnitude of the difference between the two distances, and the probability distribution may be corrected in consideration of the direction, size, shape, and type of the frontward obstruction . See also Chen, page 5164, col. 1, 1 st paragraph - Among the commonly used regression algorithms, ridge regression suits our training data the best. However, the lasso is complicated so we choose the ridge regression algorithm to get the objective weights. page 5168, Col. 2, last paragraph -page 5168, col. 1, 1 st paragraph – active safety system combines “AHP-Ridge regression and clustering algorithm to validate the safety distance while taking into consideration vehicle, driver, road, environmental, and geographical conditions see also Beglerovic – see page 3, col. 2 – numerical evaluations of cost functions passed to “Surrogate modeling” block; provides approximation function to “Stochastic Optimization”; “Stochastic Optimization block output pmin representing “most likely” location for global minimum of approximated function; see page 4, Col. 1, last paragraph – use Kriging models to use optimization tools to find appropriate coefficients ; page 4, col. 2, 2 nd paragraph – Kriging models use probabilistic approach; see page 5, Col. 1, Section C – cost function based on time to collision ttc between vehicle and obstacle; vehicle speed v is added to give higher cost value to obstacle out of sensor range and poses no threat to vehicle ) ; calculating a time threshold in each statistical region through a percentage partitioning algorithm in accordance with the fitted probability distribution curve ( Chen – see page 5161, col. 2, 2 nd paragraph - With the accurately perceived information collected, the AHP-Ridge regression evaluation model is then triggered to calculate the impact weights of different factors on the collision, which combines objective and subjective weights. To alleviate the error caused by the AHP-ridge model, Extreme Learning Machine (ELM) is introduced to verify the weight vectors. Then, the generated weights are combined with the bisecting K-means clustering model to evaluate the risk level . see also Beglerovic – see page 2, col. 1, last paragraph - conducted research on a visual emergency breaking ADAS system detecting pedestrians. The main idea was to use neural networks to model the ADAS behavior and use the model instead of the real simulation to get an evaluation and confidence level output ); and obtaining a target curve surface of the time thresholds in accordance with the distribution of the time threshold in each statistical region in the three-dimensional space ( Chen – see page 5169, FIG. 10b; page 5170, Fig. 12 – reaction time relative to safety distance; page 5171, FIG. 13 – time axis with automatic control relative to velocity and distance ) , wherein the calculating the probability in each statistical region in accordance with the point cloud so as to obtain the fitted probability distribution curve of the braking time in each statistical region comprises, with respect to all data points in the point cloud, calculating a probability of each data point in each statistical region so as to obtain the fitted probability distribution curve of the braking time, an x-axis value of the fitted probability distribution curve is the braking time, and a y-axis value of the fitted probability distribution curve is a proportion of the quantity of data points on a target time threshold to all data points in a target region ( Okubo – see par 28 - The collision likelihood evaluation unit 102E evaluates the likelihood of a collision on the basis of the distance between the host vehicle and the frontward obstruction and the predicted stopping time calculated by the stopping time prediction unit 102D. In the evaluation, the distance between the host vehicle and the frontward obstruction may simply be compared with a distance traveled by the host vehicle following the elapse of the predicted stopping time. Alternatively, a probability distribution may be assigned in accordance with the magnitude of the difference between the two distances, and the probability distribution may be corrected in consideration of the direction, size, shape, and type of the frontward obstruction . See also Chen, page 5164, col. 1, 1 st paragraph - Among the commonly used regression algorithms, ridge regression suits our training data the best. However, the lasso is complicated so we choose the ridge regression algorithm to get the objective weights. page 5168, Col. 2, last paragraph -page 5168, col. 1, 1 st paragraph – active safety system combines “AHP-Ridge regression and clustering algorithm to validate the safety distance while taking into consideration vehicle, driver, road, environmental, and geographical conditions see also Beglerovic – see page 3, col. 2 – numerical evaluations of cost functions passed to “Surrogate modeling” block; provides approximation function to “Stochastic Optimization”; “Stochastic Optimization block output pmin representing “most likely” location for global minimum of approximated function; see page 4, Col. 1, last paragraph – use Kriging models to use optimization tools to find appropriate coefficients ; page 4, col. 2, 2 nd paragraph – Kriging models use probabilistic approach; see page 5, Col. 1, Section C – cost function based on time to collision ttc between vehicle and obstacle; vehicle speed v is added to give higher cost value to obstacle out of sensor range and poses no threat to vehicle ; See page 6, Conclusion - For the surrogate modeling we used the Radial Basis Function approximation, and we have explored models with different kernel functions. we have shown that the Kriging model produced the best results leading to a lower number of real system evaluations and a good approximation inside the faulty region ) , wherein the calculating the time threshold in each statistical region through the percentage partitioning algorithm in accordance with the fitted probability distribution curve comprises: selecting the target region; obtaining a percentage of data points whose braking time is greater than a target braking time in the target region in all data points in the target region in accordance with the fitted probability distribution curve ( Beg ler ovic – see page 3, col. 1, 1 st paragraph - selecting an appropriate cost function cψ , it is possible to guide the testing towards regions where the behavior is not satisfactory and where the evaluation criterion ψ is not satisfied. see FIG. 2, page 3, col. 2, 2 nd paragraph - Fig.2 gives an overview of the proposed method. As an input to the algorithm, we provide a search space P; Usually, the cost function cψ ( Φ ( M,p )) can be modeled in such a way that a negative value represents a faulty behavior, i.e. , cthresh = 0; however, that is not mandatory and any kind of value for cthresh can be used ; A new evaluation is done with the new parameters and a better model of the approximated function ˆ cψ ( p ) is built until the algorithm reaches a faulty behavior or the maximum number of iterations ; see page 6, Summary - In this paper we introduced an iterative testing approach for autonomous driving, focusing on finding faulty behavior inside the parameter space ) ; determining whether the percentage is greater than or equal to a predetermined percentage, and when the percentage is greater than or equal to the predetermined percentage, taking the target braking time as the time threshold in the target region ( Okubo – see par 35 - In accordance with the respective thresholds, the following vehicle warning unit 106 determines an operation with which to reduce the likelihood of a collision between the host vehicle and the following vehicle while keeping the likelihood of a collision between the host vehicle and the frontward obstruction at a minimum , and outputs a brake force instruction value or the like, for example, to the brake apparatus control unit 107 in order to activate the brake apparatus ; see also Beglerovic – see page 6, col. 1, 1 st paragraph - The columns of the table are respectively: average evaluated global minimum; the best global minimum; position of the best global minimum; percentage of successfully found crashes from all test runs, i.e. , Krigin model found crashes in 97 out of 100 runs ); and obtaining the time threshold in each statistical region as the target region ( Okubo – see par 26 - The stopping time prediction unit 102D calculates a predicted stopping time required for the relative velocity between the host vehicle and the frontward obstruction to reach zero on the basis of the relative velocity between the host vehicle and the frontward obstruction and the deceleration of the host vehicle ; see par 28 - collision likelihood evaluation unit 102E evaluates the likelihood of a collision on the basis of the distance between the host vehicle and the frontward obstruction and the predicted stopping time calculated by the stopping time prediction unit 102D. In the evaluation, the distance between the host vehicle and the frontward obstruction may simply be compared with a distance traveled by the host vehicle following the elapse of the predicted stopping time ) , wherein the obtaining the target curve surface of the time thresholds in accordance with the distribution of the time threshold in each statistical region in the three-dimensional space comprises: taking coordinates of a center of the target region as coordinates of the time threshold in the target region on the two-dimensional plane ( Chen page 5165, Col. 2, Section A, 2 nd paragraph - To evaluate the risk level of all possible states, clustering algorithms are widely used. Among all clustering algorithms, K- means clustering is the most commonly used, which creates k points for starting centroids (often randomly) at the beginning and then assign each point in the dataset to a cluster. Specifically, for every centroid, the distance between the centroid and the point is calculated, and then the point is assigned to the cluster with the lowest distance ; see page 5174, col. 1, last paragraph - After obtaining the overall weights of the current driving condition, we can conduct clustering analysis based on the algorithm described in Section IV and obtain n centroids for each second-level factor. We multiply the value of the current environmental feature by the weight to get the current state vector of each indicator. Then, we assign the current state vector to the nearest centroid ) ; forming a target three-dimensional point in accordance with the center of the target region and the time threshold ( Chen – See page 5162, Table 1 – factors include… direction U22, Movement U23, Dynamics U24; See page 5164, Col. 2, Section – weights used for correcting acceleration… time; page 5165, Col. 2, Section A, 3 rd paragraph - bisecting K-means clustering uses the Euclidean distance to measure the difference between each value. Different from the traditional clustering algorithm, we define a distance that depends on the weights assigned to the third-level factors [see equation 15] ; and obtaining the target curve surface of the time thresholds through a fitting algorithm in accordance with the target three-dimensional points ( Chen page 5166, FIG. 7 – using “Bisect K—means” cluster, then the centroid discussed above, to correct the ac (acceleration) and tr (response time); see page 5167, col. 2, Section B – “Active Safety Control Model – using velocity, acceleration; observation state upon collection of new vehicle information; substituting vehicles dynamics (Equations 6 and 27 to “state space”; [continuing on page 5168]… col. 1 – estimation of state x(t) using past measurements and inputs at time t; see page 5170, col. 1, last paragraph – Once the inter-vehicle distance equals the relative safety distance, the automatic control module would be launched (the phase labeled with Automatic control). During the control phase, the system will slow down the follower until the inter-vehicle distance is equal to the absolute safety distance. see page 5173, col. 1, Conclusion - control strategy based on the calculated safety inter-vehicle distance is given, with the possible autonomous control using LQG optimal-control model. Simulation results show that our proposed scheme is able to ensure vehicle safety by dynamically adjusting the inter-vehicle distance through autonomously mechanical control. ) . Okubo, Chen, and Beglerovic are analogous art as they are directed to predictions and assessments for controlling brakes of vehicles . 1) Okubo discloses having a collision likelihood evaluation unit 102e that can predict stopping time and include a probability distribution for magnitude of difference between “distance between host vehicle and frontward obstruction” and “distance traveled by the host vehicle following the elapse of the predicted stopping time” that is corrected in consideration of direction, size, shape, and type” of obstruction (See par 28). Chen and Beglerovic improve upon O kub o by disclosing having a “space” of data (Chen page 5169; FIG. 10B) or a “parameter space” in Beglerovic (See page 2) . 2) Okubo discloses calculating different stopping times based on deceleration. Beglerovic improves upon Okubo by exploring regions of search space, as well as the known technique of a “confidence level” (See page 2) and using “surrogate modeling, stochastic optimization, and Kriging models to find coefficients (See page 3-4). One of ordinary skill in the art would be motivated to further include a search space and different models to find coefficients for different regions of search space being explored to efficiently improve upon the calculation of stopping times for different deceleration values . 3) Okubo discloses having a probability distribution corrected based on direction of a frontward obstruction (See par 28) . Chen improves upon Okubo and Beglerovic by using regression to get weights and validate safety distance (See page 5164, 5168 ) , using a clustering model to evaluate risk level on collisions and creating centroids that have coordinates (See page 5161, 5165 (see equation 15), and correcting acceleration and response time using a cluster (See page 5166), for use in controlling brakes of a vehicle (See page 5170, 5173) . One of ordinary skill in the art would be motivated to use a known technique to further include clustering, centroids, for correcting acceleration and response time for controlling braking to efficiently improve upon the probability distribution in Okubo. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of predicting stopping time between host vehicle and relative velocity to obstacle in Okubo to explore regions of search space as disclosed in Beglerovic, and further use regression, clustering, and centroids to analyze acceleration and response time for a vehicle braking as disclosed in Chen , claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success . Concerning claim 2, O kubo, Beglerovic, and Chen disclose: The time threshold model creation method according to claim 1, wherein the statistical regions are boxes in the two-dimensional plane, and the boxes are obtained through equally dividing the speed of the vehicle and the relative speed ( Beglerovic – see page 3, col. 2, 2 nd paragraph - The “grids” in Beglerovic disclose the “boxes”. It would be obvious to combine Okubo, Beglerovic, and Chen for the same reasons as claim 1 above. Concerning claim 3 , Okubo, Beglerovic, and Chen disclose: The time threshold model creation method according to claim 1, wherein the predetermined percentage is 88% to 98% ( Beglerovic – see page 6, col. 1, 1 st paragraph - The columns of the table are respectively: average evaluated global minimum; the best global minimum; position of the best global minimum; percentage of successfully found crashes from all test runs, i .e. , Krigin model found crashes in 97 out of 100 runs ) . It would be obvious to combine Okubo, Beglerovic, and Chen for the same reasons as claim 1 above. Concerning claim 4, Okubo, Beglerovic, and Chen disclose: T he time threshold model creation method according to claim 3, wherein a function of the target curve surface is expressed as T=f(V.sub.vehicle, ΔV), where T represents the time threshold, V.sub.vehicle represents the speed of the vehicle, ΔV represents the relative speed, and f represents the function for the target curve surface ( Okubo – see par 26 - The stopping time prediction unit 102D calculates a predicted stopping time required for the relative velocity between the host vehicle and the frontward obstruction to reach zero on the basis of the relative velocity between the host vehicle and the frontward obstruction and the deceleration of the host vehicle . see par 27 - the deceleration correction unit 102C does not have to be used, and instead, the stopping time prediction unit 102D may calculate the stopping time by means of a simple algebra operation using the predetermined value of the deceleration of the host vehicle ; See Chen Fig. 10A for example of target curve surface; See also Beglerovic FIG. 1 ) . It would be obvious to combine Okubo, Beglerovic, and Chen for the same reasons as claim 1 above. Concerning claim 5, Okubo, Beglerovic, and Chen disclose: A time threshold model creation system based on an autonomous braking system ( Okubo see par 20 – vehicle collision prevention apparatus in FIG. 1 includes collision likelihood calculation unit, braking processing determination unit 105 and brake apparatus control unit 107 ; see par 24 - FIG. 2 is a block diagram showing a configuration of the front end collision likelihood calculation unit provided in the vehicle collision prevention apparatus according to the first embodiment of this invention. In FIG. 2, the front end collision likelihood calculation unit 102 includes a first differentiating element 102A, a second differentiating element 102B, a deceleration correction unit 102C, a stopping time prediction unit 102D, and a collision likelihood evaluation unit 102E. ) , comprising: a data collection unit configured to obtain braking data about a target vehicle, the braking data comprising a braking time, a speed of the target vehicle, and a relative speed between the target vehicle and a target obstacle ( Okubo – [same as claim 1 above] - Okubo – see par 26 - The stopping time prediction unit 102D calculates a predicted stopping time required for the relative velocity between the host vehicle and the frontward obstruction to reach zero on the basis of the relative velocity between the host vehicle and the frontward obstruction and the deceleration of the host vehicle . see par 27 - the deceleration correction unit 102C does not have to be used, and instead, the stopping time prediction unit 102D may calculate the stopping time by means of a simple algebra operation using the predetermined value of the deceleration of the host vehicle. see par 32 - the braking processing determination unit 105 monitors the numerical value indicating the likelihood of a front end collisio n; – see FIG. 1, par 24 – calculations executed by front end collision likelihood calculation unit 102 ; See also Chen page 5172, Col. 1 – using Linux kernel ; hardware built on ARM 9 and FPGA; software processing). The remaining limitations are the same as claim 1 above. Claim 5 is rejected for the same reasons over Okubo, Beg l erovic, and Chen. It would be obvious to combine Okubo, Beglerovic, and Chen for the same reasons as claim 1 above. In addition, Okubo discloses units are “executing” different calculations. Chen improves upon Okubo by explicitly having an operating system of a Linux kernel, Hardware with FPGA, and software processing (See page 5172). Concerning claim 6, Okubo, Begerovic, and Chen disclose: An intelligent terminal, comprising a data collection device, a processor and a memory, wherein the data collection device is configured to collect data, the memory is configured to store therein one or more program instructions, and the processor is configured to execute the one or more program instructions so as to implement the time threshold model creation method according to claim 1 ( Okubo – see FIG. 1, par 24 – calculations executed by front end collision likelihood calculation unit 102; See also Chen page 5172, Col. 1 – using Linux kernel ; hardware built on ARM 9 and FPGA; software processing ) . It would be obvious to combine Okubo, Beglerovic, and Chen for the same reasons as claim 1 and claim 5 above. Concerning claim 7, Okubo, Beglerovic, and Chen disclose: A computer-readable storage medium storing therein one or more program instructions, wherein the one or more program instructions is executed by a processor so as to implement the time threshold model creation method according to claim 1. It would be obvious to combine Okubo, Beglerovic, and Chen for the same reasons as claim 1 and claim 5 abov e. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Ando (US 201 5 /0 274145 ) – directed to having probabilities graphed relative to velocities in automatic braking systems (See par 96-97) Zhang (CN 110281888) – directed to controlling deceleration and braking by curve fitting (See Abstract) Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Enter examiner's name" \* MERGEFORMAT IVAN R GOLDBERG whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-7949 . The examiner can normally be reached FILLIN "Work schedule?" \* MERGEFORMAT 830AM - 430PM . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /IVAN R GOLDBERG/ Primary Examiner, Art Unit 3619