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 .
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.
The claim(s) 1-20 are rejected under 35 USC § 101 because the claimed invention is directed to
judicial exception an abstract idea, it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated the claims under the framework provided in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register 01/07/2019, as well as subsequent USPTO eligibility guidance updates, and has provided such analysis below.
Step 1: Are the claims to a process, machine, manufacture or composition of matter?"
Yes, Claims 1-12 are directed to system and fall within the statutory category of machine;
Yes, Claims 13-19 are directed to method and fall within the statutory category of process;
Yes, Claim 20 is directed to non-transitory computer-readable storage medium and falls within the statutory category of article of manufacture.
In order to evaluate the Step 2A inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?" we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
The limitation of claim 1: “determine, based on the angle of incidence, a probability of detecting at least one LiDAR return corresponding to a reflection of the simulated transmission from the simulated at least partially transparent surface,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI) in light of specification, covers performance of the limitation in the human mind. For Example, a person is capable of observing and evaluating an angle of incidence between light beam and a transparent surface, and mentally estimating the chance that light reflected from the transparent surface would be detected. The steps include observation, evaluation, judgment, and opinion processes that can be performed mentally or with the aid of pen and paper (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).). See MPEP 2106.04(a)(2)(III).
The limitation of claim 1: “… a comparison between the probability and a target number, … a number of LiDAR returns,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI) in light of specification, covers performance of the limitation in the human mind. For Example, a person is capable of observing and evaluating an angle of incidence between light beam and a transparent surface, and mentally estimating the chance that light reflected from the transparent surface would be detected, and mentally comparing the chance with a predetermined value such as a random or target number to determine whether a reflection would occur and how may reflection returns would result. The steps include observation, evaluation, judgment, and opinion processes that can be performed mentally or with the aid of pen and paper (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).).
If a claim limitation, under its broadest reasonable interpretation in light of specification, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea under step 2A, Prong One. See MPEP 2106.04(a)(2)(III).
In MPEP 2106.04(II)(B): A claim may recite multiple judicial exceptions. For example, claim 4 at issue in Bilski v. Kappos, 561 U.S. 593, 95 USPQ2d 1001 (2010) recited two abstract ideas, and the claims at issue in Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 101 USPQ2d 1961 (2012) recited two laws of nature. However, these claims were analyzed by the Supreme Court in the same manner as claims reciting a single judicial exception, such as those in Alice Corp., 573 U.S. 208, 110 USPQ2d 1976.
As explained in MPEP 2106.4(a)(2)(I): “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a “series of mathematical calculations based on selected information” are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a “process of organizing information through mathematical correlations” are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of “managing a stable value protected life insurance policy by performing calculations and manipulating the results” as an abstract idea).
MPEP 2106.04(a)(2)(I)(A): A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.”
Further, MPEP recites: “For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
Claim 1: The limitations of “determine, based on the angle of incidence, a probability of detecting at least one LiDAR return corresponding to a reflection of the simulated transmission from the simulated at least partially transparent surface,” when given its with broadest reasonable interpretation (BRI) in light of specification, can be considered to recite mathematical concepts. For example, the instant specification describes the estimation process in terms of distance calculations and geometric relationships. For example, paragraphs [0040]-[0044]. Therefore, the limitation recites mathematical concepts (i.e., mathematical relationship and calculations) that determine or estimate the probability of detecting, which fall within the category of mathematical concepts (MPEP 2106.04(a)(2)(I)).
Claims 13 and 20 recite the similar elements as claim 1, and are rejected for the same reasons
under 35 U.S.C. 101.
Therefore, claims 1, 13 and 20 recite judicial exceptions. The claims have been identified to recite judicial exceptions, Step 2A Prong 2 will evaluate whether the claims as a whole integrates the exception into a practical application of that exception.
Step 2A Prong 2: Claims 1, 13 and 20: The judicial exception is not integrated into a practical application.
In particular, the claims recite the following additional elements: “A system comprising: a memory; and one or more processors coupled to the memory, the one or more processors being configured to:” and “A non-transitory computer-readable storage medium comprising at least one instruction for causing a computer or processor to:,” which are mere instruction to implement an abstract idea on a computer, or merely uses a computer as tool to perform an abstract idea with the broad reasonable interpretation, which does not integrate a judicial exception into practical application. See MPEP § 2106.05(f)).
Further, the following additional elements: “receive, within a simulation environment for an autonomous vehicle (AV), an angle of incidence that is formed between a simulated transmission from a simulated Light Detection and Ranging (LiDAR) sensor associated with the AV and a simulated at least partially transparent surface” and “receiving, within a simulation environment for a Light Detection and Ranging (LiDAR) sensor, an angle of incidence that is formed between a simulated transmission from a simulated LiDAR sensor and a simulated at least partially transparent surface,” are merely a recitation of insignificant extra-solution activity such as data gathering (i.e., receiving known value of angle of incidence [0044]), which does not integrate a judicial exception into practical application. Adding a step of receiving known data to a process that only recites determining a probability of detecting LiDAR return (mental process and mathematical concepts) does not add a meaningful limitation to the process of determining a probability. See MPEP 2106.05(g).
Further, the following additional elements: “generating simulated LiDAR perception data corresponding to the reflection of the simulated transmission from the simulated at least partially transparent surface …,” does not integrate the judicial exception into a practical application. The limitation merely outputs/generates data based on comparison. The step is recited at a high level of generality and does not specify any particular improvement to LiDAR sensing technology, sensor operation, or computer functionality. Instead, the limitation simply instructs a generic computer to generate data representing the result of the abstract idea by performing a generic data processing function. Therefore, this additional limitation merely applies the generic computer components with judicial exception, and does not integrate judicial exception into practical application. see MPEP 2106.05(f).
Therefore, "Do the claims recite additional elements that integrate the judicial exception into a practical application? No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
After having evaluated the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claims 1, 13 and 20 not only recite a judicial exception but that the claims are directed to the judicial exception as the judicial exception has not been integrated into practical application.
Step 2B: Claims 1, 13 and 20: The claim does not include additional elements, alone or in combination, 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, the additional elements amount to no more than generic computing components which do not amount to significantly more than the abstract idea. Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include:
i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g));
iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, …; ii. Performing repetitive calculations, … iii. Electronic recordkeeping, … (updating an activity log). iv. Storing and retrieving information in memory, …
Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
iii. A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); iv. A method of using advertising as an exchange or currency being applied or implemented on the Internet, Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715, 112 USPQ2d 1750, 1754 (Fed. Cir. 2014);
v. Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015); and
vi. A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair, In re Brown, 645 Fed. App'x 1014, 1017 (Fed. Cir. 2016) (non-precedential).
Therefore, "Do the claims recite additional elements that amount to significantly more than the judicial exception? No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claims 1, 13 and 20 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Dependent claims 2-12 and 14-19 are also similar rejected under same rationale as cited above wherein these claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These claims are merely further elaborate the mental process itself (and/or mathematical operations) or providing additional definition of process which does not impose any meaningful limits on practicing the abstract idea. Claims 2-12 and 14-19 are also rejected for incorporating the deficiency of their independent claims 1 and 13.
Claim 2 recites “The system of claim 1, wherein the one or more processors are configured to:
identify a simulated object that is positioned behind the simulated at least partially transparent surface; and
provide the angle of incidence to a decreasing linear function model to determine the probability.”
The limitation merely specifies determining whether a simulated object is positioned behind the simulated transparent surface, and selecting the decreasing linear function based on the determination with received angle of incidence to determine the probability. The step is merely an extension of mental process and a mathematical concept. Therefore, the office finds that the claim 2 is ineligible under 35 USC 101.
Claim 3 recites “The system of claim 2, wherein the one or more processors are configured to:
determine that the number of LiDAR returns is two based on the comparison between the probability and the target number, wherein the simulated LiDAR perception data includes a first LiDAR return corresponding to the simulated at least partially transparent surface and a second LiDAR return corresponding to the simulated object.”
The limitation merely specifies that the number of LiDAR returns is determined based on the comparison between the probability and the target number, and further defines that the simulated LiDAR perception data includes first and second LiDAR returns. The step is merely an extension of mental process. Therefore, the office finds that the claim 3 is ineligible under 35 USC 101.
Claim 4 recites “The system of claim 2, wherein the one or more processors are configured to:
determine that the number of LiDAR returns is one based on the comparison between the probability and the target number, wherein the simulated LiDAR perception data includes a single LiDAR return corresponding to the simulated at least partially transparent surface.”
The limitation merely specifies that the number of LiDAR returns is determined based on the comparison between the probability and the target number, and further defines that the simulated LiDAR perception data includes a single LiDAR return corresponding to the simulated transparent surface. The step is merely an extension of mental process. Therefore, the office finds that the claim 4 is ineligible under 35 USC 101.
Claim 5 recites “The system of claim 2, wherein the one or more processors are configured to:
determine that the number of LiDAR returns is one based on the comparison between the probability and the target number, wherein the simulated LiDAR perception data includes a single LiDAR return corresponding to the simulated object.”
The limitation merely specifies that the number of LiDAR returns is determined based on the comparison between the probability and the target number, and further defines that the simulated LiDAR perception data includes a single LiDAR return corresponding to the simulated object. The step is merely an extension of mental process. Therefore, the office finds that the claim 5 is ineligible under 35 USC 101.
Claim 6 recites “The system of claim 2, wherein the number of LiDAR returns is zero when a distance between the simulated object and the simulated at least partially transparent surface exceeds a distance threshold value.”
The limitation merely specifies that the number of LiDAR returns is determined when a distance exceeds a distance threshold value. The step is merely an extension of mental process and a mathematical concept. Therefore, the office finds that the claim 6 is ineligible under 35 USC 101.
Claim 7 recites “The system of claim 1, wherein the one or more processors are configured to:
determine an absence of a simulated object that is positioned behind the simulated at least partially transparent surface; and
provide the angle of incidence to a piecewise linear function model to determine the probability.”
The limitation merely specifies determining whether a simulated object is positioned behind the simulated transparent surface, and selecting the piecewise linear function based on the determination with received angle of incidence to determine the probability. The step is merely an extension of mental process and a mathematical concept. Therefore, the office finds that the claim 7 is ineligible under 35 USC 101.
Claim 8 recites “The system of claim 7, wherein the one or more processors are configured to:
determine that the number of LiDAR returns is zero based on the comparison between the probability and the target number.”
The limitation merely specifies that the number of LiDAR returns is determined based on the comparison between the probability and the target number. The step is merely an extension of mental process. Therefore, the office finds that the claim 8 is ineligible under 35 USC 101.
Claim 9 recites “The system of claim 7, wherein the one or more processors are configured to:
determine that the number of LiDAR returns is one based on the comparison between the probability and the target number, wherein the simulated LiDAR perception data includes a single LiDAR return corresponding to the simulated at least partially transparent surface.”
The limitation merely specifies that the number of LiDAR returns is determined based on the comparison between the probability and the target number, and further defines that the simulated LiDAR perception data includes a single LiDAR return corresponding to the simulated transparent surface. The step is merely an extension of mental process. Therefore, the office finds that the claim 9 is ineligible under 35 USC 101.
Claim 10 recites “The system of claim 1, wherein the target number is randomly selected based on a uniform distribution.”
The limitation merely further defines the targe number is randomly selected based on a uniform distribution. It merely reflects an extension of previous identified mental process. Therefore, the office finds that the claim 10 is ineligible under 35 USC 101.
Claim 11 recites “The system of claim 1, wherein the target number is selected based on a simulation identifier associated with the simulated at least partially transparent surface.”
The limitation merely further defines the targe number is selected based on a simulation identifier associated with the simulated transparent surface. It merely reflects an extension of previous identified mental process. Therefore, the office finds that the claim 11 is ineligible under 35 USC 101.
Claim 12 recites “The system of claim 1, wherein the target number is a weighted value based on a uniform distribution and a simulation identifier associated with the simulated at least partially transparent surface.”
The limitation merely further defines that the targe number is a weighted value based on a uniform distribution and a simulation identifier associated with the simulated transparent surface . It merely reflects an extension of previous identified mental process. Therefore, the office finds that the claim 12 is ineligible under 35 USC 101.
Claims 14-19 recite the similar elements as claims 2-7 and 10-12, and are rejected for the same reasons under 35 U.S.C. 101.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 1, 10, 13 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over
Manivasagam US20200301799A1 A1 in view of Muckenhuber (“Automotive Lidar Modelling Approach Based on Material Properties and Lidar Capabilities,” published in 2020) and Chester (“A Parameterized Simulation of Doppler Lidar,” published in 2017).
Claim 1, Manivasagam teaches A system (Abstract) comprising:
a memory; and
one or more processors coupled to the memory, the one or more processors ([0065]) being configured to:
receive, within a simulation environment for an autonomous vehicle (AV), an angle of incidence that is formed between a simulated transmission from a simulated Light Detection and Ranging (LiDAR) sensor associated with the AV and a simulated([0029] … The simulated LiDAR data can be used, for example, as simulated input for testing autonomous vehicle control systems. [0049] … performing the ray casting can include determining, for each of a plurality of rays, a ray casting location and a ray casting direction based at least in part on the trajectory. [0096] … the computing system can record sensory metadata 210 for each surfel to be used for intensity and ray drop simulation. This can include, among other information, the incidence angle, raw intensity, transmitted power level, range value as well as a unique ID per beam. [0124] … the inputs to the model include some or all of the following: Real-valued channels: range, original recorded intensity, incidence angle, original range of surfel hit, and original incidence angle of surfel hit (the original values can be obtained from the recorded metadata)… Examiner note: the reference teaches recording an “incidence angle” metadata for each surfel generated from ray casting of a simulated LiDAR sensor ([0096], [0124]). Because the ray casting simulates a LiDAR transmission intersecting a surface element (surfel), the recorded incidence angle corresponds to the angle formed between the direction of the simulated LiDAR ray and the surface element, which represents the angle of incidence between the LiDAR transmission and the surface.);
determine, based on the angle of incidence, a probability of detecting at least one LiDAR return corresponding to a reflection of the simulated transmission from the simulated ([0125] The output of the model is a ray dropout probability that predicts, for each element in the array, if it returns or not (e.g., with some probability). [0117] … a machine-learned model can process the initial ray casted three-dimensional point cloud to predict a respective dropout probability for one or more of the plurality of points in the initial cloud. [0121] While LiDAR intensity is available as a noisy proxy of surface reflectance, it is not the only indicator of ray drop, since it is a sophisticated and stochastic phenomenon impacted by factors such as incidence angle, range values, beam bias and other environment factors. [0124] As illustrated in FIG. 4, in some implementations, the inputs to the model include … incidence angle … The input channels can represent observable factors potentially influencing each ray's chance of not returning. Examiner note: the reference teaches predicting a “ray dropout probability” that determines whether a LiDAR ray returns to the sensor ([0125]). Because a dropped ray corresponds to a LiDAR transmission that does not produce a return, the dropout probability inherently determines the probability that a LiDAR return will be detected, e.g., P(return) = 1 – P(dropout). The reference further teaches the dropout probability is determined based model inputs including the incidence angle ([0121] and [0124]].); and
generate simulated LiDAR perception data corresponding to the reflection of the simulated transmission from the simulated ([0125] In some implementations … the computing system can sample from the probability mask to generate the output LiDAR point cloud. Sampling of the probability mask instead of doing direct thresholding … [0145] … the computing system can generate an adjusted three-dimensional point cloud from the initial three-dimensional point cloud based at least in part on the respective dropout probabilities. For example, each point can be dropped (or not) according to its respective dropout probability. Examiner note: the reference teaches generating an output LiDAR point cloud representing LiDAR sensor output in a simulated environment in which points correspond to detected LiDAR measurements obtained from reflected LiDAR transmissions ([0125], [0145]). The reference further teaches sampling from a probability mask to generate the output LiDAR point cloud ([0125]). Because each point may be retained or dropped according to its respective dropout probability, the sampling process uses the determined probabilities to control whether a LiDAR return is included in the generated output. A POSITA would understand that each point in the generated LiDAR point cloud corresponds to a LiDAR measurement resulting from reflected LiDAR transmissions in the simulated environment. Therefore, the generated LiDAR point cloud represents simulated LiDAR perception data corresponding to reflections of simulated LiDAR transmission and inherently includes a number of LiDAR returns.).
However, Manivasagam fails to teach the simulated at least partially transparent surface.
Muckenhuber teaches the simulated at least partially transparent surface (Table.1, Vehicle, glass(1). Page.8, under Figure 7, “Each material in the environment simulation is assigned a reflectance function Rλ (Θ) function that depends on the incidence angle Θ. By calculating the angle between laser beam direction and illuminated surface, the reflectance value Rλ of the illuminated material is derived. Depending on whether the reflectance limit RL is above or below the reflectance Rλ, the illuminated material is detected or remains undetected …” Page.21, “3. The TOF camera measurements reveal the importance of angle dependent measurements in particular for materials with specular reflection behaviour such as metal or glass.” Examiner note: the reference teaches a LiDAR modelling approach in an environment simulation in which materials in the simulated environment are assigned reflectance function that depend on the incidence angle between the laser beam and the illuminated surface, and identifies vehicle glass as one of the materials used in the simulated environment. Because glass is a material that allows transmission of light through the surface, glass corresponds to an at a least partially transparent surface. A POSITA would understand that the simulated glass material represents a simulated surface interacting with the LiDAR beam in the environment simulation, and corresponds to the simulated at least partially transparent surface recited in the receiving , determining and generating steps).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Manivasagam to incorporate the teachings of Muckenhuber, and apply modeling of material properties including glass surfaces in the LiDAR simulation in order to account for LiDAR interactions with partially transparent surface characteristics. Manivasagam teaches LiDAR simulation system that determines detection probabilities for LiDAR rays and generates simulated LiDAR point cloud (i.e., perception data) based on the modeled LiDAR interactions within a simulated environment. Muckenhuber teaches assigning material dependent reflectance functions to surfaces in a simulated environment, including materials such as glass, and determining LiDAR detection based on the incidence angle between the laser beam and the illuminated surface. The combinations of teachings would predictably provide benefit of improving the physical realism and accuracy of LiDAR simulations by incorporating partially transparent materials such as glass, into the LiDAR detection modeling process.
However, Manivasagam and Muckenhuber fail to teach a comparison between the probability and a target number.
Chester teaches a comparison between the probability and a target number (Page.24, 3.3.2 Probabilities of Detection and False Alarm, “… Then a random number is drawn and compared to these probabilities to determine if a detection drops out or if a false alarm occurs. The probability of detection is simply the probability that a detection is not dropped or the complement of the probability of drop out.” Page.26, last paragraph, “The probabilities of detection and false alarm are used to determine if a shot is detected or whether a false alarm occurs. Once the spectra are obtained for each chirp segment of a lidar shot and the probabilities of detection and false alarm are determined, a uniform random number is then drawn for each event to determine if that event occurs. For a missed detection, that point is simply excluded from the point cloud.” Examiner note: the reference teaches determining whether a LiDAR detection event occurs by comparing a detection probability with a randomly drawn number. The random drawn number as a target value that is compared with the probability to determine whether the simulated detecting event occurs. If the detection does not occur, the corresponding point is excluded from the point cloud, thereby generating simulated LiDAR perception data based on the probabilistic comparison).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Manivasagam and Muckenhuber to incorporate the teachings of Chester, and apply stochastic sampling of LiDAR detection events using a probability to random number comparison in order to determine whether simulated LiDAR returns are generated when modeling LiDAR interactions with surfaces in a simulated environment. Manivasagam teaches a LiDAR simulation system that determines detection probabilities for LiDAR rays and generates simulated LiDAR perception data based on the modeled LiDAR interactions within a simulated environment. Muckenhuber teaches assigning material dependent reluctance functions to surfaces in a simulated environment, including materials such as glass, and determining LiDAR detection based on the incidence angle between the laser beam and the illuminated surface. Chester teaches determining whether a LiDAR detection event occurs by drawing a random number and comparing the random number to probabilities of detection, and modifying the simulated LiDAR output (e.g., including or excluding points in the point cloud) based on the result of that comparison. The combinations of teachings would predictably provide benefit of improving the realism of simulated LiDAR perception data by modeling surface dependent reflection behavior and stochastically determining whether corresponding LiDAR returns are generated based on the determined detection probabilities.
Claim 10, Manivasagam and Muckenhuber fail to teach, but Chester teaches The system of claim 1, wherein the target number is randomly selected based on a uniform distribution (Page.26, last paragraph, “… Once the spectra are obtained for each chirp segment of a lidar shot and the probabilities of detection and false alarm are determined, a uniform random number is then drawn for each event to determine if that event occurs.”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Manivasagam and Muckenhuber to incorporate the teachings of Chester, and apply the use of a uniform random number for determining simulated detection events in order to enable stochastic selection of event outcomes within the LiDAR simulation framework. The combinations of teachings would predictably provide benefit of allowing the LiDAR simulation system to model probabilistic detection events using uniform random sampling, thereby improving the realism and statistical behavior of simulated LiDAR perception data used for developing and validating LiDAR perception system.
The elements of claims 13 and 19-20 are substantially the same as those of claims 1 and 10. Therefore, the elements of claims 13 and 19-20 are rejected due to the same reasons as outlined above for claims 1 and 10.
Claim(s) 2-9 and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Manivasagam
and Muckenhuber and Chester as applied to claims 1 and 13 above, and further in view of Zhao (“Mapping with Reflection - Detection and Utilization of Reflection in 3D Lidar Scans,” published in 2020).
Claim 2, Manivasagam teaches The system of claim 1, wherein the one or more processors are configured to:
identify a simulated object that ([0045] additional mesh representations of virtual objects can be placed into the three-dimensional map to generate a specific test scenario (e.g., such as an animal entering the travelway). The additional mesh representations of virtual objects can be static or can move in the environment over time (e.g., to simulate the animal entering the travelway). Thus, a particular scenario in which testing is sought can be built by adding various elements to and/or otherwise modifying the base three-dimensional map (e.g., with aspects of the modified map changing over time).); and
provide the angle of incidence ([0096], [0121], [0124] and [0125]).
However, Manivasagam fails to teach simulated at least partially transparent surface.
Muckenhuber teaches simulated at least partially transparent surface (Table.1, Vehicle, glass(1)).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Manivasagam to incorporate the teachings of Muckenhuber, and apply modeling of material properties including glass surfaces in the LiDAR simulation in order to account for LiDAR interactions with partially transparent surface characteristics. The combinations of teachings would predictably provide benefit of improving the physical realism and accuracy of LiDAR simulations by incorporating partially transparent materials such as glass, into the LiDAR detection modeling process.
However, Manivasagam and Muckenhuber and Chester fail to teach an object that is positioned behind the at least partially transparent surface and decreasing linear function model.
Zhao teaches an object that is positioned behind the at least partially transparent surface (Fig. 3: Laser Reflection Model. Page.3, B. Reflection Model of Different Material, “… if there is something behind the glass, it will diffusely reflect the light and the return light can pass through the glass again to the receiver. So a Lidar can estimate the distance of things behind the glass with weakened intensity due to twice passing through the window.”), decreasing linear function model (Page.3, B. Reflection Model of Different Material, “When the laser beam hits the glass almost perpendicularly, the intensity received peaks. When the angle of incidence decreases, the intensity drops quickly. As shown in Fig. 3b, if the angle of incidence decreases to a certain degree (also related to the distance), the return intensity will become too low to detect.” Examiner note: the reference teaches that LiDAR return behavior varies with the angle of incidence, explaining that the received intensity is highest when the laser beam hits the glass almost perpendicularly and decrease as the angle of incidence decrease. It establishes a decreasing relationship between the input variable (angle of incidence) and the resulting LiDAR return signal. A POSITA would understand that a decreasing relationship between variables can be represented by a decreasing function model, including a decreasing linear function model, to estimate the probabilities of detecting a LiDAR return.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Manivasagam and Muckenhuber and Chester to incorporate the teachings of Zhao, and apply the LiDAR interaction with transparent surfaces and the angle dependent return behavior in order to model LiDAR returns from objects located behind transparent surfaces within the LiDAR simulation framework. The combinations of teachings would predictably provide benefit of enable the simulation system to more realistically model LiDAR perception involving transparent materials and improve the realism and accuracy of LiDAR perception simulation used for developing and validating LiDAR perception systems.
Claim 3, Manivasagam and Muckenhuber and Chester teaches the one or more processors are configured to: the number of LiDAR returns is determined based on the comparison between the probability and the target number, the simulated LiDAR perception data, the simulated at least partially transparent surface and the simulated object as discussed above with respect to claims 1 and 2. However, Manivasagam and Muckenhuber and Chester fail to teach, but Zhao teaches determine that the number of LiDAR returns is two, wherein LiDAR perception data includes a first LiDAR return corresponding to the at least partially transparent surface and a second LiDAR return corresponding to the object (Page.3, last paragraph, “The first return is from the glass, because it is the nearest and thus the time of flight is the shortest. The last return is from the light that passes through the glass and is reflected by the obstacle behind glass.” ).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Manivasagam and Muckenhuber and Chester to incorporate the teachings of Zhao, and apply the LiDAR interaction with transparent surfaces and the generation of multiple LiDAR returns from transparent surfaces and objects located behind the transparent surfaces in order to model LiDAR perception scenarios involving both the transparent surface and the object behind the transparent surface within the LiDAR simulation framework. The combinations of teachings would predictably provide benefit of enabling the LiDAR simulation system to more realistically model LiDAR perception scenarios in which multiple returns are generated from transparent surface and objects located behind the transparent surfaces, thereby improving the realism and accuracy of LiDAR perception simulation used for developing and validating LiDAR perception systems.
Claim 4, Manivasagam and Muckenhuber and Chester teaches the one or more processors are configured to: the number of LiDAR returns is determined based on the comparison between the probability and the target number, the simulated LiDAR perception data and the simulated at least partially transparent surface as discussed above with respect to claims 1 and 2. However, Manivasagam and Muckenhuber and Chester fail to teach, but Zhao teaches determine that the number of LiDAR returns is one, wherein the LiDAR perception data includes a single LiDAR return corresponding to the at least partially transparent surface (Page.3, last paragraph, “The first return is from the glass, because it is the nearest and thus the time of flight is the shortest.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Manivasagam and Muckenhuber and Chester to incorporate the teachings of Zhao, and apply the LiDAR interaction with transparent surfaces and the generation of LiDAR return from transparent surfaces in order to model LiDAR perception scenarios in which a LiDAR return is generated from a transparent surface within the LiDAR simulation framework. The combinations of teachings would predictably provide benefit of enabling the LiDAR simulation system to more realistically model LiDAR perception scenarios involving reflection from transparent surface such as glass and improve the realism and accuracy of LiDAR perception simulation used for developing and validating LiDAR perception systems.
Claim 5, Manivasagam and Muckenhuber and Chester teaches the one or more processors are configured to: the number of LiDAR returns is determined based on the comparison between the probability and the target number, the simulated LiDAR perception data and the simulated object as discussed above with respect to claims 1 and 2. However, Manivasagam and Muckenhuber and Chester fail to teach, but Zhao teaches determine that the number of LiDAR returns is one, wherein the LiDAR perception data includes a single LiDAR return corresponding to the object (Page.4, first paragraph, “So in this example, the strongest return will give a reflected point, the last will be the object behind the glass, while the glass itself is ignored because it is neither the strongest nor the last return.”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Manivasagam and Muckenhuber and Chester to incorporate the teachings of Zhao, and apply the LiDAR interaction with transparent surfaces and the generation of LiDAR return from object in order to model LiDAR perception scenarios in which a LiDAR return is generated from an object within the LiDAR simulation framework. The combinations of teachings would predictably provide benefit of enabling the LiDAR simulation system to more realistically model LiDAR perception scenarios involving reflection from an object and improve the realism and accuracy of LiDAR perception simulation used for developing and validating LiDAR perception systems.
Claim 6, Manivasagam and Muckenhuber and Chester teaches the number of LiDAR, the simulated at least partially transparent surface and the simulated object as discussed above with respect to claims 1 and 2. However, Manivasagam and Muckenhuber and Chester fail to teach, but Zhao teaches the number of LiDAR returns is zero when a distance between the object and the at least partially transparent surface exceeds a distance threshold value (page.3, right column, “As shown in Fig. 3b, if the angle of incidence decreases to a certain degree (also related to the distance), the return intensity will become too low to detect … So a Lidar can estimate the distance of things behind the glass with weakened intensity due to twice passing through the window.” Page.4, under Figure 4, “The background obstacles are far enough, so they do not have enough intensity.” Examiner note: The reference teaches that LiDAR returns from objects located behind a transparent surface depend on the intensity of the reflected signal and that the return intensity may become too low to detect when conditions such as geometry and distance cause attenuation of the signal. A POSITA would understand that LiDAR sensors detect returns only when the recited signal exceeds a detection threshold. Because signal attenuation increases as the object becomes farther from the transparent surface, there exists a distance at which the attenuated return fall below the detection threshold and is no longer detected. Therefore, when the distance between the object and the transparent surface exceeds a threshold distance associated with signal attenuation and detection limits, the number of LiDAR returns becomes zero.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Manivasagam and Muckenhuber and Chester to incorporate the teachings of Zhao, and apply the LiDAR interaction with transparent surfaces and the attenuation behavior of LiDAR signals passing through transparent surfaces in order to model LiDAR perception scenarios in which LiDAR returns from objects located behind transparent surfaces may not be detected when the reflected signal intensity becomes too weak. The combinations of teachings would predictably provide benefit of enabling the LiDAR simulation system to more realistically model LiDAR perception scenarios in which LiDAR returns from objects behind transparent surfaces may not be detected due to signal attenuation, thereby improving the realism and accuracy of LiDAR perception simulation used for developing and validating LiDAR perception systems.
Claim 7, Manivasagam teaches The system of claim 1, wherein the one or more processors are configured to:
([0045] additional mesh representations of virtual objects can be placed into the three-dimensional map to generate a specific test scenario (e.g., such as an animal entering the travelway). The additional mesh representations of virtual objects can be static or can move in the environment over time (e.g., to simulate the animal entering the travelway). Thus, a particular scenario in which testing is sought can be built by adding various elements to and/or otherwise modifying the base three-dimensional map (e.g., with aspects of the modified map changing over time).); and
provide the angle of incidence to ([0096], [0121], [0124] and [0125]).
However, Manivasagam fails to teach simulated at least partially transparent surface.
Muckenhuber teaches simulated at least partially transparent surface (Table.1, Vehicle, glass(1)).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Manivasagam to incorporate the teachings of Muckenhuber, and apply modeling of material properties including glass surfaces in the LiDAR simulation in order to account for LiDAR interactions with partially transparent surface characteristics. The combinations of teachings would predictably provide benefit of improving the physical realism and accuracy of LiDAR simulations by incorporating partially transparent materials such as glass, into the LiDAR detection modeling process.
However, Manivasagam and Muckenhuber and Chester fail to teach determine an absence of an object that is positioned behind the at least partially transparent surface and a piecewise linear function model.
Zhao teaches determine an absence of an object that is positioned behind the at least partially transparent surface (page.3, right column, “As shown in Fig. 3b, if the angle of incidence decreases to a certain degree (also related to the distance), the return intensity will become too low to detect … So a Lidar can estimate the distance of things behind the glass with weakened intensity due to twice passing through the window.” Page.4, under Figure 4, “The background obstacles are far enough, so they do not have enough intensity.” Examiner note: the reference teaches that objects located behind a transparent surface may produce no detectable LiDAR return signal when the reflected signal intensity becomes too weak due to attenuation through the transparent material. A POSITA would understand that when the LiDAR sensor receives no detectable return from behind the transparent surface, the LiDAR perception system determines that no object behind the transparent surface is detected) and a piecewise linear function model (Page.3, B. Reflection Model of Different Material, “When the laser beam hits the glass almost perpendicularly, the intensity received peaks. When the angle of incidence decreases, the intensity drops quickly. Page.4, 2nd paragraph, “The intensity returned from the glass will be the strongest when the incident laser beam is perpendicular to the glass and lower as the angle decreases.” Examiner note: the reference teaches that the intensity of LiDAR returns from a transparent surface varies with the angle of incidence and decreases as the angle changes. A POSITA would understand that the angle dependent behavior may be approximated using linear relationships over different ranges of the incidence angle, such as a piecewise linear function that determines a probability of receiving a LiDAR return based on the angle of incidence.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Manivasagam and Muckenhuber and Chester to incorporate the teachings of Zhao, and apply the LiDAR interaction with transparent surfaces and the angle dependent attenuation behavior of LiDAR signals passing through transparent materials in order to model LiDAR perception scenarios in which objects located behind transparent surfaces may produce no detectable LiDAR return and to determine the probability of receiving a LiDAR return based on the angle of incidence. The combinations of teachings would predictably provide benefit of enabling the LiDAR simulation system to more realistically model LiDAR perception scenarios involving transparent materials and objects located behind transparent surfaces, thereby improving the realism and accuracy of LiDAR perception simulation used for developing and validating LiDAR perception systems.
Claim 8, Manivasagam and Muckenhuber and Chester teaches the number of LiDAR based on the comparison between the probability and the target number as discussed above with respect to claims 1 and 7. However, Manivasagam and Muckenhuber and Chester fail to teach, but Zhao teaches
determine that the number of LiDAR returns is zero (page.3, right column, “As shown in Fig. 3b, if the angle of incidence decreases to a certain degree (also related to the distance), the return intensity will become too low to detect … So a Lidar can estimate the distance of things behind the glass with weakened intensity due to twice passing through the window.” Page.4, under Figure 4, “The background obstacles are far enough, so they do not have enough intensity.” Examiner note: the reference teaches that objects located behind a transparent surface may produce no detectable LiDAR return when the reflected signal intensity becomes too weak due to attenuation through the transparent material. A POSITA would understand that when the LiDAR sensor receives no detectable return signal from behind the transparent surface, the LiDAR perception system determines that number of LiDAR returns corresponding to the object is zero.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Manivasagam and Muckenhuber and Chester to incorporate the teachings of Zhao, and apply the LiDAR interaction with transparent surfaces and the angle dependent attenuation behavior of LiDAR signals passing through transparent materials in order to model LiDAR perception scenarios in which objects located behind transparent surfaces may produce no detectable LiDAR return. The combinations of teachings would predictably provide benefit of enabling the LiDAR simulation system to more realistically model LiDAR perception scenarios involving transparent materials and objects located behind transparent surfaces, thereby improving the realism and accuracy of LiDAR perception simulation used for developing and validating LiDAR perception systems.
Claim 9, Manivasagam and Muckenhuber and Chester teaches the one or more processors are configured to: the number of LiDAR returns is determined based on the comparison between the probability and the target number, the simulated LiDAR perception data and the simulated at least partially transparent surface as discussed above with respect to claims 1 and 7. However, Manivasagam and Muckenhuber and Chester fail to teach, but Zhao teaches determine that the number of LiDAR returns is one, wherein the LiDAR perception data includes a single LiDAR return corresponding to the at least partially transparent surface (Page.3, last paragraph, “The first return is from the glass, because it is the nearest and thus the time of flight is the shortest.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Manivasagam and Muckenhuber and Chester to incorporate the teachings of Zhao, and apply the LiDAR interaction with transparent surfaces and the generation of LiDAR return from transparent surfaces in order to model LiDAR perception scenarios in which a LiDAR return is generated from a transparent surface within the LiDAR simulation framework. The combinations of teachings would predictably provide benefit of enabling the LiDAR simulation system to more realistically model LiDAR perception scenarios involving reflection from transparent surface such as glass and improve the realism and accuracy of LiDAR perception simulation used for developing and validating LiDAR perception systems.
The elements of claims 14-18 are substantially the same as those of claims 2-7. Therefore, the elements of claims 14-18 are rejected due to the same reasons as outlined above for claims 2-7.
Claim(s) 11 is rejected under 35 U.S.C. 103 as being unpatentable over Manivasagam
and Muckenhuber and Chester as applied to claim 1 above, and further in view of Hill US20200278838A1.
Claim 11, Manivasagam fail to teach, but Muckenhuber teaches The system of claim 1, wherein (Table.1, Vehicle, glass(1)).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Manivasagam to incorporate the teachings of Muckenhuber, and apply modeling of material properties including glass surfaces in the LiDAR simulation in order to account for LiDAR interactions with partially transparent surface characteristics. The combinations of teachings would predictably provide benefit of improving the physical realism and accuracy of LiDAR simulations by incorporating partially transparent materials such as glass, into the LiDAR detection modeling process.
However, Manivasagam and Muckenhuber and Chester fail to teach the target number is selected based on a simulation identifier.
Hill teaches the target number is selected based on a simulation identifier ([0002] This relates to hierarchical pseudo-random number generation for use in computer simulations that operate across more than one computing machine. [0011] … an XOR (exclusive OR logic function) of the root seed value with each agent ID value (or some other agent unique value that can be deterministically created) would produce a unique seed for each agent … Each agent with an identifier (202), (203), (204) receives the root and then operates a mixing function (206) to create the local seed (207). The local seeds, or states, are then fed into the individual PRNG (208) to generate a random sequence of numbers for the local processes. Examiner note: The reference teaches that each simulation agent has an identifier, and the identifier is used to generate a seed that derives the pseudo-random number generator for the agent. A POSITA would understand that the pseudo-random number generated by the PRNG correspond to target numbers used in stochastic simulation processes.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Manivasagam and Muckenhuber and Chester to incorporate the teachings of Hill, and apply the use of a simulation identifier to generate a seed that drives a pseudo-random number generator for generating random numbers associated with simulation agents in order to deterministically generate random numbers associated with specific simulated entities within the LiDAR simulation environment. The combinations of teachings would predictably provide benefit of enabling deterministic and reproducible stochastic simulation behavior by generating target numbers based on identifiers associated with simulation entities, thereby improving consistency and reproducibility of simulated LiDAR detection outcomes.
Claim(s) 12 is rejected under 35 U.S.C. 103 as being unpatentable over Manivasagam
and Muckenhuber and Chester as applied to claim 1 above, and further in view of Efraimidis (“Weighted Random Sampling over Data Streams,” published in 2015).
Claim 12, Manivasagam fail to teach, but Muckenhuber teaches The system of claim 1, wherein (Table.1, Vehicle, glass(1)).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Manivasagam to incorporate the teachings of Muckenhuber, and apply modeling of material properties including glass surfaces in the LiDAR simulation in order to account for LiDAR interactions with partially transparent surface characteristics. The combinations of teachings would predictably provide benefit of improving the physical realism and accuracy of LiDAR simulations by incorporating partially transparent materials such as glass, into the LiDAR detection modeling process.
However, Manivasagam and Muckenhuber and Chester fail to teach the target number is a weighted value based on a uniform distribution and a simulation identifier.
Efraimidis teaches the target number is a weighted value based on a uniform distribution and a simulation identifier (Page.1., Introduction, “If each item has an associated weight and the probability of each item to be selected is determined by these item weights, then the problem is called weighted random sampling (WRS).” Page.3, 2 weighted Random Sampling (WRS), “The distinguishability can be trivially achieved by assigning an increasing ID number to each item in the population, including the replaced items (for WRS with replacement).” Page.7, 3.2 A-ES, “In A-ES, each item vi of the population V independently generates a uniform random number ui Є (0, 1) and calculates a key ki = ui1/wi …” Examiner note: the reference teaches that items participating in the weighted random sampling process can be distinguished by assigning an ID number to each item in the population. An ID number is a value used to identify or distinguish an item within a computational a process. A POSITA would understand that when the sampling techniques are used in stochastic simulation environments, each simulated entity (e.g., an object or transparent surface in the simulation ) is represented as an item in the sampling population and is associated with an identifier. The reference further teaches generating a value for each item using a uniform random number and computing a value Ki that depends on the associated weight of the item, the generated value corresponds to a weighted value derived from a uniform random distribution for the identified item. Therefore, the reference teaches generating a weighted value based on a uniform distribution and an identifier associated with the sampled entity.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Manivasagam and Muckenhuber and Chester to incorporate the teachings of Efraimidis, and apply the weighted random sampling technique that generates a value using a uniform random distribution and an associated weight for an identified item in order to enable stochastic simulation processes to generate weighted target values for simulation entities participating in the simulation environment. The combinations of teachings would predictably provide benefit of improving probabilistic simulation operations by enabling the selection or evaluation of simulation entities based on weighted stochastic sampling derived from uniform random distributions.
Conclusion
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/YI . HAO/
Examiner, Art Unit 2187
/ANDRE PIERRE LOUIS/Primary Patent Examiner, Art Unit 2187 March 16, 2026