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 .
Response to Amendment
The amendment filed 06/18/2026 has been entered. As directed, claims 1-5, 8-10, 12-16, 19-20
have been amended, claims 7, 11, 18 have been canceled, claim 21 has been added. Thus claims 1-6, 8-10, 12-17 and 19-21 remain pending in the application.
Response to Arguments
With respect to the Applicant’s argued rejection under 35 U.S.C 101 in “Applicant Arguments/Remarks Made in an Amendment”:
Applicant argues:
…
Claims Are Not Directed to an Abstract Idea at Step 2A, Prong 1
The Office Action asserts that a person could mentally observe an angle of incidence, mentally estimate a probability of detection, and mentally compare that probability to a target number. Applicant respectfully disagrees. This characterization grossly oversimplifies the claimed inventions and does not account for the actual technical complexity recited in independent Claims 1, 13, and 20.
Claims 1, 13, and 20 recite operations "within a simulation environment for an autonomous vehicle (AV)" and receiving an angle of incidence 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." The claims then recite using whether or not the simulation environment includes a simulated object behind the transparent surface to select one of two different piecewise linear models, determining, using the selected model, a probability of detecting LiDAR returns "corresponding to a reflection of the simulated transmission from the simulated at least partially transparent surface" and generating "simulated LiDAR perception data" based on a comparison between that probability and a target number, where the data includes "a number of LiDAR returns." These are not steps that can be practically performed in the human mind, even with paper and pen. A human cannot mentally simulate LiDAR transmissions through transparent materials, determine probabilistic detection outcomes based on angle-dependent physical phenomena, or generate realistic LiDAR perception data suitable for training AV perception systems. The claims are directed to a specific technical solution of a probabilistic model for simulating LiDAR transmissions through at least partially transparent materials in an AV simulation environment that addresses a recognized technical problem in AV testing and development. See, Present Application at 11 [0012]-[0014].
Therefore, for at least these reasons, Applicant respectfully submits that independent Claims 1, 13, and 20, and any claims depending therefrom, are not directed to a mental process and patent eligible under 35 U.S.C. § 101, at least at Step 2A, Prong 1 of the Alice/Mayo test.
(see Response filed 06/18/2026 [pages 8-9]).
In response to Applicant’s arguments that the rejection oversimplifies the claimed invention and that a person cannot mentally simulate LiDAR transmissions through transparent materials, determine probabilistic detection outcomes based on angle-dependent physical phenomena, or generate realistic LiDAR perception data suitable for training AV perception systems. The argument is not persuasive because the claims do not recite the asserted level of technical complexity. The claims do not require simulating how a LiDAR transmission physically interacts with a transparent surface or determining the probability based on the physical behavior of the LiDAR transmission. Rather, the claims recite, at a high level, evaluating whether an object is present or absent behind the transparent surface, selecting a corresponding predefined model, using a provided angle of incidence and the selected model to determine a corresponding probability, evaluating whether the probability satisfies a target number, and , when the probability satisfies the target number, generating simulated LiDAR perception data representing the corresponding LiDAR return or returns. These limitations include observation, evaluation, judgment, and reasoning process that can be practically performed mentally or with the aid of pen and paper. In particular, the limitations are recited at high level of generality and does not require any particular simulation architecture, model construction technique, interpolation procedure, computer architecture, or computer specific operation that would practically prevent performance in the human mind or with the aid of pen and paper. See MPEP 2106.04(a)(2)(III). Further, the limitation of ““determine, based on the angle of incidence and the selected one of the first piecewise linear model and the second piecewise linear model, a probability of detecting …,” applies a predefined linear relationship between a numerical angle of incidence and a numerical probability to determine the probability corresponding to the recited angle. Therefore, the limitation recites a mathematical relationship between the angle of incidence and the probability of detecting a LiDAR return(s), which fall within the category of mathematical concepts (MPEP 2106.04(a)(2)(I)). Please refer to the detailed analysis in the current office action for the complete reasoning. Accordingly, Claims 1, 13, and 20 are directed to an abstract idea and patent ineligible under 35 U.S.C. § 101, at least at Step 2A, Prong 1 of the Alice/Mayo test.
With respect to the Applicant’s argued rejection under 35 U.S.C 101 in “Applicant Arguments/Remarks Made in an Amendment”:
Applicant argues:
Claims Integrate Any Alleged Judicial Exception into a Practical Application at Step 2A, Prong 2
Even assuming, arguendo, that Claims 1, 13, and 20 recite an abstract idea (which Applicant does not admit), the claims integrate any such abstract idea into a practical application. The claims generate "simulated LiDAR perception data" that can be used as training data for training machine learning models associated with AV perception stacks. This is not an abstract concept but rather a concrete technological improvement that enables more realistic simulation of LiDAR interactions with transparent materials, which in turn improves the testing and training of AV systems.
The Application explains that transparent materials such as glass present unique challenges for LiDAR simulation because LiDAR returns can vary depending on the presence or absence of objects behind the transparent surface, the angle of incidence, and other factors. Id. The claimed invention provides a unified probabilistic model that can simulate these varied scenarios without requiring individualized modeling of every possible combination of material properties, angles, distances, and object configurations. Id. This represents a practical improvement to LiDAR simulation technology that has concrete applications in AV development.
The Federal Circuit has consistently held that claims directed to improvements in computer-related technology are not directed to abstract ideas. See Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335 (Fed. Cir. 2016); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016). Here, Claims 1, 13, and 20 recite a specific technical improvement: the ability to generate realistic simulated LiDAR perception data for LiDAR transmissions interacting with partially transparent surfaces using a probabilistic model based on angle of incidence and comparison with a target number for use in training an improved AV perception stack.
Moreover, the specific combination of elements - selecting piecewise linear model, receiving angle of incidence data within an AV simulation environment, determining a probability of LiDAR return detection based on that angle, comparing the probability to a target number, and generating LiDAR perception data including a specific number of returns based on that comparison - constitutes an unconventional technical approach that was not routine or conventional in the art (see below). The claimed probabilistic model for simulating LiDAR interaction with transparent materials is not a generic computer function but a specific algorithmic solution tailored to the unique challenges of transparent material simulation for AV LiDAR systems.
Furthermore, Claims 1, 13, and 20 have been amended to recite "train, using the simulated LiDAR perception data, a machine learning model for a perception stack for an autonomous vehicle" and "send the trained machine learning model for the perception stack to the autonomous vehicle, wherein sending the trained machine learning model to the autonomous vehicle causes the autonomous vehicle to navigate the autonomous vehicle using the trained machine learning model," which represents a further practical application of the alleged abstract idea.
Therefore, for at least these reasons, Applicant respectfully submits that independent Claims 1, 13, and 20, and any claims depending therefrom, are patent eligible under 35 U.S.C. § 101, at least at Step 2A, Prong 2 of the Alice/Mayo test.
(see Response filed 06/18/2026 [pages 9-10]).
Applicant’s arguments have been fully considered but are not persuasive because the asserted improvement of limitations directed to selecting one of the first and second piecewise linear models, determining a probability based on the angle of incidence and the selected model, evaluating whether the probability satisfies a target number, and generating the simulated LiDAR perception data are part of the judicial exception identified under Step 2A, Prong One. The identified additional limitations merely recite receiving the angel of incidence within a simulation environment for an autonomous vehicle, training a machine learning model for an AV perception stack using the simulated LiDAR perception data, and sending the trained model to the autonomous vehicle for use in navigation. These identified additional limitations do not recite an improvement to the simulation environment, the machine learning model or training process, the perception stack, or the navigation operation. Rather, the additional limitations merely apply the identified judicial exception in an AV simulation and machine learning environment and use the resulting trained model fort its intended purpose. As explained in MPEP 2106.05(a), II.: "it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited satisfying persona design goals and persona capability) is not an improvement in technology." (emphasis added). According the additional limitations do not integrate the judicial exception into a practical application.
Further, Applicant’s reliance on Enfish and McRO is also not persuasive. In Enfish, the claimed self-referential table changed the structure and operation of the database itself and provided improvements such as increased flexibility, faster searching and reduced memory requirements. In McRO, the claims recited specific rules for setting morph weights and transitions that changed how computer animation was performed and automated animation tasks that changed how computer animation was performed and automated animation tasks that previously required subjective human judgement. However, the present claims do not recite a comparable improvement to the operation of a computer, or another technological process. The asserted improvement is based on the identified abstract idea. The remaining limitations merely place those operation in an AV simulation environment, use the resulting data to train a machine learning model, and send the trained model for use in navigation, without reciting a particular improvement to the simulation environment, training process, machine learning model, perception stack technique, or navigation control operation. Accordingly, Enfish and McRO do not show that the present claims integrate the judicial exception into a practical application.
Further, for Step 2B, the claim recites additional elements, at a high level of generality, receiving an angle of incidence within a simulation environment for an autonomous vehicle, training a machine learning model using the simulated LiDAR perception data, and sending the trained machine learning model to the autonomous vehicle for use in navigation. These additional elements, when considered individually and in combination, merely provide the angle of incidence as input to the recited mental processes, place the processes in an autonomous vehicle simulation environment, use the resulting simulated LiDAR perception data as training data for a machine learning model, and transmit the trained model for its ordinary use in autonomous vehicle navigation. The clam does not recite a particular simulation technique, machine learning model architecture, training procedure, transmission mechanism, or navigation control implementation. Accordingly, the addition elements do not amount to significantly more than the judicial exception. In addition, training a machine learning model using training data and deploying the trained model to autonomous vehicles are well understood, routine and conventional functions, For example, Karpathy US20210271259A1, explains in its Background that deep learning systems used for applications such as autonomous driving are developed by training a machine learning model, and that the performance of the system depends in part on the quality of the training set used to train the model. The Federal Circuit held that “patents that do no more than claim the application of generic machine learning to new data environments without disclosing improvements to the machine learning models to be applied, are patent ineligible under 35 U.S.C. § 101.” See Recentive Analytics, Inc. v, Fox Corp.
Accordingly, as discussed above, the amended claims remain directed to an abstract idea. The additional elements, considered individually and in combination, do not integrate the abstract idea into a practical application and do not amount to significantly more than the judicial exception. Therefore, the rejection of claims 1, 13 and 20, and the claims depend thereon, under 35 U.S.C. 101 is maintained.
Claim Objections
Claims 1, 13 and 20 are objected to because of the following informalities:
Claim 1 recites “train, using the simulated LiDAR perception data, a machine learning model for a perception stack for an autonomous vehicle” should read as “train, using the simulated LiDAR perception data, a machine learning model for a perception stack for the autonomous vehicle.”
Claims 13 and 20 also recite “a perception stack of an autonomous vehicle”, and are objected for the same reason.
Appropriate correction is required.
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) 1-6, 8-10, 12-17 and 19-21 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 1-6, 8-10, 12 and 21 are directed to system and fall within the statutory category of machine;
Yes, Claims 13-17 and 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: “when the simulation environment does not include a simulated object positioned behind the simulated at least partially transparent surface, select a first piecewise linear model representing a first plurality of probabilities that at least one LiDAR return corresponding to a reflection of the simulated transmission from the simulated at least partially transparent surface is detected for respective ones of a plurality of angles of incidence;
when the simulation environment includes a simulated object positioned behind the simulated at least partially transparent surface, select a second piecewise linear model different from the first piecewise linear model and representing a second plurality of probabilities that at least one LiDAR return corresponding to a reflection of the simulated transmission from the simulated at least partially transparent surface is detected for respective ones of the plurality of angles of incidence,” 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 an information presented in a simulation environment, recognizing whether the scenario includes or does not include a simulated object positioned behind the simulated at least partially transparent surface, and based on the applicable scenario, mentally selecting the corresponding one of two predefined representations, such as graphs or tables, each relating respective angels of incidence to corresponding probabilities of detecting at least one LiDAR return from the simulated at least partially transparent surface. The steps include observation, evaluation, judgment, and reasoning 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).).
The limitation of claim 1: “determine, based on the angle of incidence and the selected one of the first piecewise linear model and the second piecewise linear model, 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, after selecting predefined representation corresponding to the applicable scenario, a person is capable of locating the received angle of incidence in the selected predefined representation, such as a graph or table, and mentally identifying, reading, or estimating the corresponding probability that at least one LiDAR return from the simulated at least partially transparent surface will be detected. The steps include observation, evaluation, judgment, and reasoning 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: “generate simulated LiDAR perception data corresponding to the reflection of the simulated transmission from the simulated at least partially transparent surface when the probability satisfies a target number, wherein the simulated LiDAR perception data includes 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 comparing the determined probability with a target number, mentally evaluating whether the probability satisfies the target number, and, when the condition is satisfied, recording or preparing simulated LiDAR perception information indicating the corresponding number of LiDAR returns from the simulated at least partially transparent surface. The steps include observation, evaluation, judgment, and reasoning 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).).
Examiner note: the limitations are recited at high level of generality and does not require any particular simulation architecture, model construction technique, interpolation procedure, computer architecture, or computer specific operation that would practically prevent performance in the human mind or with the aid of pen and paper.
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 and the selected one of the first piecewise linear model and the second piecewise linear model, 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,” which, when given its broadest reasonable interpretation (BRI) in light of specification, can be considered to recite mathematical concepts. For example, paragraphs [0040]-[0044]. Thus, the limitation applies a predefined linear relationship between a numerical angle of incidence and a numerical probability to determine the probability corresponding to the recited angle. Therefore, the limitation recites a mathematical relationship between the angle of incidence and the probability of detecting a LiDAR return(s), 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 storing instructions; and one or more processors coupled to the memory, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:” and “A non-transitory computer-readable storage medium comprising at least one instruction for causing a computer or processor to:,” which are merely recitations of instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to implement the judicial exception, which does not integrate judicial exception into a 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 “send the trained machine learning model for the perception stack to the autonomous vehicle, wherein sending the trained machine learning model to the autonomous vehicle causes the autonomous vehicle to navigate using the trained machine learning model,” are merely a recitation of insignificant extra-solution activities such as data gathering (i.e., receiving known value of angle of incidence [0044]) and data output (sending/transmitting trained machine learning model to AV), which do 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 and generating simulated LiDAR perception data (mental process or mathematical concepts) does not add a meaningful limitation to the process of determining the probability and generating the simulated LiDAR perception data. Adding a final step of transmitting machine learning model to a process that only recites determining a probability of detecting LiDAR return and generating simulated LiDAR perception data (mental process or mathematical concepts) does not add a meaningful limitation to the process of determining the probability and generating the simulated LiDAR perception data. See MPEP 2106.05(g).
Further, the following additional elements: “within a simulation environment for an autonomous vehicle (AV)” and “train, using the simulated LiDAR perception data, a machine learning model for a perception stack for an autonomous vehicle,” which are merely adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea.
The recitation of performing the processes “within a simulation environment for an autonomous vehicles” merely places the judicial exception in a computer based simulation context, without requiring any particular simulation architecture or simulation technique. Likewise, the training limitation merely instructs that the resulting simulated LiDAR perception data be used to train a machine learning model, without reciting how the model is trained or specifying any particular model architecture, training algorithm, or other technological implementation. Thus, these limitations merely using generic computing components in their ordinary capacity to perform the recited simulation and machine learning functions at a high level of generality and amount to instructions to apply the judicial exception using a computer as a tool. See MPEP 2106.05(f).
Alternatively, these additional limitations merely link the use of the judicial exception to a particular technological environment or field of use, such as machine learning and simulation environment. See MPEP § 2106.05(h).
Accordingly, these additional limitations do not impose a meaningful limit on the judicial exception and do not integrate the judicial exception into practical application.
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, …
In particular, the claim recites additional elements, at a high level of generality, receiving an angle of incidence within a simulation environment for an autonomous vehicle, training a machine learning model using the simulated LiDAR perception data, and sending the trained machine learning model to the autonomous vehicle for use in navigation. These additional elements, when considered individually and in combination, merely provide the angle of incidence as input to the recited mental processes, place the processes in an autonomous vehicle simulation environment, use the resulting simulated LiDAR perception data as training data for a machine learning model, and transmit the trained model for its ordinary use in autonomous vehicle navigation. The clam does not recite a particular simulation technique, machine learning model architecture, training procedure, transmission mechanism, or navigation control implementation. Accordingly, the addition elements do not amount to significantly more than the judicial exception.
In addition, training a machine learning model using training data and deploying the trained model to autonomous vehicles are well understood, routine and conventional functions, For example, Karpathy US20210271259A1, explains in its Background that deep learning systems used for applications such as autonomous driving are developed by training a machine learning model, and that the performance of the system depends in part on the quality of the training set used to train the model. See [0004]. The reference further illustrates the ordinary implementation of this established training workflow by describing training a model using a curated data set and subsequently deploying the trained model to vehicles as an update to an autonomous vehicle system. See [0030] and [0070]. Accordingly, the claimed training and deployment functions, recited without any particular training or transmission technique, amount to well understood, routine, and conventional machine learning and transmission activity.
Further, The Federal Circuit held that “patents that do no more than claim the application of generic machine learning to new data environments without disclosing improvements to the machine learning models to be applied, are patent ineligible under 35 U.S.C. § 101.” See Recentive Analytics, Inc. v, Fox Corp.
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 2-6, 8-10, 12, 14-17 and 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 2-6, 8-10, 12, 14-17 and 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 second piecewise linear model comprises a decreasing linear function model.”
The limitation specifies the second piecewise linear model by requiring that the model comprise a decreasing linear function. It merely an extension of previous identified mental process and mathematical relationship recited in claim 1. Therefore, the office finds that the claim 2 is ineligible under 35 USC 101.
Claim 3 recites “The system of Claim 2, wherein the instructions also cause the one or more processors to determine that the number of LiDAR returns is two based on a 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. It 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 instructions also cause the one or more processors to determine that the number of LiDAR returns is one based on a 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. It 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 instructions also cause the one or more processors to determine that the number of LiDAR returns is one based on a 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. It 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. It merely an extension of mental process. Therefore, the office finds that the claim 6 is ineligible under 35 USC 101.
Claim 8 recites “The system of Claim 1, wherein the instructions also cause the one or more processors to determine that the number of LiDAR returns is zero based on a 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. It merely an extension of mental process recited in claim 1. Therefore, the office finds that the claim 8 is ineligible under 35 USC 101.
Claim 9 recites “The system of Claim 1, wherein the instructions also cause the one or more processors to determine that the number of LiDAR returns is one based on a 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. It merely an extension of mental process recited in claim 1. 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, is selected based on a simulation identifier associated with the simulated at least partially transparent surface, or selected based on a combination thereof.”
The limitation further defines the targe number by specifying the manner in which the target number is selected. It merely an extension of previous identified mental process in claim 1. Therefore, the office finds that the claim 10 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 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 an extension of previous identified mental process in claim 1. Therefore, the office finds that the claim 12 is ineligible under 35 USC 101.
Claim 21 recites “The system of Claim 1, wherein the instructions also cause the one or more processors to, when the simulation environment includes the simulated object positioned behind the simulated at least partially transparent surface, generate the simulated LiDAR perception data to include one or more LiDAR returns from the simulated object.”
The limitation further defines the generation of the simulated LiDAR perception data by specifying that the generated perception data include one or more LiDAR returns from the simulated object. It merely an extension of previous identified mental process recited in claim 1. Therefore, the office finds that the claim 12 is ineligible under 35 USC 101.
Claims 14-17 and 19 recite the similar elements as claims 2-6 and 10, and are rejected for the same reasons under 35 U.S.C. 101.
Allowable Subject Matter
Claims 1-6, 8-10, 12-17 and 19-21 would be allowable if rewritten or amended to overcome the
rejection(s) under 35 U.S.C. 101 set forth in this Office action.
The prior art of records: Regarding Claims 1, 13 and 20, the closest prior arts found:
Manivasagam (US20200301799A1), teaches generating an initial simulated LiDAR point cloud by ray casting, providing incidence angle as an input to a machine-learned model, determining a respective ray dropout probability, and generating an adjusted simulated LiDAR point could by sampling or dropping points according to the determined dropout probability (see paragraphs [0029], [0049], [0096], [0117], [0121]-[0125] and [0145]). However, Manivasagam fails to teach selecting a first piecewise linear model when no object is behind the transparent surface, selecting a different second piecewise linear model when an object is behind the transparent surface, and determining the probability using the angle and the selected one of two different piecewise models.
Muckenhuber (“Automotive Lidar Modelling Approach Based on Material Properties and Lidar Capabilities,” published in 2020), teaches assigning each simulated material an incidence angle dependent reflectance function, calculating the angle between the simulated LiDAR beam and the illuminated surface, deriving the corresponding reflectance value from the function, and determining whether the surface or object is detected based on the reflectance value and the LiDAR detection capability (see Table 1, Pages 8 and 21). However, Muckenhuber fails to teach selecting a first piecewise linear model when no object is behind the transparent surface, selecting a different second piecewise linear model when an object is behind the transparent surface, and determining the probability using the angle and the selected one of two different piecewise models.
Chester (“A Parameterized Simulation of Doppler Lidar,” published in 2017), teaches comparing a determined probability with a randomly generated value to determine whether a simulated LiDAR return is generated, thereby probabilistically generating simulated LiDAR data based on the determined probability (see Pages 24 and 26). However, Chester fails to teach selecting a first piecewise linear model when no object is behind the transparent surface, selecting a different second piecewise linear model when an object is behind the transparent surface, and determining the probability using the angle and the selected one of two different piecewise models.
Zhao (“Mapping with Reflection - Detection and Utilization of Reflection in 3D Lidar Scans,” published in 2020), teaches that the intensity of a LiDAR return from glass is greatest when the laser beam is substantially perpendicular to the glass and decreases as the incidence angel moves away from perpendicular, and further teaches that light may pass through the glass and reflect from an object positioned behind the glass, resulting in returns from the glass, the object behind the glass or both (see Page 3-4 and Fig.3). However, Zhao fails to teach selecting a first piecewise linear model when no object is behind the transparent surface, selecting a different second piecewise linear model when an object is behind the transparent surface, and determining the probability using the angle and the selected one of two different piecewise models.
(Newly applied prior art) Foster (“VisAGGE: Visible Angle Grid for Glass Environments,” published in 2013), teaches two different angle dependent probability relationships corresponding to the absence or presence of an object behind glass and using the current angle with a visibility function to determine the probability of detecting the glass return (See page.2216 and FIGs 6-7). However, Foster fails to teach selecting a first piecewise linear model when no object is behind the transparent surface, selecting a different second piecewise linear model when an object is behind the transparent surface, and determining the probability using the angle and the selected one of two different piecewise models.
(Newly applied prior art) Sun (US20230278589A1), teaches preparing a plurality of different predetermined reflection rate tables, selecting the table corresponding to the reflection information of the simulated object, inputting object attributes including the angle between the simulated sensor and the object into the selected table, obtaining a reflection rate, and generating a simulated LiDAR point based on the reflection rate (See paragraphs [0011] – [0016]). However, Sun fails to teach selecting a first piecewise linear model when no object is behind the transparent surface, selecting a different second piecewise linear model when an object is behind the transparent surface, and determining the probability using the angle and the selected one of two different piecewise models.
(Newly applied prior art) Lasram (US12298431B1), teaches generating simulated sensor data for an autonomous vehicle simulation by determining a probability based mask from attributes of simulated objects and the simulated scene, and applying the mask to determine whether simulated rays reflect from an object, pass through the object, or generate returns from another object position behind the first object (see Col, 2-3). However, Lasram fails to teach selecting a first piecewise linear model when no object is behind the transparent surface, selecting a different second piecewise linear model when an object is behind the transparent surface, and determining the probability using the angle and the selected one of two different piecewise models.
The following is an examiner’s statement of reasons for allowance:
Claim(s) 1, 13 and 20 are considered allowable since when reading the claims in light of the specification, none of the references of record alone or in combination disclose or suggest the combination of limitations specified in the independent claim 1, specifically “… when the simulation environment does not include a simulated object positioned behind the simulated at least partially transparent surface, select a first piecewise linear model representing a first plurality of probabilities that at least one LiDAR return corresponding to a reflection of the simulated transmission from the simulated at least partially transparent surface is detected for respective ones of a plurality of angles of incidence; when the simulation environment includes a simulated object positioned behind the simulated at least partially transparent surface, select a second piecewise linear model different from the first piecewise linear model and representing a second plurality of probabilities that at least one LiDAR return corresponding to a reflection of the simulated transmission from the simulated at least partially transparent surface is detected for respective ones of the plurality of angles of incidence; determine, based on the angle of incidence and the selected one of the first piecewise linear model and the second piecewise linear model, 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 presented in independent claim 1. Claims 13 and 20 recite similar limitations, and the references of record, alone or in combination, also fail to teach or suggest those limitations. Therefore, independent claims 1, 13 and 20 are considered allowable. Dependent claims are considered allowable as being dependent from allowed claims 1 and 13.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37
CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/YI . HAO/
Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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