DETAILED ACTION
Claims 1-20 have been examined and are pending.
Claims 1-20 are rejected (Non-Final Rejection).
Notice of 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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 01/05/2023is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDSs have been considered by the examiner.
Claim Rejections - 35 U.S.C. § 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 following is an analysis based on the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
To determine if a claim is directed to patent ineligible subject matter, the Court has guided the Office to apply the Alice/Mayo test, which requires:
1. Determining if the claim falls within a statutory category;
2A. Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea; and
2B. If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception.
(See MPEP 2106).
Claims 1-20 Step 1, Statutory Category?:
Yes: Claims 1-20 are directed to the statutory category of a manufacture (“non-transitory media”). See MPEP § 2106.03.
Claims 1, 5-11 and 15-17 are rejected under 35 U.S.C. § 101 because the claimed inventions are directed to an abstract idea without significantly more. The claim(s) recite a mental process. See MPEP § 2106.04(a)(2)(III).
Step 2A:
Step 2A is a two-prong inquiry. See MPEP § 2106.04(II)(A). Under the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. See MPEP § 2106.04(a)(2). The second prong is an inquiry into whether the claim integrates a judicial exception into a practical application. See MPEP § 2106.04(d).
Claim 1 Step 2A Prong One: Does the Claim Recite a Judicial Exception?
For the sake of identifying the abstract ideas, a copy of the claim is provided below. The limitations of the claims that describe abstract ideas are bolded.
A computer-implemented system, comprising: one or more non-transitory computer-readable media storing instructions, when executed by one or more processing units, cause the one or more processing units to perform operations comprising:
retrieving real-world data comprising skeleton attributes of various skeletons, each skeleton comprising a digital hierarchical framework of bones;
receiving instructions to generate a simulated skeleton for a scene in a simulation;
generating the simulated skeleton according to the scene based on the skeleton attributes, the simulated skeleton comprising the digital hierarchical framework of bones of a generic skeleton modified by scaling factors according to the scene;
building a simulation asset using the simulated skeleton; and
determining a reaction of a vehicle to the simulation asset in the scene simulated, the reaction of the vehicle being a function of a configuration of the vehicle.
The limitations “generating the simulated skeleton according to the scene based on the skeleton attributes, the simulated skeleton comprising the digital hierarchical framework of bones of a generic skeleton modified by scaling factors according to the scene” and “building a simulation asset using the simulated skeleton” are abstract ideas because they are directed to mental processes, observations, evaluations, judgments, and/or opinions. The limitations, as drafted and under broadest reasonable interpretation, “can be performed in the human mind or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III). For example, a human could draw/build an outline of a person/bystander (like Applicant’s simulation assets 106 in FIG. 1) after drawing/creating/generating a scaled down generic skeleton corresponding to a scene/environment, using pen and paper. See Applicant’s Specification, at Para. [0026].
Claim 1 Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception/Abstract idea into practical application?
Under Step 2A Prong Two, this judicial exception is not integrated into a practical application because the additional claim limitations outside of the abstract idea only present mere instructions to apply an exception, generally link the use of the judicial exception to the technological environment, or insignificant extra-solution activity. In particular, the claim recites the additional limitations of:
• “computer-implemented” and “one or more non-transitory computer-readable media storing instructions, when executed by one or more processing units, cause the one or more processing units to perform operations” (mere instructions to apply an exception to a computer – see MPEP 2106.04(d) referencing MPEP 2106.05(f); these limitations can be viewed as nothing more than high level recitations of generic computer components or computer elements used as a tool, and represent mere instructions to apply the abstract idea on a generic computer (see MPEP 2106.05(f)).
• “retrieving real-world data comprising skeleton attributes of various skeletons, each skeleton comprising a digital hierarchical framework of bones” and “receiving instructions to generate a simulated skeleton for a scene in a simulation” (insignificant extra-solution activity – mere data gathering/inputting – see MPEP 2106.04(d) referencing MPEP 2106.05(g); this limitation can be viewed as nothing more than mere data gathering in conjunction with the abstract idea (see MPEP § 2106.05(g)).
• “determining a reaction of a vehicle to the simulation asset in the scene simulated, the reaction of the vehicle being a function of a configuration of the vehicle” (general field of use or technological environment – see MPEP 2106.04(d) referencing MPEP 2106.05(h); these limitations can be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of autonomous/self-driving vehicles (see MPEP 2106.05(h)).
Claim 1 Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception?
The Examiner must consider whether each claim limitation individually or as an ordered combination amount to significantly more than the abstract idea. This analysis includes determining whether an inventive concept is furnished by an element or a combination of elements that are beyond the judicial exception. For limitations that were categorized as “apply it” or generally linking the use of the abstract idea to a particular technological environment or field of use, the analysis is the same.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above, there are three types of additional elements. The first type of additional elements is the generic computer components (“computer-implemented” and “non-transitory computer-readable media”), which are high level recitations of generic computer component(s) or computer elements used as a tool, and represent mere instructions to apply the abstract idea on a computer. See MPEP § 2106.05(f). Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. See MPEP § 2106.05(f).
The second type of additional elements (“retrieving … skeleton attributes” and “receiving instructions”), as explained previously, are insignificant extra-solution activity (mere data inputting/gathering). These recitations are recited at a high level of generality, and are also well-known. These limitations therefore remain insignificant extra-solution activity even upon reconsideration. Thus, these limitations do not amount to significantly more.
The third type of additional element is “determining a reaction of a vehicle to the simulation asset in the scene simulated, the reaction of the vehicle being a function of a configuration of the vehicle”, which is at best viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of autonomous vehicle environment. For the claim limitations that generally link the use of the judicial exception to a particular technological environment or field of use, the claim limitations do not meaningfully limit the claim because the claim limitations employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, and does not add significantly more, similar to how limiting the abstract idea in Flook to petrochemical and oil-refining industries was insufficient. See MPEP 2106.05(h).
Even when considered in combination, these additional elements represent mere instructions to apply an exception and/or data gathering, which do not provide an inventive concept. The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. See MPEP 2106.05(f).
Considering the claim limitations as an ordered combination, claim 1 does not include significantly more than the abstract idea. The claim 1 is not patent subject matter eligible. Dependent claims 5-10 are further addressed below after addressing each independent claim.
Claim 11 Step 2A Prong One: Does the Claim Recite a Judicial Exception?
For the sake of identifying the abstract ideas, a copy of the claim is provided below. The limitations of the claims that describe abstract ideas are bolded.
11. A computer-implemented system, comprising: one or more non-transitory computer-readable media storing instructions, when executed by one or more processing units, cause the one or more processing units to perform operations comprising:
retrieving real-world data comprising asset attributes of objects and people in a region;
deriving attribute rules for the asset attributes;
receiving instructions to generate a simulation asset for a scene in a simulation, the simulation asset having a subset of the asset attributes;
generating the simulation asset having characteristics according to attribute rules corresponding to the subset of the asset attributes; and
determining a reaction of a vehicle to the simulation asset in the scene simulated, the reaction of the vehicle being a function of a configuration of the vehicle.
The limitations “deriving attribute rules for the asset attributes” and “generating the simulation asset having characteristics according to attribute rules corresponding to the subset of the asset attributes” are abstract ideas because they are directed to mental processes, observations, evaluations, judgments, and/or opinions. The limitations, as drafted and under broadest reasonable interpretation, “can be performed in the human mind or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III). For example, a human could provide/write an asset rule (e.g., all humans are standing or all humans have two arms) and draw/build an outline of a person/bystander (like Applicant’s simulation assets 106 in FIG. 1) according to the asset rule. See Applicant’s Specification, at Para. [0026].
Claim 11 Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception/Abstract idea into practical application?
Under Step 2A Prong Two, this judicial exception is not integrated into a practical application because the additional claim limitations outside of the abstract idea only present mere instructions to apply an exception, generally link the use of the judicial exception to the technological environment, or insignificant extra-solution activity. In particular, the claim recites the additional limitations of:
• “computer-implemented” and “one or more non-transitory computer-readable media storing instructions, when executed by one or more processing units, cause the one or more processing units to perform operations” (mere instructions to apply an exception to a computer – see MPEP 2106.04(d) referencing MPEP 2106.05(f); these limitations can be viewed as nothing more than high level recitations of generic computer components or computer elements used as a tool, and represent mere instructions to apply the abstract idea on a generic computer (see MPEP 2106.05(f)).
• “retrieving real-world data comprising asset attributes of objects and people in a region” and “receiving instructions to generate a simulation asset for a scene in a simulation, the simulation asset having a subset of the asset attributes” (insignificant extra-solution activity – mere data gathering/inputting – see MPEP 2106.04(d) referencing MPEP 2106.05(g); this limitation can be viewed as nothing more than mere data gathering in conjunction with the abstract idea (see MPEP § 2106.05(g)).
• “determining a reaction of a vehicle to the simulation asset in the scene simulated, the reaction of the vehicle being a function of a configuration of the vehicle” (general field of use or technological environment – see MPEP 2106.04(d) referencing MPEP 2106.05(h); these limitations can be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of autonomous/self-driving vehicles (see MPEP 2106.05(h)).
Claim 11 Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception?
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above, there are three types of additional elements. The first type of additional elements is the generic computer components (“computer-implemented” and “non-transitory computer-readable media”), which are high level recitations of generic computer component(s) or computer elements used as a tool, and represent mere instructions to apply the abstract idea on a computer. See MPEP § 2106.05(f). Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. See MPEP § 2106.05(f).
The second type of additional elements (“retrieving … asset attributes” and “receiving instructions”), as explained previously, are insignificant extra-solution activity (mere data inputting/gathering). These recitations are recited at a high level of generality, and are also well-known. These limitations therefore remain insignificant extra-solution activity even upon reconsideration. Thus, these limitations do not amount to significantly more.
The third type of additional element is “determining a reaction of a vehicle to the simulation asset in the scene simulated”, which is at best viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of autonomous vehicle environment. For the claim limitations that generally link the use of the judicial exception to a particular technological environment or field of use, the claim limitations do not meaningfully limit the claim because the claim limitations employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, and does not add significantly more, similar to how limiting the abstract idea in Flook to petrochemical and oil-refining industries was insufficient. See MPEP 2106.05(h).
Even when considered in combination, these additional elements represent mere instructions to apply an exception and/or data gathering, which do not provide an inventive concept. The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. See MPEP 2106.05(f).
Considering the claim limitations as an ordered combination, claim 11 does not include significantly more than the abstract idea. The claim 11 is not patent subject matter eligible. Dependent claims 15 and 16 are further addressed below after addressing each independent claim.
Claim 17 Step 2A Prong One: Does the Claim Recite a Judicial Exception?
For the sake of identifying the abstract ideas, a copy of the claim is provided below. The limitations of the claims that describe abstract ideas are bolded.
17. A computer-implemented system, comprising: one or more non-transitory computer-readable media storing instructions, when executed by one or more processing units, cause the one or more processing units to perform operations comprising:
retrieving real-world data comprising appearances of objects in an operational design domain;
receiving instructions to generate a simulation asset comprising an individual one of the objects according to a configuration of a scene;
generating the simulation asset having a parameterized appearance representing one of various possible appearances of the individual one of the objects in the operational design domain; and
determining a reaction of a vehicle to the simulation asset in the scene simulated, the reaction of the vehicle being a function of a configuration of the vehicle.
The limitations “generating the simulation asset having a parameterized appearance representing one of various possible appearances of the individual one of the objects in the operational design domain” are abstract ideas because they are directed to mental processes, observations, evaluations, judgments, and/or opinions. The limitations, as drafted and under broadest reasonable interpretation, “can be performed in the human mind or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III). For example, a human could draw/generate an outline of a person/bystander (like Applicant’s simulation assets 106 in FIG. 1) representing an appearance of an individual. See Applicant’s Specification, at Para. [0026].
Claim 17 Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception/Abstract idea into practical application?
Under Step 2A Prong Two, this judicial exception is not integrated into a practical application because the additional claim limitations outside of the abstract idea only present mere instructions to apply an exception, generally link the use of the judicial exception to the technological environment, or insignificant extra-solution activity. In particular, the claim recites the additional limitations of:
• “computer-implemented” and “one or more non-transitory computer-readable media storing instructions, when executed by one or more processing units, cause the one or more processing units to perform operations” (mere instructions to apply an exception to a computer – see MPEP 2106.04(d) referencing MPEP 2106.05(f); these limitations can be viewed as nothing more than high level recitations of generic computer components or computer elements used as a tool, and represent mere instructions to apply the abstract idea on a generic computer (see MPEP 2106.05(f)).
• “retrieving real-world data comprising appearances of objects in an operational design domain” and “receiving instructions to generate a simulation asset comprising an individual one of the objects according to a configuration of a scene” (insignificant extra-solution activity – mere data gathering/inputting – see MPEP 2106.04(d) referencing MPEP 2106.05(g); this limitation can be viewed as nothing more than mere data gathering in conjunction with the abstract idea (see MPEP § 2106.05(g)).
• “determining a reaction of a vehicle to the simulation asset in the scene simulated, the reaction of the vehicle being a function of a configuration of the vehicle” (general field of use or technological environment – see MPEP 2106.04(d) referencing MPEP 2106.05(h); these limitations can be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of autonomous/self-driving vehicles (see MPEP 2106.05(h)).
Claim 17 Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception?
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above, there are three types of additional elements. The first type of additional elements is the generic computer components (“computer-implemented” and “non-transitory computer-readable media”), which are high level recitations of generic computer component(s) or computer elements used as a tool, and represent mere instructions to apply the abstract idea on a computer. See MPEP § 2106.05(f). Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. See MPEP § 2106.05(f).
The second type of additional elements (“retrieving … appearance of objects” and “receiving instructions”), as explained previously, are insignificant extra-solution activity (mere data inputting/gathering). These recitations are recited at a high level of generality, and are also well-known. These limitations therefore remain insignificant extra-solution activity even upon reconsideration. Thus, these limitations do not amount to significantly more.
The third type of additional element is “determining a reaction of a vehicle to the simulation asset in the scene simulated”, which is at best viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of autonomous vehicle environment. For the claim limitations that generally link the use of the judicial exception to a particular technological environment or field of use, the claim limitations do not meaningfully limit the claim because the claim limitations employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, and does not add significantly more, similar to how limiting the abstract idea in Flook to petrochemical and oil-refining industries was insufficient. See MPEP 2106.05(h).
Even when considered in combination, these additional elements represent mere instructions to apply an exception and/or data gathering, which do not provide an inventive concept. The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. See MPEP 2106.05(f).
Considering the claim limitations as an ordered combination, claim 17 does not include significantly more than the abstract idea. The claim 17 is not patent subject matter eligible.
Dependent Claims 5-10, 15 and 16
Regarding claims 5-10, 15 and 16, claim 5 depends from claim 1 and further recites: “wherein: generating the simulated skeleton comprises generating skeleton sets from the skeleton attributes, each skeleton set comprises bones from a subset of the various skeletons, the subset of various skeletons is selected according to a preconfigured rule, and different skeleton sets are associated with correspondingly different preconfigured rules”, claim 6 depends from claim 1 and further recites: “wherein the instructions comprise settings of the scene, the settings indicating one of several preconfigured rules”, claim 7 depends from claim 6 and further recites: “wherein generating the simulated skeleton further comprises choosing a corresponding one of skeleton sets according to the one of the preconfigured rules indicated in the settings”, claim 8 depends from claim 7 and further recites: “wherein generating the simulated skeleton further comprises: selecting distinct scaling factors for each bone in the skeleton set chosen; and modifying the bones of the generic skeleton by the respective scaling factors selected for the bones”, claim 9 depends from claim 7 and further recites: “wherein: different bones in each skeleton set are categorized into corresponding distributions, and an individual scaling factor is selected from a respective distribution associated with the corresponding bone”, claim 10 depends from claim 8 and further recites: “wherein modifying the bones comprises: multiplying a dimension of an individual bone by the corresponding scaling factor to generate a final dimension of the individual bone in the simulated skeleton; and repeating the multiplying for each bone in the generic skeleton”, claim 15 depends from claim 11 and further recites: “wherein each simulation asset is associated with at least one distinct attribute distribution” and claim 16 depends from claim 11 and further recites: “wherein: the instructions to generate the simulation asset are according to settings of the scene, the settings indicate one or more attribute distributions, the subset of the asset attributes is associated with a particular one of the attribute distributions, and the operations further comprise: identifying the particular one of the attribute distributions indicated by the settings; identifying the subset of asset attributes associated with the particular one of the attribute distributions; and generating simulation assets having characteristics corresponding to the subset of the asset attributes according to the attribute rules”.
These features have been considered in combination with the features required by the claim(s) from which these claims depend. The bolded portion of the additional feature are considered to further clarify the details of the human’s mental activity (e.g., with pen and paper). See MPEP §§ 2106.04(a)(2)(III). The “instructions comprising” limitation of claim 6 is considered to further clarify the details of the insignificant extra-solution activity (mere data inputting/gathering and/or data outputting). See MPEP § 2106.05(g). Therefore, these features are considered to be drawn to the abstract idea without adding significantly more, and hence claims 5-10, 15 and 16 are considered to be ineligible under 35 U.S.C. § 101.
For the foregoing reasons, claims 1, 5-11 and 15-17 are rejected under 35 U.S.C. § 101 as being directed to patent ineligible subject matter.
Claims 2, 12 and 18 are indicated as eligible under 35 U.S.C. § 101 abstract idea analysis (by intentional omission from the § 101 rejection(s) above). Claims 3, 4, 13, 14, 19 and 20 are also deemed eligible under 35 U.S.C. § 101 because of dependency from eligible claims 2, 12 and 18, respectively (i.e., for the same reasons as claims 2, 12 and 18 respectively).
Claim Rejections - 35 U.S.C. § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-8, 10-13, 14 and 17-20 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by LIANG et al. (U.S. Patent Application Publication No. 2022/0036579 A1).
Regarding claim 1, LIANG discloses a computer-implemented system, comprising: one or more non-transitory computer-readable media storing instructions, when executed by one or more processing units, cause the one or more processing units to perform operations (non-transitory, computer readable media that store instructions that when executed by the one or more processors cause the one or more processors to perform operations, Paras. [0017], [0020] & [0052] of LIANG) comprising: retrieving real-world data comprising skeleton attributes of various skeletons, each skeleton comprising a digital hierarchical framework of bones (obtain sensor information associated with a real world environment or one or more dynamic objects therein, Para. [0073] of LIANG; See also dynamic objects include pedestrians, animals, Para. [0076] of LIANG; See also template object model (e.g., enhanced LBS model, etc.) can include a template hierarchical set of interconnected bones (e.g., a skeleton) … the template object model can represent a dynamic object (e.g., a pedestrian body) using the template hierarchical set of interconnected bones, Para. [0082] of LIANG; [the set of interconnected bones (skeleton) corresponding to a pedestrian body (dynamic object) in real world environment is interpreted as corresponding to real-world data comprising skeleton attributes]; See also FIG. 3, which shows sensor data 305, model parameters 340 (including “scale factors corresponding to the template hierarchical set of interconnected bones”) and “a three-dimensional set of interconnected joints 355 representative of the dynamic object based, at least in part, on the plurality of object model parameters 340”, Paras. [0101] & [0128] of LIANG; See also an asset bank (e.g., database 505) of dynamic object sequences (e.g., pedestrian, animal, etc. sequences) and their corresponding meshes can be created directly from data captured in the real world such as, for example, by a fleet of autonomous vehicles (e.g., autonomous vehicle 205 of FIG. 2), Para. [0112] of LIANG); receiving instructions to generate a simulated skeleton for a scene in a simulation (the memory 1315 can store instructions 1345 that when executed by the one or more processors 1310 cause the one or more processors 1310 (the computing system 1300) to perform any of the operations, functions, or methods/processes described herein, including, for example, obtaining sensor data, generating object model parameters, generating simulation data, etc., Para. [0160] of LIANG; [the processor must receive the instructions to execute them, and the generation of a simulated skeleton is shown in the mapping of the immediate subsequent limitation]); generating the simulated skeleton according to the scene based on the skeleton attributes (generate a three-dimensional set of interconnected joints 355 representative of the dynamic object [which as discussed above is in the environment/scene] based, at least in part, on the plurality of object model parameters 340, Para. [0101] of LIANG; See also joint angles corresponding to the template hierarchical set of interconnected bones, Para. [0011] of LIANG; [the joints are interpreted as skeletal joints and the Para. [0011] of LIANG indicates the joint angles correspond to interconnected bones]; [thus, the object model parameters, which according to Para. [0011] of LIANG include joint angles, and offsets and scale factors corresponding to hierarchy of interconnected bones, correspond to skeleton attributes; See also annotated FIG. 6 of LIANG below), the simulated skeleton comprising the digital hierarchical framework of bones of a generic skeleton modified by scaling factors according to the scene (in order to accurately represent a plurality of different object sizes that deviate from the template mesh such as, for example, in the case of representing a number of unique human or other non-rigid bodies, the template object model can include a plurality of tunable model parameters … the plurality of tunable model parameters can include a learnable scale factor for at least one (or each) bone in the hierarchical set of interconnected bones (e.g., skeleton), Para. [0085] of LIANG; [the template mesh is interpreted as a generic]; See also scale to millions of scene elements, Para. [0116] of LIANG); building a simulation asset using the simulated skeleton (an asset bank (e.g., database 505) of dynamic object sequences (e.g., pedestrian, animal, etc. sequences) and their corresponding meshes can be created directly from data captured in the real world such as, for example, by a fleet of autonomous vehicles (e.g., autonomous vehicle 205 of FIG. 2), Para. [0112] of LIANG; See also FIG. 6 is a diagram of a simulation sequence 600, which depicts a simulation scene 605 including a plurality of three-dimensional points (e.g., LiDAR points, etc.) corresponding to a real-world or simulated environment; one or more dynamic object representations 615 including a plurality of three-dimensional points (e.g., LiDAR points, etc.) corresponding to one or more dynamic object models 610; and a simulated object scene 620 including the simulation scene 605 and the dynamic object representations 615, Para. [0113] of LIANG; See also annotated FIG. 6 of LIANG below); and determining a reaction of a vehicle to the simulation asset in the scene simulated, the reaction of the vehicle being a function of a configuration of the vehicle (the vehicle computing system 210 can obtain sensor data 255 from a sensor system 235 (e.g., sensor(s) 115, 120 of FIG. 1) onboard the vehicle 205, attempt to comprehend the vehicle's surrounding environment by performing various processing techniques on the sensor data 255, and generate an appropriate motion plan through the vehicle's surrounding environment (e.g., environment 110 of FIG. 1), Para. [0043] of LIANG; [the appropriate motion plan after comprehending the vehicle’s surrounding environment is interpreted as determining a reaction of a vehicle to the environment, which as discussed next includes the simulation asset]; See also the autonomy computing system 240 can obtain the sensor data 255 through the sensor(s) 235, process the sensor data 255 (or other data) to perceive its surrounding environment, predict [simulate] the motion of objects within the surrounding environment, and generate an appropriate motion plan through such surrounding environment … these autonomy functions can be performed by one or more sub-systems such as, for example, a perception system, a prediction system, a motion planning system, or other systems that cooperate to perceive the surrounding environment of the vehicle 205 and determine a motion plan for controlling the motion of the vehicle 205 accordingly, Para. [0061] of LIANG; [the determining a motion plan for the vehicle according to the predicted motion of objects (e.g., FIG. 6 of LIANG’s dynamic objects) is interpreted as corresponding to determining a reaction of a vehicle to the simulation asset in the scene simulated]).
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<Examiner-annotated FIG. 6 of LIANG>
Regarding claim 2, LIANG teaches the computer-implemented system of claim 1 (as shown above), the operations further comprising: in response to the reaction, updating the configuration (vehicle computing system 210 can be configured to continuously update the vehicle's motion plan and corresponding planned vehicle motion trajectories … for example, in some implementations, the vehicle computing system 210 can generate new motion planning data 275C/motion plan(s) for the vehicle 205 (e.g., multiple times per second, etc.) … each new motion plan can describe a motion of the vehicle 205 over the next planning period (e.g., next several seconds, etc.) … moreover, a new motion plan may include a new planned vehicle motion trajectory … thus, in some implementations, the vehicle computing system 210 can continuously operate to revise or otherwise generate a short-term motion plan based on the currently available data, Para. [0066] of LIANG; [Applicant’s specification, at Para. [0062], indicates “the reaction of AV 102 being the movement of AV 102 in response to the perception by the sensors”]; [thus, the continuous [multiple] updates of the vehicle’s motion plan which take into account currently available data [including how the vehicle position has changed] is interpreted as corresponding to updating in response to the reaction, i.e., in response to previous vehicle movement; See also the motion plan may define the vehicle's motion such that the vehicle 205 avoids the object(s), reduces speed to give more leeway to one or more of the object(s), proceeds cautiously, performs a stopping action, passes an object, queues behind/in front of an object, etc., Para. [0065] of LIANG]); repeating determining the reaction and updating the configuration until a desired reaction is obtained (continuously update the vehicle’s motion plan discussed above with reference to Para. [0066] of LIANG; See also vehicle computing system 210 can implement an optimization algorithm, machine-learned model, etc. that considers cost data associated with a vehicle action as well as other objective functions (e.g., cost functions based on speed limits, traffic lights, etc.), if any, to determine optimized variables that make up the motion plan … the vehicle computing system 210 can determine that the vehicle 205 can perform a certain action (e.g., pass an object, etc.) without increasing the potential risk to the vehicle 205 or violating any traffic laws (e.g., speed limits, lane boundaries, signage, etc.) … for instance, the vehicle computing system 210 can evaluate the predicted motion trajectories of one or more objects during its cost data analysis to help determine an optimized vehicle trajectory through the surrounding environment, Para. [0065] of LIANG; [evaluating predicted motion trajectories to determine an optimized vehicle trajectory is interpreted as repeating determining the reaction and updating the configuration until a desired reaction is obtained]); and exporting a final updated configuration corresponding to the desired reaction to a physical vehicle (once the optimization planner has identified the optimal motion plan (or some other iterative break occurs), the optimal motion plan (and the planned motion trajectory) can be selected and executed by the vehicle 205, Para. [0066] of LIANG).
Regarding claim 3, LIANG teaches the computer-implemented system of claim 2 (as shown above), wherein: the configuration includes settings of sensors in the vehicle, determining the reaction is by simulating sensing of the simulation asset by the sensors according to the settings (the sensor(s) 235 can include at least two different types of sensor(s). For instance, the sensor(s) 235 can include at least one first sensor (e.g., the first sensor(s) 115, etc.) and at least one second sensor (e.g., the second sensor(s) 120, etc.). The at least one first sensor can be a different type of sensor than the at least one second sensor. For example, the at least one first sensor can include one or more image capturing device(s) (e.g., one or more cameras, RGB cameras, etc.), Para. [0056] of LIANG) and the reaction of the vehicle comprises, in response to the sensing by the sensors, at least one of: (i) movement of the vehicle or (ii) classification of the simulation asset by a perception stack of the vehicle (the vehicle computing system 210 can obtain sensor data 255 from a sensor system 235 (e.g., sensor(s) 115, 120 of FIG. 1) onboard the vehicle 205, attempt to comprehend the vehicle's surrounding environment by performing various processing techniques on the sensor data 255, and generate an appropriate motion plan through the vehicle's surrounding environment (e.g., environment 110 of FIG. 1), Para. [0043] of LIANG; [the appropriate motion plan after comprehending the vehicle’s surrounding environment is interpreted as determining a reaction of a vehicle to the environment, which as discussed next includes the simulation asset]; See also the autonomy computing system 240 can obtain the sensor data 255 through the sensor(s) 235, process the sensor data 255 (or other data) to perceive its surrounding environment, predict [simulate] the motion of objects within the surrounding environment, and generate an appropriate motion plan through such surrounding environment … these autonomy functions can be performed by one or more sub-systems such as, for example, a perception system, a prediction system, a motion planning system, or other systems that cooperate to perceive the surrounding environment of the vehicle 205 and determine a motion plan for controlling the motion of the vehicle 205 accordingly, Para. [0061] of LIANG; [the determining a motion plan for the vehicle according to the predicted motion of objects (e.g., FIG. 6 of LIANG’s dynamic objects) is interpreted as corresponding to determining a reaction of a vehicle to the simulation asset in the scene simulated]).
Regarding claim 4, LIANG teaches the computer-implemented system of claim 2, wherein: the configuration includes settings of a control stack of the vehicle, the determining is by generating control messages by the control stack according to the settings (onboard systems can send or receive data, messages, signals, etc. amongst one another through the communication channel(s), Para. [0055] of LIANG) when sensors of the vehicle perceive the simulation asset (map data that provides information that assists the vehicle computing system 210 in processing, analyzing, and perceiving its surrounding environment and its relationship thereto, Para. [0059] of LIANG; See also generate perception data 275A that is indicative of one or more states (e.g., current or past state(s)) of one or more objects that are within a surrounding environment of the vehicle, Para. [0062] of LIANG), and the reaction of the vehicle comprises movement of the vehicle in response to the control messages by the control stack (the vehicle computing system 210 can obtain sensor data 255 from a sensor system 235 (e.g., sensor(s) 115, 120 of FIG. 1) onboard the vehicle 205, attempt to comprehend the vehicle's surrounding environment by performing various processing techniques on the sensor data 255, and generate an appropriate motion plan through the vehicle's surrounding environment (e.g., environment 110 of FIG. 1), Para. [0043] of LIANG; [the appropriate motion plan after comprehending the vehicle’s surrounding environment is interpreted as determining a reaction of a vehicle to the environment, which as discussed next includes the simulation asset]; See also the autonomy computing system 240 can obtain the sensor data 255 through the sensor(s) 235, process the sensor data 255 (or other data) to perceive its surrounding environment, predict [simulate] the motion of objects within the surrounding environment, and generate an appropriate motion plan through such surrounding environment … these autonomy functions can be performed by one or more sub-systems such as, for example, a perception system, a prediction system, a motion planning system, or other systems that cooperate to perceive the surrounding environment of the vehicle 205 and determine a motion plan for controlling the motion of the vehicle 205 accordingly, Para. [0061] of LIANG; [the determining a motion plan for the vehicle according to the predicted motion of objects (e.g., FIG. 6 of LIANG’s dynamic objects) is interpreted as corresponding to determining a reaction of a vehicle to the simulation asset in the scene simulated]).
Regarding claim 5, LIANG teaches the computer-implemented system of claim 1 (as shown above), wherein: generating the simulated skeleton comprises generating skeleton sets from the skeleton attributes, each skeleton set comprises bones from a subset of the various skeletons ([Examiner is interpreting “skeleton set” to correspond to a set of bones based on Applicant’s Para. [0109]]; See generate a three-dimensional set of interconnected joints 355 representative of the dynamic object based, at least in part, on the plurality of object model parameters 340 [as discussed above the object model parameters of LIANG correspond to skeleton parameters], Para. [0101] of LIANG; See also joint angles corresponding to the template hierarchical set of interconnected bones, Para. [0011] of LIANG; [the joints are interpreted as skeletal joints and the Para. [0011] of LIANG indicates the joint angles correspond to interconnected bones]; [thus, the object model parameters, which according to Para. [0011] of LIANG include joint angles, and offsets and scale factors corresponding to hierarchy of interconnected bones, correspond to skeleton attributes; See also annotated FIG. 6 of LIANG above), the subset of various skeletons is selected according to a preconfigured rule, and different skeleton sets are associated with correspondingly different preconfigured rules ([Applicant’s specification, at Para. [0032], indicates that a preconfigured rule can be a preconfigured prediction]; Likewise, LIANG discloses input data 330 (e.g., a plurality of LiDAR points with concatenated image features, etc.) is consumed by the machine-learned object parameter estimation model 335 to predict the one or more object model parameters 340, Para. [0080] of LIANG; See also dynamic objects can include pedestrians or other non-rigid objects with diverse shapes and poses, Para. [0034] of LIANG; See also generate realistic pedestrian sequences that capture the diverse shapes, poses, or movements of a number of dynamic objects operating in the real-world, Para. [0036] of LIANG).
Regarding claim 6, LIANG teaches the computer-implemented system of claim 1 (as shown above), wherein the instructions comprise settings of the scene (simulation data can further include a simulated object scene indicative of one or more movements of a respective dynamic object within a simulated environment, Para. [0010] of LIANG), the settings indicating one of several preconfigured rules (input data 330 (e.g., a plurality of LiDAR points with concatenated image features, etc.) is consumed by the machine-learned object parameter estimation model 335 to predict the one or more object model parameters 340, Para. [0080] of LIANG; [Applicant’s specification, at Para. [0032], indicates that a preconfigured rule can be a preconfigured prediction]; [As discussed above, the object model parameters correspond to skeleton attributes, and since the skeleton attributes (object model parameters) are predicted, the skeleton attributes (object model parameters) include/comprise preconfigured rules (i.e., settings)).
Regarding claim 7, LIANG teaches the computer-implemented system of claim 6 (as shown above), wherein generating the simulated skeleton further comprises choosing a corresponding one of skeleton sets according to the one of the preconfigured rules indicated in the settings (input data 330 (e.g., a plurality of LiDAR points with concatenated image features, etc.) is consumed by the machine-learned object parameter estimation model 335 to predict the one or more object model parameters 340, Para. [0080] of LIANG; [Applicant’s specification, at Para. [0032], indicates that a preconfigured rule can be a preconfigured prediction]; [As discussed above, the object model parameters correspond to skeleton attributes, and since the skeleton attributes (object model parameters) are predicted, the skeleton attributes (object model parameters) include/comprise preconfigured rules (i.e., settings)).
Regarding claim 8, LIANG teaches the computer-implemented system of claim 7, wherein generating the simulated skeleton further comprises: selecting distinct scaling factors for each bone in the skeleton set chosen; and modifying the bones of the generic skeleton by the respective scaling factors selected for the bones ([Examiner is interpreting “skeleton set” to correspond to a set of bones based on Applicant’s Para. [0109]]; See generate a three-dimensional set of interconnected joints 355 representative of the dynamic object based, at least in part, on the plurality of object model parameters 340 [as discussed above the object model parameters of LIANG correspond to skeleton parameters], Para. [0101] of LIANG; See also joint angles corresponding to the template hierarchical set of interconnected bones, Para. [0011] of LIANG; [the joints are interpreted as skeletal joints and the Para. [0011] of LIANG indicates the joint angles correspond to interconnected bones]; [thus, the object model parameters, which according to Para. [0011] of LIANG include joint angles, and offsets and scale factors corresponding to hierarchy of interconnected bones, correspond to skeleton attributes]; See also annotated FIG. 6 of LIANG above; [template is interpreted as corresponding to generic]).
Regarding claim 10, LIANG teaches the computer-implemented system of claim 8, wherein modifying the bones comprises: multiplying a dimension of an individual bone by the corresponding scaling factor to generate a final dimension of the individual bone in the simulated skeleton; and repeating the multiplying for each bone in the generic skeleton ([Examiner is interpreting “skeleton set” to correspond to a set of bones based on Applicant’s Para. [0109]]; See generate a three-dimensional set of interconnected joints 355 representative of the dynamic object based, at least in part, on the plurality of object model parameters 340 [as discussed above the object model parameters of LIANG correspond to skeleton parameters], Para. [0101] of LIANG; See also joint angles corresponding to the template hierarchical set of interconnected bones, Para. [0011] of LIANG; [the joints are interpreted as skeletal joints and the Para. [0011] of LIANG indicates the joint angles correspond to interconnected bones]; [thus, the object model parameters, which according to Para. [0011] of LIANG include joint angles, and offsets and scale factors corresponding to hierarchy of interconnected bones, correspond to skeleton attributes]; See also annotated FIG. 6 of LIANG above; [template is interpreted as corresponding to generic]).
Regarding claim 11, LIANG discloses a computer-implemented system, comprising: one or more non-transitory computer-readable media storing instructions, when executed by one or more processing units, cause the one or more processing units to perform operations (non-transitory, computer readable media that store instructions that when executed by the one or more processors cause the one or more processors to perform operations, Paras. [0017], [0020] & [0052] of LIANG) comprising: retrieving real-world data comprising asset attributes of objects and people in a region (obtain sensor information associated with a real world environment or one or more dynamic objects therein, Para. [0073] of LIANG; See also dynamic objects include pedestrians, animals, Para. [0076] of LIANG; See also template object model (e.g., enhanced LBS model, etc.) can include a template hierarchical set of interconnected bones (e.g., a skeleton) … the template object model can represent a dynamic object (e.g., a pedestrian body) using the template hierarchical set of interconnected bones, Para. [0082] of LIANG; [the set of interconnected bones (skeleton) corresponding to a pedestrian body (dynamic object) in real world environment is interpreted as corresponding to real-world data comprising skeleton attributes]; See also FIG. 3, which shows sensor data 305, model parameters 340 (including “scale factors corresponding to the template hierarchical set of interconnected bones”) and “a three-dimensional set of interconnected joints 355 representative of the dynamic object based, at least in part, on the plurality of object model parameters 340”, Paras. [0101] & [0128] of LIANG; See also an asset bank (e.g., database 505) of dynamic object sequences (e.g., pedestrian, animal, etc. sequences) and their corresponding meshes can be created directly from data captured in the real world such as, for example, by a fleet of autonomous vehicles (e.g., autonomous vehicle 205 of FIG. 2), Para. [0112] of LIANG); deriving attribute rules for the asset attributes ([Applicant’s specification, at Para. [0032], indicates that a preconfigured rule can be a preconfigured prediction]; Likewise, LIANG discloses input data 330 (e.g., a plurality of LiDAR points with concatenated image features, etc.) is consumed by the machine-learned object parameter estimation model 335 to predict the one or more object model parameters 340, Para. [0080] of LIANG; See also dynamic objects can include pedestrians or other non-rigid objects with diverse shapes and poses, Para. [0034] of LIANG; See also generate realistic pedestrian sequences that capture the diverse shapes, poses, or movements of a number of dynamic objects operating in the real-world, Para. [0036] of LIANG); receiving instructions to generate a simulation asset for a scene in a simulation, the simulation asset having a subset of the asset attributes (the memory 1315 can store instructions 1345 that when executed by the one or more processors 1310 cause the one or more processors 1310 (the computing system 1300) to perform any of the operations, functions, or methods/processes described herein, including, for example, obtaining sensor data, generating object model parameters, generating simulation data, etc., Para. [0160] of LIANG; [the processor must receive the instructions to execute them, and the generation of a simulated skeleton is shown in the mapping of the immediate subsequent limitation]); generating the simulation asset having characteristics according to attribute rules corresponding to the subset of the asset attributes (generate a three-dimensional set of interconnected joints 355 representative of the dynamic object [which as discussed above is in the environment/scene] based, at least in part, on the plurality of object model parameters 340, Para. [0101] of LIANG; See also joint angles corresponding to the template hierarchical set of interconnected bones, Para. [0011] of LIANG; [the joints are interpreted as skeletal joints and the Para. [0011] of LIANG indicates the joint angles correspond to interconnected bones]; [thus, the object model parameters, which according to Para. [0011] of LIANG include joint angles, and offsets and scale factors corresponding to hierarchy of interconnected bones, correspond to skeleton attributes; See also annotated FIG. 6 of LIANG below); and determining a reaction of a vehicle to the simulation asset in the scene simulated, the reaction of the vehicle being a function of a configuration of the vehicle (the vehicle computing system 210 can obtain sensor data 255 from a sensor system 235 (e.g., sensor(s) 115, 120 of FIG. 1) onboard the vehicle 205, attempt to comprehend the vehicle's surrounding environment by performing various processing techniques on the sensor data 255, and generate an appropriate motion plan through the vehicle's surrounding environment (e.g., environment 110 of FIG. 1), Para. [0043] of LIANG; [the appropriate motion plan after comprehending the vehicle’s surrounding environment is interpreted as determining a reaction of a vehicle to the environment, which as discussed next includes the simulation asset]; See also the autonomy computing system 240 can obtain the sensor data 255 through the sensor(s) 235, process the sensor data 255 (or other data) to perceive its surrounding environment, predict [simulate] the motion of objects within the surrounding environment, and generate an appropriate motion plan through such surrounding environment … these autonomy functions can be performed by one or more sub-systems such as, for example, a perception system, a prediction system, a motion planning system, or other systems that cooperate to perceive the surrounding environment of the vehicle 205 and determine a motion plan for controlling the motion of the vehicle 205 accordingly, Para. [0061] of LIANG; [the determining a motion plan for the vehicle according to the predicted motion of objects (e.g., FIG. 6 of LIANG’s dynamic objects) is interpreted as corresponding to determining a reaction of a vehicle to the simulation asset in the scene simulated]).
Regarding claim 17, LIANG discloses a computer-implemented system, comprising: one or more non-transitory computer-readable media storing instructions, when executed by one or more processing units, cause the one or more processing units to perform operations (non-transitory, computer readable media that store instructions that when executed by the one or more processors cause the one or more processors to perform operations, Paras. [0017], [0020] & [0052] of LIANG) comprising: retrieving real-world data comprising appearances of objects in an operational design domain (obtain sensor information associated with a real world environment or one or more dynamic objects therein, Para. [0073] of LIANG; See also dynamic objects include pedestrians, animals, Para. [0076] of LIANG; See also template object model (e.g., enhanced LBS model, etc.) can include a template hierarchical set of interconnected bones (e.g., a skeleton) … the template object model can represent a dynamic object (e.g., a pedestrian body) using the template hierarchical set of interconnected bones, Para. [0082] of LIANG; [the set of interconnected bones (skeleton) corresponding to a pedestrian body (dynamic object) in real world environment is interpreted as corresponding to real-world data comprising skeleton attributes]; See also FIG. 3, which shows sensor data 305, model parameters 340 (including “scale factors corresponding to the template hierarchical set of interconnected bones”) and “a three-dimensional set of interconnected joints 355 representative of the dynamic object based, at least in part, on the plurality of object model parameters 340”, Paras. [0101] & [0128] of LIANG; See also an asset bank (e.g., database 505) of dynamic object sequences (e.g., pedestrian, animal, etc. sequences) and their corresponding meshes can be created directly from data captured in the real world such as, for example, by a fleet of autonomous vehicles (e.g., autonomous vehicle 205 of FIG. 2), Para. [0112] of LIANG); receiving instructions to generate a simulation asset comprising an individual one of the objects according to a configuration of a scene (the memory 1315 can store instructions 1345 that when executed by the one or more processors 1310 cause the one or more processors 1310 (the computing system 1300) to perform any of the operations, functions, or methods/processes described herein, including, for example, obtaining sensor data, generating object model parameters, generating simulation data, etc., Para. [0160] of LIANG; [the processor must receive the instructions to execute them, and the generation of a simulated skeleton is shown in the mapping of the immediate subsequent limitation]); generating the simulation asset having a parameterized appearance representing one of various possible appearances of the individual one of the objects in the operational design domain attributes (generate a three-dimensional set of interconnected joints 355 representative of the dynamic object [which as discussed above is in the environment/scene] based, at least in part, on the plurality of object model parameters 340, Para. [0101] of LIANG; See also joint angles corresponding to the template hierarchical set of interconnected bones, Para. [0011] of LIANG; [the joints are interpreted as skeletal joints and the Para. [0011] of LIANG indicates the joint angles correspond to interconnected bones]; [thus, the object model parameters, which according to Para. [0011] of LIANG include joint angles, and offsets and scale factors corresponding to hierarchy of interconnected bones, correspond to skeleton attributes; See also annotated FIG. 6 of LIANG above; See also in order to accurately represent a plurality of different object sizes that deviate from the template mesh such as, for example, in the case of representing a number of unique human or other non-rigid bodies, the template object model can include a plurality of tunable model parameters … the plurality of tunable model parameters can include a learnable scale factor for at least one (or each) bone in the hierarchical set of interconnected bones (e.g., skeleton), Para. [0085] of LIANG; [the template mesh is interpreted as a generic]; See also scale to millions of scene elements, Para. [0116] of LIANG; See also an asset bank (e.g., database 505) of dynamic object sequences (e.g., pedestrian, animal, etc. sequences) and their corresponding meshes can be created directly from data captured in the real world such as, for example, by a fleet of autonomous vehicles (e.g., autonomous vehicle 205 of FIG. 2), Para. [0112] of LIANG; See also FIG. 6 is a diagram of a simulation sequence 600, which depicts a simulation scene 605 including a plurality of three-dimensional points (e.g., LiDAR points, etc.) corresponding to a real-world or simulated environment; one or more dynamic object representations 615 including a plurality of three-dimensional points (e.g., LiDAR points, etc.) corresponding to one or more dynamic object models 610; and a simulated object scene 620 including the simulation scene 605 and the dynamic object representations 615, Para. [0113] of LIANG; See also annotated FIG. 6 of LIANG below); and determining a reaction of a vehicle to the simulation asset in the scene simulated, the reaction of the vehicle being a function of a configuration of the vehicle (the vehicle computing system 210 can obtain sensor data 255 from a sensor system 235 (e.g., sensor(s) 115, 120 of FIG. 1) onboard the vehicle 205, attempt to comprehend the vehicle's surrounding environment by performing various processing techniques on the sensor data 255, and generate an appropriate motion plan through the vehicle's surrounding environment (e.g., environment 110 of FIG. 1), Para. [0043] of LIANG; [the appropriate motion plan after comprehending the vehicle’s surrounding environment is interpreted as determining a reaction of a vehicle to the environment, which as discussed next includes the simulation asset]; See also the autonomy computing system 240 can obtain the sensor data 255 through the sensor(s) 235, process the sensor data 255 (or other data) to perceive its surrounding environment, predict [simulate] the motion of objects within the surrounding environment, and generate an appropriate motion plan through such surrounding environment … these autonomy functions can be performed by one or more sub-systems such as, for example, a perception system, a prediction system, a motion planning system, or other systems that cooperate to perceive the surrounding environment of the vehicle 205 and determine a motion plan for controlling the motion of the vehicle 205 accordingly, Para. [0061] of LIANG; [the determining a motion plan for the vehicle according to the predicted motion of objects (e.g., FIG. 6 of LIANG’s dynamic objects) is interpreted as corresponding to determining a reaction of a vehicle to the simulation asset in the scene simulated]).
Claims 13 and 19 have substantially similar limitations as recited in claim 3; therefore, they are rejected under 35 U.S.C. § 103 for the same reasons.
Claims 12 and 18 have substantially similar limitations as recited in claim 2; therefore, they are rejected under 35 U.S.C. § 103 for the same reasons.
Claim 14 and 20 have substantially similar limitations as recited in claim 4; therefore, they are rejected under 35 U.S.C. § 103 for the same reasons.
Claim Rejections - 35 U.S.C. § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 9, 15 and 16 are rejected under 35 U.S.C. § 103 as being unpatentable over LIANG et al. (U.S. Patent Application Publication No. 2022/0036579 A1) in view of WANG et al. (U.S. Patent Application Publication No. 2016/0110595 A1).
Regarding claim 9, LIANG teaches the computer-implemented system of claim 7 (as shown above) and an individual scaling factor is selected from a respective distribution associated with the corresponding bone ([Examiner is interpreting “skeleton set” to correspond to a set of bones based on Applicant’s Para. [0109]]; See generate a three-dimensional set of interconnected joints 355 representative of the dynamic object based, at least in part, on the plurality of object model parameters 340 [as discussed above the object model parameters of LIANG correspond to skeleton parameters], Para. [0101] of LIANG; See also joint angles corresponding to the template hierarchical set of interconnected bones, Para. [0011] of LIANG; [the joints are interpreted as skeletal joints and the Para. [0011] of LIANG indicates the joint angles correspond to interconnected bones]; [thus, the object model parameters, which according to Para. [0011] of LIANG include joint angles, and offsets and scale factors corresponding to hierarchy of interconnected bones, correspond to skeleton attributes]; See also annotated FIG. 6 of LIANG above) but LIANG does not appear to explicitly disclose wherein: different bones in each skeleton set are categorized into corresponding distributions.
WANG, however, is in the field of using sensor data to select three-dimensional (“3D”) models that represent sensed objects (Para. [0002] of WANG) and teaches wherein: different bones in each skeleton set are categorized into corresponding distributions (creating a large synthetic 3D human body model dataset using real-world body size distributions, Para. [0018] of WANG).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the skeleton based simulation of LIANG with the distribution/rule based functionality of WANG for the purpose of providing a higher degree of accuracy (Para. [0018] of WANG).
Regarding claim 15, LIANG teaches the computer-implemented system of claim 11 (as shown above) but LIANG does not appear to explicitly disclose wherein each simulation asset is associated with at least one distinct attribute distribution.
WANG, however, is in the field of using sensor data to select three-dimensional (“3D”) models that represent sensed objects (Para. [0002] of WANG) and teaches wherein each simulation asset is associated with at least one distinct attribute distribution (creating a large synthetic 3D human body model dataset using real-world body size distributions, Para. [0018] of WANG).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the skeleton based simulation of LIANG with the distribution/rule based functionality of WANG for the purpose of providing a higher degree of accuracy (Para. [0018] of WANG).
Regarding claim 16, LIANG teaches the computer-implemented system of claim 11 (as shown above) and generating simulation assets having characteristics corresponding to the subset of the asset attributes according to the attribute rules ([Applicant’s specification, at Para. [0032], indicates that a preconfigured rule can be a preconfigured prediction]; Likewise, LIANG discloses input data 330 (e.g., a plurality of LiDAR points with concatenated image features, etc.) is consumed by the machine-learned object parameter estimation model 335 to predict the one or more object model parameters 340, Para. [0080] of LIANG; See also dynamic objects can include pedestrians or other non-rigid objects with diverse shapes and poses, Para. [0034] of LIANG; See also generate realistic pedestrian sequences that capture the diverse shapes, poses, or movements of a number of dynamic objects operating in the real-world, Para. [0036] of LIANG) but LIANG does not appear to explicitly disclose wherein: the instructions to generate the simulation asset are according to settings of the scene , the settings indicate one or more attribute distributions, the subset of the asset attributes is associated with a particular one of the attribute distributions, and the operations further comprise: identifying the particular one of the attribute distributions indicated by the settings; identifying the subset of asset attributes associated with the particular one of the attribute distributions.
WANG, however, is in the field of using sensor data to select three-dimensional (“3D”) models that represent sensed objects (Para. [0002] of WANG) and teaches wherein: the instructions to generate the simulation asset are according to settings of the scene, the settings indicate one or more attribute distributions, the subset of the asset attributes is associated with a particular one of the attribute distributions (the height and weight distributions of men and women in various age groups are known to a higher degree of accuracy than the distribution of body shapes. By generating models having various body shapes that correspond to the known heights and weights, in the same frequency distribution as the actual population, the quality of the potential matches is increased over a random model distribution, Para. [0018] of WANG), and the operations further comprise: identifying the particular one of the attribute distributions indicated by the settings; identifying the subset of asset attributes associated with the particular one of the attribute distributions (creating a large synthetic 3D human body model dataset using real-world body size distributions, Para. [0018] of WANG).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify the skeleton based simulation of LIANG with the distribution/rule based functionality of WANG for the purpose of providing a higher degree of accuracy (Para. [0018] of WANG).
Conclusion
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JOHN P. HOCKER
Examiner
Art Unit 2189
/JOHN P HOCKER/Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
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