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 .
Status of Claims
This action is in reply to the application filed on 8/3/2023, wherein:
Claims 1-20 are currently pending and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) the abstract idea of Mental Processes such as collecting information, analyzing it, and displaying certain results of the collection and analysis. This judicial exception is not integrated into a practical application and the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
This rejection follows the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed Reg 4, January 7, 2019, pp. 50-57 (“2019 PEG”).
Analysis
Step 1 – 2019 PEG pg. 53 – 2019 PEG pg. 53 (See MPEP 2106.03)
Claims 1-20 are directed to the statutory category of a process, machine, or manufacture.
Step 2A, Prong 1 (Do the claims recite an abstract idea?) – 2019 PEG pg. 54 (See MPEP 2106.04(a)-(c))
For independent claims 1 and 11, the claims recite an abstract idea of: transforming raw data into scenarios. The steps of independent claim 1 recite the abstract idea (in bold below) of: A system comprising: one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the system to perform: obtaining raw data associated with one or more events of a vehicle; inferring, within the raw data, an attribute of the vehicle according to an ontological framework, wherein the ontological framework defines at least one relationship associated with the vehicle; transforming the raw data into a seed scenario according to the ontological framework, wherein the seed scenario comprises at least one file, and the seed scenario comprises a description or a depiction of the vehicle; and generating one or more additional scenarios based on one or more modifications to the seed scenario. Independent claim 11 recites similar steps that recite the abstract idea. Independent claims 1 and 11, as drafted, are a process that, also under the broadest reasonable interpretation, covers Mental Processes, since they recite limitations that can practically be performed in the human mind, such as, observations, evaluations, judgments, and opinions. If the claim limitations, under the broadest reasonable interpretation, covers mental processes but for the recitation of additional elements including generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Other than reciting the abstract idea, the independent claims recite additional elements including generic computer components such as “a system comprising a memory storing instructions executed by one or more processors”, and nothing in the claims precludes the steps from being performed as a method of organizing human activity. Accordingly, the independent claims recite an abstract idea..
Dependent claims 2-10 and 12-20 recite similar limitations as independent claims 1 and 11; and when analyzed as a whole are held to be patent ineligible under 35 U.S.C 101 because the additional recited limitations only refine the abstract idea further. Other than reciting the abstract idea, the dependent claims recite similar additional elements including generic computer components as the independent claims, such as “the system, a large language mode (LLM), and an applications programming interface (API)”. If a claim limitation, under its broadest reasonable interpretation, covers commercial or legal interactions, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Step 2A, Prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?) – 2019 PEG pg. 54 (See MPEP 2106.04(d)-(c))
This judicial exception is not integrated into a practical application. In particular, independent claims 1 and 11 only recite the additional elements of “a system comprising a memory storing instructions executed by one or more processors”. A plain reading of the Figures and associated descriptions in the specification reveals that generic processors may be used to execute the claimed steps. The additional elements are recited at a high level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)) and limits the judicial exception to a particular environment (See MPEP 2106.05(h)). Mere instructions to apply an exception using a generic computer component and limiting the judicial exception to a particular environment doesn’t integrate the abstract idea into a practical application in Step 2A. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Hence, independent claims 1, and 11 are directed to an abstract idea.
Dependent claims 2-10 and 11-20, recite similar additional elements as the independent claims including generic computer components, such as “the system, a large language mode (LLM), and an applications programming interface (API)”. The judicial exception is not integrated into a practical application because the additional elements in the dependent claims are also recited at a high-level of generality such that it amounts to more no more than mere instructions to apply the exception using generic computer components. Therefore, the additional elements do not integrate the abstract idea into a practical application because they also do not impose any meaningful limits on practicing the abstract idea. Also, the claims do not affect an improvement to another technology or technical field; the claims do not amount to an improvement of the functioning of a computer system itself; the claims do not effect a transformation or reduction of a particular article to a different state or thing; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?) – 2019 PEG pg. 56 (See MPEP 2106.05)
Independent claims 1 and 11 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the recited additional elements amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)) and limits the judicial exception to the particular environment of computers (See MPEP 2106.05(h)). The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the function of the elements when each is taken alone. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept in Step 2B.
In addition, the dependent claims 2-19 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the dependent claims to perform the claimed limitations, amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). Similar to the independent claims, mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Also, for the same reasoning as the independent claims, the additional elements of the limitations of the dependent claims, when considered individually and as an ordered combination, together do not offer significantly more than the sum of the functions of the elements when each is taken alone and the dependent claims as a whole, do not amount to significantly more than the abstract idea itself. For these reasons, the dependent claims also are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5 and 7-20 are rejected under 35 U.S.C. 103 as being unpatentable over WO2022184363A1 to Thiem et al. (hereinafter referred to as Thiem machine translation) in view of IN 202321031887 to Dulepet et al. (hereinafter referred to as Dulepet).
In regards to claim 1, Thiem machine translation discloses a system (device for training at least one algorithm for a control unit of an autonomously or semi-autonomously driving motor vehicle to implement an autonomous driving function, Thiem machine translation page 8) comprising: one or more processors; and a memory storing instructions (computer program product 28 with a computer program product module 30 that has a self-learning neural network 32 that trains an algorithm 34, Thiem machine translation page 9) that, when executed (possible to train the computer program product module 30 both in a real environment and in a simulation environment in which a virtual twin of the motor vehicle 2 is trained, Thiem machine translation page 10) by the one or more processors, cause the system to perform (computer program product module 30 has an autonomous driving program based on input data, Thiem machine translation page 10): obtaining raw data associated with one or more events of a vehicle (model for the simulation environment 36 is an accident description in an accident database, e.g. GI DAS, in which an accident between two motor vehicles took place, Thiem machine translation page 10); inferring, within the raw data (accident information stored in database: Technical inventory of the accident: •Motor vehicle: year of construction 2021, brake: ABS, engine: 103 KW (1997 ccm), Top speed: 195 km/h • Equipment: traction control, electronic stability program, brake assistant, lane departure warning, tire pressure monitor, cruise control system • Condition after the accident: Front airbags on the driver and front passenger side (activated), knee airbag on the driver's side (activated), side airbag integrated into the seat, 1st row, side head airbag, 1st and 2nd row Belt tensioners, belt force limiters on the 1st row of seats (triggering at the accident site cannot be determined ),no obvious technical defects in the motor vehicle, damage to the entire front area, as a result of driving into the stationary motor vehicle, damage to the interior of the dashboard. Furthermore, sketches of the accident environment, for example the course of the road, the condition of the road, the weather, the position of the sun, the time, and driving dynamic data, for example speeds, trajectories, etc. are stored, Thiem machine translation pages 10 and 11), an attribute of the vehicle according to an framework (In the simulation environment 36, a road 40 is shown to simulate the previously described accident, on which a further motor vehicle 42 is located next to the motor vehicle 2 at the edge of the road 40. The time at which motor vehicle 2 collides with motor vehicle 40 is shown, Thiem machine translation pages 10 and 11), wherein the framework defines at least one relationship associated with the vehicle (selection of a critical driving situation or an accident of the motor vehicle, a plurality of driving parameters (pF) of the motor vehicle and environmental parameters (pU) being determined for the critical driving situation or the accident; Thiem machine translation, claim 1); transforming the raw data into a seed scenario (known data on a trajectory 44 of the motor vehicle 2 are iteratively imitated in the simulation environment 36 and optimized as far as possible with the aid of the optimization algorithm 39, so that the motor vehicle model 2' emulates the known behavior of the motor vehicle 2 in the best possible way, Thiem machine translation pages 10 and 11) according to the framework (since there is no real driving data from the accident to be simulated that can be imitated directly, comparative driving data that the algorithm 34 is intended to imitate are instead generated using the data from the accident database, Thiem machine translation page 11), wherein the seed scenario comprises at least one file (model for the simulation environment 36 is an accident description in an accident database, e.g. GI DAS, in which an accident between two motor vehicles took place, Thiem machine translation page 10), and the seed scenario comprises a description or a depiction of the vehicle (trajectory 44 is divided into a large number of short sections, each of which is a time increment [t_n−1; t_n] with a duration of Δt = | t_n - t_n-1 | are spaced. The journey of the motor vehicle model 2' is then simulated using the physics model 38, initially from a time period t_n-1 lying one time increment Ät before a target time t_n (time of the collision) to the target time t_n and varied by varying the changeable driving parameters pF until the simulated Motor vehicle model 2' collides with motor vehicle 42 at the same point, Thiem machine translation page 11 ); and generating one or more additional scenarios based on one or more modifications to the seed scenario (driving parameters and/or the environmental parameters have variable driving parameters and/or the variable environmental parameters, with at least one of the variable driving parameters and/or environmental parameters being varied, with the simulation being repeated with the varied parameters is carried out, Thiem machine translation page 7). However, Thiem machine translation fails to disclose inferring according to an ontological framework and wherein the ontological framework defines a relationship.
Dulepet, in the related field of generating autonomous vehicle datasets, teaches inferring according to an ontological framework (method includes preprocessing, using at least one of domain aware Large Language Model (LLMs), ontologies and Knowledge Graphs, the input text scenario associated with each driving scenario, wherein the preprocessed data provides a structured data format for each driving scenario that contextualizes and aligns for consumption by a pretrained LLM for generating a standard scenario format, Dulepet,) and wherein the ontological framework defines a relationship (Develop or Use an Ontology: Create or utilize an existing AV domain ontology that categorizes entities (e.g., Weather Conditions, Obstacles, Maneuvers, Traffic Participants) and defines their relationships, para. 0056). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the system of Thiem machine translation with the ability to use an ontological framework as taught by the system of Dulepet. The motivation for doing so would have been to combine the contextual language understanding of domain aware LLMs, that can be accessed via APIs from the cloud environment, with structured insights from ontologies and knowledge graphs tailored for extracting information from AV domain narratives efficiently (Dulepet, para. 0056).
In regards to claim 2, modified Thiem machine translation discloses the system of claim 1, and further discloses wherein the obtaining of the raw data and the transforming of the raw data are performed (known data on a trajectory 44 of the motor vehicle 2 are iteratively imitated in the simulation environment 36 and optimized as far as possible with the aid of the optimization algorithm 39, so that the motor vehicle model 2' emulates the known behavior of the motor vehicle 2 in the best possible way, Thiem machine translation pages 10 and 11) in association with a large language model (LLM) (known learning methods of supervised and unsupervised learning, as well as combinations of these learning methods, can be used, Thiem machine translation page 9; self-learning neural network can use methods of reinforcement learning with a set of methods of machine learning in which the neural network autonomously learns a strategy in order to maximize the rewards received, Thiem machine translation page 7).
In regards to claim 3, modified Thiem machine translation discloses the system of claim 2, and further discloses wherein the LLM is trained iteratively (If an acceptable deviation 46 is reached, the method described above is repeated starting from the previous point in time t_n−2 until the method has arrived at the start time t_s. The neural network 32 is then trained until the accident can be avoided, Thiem machine translation page 11), but fails to disclose wherein the LLM is trained iteratively based on a first training dataset that comprises compiled examples of correctly generated seed scenarios and previously known or obtained incorrectly generated seed scenarios before the transforming of the raw data into the seed scenario, and a second training dataset that comprises incorrectly generated scenarios during the transforming of the raw data.
Dulepet, in the related field of generating autonomous vehicle datasets, teaches wherein the LLM is trained iteratively (Iterative Refinement: Refines the conversion process through iterative feedback from evaluation, enhancing scenario accuracy and realism, para. 0064), based on a first training dataset that comprises compiled examples of correctly generated seed scenarios and previously known or obtained incorrectly generated seed scenarios before the transforming of the raw data into the seed scenario (Driving scenarios that includes crowd sources data and data from external databases, is received via multi-modal input and converted to input text scenario. Domain aware LLMs, ontologies and Knowledge Graphs preprocess it to generate a structured data for driving scenario that contextualizes and aligns with format to be efficiently consumed by a pretrained LLM, para. 0030), and a second training dataset that comprises incorrectly generated scenarios during the transforming of the raw data (Output is post processed by performing transformations based on scenario analysis and synthesis techniques for detecting syntax and semantics discrepancies in context of a logical scenario within the driving scenario to resolve potential errors in the output of the pretrained LLM, para. 0056). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the system of Thiem machine translation with the ability to use multiple training datasets as taught by the system of Dulepet. The motivation for doing so would have been to resolve potential errors in the output of the LLM (Dulepet, para. 0030).
In regards to claim 4, modified Thiem machine translation discloses the system of claim 3, and further discloses wherein the iterative training comprises training the LLM over two stages, wherein a first stage is based on the first training dataset prior to the transforming of the raw data into the seed scenario (to train the neural network 32 of the algorithm 34, comparative driving data that the algorithm 34 is intended to imitate are instead generated using the data from the accident database, Thiem machine translation page 11) and a second stage is based on the second training dataset following the transforming of the raw data into the seed scenario (In a further development it can be provided that the simulation environment is set up to use and train the at least one algorithm in further simulations of the same critical driving situation or accident or other critical driving situations or accidents, Thiem machine translation page 8).
In regards to claim 5, modified Thiem machine translation discloses the system of claim 1, and further discloses wherein the obtaining of the raw data comprises receiving any updates of new raw data (further iteration step can be carried out or, after validation of the driving function, another situation can be selected for this case, on which the at least one pre-trained algorithm is trained further, Thiem machine translation page 7) from an external data source (model for the simulation environment 36 is an accident description in an accident database, e.g. GI DAS, in which an accident between two motor vehicles took place, Thiem machine translation pages 10 and 11), but fails to disclose updates via an applications programming interface (API); and generating a queue to process the any updates.
Dulepet, in the related field of generating autonomous vehicle datasets, teaches updates via an applications programming interface (API) (system 100 can acquire driving scenarios from external databases via Application Programming Interfaces as depicted in FIG. 3, para. 036); and generating a queue to process the any updates (proposed disclosure also enables participation by anyone, anywhere, to contribute to the driving scenario database referred to as the AV dataset (database 108) through a natural language interface infinitely scaling as more road users engage across geographies with an enormous diversity of driving conditions around the world, para. 0049). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the system of Thiem machine translation with the ability to update data using an API as taught by the system of Dulepet. The motivation for doing so would have been to acquire driving scenarios from external databases via APIs (Dulepet, para. 0036).
In regards to claim 7, modified Thiem machine translation discloses the system of claim 1, but fails to disclose wherein the obtaining of the raw data comprises obtaining the raw data from different data sources, and the transforming of the raw data comprises resolving any discrepancies within the different data sources.
Dulepet, in the related field of generating autonomous vehicle datasets, teaches wherein the obtaining of the raw data comprises obtaining the raw data from different data sources (system 100 can acquire driving scenarios from external databases via Application Programming Interfaces (APIs) 7 as depicted in FIG. 3, para. 036), and the transforming of the raw data (Driving scenarios that includes crowd sources data and data from external databases, is received via multi-modal input and converted to input text scenario, para. 030) comprises resolving any discrepancies within the different data sources (Output is post processed by performing transformations based on scenario analysis and synthesis techniques for detecting syntax and semantics discrepancies in context of a logical scenario within the driving scenario to resolve potential errors in the output of the pretrained LLM, para. 0030). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the system of Thiem machine translation with the ability to transform raw data from multiple sources and detect discrepancies as taught by the system of Dulepet. The motivation for doing so would have been to detecting syntax and semantics discrepancies in context of a logical scenario within the driving scenario to resolve potential errors (Dulepet, para. 0074).
In regards to claim 8, modified Thiem machine translation discloses the system of claim 1, and further discloses wherein the raw data comprises textual data (accident information stored in database: Technical inventory of the accident: •Motor vehicle: year of construction 2021, brake: ABS, engine: 103 KW (1997 ccm), Top speed: 195 km/h • Equipment: traction control, electronic stability program, brake assistant, lane departure warning, tire pressure monitor, cruise control system • Condition after the accident: Front airbags on the driver and front passenger side (activated), knee airbag on the driver's side (activated), side airbag integrated into the seat, 1st row, side head airbag, 1st and 2nd row Belt tensioners, belt force limiters on the 1st row of seats (triggering at the accident site cannot be determined ),no obvious technical defects in the motor vehicle, damage to the entire front area, as a result of driving into the stationary motor vehicle, damage to the interior of the dashboard. Thiem machine translation pages 10 and 11) and media data (Furthermore, sketches of the accident environment, for example the course of the road, the condition of the road, the weather, the position of the sun, the time, and driving dynamic data, for example speeds, trajectories, etc. are stored, Thiem machine translation pages 10 and 11), and the transforming of the raw data (known data on a trajectory 44 of the motor vehicle 2 are iteratively imitated in the simulation environment 36 and optimized as far as possible with the aid of the optimization algorithm 39, so that the motor vehicle model 2' emulates the known behavior of the motor vehicle 2 in the best possible way, Thiem machine translation pages 10 and 11), but fails to disclose comprises recognizing one or more additional attributes of the vehicle or of the other objects from the media data that are absent or undetected from the textual data and integrating the one or more additional attributes into the seed scenario.
Dulepet, in the related field of generating autonomous vehicle datasets, teaches recognizing one or more additional attributes of the vehicle or of the other objects from the media data that are absent or undetected from the textual data (Analyzing video footage from in-car or dashboard cameras to extract keyframes and scenes, identifying essential visual elements such as lane markings, road signs, traffic flow, and interactions with other road users, para. 0042) and integrating the one or more additional attributes into the seed scenario (visual cues are translated into detailed textual descriptions of the driving experience, para. 0042). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the system of Thiem machine translation with the ability to transform media data as taught by the system of Dulepet. The motivation for doing so would have been to create detailed textual descriptions encompassing various aspects such as visual scenes, auditory cues, and contextual information (Dulepet, para. 0042).
In regards to claim 9, modified Thiem machine translation discloses the system of claim 1, and further discloses wherein the one or more events comprise an accident or a disengagement (model for the simulation environment 36 is an accident description in an accident database, e.g. GI DAS, in which an accident between two motor vehicles took place, Thiem machine learning page 10).
In regards to claim 10, modified Thiem machine translation discloses the system of claim 1, and further discloses wherein the instructions further cause the system (computer program product 28 with a computer program product module 30 that has a self-learning neural network 32 that trains an algorithm 34, Thiem machine translation page 9) to perform: implementing a testing simulation (define a virtual twin of a real motor vehicle with a real driver in a simulation environment that is modeled on a real environment, Thiem machine translation page 6, fig. 3) based on the seed scenario (known data on a trajectory 44 of the motor vehicle 2 are iteratively imitated in the simulation environment 36 and optimized as far as possible with the aid of the optimization algorithm 39, so that the motor vehicle model 2' emulates the known behavior of the motor vehicle 2 in the best possible way, Thiem machine translation page 11) and the one or more additional scenarios (driving parameters and/or the environmental parameters have variable driving parameters and/or the variable environmental parameters, with at least one of the variable driving parameters and/or environmental parameters being varied, with the simulation being repeated with the varied parameters is carried out, Thiem machine translation page 7), wherein the testing simulation comprises executing of a test driving operation involving a test vehicle based on the seed scenario (known data on a trajectory 44 of the motor vehicle 2 are iteratively imitated in the simulation environment 36 and optimized as far as possible with the aid of the optimization algorithm 39, so that the motor vehicle model 2' emulates the known behavior of the motor vehicle 2 in the best possible way, Thiem machine translation page 11) and monitoring one or more test vehicle attributes of the test vehicle (The journey of the motor vehicle model 2' is then simulated using the physics model 38, initially from a time period t_n-1 lying one time increment Ät before a target time t_n (time of the collision) to the target time t_n and varied by varying the changeable driving parameters pF until the simulated Motor vehicle model 2' collides with motor vehicle 42 at the same point, Thiem machine translation page 11).
In regards to claim 11, Thiem machine translation discloses a method (device for training at least one algorithm for a control unit of an autonomously or semi-autonomously driving motor vehicle to implement an autonomous driving function, Thiem machine translation page 8) comprising: obtaining raw data associated with one or more events of a vehicle(model for the simulation environment 36 is an accident description in an accident database, e.g. GI DAS, in which an accident between two motor vehicles took place, Thiem machine translation page 10); inferring, within the raw data (accident information stored in database: Technical inventory of the accident: •Motor vehicle: year of construction 2021, brake: ABS, engine: 103 KW (1997 ccm), Top speed: 195 km/h • Equipment: traction control, electronic stability program, brake assistant, lane departure warning, tire pressure monitor, cruise control system • Condition after the accident: Front airbags on the driver and front passenger side (activated), knee airbag on the driver's side (activated), side airbag integrated into the seat, 1st row, side head airbag, 1st and 2nd row Belt tensioners, belt force limiters on the 1st row of seats (triggering at the accident site cannot be determined ),no obvious technical defects in the motor vehicle, damage to the entire front area, as a result of driving into the stationary motor vehicle, damage to the interior of the dashboard. Furthermore, sketches of the accident environment, for example the course of the road, the condition of the road, the weather, the position of the sun, the time, and driving dynamic data, for example speeds, trajectories, etc. are stored, Thiem machine translation pages 10 and 11), an attribute of the vehicle according to a framework (In the simulation environment 36, a road 40 is shown to simulate the previously described accident, on which a further motor vehicle 42 is located next to the motor vehicle 2 at the edge of the road 40. The time at which motor vehicle 2 collides with motor vehicle 40 is shown, Thiem machine translation pages 10 and 11), wherein the framework defines at least one relationship associated with the vehicle (selection of a critical driving situation or an accident of the motor vehicle, a plurality of driving parameters (pF) of the motor vehicle and environmental parameters (pU) being determined for the critical driving situation or the accident; Thiem machine translation, claim 1); transforming the raw data into a seed scenario (known data on a trajectory 44 of the motor vehicle 2 are iteratively imitated in the simulation environment 36 and optimized as far as possible with the aid of the optimization algorithm 39, so that the motor vehicle model 2' emulates the known behavior of the motor vehicle 2 in the best possible way, Thiem machine translation pages 10 and 11) according to the framework (since there is no real driving data from the accident to be simulated that can be imitated directly, comparative driving data that the algorithm 34 is intended to imitate are instead generated using the data from the accident database, Thiem machine translation page 11), wherein the seed scenario comprises at least one file (model for the simulation environment 36 is an accident description in an accident database, e.g. GI DAS, in which an accident between two motor vehicles took place, Thiem machine translation page 10), and the seed scenario comprises a description or a depiction of the vehicle (trajectory 44 is divided into a large number of short sections, each of which is a time increment [t_n−1; t_n] with a duration of Δt = | t_n - t_n-1 | are spaced. The journey of the motor vehicle model 2' is then simulated using the physics model 38, initially from a time period t_n-1 lying one time increment Ät before a target time t_n (time of the collision) to the target time t_n and varied by varying the changeable driving parameters pF until the simulated Motor vehicle model 2' collides with motor vehicle 42 at the same point, Thiem machine translation page 11 ); and generating one or more additional scenarios based on one or more modifications to the seed scenario (driving parameters and/or the environmental parameters have variable driving parameters and/or the variable environmental parameters, with at least one of the variable driving parameters and/or environmental parameters being varied, with the simulation being repeated with the varied parameters is carried out, Thiem machine translation page 7). However, Thiem machine translation fails to disclose inferring according to an ontological framework and wherein the ontological framework defines a relationship.
Dulepet, in the related field of generating autonomous vehicle datasets, teaches inferring according to an ontological framework (method includes preprocessing, using at least one of domain aware Large Language Model (LLMs), ontologies and Knowledge Graphs, the input text scenario associated with each driving scenario, wherein the preprocessed data provides a structured data format for each driving scenario that contextualizes and aligns for consumption by a pretrained LLM for generating a standard scenario format, Dulepet,) and wherein the ontological framework defines a relationship (Develop or Use an Ontology: Create or utilize an existing AV domain ontology that categorizes entities (e.g., Weather Conditions, Obstacles, Maneuvers, Traffic Participants) and defines their relationships, para. 0056). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the system of Thiem machine translation with the ability to use an ontological framework as taught by the system of Dulepet. The motivation for doing so would have been to combine the contextual language understanding of domain aware LLMs, that can be accessed via APIs from the cloud environment, with structured insights from ontologies and knowledge graphs tailored for extracting information from AV domain narratives efficiently (Dulepet, para. 0056)..
In regards to claim 12, modified Thiem machine translation discloses the method of claim 11, and further discloses wherein the obtaining of the raw data and the transforming of the raw data are performed (known data on a trajectory 44 of the motor vehicle 2 are iteratively imitated in the simulation environment 36 and optimized as far as possible with the aid of the optimization algorithm 39, so that the motor vehicle model 2' emulates the known behavior of the motor vehicle 2 in the best possible way, Thiem machine translation pages 10 and 11) in association with a large language model (LLM) (known learning methods of supervised and unsupervised learning, as well as combinations of these learning methods, can be used, Thiem machine translation page 9; self-learning neural network can use methods of reinforcement learning with a set of methods of machine learning in which the neural network autonomously learns a strategy in order to maximize the rewards received, Thiem machine translation page 7).
In regards to claim 13, modified Thiem machine translation discloses the method of claim 12, and further discloses wherein the LLM is trained iteratively (If an acceptable deviation 46 is reached, the method described above is repeated starting from the previous point in time t_n−2 until the method has arrived at the start time t_s. The neural network 32 is then trained until the accident can be avoided, Thiem machine translation page 11), but fails to disclose wherein the LLM is trained iteratively based on a first training dataset that comprises compiled examples of correctly generated seed scenarios and previously known or obtained incorrectly generated seed scenarios before the transforming of the raw data into the seed scenario, and a second training dataset that comprises incorrectly generated scenarios during the transforming of the raw data.
Dulepet, in the related field of generating autonomous vehicle datasets, teaches wherein the LLM is trained iteratively (Iterative Refinement: Refines the conversion process through iterative feedback from evaluation, enhancing scenario accuracy and realism, para. 0064), based on a first training dataset that comprises compiled examples of correctly generated seed scenarios and previously known or obtained incorrectly generated seed scenarios before the transforming of the raw data into the seed scenario (Driving scenarios that includes crowd sources data and data from external databases, is received via multi-modal input and converted to input text scenario. Domain aware LLMs, ontologies and Knowledge Graphs preprocess it to generate a structured data for driving scenario that contextualizes and aligns with format to be efficiently consumed by a pretrained LLM, para. 0030), and a second training dataset that comprises incorrectly generated scenarios during the transforming of the raw data (Output is post processed by performing transformations based on scenario analysis and synthesis techniques for detecting syntax and semantics discrepancies in context of a logical scenario within the driving scenario to resolve potential errors in the output of the pretrained LLM, para. 0056). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the system of Thiem machine translation with the ability to use multiple training datasets as taught by the system of Dulepet. The motivation for doing so would have been to resolve potential errors in the output of the LLM (Dulepet, para. 0030).
In regards to claim 14, modified Thiem machine translation discloses the method of claim 13, and further discloses wherein the iterative training comprises training the LLM over two stages, wherein a first stage is based on the first training dataset prior to the transforming of the raw data into the seed scenario (to train the neural network 32 of the algorithm 34, comparative driving data that the algorithm 34 is intended to imitate are instead generated using the data from the accident database, Thiem machine translation page 11) and a second stage is based on the second training dataset following the transforming of the raw data into the seed scenario (In a further development it can be provided that the simulation environment is set up to use and train the at least one algorithm in further simulations of the same critical driving situation or accident or other critical driving situations or accidents, Thiem machine translation page 8).
In regards to claim 15, modified Thiem machine translation discloses the method of claim 11, and further discloses wherein the obtaining of the raw data comprises receiving any updates of new raw data (further iteration step can be carried out or, after validation of the driving function, another situation can be selected for this case, on which the at least one pre-trained algorithm is trained further, Thiem machine translation page 7) from an external data source (model for the simulation environment 36 is an accident description in an accident database, e.g. GI DAS, in which an accident between two motor vehicles took place, Thiem machine translation pages 10 and 11), but fails to disclose updates via an applications programming interface (API); and generating a queue to process the any updates.
Dulepet, in the related field of generating autonomous vehicle datasets, teaches updates via an applications programming interface (API) (system 100 can acquire driving scenarios from external databases via Application Programming Interfaces as depicted in FIG. 3, para. 036); and generating a queue to process the any updates (proposed disclosure also enables participation by anyone, anywhere, to contribute to the driving scenario database referred to as the AV dataset (database 108) through a natural language interface infinitely scaling as more road users engage across geographies with an enormous diversity of driving conditions around the world, para. 0049). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the system of Thiem machine translation with the ability to update data using an API as taught by the system of Dulepet. The motivation for doing so would have been to acquire driving scenarios from external databases via APIs (Dulepet, para. 0036).
In regards to claim 16, modified Thiem machine translation discloses the method of claim 11, and further discloses wherein the obtaining of the raw data comprises receiving any updates of new raw data (further iteration step can be carried out or, after validation of the driving function, another situation can be selected for this case, on which the at least one pre-trained algorithm is trained further, Thiem machine translation page 7) from an external data source (model for the simulation environment 36 is an accident description in an accident database, e.g. GI DAS, in which an accident between two motor vehicles took place, Thiem machine translation pages 10 and 11), but fails to disclose updates via an applications programming interface (API); and generating a queue to process the any updates.
Dulepet, in the related field of generating autonomous vehicle datasets, teaches updates via an applications programming interface (API) (system 100 can acquire driving scenarios from external databases via Application Programming Interfaces as depicted in FIG. 3, para. 036); and generating a queue to process the any updates (proposed disclosure also enables participation by anyone, anywhere, to contribute to the driving scenario database referred to as the AV dataset (database 108) through a natural language interface infinitely scaling as more road users engage across geographies with an enormous diversity of driving conditions around the world, para. 0049). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the system of Thiem machine translation with the ability to update data using an API as taught by the system of Dulepet. The motivation for doing so would have been to acquire driving scenarios from external databases via APIs (Dulepet, para. 0036).
In regards to claim 17, modified Thiem machine translation discloses the method of claim 11, but fails to disclose wherein the obtaining of the raw data comprises obtaining the raw data from different data sources, and the transforming of the raw data comprises resolving any discrepancies within the different data sources.
Dulepet, in the related field of generating autonomous vehicle datasets, teaches wherein the obtaining of the raw data comprises obtaining the raw data from different data sources (system 100 can acquire driving scenarios from external databases via Application Programming Interfaces (APIs) 7 as depicted in FIG. 3, para. 036), and the transforming of the raw data (Driving scenarios that includes crowd sources data and data from external databases, is received via multi-modal input and converted to input text scenario, para. 030) comprises resolving any discrepancies within the different data sources (Output is post processed by performing transformations based on scenario analysis and synthesis techniques for detecting syntax and semantics discrepancies in context of a logical scenario within the driving scenario to resolve potential errors in the output of the pretrained LLM, para. 0030). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the system of Thiem machine translation with the ability to transform raw data from multiple sources and detect discrepancies as taught by the system of Dulepet. The motivation for doing so would have been to detecting syntax and semantics discrepancies in context of a logical scenario within the driving scenario to resolve potential errors (Dulepet, para. 0074).
In regards to claim 18, modified Thiem machine translation discloses the method of claim 11, and further discloses wherein the raw data comprises textual data (accident information stored in database: Technical inventory of the accident: •Motor vehicle: year of construction 2021, brake: ABS, engine: 103 KW (1997 ccm), Top speed: 195 km/h • Equipment: traction control, electronic stability program, brake assistant, lane departure warning, tire pressure monitor, cruise control system • Condition after the accident: Front airbags on the driver and front passenger side (activated), knee airbag on the driver's side (activated), side airbag integrated into the seat, 1st row, side head airbag, 1st and 2nd row Belt tensioners, belt force limiters on the 1st row of seats (triggering at the accident site cannot be determined ),no obvious technical defects in the motor vehicle, damage to the entire front area, as a result of driving into the stationary motor vehicle, damage to the interior of the dashboard. Thiem machine translation pages 10 and 11) and media data (Furthermore, sketches of the accident environment, for example the course of the road, the condition of the road, the weather, the position of the sun, the time, and driving dynamic data, for example speeds, trajectories, etc. are stored, Thiem machine translation pages 10 and 11), and the transforming of the raw data (known data on a trajectory 44 of the motor vehicle 2 are iteratively imitated in the simulation environment 36 and optimized as far as possible with the aid of the optimization algorithm 39, so that the motor vehicle model 2' emulates the known behavior of the motor vehicle 2 in the best possible way, Thiem machine translation pages 10 and 11), but fails to disclose comprises recognizing one or more additional attributes of the vehicle or of the other objects from the media data that are absent or undetected from the textual data and integrating the one or more additional attributes into the seed scenario.
Dulepet, in the related field of generating autonomous vehicle datasets, teaches recognizing one or more additional attributes of the vehicle or of the other objects from the media data that are absent or undetected from the textual data (Analyzing video footage from in-car or dashboard cameras to extract keyframes and scenes, identifying essential visual elements such as lane markings, road signs, traffic flow, and interactions with other road users, para. 0042) and integrating the one or more additional attributes into the seed scenario (visual cues are translated into detailed textual descriptions of the driving experience, para. 0042). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the system of Thiem machine translation with the ability to transform media data as taught by the system of Dulepet. The motivation for doing so would have been to create detailed textual descriptions encompassing various aspects such as visual scenes, auditory cues, and contextual information (Dulepet, para. 0042).
In regards to claim 19, modified Thiem machine translation discloses the method of claim 11, and further discloses wherein the one or more events comprise an accident or a disengagement (model for the simulation environment 36 is an accident description in an accident database, e.g. GI DAS, in which an accident between two motor vehicles took place, Thiem machine learning page 10).
In regards to claim 20, modified Thiem machine translation discloses the method of claim 11, and discloses further comprising: implementing a testing simulation (define a virtual twin of a real motor vehicle with a real driver in a simulation environment that is modeled on a real environment, Thiem machine translation page 6, fig. 3) based on the seed scenario (known data on a trajectory 44 of the motor vehicle 2 are iteratively imitated in the simulation environment 36 and optimized as far as possible with the aid of the optimization algorithm 39, so that the motor vehicle model 2' emulates the known behavior of the motor vehicle 2 in the best possible way, Thiem machine translation page 11) and the one or more additional scenarios (driving parameters and/or the environmental parameters have variable driving parameters and/or the variable environmental parameters, with at least one of the variable driving parameters and/or environmental parameters being varied, with the simulation being repeated with the varied parameters is carried out, Thiem machine translation page 7), wherein the testing simulation comprises executing of a test driving operation involving a test vehicle based on the seed scenario (known data on a trajectory 44 of the motor vehicle 2 are iteratively imitated in the simulation environment 36 and optimized as far as possible with the aid of the optimization algorithm 39, so that the motor vehicle model 2' emulates the known behavior of the motor vehicle 2 in the best possible way, Thiem machine translation page 11) and monitoring one or more test vehicle attributes of the test vehicle (The journey of the motor vehicle model 2' is then simulated using the physics model 38, initially from a time period t_n-1 lying one time increment Ät before a target time t_n (time of the collision) to the target time t_n and varied by varying the changeable driving parameters pF until the simulated Motor vehicle model 2' collides with motor vehicle 42 at the same point, Thiem machine translation page 11).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Thiem machine translation, in view of Dulepet, and further in view of US 10,915,564 to Ganjam et al. (hereinafter referred to as Ganjam).
In regards to claim 6, modified Thiem machine translation discloses the system of claim 1, wherein the raw data comprises structured data (accident information stored in database: Technical inventory of the accident: •Motor vehicle: year of construction 2021, brake: ABS, engine: 103 KW (1997 ccm), Top speed: 195 km/h • Equipment: traction control, electronic stability program, brake assistant, lane departure warning, tire pressure monitor, cruise control system • Condition after the accident: Front airbags on the driver and front passenger side (activated), knee airbag on the driver's side (activated), side airbag integrated into the seat, 1st row, side head airbag, 1st and 2nd row Belt tensioners, belt force limiters on the 1st row of seats (triggering at the accident site cannot be determined ),no obvious technical defects in the motor vehicle, damage to the entire front area, as a result of driving into the stationary motor vehicle, damage to the interior of the dashboard. Thiem machine translation pages 10 and 11) and unstructured data (Furthermore, sketches of the accident environment, for example the course of the road, the condition of the road, the weather, the position of the sun, the time, and driving dynamic data, for example speeds, trajectories, etc. are stored, Thiem machine translation pages 10 and 11), and the transforming of the raw data (known data on a trajectory 44 of the motor vehicle 2 are iteratively imitated in the simulation environment 36 and optimized as far as possible with the aid of the optimization algorithm 39, so that the motor vehicle model 2' emulates the known behavior of the motor vehicle 2 in the best possible way, Thiem machine translation pages 10 and 11), but fails to disclose comprises recognizing one or more additional attributes of the vehicle or of other objects from the unstructured data () that are absent or undetected from the structured data and integrating the one or more additional attributes into the seed scenario.
Ganjam, in the related field of parsing unstructured data, teaches comprises recognizing one or more additional attributes of the vehicle or of other objects from the unstructured data (enrich unstructured data by exposing data latently related to the unstructured data by parsing and/or predicting leveraging corporal data, col. 2, lines 1-18) that are absent or undetected from the structured data and integrating the one or more additional attributes into the seed scenario (parsing unstructured data includes associating a class identifier of a plurality of identified class identifiers with the unstructured data based at least in part on an arrangement of the unstructured data, an arrangement of corporal data and/or frequency of an arrangement of corporal data, other unstructured data, and/or parsing of the other unstructured data to make the unstructured data with which the class identifier has been associated structured data, col. 4, lines 36-62). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the system of Thiem machine translation with the ability to detect additional attributes in unstructured data as taught by the system of Ganjam. The motivation for doing so would have been to parse unstructured data to obtain structured data, predict data related to the structured data, and include adding the predicted data to structured data (Ganjam, col. 5, lines 1-15).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 12,428,007 teaches a system includes a computing device configured to execute a simulation of a trained autonomous control model for a vehicle model in a simulation environment based on a predefined dataset defining a virtual driving environment and implement a fallback layer configured to detect a failure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Paul Schwarzenberg whose telephone number is (313) 446-6611. The examiner can normally be reached on Monday-Thursday (7:30-6:30).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christine Behncke, can be reached on (571) 272-8103. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PAUL S SCHWARZENBERG/Primary Examiner, Art Unit 3695 7/15/2026
Prosecution Insights