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 . Claims 1-20 are currently pending and have been examined in this application. This communication is the first action on the merits (FAOM).
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 claims are either directed to a method, a computer program product, or a computer processing system, each of which are one of the statutory categories of invention. (Step 1: YES)
The examiner has identified system claim 15 as the claim that represents the claimed invention for analysis. Claims 1 and 8 recite similar limitations are therefore are similarly evaluated below. Claim 15 recites the limitations of:
“a memory device for storing program code; and a processor device, operatively coupled to the memory device, for running the program code to:
(a) retrieve, from vehicle sensors, key parameters from real data of validation scenarios to generate corresponding scenario configurations and descriptions;
(b) transfer target scenario descriptions and validation scenario descriptions to target scenario scripts and validation scenario scripts, respectively, to create first raw simulation data pertaining to target scenario descriptions and second raw simulation data pertaining to validation scenario descriptions;
(c) train, by an adjuster network, a deep neural network model to minimize differences between the first raw simulation data and the second raw simulation data;
(d) refine the first and second raw simulation data of rare driving scenarios to generate rare driving scenario training data; and
(e) output the rare driving scenario training data to a display screen of a computing device to enable a user to train a scenario detector for an autonomic driving assistant system (ADAS)”
Limitations (b), (c), and (d), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a memory device for storing program code; and a processor device, operatively coupled to the memory device, for running the program code to”, nothing in the claim element precludes the step from practically being performed in the human mind. Limitations (b), (c), and (d), in the context of the claim encompasses a person converting data from one form to another, performing evaluations and calculations, and observing or evaluating raw data to perform an evaluation of refining the raw data to produce rare data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes” grouping of abstract ideas. Limitation (c) also encompasses a mathematical concept that can be performed mentally because it requires mathematical calculations to perform the training of the deep neural network and therefore encompasses a mathematical concept. (Step2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. Limitations that are
not indicative of integration into a practical application include: (1) Adding the words "apply it"
(or an equivalent) with the judicial exception, or mere instructions to implement an abstract
idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP
2106.05.f), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP
2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological
environment or field of use (MPEP 2106.05.h).
In particular, limitation (a) is recited at a high level of generality (i.e., a general means of data gathering) and is therefore insignificant extra solution activity. Limitation (e) is recited at a high level of generality (i.e., a general means of outputting) and is therefore insignificant extra-solution activity. The limitation of a memory device for storing program code; and a processor device, operatively coupled to the memory device, for running the program code to perform the recited steps. The memory device, program code, and processor device are recited at a high-level of generality (i.e., as generic computer components reciting generic computer functions) such that it amounts to no more than mere instructions to apply the exception using a generic computer components. In step (a), these generic computer components are used as a tool to perform the generic function of receiving data. In step (b), (c), and (d), the generic computer components are merely used to perform the abstract idea, such that it amounts to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f). Limitation (c) also recites train, by an adjuster network, a deep neural network model, which provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The judicial exception of “train, by an adjuster network, a deep neural network model” is performed “using an adjuster network.” The adjuster network is used to generally apply the abstract idea without placing any limits on how the trained DNN functions. These limitations do not include any details about how the “training” is accomplished. See MPEP 2106.05(f). Furthermore, this type of limitation merely confines the use of the abstract idea to a particular technological environment (training deep neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claim 15 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an "inventive concept") to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use. The additional elements claimed amount to insignificant extra-solution activities. As discussed in Step 2A, Prong Two above, limitation (a) of retrieving and (e) of outputting are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. As discussed in Step 2A, Prong Two above, the recitation of generic computer components to perform limitations (b), (c), and (d) amounts to no more than mere instructions to apply the exception using generic computer components. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. Thus claim 15 (and similarly claims 1 and 8) is not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Claims 2-7, 9-14, and 16-20 further define the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the aforementioned claims are not patent-eligible.
Claims 2, 9, and 16 recite additional details of what the generation of scenario scripts is based upon, which can be accounted for as part of step (b) above and is therefore part of the mental process.
Claims 3, 10, and 17 recite details of the adjuster network and is therefore part of the additional element. The LSTM encoder and decoder are both additional elements that are recited at a high level of generality as tools to perform steps.
Claims 4, 11, and 18 recite a step of “the LSTM encoder transforms the first and second raw simulation data into high dimensional features”. The step of transforming can be performed in the human mind as part of an evaluation step. The LSTM encoder is an additional element that is recited at a high level of generality as a tool to perform the step.
Claims 5, 12, and 19 recite a step of “the decoder constructs the rare driving scenario training data from the high dimensional features”. The step of constructing can be performed in the human mind as part of an evaluation step. The decoder is an additional element that is recited at a high level of generality as a tool to perform the step.
Claims 6-7, 13-14, and 20 recite a step of “a model trainer takes the first and second raw simulation data as input and outputs the rare driving scenario training data” and “the model trainer compares the rare driving scenario training data with the real data of the validation scenario and determines a difference therebetween to be minimized”. The step of taking raw data as input and outputting rare driving scenario training data can be performed in the human mind as part of an evaluation step. The step of comparing rare driving scenario training data with real data to determine a difference to be minimized can be performed in the human mind as part of an evaluation step. This step may also encompass mathematical concepts. The model trainer is an additional element that is recited at a high level of generality as a tool to perform the steps.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter:
The prior art fails to disclose or render obvious the following limitation of independent claim 1 (and similar limitations of independent claims 8 and 15) in its entirety:
“transferring target scenario descriptions and validation scenario descriptions to target scenario scripts and validation scenario scripts, respectively, to create first raw simulation data pertaining to target scenario descriptions and second raw simulation data pertaining to validation scenario descriptions; training, by an adjuster network, a deep neural network model to minimize differences between the first raw simulation data and the second raw simulation data; refining the first and second raw simulation data of rare driving scenarios to generate rare driving scenario training data; and outputting the rare driving scenario training data to a display screen of a computing device to enable a user to train a scenario detector for an autonomic driving assistant system (ADAS)”
Box (US 12,475,281 B1) teaches the following limitations: a computer-implemented method for simulating vehicle data and improving driving scenario detection, the method comprising: retrieving, from vehicle sensors, key parameters from real data of validation scenarios to generate corresponding scenario configurations and descriptions (see at least column 11, lines 17-64 - In some implementations, the simulation data generator 208 may convert the log data accessible in the logged data 211 in different ways to generate simulation data 212. For example, the log data is used as a source of data that is based on ground level truth about real world driving situations to generate simulation data 212. In many implementations, the simulation data 212 represents an editable source of truth defining a number of simulation scenarios. In some implementations, one or more components of an instance of the log data are used to aid in creating at least one aspect of a simulation scenario. For example, in some implementations, the log data is used as an aid to generate a description including a behavior, vehicle configuration (e.g., autonomous vehicle location, platform, speed, or orientation), and sensor configuration of autonomous vehicle (e.g., ego vehicle) and the environment including actors (e.g., other vehicles, traffic, pedestrians, and static objects) in a simulation scenario.); transferring target scenario descriptions to target scenario scripts, to create first simulation data pertaining to target scenario descriptions (see at least column 11, lines 17-64 - The simulation management engine 202 may access, process, and manage the simulation data 212. The simulation management engine 202 accesses a base simulation scenario in the simulation data 212 and converts the base simulation scenario into a plurality of simulation scenarios. For example, the simulation management engine 202 may use a parameter sweep to adjust a value of a parameter in a base simulation scenario through a defined range and generate configurations for a plurality of varying simulation scenarios.).
Zhou (US 2019/0004518 A1) teaches the following limitations: training, by an adjuster network, a deep neural network model to minimize differences between first data and second data (see at least [0074-0075] – train the deep neural network to minimize the difference between the state information and the target state information).
Zhu (WO 2022/133090 A1) teaches the following limitations: refining the simulation data of rare driving scenarios to generate rare driving scenario training data (see at least abstract, [0054] - identifying a first scenario for use in a simulation, which is defined by a first set of scenario parameters; identifying a first risk assessment metric of the first scenario, based on safety operations used by an autonomous driving model in the first scenario; determining a second (changed) set of scenario parameters from changes to the first set of scenario parameters, based on a second risk assessment metric; and generating a second scenario based on the second (changed) set of scenario parameters; the second autonomous driving scenario may have a greater difficulty for the safety operations than the first risk assessment metric, enabling an increase in testing complexity).
Cunningham (US 2023/0037071 A1) teaches the following limitations: outputting the driving scenario training data to a display screen of a computing device to enable a user to train a scenario detector for an autonomic driving assistant system (ADAS) (see at least [0028, 0031, 0035-0038] - A data labeling system 203 is a computing device having a display and a user interface that a user may use to example data from the data store 202 and label the data for use in training the machine learning model… After an AV follows an actual trajectory that the AV generates using the model, at 313 the system may receive feedback on the real-world trajectory and return to step 307 and identify a label for the actual trajectory indicating whether the actual trajectory was desirable or undesirable. The system may receive the label from a user, via a user interface of an in-vehicle or portable electronic device.).
It would not have been obvious to one of ordinary skill in the art before the effective filing date to have modified or combined Box, Zhou, Zhu, Cunningham, or any of the other prior art of record in a meaningful way to arrive at the claimed invention without the use of impermissible hindsight. The prior art fails to explicitly disclose or fairly suggest the recited way of generating and outputting rare driving scenario training data to a display screen so that a user can train a scenario detector for an autonomic driving assistant system.
Conclusion
The prior art made of record, and not relied upon, considered pertinent to applicant’s disclosure or directed to the state of art is listed on the enclosed PTO-892. The following is a list of the relevant prior art that was cited but not applied: Atsmon (US 2024/0199071 A1), Muehlenstaedt (US 2023/0222267 A1), Zhang (US 2019/0318267 A1), Wolff (US 2022/0055641 A1), and Pronovost (US 12,617,413 B1).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAITLIN MCCLEARY whose telephone number is (703)756-1674. The examiner can normally be reached Monday - Friday 10:00 am - 7:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Navid Z Mehdizadeh can be reached at (571) 272-7691. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CAITLIN R MCCLEARY/Examiner, Art Unit 3669
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