DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Examiner’s Note
Examiner has cited particular paragraphs/columns and line numbers or figures in the references as applied to the claims below for convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations with the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Applicant is reminded that the Examiner is entitled to give the broadest reasonable interpretation to the language of the claims. Furthermore, the Examiner is not limited to the Applicant’s definition which is not specifically set forth in the claims.
Information Disclosure Statements
The Information Disclosure Statement(s) (IDS) filed on 08/01/2025 and 09/16/2026 has/have been acknowledged.
Status of Application
The list of claims 1-20 is pending in this application. In the claim set filed 08/01/2025:
Claim(s) 1 and 11 is/are the independent claim(s) observed in the application.
Non-Final Rejection
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With respect to claims 1 and 11, claims 1 and 11 recite: “a conventional motion planner,” “a learning-based motion planner,” “an optimal motion planner,” “the first learning-based motion planner,” and “the second learning-based motion planner,” which has yielded claim construction due to improper antecedent basis such that one of ordinary skill in the art cannot clearly identify and distinguish the meets and bounds of the claimed invention.
In particular, it is unclear what constitutes the respective first and second “learning-based motion planner(s),” as the Applicant previously introduces a singular “learning-based motion planner.” It is further unclear if the “optimal motion planner” is learning-based and/or if the “conventional motion planner” is learning-based. The Examiner has reviewed the Applicant’s specification, which has not clarified the above points to the requisite degree that the Examiner can propose a reasonable claim construction interpretation to apply prior art accordingly.
Claim(s) 2-10 and 12-20 are further rejected due to their dependency on rejected claims 1 and 11, and for failing to cure the deficiencies listed above.
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.
Claim(s) 1-20 is/are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim(s) 1 and 11 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite using a tangible storage media with computer program stored thereon to perform the following method: 1) generating a first trajectory for the at least one robot device based on at least one query parameter; 2) generating a second trajectory by using a learning-based motion planner; 3) applying a post process to validate an optimized second trajectory; 4) comparing the first trajectory with the optimized second trajectory; 5) feeding an optimal motion planner that integrates path and trajectory generation with the at least one query parameter to generate training data; 6) and training the first learning-based motion planner by using the training data.
The limitations of using a tangible storage media with computer program stored thereon to perform the following method: 1) generating a first trajectory for the at least one robot device based on at least one query parameter; 2) generating a second trajectory by using a learning-based motion planner; 3) applying a post process to validate an optimized second trajectory; 4) comparing the first trajectory with the optimized second trajectory; 5) feeding an optimal motion planner that integrates path and trajectory generation with the at least one query parameter to generate training data; 6) and training the first learning-based motion planner by using the training data, 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 tangible storage media with computer program stored thereon, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the use of a tangible storage media with computer program stored thereon language, this claim encompasses the user manually performing steps of: generating a first trajectory, generating a second trajectory, optimizing the second trajectory, comparing the first trajectory and the optimized second trajectory, and generating training data based on the comparison. The subsequent step of training the first learning-based motion planner by using the training data consists of Applicant applying a judicial exception without integrating the exception into the Applicant’s claimed solution. Put another way, the Applicant recites training the “first learning-based motion planner,” but does not state how the “first learning-based motion planner” is integrated into the Applicant’s solution for generating the training data; therefore, the training is irrelevant to the operation of the Applicant’s claimed invention and is merely being included in an attempt to yield a patent eligible claim by “applying an exception.” 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. Accordingly, the claim(s) recite(s) an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites using a tangible storage media with computer program stored thereon. The using a tangible storage media with computer program stored thereon in these steps are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception by incorporating an additional element(s) in the claimed method. Accordingly, this additional element(s) does/do not integrate the abstract idea into a practical application because it/they does/do not impose any meaningful limits on practicing the abstract idea. Therefore, The claim is directed to an abstract idea.
The claim does 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 element of using a tangible storage media with computer program stored thereon amount(s) to no more than mere instructions to apply the exception by incorporating an additional element(s) in the claimed method. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Examiner’s Note #1: As stated above, the recitation of training “the first learning-based motion planner” is not satisfactory to integrate the abstract idea into a practical application as the training steps simply comprise Applicant applying a judicial exception without integrating the exception into the Applicant’s claimed solution.
Dependent claim(s) 2-10 and 12-20 when analyzed as a whole, is/are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional element(s), if any, in the dependent claim(s) is/are not sufficient to amount to significantly more than the judicial exception for the same reasons as with claim(s) 1 and 11.
Examiner’s Note #2: In order to overcome the above rejections under 35 U.S.C. 101, the Examiner suggests amending the independent claims to incorporate some aspect of controlling the robot device using the optimized motion planning rather than just performing the planning operations, which do not have a practical application on their own.
Prior Art (Not relied upon)
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached form 892.
Ogale et al. (United States Patent Publication 2019/0033085 A1) discloses: Systems, methods, devices, and other techniques for planning a trajectory of a vehicle. A computing system can implement a trajectory planning neural network configured to, at each time step of multiple time steps: obtain a first neural network input and a second neural network input. The first neural network input can characterize a set of waypoints indicated by the waypoint data, and the second neural network input can characterize (a) environmental data that represents a current state of an environment of the vehicle and (b) navigation data that represents a planned navigation route for the vehicle. The trajectory planning neural network may process the first neural network input and the second neural network input to generate a set of output scores, where each output score in the set of output scores corresponds to a different location of a set of possible locations in a vicinity of the vehicle.
Frossard et al. (United States Patent Publication 2019/0147610 A1) discloses: Systems and methods for detecting and tracking objects are provided. In one example, a computer-implemented method includes receiving sensor data from one or more sensors. The method includes inputting the sensor data to one or more machine-learned models including one or more first neural networks configured to detect one or more objects based at least in part on the sensor data and one or more second neural networks configured to track the one or more objects over a sequence of sensor data. The method includes generating, as an output of the one or more first neural networks, a 3D bounding box and detection score for a plurality of object detections. The method includes generating, as an output of the one or more second neural networks, a matching score associated with pairs of object detections. The method includes determining a trajectory for each object detection.
YIP et al. (United States Patent Publication 2019/0184561 A1) discloses: Systems and methods are provided that introduce an improved way of producing fast and optimal motion plans by using Recurrent Neural Networks (RNN) to determine end-to-end trajectories in an iterative manner. By using an RNN in this way and offloading expensive computation towards offline learning, a network is developed that implicitly generates optimal motion plans with minimal loss in performance in a compact form. This method generates near optimal paths in a single, iterative, end-to-end roll-out that that has effectively fixed-time execution regardless of the configuration space complexity. Thus, the method results in fast, consistent, and optimal trajectories that outperform popular motion planning strategies in generating motion plans.
Ogale et al. (United States Patent Publication 2020/0174490 A1) discloses: Systems, methods, devices, and other techniques for planning a trajectory of a vehicle. A computing system can implement a trajectory planning neural network configured to, at each time step of multiple time steps: obtain a first neural network input and a second neural network input. The first neural network input can characterize a set of waypoints indicated by the waypoint data, and the second neural network input can characterize (a) environmental data that represents a current state of an environment of the vehicle and (b) navigation data that represents a planned navigation route for the vehicle. The trajectory planning neural network may process the first neural network input and the second neural network input to generate a set of output scores, where each output score in the set of output scores corresponds to a different location of a set of possible locations in a vicinity of the vehicle.
Westberg et al. (United States Patent Publication 2021/0245364 A1) discloses: A method for controlling movement trajectories of a robot, the method including predicting, in an offline mode, values of at least one parameter related to the execution of alternative movement trajectories between a first position of the robot and a second position of the robot; selecting, in the offline mode, a movement trajectory based on the predicted values of the at least one parameter; and executing the selected movement trajectory by the robot. A control system for controlling movement trajectories of a robot is also provided.
YAZHEMSKY et al. (United States Patent Publication 2021/0263527 A1) discloses: A control system for a vehicle includes a first controller, a second controller, and an auto-tuner. The first controller is configured to generate an optimal trajectory of the vehicle along a path. The second controller is configured to, based on the optimal trajectory generated by the first controller, generate motoring and braking commands to a motoring and braking system of the vehicle for controlling the vehicle to travel along the path. The auto-tuner includes a processor configured to solve a real-time optimization problem to determine at least one parameter of at least one of the first controller or the second controller.
Conclusion
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/RAMI NABIH BEDEWI/Examiner, Art Unit 3666C
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