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 .
Application Status
This Office Action is in response to the application filed on 11/10/2025. The specification and drawings are objected to as detailed below. Claims 1-9 are pending and rejected as detailed below. This action is non-final.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Applicant has claimed priority to JP2023-141725 and PCT/JP2024/030971, with an earliest effective filing data of 08/31/2023.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 11/10/2025, are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “417” has been used to designate both SLAM PROCESSING UNIT, in Fig. 7 and DATA SET GENERATION UNIT, in Fig. 4. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to because Fig. 5 uses S3 twice, and does not use S4 as described in [0051]. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: Control Device 400 as described in [0026]. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Reference Signs List [0080] includes “123 traveling motor” but that is absent from any drawings or the rest of the spec
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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.
Claim 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.
Claim 6 recites the limitation "the relearned model" in the second limitation. There is insufficient antecedent basis for this limitation in the claim.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Analysis of the claim(s) regarding subject matter eligibility utilizing the 2019 Revised Patent Subject Matter Eligibility Guidance is described below.
STEP 1: STATUTORY CATEGORIES
Claim(s) 1-9 do fall into at least one of the four statutory subject matter categories. Claim 1 and its dependents are directed to a method which is the statutory category of a process. Claim 9 is directed to a device which is the statutory category of a machine.
STEP 2A: JUDICIAL EXCEPTIONS
PRONG 1: RECITATION OF A JUDICIAL EXCEPTION
The claim(s) recite(s):
- Claim 1 recite(s) an abstract idea belonging to the grouping of mental processes. Claim 1 recites, “generating the movement amount estimation model, which is a learning model learned to output a movement amount when vehicle body data is input, using, as a learning data set, the vehicle body data including at least a value related to power of a traveling body of the work vehicle and data indicating the movement amount of the work vehicle.” The claim relates to the idea of generating a model for modeling vehicle motion. The claim is recited at a high level or generality. A person within a generic computing environment would be able to create a model that can take vehicle movement data as an input and estimate the movement as an output. The claims are broad enough that this is a mental process. Claim 9 is substantially similar and would be rejected for the same rationale as recited above.
- Claim 2 recite(s) insignificant extrasolution activity. Claim 2 recites, “wherein the data indicating the movement amount of the work vehicle is generated based on measurement data of a position of the work vehicle.” This is merely a type of data to be used.
- Claim 3 recite(s) an abstract idea belonging to the grouping of mental processes. Claim 3 recites, “estimating the position of the work vehicle by a global navigation satellite system (GNSS).” This step is defined broadly so as to be reasonable for a person to achieve. The idea of estimating a location using a GPS is no different than a person looking at their GPS and guessing about where they are. The claim further states, “generating the movement amount estimation model, the movement amount estimation model is relearned using, as a new learning data set, data that is generated based on the measurement data acquired when measurement accuracy related to the measurement data is higher than a predetermined threshold and indicates the movement amount of the work vehicle.” This again is a broad step at retraining the model. A person could reasonably look at the data presented to them and determine that a new model accuracy is possible. The person could then retrain or fix their existing model to ensure the accuracy is there.
- Claim 4 recite(s) an abstract idea belonging to the grouping of mental processes. Claim 4 recites, “estimating a value related to the movement amount of the work vehicle based on the movement amount estimation model and the vehicle body data including at least the value related to the power of the traveling body of the work vehicle.” A person could reasonably estimate a motion characteristic of their vehicle. The person could guess that if they put in a certain amount of gas power the vehicle would move a certain amount. This is done every day by people driving. The claim continues, “wherein the step of generating the movement amount estimation model includes a step of relearning the movement amount estimation model using a new learning data set when accuracy of estimation of the value related to the movement amount is lower than a predetermined threshold.” This again is a broad step at retraining the model. A person could reasonably look at the data presented to them and determine that a new model accuracy is possible as compared to a threshold. The person could then retrain or fix their existing model to ensure the accuracy is there.
- Claim 5 recite(s) an abstract idea belonging to the grouping of mental processes. Claim 5 recites, “wherein in the step of generating the movement amount estimation model, when an instruction of update is received, relearning of the movement amount estimation model is started.” A person could determine that the data of their model is out of sync and reasonably determine to update it. This could be achieved using a generic computing device.
- Claim 6 recite(s) an abstract idea belonging to the grouping of mental processes. Claim 6 recites, “estimating a value related to the movement amount of the work vehicle based on the movement amount estimation model and the vehicle body data including at least the value related to the power of the traveling body of the work vehicle,” A person could reasonably estimate a motion characteristic of their vehicle. The person could guess that if they put in a certain amount of gas power the vehicle would move a certain amount. This is done every day by people driving. The claim continues, “wherein in the step of estimating the value related to the movement amount of the work vehicle, the value related to the movement amount of the work vehicle is estimated using the relearned movement amount estimation model when reliability of the relearned movement amount estimation model exceeds a predetermined threshold.” This again is a broad step at retraining the model. A person could reasonably look at the data presented to them and determine that a new model accuracy is possible as compared to a threshold. The person could then retrain or fix their existing model to ensure the accuracy is there.
- Claim 7 recite(s) an abstract idea belonging to the grouping of mental processes. Claim 7 recites, “a step of estimating the movement amount of the work vehicle using a plurality of movement amount estimation models;” a person could reasonably estimate how much a vehicle would move for a given input, “a step of measuring the movement amount of the work vehicle;” a person could again reasonably measure the data of how much it actually moved, and “a step of selecting one movement amount estimation model from the plurality of movement amount estimation models based on a deviation between the estimated movement amount and the measured movement amount.” A person could reasonably compare the data and select a model. The steps of the claim are three steps that a person within a generic computing environment could achieve. The steps are merely data processing and evaluation to determine the best model.
- Claim 8 recite(s) insignificant extrasolution activity. Claim 8 recites, “wherein in the step of generating the movement amount estimation model, the movement amount estimation model is generated using the vehicle body data acquired from a plurality of work vehicles and data indicating the movement amount.” This is merely defining that the data can come from multiple vehicles.
PRONG 2: INTEGRATION INTO A PRACTICAL APPLICATION
The additional element(s) recited in the claim(s) beyond the judicial exception are generic computing elements and generic machine learned models. The additional element(s) do not integrate the judicial exception into a practical application because the additional element(s) do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception and add insignificant extra-solution activity to the judicial exception. The computer elements are merely used as a tool to perform the abstract idea, and the use of the judicial exception is generally linked to the particular technological environment of autonomous vehicle driving without using the judicial exception in some other meaningful way (MPEP 2106.04(d)).
STEP 2B: INVENTIVE CONCEPT/SIGNIFICANTLY MORE
The additional elements recited in the claim(s) are not sufficient to amount to significantly more than the judicial exception because they do not add more than insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), and the computer functions of receiving and transmitting data have been recognized by the courts as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(d)). Further, the additional elements of a “memory” and a “processor” recited in the claim(s) are well-understood, routine, and conventional activities previously known to the industry, specified at a high level of generality (MPEP2106.05 (d)).
Based on the above analysis, claim(S) 1-9 is/are not eligible subject matter and is/are rejected under 35 U.S.C 101.
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
Claim(s) 1-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Takano (JP-2020-027333A) in view of Thompson (US PG Pub 2022/0242422).
Regarding claim 1, Takano teaches a method for generating a movement amount estimation model for a work vehicle, the method comprising a step of generating the movement amount estimation model, which is ([0020], [0038], and [0047] teach the system generating a driving trajectory model which is used to estimate the position of a vehicle. The vehicle uses the driving model to estimate its movement as described in [0022] and [0039]. This movement estimate is based on the vehicle’s body data inputs. [0022] teaches the system using the estimate position and a comparison to the measured position to determine that it is trained properly. It is noted that the claim of a work vehicle is a design choice and has no patentable weight. The system is related to a vehicle and defining it further has no bearing on the design.)
Takano does not teach a learning model learned.
However, Thompson teaches “a learning model learned.” ([0161]-[0163] teaches using a learned model to output vehicle movement characteristics based on input)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Takano with Thompson; and have a reasonable expectation of success. Both relate to models that are used to control the driving of a vehicle. As Thompson teaches in [0005]-[0007] the use of a learned model allows the model to react to changes more readily. This allows the system to perform better. As the nature of where and what the vehicle operates in the changing of the vehicle’s environment may mean that there are new challenges for it. By having a trained and retrained model the system can adapt to those changes and the vehicle be monitored accurately.
Claim 9 is substantially similar in scope and language and would be rejected for the same rationale as recited above.
Regarding claim 2, Takano teaches the method for generating the movement amount estimation model for the work vehicle according to claim 1, wherein the data indicating the movement amount of the work vehicle is generated based on measurement data of a position of the work vehicle. ([0020], [0022], and [0039] teaches the system measures the vehicle movement data)
Regarding claim 3, Takano teaches the method for generating the movement amount estimation model for the work vehicle according to claim 2, comprising a step of estimating the position of the work vehicle by a global navigation satellite system (GNSS), ([0037] teaches the system determining the vehicle’s position based on a GPS value)
wherein in the step of generating the movement amount estimation model, the movement amount estimation model is relearned using, as a new learning data set, data that is generated based on the measurement data acquired when measurement accuracy related to the measurement data is higher than a predetermined threshold and indicates the movement amount of the work vehicle. ([0022] and [0039] teach the system fixing the model in the event that the measured data differs by a significant amount. This error reduction step would be analogous to a retraining step)
Regarding claim 4, Takano teaches the method for generating the movement amount estimation model for the work vehicle according to claim 1, comprising a step of estimating a value related to the movement amount of the work vehicle based on the movement amount estimation model and the vehicle body data including at least the value related to the power of the traveling body of the work vehicle, ([0016], [0020], and [0022] teach the vehicle motion data being based in part on the wheel speed and vehicle speed data, which is analogous to a power data)
wherein the step of generating the movement amount estimation model includes a step of relearning the movement amount estimation model using a new learning data set when accuracy of estimation of the value related to the movement amount is lower than a predetermined threshold. ([0022] and [0039] teach the system fixing the model in the event that the measured data differs by a significant amount. This error reduction step would be analogous to a retraining step)
Regarding claim 5, Takano teaches the method for generating the movement amount estimation model for the work vehicle according to claim 1.
Takano does not teach wherein in the step of generating the movement amount estimation model, when an instruction of update is received, relearning of the movement amount estimation model is started.
However, Thompson teaches “wherein in the step of generating the movement amount estimation model, when an instruction of update is received, relearning of the movement amount estimation model is started.” ([0161]-[0162] teach the system training the model repeatedly on the basis of a user input)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Takano with Thompson; and have a reasonable expectation of success. Both relate to models that are used to control the driving of a vehicle. As Thompson teaches in [0005]-[0007] the use of a learned model allows the model to react to changes more readily. This allows the system to perform better. As the nature of where and what the vehicle operates in the changing of the vehicle’s environment may mean that there are new challenges for it. By having a trained and retrained model the system can adapt to those changes and the vehicle be monitored accurately.
Regarding claim 6, Takano teaches the method for generating the movement amount estimation model for the work vehicle according to claim 1, comprising a step of estimating a value related to the movement amount of the work vehicle based on the movement amount estimation model and the vehicle body data including at least the value related to the power of the traveling body of the work vehicle, ([0016], [0020], and [0022] teach the vehicle motion data being based in part on the wheel speed and vehicle speed data, which is analogous to a power data)
wherein in the step of estimating the value related to the movement amount of the work vehicle, the value related to the movement amount of the work vehicle is estimated using the relearned movement amount estimation model when reliability of the relearned movement amount estimation model exceeds a predetermined threshold. ([0022], [0039], and [0045] teaches the system correcting error data in the model to improve accuracy, and re-estimating the data with the improved model)
Regarding claim 7, Takano teaches the method for generating the movement amount estimation model for the work vehicle according to claim 1, comprising:
a step of estimating the movement amount of the work vehicle using a plurality of movement amount estimation models; ([0023]-[0022] and [0037]-[0039] teach the system having multiple initial models to select from)
a step of measuring the movement amount of the work vehicle; ([0023]-[0022] and [0037]-[0039] teach the system determining the actual movement amount of the vehicle) and
a step of selecting one movement amount estimation model from the plurality of movement amount estimation models based on a deviation between the estimated movement amount and the measured movement amount. ([0023]-[0022] and [0037]-[0039] teach the system finding the most accurate model to the current situation and selecting that model)
Regarding claim 8, Takano teaches the method for generating the movement amount estimation model for the work vehicle according claim 1, wherein in the step of generating the movement amount estimation model, the movement amount estimation model is generated using the vehicle body data acquired from a plurality of work vehicles and data indicating the movement amount. ([0028]-[0029] teaches the system having the historical data for multiple vehicles in moving in the environment and using this data for the model generation process)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Or (US PG Pub 2022/0228867) teaches a system and method for providing localization, including, during a training phase: obtaining a training dataset of accelerations, angular velocities, and known locations over time of vehicles moving in a defined area; and training a machine learning model to provide location estimation in the defined area based on the accelerations and angular velocities using the training dataset; and during runtime phase: obtaining runtime accelerations and angular velocities over time of a vehicle moving in the defined area; and using the trained model to obtain current location of the vehicle based on the runtime acceleration and angular velocities.
Towal (US PG Pub 2019/0384304) teaches a deep learning solution for path detection is implemented to generate a more abstract definition of a drivable path without reliance on explicit lane-markings—by using a detection-based approach. Using approaches of the present disclosure, the identification of drivable paths may be possible in environments where conventional approaches are unreliable, or fail—such as where lane markings do not exist or are occluded. The deep learning solution may generate outputs that represent geometries for one or more drivable paths in an environment and confidence values corresponding to path types or classes that the geometries correspond. These outputs may be directly useable by an autonomous vehicle—such as an autonomous driving software stack—with minimal post-processing.
Collin (US PG Pub 2023/0349699) teaches a declination of an object, an orientation of the object and/or a position of the object can be determined using a gyroscope. In this regard, the gyroscope can be mounted to the object. The gyroscope can be pivoted. An undetermined pivoting of the object about an axis with the gyroscope can be measured. A component of a rotation of Earth acting on the gyroscope can be determined using the undetermined pivoting and the pivoting angular velocity (w.sub.carousel). At least one parameter set can be determined. The pivoting angular velocity (w.sub.carousel) of the gyroscope about the swivel axis with a second sensor can be determined.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS STRYKER whose telephone number is (571)272-4659. The examiner can normally be reached Monday-Friday 7:30-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christian Chace can be reached at (571) 272-4190. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/N.S./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
