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 .
Examiner’s Note
Examiner has cited particular paragraphs/columns and line numbers or figures in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within 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 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 Applicants’ definition which is not specifically set forth in the claims.
Response to Amendment
The amendment filed 7/6/2026 has been entered. Claims 1-5 remain pending in the application. Applicant’s amendments to the Claims have overcome each and every rejection under U.S.C. 101 previously set forth in the Office Action mailed 4/8/2026.
Response to Arguments
Applicant’s arguments, see pages 4 and 5, filed 7/6/2026, with respect to the amendments to the claims overcoming the rejection under U.S.C. 101 have been fully considered and are persuasive. The rejection under U.S.C 101 of each claim has been withdrawn.
Applicant's arguments filed 7/6/2026 regarding the prior art rejection under U.S.C. 103 have been fully considered but they are not persuasive. The argument is moot as it is not based on the claims as examined in the office action mailed 4/8/2026 but rather on the amended claims submitted 7/6/2026. The applicant’s cited limitations to argue against the rejection, such as “wherein the patch model is deployed to expand an output portion of the control mode without changing an existing portion of the control model and wherein the patch model is generated by machine learning and the machine learning is performed by fixing a value of a parameter of the existing portion and training only an expansion portion corresponding to the patch model.” are part of amendments added to the claim after mailing of the office action, and therefore not present for the previous examination and rejection. See updated rejection of the claims below.
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.
Claims 1 and 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Friedrichs (US 20240140453), herein after referred to as Friedrichs, in view of Lu, herein after referred to as Lu, and Sandler (US 20200104706), herein after referred to as Sandler.
Regarding Claim 1, Friedrichs discloses:
collecting defective scene data (see at least [0017] “receives as an input signals corresponding to actions generated by the trained autonomous control model that will performed or are being performed by the automated control system.”)
related to a scene that is evaluated as insufficient performance during operations of automatic control using a control model of a mobile body; (see at least [0019] “when the fallback layer detects poor performance of the trained autonomous control model, the event or events leading into the action defined or performed by the trained autonomous control model are flagged.”)
generating a patch (see at least [0053] “the fallback layer 330 may determine and implement a fallback action. The fallback action may include, for example but not limited to, a stop operation, an evasive maneuver, a lane centering operation, or an adjustment to the motion trajectory for implementation by the virtual vehicle.”)
for supplementing a performance of the control model for the scene evaluated as insufficient performance from the collected defective scene data; (see at least [0053] “If the fallback layer 330 determines at block 508 that the output of the trained autonomous control model 244b fails meet the one or more predefined conditions 240c, “NO” at block 508, the method continues to block 512 and/or block 514. In embodiments, at block 512, the fallback layer 330 may determine and implement a fallback action.”)
and outputting the generated patch model. (see at least [0053] “In embodiments, if a fallback action is determined to meet the one or more predefined conditions 240c, the fallback action may be utilized to update the trained autonomous control model 244b”)
Friedrichs does not explicitly disclose:
[patch] model
wherein the patch model is deployed to expand an output portion of the control mode without changing an existing portion of the control model
and wherein the patch model is generated by machine learning and the machine learning is performed by fixing a value of a parameter of the existing portion and training only an expansion portion corresponding to the patch model.
In the same field of endeavor, Lu discloses:
[patch] model (see at least [0042] “analyzing the VAR data to generate either design data or patch data that describes a modification for one or more ADAS systems or an autonomous driving system that would have resulted in the collision being avoided or made the collision less likely to occur;”)
and wherein the patch model is generated by machine learning (see at least [0104] “the analysis system 152 includes a machine learning module configured for analyzing the VAR data (or the report data) to generate modification data for improving performance of vehicles.”)
The above pieces of prior art are considered analogous as they both represent inventions in the autonomous vehicle control modeling field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Friedrichs to generate a patch model via machine learning rather than just a patch action, as taught by Lu to modify an autonomous driving system based on collision data [0042].
In the same field of endeavor, Sandler discloses:
wherein the patch model is deployed to expand an output portion of the control mode without changing an existing portion of the control model (see at least [Fig. 1a] [0043] “The computing system can modify the machine-learned model to include a model patch, where the model patch includes a second set of learnable parameters. After modifying the machine-learned model to include the model patch, the computing system can train the machine-learned model on a second training dataset to perform a second task that is different from the first task.”)
and the machine learning is performed by fixing a value of a parameter of the existing portion (see at least [0056] “In particular, in some implementations, the new values for the patch parameters can be learned while keeping the remainder of the model parameters fixed.”)
and training only an expansion portion corresponding to the patch model. (see at least [0057] “Thus, in some implementations, only the model patch parameters are permitted to change when performing transfer-learning.”)
The above pieces of prior art are considered analogous as they both represent inventions in the model improvement field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Friedrichs to deploy that patch to expand an output portion of the control mode without changing an existing portion of the control model by fixing a value of a parameter of the existing portion and training only an expansion portion corresponding to the patch model, as taught by Sandler to train a patch model so that the model can perform a new task [0043] or add an additional layer to an existing model [0067].
Regarding Claim 3, modified Friedrichs discloses the limitations of Claim 1, and Friedrichs further discloses:
wherein the expanded output portion is configured to derive a control command that conforms to the scene evaluated as insufficient performance. (see at least [0053] “if a fallback action is determined to meet the one or more predefined conditions 240c, the fallback action may be utilized to update the trained autonomous control model 244b such that the trained autonomous control model 244b may implement such an action or a similar action when it encounters similar situations in the future.”)
Regarding Claim 4, modified Friedrichs discloses the limitations of Claim 1, and Friedrichs further discloses:
using a control command derived by the patch model instead of a control command derived by the control model, or modifying the control command derived by the control model with the patch model. (see at least [0053] “the fallback action may be utilized to update the trained autonomous control model 244b such that the trained autonomous control model 244b may implement such an action or a similar action when it encounters similar situations in the future.”)
Regarding Claim 5, modified Friedrichs discloses the limitations of Claim 1, and Friedrichs further discloses:
wherein the mobile body is a vehicle. (see at least [0015] “The systems and methods enable improved training of autonomous control systems, such as autonomous vehicle control systems.”)
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Friedrichs (US 20240140453), herein after referred to as Friedrichs, in view of Lu, herein after referred to as Lu, and Kandemir (US 20240071048), herein after referred to as Kandemir.
Regarding Claim 2, modified Friedrichs discloses the limitations of Claim 1, but Friedrichs does not explicitly disclose:
wherein the patch model is used only in scenes where insufficient performance is evaluated.
In the same field of endeavor, Kandemir discloses:
wherein the patch model is used only in scenes where insufficient performance is evaluated. (see at least [0067] “Based on the conformity assessment, the system may decide whether to use a regular control module of the lane keeping support system or to switch to a fallback control system to control e.g. the wheels 42 of the vehicle. For example, if the predictor reports middle-level uncertainty, a safe fallback functionality may be triggered, such as a lane keeping support algorithm that is less comfortable but highly interpretable and therefore safer to use.”)
The above pieces of prior art are considered analogous as they both represent inventions in the autonomous vehicle control modeling field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Friedrichs to use two control models which coexist separately on the mobile body, and use the patch model used only in scenes where insufficient performance is evaluated, as taught by Kandemir to determine whether to use a standard to fallback model based on determining whether performance is acceptable under current conditions [0067].
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACOB D UNDERBAKKE whose telephone number is (571)272-6657. The examiner can normally be reached Monday-Friday 8:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jelani Smith can be reached at 571-270-3969. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JACOB DANIEL UNDERBAKKE/Examiner, Art Unit 3662
/MAHMOUD S ISMAIL/Primary Examiner, Art Unit 3662
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