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
Applicant filed remarks on 04/06/2026. Claims 1-15 are presently pending examination.
Response to Arguments
Regarding the claim rejections under 35 USC 102/103: Applicant’s arguments, see remarks pages 7-16, filed on 04/06/2026, with respect to claim rejections under 35 USC 102/103 have been fully considered and are persuasive. The claim rejections under 35 USC 102/103 of claims 1-15 have been withdrawn. Upon further review and examination consideration of claims 1-15, a nonstatutory double patenting rejection has been issued for claims 1-15 as being unpatentable over claims 1-15 of copending Application No. 18/731,115.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claim 1-15 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-15 of copending Application No. 18/731,115 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because there were only a couple of differences in the independents claims. The Provisional application: claims contain the following differences from the current application:
Allowed Application No. 18/731,115
Pending Application No. 18/759,879
1. (Previously Presented) A computer-implemented method of providing driver assistance alerts to a driver, the method including: receiving environmental data for a sequence of driving states including at least video from a camera, returns from an optical sensor, and location data from a GNSS receiver, wherein the camera, the optical sensor, and the GNSS receiver are coupled to a processor carried by a vehicle; processing the environmental data as input to a transformer-based end-to-end neural network, wherein the end-to-end neural network is trained to generate prescriptive steering and speed control actions in response to a present driving state; analyzing hidden layer data and output data from the end-to-end neural network to estimate collision avoidance data, wherein the collision avoidance data includes, at least: one or more detected objects within the video from the camera, extracted from attention weights of the hidden layer data to generate an attention map, a directional cue, wherein the directional cue is a projection overlay based on the prescriptive steering control actions onto a heads up display, and a risk metric that quantifies a dissimilarity between the generated prescriptive steering and speed control actions, and received driver steering and speed control actions; and presenting, to the driver, a user interface including driver assistance alerts based on the collision avoidance data.
1. (Original) A computer-implemented method of providing driver assistance alerts to a driver, the method including: receiving environmental data for a sequence of driving states including at least video from a camera, returns from an optical sensor, and location data from a GNSS receiver, wherein the camera, the optical sensor, and the GNSS receiver are coupled to a processor carried by a vehicle; processing the environmental data as input to an end-to-end neural network, wherein the end-to- end neural network is trained to generate prescriptive steering and speed control actions in response to a present driving state; analyzing hidden layer data and output data from the end-to-end neural network to estimate collision avoidance data, wherein the collision avoidance data includes, at least: one or more detected objects within the video from the camera, a directional cue, wherein the directional cue is a projection overlay based on the prescriptive steering control actions onto a heads up display, and a risk metric that quantifies a dissimilarity between the generated prescriptive steering and speed control actions, and received driver steering and speed control actions; and presenting, to the driver, a user interface including driver assistance alerts based on the collision avoidance data.
2. (Original) The computer-implemented method of claim 1, wherein the directional cue projected onto the heads up display within the user interface is a dynamic whisker arrow indicating a prescriptive vehicle orientation relative to a current vehicle orientation based on the generated prescriptive steering control actions.
2. (Original) The computer-implemented method of claim 1, wherein the directional cue projected onto the heads up display within the user interface is a dynamic whisker arrow indicating a prescriptive vehicle orientation relative to a current vehicle orientation based on the generated prescriptive steering control actions.
3. (Original) The computer-implemented method of claim 1, wherein obtaining the risk metric further includes: calculating a cross entropy between the generated prescriptive steering and speed control actions and current driver steering and speed control actions and standardizing the cross entropy calculation to generate a risk metric output, wherein the risk metric output is a proxy for an imminent collision risk and increases proportionally as the current driver steering and speed control actions deviate further from the generated prescriptive steering and speed control actions.
3. (Original) The computer-implemented method of claim 1, wherein obtaining the risk metric further includes: calculating a cross entropy between the generated prescriptive steering and speed control actions and current driver steering and speed control actions and standardizing the cross entropy calculation to generate a risk metric output, wherein the risk metric output is a proxy for an imminent collision risk and increases proportionally as the current driver steering and speed control actions deviate further from the generated prescriptive steering and speed control actions.
4. (Original) The computer-implemented method of claim 3, further including, in response to the risk metric output: maintaining a manual driving mode while the risk metric output is less than a pre-determined threshold value, wherein the manual driving mode includes permitting the vehicle to apply the current driver steering and speed control input actions, and engaging in an autonomous driving mode when the risk metric output is equal to or greater than the pre-determined threshold value, wherein the autonomous driving mode includes causing the vehicle to apply the prescriptive steering and speed control input actions.
4. (Original) The computer-implemented method of claim 3, further including, in response to the risk metric output: maintaining a manual driving mode while the risk metric output is less than a pre-determined threshold value, wherein the manual driving mode includes permitting the vehicle to apply the current driver steering and speed control input actions, and engaging in an autonomous driving mode when the risk metric output is equal to or greater than the pre-determined threshold value, wherein the autonomous driving mode includes causing the vehicle to apply the prescriptive steering and speed control input actions.
5. (Original) The computer-implemented method of claim 3, further including categorizing the risk metric into risk levels by defining a particular risk level as including risk metric outputs within a pre-determined range between a lower boundary value and an upper boundary value.
5. (Original) The computer-implemented method of claim 3, further including categorizing the risk metric into risk levels by defining a particular risk level as including risk metric outputs within a pre-determined range between a lower boundary value and an upper boundary value.
6. (Original) The computer-implemented method of claim 5, wherein the user interface presents, to the driver, a quantitative risk including the risk metric output or a categorical risk including the risk level based on the risk metric value.
6. (Original) The computer-implemented method of claim 5, wherein the user interface presents, to the driver, a quantitative risk including the risk metric output or a categorical risk including the risk level based on the risk metric value.
7. (Original) The computer-implemented method of claim 1, wherein the driver assistance alerts include one or more of a visual display, an audio signal, or a haptic signal.
7. (Original) The computer-implemented method of claim 1, wherein the driver assistance alerts include one or more of a visual display, an audio signal, or a haptic signal.
8. (Original) The computer-implemented method of claim 1, wherein the user interface further presents the generated prescriptive steering and speed control actions to the driver.
8. (Original) The computer-implemented method of claim 1, wherein the user interface further presents the generated prescriptive steering and speed control actions to the driver.
9. (Original) The computer-implemented method of claim 1, wherein the environmental data is pre-processed prior to being provided to the end-to-end neural network, the pre-processing further including: tokenizing the environmental data for the present driving state to generate environmental data tokens, mapping the environmental data tokens to a reduced dimensional vector space to produce environmental data embeddings, and adding the environmental data embeddings to positional embeddings to generate input embeddings for the end-to-end neural network, wherein the positional embeddings preserve spatial information for the environmental data tokens.
9. (Original) The computer-implemented method of claim 1, wherein the environmental data is pre-processed prior to being provided to the end-to-end neural network, the pre-processing further including: tokenizing the environmental data for the present driving state to generate environmental data tokens, mapping the environmental data tokens to a reduced dimensional vector space to produce environmental data embeddings, and adding the environmental data embeddings to positional embeddings to generate input embeddings for the end-to-end neural network, wherein the positional embeddings preserve spatial information for the environmental data tokens.
10. (Previously Presented) The computer-implemented method of claim 9, wherein the transformer-based end-to-end neural network is a transformer model trained for end-to-end autonomous driving, and the transformer model processing the environmental data further includes: processing the generated input embeddings combined with compressed embeddings from nine or more earlier driving states over at least three seconds and generating, as output, a compressed embedding for the present driving state and prescriptive steering and speed control actions in response to the present driving state.
10. (Original) The computer-implemented method of claim 9, wherein the end-to-end neural network is a transformer model trained for end-to-end autonomous driving, and the transformer model processing the environmental data further includes: processing the generated input embeddings combined with compressed embeddings from nine or more earlier driving states over at least three seconds and generating, as output, a compressed embedding for the present driving state and prescriptive steering and speed control actions in response to the present driving state.
11. (Original) The computer-implemented method of claim 10, further including extracting a set of attention weights from the transformer model, and generating, using the positional embeddings, an attention map including a projection of the extracted attention weights, wherein: a magnitude of a particular attention weight increases proportionally to an importance of the particular attention weight in generating the prescriptive steering and speed actions, and an object is implicitly detected within a region of an area of real space surrounding the vehicle based on (i) a comparison of an average attention weight value within the region and another average attention weight value within one or more adjacent regions and (ii) the positional embeddings.
11. (Original) The computer-implemented method of claim 10, further including extracting a set of attention weights from the transformer model, and generating, using the positional embeddings, an attention map including a projection of the extracted attention weights, wherein: a magnitude of a particular attention weight increases proportionally to an importance of the particular attention weight in generating the prescriptive steering and speed actions, and an object is implicitly detected within a region of an area of real space surrounding the vehicle based on (i) a comparison of an average attention weight value within the region and another average attention weight value within one or more adjacent regions and (ii) the positional embeddings.
12. (Original) The computer-implemented method of claim 11, wherein presenting, via the user interface, the one or more detected objects within the heads up display further includes color- coding attention weights within the attention map enabling visual identification of implicitly detected objects and projecting an overlay of the color-coded attention map onto a heads up display.
12. (Original) The computer-implemented method of claim 11, wherein presenting, via the user interface, the one or more detected objects within the heads up display further includes color- coding attention weights within the attention map enabling visual identification of implicitly detected objects and projecting an overlay of the color-coded attention map onto a heads up display.
13. (Original) The computer-implemented method of claim 1, further including storing a history of the video from the camera and the driver assistance alerts presented to the driver within a driving database, wherein the driving database is available for additional data analysis and data auditing after a driving activity is completed.
13. (Original) The computer-implemented method of claim 1, further including storing a history of the video from the camera and the driver assistance alerts presented to the driver within a driving database, wherein the driving database is available for additional data analysis and data auditing after a driving activity is completed.
14. (Previously Presented) A computer-implemented method of training a neural network to generate driver assistance alert data, the method including: receiving environmental data for a sequence of driving states resulting from human driving, including at least video from a camera, returns from an optical sensor, and location data from a GNSS receiver, wherein the camera, the optical sensor, and the GNSS receiver are coupled to a processor carried by a vehicle; and processing the environmental data as input to imitation training of a transformer-based end-to- end neural network, including training the end-to-end neural network to generate prescriptive steering and speed control actions; whereby attention weights of the trained, transformer-based end-to-end neural network are available to be extracted from hidden layer data and to indicate areas of the video that contribute most significantly to the generated prescriptive steering and speed control actions.
14. (Original) A computer-implemented method of training a neural network to generate driver assistance alert data, the method including: receiving environmental data for a sequence of driving states resulting from human driving, including at least video from a camera, returns from an optical sensor, and location data from a GNSS receiver, wherein the camera, the optical sensor, and the GNSS receiver are coupled to a processor carried by a vehicle; processing the environmental data as input to imitation training of an end-to-end neural network, including training the end-to-end neural network to generate prescriptive steering and speed control actions in response to a present driving state; wherein the training includes analyzing hidden layer data and output data from the end-to-end neural network to estimate collision avoidance data, wherein the collision avoidance data includes, at least: a directional cue, whereby the directional cue can be projected as a prescriptive steering control action onto a heads up display, and a speed control cue, whereby the speed control cue can be projected as a prescriptive speed control action onto a heads up display; whereby attention weights of the end-to-end neural network in the hidden layer data indicate areas of the video from the camera that contribute most significantly to the generated prescriptive steering and speed control actions.
15. (Original) The method of claim 14, further including configuring a system including the end- to-end neural network and further including a risk metric generator, including: training parameters of the risk metric generator to generate a normalized risk metric that quantifies a dissimilarity between the generated prescriptive steering and speed control actions and received driver steering and speed control actions that vary from the generated prescriptive steering and speed control actions, whereby the normalized risk metric onto a heads up display.
15. (Original) The method of claim 14, further including configuring a system including the end- to-end neural network and further including a risk metric generator, including: training parameters of the risk metric generator to generate a normalized risk metric that quantifies a dissimilarity between the generated prescriptive steering and speed control actions and received driver steering and speed control actions that vary from the generated prescriptive steering and speed control actions, whereby the normalized risk metric onto a heads up display.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
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/A.A./Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
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