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
Claims 1-20 are presented for examination.
Claims 1-20 are rejected.
Claim Rejections - 35 USC § 103
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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) 1-20 is/are rejected under 35 U.S.C. 103 as -being unpatentable over Chang (US Pub. No.: 2012/0215375 A1: hereinafter “Chang”) in view of Dingli et al. (US Pat. No.: 11,673,565 B1: hereinafter “Dingli”).
Consider claims 1, 11:
Chang teaches a method, a driver training system (See Chang, e.g., “…A system for preventing driving skill atrophy comprises a trainer module that determines the driver's current skill level, disables certain automated features based on the determined skill level, and forces the driver to use and hone her driving skills…determine through on-board vehicle sensors how a driver is driving the vehicle…compares the driver's current driving skills with the driver's historical driving skills or the general population's driving skills…determines whether the driver's skill level is stagnant, improving or deteriorating. If the skill level is improving, for example, the system disables certain automated driving features to give driver more control of the vehicle…” of Abstract, ¶ [0005], ¶ [0013], ¶ [0018], ¶ [0020], and Figs. 1-3 elements 100-210, steps 302-322) comprising: memory; and one or more processors that are configured to execute machine readable instructions stored in the memory (Fig. 2 elements 202-210) to: generate one or more initial features corresponding to one or more initial traversed paths of a vehicle (See Chang, e.g., “…receiving information from remote trainer module 108, determining the driver's current skill level based on the received information, and enabling or disabling one or more automated features in vehicle 102 to help improve the driver's skill….disables an automated feature if the trainer module 104 determines that the driver has improved her driving skills…forces the driver to use and hone her own driving skill supported by the disabled driving feature…enables an automated feature if the trainer module 104 determines that the driver's skills have deteriorated…determines that the driver's skills are inadequate to handle a particular driving task without the feature's support…” of ¶ [0018], ¶ [0020]-¶ [0024], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322); based on the one or more initial features and based on a model predicting one or more skill levels associated with the one or more initial features (See Chang, e.g., “…receiving information from remote trainer module 108, determining the driver's current skill level based on the received information, and enabling or disabling one or more automated features in vehicle 102 to help improve the driver's skill…” of ¶ [0018], ¶ [0020]-¶ [0024], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322), applying one or more actuation constraints on the vehicle during one or more subsequent traversed paths (See Chang, e.g., “…The trainer module 104 next analyzes 314 the recorded parameters to determine whether the driver's skill have improved from the initial skill level. If yes, the trainer module 104 determines an altered feature set based on the improved skill level and determines 318 if it's safe to change the enabled feature set for the driver. If not, the trainer module 104 does not change the feature set and repeats steps 312-318. Otherwise, the trainer module 104 changes 320 the enabled feature set to the altered set and communicates 322 the changed feature set and skill level to the driver. The trainer module 104 then repeats steps 312-322...” of ¶ [0018], ¶ [0020]-¶ [0024], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322); generate one or more subsequent features corresponding to the one or more subsequent traversed paths (See Chang, e.g., “…The trainer module 104 next analyzes 314 the recorded parameters to determine whether the driver's skill have improved from the initial skill level. If yes, the trainer module 104 determines an altered feature set based on the improved skill level and determines 318 if it's safe to change the enabled feature set for the driver…If the trainer module 104 determines at step 314 that the driver's skills have not improved, the trainer module 104 determines 316 if the driver's skills have deteriorated. If yes, the trainer module 104 implements steps 318-322 and then repeats steps 312-322. Otherwise, the trainer module repeats steps 312-322...” of ¶ [0018], ¶ [0020]-¶ [0024], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322); evaluate the one or more subsequent features with respect to the one or more initial features (See Chang, e.g., “…determines an altered feature set based on the improved skill level and determines 318 if it's safe to change the enabled feature set for the driver…If the trainer module 104 determines at step 314 that the driver's skills have not improved, the trainer module 104 determines 316 if the driver's skills have deteriorated. If yes, the trainer module 104 implements steps 318-322 and then repeats steps 312-322. Otherwise, the trainer module repeats steps 312-322...” of ¶ [0018], ¶ [0020]-¶ [0024], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322); and based on a result of evaluating the one or more subsequent features, selectively apply one or more updated actuation constraints on the vehicle (See Chang, e.g., “…determines an altered feature set based on the improved skill level and determines 318 if it's safe to change the enabled feature set for the driver…” of ¶ [0018], ¶ [0020]-¶ [0024], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322) during one or more upcoming traversed paths following the one or more subsequent traversed paths (See Chang, e.g., “…The trainer module 104 next analyzes 314 the recorded parameters to determine whether the driver's skill have improved from the initial skill level. If yes, the trainer module 104 determines an altered feature set based on the improved skill level and determines 318 if it's safe to change the enabled feature set for the driver…If the trainer module 104 determines at step 314 that the driver's skills have not improved, the trainer module 104 determines 316 if the driver's skills have deteriorated. If yes, the trainer module 104 implements steps 318-322 and then repeats steps 312-322. Otherwise, the trainer module repeats steps 312-322...” of ¶ [0018], ¶ [0020]-¶ [0024], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322).
Chang teaches, e.g., “…receiving information from remote trainer module 108, determining the driver's current skill level based on the received information, and enabling or disabling one or more automated features in vehicle 102 to help improve the driver's skill….disables an automated feature if the trainer module 104 determines that the driver has improved her driving skills…forces the driver to use and hone her own driving skill supported by the disabled driving feature…enables an automated feature if the trainer module 104 determines that the driver's skills have deteriorated…determines that the driver's skills are inadequate to handle a particular driving task without the feature's support…” of ¶ [0018], ¶ [0020]-¶ [0024], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322. However, Chang does not explicitly teach one or more initial trajectories, one or more subsequent trajectories.
In an analogous filed of endeavor, Dingli teaches one or more initial trajectories (See Dingli, e.g., “…generating, by the first processor 550 of the vehicle computer 500, a first set of commands for a vehicle to follow the first trajectory, and a second set of commands for the vehicle to follow the second trajectory…receiving, by a first processor 550 of a vehicle computer 500, data associated with a first trajectory and data associated with a second trajectory. The first trajectory and the second trajectory are updated a plurality of times in one second…the safety processor 560 of the vehicle computer 500, the second set of commands to the vehicle for the vehicle to follow the second trajectory…” of Col. 10:19-67, Col. 11:1-67, Col. 15:22-67, Col. 16:1-44, and Figs. 4, 7 steps 400-414, 700-714), one or more subsequent trajectories (See Dingli, e.g., “…receiving, by a first processor 550 of a vehicle computer 500, data associated with a first trajectory and data associated with a second trajectory. The first trajectory and the second trajectory are updated a plurality of times in one second…the safety processor 560 of the vehicle computer 500, the second set of commands to the vehicle for the vehicle to follow the second trajectory…” of Col. 10:19-67, Col. 11:1-67, Col. 15:22-67, Col. 16:1-44, and Figs. 4, 7 steps 400-414, 700-714).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine “A system for preventing driving skill atrophy comprises a trainer module that determines the driver's current skill level, disables certain automated features based on the determined skill level, and forces the driver to use and hone her driving skills…” disclosed in Chang with “one or more initial trajectories” taught in Dingli with a reasonable expectation of success to improve safety and significantly reduce the number of fatalities.
Consider claims 2, 12:
The combination of Chang, Dingli teaches everything claimed as implemented above in the rejection of claims 1, 11. In addition, Chang teaches wherein the one or more actuation constraints or the one or more updated actuation constraints correspond to one or more permitted actuation ranges resulting from an actuation input of a driver (See Chang, e.g., “…After changing the feature set, the feature set determination module 206 informs the driver through an audible and/or visual indicator regarding the changed feature set and skill level…” of ¶ [0018], ¶ [0020]-¶ [0024]-¶ [0033], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322).
Consider claims 3, 13:
The combination of Chang, Dingli teaches everything claimed as implemented above in the rejection of claims 1, 11. In addition, Chang teaches wherein selectively applying the one or more updated actuation constraints is based on a rewards function to evaluate the one or more subsequent features with respect to the one or more initial features, or to evaluate one or more actual actuation outputs with respect to the one or more actuation constraints (See Chang, e.g., “…determines an altered feature set based on the improved skill level and determines 318 if it's safe to change the enabled feature set for the driver…” of ¶ [0018], ¶ [0020]-¶ [0024], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine “A system for preventing driving skill atrophy comprises a trainer module that determines the driver's current skill level, disables certain automated features based on the determined skill level, and forces the driver to use and hone her driving skills…” disclosed in Chang with “one or more initial trajectories” taught in Dingli with a reasonable expectation of success to improve safety and significantly reduce the number of fatalities.
Consider claims 4, 14:
The combination of Chang, Dingli teaches everything claimed as implemented above in the rejection of claims 1, 11. In addition, Chang teaches wherein the one or more subsequent traversed paths and the one or more initial traversed paths correspond to a road section, the road section comprising a nonuniform curvature (See Chang, e.g., “…the remote trainer module 108 receives the vehicle parameters from trainer module 104 or another module associated with the vehicle manufacturer. Examples of road parameters include parameters describing average speed of vehicles on the road at various times of the day, and road conditions like wet or snowy road at a particular time, traffic signs, pot holes, curves, lanes, and traffic lights present on the road…” of ¶ [0014]-¶ [0024]-¶ [0033], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322).
Consider claims 5, 15:
The combination of Chang, Dingli teaches everything claimed as implemented above in the rejection of claims 4, 14. In addition, Chang teaches wherein evaluating the one or more subsequent features with respect to the one or more initial trajectories comprises: evaluating a subsequent portion of the one or more subsequent features, the subsequent portion corresponding to a particular portion of the road section having a particular range of curvatures (See Chang, e.g., “…The trainer module 104 next analyzes 314 the recorded parameters to determine whether the driver's skill have improved from the initial skill level. If yes, the trainer module 104 determines an altered feature set based on the improved skill level and determines 318 if it's safe to change the enabled feature set for the driver…If the trainer module 104 determines at step 314 that the driver's skills have not improved, the trainer module 104 determines 316 if the driver's skills have deteriorated. If yes, the trainer module 104 implements steps 318-322 and then repeats steps 312-322. Otherwise, the trainer module repeats steps 312-322...” of ¶ [0018], ¶ [0020]-¶ [0024], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322); and at least one processor of the one or more processors is further configured to execute machine readable instructions stored in the memory to: based on a result of evaluating the subsequent portion, generating a visual or auditory feedback message (See Chang, e.g., “…After changing the feature set, the feature set determination module 206 informs the driver through an audible and/or visual indicator regarding the changed feature set and skill level…” of ¶ [0018], ¶ [0020]-¶ [0024]-¶ [0033], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322); and outputting the visual or auditory feedback message (See Chang, e.g., “…After changing the feature set, the feature set determination module 206 informs the driver through an audible and/or visual indicator regarding the changed feature set and skill level…” of ¶ [0018], ¶ [0020]-¶ [0024]-¶ [0033], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322) when the vehicle is within a threshold distance of the particular portion or a different portion of the road section satisfying the particular range of curvatures (See Chang, e.g., “…the remote trainer module 108 receives the vehicle parameters from trainer module 104 or another module associated with the vehicle manufacturer. Examples of road parameters include parameters describing average speed of vehicles on the road at various times of the day, and road conditions like wet or snowy road at a particular time, traffic signs, pot holes, curves, lanes, and traffic lights present on the road…” of ¶ [0014]-¶ [0024]-¶ [0033], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322). Dingli teaches one or more initial trajectories (See Dingli, e.g., “…generating, by the first processor 550 of the vehicle computer 500, a first set of commands for a vehicle to follow the first trajectory, and a second set of commands for the vehicle to follow the second trajectory…receiving, by a first processor 550 of a vehicle computer 500, data associated with a first trajectory and data associated with a second trajectory. The first trajectory and the second trajectory are updated a plurality of times in one second…the safety processor 560 of the vehicle computer 500, the second set of commands to the vehicle for the vehicle to follow the second trajectory…” of Col. 10:19-67, Col. 11:1-67, Col. 15:22-67, Col. 16:1-44, and Figs. 4, 7 steps 400-414, 700-714), one or more subsequent trajectories (See Dingli, e.g., “…receiving, by a first processor 550 of a vehicle computer 500, data associated with a first trajectory and data associated with a second trajectory. The first trajectory and the second trajectory are updated a plurality of times in one second…the safety processor 560 of the vehicle computer 500, the second set of commands to the vehicle for the vehicle to follow the second trajectory…” of Col. 10:19-67, Col. 11:1-67, Col. 15:22-67, Col. 16:1-44, and Figs. 4, 7 steps 400-414, 700-714).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine “A system for preventing driving skill atrophy comprises a trainer module that determines the driver's current skill level, disables certain automated features based on the determined skill level, and forces the driver to use and hone her driving skills…” disclosed in Chang with “one or more initial trajectories” taught in Dingli with a reasonable expectation of success to improve safety and significantly reduce the number of fatalities.
Consider claims 6, 16:
The combination of Chang, Dingli teaches everything claimed as implemented above in the rejection of claims 5, 15. In addition, Chang teaches wherein the one or more upcoming traversed paths corresponds to the road section (See Chang, e.g., “…The trainer module 104 next analyzes 314 the recorded parameters to determine whether the driver's skill have improved from the initial skill level. If yes, the trainer module 104 determines an altered feature set based on the improved skill level and determines 318 if it's safe to change the enabled feature set for the driver…If the trainer module 104 determines at step 314 that the driver's skills have not improved, the trainer module 104 determines 316 if the driver's skills have deteriorated. If yes, the trainer module 104 implements steps 318-322 and then repeats steps 312-322. Otherwise, the trainer module repeats steps 312-322...” of ¶ [0018], ¶ [0020]-¶ [0024], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322); and selectively applying one or more updated actuation constraints comprises: applying first actuation constraints corresponding to the particular portion (See Chang, e.g., “…The trainer module 104 next analyzes 314 the recorded parameters to determine whether the driver's skill have improved from the initial skill level. If yes, the trainer module 104 determines an altered feature set based on the improved skill level and determines 318 if it's safe to change the enabled feature set for the driver…If the trainer module 104 determines at step 314 that the driver's skills have not improved, the trainer module 104 determines 316 if the driver's skills have deteriorated. If yes, the trainer module 104 implements steps 318-322 and then repeats steps 312-322. Otherwise, the trainer module repeats steps 312-322...” of ¶ [0018], ¶ [0020]-¶ [0024], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322); and applying second actuation constraints corresponding to a different portion from the particular portion, wherein the different portion fails to satisfy the particular range of curvatures (See Chang, e.g., “…After changing the feature set, the feature set determination module 206 informs the driver through an audible and/or visual indicator regarding the changed feature set and skill level…” of ¶ [0018], ¶ [0020]-¶ [0024], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322).
Consider claims 7, 17:
The combination of Chang, Dingli teaches everything claimed as implemented above in the rejection of claims 1, 11. In addition, Chang teaches wherein selectively applying one or more updated actuation constraints comprises: in response to determining that the one or more subsequent trajectories conform more closely to a desired trajectory compared to the one or more initial trajectories, applying the one or more updated actuation constraints, the one or more updated actuation constraints having a larger permitted actuation range for a given range of actuation inputs compared to the one or more actuation constraints (See Chang, e.g., “…the received skill levels are a collection of scores in various categories…the feature set determination module 206 analyzes the scores in different categories and enables or disables features based on this analysis…if the received initial skill levels are low for turning and braking distance, the feature set determination module 206 enables the automated parallel parking feature. A combined score representing the driver's skill level does not provide this advantage as a high score in one particular category may compensate a low score in another category when a combined score is determined…” of ¶ [0018], ¶ [0020]-¶ [0024]-¶ [0033], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322).
Consider claims 8, 18:
The combination of Chang, Dingli teaches everything claimed as implemented above in the rejection of claims 1, 11. In addition, Chang teaches wherein the one or more updated actuation constraints comprise a different set of permitted actuation operations compared to the one or more actuation constraints (See Chang, e.g., “…The trainer module 104 next analyzes 314 the recorded parameters to determine whether the driver's skill have improved from the initial skill level. If yes, the trainer module 104 determines an altered feature set based on the improved skill level and determines 318 if it's safe to change the enabled feature set for the driver…If the trainer module 104 determines at step 314 that the driver's skills have not improved, the trainer module 104 determines 316 if the driver's skills have deteriorated. If yes, the trainer module 104 implements steps 318-322 and then repeats steps 312-322. Otherwise, the trainer module repeats steps 312-322...” of ¶ [0018], ¶ [0020]-¶ [0024], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322).
Consider claims 9, 19:
The combination of Chang, Dingli teaches everything claimed as implemented above in the rejection of claims 1, 11. In addition, Chang teaches wherein the one or more actuation constraints correspond to a first difficulty level and the one or more updated actuation constraints correspond to a second difficulty level (See Chang, e.g., “…The trainer module 104 next analyzes 314 the recorded parameters to determine whether the driver's skill have improved from the initial skill level. If yes, the trainer module 104 determines an altered feature set based on the improved skill level and determines 318 if it's safe to change the enabled feature set for the driver…If the trainer module 104 determines at step 314 that the driver's skills have not improved, the trainer module 104 determines 316 if the driver's skills have deteriorated. If yes, the trainer module 104 implements steps 318-322 and then repeats steps 312-322. Otherwise, the trainer module repeats steps 312-322...” of ¶ [0018], ¶ [0020]-¶ [0024], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322), and selectively applying one or more updated actuation constraints comprises: in response to determining that the one or more subsequent trajectories conform more closely to a desired trajectory compared to the one or more initial trajectories (See Chang, e.g., “…The trainer module 104 next analyzes 314 the recorded parameters to determine whether the driver's skill have improved from the initial skill level. If yes, the trainer module 104 determines an altered feature set based on the improved skill level and determines 318 if it's safe to change the enabled feature set for the driver…If the trainer module 104 determines at step 314 that the driver's skills have not improved, the trainer module 104 determines 316 if the driver's skills have deteriorated. If yes, the trainer module 104 implements steps 318-322 and then repeats steps 312-322. Otherwise, the trainer module repeats steps 312-322...” of ¶ [0018], ¶ [0020]-¶ [0024], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322), applying the one or more updated actuation constraints, wherein the second difficulty level is higher compared to the first difficulty level (See Chang, e.g., “…The trainer module 104 next analyzes 314 the recorded parameters to determine whether the driver's skill have improved from the initial skill level. If yes, the trainer module 104 determines an altered feature set based on the improved skill level and determines 318 if it's safe to change the enabled feature set for the driver…If the trainer module 104 determines at step 314 that the driver's skills have not improved, the trainer module 104 determines 316 if the driver's skills have deteriorated. If yes, the trainer module 104 implements steps 318-322 and then repeats steps 312-322. Otherwise, the trainer module repeats steps 312-322...” of ¶ [0018], ¶ [0020]-¶ [0024], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322).
Consider claims 10, 20:
The combination of Chang, Dingli teaches everything claimed as implemented above in the rejection of claims 1, 11. In addition, Chang teaches wherein the vehicle comprises a first vehicle; the one or more initial features comprise one or more first initial features; and at least one processor of the one or more processors is further configured to execute machine readable instructions stored in the memory to: update the model based on a known skill level mapped to a second initial features of a second vehicle (See Chang, e.g., “…the received skill levels are a collection of scores in various categories…the feature set determination module 206 analyzes the scores in different categories and enables or disables features based on this analysis…if the received initial skill levels are low for turning and braking distance, the feature set determination module 206 enables the automated parallel parking feature. A combined score representing the driver's skill level does not provide this advantage as a high score in one particular category may compensate a low score in another category when a combined score is determined…” of ¶ [0018], ¶ [0020]-¶ [0024]-¶ [0033], ¶ [0037]-¶ [0039], and Figs. 1-3 elements 100-210, steps 302-322). On the other hand, Dingli teaches one or more initial trajectories (See Dingli, e.g., “…generating, by the first processor 550 of the vehicle computer 500, a first set of commands for a vehicle to follow the first trajectory, and a second set of commands for the vehicle to follow the second trajectory…receiving, by a first processor 550 of a vehicle computer 500, data associated with a first trajectory and data associated with a second trajectory. The first trajectory and the second trajectory are updated a plurality of times in one second…the safety processor 560 of the vehicle computer 500, the second set of commands to the vehicle for the vehicle to follow the second trajectory…” of Col. 10:19-67, Col. 11:1-67, Col. 15:22-67, Col. 16:1-44, and Figs. 4, 7 steps 400-414, 700-714), one or more subsequent trajectories (See Dingli, e.g., “…receiving, by a first processor 550 of a vehicle computer 500, data associated with a first trajectory and data associated with a second trajectory. The first trajectory and the second trajectory are updated a plurality of times in one second…the safety processor 560 of the vehicle computer 500, the second set of commands to the vehicle for the vehicle to follow the second trajectory…” of Col. 10:19-67, Col. 11:1-67, Col. 15:22-67, Col. 16:1-44, and Figs. 4, 7 steps 400-414, 700-714).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine “A system for preventing driving skill atrophy comprises a trainer module that determines the driver's current skill level, disables certain automated features based on the determined skill level, and forces the driver to use and hone her driving skills…” disclosed in Chang with “one or more initial trajectories” taught in Dingli with a reasonable expectation of success to improve safety and significantly reduce the number of fatalities.
Obviousness 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 claims at issue 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); and 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 a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of US Patent No. 12,420,808 B2. Although the claims at issue are not identical, they are not patentably distinct from each other, take an example of claims 1, 11 of the instant application and claims 1, 15 of the US Patent No. 12,420,808 B2 (Please see the Table below):
Claims of US Pat. No.: 12,420,808 B2 (hereinafter ‘808)
Claims of Pending Application No.: 19/297,903
Reasoning
1. A driver training system comprising: a driver training circuit to, based on at least one of an inferred skill level or an emotional state of a driver operating a vehicle, dynamically adjust a driver training level for the driver while the driver is operating the vehicle during training, wherein dynamically adjusting a driver training level for the driver comprises: selecting a first driver training level, from a plurality of driver training levels, for the driver based on at least one of the inferred skill level or inferred emotional state of the driver, wherein each of the plurality of driver training levels comprises at least one of: a set of skills or a set of performance benchmarks, wherein the first driver training level applies one or more first actuation or steering ranges resulting from an actuation or a steering input of the driver; and selectively changing to a second driver training level of the plurality of driver training levels, wherein the second driver training level applies one or more second actuation or steering ranges resulting from the actuation or the steering input of the driver, the one or more second actuation or steering ranges being different compared to the one or more first actuation or steering ranges.
15. A method for driver training, comprising: inferring a skill level and emotional state of a driver operating a vehicle; and based on at least one of an inferred skill level and emotional state of the driver operating a vehicle, dynamically adjusting a driver training level for a driver while the driver is operating the vehicle during training, wherein dynamically adjusting a driver training level for the driver comprises: selecting a first driver training level, from a plurality of driver training levels, for the driver based on at least one of the inferred skill level or inferred emotional state of the driver, wherein each of the plurality of driver training levels comprises at least one of: a set of skills or a set of performance benchmarks, wherein the first driver training level applies one or more first actuation or steering ranges resulting from an actuation or a steering input of the driver; and selectively changing to a second driver training level of the plurality of driver training levels, wherein the second driver training level applies one or more second actuation or steering ranges resulting from the actuation or the steering input of the driver, the one or more second actuation or steering ranges being different compared to the one or more first actuation or steering ranges.
1. A driver training system comprising: memory; and one or more processors that are configured to execute machine readable instructions stored in the memory to: generate one or more initial trajectories corresponding to one or more initial traversed paths of a vehicle; based on the one or more initial trajectories and based on a model predicting one or more skill levels associated with the one or more initial trajectories, applying one or more actuation constraints on the vehicle during one or more subsequent traversed paths; generate one or more subsequent trajectories corresponding to the one or more subsequent traversed paths; evaluate the one or more subsequent trajectories with respect to the one or more initial trajectories; and based on a result of evaluating the one or more subsequent trajectories, selectively apply one or more updated actuation constraints on the vehicle during one or more upcoming traversed paths following the one or more subsequent traversed paths.
11. A method for driver training, comprising: generating one or more initial trajectories corresponding to one or more initial traversed paths of a vehicle; based on the one or more initial trajectories and based on a model predicting one or more skill levels associated with the one or more initial trajectories, applying one or more actuation constraints on the vehicle during one or more subsequent traversed paths; generating one or more subsequent trajectories corresponding to the one or more subsequent traversed paths; evaluating the one or more subsequent trajectories with respect to the one or more initial trajectories; and based on a result of evaluating the one or more subsequent trajectories, selectively applying one or more updated actuation constraints on the vehicle during one or more upcoming traversed paths following the one or more subsequent traversed paths.
Claims 1, 15 of ‘808 only differ from the instant application, in that the claims 1, 15 of ‘808 specify “wherein each of the plurality of driver training levels comprises at least one of: a set of skills or a set of performance benchmarks, wherein the first driver training level applies one or more first actuation or steering ranges resulting from an actuation or a steering input of the driver; and selectively changing to a second driver training level of the plurality of driver training levels, wherein the second driver training level applies one or more second actuation or steering ranges resulting from the actuation or the steering input of the driver, the one or more second actuation or steering ranges being different compared to the one or more first actuation or steering ranges.”. Nonetheless, the removal of said limitations from claims 1,11 of the instant application made claims 1, 11 a broader version of claims 1, 15 of ‘808. Therefore, since omission of an element and its function in combination is an obvious expedient if the remaining elements perform the same function as before (In re Karlson (CCPA) 136 USPQ 184 (1963)), claims 1, 11 are not patentably distinct from claims 1, 15 of '808.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Harkness (US Pub. No.: 2016/0163217 A1) teaches “Described herein are a system and method of training a motor vehicle operator in crash-avoidance skills. Critical crash-avoidance skills comprise actively scanning a driving environment to identify, recognize, and remember what is seen; adjusting vehicle speed and position to accommodate road conditions, visibility, and traffic; proactively identifying and responding to potential hazards before the potential hazards become immediate hazards; recognizing, assessing, and responding to driving risks; creating a space cushion in order to identify and maintain possible escape routes; and identifying and minimizing visual, manual, and cognitive distractions. Training comprises at least one interactive driving simulation in which use of technology by a motor vehicle operator impacts performance of the motor vehicle operator. The training comprises computer-based learning tutorials, interactive point-of-view driving simulations, and conjoint experienced driver mentor/inexperienced trainee activities, as well as objective testing of motor vehicle operators on skills and driving knowledge.”
Nemat-Nasser et al. (US Pub. No.: 2014/0210625 A1) teaches “A system for event triggering comprises an interface and a processor. An interface configured to receive a face tracking data and receive a sensor data. The processor configured to determine a degree of drowsiness based at least in part on the face tracking data and the sensor data; in the event that the degree of drowsiness is greater than a first threshold, capture data; and in the event that the degree of drowsiness is greater than a second threshold, provide a warning.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BABAR SARWAR whose telephone number is (571)270-5584. The examiner can normally be reached on Mon-Fri 9:00 AM-5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Faris S. Almatrahi can be reached on (313)446-4821. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BABAR SARWAR/Primary Examiner, Art Unit 3667
Prosecution Insights