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 the Application
This final office action is in response to Applicant’s amendment received by the Office on 17 July 2026.
Claims 1-20 have been presented in the application, of which, claims 2, 4, 6 are cancelled, claims 1, 3, 5, 7-11 are currently amended and claims 12-20 are new. Accordingly, pending claims 1, 3, 5 and 7-20 are addressed herein.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Response to Arguments
Applicant’s arguments on pages 8-14 with respect to the rejection(s) of claim(s) 1-11 under 35 U.S.C. 112(a), 35 U.S.C. 112(b), the rejection of claims 1, 3, 5, 9-11 under 35 U.S.C. 102(a)(1), the rejection of claims 7-8 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made using Kusukame et al. (US 2019/0217864 A1), Minster et al. (US 2017/0267256 A1), McCool et al. (US 2021/0354723 A1 and Zhu et al. (US 2019/0079523 A1).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 5, 7-9 and 11-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kusukame et al. (US 2019/0217864 A1, hereinafter referred to as “Kusukame”).
Regarding claim 1, Kusukame discloses a control apparatus which controls a vehicle, the control apparatus comprising: at least one processor (Figs. 1-6, 13-15 and 17-19, element 2, 220, 402, 403; paragraphs 0104-0105, 0121, 0278, 0285, 0279-0282, 0287, 0295-0297 and 0421) configured to: acquire (Fig. 7, steps S11-S13 and S16-S17 via Figs. 2-6, 13-15 and 19, element 210) multiple pieces of information (Figs. 3-6, 14-15 and 19, element 241) including information acquired from a plurality of types of sensors (Figs. 2-6, 13-15 and 19, elements 110, 111, 112, 113, 120, 121, 130, 180, 190, 511, 512; paragraphs 0054, 0059-0063, 0109-0112, 0116-0117, 0121, 0129-0133, 0138-0145, 0157-0159, 0162-0164, 0172-0191, 0196-0197, 0278, 0285, 0287, 0304-0315 and 0323); predict (Fig. 9, steps S26, S27, S29), using AI (Figs. 2-6, 13-15 and 19, element 220; machine learning, random forest, deep learning) and based on (Fig. 9, step S23) the multiple pieces of information, a danger (dangerous places: locations with puddles, ice, snow) to the vehicle (Figs. 1-6, 13-15, 17, element 1A, 401) during automated driving (paragraphs 0040, 0090, 0092, 0111-0118, 0129-0135, 0189-0190, 0197-0225, 0228, 0244-0246, 0254, 0256, 0275-0282, 0287, 0291-0297 and 0410-0411); and control a speed of the vehicle based on a result of the prediction (paragraph 0090, 0092, 0292, 0294, 0296-0297).
Regarding claim 5, Kusukame discloses a control apparatus which controls a vehicle, the control apparatus comprising: at least one processor (Figs. 1-6, 13-15 and 17-19, element 2, 220, 402, 403; paragraphs 0104-0105, 0121, 0278, 0285, 0279-0282, 0287, 0295-0297 and 0421) configured to: acquire (Fig. 7, steps S11-S13 and S16-S17 via Figs. 2-6, 13-15 and 19, element 210) multiple pieces of information (Figs. 3-6, 14-15 and 19, element 241) including information acquired from a plurality of types of sensors (Figs. 2-6, 13-15 and 19, elements 110, 111, 112, 113, 120, 121, 130, 180, 190, 511, 512; paragraphs 0054, 0059-0063, 0109-0112, 0116-0117, 0121, 0129-0133, 0138-0145, 0157-0159, 0162-0164, 0172-0191, 0196-0197, 0278, 0285, 0287, 0304-0315 and 0323); predict (Fig. 9, steps S26, S27, S29), using AI (Figs. 2-6, 13-15 and 19, element 220; machine learning, random forest, deep learning) and based on (Fig. 9, step S23) multiple pieces of information, a danger (dangerous places: locations with puddles, ice, snow) to the vehicle (Figs. 1-6, 13-15, 17, element 1A, 401) during automated driving, the danger including a condition of a road on which the vehicle travels (paragraphs 0008, 0040, 0069, 0090, 0092, 0110-0118, 0129-0135, 0189-0190, 0197-0225, 0228, 0244-0246, 0254, 0256, 0275-0282, 0287, 0291-0297 and 0410-0411).
Regarding claim 7, Kusukame discloses the control apparatus according to claim 5, wherein the at least one processor is further configured to acquire information of a shape of a road (inclined surface vs. bottom of slope; irregularity measurement via 160; depressions) and information of an amount of sunlight, predict a puddle from rainfall and forecast information (paragraphs 0040, 0130, 0133, 0144-0145, 0229, 0246, 0256, 0292, 0323, 0377, 0351, 0390 and 0409).
Regarding claim 8, Kusukame discloses the control apparatus according to claim 5, wherein the at least one processor is further configured to acquire information of a shape of a road (inclined surface vs. bottom of slope; irregularity measurement via 160; depressions) and information of an amount of sunlight, to predict road icing an snow (paragraphs 0040, 0051, 0130, 0133, 0144-0145, 0205-0206, 0229, 0246, 0256, 0292, 0296, 0323, 0377, 0351, 0390 and 0409).
Regarding claim 9, Kusukame discloses a non-transitory computer readable storage medium storing a program for causing a computer, including at least one processor), to function as a control apparatus which controls a vehicle, the at least one processor (Figs. 1-6, 13-15 and 17-19, element 2, 220, 402, 403; paragraphs 0104-0105, 0121, 0278, 0285, 0278-0282, 0287, 0295-0297 and 0421) configured to: acquire (Fig. 7, steps S11-S13 and S16-S17 via Figs. 2-6, 13-15 and 19, element 210) multiple pieces of information (Figs. 3-6, 14-15 and 19, element 241) including information acquired from a plurality of types of sensors (Figs. 2-6, 13-15 and 19, elements 110, 111, 112, 113, 120, 121, 130, 180, 190, 511, 512; paragraphs 0054, 0059-0063, 0109-0112, 0116-0117, 0121, 0129-0133, 0138-0145, 0157-0159, 0162-0164, 0172-0191, 0196-0197, 0278, 0285, 0287, 0304-0315 and 0323); predict (Fig. 9, steps S26, S27, S29), using AI (Figs. 2-6, 13-15 and 19, element 220; machine learning, random forest, deep learning) and based on (Fig. 9, step S23) the multiple pieces of information, a danger (dangerous places: locations with puddles, ice, snow) to the vehicle (Figs. 1-6, 13-15, 17, element 1A, 401) during automated driving (paragraphs 0008, 0040, 0069, 0090, 0092, 0110-0118, 0129-0135, 0189-0190, 0197-0225, 0228, 0244-0246, 0254, 0256, 0275-0282, 0287, 0291-0297 and 0410-0411); and control a speed of the vehicle based on a result of the prediction (paragraph 0090, 0092, 0292, 0294, 0296-0297).
Regarding claim 11, Kusukame discloses a non-transitory computer readable storage medium storing a program for causing a computer, including at least one processor (Figs. 1-6, 13-15 and 17-19, element 2, 220, 402, 403), to function as a control apparatus which controls a vehicle (Figs. 1-6, 13-15, 17, element 1A, 401; paragraphs 0104-0105, 0121, 0278, 0285, 0279-0282, 0287, 0295-0297 and 0421), the at least one processor configured to: acquire (Fig. 7, steps S11-S13 and S16-S17 via Figs. 2-6, 13-15 and 19, element 210) multiple pieces of information (Figs. 3-6, 14-15 and 19, element 241) including information acquired from a plurality of types of sensors (Figs. 2-6, 13-15 and 19, elements 110, 111, 112, 113, 120, 121, 130, 180, 190, 511, 512; paragraphs 0054, 0059-0063, 0109-0112, 0116-0117, 0121, 0129-0133, 0138-0145, 0157-0159, 0162-0164, 0172-0191, 0196-0197, 0278, 0285, 0287, 0304-0315 and 0323); and predict (Fig. 9, steps S26, S27, S29), using AI (Figs. 2-6, 13-15 and 19, element 220; machine learning, random forest, deep learning) and based on (Fig. 9, step S23) multiple pieces of information, a danger (dangerous places: locations with puddles, ice, snow) to the vehicle (Figs. 1-6, 13-15, 17, element 1A, 401) during automated driving, the danger including a condition of a road on which the vehicle travels (paragraphs 0008, 0040, 0069, 0090, 0092, 0110-0118, 0129-0135, 0189-0190, 0197-0225, 0228, 0244-0246, 0254, 0256, 0275-0282, 0287, 0291-0297 and 0410-0411).
Regarding claim 12, Kusukame discloses the control apparatus according to claim 1, wherein the multiple pieces of information include information of a shape of a road (inclined surface vs. bottom of slope; irregularity measurement via 160; depressions), information of an amount of sunlight and weather forecast information (paragraphs 0040, 0130, 0133, 0144-0145, 0229, 0246, 0256, 0292, 0323, 0351, 0377, 0390 and 0409).
Regarding claim 13, Kusukame discloses the control apparatus according to claim 12, wherein the at least one processor is configured to predict, using AI and based on the multiple pieces of information, a puddle when rain is forecast (paragraphs 0040, 0229, 0256, 0292 and 0351).
Regarding claim 14, Kusukame discloses the control apparatus according to claim 12, wherein the at least one processor is configured to predict, using AI and based on the multiple pieces of information, road icing when snow is forecast (paragraphs 0040, 0051, 0133, 0205-0206, 0256, 0292, 0296 and 0351).
Regarding claim 15, Kusukame discloses the control apparatus according to claim 13, wherein the at least one processor is configured to control the vehicle to avoid the predicted puddle (paragraphs 0085, 0258, 0292-0293).
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.
Claim(s) 3 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kusukame in view of Minster et al. (US 2017/0267256 A1, hereinafter referred to as “Minster”).
Regarding claim 3, Kusukame teaches a control apparatus which controls a vehicle, the control apparatus comprising: at least one processor (Figs. 1-6, 13-15 and 17-19, element 2, 220, 402, 403; paragraphs 0104-0105, 0121, 0278, 0285, 0279-0282, 0287, 0295-0297 and 0421) configured to: acquire (Fig. 7, steps S11-S13 and S16-S17 via Figs. 2-6, 13-15 and 19, element 210) multiple pieces of information (Figs. 3-6, 14-15 and 19, element 241) including information acquired from a plurality of types of sensors (Figs. 2-6, 13-15 and 19, elements 110, 111, 112, 113, 120, 121, 130, 180, 190, 511, 512; paragraphs 0054, 0059-0063, 0109-0112, 0116-0117, 0121, 0129-0133, 0138-0145, 0157-0159, 0162-0164, 0172-0191, 0196-0197, 0278, 0285, 0287, 0304-0315 and 0323); and […]; predict (Fig. 9, steps S26, S27, S29), using AI (Figs. 2-6, 13-15 and 19, element 220; machine learning, random forest, deep learning) and based on (Fig. 9, step S23) the multiple pieces of information, a danger (dangerous places: locations with puddles, ice, snow) to the vehicle (Figs. 1-6, 13-15, 17, element 1A, 401) during automated driving (paragraphs 0008, 0040, 0069, 0090, 0092, 0110-0118, 0129-0135, 0189-0190, 0197-0225, 0228, 0244-0246, 0254, 0256, 0275-0282, 0287, 0291-0297 and 0410-0411); and control a speed of the vehicle […] based on a result of the prediction (paragraphs 0090, 0092, 0292, 0294 and 0296-0297). Kusukame is silent regarding the at least one processor being configured to “acquire a preference of an occupant of the vehicle” and control a speed of the vehicle “according to the preference of the occupant.”
Minster teaches one or more processors configured to receive (Fig. 3, step S210 via Figs. 1 and 2A, element 120) passenger driving behavior preferences describing how the passenger would prefer the autonomous vehicle to drive (paragraphs 0017, 0020, 0024-0025, 0039, 0042, 0048-0050), including max speed and preferred average speed (paragraph 0044). A comfort-to-sport slider is converted to a velocity set (Fig. 2A; paragraphs 0016-0018, 0020, 0041-0042, 0066). Throttle and brake interfaces implement that velocity (paragraphs 00016, 0066). Minster identifies the problem that autonomous vehicles lack adjustment of driving style based on passenger preference (paragraph 0004). It would have been obvious to a person having ordinary skill in the art prior to Applicant’s effective filing date to apply the well-known technique of receiving a passenger driving-behavior preference and setting vehicle speed from that preference taught by Minster to the prior art control apparatus taught by Kusukame. Both use an onboard processor to command speed of an autonomous vehicle. Kusukame’s predicted-danger is a ceiling. Minster’s occupant preference is a further bound within that ceiling.
Regarding claim 10, Kusukame teaches a non-transitory computer readable storage medium storing a program for causing a computer, including at least one processor (Figs. 1-6, 13-15 and 17-19, element 2, 220, 402, 403), to function as a control apparatus which controls a vehicle (Figs. 1-6, 13-15, 17, element 1A, 401; paragraphs 0104-0105, 0121, 0278, 0285, 0279-0282, 0287, 0295-0297 and 0421), the at least one processor configured to: acquire (Fig. 7, steps S11-S13 and S16-S17 via Figs. 2-6, 13-15 and 19, element 210) multiple pieces of information (Figs. 3-6, 14-15 and 19, element 241) including information acquired from a plurality of types of sensors (Figs. 2-6, 13-15 and 19, elements 110, 111, 112, 113, 120, 121, 130, 180, 190, 511, 512; paragraphs 0054, 0059-0063, 0109-0112, 0116-0117, 0121, 0129-0133, 0138-0145, 0157-0159, 0162-0164, 0172-0191, 0196-0197, 0278, 0285, 0287, 0304-0315 and 0323); […]; predict (Fig. 9, steps S26, S27, S29), using AI (Figs. 2-6, 13-15 and 19, element 220; machine learning, random forest, deep learning) and based on (Fig. 9, step S23) the multiple pieces of information, a danger (dangerous places: locations with puddles, ice, snow) to the vehicle (Figs. 1-6, 13-15, 17, element 1A, 401) during automated driving (paragraphs 0008, 0040, 0069, 0090, 0092, 0110-0118, 0129-0135, 0189-0190, 0197-0225, 0228, 0244-0246, 0254, 0256, 0275-0282, 0287, 0291-0297 and 0410-0411); and control a speed of the vehicle […] based on a result of the prediction (paragraph 0090, 0092, 0292, 0294 and 0296-0297). Kusukame is silent regarding the at least one processor being configured to “acquire a preference of an occupant of the vehicle” and control a speed of the vehicle “according to the preference of the occupant.”
Minster teaches a medium and one or more processors configured to receive (Fig. 3, step S210 via Figs. 1 and 2A, element 120) passenger driving behavior preferences describing how the passenger would prefer the autonomous vehicle to drive (paragraphs 0017, 0020, 0024-0025, 0039, 0042, 0048-0050), including max speed and preferred average speed (Fig. 2A; paragraph 0042). A comfort-to-sport slider is converted to a velocity set (paragraphs 0016-0018, 0020, 0066). Throttle and brake implement that velocity. Minster identifies the problem that autonomous vehicles lack adjustment of driving style based on passenger preference (paragraph 0004). It would have been obvious to a person having ordinary skill in the art prior to Applicant’s effective filing date to apply the well-known technique of receiving a passenger driving-behavior preference and setting vehicle speed from that preference taught by Minster to the prior art medium taught by Kusukame. Both use an onboard processor to command speed of an autonomous vehicle. Kusukame’s predicted-danger is a ceiling. Minster’s occupant preference is a further bound within that ceiling.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kusukame as applied to claim 13 above, and further in view of McCool et al. (US 2021/0354723 A1, hereinafter referred to as “McCool”).
Regarding claim 16, Kusukame teaches the control apparatus according to claim 13, wherein the at least one processor is configured to adjust a driving speed of the vehicle […] (paragraphs 0204, 0208, 0296-0297, 0310, 0315). Kusukame is silent regarding the at least one processor being configured to adjust a driving speed of the vehicle “according to an extent or size of the predicted puddle.”
McCool teaches a classifier that determines puddle severity corresponding to a depth, and estimates puddle area and volume of water (paragraphs 0013, 0015, 0057, 0065-0066, 0071, 0082). Under BRI: depth, area, and volume are an extent or size of the puddle. For lower-severity puddles, computing devices 110 “control the vehicle in order to slow down prior to and/or while proceeding through the puddle” (paragraph 0073). Detecting 3-5 mm of water at higher speeds “could warrant reducing speed” (paragraph 0081). It would have been obvious to a person having ordinary skill in the art prior to Applicant’s effective filing date to apply the well-known technique of estimating puddle depth or size and reducing vehicle speed according to that depth or size as taught by McCool to the prior art control apparatus taught by Kusukame. Both Kusukame and McCool use onboard processors to control an autonomous vehicle around standing water. Kusukame predicts a puddle when rain is forecast and sets a speed from that prediction. Applying McCool’s size to speed control technique to Kusukame’s predicted puddle yields speed adjustment according to extent or size.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kusukame as applied to claim 1 above, and further in view of Zhu et al. (US 2019/0079523 A1, hereinafter referred to as “Zhu”).
Regarding claim 17, Kusukame teaches the control apparatus according to claim 1, wherein the at least one processor controls the speed of the vehicle […] (paragraphs 0296-0297). Kusukame is silent regarding the at least on processor controlling the speed of the vehicle using “a plurality of speed control curves corresponding respectively to acceleration, stopping and curve travel of the vehicle.”
Zhu teaches a speed state machine with states “speed up, or slow down” and a station-time graph as the speed profile (paragraphs 0062, 0069). Under BRI, the speed-up state and the speed-cost acceleration term (speed’) are a speed control curve for acceleration (paragraphs 0069, 0071). Station-time curve 701 includes barrier 705 corresponding to a decision to “stop” the AV for a red light or pedestrian ahead (Figs. 7A-7B; paragraphs 0076-0077). That stop region is a speed control curve for stopping. Perception includes “straight or curve lanes,” and the path cost includes a curvature term used with the speed profile to command throttle, brake, and steering (paragraphs 0034, 0047, 0052, 0070). The curvature constrained speed command is a speed control curve for curve travel. It would have been obvious to a person having ordinary skill in the art prior to Applicant’s effective filing date to apply the well-known technique of controlling vehicle speed with a station time speed profile and a curvature-constrained path profile taught by Zhu to the prior art control apparatus taught by Kusukame. Both Kusukame and Zhu use an onboard processor to command speed of an autonomous vehicle. Zhu teaches the missing acceleration, stopping and curve travel speed curves as the control law that implements the speed limit.
Allowable Subject Matter
Claims 18-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gawande et al. (US 2020/0148213 A1) and Zhou et al. (US 2021/0383269 A1) anticipate claims 1, 5, 9 and 11.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/Dale Moyer/Primary Examiner, Art Unit 3656
