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 .
Claim Status
Claims 1-7 are pending in the application and have been examined.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-7 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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) 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over Nakagawa et al. (US 2018/0201238 A1) hereinafter Nakagawa and Boston et al. (US 2019/0023208 A1) hereinafter Boston.
Claim 1:
Nakagawa discloses a braking control device for a vehicle which is applied to a braking device to apply a braking force to a wheel of the vehicle [Fig. 1; Items 40, 41; ¶17], the braking control device comprising: an information acquisition unit that acquires vehicle outside-condition information that is imaging information regarding a situation outside the vehicle [Items 21, 22; ¶¶20-22]; and a setting unit that sets responsivity of a braking operation, in preparation for the braking operation [¶34].
Nakagawa doesn’t explicitly disclose according to an indicator output from a learning apparatus by inputting the vehicle outside-condition information acquired by the information acquisition unit to the learning apparatus that has performed machine learning for estimating a probability of occurrence of the braking operation for applying a braking force to the wheel in the braking device based on imaging information regarding a situation outside the vehicle.
However, Boston does disclose according to an indicator output from a learning apparatus by inputting the
Further, Nakagawa discloses the vehicle outside-condition information [¶22].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the braking control of Nakagawa with the machine learning of Boston to improve response time and accuracy of potential collision events based on image data.
Claim 2:
Nakagawa and Boston, as shown in the rejection above, disclose all the limitations of claim 1.
Nakagawa doesn’t explicitly disclose wherein the learning apparatus performs machine learning based on the vehicle outside-condition information acquired when the braking operation is performed.
However, Boston foes disclose wherein the learning apparatus performs machine learning based on the
Further Nakagawa discloses the vehicle outside-condition information [¶22].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the braking control of Nakagawa with the machine learning based probability of Boston to improve response time and accuracy of potential collision events based on image data.
Claim 3:
Nakagawa and Boston, as shown in the rejection above, disclose all the limitations of claim 1.
Nakagawa doesn’t explicitly disclose wherein the setting unit causes the braking device to prepare for the braking operation when the degree of probability of the braking operation indicated by the indicator is greater than or equal to a determination value.
However, Boston foes disclose wherein the setting unit causes the braking device to prepare for the braking operation when the degree of probability of the braking operation indicated by the indicator is greater than or equal to a determination value. [¶44; Fig. 7, Steps 706-714]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the braking control of Nakagawa with the machine learning based probability of Boston to improve response time and accuracy of potential collision events based on image data.
Claim 4:
Nakagawa and Boston, as shown in the rejection above, disclose all the limitations of claim 1.
Nakagawa doesn’t explicitly disclose wherein the setting unit causes the braking device to prepare for the braking operation when the degree of probability of the braking operation indicated by the indicator is greater than or equal to a determination value.
However, Boston foes disclose wherein the setting unit causes the braking device to prepare for the braking operation when the degree of probability of the braking operation indicated by the indicator is greater than or equal to a determination value. [¶44; Fig. 7, Steps 706-714]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the braking control of Nakagawa with the machine learning based probability of Boston to improve response time and accuracy of potential collision events based on image data.
Claim 5:
Nakagawa and Boston, as shown in the rejection above, disclose all the limitations of claim 1.
Nakagawa doesn’t explicitly disclose wherein the setting unit sets the responsivity of the braking operation so that the responsivity of the braking operation increases as the indicator output indicates the probability of occurrence of the braking operation increases.
However, Boston foes disclose wherein the setting unit sets the responsivity of the braking operation so that the responsivity of the braking operation increases as the indicator output indicates the probability of occurrence of the braking operation increases. [¶44; Fig. 7, Steps 706-714; a moderate risk precharges the brakes and a high risk actuates them which is eliminating all lag thus maximizing responsivity]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the braking control of Nakagawa with the machine learning based probability of Boston to improve response time and accuracy of potential collision events based on image data.
Claim 6:
Nakagawa and Boston, as shown in the rejection above, disclose all the limitations of claim 1.
Nakagawa doesn’t explicitly disclose wherein the setting unit sets the responsivity of the braking operation so that an operation amount of the braking device required from a generation of a braking request to a braking application of the braking force to the wheel reduces as the indicator output indicates the probability of occurrence of the braking operation increases.
However, Boston foes disclose wherein the setting unit sets the responsivity of the braking operation so that an operation amount of the braking device required from a generation of a braking request to a braking application of the braking force to the wheel reduces as the indicator output indicates the probability of occurrence of the braking operation increases. [¶44; Fig. 7, Steps 706-714; a moderate risk precharges the brakes and a high risk actuates them which is eliminating all lag thus maximizing responsivity]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the braking control of Nakagawa with the machine learning based probability of Boston to improve response time and accuracy of potential collision events based on image data.
Claim 7:
Nakagawa and Boston, as shown in the rejection above, disclose all the limitations of claim 1.
Nakagawa doesn’t explicitly disclose wherein the setting unit set the responsivity of the braking operation so that an operation speed of the braking device increase as the indicator output indicates the probability of occurrence of the braking operation increases.
However, Boston foes disclose wherein the setting unit set the responsivity of the braking operation so that an operation speed of the braking device increase as the indicator output indicates the probability of occurrence of the braking operation increases. [¶44; Fig. 7, Steps 706-714; a moderate risk precharges the brakes and a high risk actuates them which is eliminating all lag thus maximizing responsivity]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the braking control of Nakagawa with the machine learning based probability of Boston to improve response time and accuracy of potential collision events based on image data.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KURT P LIETHEN whose telephone number is (313)446-6596. The examiner can normally be reached Mon - Fri, 8 AM - 4 PM.
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KURT P. LIETHEN
Primary Examiner
Art Unit 3747
/KURT PHILIP LIETHEN/Primary Examiner, Art Unit 3747