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 .
Applicant’s amendment has necessitated the withdrawal of the previous 101 rejection. While the applicant filed a terminal disclaimer to obviate the double patenting rejection, the examiner maintains the double patenting rejection because the office has not approved the terminal disclaimer.
Double Patenting
1. 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 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.
The USPTO Internet website contains terminal disclaimer forms which may be used.
Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in
which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or
PTO/AIA /26) 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 www.uspto.gov/patents/apply/applying-online/eterminal-disclamer
2. Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable
over claims 1-21 of U.S. Patent No. 12/169,787. Although the claims at issue are not identical,
they are not patentably distinct from each other because they recite substantially the same
limitations, with minor variations, that would have been obvious to one of ordinary skill in the art.
Claim Rejections - 35 USC § 103
3. 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.
4. Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Herman (US
PUB: 2020/0257308) in view of Konrardy (US PUB: 2022/0005291).
Re claim 1. Herman discloses a method comprising: determining, by a computing device, an
expected behavior of an autonomous system in a vehicle (see paras 0042-0045, 0068);
determining, an observed behavior of the autonomous system during an accident (see paras
0005). Herman does not explicitly disclose determining, by the computing device and via the machine learning, a fault proportion between a human driver of the vehicle and the autonomous system based on a difference between the expected behavior and the observed behavior of the autonomous system. However, Konrardy discloses determining, by the computing device and via the machine learning, a fault proportion between a human driver of the vehicle and the autonomous system based on a difference between the expected behavior and the observed behavior of the autonomous system (0060) and wherein the machine learning is trained based on one or more autonomous features (see paras 0126, 0127, 0131 and 0156). Thus it would have been obvious to one of ordinary skill in the art to incorporate the fault proportion determining feature and the machine learning training feature of Konrardy in the system of Herman to determine the insurance coverage level.
Re claim 2. Herman discloses the method of claim 1, further comprising: determining, by the
computing device and via machine learning, an expected behavior of a human driver of the
vehicle; determining an observed behavior of the human driver during the accident, wherein the
determining the fault proportion between the human driver of the vehicle and the autonomous
system is further based on a difference between the expected behavior and the observed behavior
of the human driver (see paras 0005, 0012, 0045). Herman does not explicitly disclose wherein the machine learning is further trained based on historical insurance data. However, Konrardy makes this disclosure (see paras 0126, 0127, 0131 and 0156). Thus it would have been obvious to one of ordinary skill in the art to incorporate the machine learning training feature of Konrardy in the system of Herman to determine the insurance coverage level.
Re claim 3. Herman does not explicitly disclose the method of claim 2, further comprising:
determining a past driving behavior of the human driver, including a mobile phone use or a route
pattern driven, wherein the determining the expected behavior of the human driver is based on
the past driving behavior of the human driver as well as behaviors of other drivers in similar
situations. However, Konrardy makes this disclosure (see paras 0143 and 0237). Thus it would
have been obvious to one of ordinary skill in the art to incorporate the fault proportion
determining feature of Konrardy in the system of Herman to determine the insurance coverage
level.
Re claim 4. Herman further discloses the method of claim 1, further comprising: modeling a
likelihood of a collision, based on information describing the accident, wherein the determining
the fault proportion is based on the modeling (see paras 0024).
Re claim 5. Herman does not explicitly disclose the method of claim 4, wherein the information
describing the accident comprises a police report or a witness statement. However, official notice
is taken that it is old and well-known in the car insurance business to collect accident report from
the written police report. Thus it would have been obvious to one of ordinary skill in the art to
incorporate the old and well-known accident report mechanism in the system of Herman to
assess fault in an automobile accident.
Re claim 6. Herman discloses the method of claim 1, wherein the expected behavior of the
autonomous system comprises an expected braking behavior, wherein the observed behavior of
the autonomous system comprises an observed braking behavior, and wherein the determining
the fault proportion between the human driver of the vehicle and the autonomous system is based
on a difference between the observed braking behavior and the expected braking behavior (see
paras 0022, 0040).
Re claim 7. Herman further discloses the method of claim 1, wherein the autonomous system in
the vehicle comprises a forward collision mitigation system, a lane keep assist system, a road
sign recognition system, or a parking assist system (see paras 0022).
Re claim 8. Herman further discloses the method of claim 1, wherein the observed behavior of
the autonomous system comprises output from an accelerometer, a GPS receiver, or a
gyroscope (see paras 0009).
Re claim 9. Herman further discloses the method of claim 1, wherein the observed behavior of
the autonomous system is based on an activation of the autonomous system, a time of the
activation, and a magnitude of the activation (see paras 0005).
Re claim 10. Herman further discloses the method comprising: modeling, by a computing device
and via machine learning, a vehicle accident to develop a model of an expected behavior of an
autonomous system in a vehicle; determining an observed outcome of the vehicle accident (see
paras 0024); and determining a fault proportion between a human driver of the vehicle and the
autonomous system based on a difference between the expected behavior and the observed
behavior of the autonomous system (see paras 0012, 0045).
Re claim 11. Claim 11 recites similar limitations to claim 5, and thus rejected using the same art
and rationale as in claim 5 above.
Re claim 12. Herman discloses the method of claim 10, wherein the modeling the vehicle
accident comprises: modeling an aspect of vehicle safety; and modeling an aspect of human
driver safety (see paras 0005).
Re claim 13. Herman discloses a method of claim 12, wherein the modeling the aspect of
vehicle safety comprises modeling an operation of the autonomous system (see paras 0005,
0011).
Re claim 14. Herman further discloses the method of claim 12, wherein the modeling the aspect
of human driver safety comprises modeling an expected reaction time of the human driver (see
paras 0011).
Re claim 15. Herman further discloses method of claim 10, wherein the modeling the vehicle
accident comprises: determining, for a plurality of combinations of human driver actions and
autonomous system actions, a likelihood of a collision, wherein the determining the fault
proportion is based on the determined likelihood (see paras 0005, 0011).
Re claim 16. Claim 16 recites similar limitations to claim 1 and thus rejected using the same art
and rationale as in claim 1, above.
Re claim 17. Herman further discloses the method of claim 16, wherein the autonomous system
in the vehicle comprises a forward collision mitigation system, a lane keep assist system, a road
sign recognition system, or a parking assist system (see paras 0022).
Re claim 18. Herman further discloses the method of claim 17, wherein the autonomous system
in the vehicle comprises the forward collision mitigation system, a vehicle state of the plurality
of potential vehicle states comprises a speed above a predetermined threshold, and wherein the
obtaining the safety score comprises determining that the safety score is a score representing a
less safe score (see paras 0014, 0022).
Re claim 19. Herman further discloses the method of claim 16, further comprising: determining,
by the computing device and via machine learning, an expected behavior of the human driver of
the vehicle; and determining an observed behavior of the human driver during the vehicle
accident, wherein the determining the fault proportion between the human driver of the vehicle
and the autonomous system is further based on a difference between the expected behavior of the
human driver and the observed behavior of the human driver (see paras 0005, 0012, 0045).
Re claim 20. Herman does not explicitly disclose the method of claim 16, wherein the obtaining
the safety score for the autonomous system of the vehicle corresponding to each of the plurality
of potential vehicle states is based on historical insurance data. However, Konrardy makes this
disclosure (see paras 0062). Thus it would have been obvious to one of ordinary skill in the art to
incorporate the teachings of Konrardy in the system of Herman to determine the insurance
coverage level.
Response to Arguments
Applicant's arguments filed on 03/04/26 have been fully considered but they are not persuasive.
In response to applicant’s argument that the prior arts of record do not recite "determining, by the
computing device and via the machine learning, a fault proportion between a human driver of the
vehicle and the autonomous system based on a difference between the expected behavior and the
observed behavior of the autonomous system," the examiner disagrees. The examiner contends that Konrardy makes this disclosure “ An automobile insurance premium may be determined by evaluating how effectively the vehicle may be able to avoid and/or mitigate crashes and/or the extent to which the driver's control of the vehicle is enhanced or replaced by the vehicle's software and artificial intelligence (see paras 0060).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OJO O OYEBISI whose telephone number is (571)272-8298. The examiner can normally be reached on Monday-Friday, 9am-7pm. Examiner interviews are
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can be reached at 571-272-8103. The fax phone number for the organization where this
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/OJO O OYEBISI/
Primary Examiner, Art Unit 3695
Prosecution Insights