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 .
This office action is in response to applicant’s arguments/remarks and amendments filed on 02/19/2026. Claims 1-2, 4, 8, 10-12, 14-16, 18-19, and 21 have been amended. Claim 22 has been cancelled. Claim 23 has been newly added. Accordingly, claims 1-6, 8-19, 21, and 23 are currently pending.
Response to Arguments
Applicant's arguments filed 02/19/2026 have been fully considered but they are not persuasive.
Applicant’s arguments, see applicant’s arguments/remarks, filed on 02/19/2026, with respect to the rejection(s) of claim(s) 1-6, 9-19, and 21 under 35 U.S.C. 103 as being unpatentable over Oh et al US 2016/0025505 A1 (hence Oh), Levinson US 2018/0136644 A1 (hence Levinson), Annapureddy et al US 2014/0278074 A1 (hence Annapureddy), and Campbell US 10,259,383 B1 (hence Campbell) and that Annapureddy doesn’t not disclose the information in association with metadata that identifies a time when each of the different driving difficulties occurred while each of the plurality of vehicles was operating in the autonomous driving mode, the examiner respectfully disagrees with that statement. Annapureddy discloses receiving crowdsourcing data from a plurality of vehicles and that crowdsourcing data includes on board diagnostics data (OBD) correlated with time stamps and GPS locations of a vehicle (abstract) and Paragraph 0027 discloses that on board diagnostics (OBD) module 202 may collect vehicle information such as collision/impact data and that reads on the limitation.
Applicant’s arguments with respect to claim(s) 1-6, 9-19, and 21 that Campbell does not disclose at least the feature of “at least one indication of severity of each of the plurality of the plurality of the different driving difficulties” have been considered but are moot because the new ground of rejection does not rely on Campbell reference applied in the prior rejection of record for any teaching or matter specifically challenged in this argument; see rejection below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-6, 9-19, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oh et al US 2016/0025505 A1 (hence Oh), in view of Levinson US 2018/0136644 A1 (hence Levinson), Annapureddy et al US 2014/0278074 A1 (hence Annapureddy), and Fields et al US 9,847,043 B1 (hence Field).
In re claims 1 and 21, Oh discloses an apparatus and method for generating a global path for an autonomous vehicle and teaches the following:
receiving, by one or more computing devices, information identifying a plurality of different driving difficulties encountered by vehicles while operating along one or more routes in the autonomous driving mode (Fig.1, #20, #30, and Paragraphs 0025-0026, “receive traffic information”, “the traffic information includes a road traffic status (traffic congestion status), accident information, road control information, weather information, autonomous driving failure probability information, nation, and the like”),
storing in memory, by the one or more computing devices, the information in association with metadata while each of the plurality of vehicles was operating in the autonomous driving mode (Paragraph 0035 “a traffic information center collects information related to the autonomous driving failure”, by virtue of data collection, the data storing is implicit, Table 1 and Paragraphs 0052-0053);
evaluating, by the one or more computing devices, the information and the metadata in view of a plurality of route options for a particular vehicle other than the plurality of vehicles (Paragraphs 0010-0011 “evaluate a difficulty of driving in each of the candidate paths in each section according to driving environment recognition rates of the one or more recognized sensors” and “a difficulty of driving in each of the candidate paths in each section based on ….. autonomous driving failure probability information of each section), Paragraph 0015 “a difficulty of driving may be evaluated based on driving environment recognition rates of the one or more sensors, traffic congestion, weather information, and autonomous driving failure probability information”, Paragraph 0027 “evaluates difficulty of driving”, Paragraph 0035 “evaluates a difficulty of driving in consideration of autonomous driving failure probability information of each section” and “analyzes the collected information to calculate and manage autonomous driving failure probability information”, Fig.2, S14 and Paragraph 0045 “evaluates the candidate paths based on the measured sensor recognition rates and traffic information”);
and selecting, by the one or more computing devices based on the evaluating, one of the plurality of route options, whereby the particular vehicle is controlled using the selected one of the plurality of route options while operating in the autonomous driving mode (Paragraph 0013 “selecting any one of the one or more candidate paths, as an autonomous driving path, based on the results of the difficulty of driving in each section”, Fig.2, S15, and Paragraph 0046)
However, Oh discloses the traffic information receiver is configured to receive traffic information provided from a traffic information center (Paragraph 0026) but doesn’t explicitly teach the following:
receiving information from a plurality of vehicles configured to operate in an autonomous mode
storing metadata that identifies a time when each of the different driving difficulties occurred, and
with at least one indication of severity of each of the plurality of the different driving difficulties
Nevertheless, Levinson discloses an application that applies artificial intelligence and/or machine-learning techniques to predict an optimal course of action (or a subset of courses of action) for an autonomous vehicle system (Abstract) and teaches the following:
receiving information from a plurality of vehicles configured to operate in an autonomous mode (Abstract, “The application may use aggregated sensor data from multiple autonomous vehicles to assist in identifying events or conditions that might affect travel (e.g., using semantic scene classification)”, Fig.2, and Paragraph 0055)
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Oh reference to include using aggregated sensor data from multiple autonomous vehicles, as taught by Levinson, with a reasonable expectation of success, in order to predict an optimal course of action (or a subset of courses of action) for an autonomous vehicle system (Levinson, Abstract)
Nevertheless, Annapureddy discloses providing navigation guidance to vehicles through crowdsourcing data from a plurality of vehicles (Abstract) and teaches the following:
storing metadata that identifies a time when each of the different driving difficulties occurred (Abstract, “crowdsourcing data includes on board diagnostics data (OBD) correlated with time stamps and GPS locations of a vehicle”, Paragraph 00027, “The crowdsourcing server 102 may be configured to data-mine the crowdsourcing data, constructing tables of road segments and associated fuel consumption, acceleration and deceleration, average speed, segment entry and exit, and unusual events such as sudden braking, swerving and impacts, in order to gather trip data and route conditions for certain road segments”, and Paragraph 0028, “a personal navigation device (PND) may be configured to read OBD2 data from vehicle 104 (for example over Bluetooth) and packages the OBD2 data with onboard GPS location and time data. The PND may then upload the packaged information to the crowdsourcing server 102”, and Paragraph 0048, “The crowdsourcing data includes on board diagnostics data (OBD) correlated with time stamps”, and Fig.5)
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Oh reference to include timestamp with the collected data, as taught by Annapureddy, with a reasonable expectation of success, in order to provide navigation guidance for a vehicle that avoids road hindrances (Annapureddy, Paragraph 0005).
Nevertheless, Fields discloses using one or more devices to allow an instructor to generate tagged driving events during a driving test session (Col.1, lines 21-24) and teaches the following:
with at least one indication of severity of each of the plurality of the different driving difficulties (Abstract and Col.11, lines 1-19, and Col.11, line 66-Col.12, line 21)
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Oh reference to include a metadata of an event including its severity, as taught by Fields, with a reasonable expectation of success, in order to generate appropriate feedback for a driver and (Fields, Col.10, lines 59-67).
In re claim 14, Oh discloses an apparatus and method for generating a global path for an autonomous vehicle and teaches the following:
controlling, by one or more processors, driving operations operation of the vehicle in the autonomous driving mode (Abstract, “method for generating a global path for an autonomous vehicle”, “a sensor module including one or more sensors installed in the vehicle”, Paragraphs 0023-0024, and Paragraph 0027 “The difficulty evaluator 40 evaluates difficulty of driving based on recognition rates (driving environment recognition rates) of the sensors constituting the sensor module 10 and traffic information”);
receiving, by the one or more processors while the vehicle is operating in the autonomous driving mode, sensor information from a perception system of the vehicle regarding objects or conditions in an external environment of the vehicle (Paragraph 0027);
generating, by the one or more processors according to the received sensor information, information identifying a plurality of driving difficulties encountered by the vehicle while operating along one or more routes in the autonomous driving mode, wherein the at least one type of difficulty includes at least one of a sensor difficulty for a sensor of the perception system or a roadway condition impacting autonomous driving (Paragraphs 0027, 0033-0036);
receiving, by the one or more processors from a remote system, information identifying different difficulties encountered by a plurality of vehicles other vehicles other than the first vehicle (Paragraph 0009 “traffic information may include a road traffic state, accident information, road control information, weather information, and autonomous driving failure probability information”, Fig.1, #20, #30, and Paragraphs 0025-0026, “receive traffic information”, “the traffic information includes a road traffic status (traffic congestion status), accident information, road control information, weather information, autonomous driving failure probability information, nation, and the like”, “Paragraphs 0035 “a traffic information center collects information related to the autonomous driving failure such as a location, a node number, a failure cause (recognition/control), and the like”);
and controlling, by the one or more processors, the operation of the vehicle in the autonomous driving mode according to the generated information and the information received from the remote system (Fig.2, S15, and Paragraph 0046)
However, Oh doesn’t explicitly teach that one or more other vehicles while operating in the autonomous driving mode, and a time when each of the different difficulties occurred and with at least one indication of severity of each of the plurality of the different driving difficulties
Nevertheless, Levinson, Annapureddy, and Fields disclose said limitations as recited above with respect to claims 1 and 21 above.
In re claims 2 and 15, Annapureddy teaches the following:
wherein the information includes a vehicle signal identifier for each one of the different driving difficulties that identifies a specific signal sent by a respective one of the one or more vehicles (Paragraph 0030 “vehicle identification number (VIN) can be extracted from OBD2 data”, the motivation to combine has been provided above)
In re claim 3, Annapureddy teaches the following:
wherein the vehicle signal identifier is either (i) included with the specific signal sent by the respective vehicle, (ii) assigned by the one or more computing devices, or (iii) composed from a combination of information from the respective vehicle and the one or more computing devices (Paragraph 0030)
In re claims 4 and 16, Oh teaches the following:
wherein the metadata includes location information identifying a location at which each one of the different driving difficulties was encountered (Paragraph 0035 and Table 1)
In re claims 5 and 17, Oh teaches the following:
wherein the location information includes at least one of a regional identifier, a street identifier, or a road segment identifier (Table 1 and Paragraph 0035)
In re claim 6, Oh teaches the following:
wherein the location information includes geographical coordinates (Paragraphs 0024 “GPS” and Paragraph 0035 “location”)
In re claim 9, Oh teaches the following:
generating a prediction model identifying which road segments are likely to present difficulty based on the information stored in the memory (Figure 3 and Paragraphs 0036-0037, 0045, and 0053-0055)
In re claim 10, Oh teaches the following:
wherein the information stored in the memory includes a signal type indicating a driving difficulty encountered by each of the one or more vehicles that transmitted corresponding information (Paragraph 0009 and Paragraph 0026)
In re claims 11 and 18, Oh teaches the following:
wherein the different driving difficulties is one or more of an extended wait to make a turn or other maneuver, an unprotected turn, a rapid brake application, an unprotected lane crossing, or a driving difficulty resulting from a driving environment (Paragraphs 0009, 0015, and 0026, “ a road traffic status (traffic congestion status), accident information, road control information, weather information, autonomous driving failure probability information, nation, and the like”)
In re claims 12 and 19, Oh teaches the following:
wherein the different driving difficulties include one or more of a fault response, a need for a human driver to take manual control, or a request for assistance from a remote operator (Paragraph 0026 “autonomous driving failure probability information”)
In re claim 13, Oh teaches the following:
transmitting the selected route option to a particular vehicle (Abstract, and Paragraph 0035)
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oh in view of Levinson, Annapureddy, and Fields, and further in view of Urano et al US 2017/0123434 A1 (hence Urano).
In re claim 8, the combination of Oh in view of Levinson, Annapureddy, and Fields discloses the claimed invention as discussed above but doesn’t explicitly teach the following:
weighting one or more values based on temporal information associated with each one of the different driving difficulties
Nevertheless, Urano discloses an autonomous driving system that performs autonomous driving control of a vehicle (Abstract) and teaches the following:
weighting one or more values based on temporal information associated with each one of the different driving difficulties (Paragraphs 0133-0134)
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Oh reference to include weighting the difficulty based on temporal information, as taught by Urano, with a reasonable expectation of success, in order to improve accuracy of objects that affects operation of a vehicle based on a time of the day (Urano, Paragraph 0135).
Allowable Subject Matter
Claim 23 is 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
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAMI KHATIB whose telephone number is (571)270-1165. The examiner can normally be reached M-F: 9:00am-5:30pm.
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/RAMI KHATIB/Primary Examiner, Art Unit 3669