DETAILED ACTION
A. This action is in response to the following communications: Request for Continued Examination filed 02/23/2026.
B. Claims 1-3,5,7-8 and 10 remains pending.
Continued Examination Under 37 CFR 1.114
C. request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/23/2023 has been entered.
Claim Interpretation
Claim 2 recites the following contingent limitations:
A1 “wherein the first category is associated with difference in properties that is important” and
A2 “the second category is associated with difference in properties that is not important or less important, wherein the importance is associated with that it is important to prepare the vehicle according to the selected path”;
B1 “when the difference in properties is of a second category, providing, by the processor device, a request for path selection to a user of the vehicle, the request presenting the at least two alternative paths to the user for selection; determining, by the processor device, if a response comprising path selection has been obtained; when the response has been obtained, then the selected path is selected to be the path indicated in the obtained response”;
B2. and when the response has not been obtained, then the selected path is selected based on a weighted state associated with each of the at least two alternative paths”.
The limitation(s) is/are contingent because they recite steps that are only required to be performed if their conditions are precedent are met. Limitation A1 only needs to be performed if first category is associated with different properties that is important and Limitation A2 only needs to be formed if second category is associated with different properties that is not important; likewise the Limitation B1 only occurs when a response is obtained and B2 if a response is not obtained . The BRI of the claim requires limitation of only one of either limitation A1 or limitation A2 and only one of either limitation B1 or limitation B2.
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.
Claim(s) 1-3,5,7-8 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over McGavran, Christine B. et al. (US Pub. 2014/0278051 A1), herein referred to as “McGavran” in view of Moustafa, Hassnaa et al. (US Pub. 2022/0126863A1).
As for claims 2 and 1, McGavran teaches. A computer-implemented method and corresponding system of claim 1 for determining a selected path to be taken by a vehicle, the method comprising (par. 42 and figure 2 shows the hardware environment):
monitoring, by a processor device of a computer system and using a navigation system of the vehicle (par. 34 engine includes a global positioning system (GPS) engine that uses GPS data to identify the current location of the user. In some of these embodiments, this engine augments the GPS data with other terrestrial tracking data, such as triangulated cellular tower data, triangulated radio tower data, and correlations to known access points (e.g., cell-ID, Wi-Fi ID/network ID), in order to improve the accuracy of the identified location) , alternative paths for the vehicle, wherein the processor device is at least partly comprised in the navigation system of the vehicle or arranged to communicate with the navigation system (par. 32 prediction engine predicts alternative routes/paths for the vehicle/user to take based upon various factors);
based on the monitoring, determining, by the processor device, that the vehicle approaches at least two alternative paths having different properties, wherein the different properties of the at least two alternative paths are associated with at least one of: topography (par. 77 road closure could be construed as a type of topography as it is not defined in the specification), traffic congestion (par. 77 traffic data), not validated path selection (par. 221 user selected path not a path predicted by the system in such would not be validated as the user has decided on their own accord), thermal conditions (par. 217 weather conditions, the “thermal condition” is not defined in the specification), Point of Interest (par. 247 points of interest), POI possibilities (par. 150 restaurants) and/or charging possibilities (par. 247 points of interest would encompass gas and charging stations pertaining to vehicle navigation) of the at least two alternative paths, wherein the at least two alternative paths do not have a predetermined destination (based upon the specification par.2 of the instant application interpretation is given such that a path is determined outside of user set navigation destination or when no destination is set; McGavran teaches a user travelling with not destination and the devices route predictor will predict destination for user and present them to said user; par. 138,139,141);
determining, by the processor device and utilizing statistical methods, a probability of the vehicle taking each of the at least two alternative paths having different properties (par. 145-148 using machine learning to determine a ranking of destinations and routes to suggest to user based upon historical, traffic, vehicle, weather etc.… data);
comparing, by the processor device, the probabilities of the at least two alternative paths to each other (par. 148 comparing routes and destinations based upon vehicle information);
based on a result of the comparison of the probabilities, selecting, by the processor device, a selected path amongst the at least two alternative paths (par. 157, 160 presenting alternative routes to user via user interface); and
initiating, by the processor device, preparation of the vehicle according to the selected path,
wherein the preparation of the vehicle is associated with one or more of: braking performance, driving performance, vehicle temperature, and/or energy consumption (par. 137,160 adjusting the driving performance to aid in a safer travel for the user through the use of this system by providing alternative paths).
McGavran does not specifically teach a decision process in which user input is triggered only if the system first determines that the probabilities of the alternative paths are close and the difference in properties is not important for preparation; however in the same field of endeavor Moustafa teaches based on a result of the comparison (par. 55 comparison based upon collected information and decided though the recommendation engine) of the probabilities, selecting, by the processor device, a selected path amongst the at least two alternative paths, wherein the result of the comparison of probabilities indicates that a difference between the probability of each of the at least two alternative paths compared to each other is below a probability threshold (par. 54 A vehicle 105 may further include a path planner 242, which may make use of the results of various other modules, such as data collection 234, sensor fusion 236, perception engine 238, and localization engine (e.g., 240) among others (e.g., recommendation engine 244) to determine a path plan and/or action plan for the vehicle, which may be used by drive controls (e.g., 220) to control the driving of the vehicle 105 within an environment; par. 216-217 selecting different paths based upon different modes L3 and L5 autonomous levels wherein the route along with what sensors and other parameters are being input into the decision making engine to determine a level of autonomous mode of operation);
when the difference is below the probability threshold, determining, by the processor device, if the difference in properties of the at least two alternative paths are of a first category or second category, wherein the first category is associated with difference in properties that is important and the second category is associated with difference in properties that is not important or less important, wherein the importance is associated with that it is important to prepare the vehicle according to the selected path (Examiner notes that important is arbitrary and subjective which does not clearly define functional limitation; Moustafa teaches various important parameters for path planning (e.g. checking sensor status and weather conditions) along with less important path planning (user’s desire for fastest route versus scenic one);
when the difference in properties is of a second category, providing, by the processor device, a request for path selection to a user of the vehicle, the request presenting the at least two alternative paths to the user for selection(par. 56 and 58 presenting information for the user to select on the user interface of the autonomous vehicle);
determining, by the processor device, if a response comprising path selection has been obtained (par. 56 user interaction; par. 58 User interfaces (e.g., 230) may capture the desires and intentions of the passenger-users and the autonomous driving stack of the vehicle 105 may consider these as additional inputs in controlling the driving of the vehicle (e.g., drive controls 220).);
when the response has been obtained, then the selected path is selected to be the path indicated in the obtained response (par. 56 and 58 user selection);
and when the response has not been obtained, then the selected path is selected based on a weighted state associated with each of the at least two alternative paths (par. 60 system selection for autonomous path planning; par. 70 using weights in the autonomous vehicle stack);
and initiating, by the processor device, preparation of the vehicle according to the selected path, wherein the preparation of the vehicle is associated with one or more of: braking performance, driving performance, vehicle temperature, and/or energy consumption (par. 57 a system manager 250 may also be provided, which monitors information collected by various sensors on the vehicle to detect issues relating to the performance of a vehicle's autonomous driving system… (e.g., provided through communication modules 212), vehicle system checks (e.g., issues relating to the motor, transmission, battery, cooling system, electrical system, tires, etc.) par. 58 affect how the vehicle is driven, including steering controls (e.g., 260), accelerator/throttle controls (e.g., 262), braking controls (e.g., 264), signaling controls (e.g., 266), among other examples.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Moustafa into McGavran because Moustafa suggests advances in the use of predictions during path planning for navigating a vehicle stating that some vehicles are configured to operate in an autonomous mode in which the vehicle navigates through an environment with little or no input from a driver. Such a vehicle typically includes one or more sensors that are configured to sense information about the environment. The vehicle may use the sensed information to navigate through the environment. For example, if the sensors sense that the vehicle is approaching an obstacle, the vehicle may navigate around the obstacle in paragraph 3.
As for claim 3, McGavran teaches. The computer-implemented method according to claim 2, wherein the result of the comparison of probabilities indicates that a difference between the probability of one of the at least two alternative paths compared to the probabilities of the other alternative paths is at or above a probability threshold, and wherein the selected path is selected to be the path with the difference at or above the probability threshold (par. 90 motion data from raw driving data is utilized by the machine learning engine in par. 91 to analyze where user was for various thresholds of time which identifies start and end times for entering and leaving each region based on analysis, statistical, and historical data that is stored; the engine 308 of some embodiments stores transitions between regions and transition probabilities or parameters from which such probabilities can be computed. Breadcrumbs are introduced in par. 92 to map thresholds of time between two different points captured via GPS which in par. 93 is to generate a more complete set of possible locations for a particular time interval on a particular day, the machine-learning engine 308 in some embodiments specifies motion data for some or all of the breadcrumb locations).
As for claim 5, McGavran teaches. The computer-implemented method according to claim 4, wherein the difference in properties is of the first category, and
wherein the selected path is selected to be the path with a highest probability compared to the other paths (par. 97-98 utilizing breadcrumbs (first category) from time information (second category) to find multiple alternative paths).
As for claim 7, McGavran teaches. The computer-implemented method according to claim 2, comprising: determining, by the processor device, a vehicle performance associated with the selected path compared to a non-selected path (par. 77 The best route might be the route most often traveled by users, the shortest route, the fastest route, etc. In some embodiments, the default best route might be one type of route (e.g., shortest route), but the route generator 226 might produce another type of route (e.g., a longer route) based on current road conditions (e.g., based on traffic, accident data, road closure, etc.), or based on user explicit or implicit preferences).
As for claim 8, McGavran teaches. A vehicle comprising a processor device configured to perform the method of claim 2 (par. 80 prediction processor).
As for claim 10, McGavran teaches. A non-transitory computer-readable storage medium comprising instructions, which when executed by a processor device, cause the processor device to perform the method of claim 2 (par. 80 prediction processor).
(Note :) It is noted that any citation to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006,1009, 158 USPQ 275, 277 (CCPA 1968)).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-3, 5, 7-8 and 10 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Inquires
Any inquiry concerning this communication should be directed to NICHOLAS AUGUSTINE at telephone number (571)270-1056.
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.
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/NICHOLAS AUGUSTINE/Primary Examiner, Art Unit 2178 April 14, 2026
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