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 .
Continued Examination Under 37 CFR 1.114
A 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 4/22/2026 has been entered.
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.
Claims 1, 2, 6-10, 13-17 are rejected under 35 U.S.C. 103 as being unpatentable over Ananthapur Bache (US 2022/0165157) in view of Barton-Sweeney (US 2024/0010198)
As to claim 1 Ananthapur Bache discloses a computer implemented method to predict high gradient wind effects to increase safety of a two-wheeled vehicle, the method comprising:
detecting, from a mobile sensor, one or more instances of high gradient wind effects based on contextual elements related to the high gradient wind effects(Paragraph 26 “Further, in at least some embodiments, subject to the collected real-time data from two-wheeler users' mobile phone GPS′, two-wheeler vehicles sensor GPSs, non-two-wheeler users' phone GPSs, and non-two-wheeler users' vehicle GPS sensors, the system disclosed herein can determine if the two-wheeler vehicles are collecting in a predetermined vicinity to suggest that inclement weather, e.g., rain, is present in that vicinity”, Paragraph 34“In some embodiments, the knowledge base 122 houses historical localized weather conditions data 126 that includes historical data including, without limitation, standard weather patterns for the associated regions, known rainfall levels and snowfall levels for the established weather patterns, and known wind speeds for those weather patterns.”);
mapping the one or more instances of high gradient wind effects into one or more generalizable feature vectors(Paragraph 34 “In some embodiments, the knowledge base 122 houses historical localized weather conditions data 126 that includes historical data including, without limitation, standard weather patterns for the associated regions, known rainfall levels and snowfall levels for the established weather patterns, and known wind speeds for those weather patterns. In some embodiments, the historical localized weather conditions data 126 are used by the machine learning module 110 to generate predictions of the localized weather and the machine leaning module 110 learns from the outcomes of the predictions from the additional real-time data as described herein to continuously improve the weather forecasting abilities.”);
generating a trained machine learning model based on a training feature dataset, where the training feature dataset is an aggregation of the one or more generalizable feature vectors (Paragraph 24 “The weather information is determined through historical weather data collected and stored by the system, real-time weather conditions, and weather predictions from one or more sources including, without limitation, localized and national weather services, commercially available weather reporting outlets (e.g., local commercial radio stations), and mobile phone-based weather apps. In addition, in some embodiments, localized road and traffic conditions (historical, real-time, and predicted) are collected from sources similar to those for the weather as well as data collected from users' and vehicles' sensors within a predetermined time frame prior to the present time. The machine learning module may assist in the weather, road, and traffic conditions determinations”); and
predicting, using the trained machine learning model, a likelihood of high gradient wind effects to take place in a specific time and a specific space partition (Paragraph 43 “The table 200 further includes a “Rain prediction” column 214 that provides the user with a prediction of pending rain. In some embodiments, the column 214 is determined by the machine learning module 110 as a function of the historical localized weather conditions data 126 relevant for the time of year, recent weather patterns, and real-time and predicted localized weather conditions data 142. For example, and without limitation, rather than a rain prediction, the table 200 includes one or more of a wind prediction column, a snow prediction column, a fog prediction column, a hail prediction column, and a sleet/freezing rain precipitation column.”).
Ananthapur Bache does not explicitly disclose mapping the one or more high gradient wind effects into one or more generalizable feature vectors indexed to a link identifier in a geographic database
Barton-Sweeney teaches mapping the one or more high gradient wind effects into one or more generalizable feature vectors indexed to a link identifier in a geographic database (Paragraph 94 “In some cases, wind measurements from vehicle 400 may be stored or communicated in a map or to one or more nearby vehicles (or a central system) to provide advance information or to augment other vehicles, which may not include one or more wind sensors.”)
based on the predicted likelihood of high gradient wind effects, automatically adapting two-wheeled vehicle settings to obviate or minimize the high gradient wind effects on the two-wheeled vehicle (Paragraph 31 “A behavior threshold curve can be predefined and may specify one or multiple navigation parameters for the vehicle to implement based on the estimated speed and/or direction of the ambient wind vector. For instance, one or multiple behavior threshold curves can specify actions for slowing down, modifying steering, and/or other adjustments (e.g., pulling over) based on the ambient ground relative wind vector exceeding or approaching one or more thresholds or values specified within the behavior threshold curve(s). In some cases, modifying vehicle behavior may include different actions, such as stopping, slowing, pulling over, changing route, and/or resuming navigation. In addition, vehicle systems may also use behavior threshold curves when recommending actions for a driver to implement during assisted navigation.”).
Barton-Sweeney further teaches wherein each of the one or more instances corresponds to a rapid change of air movement surrounding the two-wheeled vehicle (Paragraph 24-25 “As an example result, disclosed techniques can provide localized ambient wind measurements that factor the impacts of micro level terrain, canyoning or structures (e.g., tunnels or buildings). In addition, some examples involve determining the ambient crosswind speed and adjusting vehicle behavior based on the crosswind speed. For instance, the ambient crosswind can be determined relative to the vehicle based on the crosswind component of the wind speed and direction measurements from an onboard wind sensor.”).
It would have been obvious to one of ordinary skill to modify Ananthapur Bache to include the teachings of automatically adapting the behavior of the vehicle based on the high gradient wind effects for the purpose of improving safety by avoiding the areas with high winds at the particular location along the route of the vehicle.
As to claim 2 Ananthapur Bache discloses a method where the contextual elements comprises at least one of the following: a detailed map of the area; a weather forecast; temperature; wind forecast; solar irradiance; pavement temperature; pavement roughness; real-time traffic; historical traffic; predicted traffic; historical information about past accidents related to high wind gradient effects; associated contextual data related to the past accidents; historical information about the past tire temperature of this vehicle; historical information about the past tire temperature of nearby vehicles associated data related to past tire temperature; historical information about past road temperature; associated contextual data of the past road temperature; historical information about an accident involving a tire-related incident; or an associated contextual data related to the tire-related incident(Paragraph 34).
As to claim 6 Barton-Sweeney teaches a method further comprising providing mitigation information to a two- wheeled vehicle driver, where the mitigation information comprises at least one of the following:
Informing the two-wheeled driver of an upcoming area with high gradient wind effects and providing action guidance accordingly (Paragraph 24).
As to claim 7 Barton-Sweeny teaches a method where predicting, using the trained machine learning model, a likelihood of high gradient wind effects, comprises using a transfer learning model for areas where historical information on high gradient wind effects is unavailable (Paragraph 115). It would have been obvious to one of ordinary skill to modify Ananthapur Bache to include the teachings of using a transfer learning model for the purpose of reusing pre-trained models.
As to claim 8 Ananthapur Bache discloses a method further comprising classifying areas of high gradient wind effects into wind effect severity classes types based on vehicle effects and/or driver perception parameters, where the vehicle effects and/or driver perception parameters comprise a wind gradient; a vehicle instability effect; a two-wheeled vehicle type; a driver experience; a driver reaction; a driver reaction time; an effect on the driver causing careless maneuvers; a road condition or a combination thereof(Paragraph 35).
As to claim 9 the claim is interpreted and rejected as in claim 1.
As to claim 10 the claim is interpreted and rejected as in claim 2.
As to claim 13 the claim is interpreted and rejected as in claim 6.
As to claim 14 the claim is interpreted and rejected as in claim 7.
As to claim 15 Ananthapur Bache discloses a system further comprising a display configured to present areas of high gradient wind effects to alert the two-wheeled vehicle driver with guidance(Paragraph 45).
As to claim 16 the claim is interpreted and rejected as in claim 1.
As to claim 17 the claim is interpreted and rejected as in claim 2.
As to claim 21 Barton-Sweeney teaches a method wherein detecting the one or more instances of high gradient wind effects comprises detecting at least one of: (i) exiting a tunnel to an area with high wind, or (ii) transitioning form a road segment protected by a wall to another road segment without protection(Paragraph 24-25).
As to claim 22 Barton-Sweeney teaches a method, wherein the one or more instances are mapped at a link level and considering an offset on the link(Paragraph 25, 28).
Claims 3, 11-12, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ananthapur Bache (US 2022/0165157) in view of Barton-Sweeney (US 2024/0010198) as applied to claim 1 above, and in further view of Okamoto (US 12,246,751)
As to claim 3 Okamoto teaches a method where the aggregation of the one or more generalizable feature vectors comprises an aggregation of all of the one or more instances of high gradient wind effects detected on a particular link of a ride during a particular setting (Column 3 lines 23-34). It would have been obvious to one of ordinary skill to modify Ananthapur Bache to include the teachings of determining the wind data for the particular road segment for the purpose of predicting the high speed wind at the particular location along the route of the vehicle.
As to claim 11 the claim is interpreted and rejected as in claim 3.
As to claim 12 the claim is interpreted and rejected as in claim 4.
As to claim 18 the claim is interpreted and rejected as in claim 3.
Claims 5, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ananthapur Bache (US 2022/0165157) in view of Barton-Sweeney (US 2024/0010198) as applied to claim 1 above, and in further view of Cook (US 2022/0343221)
As to claim 5 Cook teaches a method where the one or more generalizable feature vectors comprise tuples of a time of occurrence of the one or more instances of high gradient wind effects; a location of the occurrence of the one or more instances of high gradient wind effects; and a description of the occurrence of the one or more instances of high gradient wind effects (Paragraph 56). IT would have been obvious to one of ordinary skill to modify Ananthapur Bache to include the teaching of getting information of the past occurrences of the high winds for the purpose of forecasting the high wind effects that may take place.
As to claim 20 the claim is interpreted and rejected as in claim 5.
Response to Arguments
Applicant's arguments filed 4/22/2026 have been fully considered but they are not persuasive.
On page 9 of the applicants arguments applicants argue that Barton-Sweeney does not teach “rapid-change instance”.
The examiner respectfully disagrees with the applicants arguments. Applicants argue that the primary reference Ananthapur Bache does not teach detecting rapid-change instance of air movement but rather teaches of wind prediction as a weather parameter. Barton-Sweeney, as stated above, teaches wherein each of the one or more instances corresponds to a rapid change of air movement surrounding the two-wheeled vehicle (Paragraph 24-25 “As an example result, disclosed techniques can provide localized ambient wind measurements that factor the impacts of micro level terrain, canyoning or structures (e.g., tunnels or buildings). In addition, some examples involve determining the ambient crosswind speed and adjusting vehicle behavior based on the crosswind speed. For instance, the ambient crosswind can be determined relative to the vehicle based on the crosswind component of the wind speed and direction measurements from an onboard wind sensor.”). Here the system provides localized ambient wind measurements such as from tunnels or buildings. The systems detects the wind speed and direction from onboard wind sensor. Thus the system detects rapid change instance from the current wind conditions observed by the vehicle sensor.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IMRAN K MUSTAFA whose telephone number is (571)270-1471. The examiner can normally be reached Mon-Fri 9-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James J Lee can be reached at 571-270-5965. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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IMRAN K. MUSTAFA
Primary Examiner
Art Unit 3668
/IMRAN K MUSTAFA/ Primary Examiner, Art Unit 3668
6/24/2026
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