DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 20 August 2026 has been entered.
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 .
Status of Claims
Claims 1, 3-6, 8-9, 11, 13-16, 18-19, and 21-24 are pending in this application.
Claims 2, 7, 10, 12, 17, and 20 are cancelled.
Claims 1, 11, and 21-24 are amended.
Claims 1, 3-6, 8-9, 11, 13-16, 18-19, and 21-24 are presented for examination.
Claim Interpretation
The examiner is interpreting the term “long distance” found in claims 8 and 18 as an area or a region within a threshold distance with time enough to identify a risk and send an alert to the user before the vehicle reaches the location of the risk as supported by the applicant’s specification (Para. 5, 25).
Response to Amendments
Applicant’s amendments, filed 20 August 2026, with respect to the claim objection for claim 1 has been fully considered. The claim objection for claim 1 has been removed.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1, 3-8, 11-18, and 21-24 are rejected under 35 U.S.C. 103 as being unpatentable over Nayak et al. (US Publication 2020/0167575 A1) in view of Tucker et al. (US Publication 2024/0067087 A1) and in further view of Dronen et al. (US Publication 2022/0203973 A1).
Regarding claim 1, Nayak teaches a vehicle-related event recognization and alert system, the vehicle-related event recognization and alert system comprising: a plurality of control systems of a plurality of original equipment manufacturer (OEM) vehicles, respectively, each OEM vehicle being associated with an OEM and each control system (Nayak: Para. 7; plurality of in-vehicle sensors installed in multiple vehicles traveling the road or from an original equipment manufacturer (OEM) cloud of the multiple vehicles) being configured to: detect a vehicle-related event in which an encounter between the OEM vehicle and one or more other objects occurred (Nayak: Para. 7; vehicular sensor data related to a number of positive observations of the physical divider by unique vehicles), verify that the vehicle-related event is a valid vehicle-related event for training of a long range vehicle-related event recognization model (Nayak: Para. 7; one or more aggregated values generated from the categorical vehicular sensor data includes selected categorical values, using a voting algorithm, for each of the physical divider measurement event type, the physical divider flag), and in response to verifying that the vehicle-related event is valid, transmitting vehicle operational data relating to the vehicle-related event via a network (Nayak: Para. 7; one or more aggregated values are collected over an extended period of time for use in training of the machine learning model for prediction of presence of the physical divider), and a computing server associated with the OEM and configured to: (Nayak: Para. 36, 81; OEM cloud for the vehicles; processing needed by the machine learning model is provided on the server side) ……… , and train the long range vehicle-related event recognization model based on the received vehicle operational data (Nayak: Para. 1, 36; using the trained machine learning model for predicting presence of one or more physical dividers on at least one segment of a road; train a machine learning model to predict physical dividers on a road segment of interest, based on map data associated with the segment of interest, vehicular sensor data collected from vehicles, an OEM cloud for the vehicles), wherein the trained long range vehicle- related event recognization model is configured to (Nayak: Para. 64-65; model or predict shape, distance from vehicle to the physical divider, and/or other attributes of the physical divider; predict a road characteristic related to the physical divider; probability of oncoming traffic, a presence of vulnerable road users) ……. and (ii) to be executed based on a set of input data to generate a model output indicative of one or more recognized vehicle-related events (Nayak: Para. 36, 81; train a machine learning model to predict physical dividers on a road segment of interest, based on map data associated with the segment of interest, vehicular sensor data collected from vehicles, an OEM cloud for the vehicles, or a combination thereof).
Nayak doesn’t explicitly teach receive, from the plurality of OEM vehicles and via the network, vehicle operational data relating to a plurality of vehicle-related events ………. (i) recognize a plurality of different types of vehicle-related events at least at a long distance relative to a particular OEM vehicle of the plurality of OEM vehicles ………… at least at the long distance relative to the particular OEM vehicle, …….. , and wherein the long distance is at least one of (i) a physical distance from the particular OEM vehicle and (ii) a time period in advance of the future encounter by the particular OEM vehicle.
However Tucker, in the same field of endeavor, teaches receive, from the plurality of OEM vehicles and via the network, vehicle operational data relating to a plurality of vehicle-related events (Tucker: Para. 37; serve to notify other vehicles, drivers, or services, when a vehicle has become disabled on a roadway; systems of the present disclosure may receive mechanical or operational data from other OEM vehicle computers) ………. (i) recognize a plurality of different types of vehicle-related events at least at a long distance relative to a particular OEM vehicle of the plurality of OEM vehicles (Tucker: Para. 38-39, 73; OEM's cloud service or tracking of vehicular location; vehicle rollovers may be detected by crash sensors, gyroscopes, and/or accelerometers; sensors may be placed to detect water levels, closure of roadway gates for toll roads and when roads are closed due to winter driving conditions, shifting or reconfiguration of lanes as occurs throughout the day in high traffic volume areas, railroad crossing gates, presence and/or speed or traffic, and other road conditions; limit to the number of times or the distance that the emergency indication or hazard may be relayed) ………… at least at the long distance relative to the particular OEM vehicle (Tucker: Para. 14; indication of a hazard at a fixed location; at least some of the plurality of passenger vehicles is within a predetermined distance of and headed toward the fixed location), …….. , and wherein the long distance is at least one of (i) a physical distance from the particular OEM vehicle and (ii) a time period in advance of the future encounter by the particular OEM vehicle (Tucker: Para. 115, 123; an approach time in addition to or instead of distance can be used to determine which vehicles should receive an alert; OEM cloud is shown interacting with the alert management system).
It would have been obvious to one having ordinary skill in the art to modify the OEM cloud machine learning model (Nayak: Para. 7) with hazards detected at a distance (Tucker: Para. 41, 115) a reasonable expectation of success because receiving an indication of a hazard event associated with a first passenger vehicle, generating an alert, forwarding the safety alert to a second passenger vehicle approaching the zone improves group vehicle safety (Tucker: Para. 8).
Nayak and Tucker don’t explicitly teach wherein the model output includes a probability score indicative of a likelihood that a particular recognized vehicle-related event will be encountered by the particular OEM vehicle in the future; wherein a control system of the particular OEM vehicle is further configured to obtain the model output, compare the probability score to a probability score threshold, and selectively generate an alert for the one or more recognized vehicle-related events when the probability score satisfies the probability score threshold.
However Dronen, in the same field of endeavor, teaches wherein the model output includes a probability score indicative of a likelihood that a particular recognized vehicle-related event will be encountered by the particular OEM vehicle in the future (Dronen: Para. 62; vehicle is characterized with indicates a level of risk that the vehicle poses to other vehicles in its vicinity; a high risk category vehicle poses a high risk of mishappening to other vehicles in its vicinity, such as in the form of high collision possibility, vehicle swerving, vehicle breakdown, erratic driving); wherein a control system of the particular OEM vehicle is further configured to obtain the model output, compare the probability score to a probability score threshold (Dronen: Para. 62, 83, 99; system may process the data in risk layer and may generate an awareness notification of this kind of dangerous situation; high risk category vehicle poses a high risk of mishappening to other vehicles in its vicinity; high or low degree may be determined based on probabilistic measures), and selectively generate an alert for the one or more recognized vehicle-related events when the probability score satisfies the probability score threshold (Dronen: Para. 47, 62, 89; sensor data from the vehicle detector, and the vehicle communication data from the V2V unit may then be fed to a local map generating unit; a single platform to provide a suite of mapping and navigation related applications for OEM devices).
It would have been obvious to one having ordinary skill in the art to modify the OEM cloud machine learning model (Nayak: Para. 7) with hazards detected at a distance (Tucker: Para. 41, 115) and the OEM cloud mapping and navigation platform for OEM devices (Dronen: Para. 47) a reasonable expectation of success because machine learning algorithms with real-time V2V data determines vehicle characterization data and associated risk data to identify risks posed by various vehicles within that region of road and alert the driver of an upcoming dangerous situation (Dronen: Para. 35, 40).
Regarding claim 3, Nayak doesn’t explicitly teach wherein the control system for the particular OEM vehicle is further configured to determine a set of vehicle information indicative of a state of the particular OEM vehicle, wherein the set of vehicle information is the set of input data for the trained vehicle-related event recognization model.
However Tucker, in the same field of endeavor, teaches wherein the control system for the particular OEM vehicle is further configured to determine a set of vehicle information indicative of a state of the particular OEM vehicle, wherein the set of vehicle information is the set of input data for the trained vehicle-related event recognization model (Tucker: Para. 38-39, 73; OEM's cloud service or tracking of vehicular location; vehicle rollovers may be detected by crash sensors, gyroscopes, and/or accelerometers; sensors may be placed to detect water levels, closure of roadway gates for toll roads and when roads are closed due to winter driving conditions, shifting or reconfiguration of lanes as occurs throughout the day in high traffic volume areas, railroad crossing gates, presence and/or speed or traffic, and other road conditions; limit to the number of times or the distance that the emergency indication or hazard may be relayed).
It would have been obvious to one having ordinary skill in the art to modify the OEM cloud machine learning model (Nayak: Para. 7) with hazards detected at a distance (Tucker: Para. 41, 115) a reasonable expectation of success because receiving an indication of a hazard event associated with a first passenger vehicle, generating an alert, forwarding the safety alert to a second passenger vehicle approaching the zone improves group vehicle safety (Tucker: Para. 8).
Regarding claim 4, Nayak doesn’t explicitly teach wherein the set of vehicle information includes at least one of (i) information captured by a set of perception sensors of the particular OEM vehicle and (ii) vehicle-to-anything (V2X) information obtained by the particular OEM vehicle.
However Tucker, in the same field of endeavor, teaches wherein the set of vehicle information includes at least one of (i) information captured by a set of perception sensors of the particular OEM vehicle and (ii) vehicle-to-anything (V2X) information obtained by the particular OEM vehicle (Tucker: Para. 31, 59; camera, radar, sonar, or other detection system, is a component that may gather information from the roadway or other location associated with a vehicle; vehicle-to-vehicle communication system).
It would have been obvious to one having ordinary skill in the art to modify the OEM cloud machine learning model (Nayak: Para. 7) with hazards detected at a distance (Tucker: Para. 41, 115) a reasonable expectation of success because receiving an indication of a hazard event associated with a first passenger vehicle, generating an alert, forwarding the safety alert to a second passenger vehicle approaching the zone improves group vehicle safety (Tucker: Para. 8).
Regarding claim 5, Nayak doesn’t explicitly teach wherein the plurality of vehicle-related events includes at least one of (i) accidents, (ii) police chases, (iii) public threats, (iv) careless driving, (v) roadside events, (vi) fire incidents, and (vii) natural disasters.
However Tucker, in the same field of endeavor, teaches wherein the plurality of vehicle-related events includes at least one of (i) accidents, (ii) police chases, (iii) public threats, (iv) careless driving, (v) roadside events, (vi) fire incidents, and (vii) natural disasters (Tucker: Para. 30, 83, 110; an emergency alert, a disabled vehicle alert, road hazard alert, a road closure alert, a traffic alert, or another type of alert).
It would have been obvious to one having ordinary skill in the art to modify the OEM cloud machine learning model (Nayak: Para. 7) with hazards detected at a distance (Tucker: Para. 41, 115) a reasonable expectation of success because receiving an indication of a hazard event associated with a first passenger vehicle, generating an alert, forwarding the safety alert to a second passenger vehicle approaching the zone improves group vehicle safety (Tucker: Para. 8).
Regarding claim 6, Nayak and Tucker don’t explicitly teach wherein the computing server is further configured to provide the model output to a user device logged into an account or an application associated with the OEM.
However Dronen, in the same field of endeavor, teaches wherein the computing server is further configured to provide the model output to a user device logged into an account or an application associated with the OEM (Dronen: Para. 46; user equipment may be configured to execute and run mobile applications such as a messaging application, a browser application, a navigation application; user equipment may be coupled to the system via the OEM cloud 105 and the network).
It would have been obvious to one having ordinary skill in the art to modify the OEM cloud machine learning model (Nayak: Para. 7) with hazards detected at a distance (Tucker: Para. 41, 115) and the OEM cloud mapping and navigation platform for OEM devices (Dronen: Para. 47) a reasonable expectation of success because machine learning algorithms with real-time V2V data determines vehicle characterization data and associated risk data to identify risks posed by various vehicles within that region of road and alert the driver of an upcoming dangerous situation (Dronen: Para. 35, 40).
Regarding claim 8, Nayak teaches the vehicle-related event recognization and alert system of claim 1, wherein the long distance is a physical distance from the particular OEM vehicle (Nayak: Para. 73; present a notification to the driver; notification can alert the driver that a change in the autonomous mode will occur shortly (e.g., within a specified period of time)).
Regarding claim 11, Nayak teaches a vehicle-related event recognization and alert method, the vehicle-related event recognization and alert method comprising: detecting, by each control system of a plurality of control systems of a plurality of original equipment manufacturer (OEM) vehicles, respectively, each OEM vehicle being associated with an OEM, a vehicle-related event in which an encounter between the OEM vehicle and one or more other objects occurred (Nayak: Para. 7; plurality of in-vehicle sensors installed in multiple vehicles traveling the road or from an original equipment manufacturer (OEM) cloud of the multiple vehicles; vehicular sensor data related to a number of positive observations of the physical divider by unique vehicles); verifying, by each control system, that the vehicle-related event is a valid vehicle- related event for training of a long range vehicle-related event recognization model (Nayak: Para. 7; one or more aggregated values generated from the categorical vehicular sensor data includes selected categorical values, using a voting algorithm, for each of the physical divider measurement event type, the physical divider flag); in response to verifying that the vehicle-related event is valid, transmitting, from each control system via a network, vehicle operational data relating to the vehicle-related event (Nayak: Para. 7; one or more aggregated values are collected over an extended period of time for use in training of the machine learning model for prediction of presence of the physical divider); receiving, by a computing server associated with the OEM and via the network (Nayak: Para. 36, 81; OEM cloud for the vehicles; processing needed by the machine learning model is provided on the server side), ……… ; training, by the computing server, the long range vehicle-related event recognization model based on the received vehicle operational data (Nayak: Para. 1, 36; using the trained machine learning model for predicting presence of one or more physical dividers on at least one segment of a road; train a machine learning model to predict physical dividers on a road segment of interest, based on map data associated with the segment of interest, vehicular sensor data collected from vehicles, an OEM cloud for the vehicles), wherein the trained long range vehicle-related event recognization model is configured to wherein the trained long range vehicle-related event recognization model is configured to (Nayak: Para. 64-65; model or predict shape, distance from vehicle to the physical divider, and/or other attributes of the physical divider; predict a road characteristic related to the physical divider; probability of oncoming traffic, a presence of vulnerable road users) ……….. and (ii) to be executed based on a set of input data to generate a model output indicative of one or more recognized vehicle-related events (Nayak: Para. 36, 81; train a machine learning model to predict physical dividers on a road segment of interest, based on map data associated with the segment of interest, vehicular sensor data collected from vehicles, an OEM cloud for the vehicles, or a combination thereof).
Nayak doesn’t explicitly teach vehicle operational data relating to a plurality of vehicle-related events ………. (i) recognize a plurality of different types of vehicle-related events at least at a long distance relative to a particular OEM vehicle of the plurality of OEM vehicles ……………. wherein the long distance is at least one of (i) a physical distance from the particular OEM vehicle and (ii) a time period in advance of the future encounter by the particular OEM vehicle.
However Tucker, in the same field of endeavor, teaches vehicle operational data relating to a plurality of vehicle-related events (Tucker: Para. 37; serve to notify other vehicles, drivers, or services, when a vehicle has become disabled on a roadway; systems of the present disclosure may receive mechanical or operational data from other OEM vehicle computers) ………. (i) recognize a plurality of different types of vehicle-related events at least at a long distance relative to a particular OEM vehicle of the plurality of OEM vehicles (Tucker: Para. 38-39, 73; OEM's cloud service or tracking of vehicular location; vehicle rollovers may be detected by crash sensors, gyroscopes, and/or accelerometers; sensors may be placed to detect water levels, closure of roadway gates for toll roads and when roads are closed due to winter driving conditions, shifting or reconfiguration of lanes as occurs throughout the day in high traffic volume areas, railroad crossing gates, presence and/or speed or traffic, and other road conditions; limit to the number of times or the distance that the emergency indication or hazard may be relayed) ……………. wherein the long distance is at least one of (i) a physical distance from the particular OEM vehicle and (ii) a time period in advance of the future encounter by the particular OEM vehicle (Tucker: Para. 115, 123; an approach time in addition to or instead of distance can be used to determine which vehicles should receive an alert; OEM cloud is shown interacting with the alert management system).
It would have been obvious to one having ordinary skill in the art to modify the OEM cloud machine learning model (Nayak: Para. 7) with hazards detected at a distance (Tucker: Para. 41, 115) a reasonable expectation of success because receiving an indication of a hazard event associated with a first passenger vehicle, generating an alert, forwarding the safety alert to a second passenger vehicle approaching the zone improves group vehicle safety (Tucker: Para. 8).
Nayak and Tucker don’t explicitly teach wherein the model output includes a probability score indicative of a likelihood that a particular recognized vehicle-related event will be encountered by the particular OEM vehicle in the future; and obtaining, by control system associated with the particular OEM vehicle, the model output, comparing, by the control system, the probability score to probability score threshold, and selectively generating, by the control system of the particular OEM, an alert for the one or more recognized vehicle-related events when the probability score satisfies the probability score threshold.
However Dronen, in the same field of endeavor, teaches wherein the model output includes a probability score indicative of a likelihood that a particular recognized vehicle-related event will be encountered by the particular OEM vehicle in the future (Dronen: Para. 62; vehicle is characterized with indicates a level of risk that the vehicle poses to other vehicles in its vicinity; a high risk category vehicle poses a high risk of mishappening to other vehicles in its vicinity, such as in the form of high collision possibility, vehicle swerving, vehicle breakdown, erratic driving); and obtaining, by control system associated with the particular OEM vehicle, the model output, comparing, by the control system, the probability score to probability score threshold (Dronen: Para. 62, 83, 99; system may process the data in risk layer and may generate an awareness notification of this kind of dangerous situation; high risk category vehicle poses a high risk of mishappening to other vehicles in its vicinity; high or low degree may be determined based on probabilistic measures), and selectively generating, by the control system of the particular OEM, an alert for the one or more recognized vehicle-related events when the probability score satisfies the probability score threshold (Dronen: Para. 47, 62, 89; sensor data from the vehicle detector, and the vehicle communication data from the V2V unit may then be fed to a local map generating unit; a single platform to provide a suite of mapping and navigation related applications for OEM devices).
It would have been obvious to one having ordinary skill in the art to modify the OEM cloud machine learning model (Nayak: Para. 7) with hazards detected at a distance (Tucker: Para. 41, 115) and the OEM cloud mapping and navigation platform for OEM devices (Dronen: Para. 47) a reasonable expectation of success because machine learning algorithms with real-time V2V data determines vehicle characterization data and associated risk data to identify risks posed by various vehicles within that region of road and alert the driver of an upcoming dangerous situation (Dronen: Para. 35, 40).
Regarding claim 13, Nayak doesn’t explicitly teach further comprising determining, by the control system for the particular OEM vehicle, a set of vehicle information indicative of a state of the for the particular OEM vehicle, wherein the set of vehicle information is the set of input data for the trained vehicle-related event recognization model.
However Tucker, in the same field of endeavor, teaches further comprising determining, by the control system for the particular OEM vehicle, a set of vehicle information indicative of a state of the for the particular OEM vehicle, wherein the set of vehicle information is the set of input data for the trained vehicle-related event recognization model (Tucker: Para. 38-39, 73; OEM's cloud service or tracking of vehicular location; vehicle rollovers may be detected by crash sensors, gyroscopes, and/or accelerometers; sensors may be placed to detect water levels, closure of roadway gates for toll roads and when roads are closed due to winter driving conditions, shifting or reconfiguration of lanes as occurs throughout the day in high traffic volume areas, railroad crossing gates, presence and/or speed or traffic, and other road conditions; limit to the number of times or the distance that the emergency indication or hazard may be relayed).
It would have been obvious to one having ordinary skill in the art to modify the OEM cloud machine learning model (Nayak: Para. 7) with hazards detected at a distance (Tucker: Para. 41, 115) a reasonable expectation of success because receiving an indication of a hazard event associated with a first passenger vehicle, generating an alert, forwarding the safety alert to a second passenger vehicle approaching the zone improves group vehicle safety (Tucker: Para. 8).
Regarding claim 14, Nayak doesn’t explicitly teach wherein the set of vehicle information includes at least one of (i) information captured by a set of perception sensors of the particular OEM vehicle and (ii) vehicle-to-anything (V2X) information obtained by the particular OEM vehicle.
However Tucker, in the same field of endeavor, teaches wherein the set of vehicle information includes at least one of (i) information captured by a set of perception sensors of the particular OEM vehicle and (ii) vehicle-to-anything (V2X) information obtained by the particular OEM vehicle (Tucker: Para. 31, 59; camera, radar, sonar, or other detection system, is a component that may gather information from the roadway or other location associated with a vehicle; vehicle-to-vehicle communication system).
It would have been obvious to one having ordinary skill in the art to modify the OEM cloud machine learning model (Nayak: Para. 7) with hazards detected at a distance (Tucker: Para. 41, 115) a reasonable expectation of success because receiving an indication of a hazard event associated with a first passenger vehicle, generating an alert, forwarding the safety alert to a second passenger vehicle approaching the zone improves group vehicle safety (Tucker: Para. 8).
Regarding claim 15 Nayak doesn’t explicitly teach wherein the plurality of vehicle-related events includes at least one of (i) accidents, (ii) police chases, (iii) public threats, (iv) careless driving, (v) roadside events, (vi) fire incidents, and (vii) natural disasters.
However Tucker, in the same field of endeavor, teaches wherein the plurality of vehicle-related events includes at least one of (i) accidents, (ii) police chases, (iii) public threats, (iv) careless driving, (v) roadside events, (vi) fire incidents, and (vii) natural disasters (Tucker: Para. 30, 83, 110; an emergency alert, a disabled vehicle alert, road hazard alert, a road closure alert, a traffic alert, or another type of alert).
It would have been obvious to one having ordinary skill in the art to modify the OEM cloud machine learning model (Nayak: Para. 7) with hazards detected at a distance (Tucker: Para. 41, 115) a reasonable expectation of success because receiving an indication of a hazard event associated with a first passenger vehicle, generating an alert, forwarding the safety alert to a second passenger vehicle approaching the zone improves group vehicle safety (Tucker: Para. 8).
Regarding claim 16, Nayak and Tucker don’t explicitly teach further comprising providing, by the computing server, the model output to a user device logged into an account or an application associated with the OEM.
However Dronen, in the same field of endeavor, teaches further comprising providing, by the computing server, the model output to a user device logged into an account or an application associated with the OEM (Dronen: Para. 46; user equipment may be configured to execute and run mobile applications such as a messaging application, a browser application, a navigation application; user equipment may be coupled to the system via the OEM cloud and the network).
It would have been obvious to one having ordinary skill in the art to modify the OEM cloud machine learning model (Nayak: Para. 7) with hazards detected at a distance (Tucker: Para. 41, 115) and the OEM cloud mapping and navigation platform for OEM devices (Dronen: Para. 47) a reasonable expectation of success because machine learning algorithms with real-time V2V data determines vehicle characterization data and associated risk data to identify risks posed by various vehicles within that region of road and alert the driver of an upcoming dangerous situation (Dronen: Para. 35, 40).
Regarding claim 18, Nayak teaches the vehicle-related event recognization and alert method of claim 17, wherein the long distance is a physical distance from the particular OEM vehicle (Nayak: Para. 73; present a notification to the driver; notification can alert the driver that a change in the autonomous mode will occur shortly (e.g., within a specified period of time)).
Regarding claim 21, Nayak teaches the vehicle-related event recognization and alert system of claim 7, wherein the long distance is the time period in advance of the future encounter with the particular OEM vehicle (Nayak: Para. 73; present a notification to the driver; notification can alert the driver that a change in the autonomous mode will occur shortly (e.g., within a specified period of time)).
Regarding claim 22, Nayak teaches the vehicle-related event recognization and alert system of claim 21, wherein the long distance is a combination of both (i) the time period in advance of the future encounter with the particular OEM vehicle and (ii) the physical distance from the particular OEM vehicle (Nayak: Para. 73; present a notification to the driver; notification can alert the driver that a change in the autonomous mode will occur shortly (e.g., within a specified period of time)).
Regarding claim 23, Nayak teaches the vehicle-related event recognization and alert method of claim 17, wherein the long distance is the time period in advance of the future encounter with the particular OEM vehicle (Nayak: Para. 73; present a notification to the driver; notification can alert the driver that a change in the autonomous mode will occur shortly (e.g., within a specified period of time)).
Regarding claim 24, Nayak doesn’t explicitly teach wherein the long distance is a combination of both (i) the time period in advance of the future encounter with the particular OEM vehicle and (ii) the physical distance from the particular OEM vehicle.
However Tucker, in the same field of endeavor, teaches wherein the long distance is a combination of both (i) the time period in advance of the future encounter with the particular OEM vehicle and (ii) the physical distance from the particular OEM vehicle (Tucker: Para. 115, 123; an approach time in addition to or instead of distance can be used to determine which vehicles should receive an alert; OEM cloud is shown interacting with the alert management system).
It would have been obvious to one having ordinary skill in the art to modify the OEM cloud machine learning model (Nayak: Para. 7) with hazards detected at a distance (Tucker: Para. 41, 115) a reasonable expectation of success because receiving an indication of a hazard event associated with a first passenger vehicle, generating an alert, forwarding the safety alert to a second passenger vehicle approaching the zone improves group vehicle safety (Tucker: Para. 8).
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Nayak et al. (US Publication 2020/0167575 A1) in view of Dronen et al. (US Publication 2022/0203973 A1) and in further view of Kim et al. (US Publication 2024/0375642 A1).
Regarding claim 9, Nayak teaches the vehicle-related event recognization and alert system of claim 7, wherein the alert includes at least one of a visual alert, an audio alert, and a haptic alert wherein the alert includes at least one of a visual alert, an audio alert, and a haptic alert (Nayak: Para. 73; present a notification to the driver or occupant of the vehicle).
Nayak doesn’t explicitly teach wherein the control system for the particular OEM vehicle is further configured to generate and output different alerts for different recognized vehicle-related events.
However Tucker, in the same field of endeavor, teaches wherein the control system for the particular OEM vehicle is further configured to generate and output different alerts for different recognized vehicle-related events (Tucker: Para. 30, 83, 110; an emergency alert, a disabled vehicle alert, road hazard alert, a road closure alert, a traffic alert, or another type of alert).
It would have been obvious to one having ordinary skill in the art to modify the OEM cloud machine learning model (Nayak: Para. 7) with hazards detected at a distance (Tucker: Para. 41, 115) a reasonable expectation of success because receiving an indication of a hazard event associated with a first passenger vehicle, generating an alert, forwarding the safety alert to a second passenger vehicle approaching the zone improves group vehicle safety (Tucker: Para. 8).
Nayak and Tucker don’t explicitly teach wherein more intense alerts are provided for more severe recognized vehicle-related events.
However Kim, in the same field of endeavor, teaches wherein more intense alerts are provided for more severe recognized vehicle-related events (Kim: Para. 139; as the warning level increases, auditory alerts may have a louder sound level).
It would have been obvious to one having ordinary skill in the art to modify the OEM cloud machine learning model (Nayak: Para. 7) with hazards detected at a distance (Tucker: Para. 41, 115) and the with louder sound level with increased warning level (Kim: Para. 139) with a reasonable expectation of success because providing escalating auditory or haptic warnings to the user attempts to gain the user’s attention as the warning level escalates (Kim: Para. 108).
Regarding claim 19, Nayak teaches the vehicle-related event recognization and alert method of claim 17, wherein the alert includes at least one of a visual alert, an audio alert, and a haptic alert (Nayak: Para. 73; present a notification to the driver or occupant of the vehicle).
Nayak doesn’t explicitly teach further comprising generating and outputting, by the control system of the particular OEM, different alerts for different recognized vehicle-related events.
However Tucker, in the same field of endeavor, teaches further comprising generating and outputting, by the control system of the particular OEM, different alerts for different recognized vehicle-related events (Tucker: Para. 30, 83, 110; an emergency alert, a disabled vehicle alert, road hazard alert, a road closure alert, a traffic alert, or another type of alert).
It would have been obvious to one having ordinary skill in the art to modify the OEM cloud machine learning model (Nayak: Para. 7) with hazards detected at a distance (Tucker: Para. 41, 115) a reasonable expectation of success because receiving an indication of a hazard event associated with a first passenger vehicle, generating an alert, forwarding the safety alert to a second passenger vehicle approaching the zone improves group vehicle safety (Tucker: Para. 8).
Nayak and Dronen don’t explicitly teach wherein more intense alerts are provided for more severe recognized vehicle-related events.
However Kim, in the same field of endeavor, teaches wherein more intense alerts are provided for more severe recognized vehicle-related events (Kim: Para. 139; as the warning level increases, auditory alerts may have a louder sound level).
It would have been obvious to one having ordinary skill in the art to modify the OEM cloud machine learning model (Nayak: Para. 7) with hazards detected at a distance (Tucker: Para. 41, 115) and the with louder sound level with increased warning level (Kim: Para. 139) with a reasonable expectation of success because providing escalating auditory or haptic warnings to the user attempts to gain the user’s attention as the warning level escalates (Kim: Para. 108).
Response to Arguments
Applicant’s arguments, filed 20 August 2026, with respect to the rejection of claims 1, 3-9, 11, 13-19, and 21-24 under 35 U.S.C. 103 have been fully considered, but are not persuasive.
The applicant’s attorney argues that Nayak is directed to non-analogous road infrastructure prediction.
In response to applicant's argument that Nayak is nonanalogous art, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, Nayak teaches an original equipment manufacturer cloud of multiple vehicles (Nayak: Para. 7). Nayak teaches training a machine learning model through aggregated values from multiple sensors of multiple vehicles to predict the presence of the physical divider (Nayak: Para. 7). The machine learning model can be used to predict a road characteristic related to the physical divider, such as a probability of oncoming traffic and a presence of vulnerable road users (Nayak: Para. 65). Nayak is in the field of collected data from OEM vehicles, to train a model important to vehicles, in order to predict upcoming issues and locations for a particular vehicle advancing to a location.
The applicant next argues that Nayak does not disclose a model configured to recognize a plurality of different types of vehicle-related events - accidents, police chases, public threats, careless driving, roadside events, fire incidents, and natural disasters - at a long distance relative to a particular OEM vehicle.
In response to the applicant’s argument above, Nayak teaches training a machine learning model through aggregated values from multiple sensors of multiple vehicles to predict the presence of the physical divider (Nayak: Para. 7). The machine learning model can be used to predict a road characteristic related to the physical divider, such as a probability of oncoming traffic and a presence of vulnerable road users (Nayak: Para. 65). Nayak’s model takes in a collection of vehicle data from a collection of vehicles in order to train a machine learning model that predicts the probability of oncoming traffic and vulnerable road users. It would be obvious to one of ordinary skill in the art to see bikers, pedestrians, or construction workers as vulnerable road users. It would be obvious of one of ordinary skill in the art to calculate the probability of accidents or careless driving based on the oncoming traffic information.
The applicant next argues that Nayak does not disclose a model output including a probability score indicative of a likelihood that a particular recognized vehicle-related event will be encountered by a particular OEM vehicle in the future.
In response to the applicant’s argument above, the machine learning model can be used to predict a road characteristic related to the physical divider, such as a probability of oncoming traffic and a presence of vulnerable road users (Nayak: Para. 65). Nayak’s model takes in a collection of vehicle data from a collection of vehicles in order to train a machine learning model that predicts the probability of oncoming traffic and vulnerable road users. It would be obvious to one of ordinary skill in the art to see bikers, pedestrians, or construction workers as vulnerable road users. It would be obvious of one of ordinary skill in the art to calculate the probability of accidents or careless driving based on the oncoming traffic information. Nayak’s machine learning model calculated probability is a probability score indicative of a likelihood of a particular event will be encountered.
The applicant next argues that Dronen does not disclose a trained long range vehicle-related event recognization model configured to recognize a plurality of different types of vehicle-related events at a long distance relative to a particular OEM vehicle.
In response to the argument above, Dronen teaches machine learning model calculating the risk probability for a vehicle in the vicinity of the other (Dronen: Para. 13, 62). Dronen teaches calculating high, medium and low risk of a vehicle related event. The events are different such as a risk of high collision possibility, vehicle swerving, vehicle breakdown, and erratic driving of a vehicle in that region for another vehicle in the vicinity (Dronen: Para. 62). Dronen teaches a vehicle characterized with a predicted level of risk for that vehicle poses to other vehicles in its vicinity (Dronen: Para. 62). The system calculates the risk vehicle A poses to any vehicle B that arrives or passes in vicinity of vehicle A. Vehicle B would get an alert as it approaches the vicinity of risky vehicle A. Dronen sends an alert and safety message to OEM vehicles approaching the vicinity of vehicle A (Dronen: Para. 47, 99).
The applicant next argues Dronen does not disclose a probability score for a future encounter with a recognized vehicle-related event category at a long distance.
In response to the argument above, Dronen teaches machine learning model calculating the risk probability for a vehicle in the vicinity of the other (Dronen: Para. 13, 62). Dronen teaches calculating high, medium and low risk of a vehicle related event. The events are different such as a risk of high collision possibility, vehicle swerving, vehicle breakdown, and erratic driving of a vehicle in that region for another vehicle in the vicinity (Dronen: Para. 62). Dronen teaches a vehicle characterized with a predicted level of risk for that vehicle poses to other vehicles in its vicinity (Dronen: Para. 62). The system calculates the risk vehicle A poses to any vehicle B that arrives or passes in vicinity of vehicle A. Vehicle B would get an alert as it approaches the vicinity of risky vehicle A. Dronen sends an alert and safety message to OEM vehicles approaching the vicinity of vehicle A (Dronen: Para. 47, 99). Dronen generates an awareness notification of a predicted type of dangerous situation allowing for the driver to react to an upcoming situation accordingly (Dronen: Para. 83). It would be obvious to one of ordinary skill in the art to create the probability of 0 to 100% from a high, medium, or low risk. It also would be obvious to one of ordinary skill in the art to decide that high risk get an automatic alert, thus the predicted risk is compared to a threshold.
The applicant next argues that the combination of Nayak and Dronen to create the claimed invention would be through hindsight bias.
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
The applicant next argues that the combination of Nayak and Dronen does not arrive at the amended claims as a whole.
In response to the argument above, the claim as a whole is rejected by Nayak, Tucker, and Dronen.
The applicant next argues that there is no reason to combine Nayak and Dronen.
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
In this case, both Nayak and Dronen teach a system where data is collected from a group of OEM vehicles. Nayak teaches training a machine learning model through aggregated values from multiple sensors of multiple vehicles to predict the presence of the physical divider (Nayak: Para. 7). The machine learning model can be used to predict a road characteristic related to the physical divider, such as a probability of oncoming traffic and a presence of vulnerable road users (Nayak: Para. 65). Dronen teaches machine learning model calculating the risk probability for a vehicle in the vicinity of the other (Dronen: Para. 13, 62). One of ordinary skill in the art would see the similarities between two machine learning model systems using a group of OEM vehicles. There is an obvious similarity between using a machine learning model to predict the probability of oncoming traffic for the vehicle approaching a road segment and a machine learning model predicting the risk of collision for the vehicle approaching a road segment with a predicted high risk vehicle located in that upcoming segment.
The applicant next argues that the dependent claims are allowable at least based on their dependencies.
In response to the argument above, independent claims 1 and 11 are rejected. Dependent claims 3-6, 8-9, 13-16, 18-19, 21-24 are rejected at least based on their dependencies on claims 1 and 11.
The applicant’s arguments have failed to point out the distinguishing characteristics of the amended claim language over the prior art. For the above reasons, Nayak’s OEM vehicle server-based machine learning in view of Tucker’s time and distance calculations with Dronen’s collision probability reads on applicant’s long-distance vehicle-related event recognization and alert. The rejection is maintained.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAURA E LINHARDT whose telephone number is (571) 272-8325. The examiner can normally be reached on M-TR, M-F: 8am-4pm.
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/L.E.L./Examiner, Art Unit 3663
/ADAM D TISSOT/ Primary Examiner, Art Unit 3663
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