DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to communications: Amendment filed on 12/19/2025.
Claims 1-15 and 17-20 are pending. Claims 1, 9, and 12 are independent. Claim 21 is newly canceled.
The previous rejection of claims 1-15, and 17-20 under 35 USC § 103 have been maintained in view of the amendment.
Claim Rejections - 35 USC § 103
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) 1-15, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over McBeth et al. (US2021/0097311) in view of Mercelis et al. (“Towards Detection of Road Weather Conditions using Large-Scale Vehicle Fleets”) and Bures et al. (US2020/0322703).
In regards to claim 1, McBeth et al. substantially discloses a method for generating a model for estimation of reliability of hazard sensor observations associated with vehicles, the method comprising:
Receiving, by a communication interface and a wireless network, one or more hazard observations collected from at least one hazard sensor a vehicle, wherein each of the one or more hazard observations is associated with a hazard location and a hazard timestamp (McBeth et al. para[0051], receives GPS location and time stamp of roadway hazards);
training, by the controller, a model for estimation of reliability of subsequent hazard sensor observations based on the comparison of the one or more hazard observations to the one or more historical weather records (McBeth et al. para[0052], training a model to detect hazards from sensor data, para[0056] determines confidence a hazard has been accurately identified.).
McBeth et al. does not explicitly disclose performing, by a hazard estimation module of the controller, a comparison of the one or more hazard observations to the one or more weather records.
However Mercelis et al. substantially discloses performing, by a hazard estimation module of the controller, a comparison of the one or more hazard observations to the one or more weather records (Mercelis et al. pg3 section IV.A para1, compares hazards observations to weather records for validation).
It would have been obvious to one of ordinary skill in the art before the filing date of the application to have combined the road hazard detection method of McBeth with weather condition detection in order to allow drivers to anticipate and adjust driving behavior in response to hazardous weather conditions (Mercelis et al. pg1 section I para1).
Mcbeth et al. does not explicitly disclose Identifying, by a controller, a time period before the one or more hazard observations were collected;
accessing, by the controller, one or more historical weather records, recorded in the time period, from a historical weather database, wherein the accessing is based on the hazard location and the hazard timestamp.
However Bures et al. substantially discloses Identifying, by a controller, a time period before the one or more hazard observations were collected (Bures et al. para[0071], identifies new measurement and retrieves historical measurement that would be from a period before the new measurement to compare the new measurement to the historical measurement);
accessing, by the controller, one or more historical weather records, recorded in the time period, from a historical weather database, wherein the accessing is based on the hazard location and the hazard timestamp (Bures et al. para[0272], the contextual database can include expected current conditions for the location, for example, generated based on performing one or more statistical measurement functions of the function database 543 and/or otherwise determining a statistical trend of one or more conditions of the location based on historical data included in the measurement database 542, for example, indicating a historical average and/or standard deviation of one or more measurements for that location).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the road hazard detection method of McBeth et al. with the measurement database of Bures et al. in order to track discrepancies between measured value and expected historical value (Bures et al. para[0071]).
In regards to claim 2, McBeth et al. as modified by Mercelis et al. and Bures et al. discloses the method of claim 1, further comprising:
receiving, by the communication interface, a subsequent hazard observation (McBeth et al. para[0048], receives information identifying roadway hazards);
receiving, by the controller, a weather observation (McBeth et al. para[0051], receives weather forecast); and
querying, by the communication interface, the model for estimation of reliability of the hazard sensor based on the subsequent hazard observation and the weather observation (McBeth et al. para[0055]).
In regards to claim 3, McBeth et al. as modified by Mercelis et al. and Bures et al. discloses the method of claim 1, further comprising: defining a field of observation based on the location from the one or more hazard observations (McBeth et al. para[0041]).
In regards to claim 4, McBeth et al. as modified by Mercelis et al. and Bures et al. discloses the method of claim 3, wherein the field of observation has a predetermined size or dimension defined according to the location from the one or more hazard observation (Mercelis et al. pg2 section II.B para2).
It would have been obvious to one of ordinary skill in the art before the filing date of the application to have combined the road hazard detection method of McBeth with weather condition detection in order to allow drivers to anticipate and adjust driving behavior in response to hazardous weather conditions (Mercelis et al. pg1 section I para1).
In regards to claim 5, McBeth et al. as modified by Mercelis et al. and Bures et al. discloses the method of claim 3, wherein the field of observation comprises a plurality of map tiles (Mercelis et al. pg2 section II.A para1, field of observation consists of map tiles (road segments)).
It would have been obvious to one of ordinary skill in the art before the filing date of the application to have combined the road hazard detection method of McBeth with weather condition detection in order to allow drivers to anticipate and adjust driving behavior in response to hazardous weather conditions (Mercelis et al. pg1 section I para1).
In regards to claim 6, McBeth et al. as modified by Mercelis et al. and Bures et al. discloses the method of claim 5, wherein each of the plurality of map tiles is associated with at least one weather record from the historical weather database (Mercelis et al. pg3 section III para1, provides local road weather data (road segment level)).
It would have been obvious to one of ordinary skill in the art before the filing date of the application to have combined the road hazard detection method of McBeth with weather condition detection in order to allow drivers to anticipate and adjust driving behavior in response to hazardous weather conditions (Mercelis et al. pg1 section I para1).
In regards to claim 7, McBeth et al. as modified by Mercelis et al. and Bures et al. discloses the method of claim 6, wherein the at least one hazard variables includes an intensity of precipitation, a precipitation indicator, a visibility distance, and a visibility indicator (Mercelis et al. pg1 section I para2, targeted weather conditions include visibility and precipitation (intensity, type)).
It would have been obvious to one of ordinary skill in the art before the filing date of the application to have combined the road hazard detection method of McBeth with weather condition detection in order to allow drivers to anticipate and adjust driving behavior in response to hazardous weather conditions (Mercelis et al. pg1 section I para1).
In regards to claim 8, McBeth et al. as modified by Mercelis et al. and Bures et al. discloses the method of claim 1, wherein the one or more weather records from the historical weather database is associated with a weather record timestamp, the method further comprising: calculating, by the controller, a time gap between the hazard timestamp of the weather record timestamp, wherein the model for estimation of reliability of the hazard sensor is based on the time gap (McBeth et al. para[0046]).
In regards to claim 9, McBeth et al. substantially discloses an apparatus for generating a model for estimation of reliability of a hazard sensor observations at a vehicle, the apparatus comprising:
A controller of a server(McBeth et al. para[0030], vehicular computer executes instructions based processing data related to control of the vehicle and the external environments), wherein the controller includes:
a hazard observation interface configured to receive one or more hazard observations from a hazard sensor of the vehicle, wherein each of the one or more hazard observations is associated with a hazard location and a hazard timestamp (McBeth et al. para[0051], receives GPS location and time stamp of roadway hazards).
McBeth et al. does not explicitly disclose a ground truth module configured to determine ground truth data from one or more historical weather records from a historical weather database based on the hazard location and the hazard timestamp.
However Mercelis et al. substantially discloses a ground truth module configured to determine ground truth data from one or more historical weather records from a historical weather database based on the hazard location and the hazard timestamp (Mercelis et al. fig. 1 pg2 section II para2, accesses records of Royal Meteorological Institute (RMI)).
It would have been obvious to one of ordinary skill in the art before the filing date of the application to have combined the road hazard detection method of McBeth with weather condition detection in order to allow drivers to anticipate and adjust driving behavior in response to hazardous weather conditions (Mercelis et al. pg1 section I para1).
McBeth et al. does not explicitly disclose wherein the controller is configured to identify a time window based on the hazard timestamp and access one or more weather records, recorded in the identified time window, from the historical weather database based on the hazard location and the hazard timestamp;
a model configured to perform a comparison of the one or more hazard observations to the one or more historical weather records, the model trained for estimation of reliability of the hazard sensor observations based on the comparison of the one or more hazard observations to the one or more historical weather records.
However Bures et al. disclose wherein the controller is configured to identify a time window based on the hazard timestamp and access one or more weather records, recorded in the identified time window, from the historical weather database based on the hazard location and the hazard timestamp (Bures et al. para[0071], identifies new measurement and retrieves historical measurement that would be from a period before the new measurement to compare the new measurement to the historical measurement).
a model configured to perform a comparison of the one or more hazard observations to the one or more historical weather records, the model trained for estimation of reliability of the hazard sensor observations based on the comparison of the one or more hazard observations to the one or more historical weather records (Bures et al. para[0272], Alternatively or in addition, predicted average and/or standard deviation for current conditions can be included in the contextual database, for example, automatically generated as a function of one or more other factors such as current time of day, day of the week, month of the year, recent measurements, weather data, other current measurements determined to be correlated to the condition being predicted, and/or other data determined to dictate a current state that is expected to impacts the condition being predicted).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the road hazard detection method of McBeth et al. with the measurement database of Bures et al. in order to track discrepancies between measured value and expected historical value (Bures et al. para[0071]).
In regards to claim 10, McBeth et al. as modified by Mercelis et al. and Bures et al. discloses the apparatus of claim 9, wherein the ground truth model is configured to determine the ground truth data based on a field of observation based on the location from the one or more hazard observations (McBeth et al. para[0041]).
In regards to claim 11, McBeth et al. as modified by Mercelis et al. and Bures et al. discloses the apparatus of claim 9, wherein the ground truth model is configured to determine the ground truth data based on weather records for a plurality of map tiles (Mercelis et al. pg3 section III para1, provides local road weather data (road segment level)).
It would have been obvious to one of ordinary skill in the art before the filing date of the application to have combined the road hazard detection method of McBeth with weather condition detection in order to allow drivers to anticipate and adjust driving behavior in response to hazardous weather conditions (Mercelis et al. pg1 section I para1).
In regards to claim 12, McBeth et al. substantially discloses a method for determining reliability of observations from a hazard sensor at a vehicle, the method comprising:
Receiving, by a communication interface and a wireless network of a server, a hazard observation collected at a vehicle in association with a location and a timestamp (McBeth et al. para[0051], receives GPS location and time stamp of roadway hazards).
McBeth et al. does not explicitly disclose querying a model for an estimation of reliability of the hazard sensor based on the plurality of historical weather records and the timestamp for the hazard observation.
However Mercelis et al. substantially discloses querying a model for an estimation of reliability of the hazard sensor based on the plurality of weather records and the timestamp for the hazard observation (Mercelis et al. pg3 section III para1, machine learning algorithm trained to validate sensor data with weather models)).
It would have been obvious to one of ordinary skill in the art before the filing date of the application to have combined the road hazard detection method of McBeth with weather condition detection in order to allow drivers to anticipate and adjust driving behavior in response to hazardous weather conditions (Mercelis et al. pg1 section I para1).
McBeth et al. does not explicitly disclose Identifying, by a controller, a time window before the hazard observation was collected;
accessing a plurality of historical weather records, recorded in the time window, from a historical weather database based on the location and the hazard timestamp, wherein the plurality of historical weather records correspond to different map tiles in an area surrounding the location of the hazard observation;.
However Bures et al. discloses accessing a plurality of historical weather records, recorded in the time window, from a historical weather database based on the location and the hazard timestamp, wherein the plurality of historical weather records correspond to different map tiles in an area surrounding the location of the hazard observation (Bures et al. para[0272], the contextual database can include expected current conditions for the location, for example, generated based on performing one or more statistical measurement functions of the function database 543 and/or otherwise determining a statistical trend of one or more conditions of the location based on historical data included in the measurement database 542, for example, indicating a historical average and/or standard deviation of one or more measurements for that location).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the road hazard detection method of McBeth et al. with the measurement database of Bures et al. in order to track discrepancies between measured value and expected historical value (Bures et al. para[0071]).
In regards to claim 13, McBeth et al. as modified by Mercelis et al. and Bures et al. discloses the method of claim 12, further comprising: defining a field of observation for the different map tiles in the area surrounding the location from the hazard observation (McBeth et al. para[0041]).
In regards to claim 14, McBeth et al. as modified by Mercelis et al. and Bures et al. discloses the method of claim 13, wherein the field of observation has a predetermined size or dimension based defined according to the location from the hazard observation (Mercelis et al. pg2 section II.B para2).
It would have been obvious to one of ordinary skill in the art before the filing date of the application to have combined the road hazard detection method of McBeth with weather condition detection in order to allow drivers to anticipate and adjust driving behavior in response to hazardous weather conditions (Mercelis et al. pg1 section I para1).
In regards to claim 15, McBeth et al. as modified by Mercelis et al. and Bures et al. discloses the method of claim 14, wherein the field of observation comprises a plurality of map tiles (Mercelis et al. pg2 section II.A para1, field of observation consists of map tiles (road segments)), and
Wherein each of the plurality of map tiles is associated with at least one weather record from the historical weather database (Mercelis et al. pg3 section III para1, provides local road weather data (road segment level)).
It would have been obvious to one of ordinary skill in the art before the filing date of the application to have combined the road hazard detection method of McBeth with weather condition detection in order to allow drivers to anticipate and adjust driving behavior in response to hazardous weather conditions (Mercelis et al. pg1 section I para1).
In regards to claim 17, McBeth et al. as modified by Mercelis et al. and Bures et al. discloses the method of claim 16, wherein the at least one hazard variables includes an intensity of precipitation, a precipitation indicator, a visibility distance, and a visibility indicator (Mercelis et al. pg1 section I para2, targeted weather conditions include visibility and precipitation (intensity, type)).
It would have been obvious to one of ordinary skill in the art before the filing date of the application to have combined the road hazard detection method of McBeth with weather condition detection in order to allow drivers to anticipate and adjust driving behavior in response to hazardous weather conditions (Mercelis et al. pg1 section I para1).
In regards to claim 18, McBeth et al. as modified by Mercelis et al. and Bures et al. discloses the method of claim 12, further comprising: performing, by a hazard estimation module of the controller, a comparison of the estimation of the reliability of the hazard sensor to a threshold value (Bures et al. para[0285], In some embodiments, the detection functions can take one or more measurement entries as input, and can generate a probability value, where the probability value indicates a probability indicating whether or not the condition of interest exists. The binary value can be generated by comparing the probability value to a probability threshold).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the road hazard detection method of McBeth et al. with the measurement database of Bures et al. in order to track discrepancies between measured value and expected historical value (Bures et al. para[0071]).
In regards to claim 19, McBeth et al. as modified by Mercelis et al. and Bures et al. discloses the method of claim 18, further comprising: calculating a routing instruction in response to the comparison (McBeth et al. para[0043]).
In regards to claim 20, McBeth et al. as modified by Mercelis et al. and Bures et al. discloses the method of claim 18, further comprising: displaying, by a display of the server, an alert in response to the comparison (McBeth et al. para[0019]).
Response to Arguments
Applicant's arguments filed 12/29/2025 have been fully considered but they are not persuasive.
In regards to claim 1, applicant argues on page 8 that Bures does not teach “identifying, by a controller, a time period before the one or more hazard observations”.
However McBeth et al. as modified by Mercelis et al. and Bures et al. discloses identifying, by a controller, a time period before the one or more hazard observations (Bures et al. para[0071], receives new raw and/or processed measurement and historical data from before time period of new data.)
Applicant argues on page 9, that Bures does not teach “accessing by the controller, one or more historical weather records, recorded in the time period, from a historical weather database”.
However McBeth et al. as modified by Mercelis et al. and Bures et al. discloses accessing by the controller, one or more historical weather records, recorded in the time period, from a historical weather database, wherein accessing is based on the hazard location and the hazard timestamp (Bures et al. para[0272], accesses measurement database 542 for historical average measurement for time window before new measurement was collected).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/N.H/Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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