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 .
Status of the Claims
Claims 1-24 are pending for examination.
Claims 1, 9 and 16 are independent Claims.
Claims 1-8 and 16-23 are allowable.
Claims 9-15 and 24 are currently rejected under 35 U.S.C. §103.
Allowable Subject Matter
Claims 1-8 and 16-23 are allowable.
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) 9-13 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Braybrook in view of Coleman, II et al. (U.S. 12,282,327 hereinafter Coleman) .
As Claim 9, besides Claim 1, Braybrook teaches a method comprising:
upon activation of an application stored on a mobile device (Braybrook (¶0044 line 1-4, ¶0066 line 3-5), application is activated to verify the route and correct any errors. Braybrook (¶0066 line 1-10) teaches the portable device (mobile phone) located on a vehicle and capture road feature data. Therefore, the motion characteristic of the trip is the same as “a motion characteristic of the mobile device”);
receiving, by the application, first sensor data from a first sensor, the first sensor data including measurements from the first sensor collected during a recorded time interval that preceded the activation of the application (Braybrook (¶0044 line 6-10, 14-27), GPS data is only available before time T1 and after time T2. SLAM data is used to resolve the gap in GPS data);
receiving, by the application, second sensor data from one or more second sensors (Braybrook (¶0044 line 6-10, 14-27), GPS data is only available before time T1 and after time T2. SLAM data is used to resolve the gap in GPS data) after the activation of the application, the second sensor data including measurements from the one or more second sensors collected after the activation of the application (Braybrook (¶0043 last 4 lines), topographic data may be received before, during or after the journey);
generating a set of sensor signals from the first sensor data and the second sensor data (Braybrook (¶0044 line 6-10, 14-27), GPS data is only available before time T1 and after time T2. SLAM data is used to resolve the gap in GPS data);
Braybrook does not explicitly disclose:
extracting from the set of sensor signals one or more statistical features; and
executing a classifier using the one or more statistical features to generate a prediction of a motion characteristic of the mobile device, wherein the prediction of the motion characteristic corresponds to the motion characteristic included in a set of predefined motion characteristics.
Coleman teaches:
executing a classifier using the one or more statistical features to generate a prediction of a motion characteristic of the mobile device (Coleman (col. 23 line 24-34), “driving characteristic (e.g. Hard breaking occurrences, swerving, lane departures, etc.) to the machine learning datasets, the computing platform 210 may predict how safe ( e.g., a likelihood of an accident or incident occurring) the driving associated with the driving data is (e.g., a level or safety score). In some examples, the analysis may include evaluating a number of times different behaviors occurred in a predetermined time period or data window (e.g., number of minutes spent driving at night in a week”), wherein the prediction of the motion characteristic corresponds to the motion characteristic included in a set of predefined motion characteristics (Coleman (col. 23 line 14-21), “the safety output may be a determination of whether the data evaluated is either deemed "safe" or "unsafe.").
Braybrook discloses a system/method to complete trip data from missing data. Coleman discloses a machine learning method to classify trip data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify trip data of Braybrook instead be a machine learning system/method taught by Coleman, with a reasonable expectation of success. The motivation would be to provide an advantage by “identifying a ride performance characteristic associated with driving data, determining a safety output” (Coleman (col. 1 line 27-28)).
As Claim 10, besides Claim 9, Braybrook in view of Coleman teaches wherein the first sensor is an accelerometer (Braybrook (¶0066 line 19-24), inertial measurement unit), and wherein the prediction is used to generate an alert to present (Coleman (col. 26 line 13-16), “evaluated to predict or determine a ride performance characteristic, such as a level of safety ( e.g., a safety score), level of smoothness, level of satisfaction, or the like, associated with the driving data”).
As Claim 11, besides Claim 9, Braybrook in view of Coleman teaches wherein a first sampling rate of the first sensor is fixed (Braybrook (¶0050 line 1-5), first sensor data is GPS signal) and a second sampling rate of the one or more second sensors is adjustable (Braybrook (¶0050 last 7 lines), second sensor data is sampling around 50-100 meters).
As Claim 12, besides Claim 9, Braybrook in view of Solano teaches wherein the one or more statistical features correspond to a subset of the set of sensor signals that are associated with the motion characteristic of the mobile device (Coleman (col. 23 line 14-21), “the safety output may be a determination of whether the data evaluated is either deemed "safe" or "unsafe".”).
As Claim 13, besides Claim 9, Braybrook in view of Coleman teaches wherein the prediction of the motion characteristic of the mobile device corresponds to a speed of a vehicle (Coleman (col. 23 line 14-21, 24-25), “For instance, by comparing various driving characteristic ( e.g., hard braking occurences, swerving, lane departures, etc.").
As Claim 15, besides Claim 9, Braybrook in view of Coleman teaches further comprising identify a non-binary numerical probability associated with the prediction of the motion characteristic of the mobile device (Coleman (col. 23 line 24-34), “driving characteristic (e.g. Hard breaking occurrences, swerving, lane departures, etc.) to the machine learning datasets, the computing platform 210 may predict how safe ( e.g., a likelihood of an accident or incident occurring) the driving associated with the driving data is (e.g., a level or safety score). In some examples, the analysis may include evaluating a number of times different behaviors occurred in a predetermined time period or data window (e.g., number of minutes spent driving at night in a week.” Coleman (col. 13 line 7-8 and 23-30), “Additionally or alternatively, the safety output may include a score or rating associated with a level of safety for the driving data. Data having a score at or above a threshold may be considered safe, while scores below the threshold may be considered unsafe.”, rating is associated with a level of safety with higher score indicates a higher probability of safety).
Claim(s) 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Braybrook in view of Coleman in further view of Solano (U.S. 20210318129 hereinafter Solano) .
As Claim 24, besides Claim 9, Braybrook in view of Coleman does not disclose while Solano teaches wherein the generated prediction of the motion characteristic of the mobile device corresponds to movement in a vehicle (Solano (¶0032 line 6-10), the system determines whether the mobile device is in a vehicle).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify trip system of Braybrook in view of Coleman instead be a missing trip module taught by Solano, with a reasonable expectation of success. The motivation would be to easily and conveniently detect inaccuracy in telemetric data (Solano (¶0052)).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Braybrook in view of Coleman in further view of Pal et al. (U.S. 2017/0053461 hereinafter Pal).
As Claim 14, besides Claim 9, Braybrook in view of Coleman does not explicitly disclose wherein the generated prediction of the motion characteristic of the mobile device corresponds to a vehicle collision.
Pal teaches:
wherein the generated prediction of the motion characteristic of the mobile device corresponds to a vehicle collision (Pal (¶0035), system increases confidence level associated with detecting vehicular accident events and/or determining vehicular accident characteristics).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify trip system of Braybrook in view of Coleman instead be an accident detection module teaches by Pal, with a reasonable expectation of success. The motivation would be to increase confidence level associated with detecting vehicular accident events and/or determining vehicular accident characteristics (Pal (¶0035)).
Response to Amendment
Claim Rejection under 35 U.S.C. §101:
Applicants’ arguments are persuasive; therefore, 35 U.S.C. §101 rejections are respectfully withdrawn.
Claim Rejection under 35 U.S.C. §103:
As Claim 9, Applicant(s) argues that Solano does not disclose “a classification machine learning model” (first paragraph of page 15 in the remarks).
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Applicant(s) argument(s) are not persuasive because Solano (¶0023 line 7-12, ¶0052 last 5 lines) teaches “if the data source analysis platform 103 identifies that there is a distance gap, the data source analysis platform 103 may determine that data corresponding to at least one driving trip may be missing.” Therefore, the machine learning of solano (data source analysis platform) can classify the driving trip as being missing.
As Claim 9, Applicant(s) argues that Solano does not disclose “executing a classification model …” as currently amended (last paragraph of page 15 in the remarks).
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Applicants’ arguments are moot because new reference Coleman teaches the limitation(s).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NHAT HUY T NGUYEN whose telephone number is (571)270-7333. The examiner can normally be reached M-F: 12:00-8:00 EST.
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/NHAT HUY T NGUYEN/Primary Examiner, Art Unit 2147
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