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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/08/2026 has been entered.
Response to Amendments and Arguments
The amendments and arguments filed 12/01/2025 are acknowledged and have been fully considered. Claims 1, 2, 10, and 19 are amended; claim 18 has been canceled; no claims have been added or withdrawn. Claims 1-17 and 19 are now pending and under consideration.
The previous objections to the drawings have been withdrawn, in light of the amendments to the drawings.
The previous objections to claims 2 and 10 have been withdrawn, in light of the amendments to the claims.
The previous rejection of claim 19 under 35 U.S.C. 112(b) has been withdrawn, in light of the amendments to the claim.
The previous rejection of claim 19 under 35 U.S.C. 101 has been withdrawn, in light of the amendments to the claim.
Applicant asserts on pages 12-15 of the remarks that it would be improper to maintain the prior art rejections of amended independent claims 1, 10, and 19 under 35 U.S.C. 102(a)(1) as being anticipated by CN 108791303 A to Zhang et al., with reference to ¶ 72-73, 88 & 115 of Zhang, because:
As acknowledged by the Continuation Sheet of the Advisory Action mailed 12/05/2025, the prior art rejections of amended independent claims 1, 10, and 19 under 35 U.S.C. 102(a)(1) as being anticipated by CN 108791303 A to Zhang et al. cannot be maintained in view of the amendments to the claims. Therefore, the rejections have been withdrawn. However, upon further consideration, a new ground of rejection of the amended independent claims 1, 10, and 19 is now made under 35 U.S.C. 103 as being unpatentable over Zhang in view of U.S. Patent Application Publication No. 2016/0016590 to Fernandez Pozo et al.
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 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 1-7, 10-15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over CN 108791303 A to Zhang et al. (hereinafter: “Zhang”) in view of U.S. Patent Application Publication No. 2016/0016590 to Fernandez Pozo et al. (hereinafter: “Fernandez Pozo”).
*Note: the machine-translation of Zhang provided with the non-final Office Action mailed 03/26/2025 was only modified by the examiner to introduce paragraph numbering which corresponds to paragraph citations used throughout the prior art rejections of the claims below.
With respect to claim 1, Zhang teaches a computer-implemented method for monitoring operation of a vehicle (for example, as discussed by at least ¶ 3 & 57-65), comprising: providing, by a mobile device located in the vehicle, a trip dataset comprising time data and accelerometer data, the accelerometer data being acquired by an accelerometer of the mobile device that measures acceleration in 3 acceleration axes, wherein the mobile device is transported by the vehicle during an acquisition of the trip dataset, and the trip dataset corresponds to a vehicle trajectory [for example, as discussed by at least ¶ 58-59, 64-73, 86-89 & 120, acceleration data (e.g., “accelerometer data”) is collected at a fixed acquisition frequency during driving of a vehicle, and a data interval (e.g., “time data”) is determined during the driving of the vehicle, such that the acceleration data and the data interval are together definable as a “trip dataset,” where the acceleration data is collected using a three-axis acceleration sensor (e.g., “accelerometer”) on a mobile terminal (e.g., “mobile device”) within the vehicle during the driving of the vehicle, such that the “trip dataset” necessarily corresponds to a definable “vehicle trajectory”]; dividing the trip dataset into a plurality of trip segments (for example, as discussed by at least ¶ 71-72, 88 & 115, the acceleration data corresponding to the data interval is composed of a series of acceleration data sets ax, ay, az divided according to the fixed acquisition frequency, such that the “trip dataset” is necessarily divided into definable “trip segments”); and for each trip segment of the plurality of trip segments, by any of one or more microprocessors (120): processing the trip dataset into a uniformly sampled dataset by filtering the trip dataset and decimating the trip dataset [for example, as discussed by at least ¶ 16, 29, 73, 88 & 115, each acceleration data set ax, ay, az of the series of acceleration data sets ax, ay, az corresponding to the data interval is extracted according to the fixed acquisition frequency, including by performing high-pass filtering on the extracted acceleration data (e.g., “filtering the trip dataset”) to separate low-frequency acceleration data (e.g., “decimating the trip dataset”)]; producing gravitational vector direction data by determining a gravitational vector direction; transforming coordinates from mobile device coordinates into vehicle coordinates (for example, as discussed by at least ¶ 74-75 & 118-135, coordinate transformation is performed on each of the acceleration data sets ax, ay, az to normalize each of the acceleration data sets ax, ay, az to a vehicle posture from a mobile terminal posture based on adjusting a z-axis of each of the acceleration data sets ax, ay, az to match a gravity direction and adjusting an x-axis and a y-axis of each of the acceleration data sets ax, ay, az to match a driving direction of the vehicle); identifying one or more acceleration events by comparing the acceleration data of the trip segment with predetermined thresholds [for example, as discussed by at least ¶ 77-78 & 107-115, an acceleration-based driving behavior (e.g., “acceleration event”) is identified based on: determining a consecutive (continuous) number by comparing a total acceleration determined for (and based on) each of the acceleration sets ax, ay, az with a set threshold (e.g., “predetermined threshold”), and comparing the consecutive (continuous) number with a set number (e.g., “predetermined threshold”)]; and reducing usage of the vehicle for transport tasks if the number of identified one or more acceleration events exceeds a safety condition [claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure (e.g., see: MPEP 2111.04_I), and the broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met (e.g., see: MPEP 2111.04_II), and the step “reducing usage of the vehicle for transport tasks” would not necessarily be performed as part of the claimed method at times including when the condition “if the number of identified one or more acceleration events exceeds a safety condition” is not met during performing of the claimed method (e.g., when the number of identified one or more acceleration events does not exceed the safety condition during the performing of the claimed method), such that “reducing usage of the vehicle for transport tasks if the number of identified one or more acceleration events exceeds a safety condition” does not necessarily further limit the scope of the claimed method under a broadest reasonable interpretation; even so, for example, as discussed by at least ¶ 38, 77-79 & 99-101, a dispatch strategy is adjusted such that the usage of the vehicle on a certain road section is reduced to increase passenger safety at times including when the identified acceleration-based driving behavior is determined to include a dangerous driving behavior based on the consecutive (continuous) number exceeding the set number (e.g., “safety condition”); note that each instance of a vehicle transporting itself and/or a driver and/or a passenger and/or an object (e.g., the “mobile device,” or cargo) via driving of the vehicle over at least a minimal distance is definable as a “transport task”].
Zhang appears to lack a clear teaching as to whether, for each trip segment of the plurality of trip segments, processing the trip dataset into the uniformly sampled dataset is further by increasing a data rate of the trip dataset by interpolation.
Fernandez Pozo teaches an analogous method including: providing, by a mobile device located in the vehicle, a trip dataset comprising time data and accelerometer data, the accelerometer data being acquired by an accelerometer of the mobile device that measures acceleration in 3 acceleration axes, wherein the mobile device is transported by the vehicle during an acquisition of the trip dataset (as discussed by at least ¶ 0041-0043, 0052-0054, & 0087, accelerometer data is collected with respect to time at a fixed sampling rate during driving of a vehicle, where the accelerometer data is collected using an accelerometer sensor of a mobile device transported by the vehicle during the driving of the vehicle, where the accelerometer data includes three axis accelerometer data), and the trip dataset corresponds to a vehicle trajectory; and processing the trip dataset into the uniformly sampled dataset is further by increasing a data rate of the trip dataset by interpolation, filtering the trip dataset, and decimating the trip dataset (as depicted by at least Figs. 1 & 2 and as discussed by at least ¶ 0041-0043 & 0052-0064, each set of accelerometer data with respect to time is collected at the fixed sampling rate, including by performing an interpolation process to provide the fixed sampling rate for all the sensor data independently of technological characteristics, thereby providing consistent sampling frequency for all data, filtering the accelerometer data, and removing unrelated data).
It would have been obvious to one having ordinary skill in the art at the time the invention was made to have modified the computer-implemented method of Zhang with the teachings of Fernandez Pozo, if even necessary, such that for each trip segment of the plurality of trip segments, the processing the trip dataset into the uniformly sampled dataset is further by increasing a data rate of the trip dataset by interpolation because Zhang already Fernandez Pozo further teaches that performing such an interpolation process beneficially provides the fixed sampling rate for all the sensor data independently of technological characteristics, thereby providing consistent sampling frequency for all data.
With respect to claim 2, Zhang modified supra teaches the computer-implemented method of claim 1, wherein the predetermined thresholds comprise a predetermined acceleration threshold (the set threshold, as discussed in detail above with respect to claim 1, is definable as a “predetermined acceleration threshold”); wherein the one or more acceleration events being at least one of vehicle speeding up and braking events along the vehicle trajectory (for example, as discussed by at least ¶ 65, 78 & 165 of Zhang, the identified acceleration-based driving behavior at least at times includes a sudden braking of the vehicle; because vehicle speeding up event and vehicle braking event are recited in the alternative, it is sufficient to address one of the claimed alternatives) and identifying comprises comparing an acceleration along an acceleration axis that follows the vehicle trajectory against the predetermined acceleration threshold (as discussed in detail above with respect to claims 1 and 2).
With respect to claim 3, Zhang modified supra teaches the computer-implemented method of claim 1, wherein the predetermined thresholds comprise a predetermined lateral acceleration threshold (the set threshold, as discussed in detail above with respect to claim 1, is definable as a “predetermined lateral acceleration threshold” by virtue of the set threshold being compared to the total acceleration which includes lateral acceleration of the vehicle), the one or more acceleration events being vehicle turning events which are lateral movements in the vehicle trajectory (for example, as discussed by at least ¶ 65, 78 & 165 of Zhang, the identified acceleration-based driving behavior at least at times includes a sharp turn of the vehicle), and the identifying of the one or more acceleration events comprises comparing an acceleration along an acceleration axis, which is orthogonal to the vehicle trajectory and orthogonal to the gravitational vector direction, against the predetermined lateral acceleration threshold (as discussed in detail above with respect to claims 1 and 3).
With respect to claim 4, Zhang modified supra teaches the computer-implemented method of claim 3, wherein the safety condition is a predefined safety limit (as discussed in detail above with respect to claim 1), and the method further comprises: determining that the number of identified one or more acceleration events exceeds the predefined safety limit (as discussed in detail above with respect to claim 1).
With respect to claim 5, Zhang modified supra teaches the computer-implemented method claim 1, further comprising generating a report including the one or more acceleration events and generating a safety score based on the report [for example, as discussed by at least ¶ 97-100 & 181-183 of Zhang, the dangerous driving behavior data (e.g., “report”) of a driver is generated, and a driver rating (level) (e.g., “safety score”) is generated based on the dangerous driving behavior data, where the dangerous driving behavior data includes the identified dangerous driving behavior].
With respect to claim 6, Zhang modified supra teaches the computer-implemented method of claim 5, further comprising sending the safety score to a service allocation server (for example, as discussed by at least ¶ 22, 59-65, 97-100 & 182-183 of Zhang).
With respect to claim 7, Zhang modified supra teaches the computer-implemented method of claim 6, wherein the number of identified one or more acceleration events exceed the safety condition when the safety score exceeds a predefined safety limit (as discussed in detail above with respect to claims 1, 5, and 6).
With respect to claim 10, Zhang modified supra teaches a computer system for monitoring operation of a vehicle, comprising: a mobile device for being placed in the vehicle, the mobile device being configured to provide a trip dataset comprising time data and accelerometer data, wherein the trip dataset corresponds to a vehicle trajectory [claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure (e.g., see: MPEP 2111.04_I), and an apparatus claims cover what a device is, not what a device does, and a claim containing a recitation with respect to the manner in which a claimed apparatus is intended to be employed does not differentiate the claimed apparatus from a prior art apparatus if the prior art apparatus teaches all the structural limitations of the claim (e.g., see: MPEP 2114_II), and “for being placed in the vehicle” merely defines the manner in which the claimed “mobile device” is intended to be used, such that “for being placed in the vehicle” does not necessarily further limit the scope of the claimed “computer system” under a broadest reasonable interpretation; even so, as discussed in detail above with respect to at least claim 1]; an accelerometer comprised in the mobile device and configured to acquire the accelerometer data by measuring acceleration in 3 acceleration axes during transportation of the mobile device by the vehicle (as discussed in detail above with respect to at least claim 1); a computer executable code stored in a memory, configured to cause one or more microprocessors to: process the trip dataset into a uniformly sampled dataset by increasing a data rate of the trip dataset by interpolation, filtering the trip dataset, and decimating the trip dataset; divide the uniformly sampled dataset into a plurality of trip segments; and for each trip segment of the plurality of trip segments: produce gravitational vector direction data by determining a gravitational vector direction; transform coordinates from mobile device coordinates into vehicle coordinates; identify one or more acceleration events by comparing the acceleration data of the trip segment with predetermined thresholds; and reduce usage of the vehicle for transport tasks if the number of identified one or more acceleration events exceeds a safety condition (as discussed in detail above with respect to at least claim 1).
With respect to claim 11, Zhang modified supra teaches the computer system of claim 10, wherein the predetermined thresholds comprise a predetermined acceleration threshold, the one or more acceleration events being at least one of vehicle speeding up and braking events along the vehicle trajectory, and the identifying of the one or more acceleration events comprises comparing an acceleration along an acceleration axis that follows the vehicle trajectory against the predetermined acceleration threshold (as discussed in detail above with respect to claims 2 and 10).
With respect to claim 12, Zhang modified supra teaches the computer system of claim 10, wherein the predetermined thresholds comprises a predetermined lateral acceleration threshold, the one or more acceleration events being vehicle turning events that are lateral movements in the vehicle trajectory, and the identifying of the one or more acceleration events comprises comparing an acceleration along an acceleration axis that is orthogonal to the vehicle trajectory and orthogonal to the gravitational vector direction against the predetermined lateral acceleration threshold (as discussed in detail above with respect to claims 3 and 10).
With respect to claim 13, Zhang modified supra teaches the computer system of claim 10, wherein the computer executable code is further configured to cause the one or more microprocessors to generate a report including the one or more acceleration events and to generate a safety score based on the report (as discussed in detail above with respect to claims 5 and 10).
With respect to claim 14, Zhang modified supra teaches the computer system of claim 13, wherein the computer executable code is further configured to cause the one or more microprocessors to send the safety score to a service allocation server (as discussed in detail above with respect to claims 6 and 10).
With respect to claim 15, Zhang modified supra teaches the computer system of claim 10, wherein the mobile device is configured to send the trip dataset to a processing server (as discussed by at least ¶ 59-60, 70, 89, 97-88 & 238-246 of Zhang), wherein the processing server comprises the memory and the one or more microprocessors [claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure (e.g., see: MPEP 2111.04_I), and no part of “wherein the processing server comprises the memory and the one or more microprocessors” necessarily requires the claimed “computer system” to include the “processing server” and/or the “memory” and/or the “one or more microprocessors,” such that no part of “wherein the processing server comprises the memory and the one or more microprocessors” necessarily further limits the scope of the claimed “computer system” under a broadest reasonable interpretation; even so, as discussed by at least ¶ 59-60, 70, 89, 97-88 & 238-246 of Zhang].
With respect to claim 19, Zhang modified supra teaches a non-transitory computer readable medium comprising program instructions, which when executed by one or more processors, cause the one or more processors to perform a method comprising: providing a trip dataset comprising time data and accelerometer data, the accelerometer data being acquired by an accelerometer configured to measure acceleration in 3 acceleration axes, wherein the accelerometer is included in a mobile device being transported by the vehicle during data acquisition of the trip dataset and the trip dataset corresponds to a vehicle trajectory; dividing the trip dataset into a plurality of trip segments; and for each trip segment of the plurality of trip segments: processing the trip dataset into a uniformly sampled dataset by increasing a data rate of the trip dataset by interpolation, filtering the trip dataset, and decimating the trip dataset; producing gravitational vector direction data by determining a gravitational vector direction; transforming coordinates from mobile device coordinates into vehicle coordinates; identifying one or more acceleration events by comparing the acceleration data of the trip segment with predetermined thresholds; and reducing usage of the vehicle for transport tasks if the number of identified one or more acceleration events exceeds a safety condition (as discussed in detail above with respect to at least claims 1 and 10).
Claims 8, 9, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Fernandez Pozo, and in view of U.S. Patent Application Publication No. 2015/0382156 to Gruteser et al. (hereinafter: “Gruteser”).
With respect to claim 8, Zhang modified supra teaches the computer-implemented method of claim 1, wherein the trip dataset further comprises gyroscope data (for example, as discussed by at least ¶ 70 of Zhang); however, Zhang appears to lack a clear teaching as to whether the method further comprises: determining, from the gyroscope data, instances of lateral acceleration.
Gruteser teaches an analogous computer-implemented method including determining, from gyroscope data of a trip data set, instances of lateral acceleration (as discussed by at least ¶ 0008 & 0073-0088).
It would have been obvious to one having ordinary skill in the art at the time the invention was made to have modified the computer-implemented method of Zhang with the teachings of Gruteser, if even necessary, to further include determining, from the gyroscope data, instances of lateral acceleration because Gruteser demonstrates that instances of acceleration, including at least one of a centripetal acceleration (e.g., lateral acceleration) or a longitudinal acceleration, are determinable using accelerometer data AND gyroscope data in the alternative to using only accelerometer data. Therefore, such a modification, if even necessary, would amount to a simple substitution of one known element for another to obtain predictable results (e.g., see: MPEP 2143_I_B).
With respect to claim 9, Zhang modified supra teaches the computer-implemented method of claim 8, further comprising: disregarding the instances of lateral acceleration when producing the gravitational vector direction data (as discussed by at least ¶ 29 & 122-128 of Zhang; also, see at least ¶ 0085 of Gruteser).
With respect to claim 16, Zhang modified supra teaches the computer system of claim 10, wherein the trip dataset further comprises gyroscope data; and the computer executable code is further configured to: determine, from the gyroscope data, instances of lateral acceleration (as discussed in detail above with respect to claims 8 and 10).
With respect to claim 17, Zhang modified supra teaches the computer system of claim 16, wherein the producing of the gravitation vector direction data disregards the instances of lateral acceleration (as discussed in detail above with respect to claims 8-10 and 16).
Conclusion
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/JOHN M ZALESKAS/Primary Examiner, Art Unit 3747