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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1/23/2026 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s amendments have been fully reviewed and the Claim Objections and 112 rejections of the previous office action have been withdrawn accordingly.
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 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.
Claim(s) 1, 7, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cordova (US-11209275-B2), Perl (US-20170372431-A1), Giffard (US-6407700-B1), and Pal US-20150051785-A1, Qin US-20180300565-A1, in view of Liu (US-20110264609-A1).
1. (Currently Amended) Cordova (US-11209275-B2) discloses
A method for automated transportation mode recognition [col.21] based on sensory data measured by a plurality of sensors of a mobile device of a user, the plurality of sensors at least comprising an accelerometer and a GPS sensor, the mobile device comprising one or more wireless connections,
(Cordova [claim(s) 1] collecting trip data utilizing sensors included in a mobile device during a trip)
(Cordova [claim(s) 2] wherein the sensors included in the mobile device include at least an accelerometer and a gyroscope)
(Cordova [col.4 ln.60] Typically, the trip data is measured using a mobile device, such as a smart phone. The trip data can include location data (e.g., GPS data))
(Cordova [col.22 ln.25] Mobile device 1801, which can be a mobile phone, includes a sensor data block 1805, a data processing block 1820, and a data transmission block 1830. The sensor data block 1805 includes data collection sensors as well as data collected from these sensors that are available to mobile device 1801. This can include external devices connected via Bluetooth, USB cable, and the like)
Regarding the limitation; “…the mobile device acting as a wireless node within a cellular data transmission network by means of antenna connections of the mobile device to the cellular data transmission network”,
Perl (US-20170372431-A1) discloses in a similar invention field of endeavor, a consideration for [0010] For providing the wireless connection, the telematics device can e.g. act as wireless node within a corresponding data transmission network by means of antenna connections of the telematics device, in particular mobile telecommunication networks as e.g. 3G, 4G, 5G LTE (Long-Term Evolution) networks or mobile WiMAX or other GSM/EDGE and UMTS/HSPA based network technologies etc., and more particular with appropriate identification means as SIM (Subscriber Identity Module) etc.
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to include the mobile device acting as a wireless node within a cellular data transmission network by means of antenna connections of the mobile device to the cellular data transmission network with a reasonable expectation for success, as taught by Perl, for the benefit of providing wireless connection [0010], increasing communication capabilities.
the plurality of sensors being connected to a monitoring mobile node application of the mobile device, and the monitoring mobile node application capturing usage-based and/or user-based sensory data of the mobile device and/or the user of the mobile device, the method comprising:
(Cordova [col.7 ln.15] analysis of the temporal data profile can include analysis of the acceleration patterns, variation in course, and other data…)
(Cordova [FIG.1; col.1 ln.40] to determine modes of transportation used by a user having a mobile device. In a particular embodiment, location data collected using a mobile device is analyzed to determine the mode of transportation for a user during a trip)
measuring time series of sensory parameter values based on measuring parameters obtained from the plurality of sensor of the mobile device, the measuring parameters comprising a time series of sensory parameter values of accelerometer measurements and a time series of sensory parameter values of GPS-based speed measurements of the GPS sensor, the GPS sensor measuring longitude, latitude, and altitude positions of the mobile device
(Cordova [col.4 ln. 60, col.16 ln.15] The trip data can include location data (e.g., GPS data) as a function of time, accelerometer data, combinations thereof, or the like… The acceleration profile is analyzed to determine the percentage of the samples for which the sign of the acceleration differs from a first time (t−1) to a second time (t))
(Cordova [col.6 ln.40] As illustrated in FIG. 3, location data (e.g., GPS data) collected using the location determination system 1810 of the mobile device can be used to generate vehicle speed data for the trip)
(Cordova [col.4 ln.25] A trip can be considered as a collection of points (e.g., locations defined by a latitude, longitude, and a time-stamp), segments (e.g., a collection of points, including the route between adjacent points) and stages (e.g., a group of segments), all of which are characterized by being in the same temporal vicinity
(Cordova [col.14 ln.10] The location data can include speed data derived from the location data as well as altitude data for the mobile device as a function of time during the trip)
Regarding the limitation; “…by measuring different speeds of light delays in signals coming from two or more satellites”,
Giffard (US-6407700-B1) discloses in a similar invention field of endeavor, a consideration for [claim(s) 12] autonomous, substantially real-time ionospheric delay measuring apparatus for calculating an ionospheric delay of a GPS signal transmitted by a first satellite, the first satellite having an obliquity, and received by a GPS front end capable of receiving a GPS signal from each of at least two satellites and configured to calculate, from each GPS signal…
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to include measuring different speeds of light delays in signals coming from two or more satellites with a reasonable expectation for success, as taught by Giffard, for the benefit of providing at least two satellites used in GPS calculations, ensuring accuracy and redundancy of a system.
the measured time series of sensory parameter values being (i) processed by rotation of
(Cordova [col.18] accelerometer data collecting using the mobile device, particularly, the accelerometer gravity values are utilized to determine the inclination/orientation of the mobile device during the trip. As an example, for a person on a bus, they may be reading text on their mobile device. The sign of gz is determined, where gz is the component of the gravity vector along the z-axis of the phone's reference frame. gy is also determined, where gy is the component of the gravity vector along the y-axis of the phone's reference frame.)
(ii)
(Cordova [col.19] The GPS coordinates as a function of time during the trip are used to compute the total trip distance (1462) and, using the distance from the start coordinate to the end coordinate (1446), the distance from the start coordinate to the end coordinate divided by the total trip distance is computed (1464). This straight line distance divided by the total trip distance provides a measure of the amount of deviation during the trip.)
Pal US-20150051785-A1 discloses in a similar invention field of endeavor, a consideration for car prognosis comprising “…measured time series of sensory parameter values being (i) processed by rotation of a 3-axis accelerometer from a smart phone reference system to a vehicle reference system (ii) aligned between accelerometer and GPS-based speed measurements sharing a common 10Hz sampling grid,;
(Pal [0005] The disclosure teaches the use of positioning system like GPS to capture the location of the vehicle and also takes into account the velocity of the vehicle to orient the accelerometer. The disclosure does not disclose the sampling frequency used to capture data.)
(Pal [0018] MEMS usually embedded in a PCD are 3-axis accelerometer configured to capture rate of change of acceleration in any co-ordinate axis, and global position system (GPS) configured to capture the location… A GPS embedded on a PCD can also be used to determine the location of the vehicle, so that the data relating to the road condition is shared with other drivers or subscriber.)
(Pal [0021] In another aspect of the present invention the 3-axis accelerometer utilizes a lower sampling rate/frequency about 4 Hertz (Hz) to 10 Hertz (Hz) to capture the data and only using four analysis points, hence reducing the battery consumption when compared to 256 or above analysis points.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to include measured time series of sensory parameter values being (i) processed by rotation of a 3-axis accelerometer from a smart phone reference system to a vehicle reference system (ii) aligned between accelerometer and GPS-based speed measurements sharing a common 10Hz sampling grid with a reasonable expectation for success, as taught by Pal, for the benefit of providing a common sampling grid for use with an accelerometer configured to measure in 3 axis perspectives, improving overall measurement capability and transmission.
Qin US-20180300565-A1 discloses in a similar invention field of endeavor, a consideration for perceiving travel signals wherein vehicle travel may be classified according to “…5 minute long mini-trips providing a 4- dimensional time series with a fixed time length;
(Qin [0100] In some implementations of classifying relevant travel signals 508 in FIG. 5, the relevance may be based on a moving direction of a vehicle, a route of a vehicle, a distance (e.g., within 5 meters, 10 meters, 50 meters, 100 meters, or 200 meters) from which a vehicle may reach, or a time interval (e.g., within 1 second, 5 seconds, 10 seconds, 20 seconds, 30 seconds, 1 minutes, or 5 minutes) for which a vehicle may reach the travel signal, or combinations of them. Since not all true travel signals are relevant, for example, to a driving decision of the vehicle, relevant travel signals are classified from among the true travel signals...)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to include 5 minute long mini-trips (i.e. a time-series of directional data based on a fixed length) with a reasonable expectation for success, as taught by Qin, for the benefit of providing a time length with which to monitor and classify processed information in relation to all data collected, providing time orientation associated with data collection.
Examiner’s Note: It should be noted that the limitation(s) of “…providing a 4- dimensional time series with a fixed time length” is being interpreted at this time as a collection of spatial and time related data in regards to vehicle monitoring and operation, which is in line with the disclosure in the specification (see page 19, line 5)
triggering the automated transportation mode recognition using the measured time series of the sensory parameter values as input feature values
(Cordova [col.22 ln.50-60] Utilizing data collected by the sensors, the particular mode of transportation utilized by the user during a trip can be determined )
(Cordova [col.21 ln.50] the analysis of the trip data provides a probability value for each of the particular modes of transportation that were analyzed, for example, plane as a mode of transportation, off-road as a mode of transportation, bicycle as a mode of transportation, train as a mode of transportation, and bus as a mode of transportation)
Regarding the limitation; “…to a gradient boosting machine-learning classifier”,
Liu (US-20110264609-A1) discloses in a similar invention field of endeavor, a consideration for [0002] Machine learning is a scientific discipline that pertains to design and development of algorithms/functions that allow computer programs to intelligently evolve based upon observed data such as data from a sensor or retained in one or more databases. Gradient boosting is one form of machine learning technique that is commonly utilized for learning mathematical models. Generally, a gradient boosted machine is utilized to learn a function such that the function can output a value of a target attribute of an entity. Specifically, an entity can be represented by a feature vector, wherein the feature vector includes a plurality of attributes corresponding to the entity. Observations of a certain target attribute pertaining to the entity can be obtained and these observations together with the feature vector can be utilized to learn a function (through employment of a gradient boosted machine) that can be configured to predict a value for the target attribute for another entity of the same type (but with a different feature vector).
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to include a gradient boosting machine-learning classifier with a reasonable expectation for success, as taught by Liu, for the benefit of enabling a systemin configured to predict a value for the target attribute for another entity of the same type [0010].
the transportation mode includes at least one of public transportation, motorcycle, cycling, train, tram, plane, car, skiing, and boat, and
(Cordova [col.21])
the transport mode label denotes the transportation mode detected by the automated transportation mode recognition.
(Cordova [col.12 ln.10] The classifiers can include, but do not require, a plane classifier (1020), an off-road classifier (1025), a bicycle classifier (1030), a train classifier (1035), a bus classifier (1040), additional classifiers, or the like. The various classifiers can include multiple sub-classifiers )
7. Cordova (US-11209275-B2) discloses The method for automated transportation mode recognition according to claim 1, wherein,
**** and
the generation of the transport mode label for the transport mode movement pattern of the trip, the trips are postprocessed verification process by a set of hard coded rules reinforcing avoidance of incorrect and/or insufficiently confident recognition [col.13 ln.30] Thus, the user verification can extend to cover more than just the mode of transportation, but additional characteristics of the trip, including the occupant status as driver or passenger, the exit door, whether the occupant was seated in the front or back seat, and the like. As a result, the verification process can provide verification of a correct prediction for some characteristics of the trip and correction of incorrect predictions for other characteristics of the trip. One of ordinary skill in the art would recognize many variations, modifications, and alternatives… Using this feedback, the trip data analysis module can be updated (1066)
Cordova lacks the following underlined limitations:
wherein, upon detection by the gradient boosting machine-learning classifier
Regarding the limitation; “…gradient boosting machine-learning classifier”, the limitation is similar in scope to features disclosed in re claim(s) 1 and are therefore rejected under the same premise, for more information please see the rejection in re Liu (US-20110264609-A1).
13. Cordova (US-11209275-B2) discloses The method for automated transportation mode recognition according to claim 1, wherein the measured time series of the sensory parameter values comprise GPS-based acceleration measurements derived based on a measured ratio between (a) a measured speed difference between a measured set of GPS parameter values and a measured subsequent set of GPS parameter values, and (b) a measured time difference between the measured set of GPS parameter values and the measured subsequent set of GPS parameter values [col.7 ln.15] As discussed in relation to element 630 below, analysis of the temporal data profile can include analysis of the acceleration patterns, variation in course, and other data in addition to analysis of the contextual data discussed in relation to element 614.
Claim(s) 2-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cordova (US-11209275-B2), Perl (US-20170372431-A1), Giffard (US-6407700-B1), Pal US-20150051785-A1, Qin US-20180300565-A1, and Liu (US-20110264609-A1), as applied to claim 1 above and further in view of Laskey (US-20170274855-A1).
2. Cordova (US-11209275-B2) discloses The method for automated transportation mode recognition according to claim 1, wherein
a supervised learning structure [col.5 ln.35] method also includes determining if segments of the trip are associated with planes (112) and removing these segments of the trip that are associated with airplanes. As described more fully below, segments are analyzed so that segments not associated with car travel (for example, starting with plane segments) are removed from the data set, leaving a data set only including car segments is applied to the
****
during a supervised learning phase,
transport mode movement patterns of measured trips [claim(s) 1] forming one or more segments using the collected trip data are stored in a trips database [col.22 ln.50] The system for collecting trip data also can include a server 1850 that communicates with the mobile device 1801. The server 1850 provides functionality including data collection frequency adjuster 1852, driving model builder 1858, and transportation mode classifier ... The transportation mode classifier 1854 can be referred to as a trip data analysis module and can utilize the various classifiers discussed herein. Utilizing data collected by the sensors, … analysis of the data collected using the sensors during the trip is utilized to update the classifiers included in the transportation mode classifier 1854. These components are executed by processors (not shown) in conjunction with memory (not shown). Server 1850 also includes data storage 1856, *Examiner’s Note: see also Eyler (US 20190019329 A1) discussed below in re claim(s) 8
the sensory parameter values include sensory movement parameter values [col.6 ln.40] As illustrated in FIG. 3, location data (e.g., GPS data) collected using the location determination system 1810 of the mobile device can be used to generate vehicle speed data for the trip,
transport mode movement patterns of the trip are identified from the sensory movement parameter values [col.22 ln.50-60] Utilizing data collected by the sensors, the particular mode of transportation utilized by the user during a trip can be determined,
each of the trips comprises the sensory movement parameter values of GPS positions by the GPSsensor and acceleration [col.23 ln.10] It should be noted that although some methods are illustrated in terms of only using location (e.g., GPS) data, embodiments of the present invention can also utilize accelerometer data in conjunction with the location data forces being applied to the mobile device [col.18 ln.55] The accelerometer data collecting using the mobile device, particularly, the accelerometer gravity values are utilized to determine the inclination/orientation of the mobile device during the trip… gravity vector along the z-axis… gravity vector along the y-axis
****,
operating system activities parameter values [col.6 ln.65] using the time-stamped location signal, the mode of transportation can be determined, even in the absence of contextual data of an operating system of the mobile device, and a transport mode label value [co.12 ln.10], and
trips with transport mode labels detected
****
are fed into a user back-loop for dynamic correction by a user associated with a respective trip [col.12 ln.30] the user can optionally be prompted with the determined mode of transportation and asked to verify that the classification is correct (1052). Using this feedback, the trip data analysis module can be updated (1066) and saved to the trips database by updating learning transport mode movement patterns of the measured trips in the trips database [col.13 ln.30] Thus, the user verification can extend to cover more than just the mode of transportation, but additional characteristics of the trip, including the occupant status as driver or passenger, the exit door, whether the occupant was seated in the front or back seat, and the like. As a result, the verification process can provide verification of a correct prediction for some characteristics of the trip and correction of incorrect predictions for other characteristics of the trip. One of ordinary skill in the art would recognize many variations, modifications, and alternatives… Using this feedback, the trip data analysis module can be updated (1066)
Cordova lacks the following underlined limitations:
applied to the gradient boosting machine-learning classifier during a supervised learning phase
on all three physical axes by the accelerometer,
transport mode labels detected by the gradient boosting machine-learning classifier
Regarding the limitation; “…gradient boosting machine-learning classifier”, the limitation is similar in scope to features disclosed in re claim(s) 1 and are therefore rejected under the same premise, for more information please see the rejection in re Liu (US-20110264609-A1).
Regarding the limitation; “…all three physical axes by the accelerometer”, Laskey (US-20170274855-A1) discloses in a similar invention field of endeavor, a consideration for [0026] As a non-limiting example, the one or more sensors 26 are one or more accelerometers or tri-axis accelerometers that are capable of measuring acceleration in the X, Y and Z directions.
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to include all three physical axes by the accelerometer with a reasonable expectation for success, as taught by Laskey, for the benefit of providing sensor data in a 3-D space for the benefit of improving orientation and system placement data/monitoring.
3. Cordova (US-11209275-B2) discloses The method for automated transportation mode recognition according to claim 2, further comprising monitoring the trips database to automatically detect changes in the trips database, wherein upon detecting a change in the trips database [col.8 ln.15] If the temporal profile of the trip does not correlate with the train system, then an additional analysis can be performed to determine if the trip data correlates with train motion data (644). As discussed above, the course data can be analyzed to compare the variation in course with different modes of transportation, the acceleration data can be analyzed, and the like, to determine the mode of transportation…, the supervised learning phase is reinitiated [col.5 ln.35] method also includes determining if segments of the trip are associated with planes (112) and removing these segments of the trip that are associated with airplanes. As described more fully below, segments are analyzed so that segments not associated with car travel (for example, starting with plane segments) are removed from the data set, leaving a data set only including car segments.
4. Cordova (US-11209275-B2) discloses The method for automated transportation mode recognition according to claim 2, wherein the trips database is updated continuously and/or dynamically based on the user back-loop [col.12] Using this feedback, the trip data analysis module can be updated (1066). The trip data analysis module can include the various classifiers illustrated and discussed in FIG. 10. The updated trip data analysis module can then be utilized when future trip data is received (1010).
5. Cordova (US-11209275-B2) discloses The method for automated transportation mode recognition according to claim 2, wherein, for the supervised learning phase, the trips of the trips database are preprocessed by filtering out transport mode movement patterns, which have a time duration shorter than 1 minute and/or comprise less than 30 GPS positions and/or do not have a proper transport mode labelling [col.5 ln.35] method also includes determining if segments of the trip are associated with planes (112) and removing these segments of the trip that are associated with airplanes. As described more fully below, segments are analyzed so that segments not associated with car travel (for example, starting with plane segments) are removed from the data set, leaving a data set only including car segments.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cordova (US-11209275-B2), Perl (US-20170372431-A1), Giffard (US-6407700-B1), Pal US-20150051785-A1, Qin US-20180300565-A1, Liu (US-20110264609-A1), and Laskey (US-20170274855-A1), as applied to claim 2 above and further in view of Xu (US-20220373339-A1).
6. Cordova (US-11209275-B2) discloses The method for automated transportation mode recognition according to claim 2, wherein, for the supervised learning phase, the trips of the trips database are preprocessed by filtering out transport mode movement patterns [col.5 ln.35] method also includes determining if segments of the trip are associated with planes (112) and removing these segments of the trip that are associated with airplanes. As described more fully below, segments are analyzed so that segments not associated with car travel (for example, starting with plane segments) are removed from the data set, leaving a data set only including car segments
****.
Cordova lacks the following underlined limitations:
having duplicated GPS locations by timestamp and/or transport mode movement patterns with GPS locations having negative speed and/or transport mode movement patterns having GPS locations with an accuracy > 50m
Regarding the limitation; Xu (US-20220373339-A1) discloses in a similar invention field of endeavor, a consideration for [0059] preprocessing the raw GPS data to obtain the filtered trajectory is depicted in FIG. 5. In some embodiments, during the preprocessing, the raw GPS data can be first processed to remove duplicate GPS pings and rearranged to become time-ordered.
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to include filtering out duplicated GPS locations by timestamp and/or transport mode movement patterns with a reasonable expectation for success, as taught by Xu, for the benefit of providing a filtered trajectory as the raw GPS pings are usually very noisy [0059].
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cordova (US-11209275-B2), Perl (US-20170372431-A1), Giffard (US-6407700-B1), Pal US-20150051785-A1, Qin US-20180300565-A1, and Liu (US-20110264609-A1), as applied to claim 1 above and further in view of Eyler (US-20190019329-A1).
8. Cordova (US-11209275-B2) lacks The method for automated transportation mode recognition according to claim 1, wherein user's routines are automatically detected by a trip familiarity-based recognition structure increasing the accuracy of the automated transportation mode recognition.
Regarding the limitation; Eyler (US-20190019329-A1) discloses in a similar invention field of endeavor, a consideration for [0096] the virtual reality transportation system 106 analyzes the historical information stored within the route database 108… the virtual reality transportation system 106 determines whether the previous travel routes and the new travel route are within a threshold distance from start to finish, are within an expected total transit time, etc. By comparing the maneuvers and other route traits (e.g., distance, timing, etc.), the virtual reality transportation system 106 determines which previous travel routes are within a threshold similarity of the new travel route (e.g., an 85% match or greater) and identifies one of the previous travel routes that is the most similar (e.g., a 95% match) as a substitute travel route… the virtual reality transportation system 106 analyzes the historical information stored within the route database 108
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to include wherein user's routines are automatically detected by a trip familiarity-based recognition structure with a reasonable expectation for success, as taught by Eyler, for the benefit of increasing the accuracy route navigation based upon historical data.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cordova (US-11209275-B2), Perl (US-20170372431-A1), Giffard (US-6407700-B1), Pal US-20150051785-A1, Qin US-20180300565-A1, Liu (US-20110264609-A1, and Laskey (US-20170274855-A1), as applied to claim 2 above and further in view of Eyler (US-20190019329-A1).
9. Cordova (US-11209275-B2) lacks The method for automated transportation mode recognition according to claim 2, wherein the transport mode movement patterns of measured trips stored to the trips database are processed for data enrichment, where the data enrichment process comprises route-matching of the trips with the transport mode movement patterns based on roadmaps and/or GIS-geometry mapping of the trips with the transport mode movement patterns based on spatial and geographic GIS data, and/or public transport mapping based on public transport road maps and timetable data.
Regarding the limitation; Eyler (US-20190019329-A1) discloses in a similar invention field of endeavor, a consideration for [0096] the virtual reality transportation system 106 analyzes the historical information stored within the route database 108… the virtual reality transportation system 106 determines whether the previous travel routes and the new travel route are within a threshold distance from start to finish, are within an expected total transit time, etc. By comparing the maneuvers and other route traits (e.g., distance, timing, etc.), the virtual reality transportation system 106 determines which previous travel routes are within a threshold similarity of the new travel route (e.g., an 85% match or greater) and identifies one of the previous travel routes that is the most similar (e.g., a 95% match) as a substitute travel route. Elyer further disclosed wherein [0140] generating a three-dimensional virtual environment that includes the sensory view of the environment surrounding the autonomous transportation vehicle. The act 908 can further involve incorporating mapping data (e.g., from a third-party mapping service) to more accurately determine size, shape, and locations of buildings or other objects for generating within the three-dimensional virtual environment.
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to include wherein transport mode movement patterns of measured trips stored to the trips database are processed for data enrichment, where the data enrichment process comprises route-matching of the trips with the transport mode movement patterns based on roadmaps with a reasonable expectation for success, as taught by Eyler, to more accurately determine size, shape, and locations of buildings or other objects for generating within the three-dimensional virtual environment [0140].
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cordova (US-11209275-B2), Perl (US-20170372431-A1), Giffard (US-6407700-B1), Pal US-20150051785-A1, Qin US-20180300565-A1, and Liu (US-20110264609-A1), as applied to claim 1 above and further in view of Krulwich (US-20150355336-A1).
10. Cordova (US-11209275-B2) discloses The method for automated transportation mode recognition according to claim 1, wherein the measuring parameters of the time series of sensory parameter values of the GPS-based speed measurements further comprise extracted average GPS-based speed measurements and/or standard deviation of the GPS-based speed measurements and/or percentiles values from 0 to 100 of the GPS-based speed measurements in predefined percentile steps.
Regarding the limitation; Krulwich (US-20150355336-A1) discloses in a similar invention field of endeavor, a consideration for [0018] Alternatively, GPS location data for each of vehicle 105 and vehicle 110 may be periodically or randomly transmitted to service provider 115 at various times based on factors including, but not limited to, current traffic conditions, current weather conditions, current vehicle speed, a frequency of user access to the GPS location data, etc. [0035] For example, current location calculator may calculate an average GPS location for each of vehicle 105 and vehicle 110, based on the collected and collated GPS data, received over a period of time.
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to include wherein the measuring parameters of the time series of sensory parameter values of the GPS-based speed measurements further comprise extracted average GPS-based speed measurements with a reasonable expectation for success, as taught by Krulwich, for the benefit of providing an accurate estimation of a vehicles true GPS location.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cordova (US-11209275-B2), Perl (US-20170372431-A1), Giffard (US-6407700-B1), Pal US-20150051785-A1, Qin US-20180300565-A1, Liu (US-20110264609-A1), and Krulwich (US-20150355336-A1), as applied to claim 10 above and further in view of Kenthapadi (US-10552741-B1).
11. Cordova (US-11209275-B2) lacks The method for automated transportation mode recognition according to claim 10, wherein the percentile steps comprise a granularity of a factor 10.
Regarding the limitation; “…percentiles values”, Kenthapadi (US-10552741-B1) discloses in a similar invention field of endeavor, a consideration for [col.10 ln.45] In the example of compensation information, percentile estimates of compensation can be calculated for cohorts of various degrees of granularity, but those percentiles estimated for cohorts with small sample sizes can wind up being unstable in that they can fluctuate significantly with the addition or exclusion of a few entries.
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to include percentiles values with a reasonable expectation for success, as taught by Kenthapadi, for the benefit of providing information according to a percentile presentation, allowing data to be processed accordingly.
Kenthapadi (US-10552741-B1) teaches various degrees of granularity, as discussed above, but is silent as to distinctly disclosing a granularity of a factor 10.
However; It would have been obvious to one having ordinary skill in the art at the time of the invention to modify the device of Cordova, as taught by Kenthapadi, by adjusting granularity factors associated with percentiles as a matter of routine optimization since it has been held that “where the general conditions of a claim are disclosed in the prior art, it is not inventive to discover the optimum or workable ranges by routine experimentation." In re Aller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cordova (US-11209275-B2), Perl (US-20170372431-A1), Giffard (US-6407700-B1), Pal US-20150051785-A1, Qin US-20180300565-A1, and Liu (US-20110264609-A1), as applied to claim 1 above and further in view of Vassilev (US-20200050964-A1).
12. Cordova (US-11209275-B2) lacks The method for automated transportation mode recognition according to claim 1, wherein the altitude position further comprises a standard deviation extracted from the altitude position measurements.
Regarding the limitation; Vassilev (US-20200050964-A1) discloses in a similar invention field of endeavor, a consideration for [0069] These predictors P.sub.u are constructed by applying for example statistical position (average, median, etc.) or dispersion (variance, standard deviation, etc.) criteria to the raw data (for example, acceleration, speed, altitude, etc.) derived from the sensors.
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to include a standard deviation extracted from the altitude position measurements with a reasonable expectation for success, as taught by Vassilev, for the benefit of applying statistical analysis to raw data, increasing operational agency and awareness.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cordova (US-11209275-B2), Perl (US-20170372431-A1), Giffard (US-6407700-B1), Pal US-20150051785-A1, Qin US-20180300565-A1, and Liu (US-20110264609-A1), as applied to claim 13 above and further in view of Vassilev (US-20200050964-A1).
14. Cordova (US-11209275-B2) discloses The method for automated transportation mode recognition according to claim 13, wherein the GPS-based acceleration measurements [col.23 ln.10] It should be noted that although some methods are illustrated in terms of only using location (e.g., GPS) data, embodiments of the present invention can also utilize accelerometer data in conjunction with the location data
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extracted from the GPS-based acceleration measurements and/or a variance value of the GPS-based acceleration measurements derived based on an angle between triplets of consecutive GPS points.
Cordova lacks the following underlined limitations:
comprise a standard deviation extracted from the GPS-based acceleration measurements and/or a variance value of the GPS-based acceleration measurements derived based on an angle between triplets of consecutive GPS points
Regarding the limitation; “…a standard deviation”, Vassilev (US-20200050964-A1) discloses in a similar invention field of endeavor, a consideration for [0069] These predictors P.sub.u are constructed by applying for example statistical position (average, median, etc.) or dispersion (variance, standard deviation, etc.) criteria to the raw data (for example, acceleration, speed, altitude, etc.) derived from the sensors.
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to include a standard deviation of collected data with a reasonable expectation for success, as taught by Vassilev, for the benefit of applying statistical analysis to raw data, increasing operational agency and awareness.
Claim(s) 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cordova (US-11209275-B2), Perl (US-20170372431-A1), Giffard (US-6407700-B1), Pal US-20150051785-A1, Qin US-20180300565-A1, and Liu (US-20110264609-A1), as applied to claim 1 above and further in view of Laskey (US-20170274855-A1).
15. Cordova (US-11209275-B2) lacks The method for automated transportation mode recognition according to claim 1, wherein the measuring parameters of the time series of sensory parameter values of the accelerometer measurements further comprise percentile values from 0 to 100 of the accelerometer measurements in predefined percentile steps and/or interquartile range values measured by a difference between the 75th and the 25th percentile.
Regarding the limitation; “…percentiles values”, Kenthapadi (US-10552741-B1) discloses in a similar invention field of endeavor, a consideration for [col.10 ln.45] In the example of compensation information, percentile estimates of compensation can be calculated for cohorts of various degrees of granularity, but those percentiles estimated for cohorts with small sample sizes can wind up being unstable in that they can fluctuate significantly with the addition or exclusion of a few entries.
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to include percentiles values with a reasonable expectation for success, as taught by Kenthapadi, for the benefit of providing information according to a percentile presentation, allowing data to be processed accordingly.
Kenthapadi (US-10552741-B1) is silent as to distinctly disclosing a values from 0 to 100 of the accelerometer measurements in predefined percentile steps and/or interquartile range values measured by a difference between the 75th and the 25th percentile.
However; It would have been obvious to one having ordinary skill in the art at the time of the invention to modify the device of Cordova, as taught by Kenthapadi, by adjusting value factors associated with percentiles as a matter of routine optimization since it has been held that “where the general conditions of a claim are disclosed in the prior art, it is not inventive to discover the optimum or workable ranges by routine experimentation." In re Aller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955).
16. Cordova (US-11209275-B2) discloses The method for automated transportation mode recognition according to claim 15, wherein said percentile steps comprise a granularity of a factor 10.
Regarding the limitation; “…percentiles steps”, Kenthapadi (US-10552741-B1) discloses in a similar invention field of endeavor, a consideration for [col.10 ln.45] In the example of compensation information, percentile estimates of compensation can be calculated for cohorts of various degrees of granularity, but those percentiles estimated for cohorts with small sample sizes can wind up being unstable in that they can fluctuate significantly with the addition or exclusion of a few entries.
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to include percentiles values with a reasonable expectation for success, as taught by Kenthapadi, for the benefit of providing information according to a percentile presentation, allowing data to be processed accordingly.
Kenthapadi (US-10552741-B1) is silent as to distinctly disclosing a granularity of a factor 10.
However; It would have been obvious to one having ordinary skill in the art at the time of the invention to modify the device of Cordova, as taught by Kenthapadi, by adjusting granularity factors associated with percentiles as a matter of routine optimization since it has been held that “where the general conditions of a claim are disclosed in the prior art, it is not inventive to discover the optimum or workable ranges by routine experimentation." In re Aller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cordova (US-11209275-B2), Perl (US-20170372431-A1), Giffard (US-6407700-B1), Pal US-20150051785-A1, Qin US-20180300565-A1, Liu (US-20110264609-A1), and Laskey (US-20170274855-A1), as applied to claim 2 above and further in view of Xu (US-20220373339-A1), Grokop (US-20130245986-A1), and Namba (US-20200302787-A1).
17. Cordova (US-11209275-B2) lacks The method for automated transportation mode recognition according to claim 2, wherein
in a case of two or more accelerometer measurements having the same timestamps, a most recent accelerometer measurement order is selected, and an accelerometer measurement norm value is generated over the accelerometer measurements of a measured trip, and
an average of the accelerometer measurements of the measured trip is removed form said measured trip.
Cordova lacks the following underlined limitations:
in a case of two or more accelerometer measurements having the same timestamps a most recent accelerometer measurement order is selected, and an accelerometer measurement norm value is generated over the accelerometer measurements of a measured trip, and
an average of the accelerometer measurements of the measured trip is removed form said measured trip.
Regarding the limitation; “…in a case of two or more measurements having the same timestamps, a most recent measurement order is selected”, Xu (US-20220373339-A1) discloses in a similar invention field of endeavor, a consideration for [0059] preprocessing the raw GPS data to obtain the filtered trajectory is depicted in FIG. 5. In some embodiments, during the preprocessing, the raw GPS data can be first processed to remove duplicate GPS pings and rearranged to become time-ordered.
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to include in a case of two or more measurements having the same timestamps, a most recent to a measurement order is selected with a reasonable expectation for success, as taught by Xu, for the benefit of providing a filtered trajectory as the raw information collected is usually very noisy [0059].
Xu is silent however to distinctly disclosing a consideration for acceleromter measurements.
However regarding the limitation; “…accelerometer measurements having the same timestamps, a most recent accelerometer measurement order is selected, and an accelerometer measurement norm value is generated over the accelerometer measurements of a measured trip”, Grokop (US-20130245986-A1) discloses in a similar invention field of endeavor, a consideration for [0053] To improve drive detection as described above, various additional features and parameters can be introduced. For instance, spectral entropy (se) can be defined as the entropy of the distribution obtained by normalizing the FFT, e.g., se=-.SIGMA.p(x)log p(x), were p(x)=|fft( {square root over (a.sub.x.sup.2+a.sub.y.sup.2+a.sub.z.sup.2)})|. Further, a mean of norms (mn) can be defined as the mean of the norm of accelerometer values over the main window of 1 s, e.g.,
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to include an accelerometer measurement norm value generated over the accelerometer measurements with a reasonable expectation for success, as taught by Grokop, for the benefit of providing processed data as it relates to accelerometer measurements used in operational methods.
Regarding the limitation; “…average of the accelerometer measurements of the measured trip is removed form said measured trip”, Namba (US-20200302787-A1) discloses in a similar invention field of endeavor, a consideration for [0047] information collector 201 removes old data and data deviating from an average value, extracts highly reliable data, and transmits the extracted data
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to include removing an average data value from processing with a reasonable expectation for success, as taught by Namba, for the benefit of transmitting highly reliable data [0047]
Claim(s) 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cordova (US-11209275-B2), Perl (US-20170372431-A1), Giffard (US-6407700-B1), Pal US-20150051785-A1, Qin US-20180300565-A1, Liu (US-20110264609-A1), and Laskey (US-20170274855-A1), as applied to claim 2 above and further in view of Yoshimoto (US-20110131169-A1).
18. Cordova (US-11209275-B2) discloses The method for automated transportation mode recognition according to claim 2, wherein
the operating system activities parameter values of the operating system of the mobile device comprise a unique timestamp [col.4] A trip can be considered as a collection of points (e.g., locations defined by a latitude, longitude, and a time-stamp), segments (e.g., a collection of points, including the route between adjacent points) and stages (e.g., a group of segments) and a map of labels with probability measures [col.12 ln.5] As illustrated in FIG. 10, a set of classifiers are utilized to determine the probability that the trip data is associated with each of the various modes of transportation., and
in a case of an absence of a label, the probability measure is set to 0 [Examiner’s Note] It should be noted that unlabeled information, information not falling under the classifiers as taught by Cordova, have no associated operational probability or operations and as such have null values within operation calculations.
Regarding the limitation; “…in a case of an absence of a label, the probability measure is set to 0”, Yoshimoto (US-20110131169-A1) discloses in a similar invention field of endeavor, a consideration for [col.4] when the probability of a component containing a missing value is designated as 1, the contribution of the component having the missing value to the dissimilarity, (Formula 5), becomes 0.
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to include in a case of an absence of a label, the probability measure is set to 0 with a reasonable expectation for success, as taught by Yoshimoto, for the benefit of preventing erroneous data from affecting computational operations.
19. Cordova (US-11209275-B2) discloses The method for automated transportation mode recognition according to claim 18, wherein said labels of the map are normalized to 'Automotive', 'Cycling', 'OnFoot', 'Running', 'Stationary', 'Unknown', 'Walking', and 'Tilting' denoting a feature vector for naming compliance between two operating systems.
[col.12 ln.10] The classifiers can include, but do not require, a plane classifier (1020), an off-road classifier (1025), a bicycle classifier (1030), a train classifier (1035), a bus classifier (1040), additional classifiers, or the like. The various classifiers can include multiple sub-classifiers.
[col. 4, l. 23] A variety of modes of transportation are amenable to use according to embodiments of the present invention, including, without limitation, walking, riding a bus, driving a car, riding in a car as a passenger, riding in a train, taking the subway, riding a bike, and the like. A trip can be considered as a collection of points (e.g., locations defined by a latitude, longitude, and a time-stamp), segments (e.g., a collection of points, including the route between adjacent points) and stages (e.g., a group of segments), all of which are characterized by being in the same temporal vicinity. Points can be referred to as GPS points, with the location referenced to GPS locations. The stages can be defined to begin and end when a change from one mode of transport to another is detected or when there is a change in vehicle of the same mode of transport. These stage transitions can be referenced by transition points, which are points (e.g., latitude/longitude location and time-stamp) associated with the stage transition.
[col. 15, l. 62] If the speed values are not realistic or erroneous, for example, negative, the speed data points that are erroneous, which can be referred to as missing data points, are calculated given the speed data and the location data represented by the GPS coordinates (1316). In other words, any speeds for which the speed values are less than zero usually indicates that the speed value couldn't be calculated based on the location data collected by the mobile device. Thus, these missing data points can be filled in to provide a complete speed profile for the trip. The speed between samples can be calculated by dividing the great circle distance (between one sample's coordinates and the next) by the time difference between both samples. In some embodiments, distance is measured in meters, time in seconds, speed in meters/second.
Regarding the limitation, the limitations characterizing the exact language of the map labels does not constitute a functional relationship with the system of the parent claims, and therefore is representative of printed matter and/or design choice, when the system supports representative classifier determination. Therefore, the exact wording of the classifier does not differentiate from the prior art.
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cordova (US-11209275-B2), Perl (US-20170372431-A1), Giffard (US-6407700-B1), Pal US-20150051785-A1, Qin US-20180300565-A1, Liu (US-20110264609-A1, Laskey (US-20170274855-A1), and Eyler (US-20190019329-A1), as applied to claim 9 above and further in view of Sarma (US-20110320111-A1).
22. Cordova (US-11209275-B2) discloses The method for automated transportation mode recognition according to claim 9, wherein
the public transport mapping based on public transport road maps and timetable data comprises identifying for a set of measured GPS location parameters [col.4 ln.50] contextual data, also referred to as contextual map data, is utilized in determining the modes of transportation during a trip. The contextual map data can be stored in a database that includes data related to transportation systems, including roads, trains, buses, bodies of water, and the like. As an example, location data related to trains could include locations of train stations, locations of train tracks, timetables and schedules, and the like. Furthermore, location data related to a bus system could include bus routes, bus schedules, bus stops, and the like. candidate stops as sequences of points [col.5 ln.20] segments can be formed by defining the segments as time periods between stops in the trip data (110). Accordingly, for a given trip, a number of segments can be formed, with each segment separated by a stop in the trip data that fulfill conditions of:
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for each of the candidate sequences, an average [col.10 ln.35] average accuracy of location data (e.g., GPS coordinates), latitude and an average longitude [col.4] A trip can be considered as a collection of points (e.g., locations defined by a latitude, longitude, and a time-stamp) is generated, obtaining a candidate stop position for each sequence/stop [col.5 ln.15] Wherever the measured speed is close to zero, the corresponding GPS point marks the beginning or end of a segment.
Cordova lacks the following underlined limitations:
(i) measured speed <= 3 m/s; and
(ii) candidate sequences are longer than 5 seconds,
the identification is performed after applying a moving average within a window of time over an array of measured speeds, replacing each sample an average of the sample itself and the 4 samples before and after, and
Cordova considers speed and sequencing variables during transport analysis. Therefore, it would have been obvious to one having ordinary skill in the art at the time of the invention to modify the device of Cordova, by adjusting parameters associated with data processing configurations comprising: “(i) measured speed <= 3 m/s; and (ii) candidate sequences are longer than 5 seconds” as a matter of routine optimization since it has been held that “where the general conditions of a claim are disclosed in the prior art, it is not inventive to discover the optimum or workable ranges by routine experimentation." In re Aller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955).
Regarding the limitation; “…the identification is performed after applying a moving average within a window of time over an array of measured speeds, replacing each sample an average of the sample itself and the 4 samples before and after” Sarma (US-20110320111-A1) discloses in a similar invention field of endeavor, a consideration for [0071] …calculate the moving averages of the speed for time windows of various size.
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to include wherein applying a moving average within a window length over an array of measured speeds, replacing each sample with a reasonable expectation for success, as taught by Sarma, for the benefit of increasing informational accuracy by creating an average data reading in real-time segments.
Sarma discloses applying a moving average within a window length over an array of measured speeds but does not distinctly disclose wherein:
“the identification is performed after applying a moving average within a window of time over an array of measured speeds, replacing each sample an average of the sample itself and the 4 samples before and after.”
However; It would have been obvious to one having ordinary skill in the art at the time of the invention to modify the device of Cordova, by adjusting parameters associated with a moving average, as taught by Sarma, comprising: “a moving average within a window of time over an array of measured speeds, replacing each sample an average of the sample itself and the 4 samples before and after” as a matter of routine optimization since it has been held that “where the general conditions of a claim are disclosed in the prior art, it is not inventive to discover the optimum or workable ranges by routine experimentation." In re Aller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955).
Claim(s) 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cordova (US-11209275-B2), Perl (US-20170372431-A1), Giffard (US-6407700-B1), Pal US-20150051785-A1, Qin US-20180300565-A1, and Liu (US-20110264609-A1), as applied to claim 1 above and further in view of SciKit-Learn (“Gradient Boosting Classifier”).
23. Cordova (US-11209275-B2) lacks The method for automated transportation mode recognition according to claim 1, wherein a hyperparameter configuration is applied to the gradient boosting machine-learning classifier comprising the values 225 for n-estimators, 0.03 for learning-rate, 30 for max-depth, 50 for num-leaves, 0.8 for subsample, 0.7 for colsample-bytree, and 5 for min-sum-hessian-in-leaf.
Regarding the limitation; SciKit-Learn (Gradient Boosting Classifier) discloses in a similar invention field of endeavor, a consideration for hyperparameter configurations for gradient boosting algorithms [parameters, source code].
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Cordova to a hyperparameter configuration applied to the gradient boosting machine-learning classifier with a reasonable expectation for success, as taught by SciKit-Learn, for the benefit of controlling parameters within an additive model for optimizing differentiable loss functions [Gradient Boosting for classification].
SciKit-Learn discloses hyperparameter configuration(s) but does not distinctly disclose wherein:
“values 225 for the n-estimators, 0.03 for the learning-rate, 30 for the max-depth, 50 for the num-leaves, 0.8 for the subsample, 0.7 for the colsample-bytree, and 5 for the min-sum-hessian-in-leaf.”
However; It would have been obvious to one having ordinary skill in the art at the time of the invention to modify the device of Cordova, by adjusting parameters associated with machine-learning configurations, as taught by SciKit-Learn, comprising: “values 225 for the n-estimators, 0.03 for the learning-rate, 30 for the max-depth, 50 for the num-leaves, 0.8 for the subsample, 0.7 for the colsample-bytree, and 5 for the min-sum-hessian-in-leaf” as a matter of routine optimization since it has been held that “where the general conditions of a claim are disclosed in the prior art, it is not inventive to discover the optimum or workable ranges by routine experimentation." In re Aller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955).
Allowable Subject Matter
Claim(s) 20 (to include dependent claim(s) 21) are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
The problem to be solved by the present invention may be regarded as how to determine a route probability and priority with respect to a mathematical relationship regarding historical data computations.
The closest available prior art at hand;
Eyler (US-20190019329-A1) discloses in a similar invention field of endeavor, a consideration for [0096] the virtual reality transportation system 106 analyzes the historical information stored within the route database 108… the virtual reality transportation system 106 determines whether the previous travel routes and the new travel route are within a threshold distance from start to finish, are within an expected total transit time, etc. By comparing the maneuvers and other route traits (e.g., distance, timing, etc.), the virtual reality transportation system 106 determines which previous travel routes are within a threshold similarity of the new travel route (e.g., an 85% match or greater) and identifies one of the previous travel routes that is the most similar (e.g., a 95% match) as a substitute travel route. Elyer further disclosed wherein [0140] generating a three-dimensional virtual environment that includes the sensory view of the environment surrounding the autonomous transportation vehicle. The act 908 can further involve incorporating mapping data (e.g., from a third-party mapping service) to more accurately determine size, shape, and locations of buildings or other objects for generating within the three-dimensional virtual environment.
Cordova (US-11209275-B2) discloses A method for automated transportation mode recognition [col.21] based on sensory data measured by a plurality of sensors of a mobile device of a user [claim(s) 1] collecting trip data utilizing sensors included in a mobile device during a trip
A solution to the problem detailed above as proposed in claim(s) 20 of the present application is neither distinctly disclosed in, nor render obvious by, the available prior art at hand, and appears therefor to involve an inventive step.
As such, it should be noted that while the combination of references discloses aspects of the claimed limitations, the disclosure fails to fully capture the structure and interplay of the elements as recited in the claims. Therefore, upon review of the evidence at hand, it is hereby concluded that the evidence obtained and made of record, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious all the features of applicant’s invention as the features amount to more than a predictable use of elements in the prior art. As such, it is this examiner’s opinion that there lacks a sufficient nexus between the prior art and the instant application.
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 extension fee 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 date of this final action.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW JOHN MOSCOLA whose telephone number is (571)272-6944.
The examiner can normally be reached M-F 7:30-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abby Flynn can be reached on (571) 272-9855. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.J.M./Examiner, Art Unit 3663
/ABBY J FLYNN/Supervisory Patent Examiner, Art Unit 3663
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