DETAILED ACTION
This is the First Office Action on the Merits and is directed towards claims 1, 3, 4 and 6-15 as originally amended and/or filed on 06/06/2024.
Notice of Pre-AIA or AIA Status
Priority is claimed as set forth below, accordingly the earliest effective filing date is 07 December, 2021 (20211207).
The present application, effectively filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
This application is a U.S. National Phase Application of International patent application number PCT/EP2021/084492 filed on 07 December, 2021 (20211207).
Information Disclosure Statement
As required by M.P.E.P. 609 [R-07.2022], Applicant's 06/06/2024 submission(s) of Information Disclosure Statement (IDS)(s) is/are acknowledged by the Examiner and the reference(s) cited therein has/have been considered in the examination of the claim(s) now pending. A copy of the submitted IDS(s) initialed and dated by the Examiner is/are attached to the instant Office action.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3, 4 and 6-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210053643 A1 to Murphy; Conrad Xavier (Cited in the 6/6/2024 IDS) in view of GB 2579559 A to SESHADRI ARJUN KRISHNA et al. (SESHADRI Cited in the 6/6/2024 IDS).
Regarding claim 1 Murphy teaches in for example the Figure(s) reproduced immediately below:
PNG
media_image1.png
494
595
media_image1.png
Greyscale
PNG
media_image2.png
421
604
media_image2.png
Greyscale
PNG
media_image3.png
762
423
media_image3.png
Greyscale
PNG
media_image4.png
705
517
media_image4.png
Greyscale
PNG
media_image5.png
763
541
media_image5.png
Greyscale
PNG
media_image6.png
756
419
media_image6.png
Greyscale
PNG
media_image7.png
489
682
media_image7.png
Greyscale
PNG
media_image8.png
705
521
media_image8.png
Greyscale
and associated descriptive texts a method for detecting a tandem ride condition on an electric scooter (as shown in at least figures 3, 5, 6 and 9 above there is inter alia a method for detecting the mass or weight of a micromobility vehicle (MV) which connotes the claimed “electric scooter” which would include the mass of ANY and ALL riders of the electric scooter 502/914 ( c ) including “a tandem ride condition” as explained in for example paras:
“[0034] FIG. 5 is a block diagram illustrating an example system 500 for operating a micromobility vehicle 502 having mounted computing devices 510 that include one or more antennas 426(d). The MV 502 may use the antennas 426(d) to communicate with a dynamic transportation management system 506 over a wireless network 504, such as a cellular network or the Internet. MV 502 mounted computing devices 510 may also have a user interface 426(e), such as touchscreen or docking station for a rider's computing device (e.g., smartphone), and/or may use antennas 426(d) to interface with a rider's computing device. Accordingly, a rider may create, access, and/or update a rider profile stored as user data at dynamic transportation management system 506, which is described in detail below with reference to FIGS. 9 and 10.
[0036] The mounted computing devices 510 may also include modules 404-410, as previously described, and throttle position detection module 404 may detect a throttle position 507 which is associated with an amount of deflection 509 of throttle 422 from a zero acceleration set point or end point of throttle 422 travel. The throttle 422 may be labeled to indicate one or more amounts of acceleration based on throttle position 507, such as zero acceleration and maximum acceleration setpoints, one or more of which may correspond to a throttle travel endpoint. The mounted computing devices 510 may further include accelerometer 424(a), and various interfaces 426(a)-426(c) for communicating with peripheral devices 508 (e.g., GPS 424(b), speed sensor 424(c), inclinometer 424(d)), and weight sensor 424(e). The inclinometer 424(d) may provide input that can be used to determine when the MV 502 is operating on a level surface, which may aid the MV in determining when an acceleration delta represents MV load (e.g., rider weight plus cargo) and not inclination. Accordingly, the MV 502 may use the acceleration delta and inclinometer to compute or update rider weight, detect addition of cargo, etc. Alternatively or additionally, weight sensor 424(e) may be used to detect weight of riders and/or cargo, and thus a change in measured weight may be used to detect addition of cargo or additional riders.”),
the scooter comprising an electric motor (as explained in for example para:
“[0028] At step 340, the method includes modifying the torque magnitude based at least on the comparison of the acceleration of the micromobility vehicle with the target acceleration associated with the throttle position. For example, the ECU may calculate a torque based on the target acceleration, calculate another torque based on the acceleration delta, and add the two torques to arrive at a torque magnitude that will achieve the target acceleration. This torque magnitude may be used as an input to a torque drive system of the ECU, and thus control operation of the MV in such a way that the output torque of the MV motor is caused to match the input torque magnitude (i.e., torque demand). Accordingly, based at least on the comparison of the acceleration with the target acceleration, the method may include determining a delta between the acceleration and the target acceleration, and determining a rate for modifying the torque magnitude based at least on the delta, wherein the torque magnitude is modified based on the determined rate. After step 340, processing may end. Alternatively, processing may return to a previous step in the process, such as step 310.”),
the method comprising estimating a mass on the scooter (in Fig. 6, step 630 “variables based on measurements” as explained in for example para:
“[0039] At step 630, the method 600 includes initiating variables based on the information and operating the MV while updating the variables based on measurements. For example, the rider skill level may be used to select a target acceleration profile, with more skilled riders being provided with a more aggressive acceleration profile. Additionally, a rider weight and cargo weight may be added and used to select, via a lookup table or other data structure, a semi-static acceleration delta that represents an initial estimate of vehicle load. If this information is not available, a less aggressive acceleration profile and a default semi-static acceleration delta may be used initially to ensure safe operation even for an unskilled rider of exceptionally low weight. Other variables may be initially set to a value, such as zero, that is suitable for initial operation of the MV. The vehicle may then be operated initially in a safety mode on level ground so that vehicle load can be accurately assessed, and the semi-static acceleration delta can be updated accordingly. Once this assessment has occurred, then if the rider frequently maximizes the throttle position, a higher skill level may be assigned and a more aggressive acceleration profile utilized. Addition of cargo during the ride may be detected and the semi-static acceleration delta updated accordingly. Processing may proceed from step 630 to step 640.”),
determining, for longer than two seconds, and storing movement data of the scooter, the movement data including speed data of the scooter (as shown in Figures 3 and 6 it is considered that the method is continuously updating the user profile as the weight changes during the ride which takes longer that two seconds in order to process the data of the scooter or to even accelerate to a speed that the calculations could be made as shown in steps 330 and 630 as explained in for example paras:
“[0026] At step 320, the method includes determining an acceleration of the micromobility vehicle based at least on the throttle position, wherein the acceleration is associated with a torque magnitude of the micromobility vehicle. For example, the micromobility vehicle may measure the acceleration based on a wheel speed or angular acceleration from a sensor disposed at a wheel or axle of the MV. Alternatively or additionally, the micromobility vehicle may be equipped with GPS, an accelerometer, and/or wireless positioning capability that allows the MV to determine the acceleration. The micromobility vehicle may receive a signal and/or determine an acceleration and store information regarding the measured acceleration in a computer memory of the ECU. Processing may proceed from step 320 to step 330.”),
determining force or power data of the motor (connotes “torque magnitude” as shown in Figure 3, step 340 and Fig. 6, step 640 as explained in for example para:
“[0028] At step 340, the method includes modifying the torque magnitude based at least on the comparison of the acceleration of the micromobility vehicle with the target acceleration associated with the throttle position. For example, the ECU may calculate a torque based on the target acceleration, calculate another torque based on the acceleration delta, and add the two torques to arrive at a torque magnitude that will achieve the target acceleration. This torque magnitude may be used as an input to a torque drive system of the ECU, and thus control operation of the MV in such a way that the output torque of the MV motor is caused to match the input torque magnitude (i.e., torque demand). Accordingly, based at least on the comparison of the acceleration with the target acceleration, the method may include determining a delta between the acceleration and the target acceleration, and determining a rate for modifying the torque magnitude based at least on the delta, wherein the torque magnitude is modified based on the determined rate. After step 340, processing may end. Alternatively, processing may return to a previous step in the process, such as step 310.”), and
wherein the estimation is based on:
comparing the estimated mass to a (as shown in Fig. 6, step 600 “updating a rider profile with rider weight” and step 630 as explained in for example para:
“[0037] FIG. 6 is a flow diagram of an exemplary method 600 of implementing a rider profile. At step 610, the method 600 includes prompting a rider to provide information by selecting, creating, or updating a rider profile with rider weight and/or skill level. An existing rider profile selected by the rider may be obtained from user data stored on a dynamic transportation management system 506 (see FIG. 5) as described below with reference to FIGS. 9 and 10. Alternatively or additionally, a rider and/or cargo weight can be determined using sensors of the MV and/or in an staging area from which such MVs are deployed (e.g., weight sensors of the MV, weight sensors in the cargo are of the MV, a scale in a staging area of the MV, pressure sensors under a road surface in the staging area of the MV, etc.). The MV and/or dynamic transportation management system 506 (see FIG. 5) can determine an initial rider and/or cargo weight for formulation or refinement of the rider profile. For example, cargo weight and gross vehicle weight can be measured using sensors, and the rider weight may be determined as a difference between the gross vehicle weight and a total of the cargo weight and a known MV weight. These weights or weight classes, whether obtained from a stored profile, provided by the rider, or determined using sensors, can iteratively refined during vehicle operation as further detailed below. Alternatively, the information from the sensors (e.g., weight, acceleration, and/or incline) and/or initial information (e.g., rider weight, cargo weight, and/or skill level) may be employed during operation of the MV without using iterative refinement of the initial information. The rider profile information may be stored in memory of the MV ECU during a ride. Processing may proceed from step 610 to step 620.”);
and classifying current ride as exhibiting a tandem ride condition if the estimated mass of the current ride is higher than the (in fig. 6 step 640 as explained in for example para:
“[0040] At step 640, the method 600 includes creating or updating rider profile based on the information and/or update of the variables based on the measurements. For example, if the rider chose to create a rider profile and provide a rider weight at step 610, if the rider indicated no cargo, and if no addition of cargo was detected during the ride, then the semi-static acceleration delta that resulted from measurements during the ride, or a corresponding weight, may be stored in a rider profile as rider weight. Otherwise, if the rider chose to create a rider profile and provide a rider weight, but the rider indicated presence of cargo, or if addition of cargo was detected during the ride, then the rider weight provided by the rider may be stored in a rider profile as rider weight. If the rider did not create or select a profile, and if no addition of cargo was detected during the ride, then the semi-static acceleration delta that resulted from measurements during the ride, or a corresponding weight, may be stored in a rider profile as rider weight. Storing this information as rider weight may be conditioned on prompting the rider to confirm that there was little or no cargo, and if the rider indicates that there was cargo, then the rider may be offered another opportunity to create a rider profile. A same or similar procedure may be used for riders who select a rider profile at step 610 and indicate no cargo at step 620, but for whom a semi-static acceleration delta determined at step 630 is significantly different than expected based on the rider weight of the profile. If lighter than expected, the rider weight of the profile may be reduced. If heavier than expected, the rider may be prompted to confirm that no cargo was carried or added before increasing the rider weight of the profile. A selected or detected rider skill level may also be saved in the rider profile. The created or updated rider profile may be stored as part of user data stored on a dynamic transportation management system 506 (see FIG. 5) as described below with reference to FIGS. 9 and 10.”).
Murphy does not appear to expressly disclose applying a vehicle dynamics filter to select a time period of the stored movement data during which scooter motion has primarily been longitudinal,
wherein the vehicle dynamics filter includes predefined criteria including
the determined speed being equal to or higher than a speed threshold
and force or power in the determined force or power data being higher than zero;
comparing the estimated mass to a statistical mass value determined based on stored data associated with a user profile;
and classifying current ride as exhibiting a tandem ride condition if the estimated mass of the current ride is higher than the statistical mass value.
In analogous art SESHADRI teaches in for example, the figures below:
PNG
media_image9.png
532
749
media_image9.png
Greyscale
PNG
media_image10.png
555
574
media_image10.png
Greyscale
PNG
media_image11.png
762
575
media_image11.png
Greyscale
PNG
media_image12.png
762
579
media_image12.png
Greyscale
And associated descriptive texts applying a vehicle dynamics filter to select a time period of the stored movement data during which scooter motion has primarily been longitudinal (in for example Fig. 8, step 20 as explained in for example para:
“In step 20, vehicle fuel consumption, forward acceleration and speed data is obtained with respect to time. This may be via a live feed, e.g. of time stamped data. Such data may be obtained from the CAN bus of the vehicle, or determined from OBD data, e.g. via an OBD adapter of the vehicle.”) ,
wherein the vehicle dynamics filter includes predefined criteria including
the determined speed being equal to or higher than a speed threshold
and force or power in the determined force or power data being higher than zero (as set forth in for example paras:
“In step 22 those times where vehicle speed exceeds a predefined threshold, or both vehicle speed and vehicle forward acceleration exceed respective predefined thresholds, are identified.
In step 24, vehicle mass is estimated for each identified time, using the fuel consumption, forward acceleration and speed data for that time. This may be achieved using equation 2, with the assumption that tank to wheel efficiency is at its maximum.”);
comparing the estimated mass to a statistical mass value determined based on stored data associated with a user profile (connotes inter alia the predefined mass based on vehicle class and percentage thereof based upon the perceived load of the vehicle as set forth in for example only paras:
“It will be appreciated that the function used in determining the forward acceleration threshold value(s) is dependent upon vehicle mass. In some embodiments, a vehicle mass value is obtained for use in the function by selecting a predefined mass based upon vehicle class. For example, a number of vehicle classes may be predefined, each associated with a predefined vehicle mass. The user may be able to manually update the mass of the vehicle, where the exact value is known. However, the Applicant has recognised that the mass of a vehicle, particularly in the context of operational fleets, may fluctuate greatly, for example depending upon the size of the load the vehicle is carrying, number of passengers and/or fuel load. To address this issue, and be able to provide a more accurate estimate of mass, the driver may be able to select an intermediate value between predefined minimum and maximum mass values for a particular vehicle class, e.g. as a percentage, based upon the perceived load of the vehicle.
It is believed that such techniques for estimating vehicle mass based on instantaneous fuel consumption are advantageous in their own right, independent of the methods described herein for identifying excessive forward acceleration of a vehicle. For example, the ability to estimate vehicle mass may be useful in determining other performance indicators indicative of braking or steering of the vehicle, or providing a warning if the vehicle is overloaded, etc. In accordance with a further aspect of the invention, there is provided a method of, preferably automatically, estimating a mass of a vehicle, comprising: obtaining data indicative of the fuel consumption of the vehicle; and using the fuel consumption data to determine the mass of the vehicle.”);
and classifying current ride as exhibiting a tandem ride condition if the estimated mass of the current ride is higher than the statistical mass value (in for example para:
“It will be appreciated that equations 1A and 1B, and hence the forward acceleration threshold function, are dependent upon the mass of the vehicle. There are various ways in which a mass may be determined for a particular vehicle. For example, one of a set of predefined vehicle masses may be used based on vehicle class. A user may be provided with the option to manually update the mass if the actual mass is known. However, the mass of a vehicle may fluctuate considerably over time, e.g. based upon the size of a load being carried, a fuel load, number of passengers, etc. This is particularly likely where the vehicle is part of an operational fleet. In order to address this issue, a driver may be offered the option to specify the mass of the vehicle within a range of mass values for the vehicle between predefined minimum and maximum values, dependent upon, for example, the load being carried. For example, the driver may be able to set an intermediate value, e.g. percentage, based upon the perceived load of the vehicle. However, in some further embodiments, the present invention provides a way of automatically estimating vehicle mass. The mass estimated in accordance with these techniques may be used in obtaining forward acceleration thresholds as described above, or in other contexts where it is required to know vehicle mass, e.g. to obtain acceleration thresholds in respect of braking or cornering, in determining other driving performance indicators not related to acceleration, e.g. in relation to braking or steering in general, or in other contexts, e.g. fuel consumption determination. Being able to automatically estimate mass enables, for example, thresholds for use in such determinations to be made more dynamic. In some applications, knowledge of mass is advantageous in its own right, and may not necessarily be used in further calculations, e.g. to provide a current estimate of mass of a lorry where this is required by regulation.”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the load detection disclosed in SESHADRI with the load detection taught in Murphy with a reasonable expectation of success because it would have “prevented excessive acceleration” as taught by SESHADRI Para:
“The Applicant has recognised that such techniques in which a driving event is identified based upon excessive acceleration detected using a predefined, fixed minimum acceleration threshold selected based upon vehicle speed, with the same threshold being used throughout a range of vehicle speeds, may not be appropriate for identifying a driving event that is based on forward acceleration due to the dynamics of a moving vehicle.”.
Regarding claim 3 and the limitation the method of claim 1, wherein the movement data includes acceleration data measured by an inertial measurement unit of the scooter (see Murphy fig. 5, item 424(a) Accelerometer and para [0036] above).
Regarding claim 4 and the limitation the method of claim 3, comprising comparing the force or power data and the acceleration data, and determining a coefficient based on the comparison (to a Person of Ordinary Skill In The Art (POSITA) connotes Murphy steps 340 and 640).
Regarding claim 6 and the limitation the method of claim 3, wherein the acceleration data is measured by a 3-axis accelerometer of the inertial measurement unit (see the teachings of SESHADRI:
“In order to provide an accurate acceleration measurement, the accelerometer must be calibrated, such that the raw accelerometer data in the coordinate frame of the accelerometer system can be transformed into the coordinate frame of the vehicle. Various techniques may be used to achieve this. One such method is described in WO 2011/003461 Al entitled "Accelerometer system and method", and published 13 January 2011, that involves collecting a plurality of acceleration samples during vehicle standstill and using the samples to compute an average gravitation vector. Based on this average gravitation vector, the angle of rotation between the accelerometer and the horizontal vehicle plane can thus be determined. “
which incorporates WO 2011003461 A1 to SCHMIDT ALEXANDER et al. which teaches:
“The accelerometer in certain embodiments is a three-axis accelerometer and measures acceleration along each of three orthogonal axes (x, y, z). In alternative embodiments the accelerometer is a one or two axis accelerometer. The accelerometer may be an analogue or digital acceleration sensor and can be of any type. In one embodiment, the accelerometer is a Bosch Sensortec SMB380 triaxial acceleration sensor.”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the accelerometer disclosed in SESHADRI with the accelerometer taught in Murphy with a reasonable expectation of success because it would have “provided an accurate measurement” as taught by SESHADRI:
“In order to provide an accurate acceleration measurement, the accelerometer must be calibrated, such that the raw accelerometer data in the coordinate frame of the accelerometer system can be transformed into the coordinate frame of the vehicle. Various techniques may be used to achieve this. One such method is described in WO 2011/003461 Al entitled "Accelerometer system and method", and published 13 January 2011, that involves collecting a plurality of acceleration samples during vehicle standstill and using the samples to compute an average gravitation vector. Based on this average gravitation vector, the angle of rotation between the accelerometer and the horizontal vehicle plane can thus be determined.”.
Regarding claim 7 and the limitation the method of claim 1, wherein the speed data relates to a wheel speed of the scooter (see the teachings of Murphy fig. 5, items 424(c) “Speed Sensor” “receive a signal from a sensor 424, such as a wheel sensor “ and 426(a) and para:
“[0031] Additionally, acceleration determination module 406 has instructions that cause the processor 430 to carry out operations detailed above with respect to step 320 (see FIG. 3). In operation, module 406 may cause processor 430 to receive a signal from a sensor 424, such as a wheel sensor, GPS, accelerometer, weight sensor etc. Alternatively or additionally, module 406 may cause processor 430 to operate wireless antennas of interfaces 426 to carry out wireless position determination operations (e.g., received signal strength indication (RSSI), fingerprinting, angle of arrival, time of flight, etc.). Module 406 may cause processor 430 to carry out positioning measurements (e.g., GPS or wireless) and determine vehicle acceleration from such measurements over time.”).
Regarding claim 8 and the limitation the method of claim 1, wherein the estimating comprises determining the force or power data based on electrical power of the motor and the speed data (see the teachings of SESHADRI para:
“The present invention in its various aspects is applicable to any type of vehicle including a conventional (fossil fuel) powered vehicle, e.g. petrol or diesel, a hybrid vehicle, a hydrogen powered vehicle, a fuel cell powered vehicle or, (in respect of the first and second aspects of the invention which do not necessarily require fuel consumption to be determined), an electric vehicle. Embodiments of the invention will be described with reference to a vehicle having an engine (or drive unit) operative at an engine speed and fuel being supplied to the engine (or being consumed by the engine) at a fuel rate. It will, however, be realised that these terms may be construed accordingly to encompass the aforementioned types of vehicles.”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the load detection disclosed in SESHADRI with the load detection taught in Murphy with a reasonable expectation of success because it would have “prevented excessive acceleration” as taught by SESHADRI Para:
“The Applicant has recognised that such techniques in which a driving event is identified based upon excessive acceleration detected using a predefined, fixed minimum acceleration threshold selected based upon vehicle speed, with the same threshold being used throughout a range of vehicle speeds, may not be appropriate for identifying a driving event that is based on forward acceleration due to the dynamics of a moving vehicle.”.
Regarding claim 9 and the limitation the method of claim 8, wherein the electrical power is determined by at least one or two of:
motor current,
motor voltage (as set forth in the rejection of claim 8 immediately above and incorporated herein by reference wherein SESHADRI teaches:
“an electric vehicle. Embodiments of the invention will be described with reference to a vehicle having an engine (or drive unit) operative at an engine speed and fuel being supplied to the engine (or being consumed by the engine) at a fuel rate. It will, however, be realised that these terms may be construed accordingly to encompass the aforementioned types of vehicles.”).“
Wherein it is understood that a Person of Ordinary Skill In The Art (POSITA) is well apprised that an electric vehicle consumes electrical power per mile and that the notoriously old and well known equation for Power equals Current times Voltage (P=IE)).
Regarding claim 10 and the limitation the method of claim 1, wherein the estimated mass is scaled by a correction factor, wherein the correction factor takes into account at least an electric scooter mass (see the teachings of Murphy para:
“[0045] As set forth above, where the semi-static acceleration delta (Δα.sub.S) and the recent dynamic acceleration delta (Δα.sub.R) are zero, block 806 performs the operation of comparing the measured acceleration (α.sub.M) with the target acceleration (α.sub.T) associated with the current throttle position (P.sub.C) 708, and it does so directly. Similarly, where the semi-static acceleration delta (Δα.sub.S) and/or the recent dynamic acceleration delta (Δα.sub.R) are not zero, then block 806 also performs the operation of comparing the measured acceleration (α.sub.M) with the target acceleration (α.sub.T) associated with the throttle position 708, but with the target acceleration (α.sub.T) being previously modified based on correction factors that correspond to the semi-static acceleration delta (Δα.sub.S) and/or the recent dynamic acceleration delta (Δα.sub.R). With these correction factors being iteratively updated at blocks 728 and 732 (see FIG. 7), the current dynamic acceleration delta (Δα.sub.D) is effectively driven to zero over time (e.g., in a single iteration or after expiration of a counter as described herein). Also, when the measured acceleration (α.sub.M) changes due to addition of cargo, the operation at block 806 compares this changed acceleration (α.sub.M) with the target acceleration (α.sub.T), and the torque magnitude is modified, at block 808, based on this comparison of the changed acceleration (α.sub.M) with the target acceleration (α.sub.T).”) .
Regarding claim 11 and the limitation the method of claim 1, wherein the estimated mass is scaled by a correction factor, wherein the correction factor takes into account a driver mass (wherein it is understood that the mass of the scooter contains the mass of the driver included in at least the users profile as explained above as well as in the teachings of Murphy para:
“[0045] As set forth above, where the semi-static acceleration delta (Δα.sub.S) and the recent dynamic acceleration delta (Δα.sub.R) are zero, block 806 performs the operation of comparing the measured acceleration (α.sub.M) with the target acceleration (α.sub.T) associated with the current throttle position (P.sub.C) 708, and it does so directly. Similarly, where the semi-static acceleration delta (Δα.sub.S) and/or the recent dynamic acceleration delta (Δα.sub.R) are not zero, then block 806 also performs the operation of comparing the measured acceleration (α.sub.M) with the target acceleration (α.sub.T) associated with the throttle position 708, but with the target acceleration (α.sub.T) being previously modified based on correction factors that correspond to the semi-static acceleration delta (Δα.sub.S) and/or the recent dynamic acceleration delta (Δα.sub.R). With these correction factors being iteratively updated at blocks 728 and 732 (see FIG. 7), the current dynamic acceleration delta (Δα.sub.D) is effectively driven to zero over time (e.g., in a single iteration or after expiration of a counter as described herein). Also, when the measured acceleration (α.sub.M) changes due to addition of cargo, the operation at block 806 compares this changed acceleration (α.sub.M) with the target acceleration (α.sub.T), and the torque magnitude is modified, at block 808, based on this comparison of the changed acceleration (α.sub.M) with the target acceleration (α.sub.T).”) .
Regarding claim 12 and the limitation the method of claim l, comprising storing the estimated mass to a memory in association with the user profile (see Murphy Fig. 6 step 640 “update rider profile…”).
Regarding claim 13 and the limitation the method of claim 11, wherein the driver mass is adapted based on previous estimated masses associated with the user profile (see Murphy, Fig. 6, step 640, “update rider profile…” and the teachings of SESHADRI with regard to “estimating mass” in the motivation to combine and the rejection of corresponding parts of claims 11 and 1 above incorporated herein by reference.).
Regarding claim 14 and the limitation An electric scooter control system comprising at least a processing unit and a memory,
wherein the electric scooter control system is configured to execute the method of claim 1 (see the rejection of corresponding parts of claim 1 above incorporated herein by reference wherein it is understood that (see Murphy expressly teaches an electric scooter with a processing unit and memory in for example Figs. 4 and 5 and the combination of Murphy and SESHADRI teach the method of claim 1).
Regarding claim 15 and the limitation an electric scooter comprising:
an electric motor,
at least two wheels,
wherein at least one of the wheels is arranged to be rotated by the electric motor, and an electric scooter control system of claim 14 (see the rejection of corresponding parts of claims 14 and 1 above incorporated herein by reference wherein it is clear that Murphy teaches an electric scooter comprising an electric motor, at least two wheels and which is operated as claimed and explained in the combination of Murphy and SESHADRI in the motivation to combine and the rejection of corresponding parts as explained above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure as teaching, inter alia, the state of the art of scooter control and detecting the load of a vehicle/scooter at the time of the invention. For example:
US 6347269 B1 to Hayakawa; Kisaburo et al. teaches, inter alia a Vehicle mass calculation device in for example the ABSTRACT, Figures and/or Paragraphs below:
“To calculate a vehicle mass based on a driving force caused by an engine, running resistance, and vehicle acceleration, influence of gradient resistance is removed. A gross driving force calculating section calculates a gross driving force F of a vehicle by deducting running resistance from a driving force of a vehicle caused by an engine. An acceleration sensor calculates a longitudinal acceleration .alpha. of the vehicle. Relationship among a gross driving force F, a longitudinal acceleration .alpha., a vehicle mass M, and road gradient .THETA. can be expressed as (.alpha.=F/M-g sin .THETA.). Because the change of gradient contains only a low frequency component, by processing a gross driving force F and an acceleration .alpha., using a high-pass filter with a predetermined cut-off frequency, the influence of the gradient .THETA. can be removed. Based on the resultant processed gross driving force F and the processed acceleration .alpha., a vehicle mass can be obtained from the above expression without being affected by the influence of the gradient”.
US 20070135982 A1 to Breed; David S. et al. teaches, inter alia Methods for Sensing Weight of an Occupying Item in a Vehicular Seat in for example the ABSTRACT, Figures and/or Paragraphs below:
“Method for determining weight of an occupant of an automotive seat involves arranging a bladder having at least one chamber in a seat portion of the seat, measuring the pressure in each chamber and deriving the weight of the occupant based on the measured pressure. The pressure in each chamber may be measured by a respective transducer associated therewith. The weight distribution of the occupant, the center of gravity of the occupant and/or the position of the occupant can be determined based on the pressure measured by the transducer(s). In one embodiment, the bladder is arranged in a container and fluid flow between the bladder and the container is permitted and optionally regulated, for example, via an adjustable orifice between the bladder and the container.”.
US 20160355189 A1 to Lin; Sung-Ching et al. teaches, inter alia XXX in for example the ABSTRACT, Figures and/or Paragraphs below:
“A torque-speed curve or data of load that is used as a standard to determine an external condition in which an electric vehicle is operating such as incline or no incline, head wind or no headwind, high temperature or low temperature. The system compares samples of actual torque-speed of load data to the standard. Based on the comparison, the system determines the external condition (going up a hill, traveling into a headwind, operating at high temperature) or an abnormal operation of the vehicle powertrain, for example, low tire pressure, elevated friction, wheels out of alignment. Based on the determination, the system takes an action to govern a maximum torque output of the motor to control temperature of the vehicle battery; to raise a wind deflector; to govern maximum speed of the vehicle to reduce danger resulting from low tire pressure, elevated powertrain friction or out of alignment wheels; or to initiate an indication of abnormal conditions.”.
US 20180170394 A1 to Bedegi; Peter A. et al. teaches, inter alia AUTOMATED VEHICLE CONTROL WITH PAYLOAD COMPENSATION in for example the ABSTRACT, Figures and/or Paragraphs below:
“A vehicle-control system for an automated vehicle includes a load-sensor and a controller. The load-sensor is used to determine a weight of a payload transported by a host-vehicle. The controller determines a response-characteristic used to operate the host-vehicle, wherein the response-characteristic is determined based on the weight of the payload. The load-sensor may be used to measure a ride-height of the host-vehicle, estimate the weight based on a test-acceleration of the host-vehicle, or estimate the weight based on a manifest that indicates a package-weight of a package transported by the host-vehicle.”.
US 20170259697 A1 to Dastoor; Sanjay et al. teaches, inter alia DYNAMIC CONTROL FOR LIGHT ELECTRIC VEHICLES in for example the ABSTRACT, Figures and/or Paragraphs below:
“A method for dynamic control of an electric vehicle operable based on a throttle value received from a throttle and a default throttle map correlating default output values with throttle values, the method including: determining a user parameter; detecting a condition indicative of perturbation; in response to detecting the condition indicative of perturbation, determining a replacement output value for a first throttle value based on the user parameter; and controlling vehicle operation to meet the replacement output value in response to receipt of the first throttle value.”.
US 20200124430 A1 to Bradlow; Henry Weston et al. teaches, inter alia DETECTING TYPES OF TRAVEL CORRIDORS ON WHICH PERSONAL MOBILITY VEHICLES TRAVEL in for example the ABSTRACT, Figures and/or Paragraphs below:
“The disclosed embodiments relate to detecting sidewalk riding by a personal mobility vehicle (e.g., an electric scooter). For example, a method includes collecting sensor data (e.g., vibration data of an accelerometer) generated by the scooter while traveling on a surface of a travel pathway. The method further includes identifying a surface type by processing the collected sensor data with a computer model that can distinguish among different surface types, and determining that the travel pathway is unsuitable (e.g., a sidewalk) for the scooter based at least in part on the identified surface type (e.g., a pattern of concrete sections). In response to determining that the travel pathway is a sidewalk, causing the personal mobility vehicle to assist the user in navigating the personal mobility vehicle, alter a mobility operation of the personal mobility vehicle, or notify a surrounding area of a presence of the personal mobility vehicle.”.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL LAWSON GREENE JR whose telephone number is (571)272-6876. The examiner can normally be reached on MON-THUR 7-5:30PM (EST) .
Examiner interviews are available via telephone and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hunter Lonsberry can be reached on (571) 272-7298. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DANIEL L GREENE/Primary Examiner, Art Unit 3665 20260108