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 .
Contingent Limitations
Claim 3 comprises contingent limitations recited in phrases “in a case if …”. The broadest reasonable interpretation of a method claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. The conditions followed the phrase “if” may not be met, hence the corresponding steps may not be required to be conducted. Therefore, these limitations have no patentable weight. See MPEP 2111.04 (II) for details.
Applicant is deemed to intend to make the limitation to have patentable weight in the claim, therefore for continuing examination purpose, the limitation has been construed as “in response to the updated distance [[is]] being less than the calculated distance”.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 18 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
claim 18 recites a limitation “The method as claimed in claim 17”, which lacks sufficient antecedent basis. For continuing examination purpose, the limitation has been construed as “The system as claimed in claim 17”.
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.
Claims 1-3, 6-8 and 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over POLL (US 20220176939 A1, hereinafter as “POLL”) in view of Adetola (US 20190283530 A1, hereinafter as “Adetola”).
Regarding claim 1, POLL teaches:
A method for managing power consumption in a battery electric vehicle (BEV) (BEV 102 in FIG. 1 and [0022]), comprising:
obtaining, via an electronic control unit (ECU) of the BEV (ECU 140 in FIG.1 and [0029]), real time vehicle data including at least a state of charge of an energy storage unit of the BEV, each of a power limitation maximum current and a power limitation maximum voltage of the energy storage unit, a charging rate of the energy storage unit, information related to load conditions of the BEV, a health status of the energy storage unit, and live weather forecast information ([0024]: “Vehicle operating data and/or ambient data such as cabin temperature, climate control status, accessory use, ambient temperature, battery state of charge (SOC), trip destination and/or selected route, etc. … ”; [0027]: “Trip destination and associated route planning or actual route/trip data as well as planned and/or actual vehicle charging data, battery SOC, climate control use, accessory use, energy efficiency, etc. may be selectively transmitted to an external cloud server 146 for use in collecting crowd-sourced data associated with determining a predicted energy consumption or efficiency associated with particular road links, ambient temperatures, weather, traffic, etc.”; and [0035]: “The vehicle 102 may be further provided with a battery electronic control module (BECM) 160 to control cell balancing, charging, discharging, and other operations of a vehicle traction battery. BECM 160 may be connected to the in-vehicle network 142 and configured to communicate with various ECUs 140 of the vehicle 102 and collect data therefrom. The BECM may provide data to monitor or calculate a battery health of life (HOL) based on published calculation strategies. The BECM may also measure various battery parameters such as cell voltage, current, and or resistance to monitor battery operation and performance”);
receiving, based on one of a user input or an acquisition from a remote database, information regarding one or more deliveries that are planned by a user for a specific day ([0008]: “the trip data including a plurality of road segments associated with a selected route to a trip destination”);
determining a consumption of energy for completion of the one or more deliveries on the specific day based on the obtained real time vehicle data and the received information regarding the one or more deliveries ([0037]: “The DTE is calculated or otherwise determined based on a remaining trip distance, available energy of the traction battery 101, an estimated traction battery energy required for the remaining trip distance based on the received trip data, and a vehicle efficiency”);
calculating a distance that can be traveled by the BEV based on the determined consumption of energy ([0037]: “The DTE is calculated or otherwise determined based on a remaining trip distance, available energy of the traction battery 101, an estimated traction battery energy required for the remaining trip distance based on the received trip data, and a vehicle efficiency”. DTE, i.e., distance that can be traveled before the battery is empty, is calculated); and
displaying a result of the estimation on a human machine interface (HMI) (HMI 112 and/or display 114 in FIG. 1 and [0021]) of the BEV, indicating an updated distance that can be traveled by the BEV ([0021]: “… providing a segment-based DTE for display via a human-machine interface 112 and/or separate display 114”; And [0024]: “The computer-readable media 110 (also referred to as a processor-readable medium or storage) includes any non-transitory medium (e.g., tangible medium) that participates in providing instructions or other data, settings, or parameters that may be read or accessed by the controller or processor 106 of the computing platform 104 to calculate and instruct display of a dynamically updated segment-based single DTE”).
POLL teaches all the limitations except the BEV comprises a transport refrigeration unit (TRU); and determining, in real time, a temperature difference between an internal temperature of the TRU and an external temperature outside the TRU; estimating an impact of the determined temperature difference on the determined consumption of energy, wherein the updated distance that can be traveled by the BEV is based on the estimated impact of the temperature difference.
However, Adetola teaches in an analogous art:
a transport refrigeration unit (TRU) (TRU 104 in FIG. 1 and [0023]];
determining, in real time, a temperature difference between an internal temperature of the TRU and an external temperature outside the TRU ([0041]: “The mission specific parameters may include one or more of actual and forecasted in-route weather and traffic conditions, driver preferences, in-route proximity to an electrical grid charging station, loaded cargo type, cargo required temperature set-points and airflow, cargo current temperature and refrigeration cycle efficiency. For example, the loaded cargo type may be perishable or unperishable, cargo required temperature set-points are below or above freezing temperatures for loaded cargo, and driver preferences define a minimum duration of time between connecting to an electrical grid charging station. TRU power health includes an observed and a pan-mission anticipated state of TRU power supply components and power demand components. The refrigeration cycle efficiency may be learned from recorded trends/historical data related to the TRU operational mode, outside air temperature, altitude, and cooling/heating load”);
estimating an impact of the determined temperature difference on the determined consumption of energy )[0042]: “With this collected dataset of information the TRU controller may perform step S280 of predicting the TRU's cooling power demand”);
The TRU is a load/accessory consuming power. POLL teaches to calculate DTE based on also the power consumed by vehicle accessory and/or climate control system ([0037]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified POLL based on the teaching of Adetola, to make the method to further comprise determining, in real time, a temperature difference between an internal temperature of the TRU and an external temperature outside the TRU; estimating an impact of the determined temperature difference on the determined consumption of energy, wherein the BEV has a transport refrigeration unit (TRU), and the BEV is based on the estimated impact of the temperature difference. One of ordinary skill in the art would have been motivated to do this modification in order to help “provide proper conditioning for cargo”, as Adetola suggests in [0003].
Regarding claim 2, POLL-Adetola teach(es) all the limitations of its base claim from which the claim depends on.
POLL further teaches:
calculating the updated distance that can be traveled by the BEV based on the estimated impact of the determined temperature difference on the determined consumption of energy, wherein the updated distance corresponds to one of a decrease or increase in the calculated distance ([0037]: “Each of the plurality of road segments may include an associated segment distance and estimated energy consumption by the traction battery 101 for: propelling the vehicle 102 through the segment using the electric machine 105, operating the climate control system while traveling through the segment, and powering the at least one vehicle accessory while traveling through the segment. The DTE is calculated or otherwise determined based on a remaining trip distance, available energy of the traction battery 101, an estimated traction battery energy required for the remaining trip distance based on the received trip data, and a vehicle efficiency”. This teaches to calculate updated DTE based on the power consumed by the accessory/TRU which depends on the temperature difference TRU makes. The more power the TRU consumes, the shorter DTE of the BEV can make); and
displaying the updated distance on the HMI ([0021], [0024]).
Regarding claim 3, POLL-Adetola teach(es) all the limitations of its base claim from which the claim depends on.
POLL further teaches:
displaying, in response to the updated distance being less than the calculated distance, a current status of a remaining distance that can be traveled by the BEV on the HMI based on the determined consumption of energy ([0037], [0021] and [0024]: the updated TDE, which is shorter or longer than the previously calculated value, is displayed on the HMI).
Regarding claim 6, POLL-Adetola teach(es) all the limitations of its base claim from which the claim depends on.
POLL further teaches:
the energy storage unit includes one or more batteries (battery 101 in FIG. 1).
Regarding claim 7, POLL-Adetola teach(es) all the limitations of its base claim from which the claim depends on.
POLL further teaches:
the ECU receives the real time vehicle data via one of a transceiver or an antenna included in the ECU (FIG. 1 and [0037]: “One or more controllers 106, 140 are in communication with the traction battery 101 and the electric machine 105 via a wired or wireless vehicle network 142”. Network 142 comprises transceiver included in the ECU 140).
Regarding claim 8, POLL-Adetola teach(es) all the limitations of its base claim from which the claim depends on.
POLL further teaches:
the HMI is installed in a driving compartment of the BEV, and wherein the user input is received via the HMI (FIG.1 and [0025]: “the computing platform 104 may receive input from human-machine interface (HMI) controls 112 configured to provide for occupant interaction with the vehicle 102”. This teaches the HMI 112 is for user interaction and installed in driving compartment).
Regarding claim 13, POLL-Adetola teach(es) all the limitations of its base claim from which the claim depends on.
POLL further teaches:
displaying the result of the estimation on one of a Graphical User Interface (GUI) of a mobile application using a telematic connection (FIG. 1 and [0025]: “HMI 112 may include one or more video screens or displays to present DTE information to the driver/occupants, such as display 114 or a connected/coupled display of a mobile device 126”) or a GUI of an external device connected to the ECU using a Wi-Fi or a Bluetooth connection.
Claim 14 recites a system conducting the operational steps of the method in claim 1 with patentably the same limitations. Therefore, claim 14 is rejected for the same reason recited in the rejection of claim 1.
Claims 15 and 16 recite a system conducting the operational steps of the method in claims 2 and 3 respectively with patentably the same limitations. Therefore, claims 15 and 16 are rejected for the same reason recited in the rejection of claims 2 and 3, respectively.
Claims 4 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over POLL in view of Adetola, and in further view of LEE (US 20220097557 A1, hereinafter as “LEE”).
Regarding claim 4, POLL-Adetola teach(es) all the limitations of its base claim from which the claim depends on, but do not teach determining, using a machine learning model, information regarding a driving pattern of the user while completing one or more deliveries in past based on historical information of the user, wherein the historical information includes associated vehicle parameters related to activities of the user in the past; and calculating the distance that can be traveled by the BEV based on the determined consumption of energy and the determined information regarding the driving pattern of the user.
However, LEE teaches in an analogous art:
determining, using a machine learning model, information regarding a driving pattern of the user while completing one or more deliveries in past based on historical information of the user, wherein the historical information includes associated vehicle parameters related to activities of the user in the past ([0080]: “The battery status estimation unit 231 analyzes the big data collected and stored by the data collection server 21, by using a machine learning technique, so that a correlation of the battery consumption characteristics with types of vehicle and battery, topography, a driving pattern, weather information, and road weather information is derived. The battery status estimation unit 231 may estimate a current distance to empty considering the current status of the battery and weather information. More preferably, the battery status estimation unit 231 may analyze the battery consumption characteristics considering topography, a driving pattern, and weather information for a predetermined path from the current location of the vehicle, and estimates battery consumption in accordance with the battery consumption characteristics”); and
calculating the distance that can be traveled by the BEV based on the determined consumption of energy and the determined information regarding the driving pattern of the user ([0080]: “The battery status estimation unit 231 analyzes the big data collected and stored by the data collection server 21, by using a machine learning technique, so that a correlation of the battery consumption characteristics with types of vehicle and battery, topography, a driving pattern, weather information, and road weather information is derived. The battery status estimation unit 231 may estimate a current distance to empty considering the current status of the battery and weather information. More preferably, the battery status estimation unit 231 may analyze the battery consumption characteristics considering topography, a driving pattern, and weather information for a predetermined path from the current location of the vehicle, and estimates battery consumption in accordance with the battery consumption characteristics”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified POLL-Adetola based on the teaching of LEE, to make the method to further comprise determining, using a machine learning model, information regarding a driving pattern of the user while completing one or more deliveries in past based on historical information of the user, wherein the historical information includes associated vehicle parameters related to activities of the user in the past; and calculating the distance that can be traveled by the BEV based on the determined consumption of energy and the determined information regarding the driving pattern of the user. One of ordinary skill in the art would have been motivated to do this modification in order to help “achieving accurate estimation”, as LEE suggests in [0002].
Claim 17 recites a system conducting the operational steps of the method in claim 4 with patentably the same limitations. Therefore, claim 17 is rejected for the same reason recited in the rejection of claim 4.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over POLL in view of Adetola, and in further view of Uyeki (US 20130079978 A1, hereinafter as “Uyeki”).
Regarding claim 11, POLL-Adetola teach(es) all the limitations of its base claim from which the claim depends on, but do not teach the live weather forecast information includes at least temperature information, humidity information, wind speed, a direction of the wind speed, and information indicating one of sunny or cloudy weather conditions.
However, Uyeki teaches in an analogous art:
the live weather forecast information includes at least temperature information, humidity information, wind speed, a direction of the wind speed, and information indicating one of sunny or cloudy weather conditions ([0031]: “The forecast weather information maintained by the weather server 108 for a location at a time and date in the future may include one or more of the following: outdoor temperature, humidity, wind speed, wind direction, condition summary (e.g., cloudy, partly cloudy, sunny, showers, snowing, etc.), and the rate of rain or snow fall”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified POLL-Adetola based on the teaching of Uyeki, to make the method wherein the live weather forecast information includes at least temperature information, humidity information, wind speed, a direction of the wind speed, and information indicating one of sunny or cloudy weather conditions. One of ordinary skill in the art would have been motivated to do this modification in order to help “manage the climate control system of an electric vehicle in a manner that minimizes the effect on the vehicle's driving range”, as Uyeki suggests in [0004].
Allowable Subject Matter
Claims 5, 9, 10, 12, 18, 19 and 20 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES CAI whose telephone number is (571)272-7192. The examiner can normally be reached on M-F 8-5 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini Shah can be reached on 571-272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHARLES CAI/Primary Patent Examiner, Art Unit 2115