DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
This action is in response to the Applicant’s filing on May 24, 2024. Claims 1-10 are pending and examined below.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
a receiving unit configured to:
receive…(claims 7 and 8)
The Examiners broadest reasonable interpretation for the receiving unit is based on paragraph [0056] of the disclosure where it states “The receiving unit 204 may be, for example, a receiver that may include an antenna, an antenna array, an input interface, a pin, a circuit, or the like.” The Examiner interprets the receiving unit to comprise of hardware for receiving information encoded in a signal.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 Analysis:
STEP 1: Does claim 1 fall within one of the statutory categories? Yes. The claim is directed toward a method, which falls within one of the statutory categories.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claim is directed to an abstract idea.
Claim 1 recites: A method (700) for recommending energy efficient routes to vehicles, for V2V energy exchange, comprising:
identifying (702) at least one energy consumer vehicle and one or more energy supplier vehicles, among a plurality of vehicles, based on route information of the plurality of vehicles and energy consumption associated with the route information;
selecting (704), for each of the identified at least one energy consumer vehicle, at least one corresponding energy supplier vehicle from the identified one or more energy supplier vehicles based on at least one proximity location, wherein the proximity location corresponds to a location at which routes of the selected energy consumer vehicle and the selected at least one energy supplier vehicle either interact or are in close proximity to each other;
identifying (706) a plurality of energy exchange locations based on the proximity locations;
extracting (708) the route information and the energy consumption associated with the route information of the selected at least one energy supplier vehicle and the at least one energy consumer vehicle;
identifying (710) an optimal route, from the extracted route information, for the at least one energy consumer vehicle and the selected at least one energy supplier vehicle;
calculating (712) an updated optimal route for the selected at least one energy supplier vehicle and for the at least one energy consumer vehicle based on at least one of the identified optimal route, the plurality of energy exchange locations, the extracted route information and energy consumption associated with the extracted route information, and one or more constraints; and
recommending (714) the updated optimal route for the at least one energy supplier vehicle and the at least one energy consumer vehicle for V2V energy exchange, indicating at least one energy exchange location from the identified plurality of energy exchange locations and duration of energy exchange.
The limitations highlighted in claim 1 above are mental processes that can be practicably performed in the human mind and, therefore, abstract ideas. The limitations of claim 1 highlighted above merely consists of selecting an energy supplier vehicle for each energy consumer vehicle based on route information, energy consumption, and proximity locations of vehicles; identifying potential energy exchange locations based on the proximity locations; extracting route information for the vehicles; identifying an optimal route from the route information; and calculating an updated optimal route based on optimal route, energy exchange locations, route information, energy consumption, and one or more constraints.. This is the equivalent of a person selecting an energy supplier vehicle for each energy consumer vehicle, identifying possible locations where they might meet, observing route information for each vehicle, determining a route each vehicle is currently traversing, and calculating a new route for the vehicles based on a person's knowledge of interpreting sensor data related to vehicle proximity, current route, and energy consumption for each vehicle. Thus, the claim recites a mental process.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
Claim 1 recites: A method (700) for recommending energy efficient routes to vehicles, for V2V energy exchange, comprising:
identifying (702) at least one energy consumer vehicle and one or more energy supplier vehicles, among a plurality of vehicles, based on route information of the plurality of vehicles and energy consumption associated with the route information;
selecting (704), for each of the identified at least one energy consumer vehicle, at least one corresponding energy supplier vehicle from the identified one or more energy supplier vehicles based on at least one proximity location, wherein the proximity location corresponds to a location at which routes of the selected energy consumer vehicle and the selected at least one energy supplier vehicle either interact or are in close proximity to each other;
identifying (706) a plurality of energy exchange locations based on the proximity locations;
extracting (708) the route information and the energy consumption associated with the route information of the selected at least one energy supplier vehicle and the at least one energy consumer vehicle;
identifying (710) an optimal route, from the extracted route information, for the at least one energy consumer vehicle and the selected at least one energy supplier vehicle;
calculating (712) an updated optimal route for the selected at least one energy supplier vehicle and for the at least one energy consumer vehicle based on at least one of the identified optimal route, the plurality of energy exchange locations, the extracted route information and energy consumption associated with the extracted route information, and one or more constraints; and
recommending (714) the updated optimal route for the at least one energy supplier vehicle and the at least one energy consumer vehicle for V2V energy exchange, indicating at least one energy exchange location from the identified plurality of energy exchange locations and duration of energy exchange.
Claim 1 does not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. The additional elements underlined above do not integrate the abstract idea into practical application. The identifying step is recited at a high level of generality (as a general means of data gathering) and amount to mere data gathering, which is a form of insignificant extra solution activity. Further, the recommending step is recited at a high level of generality (as a generic outputting of information such as displaying or transmitting of information) and amounts to mere post solution actions, which is also a form of insignificant extra solution activity. Still further, the method amounts to instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea which is indicative that the judicial exception has not been integrated into a practical application. In the instant case, the steps of identifying, selecting, extracting, calculating, and recommending are performed by a processor. Thus, it is clear that the abstract idea is merely implemented on a computer, which is indicative of the abstract idea having not been integrated into a practical application.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claim does not recite additional elements that amount to significantly more than the judicial exception.
Independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. A conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitation/s of identifying and recommending are well-understood, routine, and conventional (WURC) activities in the field. Identifying and recommending are fundamental, i.e. WURC, activities performed by processors.
Independent claim 6 is rejected using a similar rationale as applied to claim 1 above, as claim 6 is commensurate in scope with claim 1 but is drawn to a system.
Dependent claims 2-5 and 7-10 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-5 and 7-10 are not patent eligible under the same rationale as provided for in the rejection of independent claim 1.
Therefore, claims 1-10 are ineligible under 35 U.S.C. §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3 and 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2025/0310736 by Brannan (herein after "Brannan"), in view of U.S. Patent Application Publication No. 2019/0107406 by Cox et al. (herein after "Cox").
Note: Text written in bold typeface is claim language from the instant application. Text written in normal typeface are comments made by the Examiner and/or passages from the prior art reference(s).
Regarding claim 1, Brannan discloses a method (700) for recommending energy efficient routes to vehicles, for V2V energy exchange, (Brannan ¶ [0034]: applications may include a status evaluation application for determining which of the electric vehicles are located nearby within a predetermined radius, whether the vehicles have enough SOC such that the vehicles can travel at least a certain predetermined distance after charging another vehicle, and which of the vehicles are capable of reaching the vehicle that requires charging with minimal rerouting) comprising:
identifying (702) at least one energy consumer vehicle (Brannan ¶ [0074]: The method 800 may include determining at step 802, via one or more processors, an electric vehicle having a battery with a state of charge (SOC) below a predetermined threshold; 802 in Fig. 8) and one or more energy supplier vehicles (Brannan ¶ [0076]: The method 800 may include at step 806 ranking, from among the electric vehicles within the predetermined distance of the low SOC vehicle, via the one or more processors, the electric vehicles based upon various factors; 806 in Fig. 8), among a plurality of vehicles, based on route information of the plurality of vehicles and energy consumption associated with the route information (Brannan ¶ [0029]: determine how much power is stored in a battery or batteries implemented in the vehicle 100 using the sensors 208 as well as a predicted range of the vehicle 100 based upon the measured power. The sensors 208 may be a voltmeter coupled to the battery to measure the state of charge (SOC), for example. The measurement taken by the sensors 208 are then used by the processing unit 202 to calculate how far the vehicle 100 may be able to travel before the vehicle 100 completely runs out of battery, which is also called its predicted range of operation; Brannan ¶ [0074]: Such threshold may be defined as a percentage of the SOC remaining in the electric vehicle, such as 20%, 25%, 30%, etc., or as a distance that the electric vehicle is determined to be capable of traveling based upon the SOC remaining in the electric vehicle, such as 10 or 20 miles, etc; Brannan ¶ [0076]: the electric vehicles within the predetermined distance of the low SOC vehicle may be ranked based upon (a) distance to the low SOC vehicle, (b) similarity of route being traveled to the route of the low SOC vehicle; (c) travel distance to destination remaining; (d) remaining battery power or remaining miles based on current battery power; and/or (e) power available to transfer (such as determined from distance remaining to destination, or amount of power that would be “left over”));
selecting (704), for each of the identified at least one energy consumer vehicle, at least one corresponding energy supplier vehicle from the identified one or more energy supplier vehicles based on at least one proximity location, wherein the proximity location corresponds to a location at which routes of the selected energy consumer vehicle and the selected at least one energy supplier vehicle either interact or are in close proximity to each other (Brannan ¶ [0076]: the electric vehicles within the predetermined distance of the low SOC vehicle may be ranked based upon (a) distance to the low SOC vehicle, (b) similarity of route being traveled to the route of the low SOC vehicle; (c) travel distance to destination remaining; (d) remaining battery power or remaining miles based on current battery power; and/or (e) power available to transfer (such as determined from distance remaining to destination, or amount of power that would be “left over”));
identifying (706) a plurality of energy exchange locations based on the proximity locations (Brannan ¶ [0078]: route information for each vehicle may be retrieved and analyzed to determine available locations for charging the low SOC vehicle; Brannan ¶ [0080]: The method 800 may include scheduling at step 812, via the one or more processors and/or associated transceivers, a rendezvous for the low SOC vehicle with the highest, or one of the highest, remaining ranked electric vehicle for recharging the low SOC vehicle. If one or both vehicles are autonomous, the one or more processors may determine routes for each autonomous electric vehicle, and automatically route each electric vehicle to the rendezvous location);
extracting (708) the route information and the energy consumption associated with the route information of the selected at least one energy supplier vehicle and the at least one energy consumer vehicle (Brannan ¶ [0034]: The various software applications on the server 212 may include a vehicle operation information monitoring application or receiving information regarding the electric vehicles and their current locations, predicted routes, vehicle conditions, battery SOC; Brannan ¶ [0074]: The vehicle telematics data may include acceleration, braking, cornering, speed, direction, route, GPS (Global Positioning System) location, and other information, such as SOC or battery level data; Brannan ¶ [0076]: the electric vehicles within the predetermined distance of the low SOC vehicle may be ranked based upon (a) distance to the low SOC vehicle, (b) similarity of route being traveled to the route of the low SOC vehicle; (c) travel distance to destination remaining; (d) remaining battery power or remaining miles based on current battery power; and/or (e) power available to transfer (such as determined from distance remaining to destination, or amount of power that would be “left over”); Brannan ¶ [0078]: route information for each vehicle may be retrieved and analyzed to determine available locations for charging the low SOC vehicle);
identifying (710) an optimal route, from the extracted route information, for the at least one energy consumer vehicle and the selected at least one energy supplier vehicle (Brannan ¶ [0076]: the electric vehicles within the predetermined distance of the low SOC vehicle may be ranked based upon (a) distance to the low SOC vehicle, (b) similarity of route being traveled to the route of the low SOC vehicle; (c) travel distance to destination remaining; Brannan ¶ [0078]: route information for each vehicle may be retrieved and analyzed to determine available locations for charging the low SOC vehicle);
calculating (712) an updated optimal route for the selected at least one energy supplier vehicle and for the at least one energy consumer vehicle based on at least one of the identified optimal route, the plurality of energy exchange locations, the extracted route information and energy consumption associated with the extracted route information, and one or more constraints (Brannan ¶ [0077]: The method 800 may include determining or evaluating at step 808, via the one or more processors, time constraints and/or the availability of the ranked electric vehicles to charge. For instance, vehicle and/or operator electronic calendars (or autonomous vehicle and/or passenger electronic calendars) may be retrieved and analyzed to find available times for charging the low SOC vehicle; Brannan ¶ [0078]: route information for each vehicle may be retrieved and analyzed to determine available locations for charging the low SOC vehicle; Brannan ¶ [0080]: The method 800 may include scheduling at step 812, via the one or more processors and/or associated transceivers, a rendezvous for the low SOC vehicle with the highest, or one of the highest, remaining ranked electric vehicle for recharging the low SOC vehicle. If one or both vehicles are autonomous, the one or more processors may determine routes for each autonomous electric vehicle, and automatically route each electric vehicle to the rendezvous location); and
recommending (714) the updated optimal route for the at least one energy supplier vehicle and the at least one energy consumer vehicle for V2V energy exchange, indicating at least one energy exchange location from the identified plurality of energy exchange locations and duration of energy exchange (Brannan ¶ [0080]: The method 800 may include scheduling at step 812, via the one or more processors and/or associated transceivers, a rendezvous for the low SOC vehicle with the highest, or one of the highest, remaining ranked electric vehicle for recharging the low SOC vehicle. If one or both vehicles are autonomous, the one or more processors may determine routes for each autonomous electric vehicle, and automatically route each electric vehicle to the rendezvous location).
It is noted Brannan fails to particularly disclose identifying (702) at least one energy consumer vehicle based on route information and energy consumption associated with the route information; and extracting (708) the route information and the energy consumption associated with the route information of the at least one energy consumer vehicle.
However, Cox, in the same field of endeavor, teaches identifying (702) at least one energy consumer vehicle based on route information and energy consumption associated with the route information (Cox ¶ [0126]: the vehicle can request a charge from the charging system when, for example, the vehicle needs or is predicted to need power. As an example, the vehicle can establish communications with the charging device/vehicle to one or more of coordinate interconnectivity between the two for charging; Cox ¶ [0143]: The route configuration, determination or selection can consider not only the level of stored energy but other factors, including location of a charging facility or station (e.g., a manual charging station 310J, robotic charging station 310K, roadway charging system 310L, emergency charging system 310M or 310N, and/or overhead charging system 258), power consumption over the route, power regeneration over the route, and other factors as set forth below); and
extracting (708) the route information and the energy consumption associated with the route information of the at least one energy consumer vehicle (Cox ¶ [0162]: The map information can be used by the vehicle navigation system 2104 or map database manager 2012 to determine and assign, for at least a portion of each possible route and using an aggregate energy usage or consumption indicator, an expected, projected, or probable energy usage required to travel the portion of the route).
Therefore, given the teachings as a whole, it would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle charging management system of Brannan to include the calculation of energy consumption associated with a route of Cox. A person of ordinary skill in the art would be motivated to make this modification in order to provide an electric vehicle routing solution that takes into account uncertainty in waiting time, charging time, and vehicle state of charge (Cox ¶ [0004]).
Regarding claim 2, Brannan discloses wherein the identification of the at least one energy consumer vehicle comprises: receiving (702) the route information of each of the plurality of vehicles (Brannan ¶ [0034]: The various software applications on the server 212 may include a vehicle operation information monitoring application or receiving information regarding the electric vehicles and their current locations, predicted routes, vehicle conditions, battery SOC; Brannan ¶ [0074]: The vehicle telematics data may include acceleration, braking, cornering, speed, direction, route, GPS (Global Positioning System) location, and other information, such as SOC or battery level data; Brannan ¶ [0078]: route information for each vehicle may be retrieved and analyzed to determine available locations for charging the low SOC vehicle).
categorizing (702) the plurality of vehicles as energy consumer vehicle based on energy consumption on the route (Brannan ¶ [0029]: determine how much power is stored in a battery or batteries implemented in the vehicle 100 using the sensors 208 as well as a predicted range of the vehicle 100 based upon the measured power. The sensors 208 may be a voltmeter coupled to the battery to measure the state of charge (SOC), for example. The measurement taken by the sensors 208 are then used by the processing unit 202 to calculate how far the vehicle 100 may be able to travel before the vehicle 100 completely runs out of battery, which is also called its predicted range of operation).
It is noted Brannan fails to particularly disclose predicting (702) energy consumption of each of the plurality of vehicles based on the route information, wherein the energy consumption of each of the plurality of vehicles is predicted at multiple time instances during a travel; generating (702) the optimal route for each of the plurality of vehicles based on the predicted energy consumption; and categorizing (702) the plurality of vehicles as energy consumer vehicle based on energy consumption on the generated optimal route.
However, Cox, in the same field of endeavor, teaches wherein the identification of the at least one energy consumer vehicle comprises:
receiving (702) the route information of each of the plurality of vehicles (Cox ¶ [0209]: a route-based range prediction algorithm may comprise predicting a total amount of power consumption required for a given route. The prediction may be made at a beginning of a trip or upon a user entering a starting location and/or a destination into a user interface; Cox ¶ [0211]: Upon determining the origin and destination, one or more routes may be generated);
predicting (702) energy consumption of each of the plurality of vehicles based on the route information, wherein the energy consumption of each of the plurality of vehicles is predicted at multiple time instances during a travel (Cox ¶ [0212]: In order to predict a power consumption for a particular route, explicit features and factors may be considered 2808. For example, in some embodiments, factors including, but not limited to, total distance of the route to the destination, elevation changes in the route, temperature, windspeed and wind-direction, drag coefficient of the vehicle, weight of the vehicle including occupants, and other factors may be considered; Cox ¶ [0216]: Predictions may be updated in real time, periodically, or at particular points during a journey 2820);
generating (702) the optimal route for each of the plurality of vehicles based on the predicted energy consumption (Cox ¶ [0143]: The route configuration, determination or selection can consider not only the level of stored energy but other factors, including location of a charging facility or station (e.g., a manual charging station 310J, robotic charging station 310K, roadway charging system 310L, emergency charging system 310M or 310N, and/or overhead charging system 258), power consumption over the route, power regeneration over the route, and other factors as set forth below); and
categorizing (702) the plurality of vehicles as energy consumer vehicle based on energy consumption on the generated optimal route (Cox ¶ [0126]: the vehicle can request a charge from the charging system when, for example, the vehicle needs or is predicted to need power. As an example, the vehicle can establish communications with the charging device/vehicle to one or more of coordinate interconnectivity between the two for charging).
Therefore, given the teachings as a whole, it would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle charging management system of Brannan modified by the calculation of energy consumption associated with a route of Cox to further include the multiple predictions of energy consumption on a route and generation of an optimal route of Cox. A person of ordinary skill in the art would be motivated to make this modification in order to provide an electric vehicle routing solution that takes into account uncertainty in waiting time, charging time, and vehicle state of charge (Cox ¶ [0004]).
Regarding claim 3, Brannan discloses wherein the selection of the at least one energy supplier vehicle further comprising: receiving (704) the route information of the one or more energy supplier vehicles (Brannan ¶ [0076]: The method 800 may include at step 806 ranking, from among the electric vehicles within the predetermined distance of the low SOC vehicle, via the one or more processors, the electric vehicles based upon various factors; Brannan ¶ [0076]: the electric vehicles within the predetermined distance of the low SOC vehicle may be ranked based upon (a) distance to the low SOC vehicle, (b) similarity of route being traveled to the route of the low SOC vehicle; (c) travel distance to destination remaining; (d) remaining battery power or remaining miles based on current battery power; and/or (e) power available to transfer (such as determined from distance remaining to destination, or amount of power that would be “left over”); Brannan ¶ [0078]: route information for each vehicle may be retrieved and analyzed to determine available locations for charging the low SOC vehicle); and
identifying (704) at least one energy supplier vehicle from the one or more energy supplier vehicles based on the received route information and at least one of waiting time, delivery information, minimum detour, charging rate of the one or more energy supplier vehicles, wherein the charging rate is energy exchange rate of the one or more energy supplier vehicles (Brannan ¶ [0034]: applications may include a status evaluation application for determining which of the electric vehicles are located nearby within a predetermined radius, whether the vehicles have enough SOC such that the vehicles can travel at least a certain predetermined distance after charging another vehicle, and which of the vehicles are capable of reaching the vehicle that requires charging with minimal rerouting; Brannan ¶ [0076]: the electric vehicles within the predetermined distance of the low SOC vehicle may be ranked based upon (a) distance to the low SOC vehicle, (b) similarity of route being traveled to the route of the low SOC vehicle; (c) travel distance to destination remaining; (d) remaining battery power or remaining miles based on current battery power; and/or (e) power available to transfer (such as determined from distance remaining to destination, or amount of power that would be “left over”); Brannan ¶ [0080]: The method 800 may include scheduling at step 812, via the one or more processors and/or associated transceivers, a rendezvous for the low SOC vehicle with the highest, or one of the highest, remaining ranked electric vehicle for recharging the low SOC vehicle).
Regarding claim 6, Brannan discloses a system (200) for recommending energy efficient routes to vehicles, for V2V energy exchange, (Brannan ¶ [0034]: applications may include a status evaluation application for determining which of the electric vehicles are located nearby within a predetermined radius, whether the vehicles have enough SOC such that the vehicles can travel at least a certain predetermined distance after charging another vehicle, and which of the vehicles are capable of reaching the vehicle that requires charging with minimal rerouting) comprising: one or more processing unit (202) (Brannan ¶ [0034]: The various software applications may be executed on the same computer processor or on different computer processors) configured to:
identify at least one energy consumer vehicle (Brannan ¶ [0074]: The method 800 may include determining at step 802, via one or more processors, an electric vehicle having a battery with a state of charge (SOC) below a predetermined threshold; 802 in Fig. 8) and one or more energy supplier vehicles (Brannan ¶ [0076]: The method 800 may include at step 806 ranking, from among the electric vehicles within the predetermined distance of the low SOC vehicle, via the one or more processors, the electric vehicles based upon various factors; 806 in Fig. 8), among a plurality of vehicles, based on route information of the plurality of vehicles and energy consumption associated with the route information (Brannan ¶ [0029]: determine how much power is stored in a battery or batteries implemented in the vehicle 100 using the sensors 208 as well as a predicted range of the vehicle 100 based upon the measured power. The sensors 208 may be a voltmeter coupled to the battery to measure the state of charge (SOC), for example. The measurement taken by the sensors 208 are then used by the processing unit 202 to calculate how far the vehicle 100 may be able to travel before the vehicle 100 completely runs out of battery, which is also called its predicted range of operation; Brannan ¶ [0074]: Such threshold may be defined as a percentage of the SOC remaining in the electric vehicle, such as 20%, 25%, 30%, etc., or as a distance that the electric vehicle is determined to be capable of traveling based upon the SOC remaining in the electric vehicle, such as 10 or 20 miles, etc; Brannan ¶ [0076]: the electric vehicles within the predetermined distance of the low SOC vehicle may be ranked based upon (a) distance to the low SOC vehicle, (b) similarity of route being traveled to the route of the low SOC vehicle; (c) travel distance to destination remaining; (d) remaining battery power or remaining miles based on current battery power; and/or (e) power available to transfer (such as determined from distance remaining to destination, or amount of power that would be “left over”));
select, for each of the identified at least one energy consumer vehicle, at least one corresponding energy supplier vehicle from the identified one or more energy supplier vehicles based on at least one proximity location, wherein the proximity location corresponds to a location at which routes of the selected energy consumer vehicle and the selected at least one energy supplier vehicle either interact or are in close proximity to each other (Brannan ¶ [0076]: the electric vehicles within the predetermined distance of the low SOC vehicle may be ranked based upon (a) distance to the low SOC vehicle, (b) similarity of route being traveled to the route of the low SOC vehicle; (c) travel distance to destination remaining; (d) remaining battery power or remaining miles based on current battery power; and/or (e) power available to transfer (such as determined from distance remaining to destination, or amount of power that would be “left over”));
identify a plurality of energy exchange locations based on the proximity locations (Brannan ¶ [0078]: route information for each vehicle may be retrieved and analyzed to determine available locations for charging the low SOC vehicle; Brannan ¶ [0080]: The method 800 may include scheduling at step 812, via the one or more processors and/or associated transceivers, a rendezvous for the low SOC vehicle with the highest, or one of the highest, remaining ranked electric vehicle for recharging the low SOC vehicle. If one or both vehicles are autonomous, the one or more processors may determine routes for each autonomous electric vehicle, and automatically route each electric vehicle to the rendezvous location);
extract the route information and the energy consumption associated with the route information of the selected at least one energy supplier vehicle and the at least one energy consumer vehicle (Brannan ¶ [0034]: The various software applications on the server 212 may include a vehicle operation information monitoring application or receiving information regarding the electric vehicles and their current locations, predicted routes, vehicle conditions, battery SOC; Brannan ¶ [0074]: The vehicle telematics data may include acceleration, braking, cornering, speed, direction, route, GPS (Global Positioning System) location, and other information, such as SOC or battery level data; Brannan ¶ [0076]: the electric vehicles within the predetermined distance of the low SOC vehicle may be ranked based upon (a) distance to the low SOC vehicle, (b) similarity of route being traveled to the route of the low SOC vehicle; (c) travel distance to destination remaining; (d) remaining battery power or remaining miles based on current battery power; and/or (e) power available to transfer (such as determined from distance remaining to destination, or amount of power that would be “left over”); Brannan ¶ [0078]: route information for each vehicle may be retrieved and analyzed to determine available locations for charging the low SOC vehicle);
identify an optimal route, from the extracted route information, for the at least one energy consumer vehicle and the selected at least one energy supplier vehicle (Brannan ¶ [0076]: the electric vehicles within the predetermined distance of the low SOC vehicle may be ranked based upon (a) distance to the low SOC vehicle, (b) similarity of route being traveled to the route of the low SOC vehicle; (c) travel distance to destination remaining; Brannan ¶ [0078]: route information for each vehicle may be retrieved and analyzed to determine available locations for charging the low SOC vehicle); and
calculate an updated optimal route for the at least one energy consumer vehicle and the selected at least one energy supplier vehicle based on at least one of the identified optimal route, the plurality of energy exchange locations, the extracted route information and energy consumption associated with the extracted route information, and one or more constraints (Brannan ¶ [0077]: The method 800 may include determining or evaluating at step 808, via the one or more processors, time constraints and/or the availability of the ranked electric vehicles to charge. For instance, vehicle and/or operator electronic calendars (or autonomous vehicle and/or passenger electronic calendars) may be retrieved and analyzed to find available times for charging the low SOC vehicle; Brannan ¶ [0078]: route information for each vehicle may be retrieved and analyzed to determine available locations for charging the low SOC vehicle; Brannan ¶ [0080]: The method 800 may include scheduling at step 812, via the one or more processors and/or associated transceivers, a rendezvous for the low SOC vehicle with the highest, or one of the highest, remaining ranked electric vehicle for recharging the low SOC vehicle. If one or both vehicles are autonomous, the one or more processors may determine routes for each autonomous electric vehicle, and automatically route each electric vehicle to the rendezvous location); and
recommend the updated optimal route for the at least one energy supplier vehicle and the at least one energy consumer vehicle for V2V energy exchange, indicating at least one energy exchange location from the identified plurality of energy exchange locations and duration of energy exchange (Brannan ¶ [0080]: The method 800 may include scheduling at step 812, via the one or more processors and/or associated transceivers, a rendezvous for the low SOC vehicle with the highest, or one of the highest, remaining ranked electric vehicle for recharging the low SOC vehicle. If one or both vehicles are autonomous, the one or more processors may determine routes for each autonomous electric vehicle, and automatically route each electric vehicle to the rendezvous location).
It is noted Brannan fails to particularly disclose identify at least one energy consumer vehicle based on route information and energy consumption associated with the route information; and extract the route information and the energy consumption associated with the route information of the at least one energy consumer vehicle.
However, Cox, in the same field of endeavor, teaches identify at least one energy consumer vehicle based on route information and energy consumption associated with the route information (Cox ¶ [0126]: the vehicle can request a charge from the charging system when, for example, the vehicle needs or is predicted to need power. As an example, the vehicle can establish communications with the charging device/vehicle to one or more of coordinate interconnectivity between the two for charging; Cox ¶ [0143]: The route configuration, determination or selection can consider not only the level of stored energy but other factors, including location of a charging facility or station (e.g., a manual charging station 310J, robotic charging station 310K, roadway charging system 310L, emergency charging system 310M or 310N, and/or overhead charging system 258), power consumption over the route, power regeneration over the route, and other factors as set forth below); and
extract the route information and the energy consumption associated with the route information of the at least one energy consumer vehicle (Cox ¶ [0162]: The map information can be used by the vehicle navigation system 2104 or map database manager 2012 to determine and assign, for at least a portion of each possible route and using an aggregate energy usage or consumption indicator, an expected, projected, or probable energy usage required to travel the portion of the route).
Therefore, given the teachings as a whole, it would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle charging management system of Brannan to include the calculation of energy consumption associated with a route of Cox. A person of ordinary skill in the art would be motivated to make this modification in order to provide an electric vehicle routing solution that takes into account uncertainty in waiting time, charging time, and vehicle state of charge (Cox ¶ [0004]).
Regarding claim 7, Brannan discloses further comprising a receiving unit (204) (Brannan ¶ [0098]: The method may be implemented via one or more local or remote processors, transceivers, servers, and/or sensors) configured to receive the route information of each of the plurality of vehicles (Brannan ¶ [0034]: The various software applications on the server 212 may include a vehicle operation information monitoring application or receiving information regarding the electric vehicles and their current locations, predicted routes, vehicle conditions, battery SOC; Brannan ¶ [0074]: The vehicle telematics data may include acceleration, braking, cornering, speed, direction, route, GPS (Global Positioning System) location, and other information, such as SOC or battery level data; Brannan ¶ [0078]: route information for each vehicle may be retrieved and analyzed to determine available locations for charging the low SOC vehicle), wherein one or more processing unit (202) is further configured to:
categorize the plurality of vehicles as energy consumer vehicle based on energy consumption on the generated optimal route (Brannan ¶ [0029]: determine how much power is stored in a battery or batteries implemented in the vehicle 100 using the sensors 208 as well as a predicted range of the vehicle 100 based upon the measured power. The sensors 208 may be a voltmeter coupled to the battery to measure the state of charge (SOC), for example. The measurement taken by the sensors 208 are then used by the processing unit 202 to calculate how far the vehicle 100 may be able to travel before the vehicle 100 completely runs out of battery, which is also called its predicted range of operation).
It is noted Brannan fails to particularly disclose predict energy consumption of each of the plurality of vehicles based on the route information, wherein the energy consumption of each of the plurality of vehicles is predicted at multiple time instances during a travel; and generate the optimal route for each of the plurality of vehicles based on the predicted energy consumption.
However, Cox, in the same field of endeavor, teaches receive the route information of each of the plurality of vehicles (Cox ¶ [0209]: a route-based range prediction algorithm may comprise predicting a total amount of power consumption required for a given route. The prediction may be made at a beginning of a trip or upon a user entering a starting location and/or a destination into a user interface; Cox ¶ [0211]: Upon determining the origin and destination, one or more routes may be generated);
predict energy consumption of each of the plurality of vehicles based on the route information, wherein the energy consumption of each of the plurality of vehicles is predicted at multiple time instances during a travel (Cox ¶ [0212]: In order to predict a power consumption for a particular route, explicit features and factors may be considered 2808. For example, in some embodiments, factors including, but not limited to, total distance of the route to the destination, elevation changes in the route, temperature, windspeed and wind-direction, drag coefficient of the vehicle, weight of the vehicle including occupants, and other factors may be considered; Cox ¶ [0216]: Predictions may be updated in real time, periodically, or at particular points during a journey 2820);
generate the optimal route for each of the plurality of vehicles based on the predicted energy consumption (Cox ¶ [0143]: The route configuration, determination or selection can consider not only the level of stored energy but other factors, including location of a charging facility or station (e.g., a manual charging station 310J, robotic charging station 310K, roadway charging system 310L, emergency charging system 310M or 310N, and/or overhead charging system 258), power consumption over the route, power regeneration over the route, and other factors as set forth below); and
categorize the plurality of vehicles as energy consum