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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/14/2025 was considered by the examiner.
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 charging information acquisition module”, “a charging information processing module”, “a storage module”, “navigation module”, “allocation module” and an information notification module” in claim 25.
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 § 112
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 14-24 is/are 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 14 recites “…of the vehicle of which charging behavior is regular”. “regular” renders the claim indefinite because the term is subjective and Examiner is unable to determine the scope of the limitation. A review of the specification also does not reveal a definition for the term. For examination purposes, the term limitation will be interpreted to mean “…of the vehicle of which charging behavior can be predicted”.
Claims 15-24 are also rejected under 112b for failing to cure the deficiency above.
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 14-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim(s) 14/25/26 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim(s) 14/25/26 is/are directed towards a method (i.e. a process), a computer system (i.e. machine), and computer readable medium (i.e. a manufacture), respectively. Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Claim(s) 14/26 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “step s1: acquiring vehicle charging information; step S2: selecting, on a basis of the acquired vehicle charging information, a vehicle of which charging behavior is regular; and step S3: obtaining navigation information of a target vehicle and allocating a charging station to the target vehicle on a basis of the navigation information of the target vehicle and charging information of the vehicle of which charging behavior is regular.”.
The limitations above, as drafted, is a process that, under its broadest reasonable interpretation, covers a method of “allocating a charging station to a vehicle” which is a method of organizing a human activity. That is, the method allows for fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
This judicial exception is not integrated into a practical application. In particular, the claim only recites charging station (claim 14, 26) and computer readable program carrier (claim 26). The charging station is recited at a high level of generality and amounts to no more than field of use and the computer readable program carrier is recited at a high level of generality and amounts to no more than apply it. Accordingly, these additional element(s), alone or in combination, do(es) not integrate the abstract idea into a practical application because it/they do(es) not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) is/are nothing more than mere instructions to apply the exception on a general computer.
Dependent claim(s) 15-24 is/are also directed to an abstract idea without significantly more because it/they further narrow(s) the abstract idea described in relation to claim 14 without successfully integrating the exception into a practical application or providing significantly more limitations.
Claim(s) 25 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “acquire vehicle charging information; select, on a basis of the acquired vehicle charging information, a vehicle of which charging behavior is regular; store the charging information of the vehicle of which charging behavior is regular; obtain navigation information input by a user of a target vehicle, wherein high-definition map information labeled with charging station basic information is stored; allocate a charging station to the target vehicle according to the navigation information and the stored charging information of the vehicle of which charging behavior is regular; notify, to the user, location information of the allocated charging station and a navigation route to the allocated charging station.”.
The limitations above, as drafted, is a process that, under its broadest reasonable interpretation, covers a method of “allocating a charging station” which is a method of organizing a human activity. That is, the method allows for fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
This judicial exception is not integrated into a practical application. In particular, the claim only recites “a charging information acquisition module”, “a charging information processing module”, “a storage module”, “navigation module”, “allocation module” and an information notification module”. Each of the additional limitations is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional element(s), alone or in combination, do(es) not integrate the abstract idea into a practical application because it/they do(es) not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s), alone or in combination, is/are nothing more than mere instructions to apply the exception on a general computer.
Non-transitory rejection: Claim 26 recites “a computer readable program carrier”. A review of the specification does not define or exclude signals or carrier waves from a computer readable program carrier. Therefore, claim 26 is rejected under 35 USC 101 “transitory medium”. In order to overcome this rejection, the Office recommends amending the claims so that they recite only non-transitory media (see USPTO Notice 1351 OG 212).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 14, 24 and 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saito (US 2015/0294329) in view of Hershkovitz (US 2015/0039391)
As per claim 14/26, Saito discloses a method for smart allocation of a charging station, comprising:
step S1: acquiring vehicle charging information ([0021] The EV 11 has a data logger 14 configured to store data associated with a charging event. The stored data regarding the EV's usage history data is probe data. The probe data includes, but is not limited to, location of the charging station, an arrival time at the charging station, a start time for the start of charging, and an end time for the end of charging…[0023] The data center 16 includes a data collector or data collection unit 17 configured to receive probe data from a plurality of EVs 8, 9 and 10, as shown in FIG. 3. The probe data received by the data center 16 can include probe data from the requesting EV 11 when the requesting EV 11 has probe data relevant to the particular charging station.);
step S3: obtaining navigation information of a target vehicle ([0025] The navigation/display unit 21 of the EV 11 is configured to communicate with a global positioning system 22, as shown in FIG. 2. When programming a route to be traveled, the user inputs a start point 23 and a destination 24, as shown in FIGS. 3 and 4. A route 25 to be followed is calculated based on input parameters to travel from the start point 23 to the destination 24; [0021] The data logger 14 is connected to a navigation and display unit 21, as shown in FIG. 2, such that the data logger 14 can obtain and store information related to a destination, a travel route to reach the destination and a current location on the travel route. A vehicle control unit 30 is connected to a controlled device 31, such as a motor, air conditioning or a brake, to control operation of the device.)
and allocating a charging station to the target vehicle on a basis of the navigation information of the target vehicle and historical charging information charging station associated with the requesting EV 11 (FIG. 2), the charging station can be along the route of travel, proximal to a location of the requesting EV or proximal to the route of travel, as shown in FIGS. 2 and 3; [0058] A route to a most appropriate charging station can be provided to the requesting EV 11 based on the predicted usage value of step S34 and a state of charge of the requesting EV…[0054] In accordance with another exemplary embodiment of the present invention, as shown in FIG. 8, a method of predicting usage of a charging station includes a step S31 in which charging activity history of a plurality of charging stations is collected. A charging station associated with a requesting EV is determined in step S32. A future demand for the charging station based on the collected charging activity history is predicted in step S33. A predicted usage value of the charging station is provided in step S34 based on the predicted future demand (step S33) for an estimated time of arrival of the requesting electric vehicle at the charging station.” Saito identifies a charging station along or near the vehicle’s route or location and provides station usage information based on the vehicle’s estimated arrival. It also teaches using historical charging information when determining the predicted station usage because the future demand is based on collected charging activity history).
However, Saito does not expressly disclose selecting, on a basis of the acquired vehicle charging information, a vehicle of which charging behavior is regular. Although Saito recognizes recurring charging patterns derived from EV charging histories, it principally determines aggregate station usage patterns. It does not expressly select a particular vehicle because that vehicle’s individual charging behavior has been determined to be regular. Saito also does not expressly establish that the charging information used in step S3 is charging information of the vehicle of which charging behavior is regular (where the information belongs to the vehicle selected in step s2).
But, Hersh teaches selecting, on a basis of the acquired vehicle charging information , a vehicle of which charging behavior is regular and using charging information collected from the vehicle of which charging behavior is regular to predict station charging station demand ([0115] The data of each vehicle data record 40 may be gathered and updated for each vehicle 102 in the memory 310 of the control center system 112 based on data received from the vehicles 102 and/or based on data extracted/determined from/by the various databases/modules (depicted in FIG. 3) of the control center system 112. The gathered data may be then used by the processor 302 and/or the demand prediction module 322 to determine the likely service stations 45 and arrival times 46, and the arrival battery status 47, for each respective vehicle 102, and based thereon to predict the demand 50 at one or more battery service stations and/or geographical regions. [117]… In some embodiments, the control center system 112 also determines the battery status (e.g., charge level) at which a particular user is likely to visit a battery service station 130. For example, the control center system 112 may have stored historical data for a particular user 110 that the user typically exchanges or charges the battery of his vehicle when the vehicle's battery still has enough charge to travel 15 miles…[0174] In some embodiments, the control center system 112 uses historical charging demand data in order to better predict future minimum charging loads. In some embodiments, before the control center system 112 adjusts the one or more battery policies, the control center system 112 measures (901) an actual energy demand of the electric vehicle network over a predetermined time window. In some embodiments, the energy demand corresponds to the actual amount of energy used by the electric vehicle network 100 over the predetermined time window (e.g., the amount of energy used within a particular time span of any suitable duration, such as minutes, hours, days, etc.). In some embodiments, the energy demand corresponds to the aggregated individual energy usage of each of (or a subset of) the vehicles 102 of the electric vehicle network 100. In some embodiments, the control center system 112 stores (902) the historical data in order to extract historical trends in energy usage. [0175] Historical data can be analyzed at a vehicle level, or at a network level. For example, in some embodiments, the control center system 112 may determine that particular users 110 of vehicles 102 have predictable driving habits, and therefore predictable charging behavior.” )
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations as taught by Hersh in the teaching of Saito, in order to adjust battery policies in order to provide improved battery services to users of electric vehicles (please see Hersh abstract).
As per claim 24, Saito discloses wherein the acquired vehicle charging information comprises a vehicle identifier, charging station basic information, an on/off signal of a charging pile, a charging current signal of the charging pile, vehicle start information, vehicle speed information, and/or vehicle location information, wherein the charging station basic information comprises a charging station identifier, charging station location information, a number of charging piles of a charging station, and/or a charging pile type ([0020] The EV 11 has a data logger 14 configured to store data associated with a charging event. The stored data regarding the EV's usage history data is probe data. The probe data includes, but is not limited to, location of the charging station, an arrival time at the charging station, a start time for the start of charging, and an end time for the end of charging. Situational identifiers can also be stored by the data logger. The situational identifiers can include, but are not limited to, a power on time for when the EV is powered on, a running time for the length of time the EV is being driven while powered on, a power off time for when the EV is powered off, and a route path indicating the route the EV has taken. Additionally, the data logger 14 can store the state of charge (SOC), i.e., the remaining battery life, associated with any of the other stored data. For example, the remaining battery life can be stored with the time associated with powering on the EV.)
Claim(s) 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saito (US 2015/0294329) in view of Hershkovitz (US 2015/0039391), as disclosed in the rejection of claim 14, in further view of Ogawa (JP 2011083165) in view of Bianco (US 2014/0049213)
As per claim 15, Saito does not disclose but Hersh discloses per claim 14 a vehicle of which charging behavior is regular. Saito in view of Hersh does not disclose but Ogawa discloses assessing, on the basis of the acquired vehicle charging information, and storing a leaving time when the vehicle leaves the charging station (further, the staying time is predicted by a neural network from the past visit record of the electric vehicle 6 stored in association with the vehicle ID (S405).. FIG. 5 shows an example in which the order of the electric vehicles 6 waiting for charging is determined so that the charging is completed within the staying time of the customer. It is assumed that the current time is 10:00, and that charging takes 30 minutes when fully charged from the remaining battery level 0 (the same applies to the examples in FIG. 6 and subsequent figures) … The scheduled departure time 55A12 is a time at which the electric vehicle 6 is scheduled to leave, and charging needs to be completed by this time, and a time obtained by adding the stay time 55A11 to the reception time 55A2 is set. The state 55A13 indicates the charging state of the electric vehicle 6, and charging completion, charging, waiting, etc. are set. Of the charge management data 55A, the record waiting for the state 55A13 indicates the charge queue of the electric vehicle 6 in the charger 2… The vehicle ID 55A1, the reception time 55A2, the remaining battery level 55A3, the full charge time 55A4, the minimum required charge 55A5, the minimum It consists of a record for each charger 2 including a required time 55A6, a chargeable time 55A7, a charge speed 55A8, a charge start time 55A9, a charge end time 55A10, a stay time 55A11, a scheduled departure time 55A12, and a state 55A13.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations as taught by Ogawa in the teaching of Saito in view of Hersh, in order to efficiently charge a plurality of electric vehicles (please see Ogawa abstract).
However, Ogawa predicts and stores a scheduled departure time. It does not expressly disclose detecting the instant at which the vehicle physically leaves the charging space and recording that actual time. But, Bianco discloses a vehicle sensor for the charging parking space that record when a vehicle actually leaves the charging station ([0031]The vehicle detector 60 functions so that when the vehicle leaves the parking space (the presence signal ceases), the power supply is terminated by the EVSE controller 50 until a new input which authorizes the supply of power to the specific station (which may be selected at the keyboard 32 or a reader at the payment station) is provided.).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations as taught by Bianco in the teaching of Saito in view of Hersh and Ogawa, in order to control the flow of power to each charging station (please see Bianco abstract).
As per claim 16, Saito in view of Hersh, Ogawa and Bianco discloses all the limitation of claim 15. Saito further discloses wherein in step S3, the charging station is allocated to the target vehicle on the basis of the navigation information of the target vehicle ([0055] In step S32, when determining the charging station associated with the requesting EV 11 (FIG. 2), the charging station can be along the route of travel, proximal to a location of the requesting EV or proximal to the route of travel, as shown in FIGS. 2 and 3… [0042] The probe data can also include a charging indicator when one of the plurality of electric vehicles is presently charging at the charging station. The data center 16 can provide the requesting EV 11 with an estimated vacant, or available, time of the charging station based on the charging status of the presently charging vehicle… [0058] A route to a most appropriate charging station can be provided to the requesting EV 11 based on the predicted usage value of step S34 and a state of charge of the requesting EV. The predicted usage value can be provided as an average waiting time at the charging station, as a segmented waiting probability based on a percentage of chargers utilized at the charging station, as an indication of congestion at the charging station, or any other suitable representative factor of interest.).
However, Saito does not explicitly disclose that Ogawa discloses allocating a charging station based on the stored leaving time, wherein the stored leaving time is used to predict a status of the charging station (Subsequently, the control device 5 refers to the charge management data 55A of each charger 2 and searches for a charger 2 that is currently vacant because only the record of charge completion in the state 55A13 is stored (S407). If there is no vacant charger 2 (NO in S407), the charger that has the earliest charge end time 55A10 of the record at the end of the current queue, that is, the charge start time of the new passenger car is likely to be the earliest. The record of the electric vehicle 6 is additionally registered in the charge management data 55A of No. 2 (S408). At this time, among the records to be additionally registered, the previously acquired data values and the calculated values are the vehicle ID 55A1, the remaining battery level 55A3, the full charge time 55A4, the minimum required charge amount 55A5, the minimum required time 55A6 and the stay. Set to time 55A11. And the time which received parking data is set to reception time 55A2. In the scheduled departure time 55A12, a time obtained by adding the stay time 55A11 to the reception time 55A2 is set. A wait is set in the state 55A13.)(please see claim 15 rejection for combination rationale).
Claim(s) 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saito (US 2015/0294329) in view of Hershkovitz (US 2015/0039391), as disclosed in the rejection of claim 14, in further view of Tate (US 2012/0233077).
As per claim 17, Saito discloses step S301: obtaining the navigation information of the target vehicle, wherein the navigation information comprises a navigation destination ([0025] The navigation/display unit 21 of the EV 11 is configured to communicate with a global positioning system 22, as shown in FIG. 2. When programming a route to be traveled, the user inputs a start point 23 and a destination 24, as shown in FIGS. 3 and 4. A route 25 to be followed is calculated based on input parameters to travel from the start point 23 to the destination 24… [0033] The current locations of the EVs can be included with the probe data such that a relationship can be determined between the location distribution of the EVs and the usage patterns of the charging stations. A trend model for each pattern characteristic can be made. The most similar EV distribution situation can be determined for a particular period (e.g., the present and the corresponding occupancy trend can be provided to the requesting EV as forecasting information.);
step S302: predicting, on the basis of the stored leaving time of the vehicle, status information of a charging station ([0050] The probe data sent in step S21 can include information regarding arrival and departure of the plurality of electric vehicles at the charging station 33 (FIG. 5). In step S22, predicting the usage pattern includes determining a relationship between an occupancy pattern of the charging station and a distribution of the plurality of electric vehicles. A similar usage pattern can be determined based on the requesting EV's direction of travel and providing a corresponding occupancy trend to the requesting EV 11… [0041] The probe data sent by the plurality of EVs in step S11 can include a location of the charging station and start and end timestamps of charging activity at the charging station. The data center 16 can predict a vacant, or available, time of the charging station based on the received start and end timestamps.)
step S304: presenting, to a user of the target vehicle, location information of the allocated charging station and a navigation route to the allocated charging station ([0058] A route to a most appropriate charging station can be provided to the requesting EV 11 based on the predicted usage value of step S34 and a state of charge of the requesting EV.)
However, Saito does not disclose that the vehicles at the charging station are vehicle which charging behavior is regular but Hersh discloses that as per claim 14.
However, Saito in view of Hersh does not disclose but Tate discloses that the charging stations are within a preset region range of the navigation destination ([0011] When the client device 12 is configured as a navigation system of any type, the driver of the vehicle 13 can view the available charging stations 20 as points of interest on a displayed map, and can select, e.g., via a touch screen of the client device 12 when the display screen 32 is so configured, the particular charging station 20 that the driver wishes to reserve. Because driving route information can be displayed by such a client device 12, a driver of the vehicle 13 can readily select a charging station 20 that is within a particular range of a desired trip destination.)
step S303: allocating a charging station to the target vehicle on the basis of the navigation information of the target vehicle and the predicted status information of the charging station ([0004] In particular, a method for reserving an electric charging station includes receiving a desired destination from a client device, e.g., a navigation system or a smart phone, using a server, and automatically verifying the availability of the station at an expected arrival time at the desired destination. The method includes reserving the station when the station is available at the expected arrival time and transmitting an electronic token to the client device. The electronic token confirms the reservation and uniquely identifies the vehicle.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations as taught by Tate in the teaching of Saito in view of Hersh, in order to transmit an electronic token to the client device (please see Tate abstract).
As per claim 18, Saito in view of Hersh and Tate disclose all the limitation of claim 17. Saito discloses wherein in step S303, it is determined, on the basis of the navigation information of the target vehicle and the predicted status information of the charging station, whether an idle charging station is present of the navigation destination, wherein when an idle charging station is present in the preset region range of the navigation destination at the predicted time, then a charging station closest to the navigation destination is allocated to the target vehicle ([0007] In view of the state of the known technology, one aspect of the present invention is a method of predicting usage of a charging station. Charging activity history of a plurality of charging stations is collected. A charging station is determined associated with a requesting electric vehicle. A future demand for the charging station is predicted based on the collected charging activity history. A predicted usage value of the charging station is provided based on the predicted future demand for an estimated time of arrival of the requesting electric vehicle at the charging station.).
However, Saito does not disclose but Tate discloses whether an idle charging station is present in the preset region range of the navigation destination at a predicted time when the target vehicle reaches the navigation destination, wherein when an idle charging station is present in the preset region range of the navigation destination at the predicted time, then a charging station closest to the navigation destination is allocated to the target vehicle ([0004] In particular, a method for reserving an electric charging station includes receiving a desired destination from a client device, e.g., a navigation system or a smart phone, using a server, and automatically verifying the availability of the station at an expected arrival time at the desired destination. The method includes reserving the station when the station is available at the expected arrival time, and transmitting an electronic token to the client device. The electronic token confirms the reservation and uniquely identifies the vehicle…[0011] When the client device 12 is configured as a navigation system of any type, the driver of the vehicle 13 can view the available charging stations 20 as points of interest on a displayed map, and can select, e.g., via a touch screen of the client device 12 when the display screen 32 is so configured, the particular charging station 20 that the driver wishes to reserve. Because driving route information can be displayed by such a client device 12, a driver of the vehicle 13 can readily select a charging station 20 that is within a particular range of a desired trip destination)(please see claim 17 rejection for combination rationale);
Claim(s) 19 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saito (US 2015/0294329) in view of Hershkovitz (US 2015/0039391), as disclosed in the rejection of claim 14, in further view of Wilding (US 20190202315) and Rajmohan (US 20220089056)
As per claim 19, Saito discloses wherein step S3 comprises: step S311: obtaining the navigation information of the target vehicle ([0021] The data logger 14 is connected to a navigation and display unit 21, as shown in FIG. 2, such that the data logger 14 can obtain and store information related to a destination, a travel route to reach the destination and a current location on the travel route. A vehicle control unit 30 is connected to a controlled device 31, such as a motor, air conditioning or a brake, to control operation of the device. The data logger 14 is also connected to the vehicle control unit 30 to obtain and store information related to the vehicle control unit 30, such as powering on and off of the motor. An electricity and battery managing unit 32 is connected to the battery 12 to manage operation of the battery 12. The data logger 14 is connected to the electricity and battery managing unit 32 to obtain and store information related to the battery 12, such as the time charging of the battery starts and stops and the current state of charge of the battery. The EV 11 has a communicator or communication unit 15 configured to communicate with a data center 16, as shown in FIG. 2. The data logger 14 provides the probe data to the communication unit 15, which transmits the probe data to the data center 16, as shown in FIGS. 2 and 3…[0022] The data center 16 includes a data collector or data collection unit 17 configured to receive probe data from a plurality of EVs 8, 9 and 10, as shown in FIG. 3. The probe data received by the data center 16 can include probe data from the requesting EV 11 when the requesting EV 11 has probe data relevant to the particular charging station. A single data center 16 can be used to receive the probe data from the EVs 8, 9 and 10. Alternatively, a plurality of data centers 16 can be used such that each data center covers a predetermined area or region).
step S313: predicting, on the basis of the stored leaving time of the vehicle, status information of a charging station in a preset region range of the predicted charging location ([0034] The departure and arriving locations of the EVs, as shown in map 35 of FIG. 5, can be included with the probe data. Relationships can be determined between the occupancy pattern of the charging station and the distribution of the EVs, particularly using the departure point of a route for each EV that arrived at and eventually used the charging station. The most similar pattern for departing EVs at a specific time, e.g., the present, can be determined and the corresponding occupancy trend can be provided as forecasting information to the requesting EV. [0050] The probe data sent in step S21 can include information regarding arrival and departure of the plurality of electric vehicles at the charging station 33 (FIG. 5). In step S22, predicting the usage pattern includes determining a relationship between an occupancy pattern of the charging station and a distribution of the plurality of electric vehicles. A similar usage pattern can be determined based on the requesting EV's direction of travel and providing a corresponding occupancy trend to the requesting EV 11. [0053] Additionally, a similar pattern for departures of the plurality of EVs 8, 9 and 10 can be determined. A corresponding occupancy trend based on the departure pattern is sent to the requesting EV 11. To facilitate determining the departure patterns, the probe data sent in step S21 includes a direction of travel of the plurality of electric vehicles. A relationship between the occupancy pattern of the charging station 33 (FIG. 5) and the direction of travel of the plurality of EVs can be determined.);
step S314: allocating a charging station to the target vehicle on the basis of the navigation information of the target vehicle and the predicted status information of the charging station (paragraph 58);
step S315: presenting, to a user of the target vehicle, location information of the allocated charging station and a navigation route to the allocated charging station (([0058] A route to a most appropriate charging station can be provided to the requesting EV 11 based on the predicted usage value of step S34 and a state of charge of the requesting EV.).
However, Saito does not disclose but Hersh discloses determining, on the basis of the acquired vehicle charging information, whether charging behavior of the target vehicle is regular; step S312: when the charging behavior of the target vehicle is regular, then assessing an arrival time ([0027] In possible embodiments determining the respective likely battery service station and the respective likely vehicle arrival time for a respective electric vehicle is further based on a speed of the respective electric vehicle… [0175] Historical data can be analyzed at a vehicle level, or at a network level. For example, in some embodiments, the control center system 112 may determine that particular users 110 of vehicles 102 have predictable driving habits, and therefore predictable charging behavior. The energy demands and charging behavior of individual users 110 can be aggregated in order to determine overall, network-level energy demand predictions.)(please see claim 14 rejection for combination rationale).
However, Saito in view of Hersh does not disclose but Wilding discloses step S312: when the charging behavior of the target vehicle is regular, then assessing a charging start time of the target vehicle on the basis of the acquired charging information of the target vehicle, and predicting a predicted charging location of the target vehicle on the basis of the navigation information and the charging start time of the target vehicle ([0023] In a further advantageous embodiment, the data processing unit is configured in such a way that, when receiving further information about the current position of a particular motor vehicle and a request for the control of a destination by a positioning system of the motor vehicle, the data processing unit determines the charge status of the motor vehicle along the route between the current position of the motor vehicle and the destination, and determines the point in time for a recommended charging of the energy storage device and the charge status at this point in time. Thereby, the user of a motor vehicle, when searching for an appropriate charging station, no longer has to self-estimate the charge status of the energy storage device of his motor vehicle at the point in time of a possible charging-start. The data processing unit calculates where and at what point in time the level of charge status of the energy storage device of the motor vehicle is, by knowing the traveled distance, and the charge status of the particular motor vehicle at its current geographical position and the geographical position of one or more charging stations along the route. By predefinable parameters, the data processing unit creates a strategy that assigns a motor vehicle to a particular or more a particular charging station along the route. The planning of the total distance to be traveled takes into account the stays at one or more assigned charging stations. The point in time of charging-start at a charging station is accurately calculated. For example, information about the current traffic on the route can be sent to the data processing unit, which includes this information in the determination of the point in time of charging-start and charging period of time, via the positioning system of the motor vehicle.).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations as taught by Wilding in the teaching of Saito in view of Hersh, in order to provide a reliable and easier planning for the use of a charging station for charging this energy storage device and to reduce waiting times at the charging station as much as possible (please see Wilding, paragraph 14).
However, Saito in view of Hersh does not disclose but Rajmohan discloses a charging station in a preset region range of the predicted charging location ([0012]… For example, embodiments of the present invention can dynamically identify an optimal charging station such that the user's vehicle is charged within a certain radius of the user while the user is eating at a restaurant along the route to the user's final destination” Rajmohan discloses a present geographic region around an intended route location. It does not expressly call the center of that radius the claimed “predicted charging location”. Wilding supplies the predicted route charging location).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations as taught by Rajmohan in the teaching of Saito in view of Hersh and Wilding, in order to dynamically determine an optimal charging station using a bipartite graph (please see Rajmohan abstract).
As per claim 21, Saito discloses wherein in step S314, it is determined, on the basis of the charging start time of the target vehicle and the status information of the charging station, whether an idle charging station is at the charging start time, wherein when an idle charging station is present at the charging start time, then an idle charging station is allocated to the target vehicle ([0041] The probe data sent by the plurality of EVs in step S11 can include a location of the charging station and start and end timestamps of charging activity at the charging station. The data center 16 can predict a vacant, or available, time of the charging station based on the received start and end timestamps… [0042] The probe data can also include a charging indicator when one of the plurality of electric vehicles is presently charging at the charging station. The data center 16 can provide the requesting EV 11 with an estimated vacant, or available, time of the charging station based on the charging status of the presently charging vehicle.).
However, Saito does not disclose but Wilding discloses determining a charging start time of the target vehicle ([0023] In a further advantageous embodiment, the data processing unit is configured in such a way that, when receiving further information about the current position of a particular motor vehicle and a request for the control of a destination by a positioning system of the motor vehicle, the data processing unit determines the charge status of the motor vehicle along the route between the current position of the motor vehicle and the destination, and determines the point in time for a recommended charging of the energy storage device and the charge status at this point in time. Thereby, the user of a motor vehicle, when searching for an appropriate charging station, no longer has to self-estimate the charge status of the energy storage device of his motor vehicle at the point in time of a possible charging-start. The data processing unit calculates where and at what point in time the level of charge status of the energy storage device of the motor vehicle is, by knowing the traveled distance, and the charge status of the particular motor vehicle at its current geographical position and the geographical position of one or more charging stations along the route. By predefinable parameters, the data processing unit creates a strategy that assigns a motor vehicle to a particular or more a particular charging station along the route. The planning of the total distance to be traveled takes into account the stays at one or more assigned charging stations. The point in time of charging-start at a charging station is accurately calculated. For example, information about the current traffic on the route can be sent to the data processing unit, which includes this information in the determination of the point in time of charging-start and charging period of time, via the positioning system of the motor vehicle.)(please see claim 19 rejection for combination rationale).
However, Saito in view of Wilding does not disclose but Rajmohan discloses finding the closest charging station within a preset region of the predicted charging location ([0012]… For example, embodiments of the present invention can dynamically identify an optimal charging station such that the user's vehicle is charged within a certain radius of the user while the user is eating at a restaurant along the route to the user's final destination.)(please see claim 19 rejection for combination rationale).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saito (US 2015/0294329) in view of Hershkovitz (US 2015/0039391), as disclosed in the rejection of claim 14, in further view of Hansen (US 2013/0197710).
As per claim 20, Saito discloses wherein the vehicle charging information, on which the selection is based, varies over time during acquisition ([0030] The probe data received from at least one EV is used to analyze and predict the charging demand for each charging station. The predicted charging demand is provided to EV users as a usage value for each charging station. Saito in view of Hersh does not disclose but Hansen discloses wherein in step S2, the selecting is performed at a preset time interval [0094] In the present embodiment of the invention, the dispatch controller is configured to re-compute the charge priority for each rechargeable power unit at regular time intervals such as every 15 minutes or less. Once, appropriate charge priorities are recomputed for all the electrical cars, a new or revised charging sequence is determined by the dispatch controller and the above-mentioned process of selecting the particular subset of electrical cars eligible for charging based on the revised charging sequence is repeated so as to determine a new subset of electrical cars.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations as taught by Hansen in the teaching of Saito, in order to controlling the supply of electrical power to the plurality of rechargeable power units in accordance with computed charge priorities (please see Hansen, abstract).
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saito (US 2015/0294329) in view of Hershkovitz (US 2015/0039391), as disclosed in the rejection of claim 14, in further view of Qui (US 20200333148).
As per claim 22, Saito discloses step 8321: obtaining the navigation information and current charge level information of the target vehicle, wherein the navigation information comprises a navigation destination ([0021] The data logger 14 is connected to a navigation and display unit 21, as shown in FIG. 2, such that the data logger 14 can obtain and store information related to a destination, a travel route to reach the destination and a current location on the travel route. A vehicle control unit 30 is connected to a controlled device 31, such as a motor, air conditioning or a brake, to control operation of the device. The data logger 14 is also connected to the vehicle control unit 30 to obtain and store information related to the vehicle control unit 30, such as powering on and off of the motor. An electricity and battery managing unit 32 is connected to the battery 12 to manage operation of the battery 12. The data logger 14 is connected to the electricity and battery managing unit 32 to obtain and store information related to the battery 12, such as the time charging of the battery starts and stops and the current state of charge of the battery. The EV 11 has a communicator or communication unit 15 configured to communicate with a data center 16, as shown in FIG. 2. The data logger 14 provides the probe data to the communication unit 15, which transmits the probe data to the data center 16, as shown in FIGS. 2 and 3… [0043] The probe data can include a current location and a state of charge, i.e., remaining battery life, of the requesting EV 11. When the current location of the EV is proximal a charging station and the state of charge of the EV is not increasing (i.e., the EV is not being charged), the usage value of the proximal charging station indicates a waiting time for use of the charging station.);
step S324: predicting, on the basis of the stored leaving time of the vehicle, status information of a charging station ([0041] The probe data sent by the plurality of EVs in step S11 can include a location of the charging station and start and end timestamps of charging activity at the charging station. The data center 16 can predict a vacant, or available, time of the charging station based on the received start and end timestamps. ...[0050] The probe data sent in step S21 can include information regarding arrival and departure of the plurality of electric vehicles at the charging station 33 (FIG. 5). In step S22, predicting the usage pattern includes determining a relationship between an occupancy pattern of the charging station and a distribution of the plurality of electric vehicles. A similar usage pattern can be determined based on the requesting EV's direction of travel and providing a corresponding occupancy trend to the requesting EV 11… [0056] The charging activity history collected in step S31 can include a location and charging start and end timestamps of the plurality of charging stations. The charging activity history can be the probe data received from the plurality of EVs.)
step S325: allocating a charging station to the target vehicle on the basis of the navigation information of the target vehicle and the predicted status information of the charging station ([0055] In step S32, when determining the charging station associated with the requesting EV 11 (FIG. 2), the charging station can be along the route of travel, proximal to a location of the requesting EV or proximal to the route of travel, as shown in FIGS. 2 and 3… [0058] A route to a most appropriate charging station can be provided to the requesting EV 11 based on the predicted usage value of step S34 and a state of charge of the requesting EV. The predicted usage value can be provided as an average waiting time at the charging station, as a segmented waiting probability based on a percentage of chargers utilized at the charging station, as an indication of congestion at the charging station, or any other suitable representative factor of interest.);
step S326: presenting, to a user of the target vehicle, location information of the allocated charging station and a navigation route to the allocated charging station ([0058] A route to a most appropriate charging station can be provided to the requesting EV 11 based on the predicted usage value of step S34 and a state of charge of the requesting EV. The predicted usage value can be provided as an average waiting time at the charging station, as a segmented waiting probability based on a percentage of chargers utilized at the charging station, as an indication of congestion at the charging station, or any other suitable representative factor of interest.)
Saito does not disclose but Hersh discloses determining a predicted charging time of the vehicle of which charging behavior is regular ([0008] The method may include determining, for each respective electric vehicle, a likely battery service station (i.e. the battery service station where a vehicle might receive battery related services) and a likely vehicle arrival time at such battery service station. For example, this determination may be based at least partially on the location, final destination, and the battery charge status for each of the electric vehicles. In some embodiments, the determination is further based on the speed of the vehicle, speed limits, traffic conditions, and/or the average speed of a group of other vehicles in proximity to the respective electric vehicle.)(please see claim 14 rejection for combination rationale).
However, Saito in view of Hersh does not disclose but Qui discloses step 8322: determining, on a basis of the current charge level information and the navigation information of the target vehicle, whether a current charge level of the target vehicle is sufficient to enable the target vehicle to reach the navigation destination; step 8323: when the current charge level of the target vehicle is not sufficient to enable the target vehicle to reach the navigation destination, then determining a predicted charging location of the target vehicle on the basis of the navigation information and the current charge level information of the target vehicle ([0003] An electrified vehicle may include a vehicle battery, and a processor configured to receive map data and generate a route based on the map data, and in response to a required vehicle energy needed to complete the route exceeding the current vehicle energy, modify the route to include at least one charging stop, claim 4: “ wherein the current vehicle energy is calculated based on a current state of charge.”)
A charging station in a preset region range of the predicted charging location ([0027] Each charge point 210 may be associated with a charging station 212. The charging stations 212 may be located within a maximum proximity (either predefined distance and/or time) to the charge point 210…claim 6: “wherein the processor is further configured to identify charge stations within a predefined distance of the charge points along the route.”)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations as taught by Qui in the teaching of Saito in view of Hersh, in order to minimize combined charging time of the at least one charging stop (please see Qui, paragraph 3).
Claim(s) 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saito (US 2015/0294329) in view of Hershkovitz (US 2015/0039391), Qui (US 20200333148), as disclosed in the rejection of claim 22, in further view of Rajmohan (US 20220089056).
As per claim 23, Saito discloses wherein in step S325, it is determined, on the basis of the charging time of the target vehicle and the status information of the charging station The probe data can also include a charging indicator when one of the plurality of electric vehicles is presently charging at the charging station. The data center 16 can provide the requesting EV 11 with an estimated vacant, or available, time of the charging station based on the charging status of the presently charging vehicle.).
However, Saito does not disclose but Hersh discloses predicting a charge time for the target vehicle ([0008] The method may include determining, for each respective electric vehicle, a likely battery service station (i.e. the battery service station where a vehicle might receive battery related services) and a likely vehicle arrival time at such battery service station. For example, this determination may be based at least partially on the location, final destination, and the battery charge status for each of the electric vehicles. In some embodiments, the determination is further based on the speed of the vehicle, speed limits, traffic conditions, and/or the average speed of a group of other vehicles in proximity to the respective electric vehicle.)(please see claim 14 rejection for combination rationale).
However, Saito in view of Hersh does not disclose but Rajmohan discloses However, Saito in view of Hersh does not disclose but Rajmohan discloses finding the closest charging station within a preset region of the predicted charging location ([0012]… For example, embodiments of the present invention can dynamically identify an optimal charging station such that the user's vehicle is charged within a certain radius of the user while the user is eating at a restaurant along the route to the user's final destination.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations as taught by Rajmohan in the teaching of Saito in view of Hersh, in order to dynamically determine an optimal charging station using a bipartite graph (please see Rajmohan abstract).
Claim(s) 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saito (US 2015/0294329) in view of Hershkovitz (US 2015/0039391) in further view of Tsuji (UIS 2020/022449).
As per claim 25, Saito discloses a system (1) for smart allocation of a charging station, wherein the system (1) is configured to perform the method according to claim 14, comprising:
a charging information acquisition module (11) configured to acquire vehicle charging information ([0021] The EV 11 has a data logger 14 configured to store data associated with a charging event. The stored data regarding the EV's usage history data is probe data. The probe data includes, but is not limited to, location of the charging station, an arrival time at the charging station, a start time for the start of charging, and an end time for the end of charging…[0023] The data center 16 includes a data collector or data collection unit 17 configured to receive probe data from a plurality of EVs 8, 9 and 10, as shown in FIG. 3. The probe data received by the data center 16 can include probe data from the requesting EV 11 when the requesting EV 11 has probe data relevant to the particular charging station.);
a navigation module (14) configured to obtain navigation information input by a user of a target vehicle, wherein map information labeled with charging station basic information is stored ([0004] Some services are provided to EV users to facilitate charging the EV. A global positioning system (GPS) can provide a geographical location of a charging station. Locations can be provided for the charging station nearest the current location of the EV. Additionally, locations of charging stations along a planned route can be provided. Availability information, such as the business hours of the charging station and the number of charging spots provided at the charging station, can also be provided with the geographical location of the charging station to further facilitate charging by the EV user. Still further, real-time vacancy information can be provided to the EV user as to the current availability of a charging spot at a charging station. [0025] The navigation/display unit 21 of the EV 11 is configured to communicate with a global positioning system 22, as shown in FIG. 2. When programming a route to be traveled, the user inputs a start point 23 and a destination 24, as shown in FIGS. 3 and 4. A route 25 to be followed is calculated based on input parameters to travel from the start point 23 to the destination 24; [0021] The data logger 14 is connected to a navigation and display unit 21, as shown in FIG. 2, such that the data logger 14 can obtain and store information related to a destination, a travel route to reach the destination and a current location on the travel route. A vehicle control unit 30 is connected to a controlled device 31, such as a motor, air conditioning or a brake, to control operation of the device.)
an allocation module (15) configured to allocate a charging station to the target vehicle according to the navigation information and the stored charging information of the vehicle ([0055] In step S32, when determining the charging station associated with the requesting EV 11 (FIG. 2), the charging station can be along the route of travel, proximal to a location of the requesting EV or proximal to the route of travel, as shown in FIGS. 2 and 3; [0058] A route to a most appropriate charging station can be provided to the requesting EV 11 based on the predicted usage value of step S34 and a state of charge of the requesting EV…[0054] In accordance with another exemplary embodiment of the present invention, as shown in FIG. 8, a method of predicting usage of a charging station includes a step S31 in which charging activity history of a plurality of charging stations is collected. A charging station associated with a requesting EV is determined in step S32. A future demand for the charging station based on the collected charging activity history is predicted in step S33. A predicted usage value of the charging station is provided in step S34 based on the predicted future demand (step S33) for an estimated time of arrival of the requesting electric vehicle at the charging station.” Saito identifies a charging station along or near the vehicle’s route or location and provides station usage information based on the vehicle’s estimated arrival. It also teaches using historical charging information when determining the predicted station usage because the future demand is based on collected charging activity history).
an information notification module (16) configured to notify, to the user, location information of the allocated charging station and a navigation route to the allocated charging station ([0055] In step S32, when determining the charging station associated with the requesting EV 11 (FIG. 2), the charging station can be along the route of travel, proximal to a location of the requesting EV or proximal to the route of travel, as shown in FIGS. 2 and 3. [0058] A route to a most appropriate charging station can be provided to the requesting EV 11 based on the predicted usage value of step S34 and a state of charge of the requesting EV.).
However, Saito does not expressly disclose selecting, on a basis of the acquired vehicle charging information, a vehicle of which charging behavior is regular. Although Saito recognizes recurring charging patterns derived from EV charging histories, it principally determines aggregate station usage patterns. It does not expressly select a particular vehicle because that vehicle’s individual charging behavior has been determined to be regular. Saito also does not expressly establish that the charging information used in step S3 is charging information of the vehicle of which charging behavior is regular (where the information belongs to the vehicle selected in step s2).
But, Hersh teaches a charging information processing module (12) configured to select, on a basis of the acquired vehicle charging information, a vehicle of which charging behavior is regular and using charging information collected from the vehicle of which charging behavior is regular to predict station charging station demand ([0115] The data of each vehicle data record 40 may be gathered and updated for each vehicle 102 in the memory 310 of the control center system 112 based on data received from the vehicles 102 and/or based on data extracted/determined from/by the various databases/modules (depicted in FIG. 3) of the control center system 112. The gathered data may be then used by the processor 302 and/or the demand prediction module 322 to determine the likely service stations 45 and arrival times 46, and the arrival battery status 47, for each respective vehicle 102, and based thereon to predict the demand 50 at one or more battery service stations and/or geographical regions. [117]… In some embodiments, the control center system 112 also determines the battery status (e.g., charge level) at which a particular user is likely to visit a battery service station 130. For example, the control center system 112 may have stored historical data for a particular user 110 that the user typically exchanges or charges the battery of his vehicle when the vehicle's battery still has enough charge to travel 15 miles…[0174] In some embodiments, the control center system 112 uses historical charging demand data in order to better predict future minimum charging loads. In some embodiments, before the control center system 112 adjusts the one or more battery policies, the control center system 112 measures (901) an actual energy demand of the electric vehicle network over a predetermined time window. In some embodiments, the energy demand corresponds to the actual amount of energy used by the electric vehicle network 100 over the predetermined time window (e.g., the amount of energy used within a particular time span of any suitable duration, such as minutes, hours, days, etc.). In some embodiments, the energy demand corresponds to the aggregated individual energy usage of each of (or a subset of) the vehicles 102 of the electric vehicle network 100. In some embodiments, the control center system 112 stores (902) the historical data in order to extract historical trends in energy usage. [0175] Historical data can be analyzed at a vehicle level, or at a network level. For example, in some embodiments, the control center system 112 may determine that particular users 110 of vehicles 102 have predictable driving habits, and therefore predictable charging behavior.” )
a storage module (13) configured to store the charging information of the vehicle of which charging behavior is regular ([174]… the control center system 112 stores (902) the historical data in order to extract historical trends in energy usage. In some embodiments, the control center system 112 stores the actual energy demand to be used later as historical data in the predicted demand database 332 (FIG. 3). In some embodiments, the historical actual energy demand data is used to predict the energy demand of the electric vehicle network 100, and thus predict the estimated minimum charging load for future time windows.).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations as taught by Hersh in the teaching of Saito, in order to adjust battery policies in order to provide improved battery services to users of electric vehicles (please see Hersh abstract).
However, Saito in view of Hersh does not disclose but Tsuji discloses wherein high-definition map information labeled with basic information is stored in the navigation module ([0019]… A database of the navigation map information and the high-definition map information may be controlled by a server, so that the storage device 4 can acquire only difference data of the navigation map information and the high-definition map information after being updated through telematics so as to update the navigation map information stored in the navigation map information storage unit 42 and the high-definition map information stored in the high-definition map information storage unit 41.).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations as taught by Tsuji in the teaching of Saito in view of Hersh, in order to generates a first traveling path based on map information around a circumference of a host vehicle (please see Tsuji Abstract).
Conclusion
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OMAR . ZEROUAL
Examiner
Art Unit 3628
/OMAR ZEROUAL/Primary Examiner, Art Unit 3629
Prosecution Insights