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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/23/2023 have been fully considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claims 1-20 are directed to a method (i.e. a process).
101 Analysis – Step 2A, Prong 1
Regarding Prong 1 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Claims 1-20 includes limitations that recite an abstract idea (emphasized below in bold) and will be used as a representative claim for the remainder of the 101 rejection.
Claim 1:
A method for determining a vehicle charging intention, comprising:
acquiring travel information of a target travel as target travel information;
determining a confidence level of a charging intention in the target travel according to the target travel information, wherein the confidence level of the charging intention is determined by a charging probability of at least one preset dimension, wherein the at least one preset dimension comprises a charging position dimension, a charging time dimension and a battery state dimension;
and determining whether the charging intention exists in the target travel according to the confidence level of the charging intention.
The examiner submits that the foregoing bolded limitations constitute a “mental process” because user its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, the limitation “determining a confidence level of a charging intention in the target travel according to the target travel information, wherein the confidence level of the charging intention is determined by a charging probability of at least one preset dimension, wherein the at least one preset dimension comprises a charging position dimension, a charging time dimension and a battery state dimension” in the context of the claim encompasses a user making a determination (i.e. a mental process), wherein target travel information data is used to determine a confidence level of a charging intention. Since this limitation can be done mentally, then this limitation recites an abstract idea. Furthermore, the limitation of “determining whether the charging intention exists in the target travel according to the confidence level of the charging intention” in the context of the claims encompasses the user making a determination (i.e. a mental process) of whether there is a charging intention according to the confidence level. Since this limitation can be done in the mind, then this limitation recites a mental process (i.e. an abstract idea).Accordingly, the claim recites at least one abstract idea. The same rational applies to independent claims 15 and 20.
Claim 2:
wherein the target travel information comprises a target final position of the target travel, and determining the confidence level of the charging intention in the target travel according to the target travel information comprises:
determining a charging position matched with the target final position as a target charging position, wherein the charging position is a position where a vehicle is charged in a historical charging travel, and the historical charging travel is a travel in which the vehicle is charged;
determining the charging probability of the at least one preset dimension for the vehicle according to the target charging position and the target travel information; and
determining the confidence level of the charging intention according to the charging probability of the at least one preset dimension.
Regarding claim 2, the bolded limitations of making a determination (i.e. determining a charging position matched with the target final position as a target charging position, determining the charging probability, and determining the confidence level according to the probability) in the context of the claim encompasses the user determining a charging probability based on the received target charging position and target travel information data, wherein the confidence level is determined based on a probability. Since this limitation can be done in the mind, this limitation recites a mental process i.e. an abstract idea. The same rational applies to dependent claim 16.
Claim 3:
wherein the target travel information further comprises a target starting position and a target driving route of the target travel; and when the at least one preset dimension comprises the charging position dimension, the charging probability of the charging position dimension for the vehicle is acquired by:
determining a probability of the vehicle being charged at the target charging position as a first probability; determining a probability of the target final position being the charging position as a second probability;
and determining the charging probability of the charging position dimension for the vehicle according to a product of the first probability and the second probability.
Regarding claim 3, the limitations as recited in bold above encompasses determining a first and second probability, and then further determining the charging probability based on a product of the first probability and the second probability. Since this can be done on a piece of paper (i.e. multiplying one probability to the other), then this limitation recites a mental process i.e. an abstract idea. The same rational applies to dependent claim 17.
Claim 4:
wherein determining the probability of the vehicle being charged at the target charging position as the first probability comprises:
acquiring a first number of times the vehicle is charged at the target charging position in the historical charging travels and a number of matching travels of which a final position is the target charging position in the historical charging travels;
and determining the first probability according to a ratio of the first number of times to the number of matching travels.
Regarding claim 4, the limitation of acquiring information from historical data is a limitation that can be done by a user looking at the historical data to acquire it. Since this limitation can be done in the mind, then this limitation recites an abstract idea. Furthermore, the limitation of determining the first probability according to a ratio is a limitation that can be done on a piece of paper with a pen, and therefore recites a mental process i.e. an abstract idea.
Claim 5:
wherein determining the probability of the target final position being the charging position as the second probability comprises: determining a similarity between the target driving route and a driving route of a designated charging travel, wherein the designated charging travel is the historical charging travel of which a travel starting position matches the target starting position;
and determining the second probability according to the similarity corresponding to each designated charging travel.
Regarding claim 5, the limitation of determining a similarity between the target route and a driving route of a designated charging travel in the context of the claim encompasses the user drawing the routes on a map, and determining their similarity, which is a limitation that can be done in the human mind. Furthermore, determining the second probability according to the similarity is a process that can be done in the mind of a user, and therefore recites a mental process i.e. an abstract idea.
Claim 6:
wherein the target travel information further comprises a target starting time point of the target travel, and when the at least one preset dimension comprises the charging time dimension, the charging probability of the charging time dimension is acquired by:
determining a charging starting time interval, in which the target starting time point is located, as a target time interval, wherein the charging starting time interval is preset according to a starting time point of every historical charging travel;
acquiring a second number of times the vehicle is charged in the target time interval in the historical charging travels and a first number of times the vehicle is charged at the target charging position in the historical charging travels;
and determining the charging probability of the charging time dimension for the vehicle according to a ratio of the second number of times to the first number of times.
Regarding claim 6, the limitation of determining a charging starting time interval, and acquiring a number of times the vehicle is charged in the historical charging travels and the number of times the vehicle is charged at the second charging position in the historical charging travels encompasses a user making a determination (i.e. determining a charging starting time interval), which is a mental process. Furthermore, looking up data in a historical database of the charging travels is another process that can be done mentally. Lastly, determining the charging probability based on data is a step that can further be done mentally. Therefore, these limitations recite at least one abstract idea. The same rational applies to dependent claim 18.
Claim 7:
wherein the target travel information further comprises a target initial SOC value of a vehicle battery at a beginning of the target travel, and when the at least one preset dimension comprises the battery state dimension, the charging probability of the battery state dimension for the vehicle is acquired by:
determining an SOC range, in which the target initial SOC value is located, as a target SOC range, wherein the SOC range is preset according to an initial SOC value of every historical charging travel;
acquiring a third number of times the vehicle is charged in the target SOC range in the historical charging travels and a first number of times the vehicle is charged at the target charging position in the historical charging travels;
and determining the charging probability of the battery state dimension for the vehicle according to a ratio of the third number of times to the first number of times.
Regarding claim 6, the limitations as recited above in the context of the claim encompasses the user determining an SOC range (i.e. which can be done by looking up a table), and is deemed a mental process. Furthermore, acquiring and looking up data from a database (i.e. acquiring a third number of times the vehicle is charged) is a process that can be done mentally. Lastly, determining the charging probability based on a ratio is a process that can be done on a piece of paper, and therefore recites a mental process i.e. an abstract idea. The same rational applies to dependent claim 19.
Claim 8:
wherein determining the confidence level of the charging intention according to the charging probability of the at least one preset dimension comprises: determining the confidence level of the charging intention according to a product of the charging probability of the at least one preset dimension.
Regarding claim 8, the limitation in the context of the claim encompasses the user determining the confidence level according to a product of the charging probability of the at least one present information, which is a process that can be done mentally, by calculating the product of the charging probability. Since this limitation can be done on a piece of paper, then this limitation recites a mental process i.e. an abstract idea.
Claim 9:
wherein determining whether the charging intention exists in the target travel according to the confidence level of the charging intention comprises: determining that the charging intention exists in the target travel when the confidence level of the charging intention is greater than or equal to a confidence threshold;
and determining that no charging intention exists in the target travel when the confidence level of the charging intention is less than the confidence threshold.
Regarding claim 9, the limitation in the context of the claim encompasses a user determining whether or not the charging intention exists based on a confidence level being above or lower than a threshold. Since this determining can be done mentally, by comparing the confidence level to a threshold, then this limitation recites a mental process i.e. an abstract idea.
Claim 10:
wherein the target travel information comprises a target final position, a target starting time point, and a target initial SOC value of a vehicle battery at a beginning of the target travel, and before determining the confidence level of the charging intention in the target travel according to the target travel information, the method further comprises:
determining that the target travel information satisfies a preset condition comprising at least one of the following conditions: among preset charging positions, at least one charging position being within a preset distance away from the target final position; among preset charging starting time intervals, a charging starting time interval comprising the target starting time point existing; and among preset SOC ranges, an SOC range comprising the target initial SOC value existing.
Regarding claim 10, the limitation in the context of the claim encompasses a user determining whether the target travel information satisfies a preset condition. Since this limitation can be done mentally, then this limitation recites a mental process i.e. an abstract idea.
Claim 11:
wherein the target travel is determined by: acquiring vehicle position information;
acquiring historical travel information of a vehicle, wherein the historical travel information comprises a historical starting time point, a historical initial SOC value, and a historical charging position of every historical charging travel, and further comprises a number of historical travels for each historical charging position of which a final position is the historical charging position and a number of times the vehicle is charged at each historical charging position;
generating a predicted travel according to the historical travel information and the vehicle position information; and determining the target travel according to the predicted travel.
Regarding claim 11, the limitation in the context of the claim encompasses a user looking up data regarding historical travel information of a vehicle and generating a predicted travel based on the historical travel information and vehicle position that is acquired. Since this limitation can be done mentally (i.e. drawing a route on a map), then this limitation recites a mental process i.e. an abstract idea.
Claim 12:
wherein determining the target travel according to the predicted travel comprises: outputting predicted travel information for characterizing the predicted travel; and determining the predicted travel as the target travel when a confirmation instruction for the predicted travel information is received.
Regarding claim 12, the limitation in the context of the claim encompasses a user outputting predicted travel information (i.e. outputting information by speaking or writing down information) and making a determination based on an instruction, which Is a step that can be done mentally, and therefore recites a mental process. Accordingly, this claim recites at least one abstract idea.
Claim 13:
wherein the target travel is determined by: receiving a setting instruction for the target travel, wherein the setting instruction is used to indicate the travel information of the target travel; and determining a travel indicated by the setting instruction as the target travel.
Regarding claim 13, the limitation in the context of the claim encompasses a user making a determination based on an instruction, which is a process that can be done mentally, and therefore recites a mental process i.e. an abstract idea.
101 Analysis – Step 2A, Prong 2
Regarding prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
Claim 1:
A method for determining a vehicle charging intention, comprising:
acquiring travel information of a target travel as target travel information;
determining a confidence level of a charging intention in the target travel according to the target travel information, wherein the confidence level of the charging intention is determined by a charging probability of at least one preset dimension, wherein the at least one preset dimension comprises a charging position dimension, a charging time dimension and a battery state dimension;
and determining whether the charging intention exists in the target travel according to the confidence level of the charging intention.
Claim 11:
wherein the target travel is determined by: acquiring vehicle position information;
acquiring historical travel information of a vehicle, wherein the historical travel information comprises a historical starting time point, a historical initial SOC value, and a historical charging position of every historical charging travel, and further comprises a number of historical travels for each historical charging position of which a final position is the historical charging position and a number of times the vehicle is charged at each historical charging position;
generating a predicted travel according to the historical travel information and the vehicle position information; and determining the target travel according to the predicted travel.
Claim 13:
wherein the target travel is determined by: receiving a setting instruction for the target travel, wherein the setting instruction is used to indicate the travel information of the target travel; and determining a travel indicated by the setting instruction as the target travel.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “acquiring information”, such as travel information data and receiving setting instruction data, the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (i.e. processors) to perform the process. In particular, the acquiring step by the processors are recited at a high level of generality (i.e. as a general means of gathering data), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Lastly, the “one or more processors” merely describes how to generally “apply” the otherwise mental judgements in a generic or general purpose vehicle environment. The vehicle control system is recited at a high level of generality and merely automates the determining steps.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claims 1,8, and 15 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. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the determining and comparing amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, with regards to the additional limitations of “acquiring” data, the examiner submits that these limitations are insignificant extra-solution activities.
Further, 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 limitations of “acquiring” data are well-understood, routine, and conventional activities because the background recites that the data is obtained from a database and the specification does not provide any indication that the processor is anything other than a conventional processor. The step of “acquiring” data is taught in the primary reference Li Yanan CN112435053A, see at least Page 5 lines 59-60. The step of acquiring data is well-understood, routine, and conventional activity in the field. For these reasons, there is no inventive concept and the claim is not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 8-11, 13-16 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li Yanan CN112435053A (henceforth Li).
Regarding claim 1,
Li discloses:
A method for determining a vehicle charging intention (See at least Page 6 lines 7-8, “it is determined that the electric vehicle has a charging intention for this trip.”), comprising: acquiring travel information of a target travel as target travel information; (See at least Page 5 lines 59-60, “Step S104, acquiring the current location, current time, and current battery SOC of the electric vehicle for this trip”. Travel information of a target travel is acquired. Further see Page 10 lines 40-48, wherein the node that represents the end of the trip (i.e. target travel) is acquired.)
determining a confidence level of a charging intention in the target travel according to the target travel information, wherein the confidence level of the charging intention is determined by a charging probability of at least one preset dimension, wherein the at least one preset dimension comprises a charging position dimension, a charging time dimension and a battery state dimension;
(See at least Page 6 lines 2-3, “Step S106, based on the current location, current time and current battery SOC, it is determined whether the current trip of the electric vehicle conforms to the historical charging habit”. Further see Page 8 lines 31-37, “The above-mentioned probability of reaching a fixed charging node from the current position represents the probability of going to the charging node when the road has a fork. This value can be calculated according to the ratio of the number of historical trips. c) If the fixed charging node cannot be reached from the current position, or the probability of reaching the fixed charging node from the current position is not higher than the preset threshold, it is determined that the current trip of the electric vehicle does not conform to the historical charging habits.” A confidence level of whether the electric conforms to the historical charging habits is determined according to the target travel information.
Further see Page 5 lines 50-51, “whether the charging location is fixed and whether the charging start time is fixed”, wherein a charging position dimension and a charging time is a preset dimension.
Further see page 11 lines 58-60 to page 12 lines 1-6, wherein the SOC of the current battery is a preset dimension that comprises a battery state dimension.)
and determining whether the charging intention exists in the target travel according to the confidence level of the charging intention. (See at least Page 6 lines 7-8, “In step S108, if it meets the requirements, it is determined that the electric vehicle has a charging intention for this trip.”)
Regarding claim 2,
Li discloses:
wherein the target travel information comprises a target final position of the target travel (See Page 1, lines 58-59, “travel end point”), and determining the confidence level of the charging intention in the target travel according to the target travel information comprises: determining a charging position matched with the target final position as a target charging position, wherein the charging position is a position where a vehicle is charged in a historical charging travel, and the historical charging travel is a travel in which the vehicle is charged; determining the charging probability of the at least one preset dimension for the vehicle according to the target charging position and the target travel information; and determining the confidence level of the charging intention according to the charging probability of the at least one preset dimension. (See at least Page 10 lines 40-45, “obtain historical travel data and historical charging data of electric vehicles; construct a travel trajectory network based on the historical travel data, where the travel trajectory network includes: multiple nodes and connections between nodes , Each node represents any of the following: the starting point of the trip, the end of the trip, the intersection point of the trip, and the distance and speed between the two connected nodes are marked on the connection; the charging is marked in the travel trajectory network according to the historical charging data” and page 10 lines 50-58, wherein a confidence level (i.e. determining that the electric vehicle has a charging intention for this trip involves determining if the current trip conforms to the historical charging habit) is determined according to charging probability.)
Regarding claim 8,
Li discloses:
wherein determining the confidence level of the charging intention according to the charging probability of the at least one preset dimension comprises: determining the confidence level of the charging intention according to a product of the charging probability of the at least one preset dimension.
(See at least Page 8 lines 31-33, “The above-mentioned probability of reaching a fixed charging node from the current position represents the probability of going to the charging node when the road has a fork. This value can be calculated according to the ratio of the number of historical trips” and at least page 10 lines 50-58, wherein the confidence level (i.e. of determining that the vehicle has a charging intention) is based on a product of the charging probability of the at least one preset dimension.)
Regarding claim 9,
Li discloses:
wherein determining whether the charging intention exists in the target travel according to the confidence level of the charging intention comprises: determining that the charging intention exists in the target travel when the confidence level of the charging intention is greater than or equal to a confidence threshold; and determining that no charging intention exists in the target travel when the confidence level of the charging intention is less than the confidence threshold.
(See at least Page 6 lines 7-8, “In step S108, if it meets the requirements, it is determined that the electric vehicle has a charging intention for this trip.” When the requirements are not met (i.e. the confidence level is lower than a threshold), then it is determined that the charging intention does not exist, and vice versa.)
Regarding claim 10,
Li discloses:
wherein the target travel information comprises a target final position (See Page 1, lines 58-59, “travel end point”), a target starting time point, and a target initial SOC value of a vehicle battery at a beginning of the target travel, (See at least Page 1 line 40, “Acquiring the current location, current time, and current battery SOC of the electric vehicle for this trip”.)
and before determining the confidence level of the charging intention in the target travel according to the target travel information, the method further comprises: determining that the target travel information satisfies a preset condition comprising at least one of the following conditions: among preset charging positions, at least one charging position being within a preset distance away from the target final position; among preset charging starting time intervals, a charging starting time interval comprising the target starting time point existing; and among preset SOC ranges, an SOC range comprising the target initial SOC value existing.
(See at least Page 2 lines 56-60, “Predicting the battery SOC reaching the fixed charging node based on the current battery SOC; If the fixed charging node can be reached from the current position, and the battery SOC reaching the fixed charging node is consistent with the charging start SOC interval, it is determined that the current trip of the electric vehicle conforms to the history Charging habits”. Further see at least Page 4, lines 54-58, “acquiring the historical charging habits of the vehicle owner charging the electric vehicle; acquiring the current location, current time, and current battery SOC of the electric vehicle during the trip; Based on the current location, current time and current battery SOC, it is determined whether the current trip of the electric vehicle conforms to the historical charging habits; if so, it is determined that the current trip of the electric vehicle carries a charging intention.” Determining that the target travel information satisfies a preset condition comprises a charging starting time interval comprising the target starting time point existing. Additionally see Page 11, lines 41-43, “the battery SOC reaching any charging node is consistent with the charging start SOC range, it is determined that the electric vehicle’s current trip conforms to the historical charging habits”. The SOC range comprises the target initial SOC value existing.)
Regarding claim 11,
Li discloses:
wherein the target travel is determined by: acquiring vehicle position information; (See at least Page 10, lines 18-19, “acquire the current location of the electric vehicle for this trip”.)
acquiring historical travel information of a vehicle, wherein the historical travel information comprises a historical starting time point, a historical initial SOC value, and a historical charging position of every historical charging travel, (See at least Page 10 lines 40-48, “obtain historical travel data and historical charging data of electric vehicles; construct a travel trajectory network based on the historical travel data, where the travel trajectory network includes: multiple nodes and connections between nodes , Each node represents any of the following: the starting point of the trip, the end of the trip, the intersection point of the trip, and the distance and speed between the two connected nodes are marked on the connection; the charging is marked in the travel trajectory network according to the historical charging data Node, and count the charging information of electric vehicles at each charging node. The charging information includes: charging frequency, time to start charging, charging start SOC interval and historical travel trajectory to the charging node; 47 according to the charging of each charging node The information determines the historical charging habits”. The historical travel information of the vehicle is acquired, which includes a historical starting time point, a historical initial SOC value, and a historical charging position of every historical charging travel.)
and further comprises a number of historical travels for each historical charging position of which a final position is the historical charging position and a number of times the vehicle is charged at each historical charging position; (See at least Page 10 lines 45-46, “count the charging information at each charging nodes” and “charging frequency”.)
generating a predicted travel according to the historical travel information and the vehicle position information; and determining the target travel according to the predicted travel. (See at least Page 10 lines 27-34.)
Regarding claim 13,
Li discloses:
wherein the target travel is determined by: receiving a setting instruction for the target travel, wherein the setting instruction is used to indicate the travel information of the target travel; and determining a travel indicated by the setting instruction as the target travel. (See at least Page 7, lines 1-5, “For example, it can be set when the charging frequency of a certain charging node is greater than the preset threshold, it means that the charging location for the car owner to charge the electric vehicle is fixed; it can also be set that the car owner can only charge the charging node within the preset number (such as 3) Charging means that the charging location for the vehicle owner to charge the electric vehicle is fixed”. The setting instruction (i.e. by setting the charging frequency of a certain charging node)is used to indicate the travel information of the target travel.)
Regarding claim 14,
Li discloses:
further comprising: when it is determined that the charging intention exists in the target travel, during driving process of a vehicle, controlling a battery temperature of the vehicle to be a target temperature, wherein the target temperature is a temperature suitable for charging a vehicle battery.
(See at least Page 9 lines 54-55, “For example, it is predicted that this trip 54 carries a charging intention, and the battery can be heated in advance to save charging time”. When it is determined that the charging intention exists, the battery temperature is controlled to be a target temperature.)
Regarding claim 15,
Li discloses the same limitations as recited in claim 1 above, and is therefore rejected under the same rational.
Regarding claim 16,
Li discloses the same limitations as recited in claim 2 above, and is therefore rejected under the same rational.
Regarding claim 20
Yang discloses the same limitations as recited in claim 1 above, and is therefore rejected under the same rational.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of YANG GENG CN111192451A (henceforth Yang).
Regarding claim 3,
Li discloses the limitations as recited in claims 1 and 2 above.
Li further discloses:
wherein the target travel information further comprises a target starting position and a target driving route of the target travel; See at least Page 10 lines 40-45, “obtain historical travel data and historical charging data of electric vehicles; construct a travel trajectory network based on the historical travel data, where the travel trajectory network includes: multiple nodes and connections between nodes , Each node represents any of the following: the starting point of the trip, the end of the trip, the intersection point of the trip”. The target travel information comprises a target starting position and a target driving route.)
and when the at least one preset dimension comprises the charging position dimension, (See at least Page 2, lines 18, “charging node”.)the charging probability of the charging position dimension for the vehicle is acquired by:
determining a probability of the vehicle being charged at the target charging position as a first probability; (See at least Page 3 lines 14-16, “probability of reaching the fixed charging node”.)
Li discloses determining a probability of the charging position dimension, but does not specifically state the limitation “determining a probability of the target final position being the position as a second probability; and determining the probability of the position dimension for the vehicle according to a product of the first probability and the second probability”. However, Yang teaches:
determining a probability of the target final position being the position as a second probability; and determining the probability of the position dimension for the vehicle according to a product of the first probability and the second probability
(See at least Page 9 lines 8-9, “to obtain the first arrival probability of the vehicle and the second arrival probability, the probability of the product of the first probability of arrival and the second arrival probability as the vehicle to the destination”. The probability of the position dimension is the product of the first and second probability.)
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Li to incorporate the teachings of Yang to include “determining a probability of the target final position being the position as a second probability; and determining the probability of the position dimension for the vehicle according to a product of the first probability and the second probability” in order to “accurately predict all vehicles that may reach the destination and the arrival time of the destination” (Page 1, lines 38-39, Yang). This would create a more robust system for determining the probability of the position dimension of the vehicle, such as when it needs to charge. Additionally, a person having ordinary skill in the art would have a reasonable expectation of success in combining the teachings of Li and Yang. The claimed invention is merely a combination of known elements and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 17,
Yang and Li discloses the same limitations as recited in claim 3 above, and is therefore rejected under the same rational.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Li and Yang further in view of Hanaoka US20150293576A1.
Regarding claim 4,
Li and Yang discloses the limitations as recited in claims 1-3 above.
Li does not specifically state wherein determining the probability of the vehicle being charged at the target charging position as the first probability comprises: acquiring a first number of times the vehicle is charged at the target charging position in the historical charging travels and a number of matching travels of which a final position is the target charging position in the historical charging travels; and determining the first probability according to a ratio of the first number of times to the number of matching travels. However, Hanaoka teaches:
wherein determining the probability of the vehicle being charged at the target charging position as the first probability comprises: acquiring a first number of times the vehicle is charged at the target charging position in the historical charging travels and a number of matching travels of which a final position is the target charging position in the historical charging travels; and determining the first probability according to a ratio of the first number of times to the number of matching travels. (See at least Para. 0020, “At spots where the number of times of charging is less than the predetermined number of times based on the charging history, the probability that the rechargeable battery cannot be charged is high. In the above configuration, therefore, when the number of times of charging is less than the predetermined number of times, it is determined that the rechargeable battery cannot be charged even if the remaining level of the rechargeable battery is decreased, and thus the power consumption is suppressed. As a result, the power consumption is appropriately suppressed under the circumstance where the charging of the rechargeable battery is difficult”. The first probability is determined according to a ratio of the first number of times to the number of matching travels.)
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Li to incorporate the teachings of Hanaoka to include “wherein determining the probability of the vehicle being charged at the target charging position as the first probability comprises: acquiring a first number of times the vehicle is charged at the target charging position in the historical charging travels and a number of matching travels of which a final position is the target charging position in the historical charging travels; and determining the first probability according to a ratio of the first number of times to the number of matching travels” in order to determine whether the battery can or cannot be charged, such that “ the power consumption is appropriately suppressed under the circumstance where the charging of the rechargeable battery is difficult” (Para. 0020, Hanaoka). This would create a more robust system for determining the probability of the vehicle being charged at a target position. Additionally, a person having ordinary skill in the art would have a reasonable expectation of success in combining the teachings of Li and Hanaoka. The claimed invention is merely a combination of known elements and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Li and Yang further in view of McGee et al. US20120290159A1 (henceforth McGee).
Regarding claim 5,
Li and Yang discloses the limitations as recited in claims 1-3 above.
Li does not specifically state “wherein determining the probability of the target final position being the charging position as the second probability comprises: determining a similarity between the target driving route and a driving route of a designated charging travel, wherein the designated charging travel is the historical charging travel of which a travel starting position matches the target starting position; and determining the second probability according to the similarity corresponding to each designated charging travel.” However, McGee teaches:
wherein determining the probability of the target final position being the charging position as the second probability comprises: determining a similarity between the target driving route and a driving route of a designated charging travel, wherein the designated charging travel is the historical charging travel of which a travel starting position matches the target starting position; and determining the second probability according to the similarity corresponding to each designated charging travel. (See at least Para. 0012, “the method includes storing, for each of a plurality of past charging events of a vehicle, information indicative of the location of the past charging event, the location of the next charging event after the past charging event, and distance traveled by the vehicle from the location of the past charging event until the next charging event. The method further includes generating from the stored information a probability matrix indicative of the probability of the location of the next charging event for each location of the past charging events. The method further includes generating from the stored information a journey matrix indicative of the distance traveled by the vehicle from each location of the past charging events until the corresponding next charging events.“ The second probability is determined according to the similarity between the target driving route and a driving route of a designated charging travel.)
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Li to incorporate the teachings of McGee to include the limitation as recited above in order to more efficiently determine the probability of the next charging location. (Para. 0012, McGee). This would create a more robust system for determining the probability according to the route similarity. Additionally, a person having ordinary skill in the art would have a reasonable expectation of success in combining the teachings of Li and McGee. The claimed invention is merely a combination of known elements and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Dufford US20170232915A1.
Regarding claim 12,
Li discloses the limitations as recited in claims 1 and 11 above.
Li further discloses:
wherein determining the target travel according to the predicted travel comprises: outputting predicted travel information for characterizing the predicted travel;
(See at least Page 10 and lines 53-54, “predict the time to reach the fixed charging node based on the current time; predict the battery SOC to reach the fixed charging node”. Predicted travel information characterizing the predicted travel is outputted.)
Li does not specifically state determining the predicted travel as the target travel when a confirmation instruction for the predicted travel information is received.
However, Dufford teaches:
determining the predicted travel as the target travel when a confirmation instruction for the predicted travel information is received.
(See at least Para. 0062, “The predicted route set may be validated by user confirmation and/or traversing the predicted route.”)
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Li to incorporate the teachings of Dufford to include “determining the predicted travel as the target travel when a confirmation instruction for the predicted travel information is received” in order to indicate “whether the predicted route set and/or ride interruption events are accurate” (Para. 0062, Dufford), which would create a more robust system for determining the predicted travel. Additionally, a person having ordinary skill in the art would have a reasonable expectation of success in combining the teachings of Li and Dufford. The claimed invention is merely a combination of known elements and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Allowable Subject Matter
Claims 6-7 and 18-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 6, the claim limitations require “wherein the target travel information further comprises a target starting time point of the target travel, and when the at least one preset dimension comprises the charging time dimension, the charging probability of the charging time dimension is acquired by: determining a charging starting time interval, in which the target starting time point is located, as a target time interval, wherein the charging starting time interval is preset according to a starting time point of every historical charging travel; acquiring a second number of times the vehicle is charged in the target time interval in the historical charging travels and a first number of times the vehicle is charged at the target charging position in the historical charging travels; and determining the charging probability of the charging time dimension for the vehicle according to a ratio of the second number of times to the first number of times.“ The limitation of acquiring a second number of times the vehicle is charged in the target time interval in the historical charging travels and a first number of times the vehicle is charged at the target charging position in the historical charging travels, and determining the charging probability of the charging time dimension according to the ratio of the second number of times to the first number of times, is not captured in the prior art. For example, the primary reference Li Yanan CN112435053A discloses a charging frequency at a charging node based on historical data, but does not disclose the limitation as recited above. Therefore, this limitation and in combination with the other elements in the claim are not anticipated nor made obvious by the prior art on record. The same rational applies to claim 18.
Regarding claim 7, the claim limitations require “wherein the target travel information further comprises a target initial SOC value of a vehicle battery at a beginning of the target travel, and when the at least one preset dimension comprises the battery state dimension, the charging probability of the battery state dimension for the vehicle is acquired by: determining an SOC range, in which the target initial SOC value is located, as a target SOC range, wherein the SOC range is preset according to an initial SOC value of every historical charging travel; acquiring a third number of times the vehicle is charged in the target SOC range in the historical charging travels and a first number of times the vehicle is charged at the target charging position in the historical charging travels; and determining the charging probability of the battery state dimension for the vehicle according to a ratio of the third number of times to the first number of times.” The limitation of acquiring a third number of times the vehicle is charged in the target SOC range in the historical charging travels and a first number of times the vehicle is charged at the target charging position in the historical charging travels; and determining the charging probability of the battery state dimension for the vehicle according to a ratio of the third number of times to the first number of times, is not captured in the prior art on record. For example, the primary reference Li Yanan CN112435053A discloses historical SOC at a charging destination, but does not disclose the limitation as recited above. Therefore, this limitation and in combination with the other elements in the claim are not anticipated nor made obvious by the prior art on record. The same rational applies to claim 19.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Miller et al. US11,214,161B2 discloses determining, via a processor, a remaining trip distance for an electric vehicle. The example method further includes determining, via the processor, a remaining expected range of the electric vehicle. The example method also includes transmitting a request for a mobile charging unit to meet the electric vehicle at a location when a ratio of the remaining trip distance to a remaining expected range exceeds a first threshold. (See abstract).
Maki et al. US20130013139A1 discloses an information terminal for electric-powered vehicle configured to calculate a required time for the vehicle to travel a route from a current position to a destination in accordance with map information, includes: route setting means that searches for a route to the destination; recharging place setting means that sets a charging place on a way of the route to the destination set by the route setting means; and charging facility information acquisition means that acquires information on the charging place set by the recharging place setting means. A recommended vehicle speed or a vehicle speed range including the recommended vehicle speed at a section to the charging place is calculated. (See abstract).
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/Erin M Piateski/Supervisory Patent Examiner, Art Unit 3669
/G.J.L./
Examiner
Art Unit 3669