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 .
DETAILED ACTION
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 8/1/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 15 – 23, 25 – 27, & 29 – 34 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The determination of whether a claim recites patent ineligible subject matter is a 2 step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2) and 2106.05(a) thru (d) for explanations.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
Claim 15 is directed to a computer-implemented method of generating map data (i.e., a process). Therefore, claim 15 is within at least one of the four statutory categories. Similarly, Claim 26 is directed to a system for generating map data (i.e., a machine) and is also within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, 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. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c)
Independent claim 25 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]) and will be used as a representative claim for the remainder of the 101 rejection. Claim 25 recites:
A system for generating map data with energy consumption forecast values for use in energy management of an electric vehicle, the system comprising:
at least one memory containing an energy consumption forecast model and/or base map data which comprises a network of road sections; a backend computing device which is designed to carry out the following actions:
a) loading the base map data;
b) carrying out the following actions for each road section: b1) determining a set of neighboring road sections using the base map data; [mental process/step]
b2) calculating transition vectors for each combination of the road section and a neighboring road section, using the base map data; [mental process/step]
b3) calculating an energy consumption value for each transition vector using the energy consumption forecast model; [mental process/step]
b4) calculating an energy consumption forecast value of the road section using the calculated energy consumption values; and [mental process/step]
c) providing map data comprising the network of road sections and the calculated energy consumption forecast values.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “carrying… determining…” in the context of this claim encompasses a person (driver) looking at map data and forming a simple judgement as to connected roads. Further, “calculating transition vectors…” in the context of the claim encompasses a person determining a mathematical representation of a transition from one road to another, which is an abstract idea under the broadest reasonable interpretation of the claim. Finally, the limitations “calculating an energy consumption value…” and “calculating an energy consumption forecast value…” in the context of the claim encompass determining an amount of energy consumed by the vehicle were the vehicle to traverse the relevant segments, which are mathematical evaluations under the broadest reasonable interpretation of the claim. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). 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” [with a description of the additional limitations in brackets], while the bolded portions continue to represent the “abstract idea”.):
A system for generating map data with energy consumption forecast values for use in energy management of an electric vehicle, the system comprising: [generic linking to technical field, 2106.05(h), Apply it, 2106.05(f)]
at least one memory containing an energy consumption forecast model and/or base map data which comprises a network of road sections; a backend computing device which is designed to carry out the following actions: [applying the abstract idea using generic computing module, Apply it 2106.05(f)]
a) loading the base map data; [pre-solution activity (data gathering) 2106.05(g)]
b) carrying out the following actions for each road section: b1) determining a set of neighboring road sections using the base map data;
b2) calculating transition vectors for each combination of the road section and a neighboring road section, using the base map data;
b3) calculating an energy consumption value for each transition vector using the energy consumption forecast model;
b4) calculating an energy consumption forecast value of the road section using the calculated energy consumption values; and
c) providing map data comprising the network of road sections and the calculated energy consumption forecast values. [insignificant post-solution activity (outputting results of the mental process) 2106.05(g)].
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 “at least one memory…,” “loading…,” and “providing…,” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (vehicle controller) to perform the process. In particular, the “loading…” step is recited at a high level of generality (i.e. as a general means of gathering map data for use in the computations), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The “providing…” step is also recited at a high level of generality (i.e. as a general means of outputting the map data), and amounts to mere post solution output of data, which is a form of insignificant extra-solution activity. Lastly, the “at least one memory…” is recited at a high-level of generality (i.e., as a generic processor and storage device performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
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. see 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 Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computing element to perform the steps of the mental process amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “loading…” and “providing…,” the examiner submits that these limitations are insignificant extra-solution activities.
Dependent claim(s) 16 – 25 & 27 – 34 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and do not integrate the judicial exception into a practical application. Specifically,
Claim 16 recites wherein the neighboring road sections are directly connected to the road section, which merely narrows the data evaluation performed as part of the asserted mental process without significantly more.
Claim 17 recites wherein the neighboring road sections are connected to the road section by at least one intermediary road, which merely narrows the data evaluation performed as part of the asserted mental process without significantly more.
Claim 18 recites wherein the base maps include information regarding the road, which merely narrows the insignificant extra-solution activity of data gathering to specific embodiments.
Claim 19 recites wherein the transition vectors include differences in parameter values, which is a mathematical evaluation under the broadest reasonable interpretation of the claim.
Claim 20 recites wherein the energy consumption forecast model comprises a trained regression model, which is a mathematical evaluation framework under the broadest reasonable interpretation of the claim.
Claim 21 recites wherein the at least one regression model comprises a random forest decision tree, which is a mathematical evaluation framework under the broadest reasonable interpretation of the claim.
Claim 22 recites wherein an energy consumption forecast corresponds to a largest energy consumption value computed, which is a mental evaluation under the broadest reasonable interpretation of the claim.
Claim 23 recites wherein a GPS location of the vehicle is determined and used to determine an energy forecast value in the environment, which is a mental evaluation based on generically collected data under the broadest reasonable interpretation of the claim.
Claim 25 recites wherein the method is implemented by at least one processor and computer readable storage medium, which is the insignificant extra-solution activity of applying the abstract idea using generic computing components.
Therefore, dependent claims 16 – 23, 25, 27, & 29 – 34 are not patent eligible under the same rationale as provided for in the rejection of Independent Claims 15 & 26.
Therefore, claim(s) 15 – 23, 25 – 27, & 29 – 34 is/are ineligible under 35 USC §101.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 15 - 19, 22, 23, 25 - 27, & 29 - 32 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wray (US 2022/0205796 A1).
Regarding Claim 15:
Wray discloses: A computer-implemented method for generating map data with energy consumption forecast values for use in energy management of an electric vehicle, the method comprising: (Wray discloses in at least Paragraph 0004 a method for route planning for a hybrid electric vehicle, including the optimization of the control of a vehicle along a route from origin to destination for optimal energy consumption [i.e. a method for generating map data with energy consumption forecast values for use in energy management of an electric vehicle]. At least Paragraphs 0006 & 0043 of Wray further discloses wherein the method may be implemented by a non-transitory computer-readable storage medium and associated processor [i.e. the method is computer-implemented])
a) loading base map data which comprises a network of road sections; (Wray discloses in at least Paragraph 0097 wherein a navigation map may be obtained or otherwise learned, from which a state space may be derived from the road segments in the map as disclosed in at least Paragraphs 0105 & 0106 [i.e. loading base map data which comprises a network of road sections])
b) carrying out the following actions for each road section: b1) determining a set of neighboring road sections using the base map data; (Wray discloses in at least Paragraphs 0112 & 0187 wherein neighboring successor edges corresponding to a next road to traverse may be determined, with the transition function determining a probability of transition onto the next segment, which may be based in part on the connections between road segments, such that if the road segments are not connected, the probability of transition between the two is zero [i.e. determining a set of neighboring road sections using the base map data])
b2) calculating transition vectors for each combination of the road section and a neighboring road section using the base map data; (Wray discloses in at least Paragraphs 0076 & 0109 – 0112 wherein a transition function may be defined that includes a cost vector for transitioning from one state to another, which may include traveling from one road to another [i.e. transition vectors for each combination of the road section and a neighboring road section are calculated])
b3) calculating an energy consumption value for each transition vector using an energy consumption forecast model; and (Wray discloses in at least Paragraphs 0122 & 0123 wherein the cost vector may include an expected energy expenditure, including the contribution from engine switching/activation required when traversing a road segment as disclosed in at least Paragraphs 0133 & 0158 – 0161, the engine switching being based on a model as disclosed in at least Paragraphs 0165 & 0166 [i.e. calculating an energy consumption value for each transition vector using an energy consumption forecast model])
b4) calculating an energy consumption forecast value of the road section using the calculated energy consumption value for each transition vector; and (Wray discloses in at least Paragraphs 0122 & 0123 wherein the cost vector may include an expected energy expenditure, including the contribution from engine switching/activation required when traversing a road segment as disclosed in at least Paragraphs 0133 & 0158 – 0161, the engine switching being based on a model as disclosed in at least Paragraphs 0165 & 0166, as well as an expected regeneration for the segment as disclosed in at least Paragraphs 0124 – 0127 [i.e. calculating an energy consumption forecast value of the road section using the calculated energy consumption value for each transition vector]. Wray further discloses in at least Paragraphs 0156 – 0163 wherein a total energy cost for a route may be determined, the route being based on a plurality of road segments)
c) providing map data comprising the network of road sections and the calculated energy consumption forecast values. (Wray discloses in at least Paragraphs 0047 & 0201 wherein the route planner output may be displayed to the driver on the display of a vehicle, which may be based on a learned map output as disclosed in at least Paragraphs 0204 – 0206 & 0212 – 0214 of Wray [i.e. map data comprising the network of road sections and the calculated energy consumption forecast values is provided])
Regarding Claim 16:
The computer-implemented method as claimed in claim 15, wherein the set of neighboring road sections contains all road sections that are directly connected to the road section.
Wray discloses in at least Paragraph 0187 wherein neighboring successor edges corresponding to a next road to traverse may be determined, with the transition function determining a probability of transition onto the next segment, which may be based in part on the connections between road segments, such that if the road segments are not connected, the probability of transition between the two is zero [i.e. the set of neighboring road sections contains all road sections that are directly connected to the road section].
Regarding Claim 17:
The computer-implemented method as claimed in claim 15, wherein the set of neighboring road sections contains all road sections that are connected to the road section by at least one other road section.
Wray discloses in at least Paragraphs 0154 – 0160 and Figure 6A, below, wherein road segments [such as 550D] connected to the road section by at least one other road section [such as 550B] are evaluated for required engine activation actions [i.e. the set of neighboring road sections contains all road sections that are connected to the road section by at least one other road section].
PNG
media_image1.png
512
680
media_image1.png
Greyscale
Regarding Claim 18:
The computer-implemented method as claimed in claim 15, wherein the base maps for at least some of the road sections have one or more of the following values: speed limit, road type, gradient, length.
Wray discloses in at least Paragraphs 0094 & 0096 wherein the map data for the road may include road slope [i.e. road gradient].
Regarding Claim 19:
The computer-implemented method as claimed in claim 15 wherein the transition vectors have one or more of the following values: difference in speed limit, change of road type, gradient difference.
Wray discloses in at least Paragraphs 0034, 0102, 0134 wherein based on a change in uphill or downhill state for an upcoming road, an engine activation action may be determined and performed [i.e. the transition vector may include a gradient difference of the road].
Regarding Claim 22:
The computer-implemented method as claimed in claim 15 wherein in b4) the calculated energy consumption forecast value of the road section corresponds to a largest of the energy consumption values calculated in b3).
Wray discloses in at least Paragraph 0127 wherein the expected battery consumption may be computed as a maximum of zero or the negative of energy consumption/regeneration [i.e. the calculated energy consumption forecast value of the road section corresponds to a largest of the energy consumption values].
Regarding Claim 23:
The computer-implemented method as claimed in claim 15, the method further comprising the following actions: d) determining a GPS location of an electric vehicle; e) determining a maximum, reference energy consumption forecast value in an environment of the electric vehicle according to the GPS location using the map data.
Wray discloses in at least Paragraph 0046 wherein a location unit of a vehicle may include a GPS unit to determine a current vehicle position and orientation [i.e. determining a GPS location of an electric vehicle] which may be used to determine an optimized route of the vehicle based on the costs of a plurality of road segments as disclosed in at least Paragraphs 0054, 0112, & 0156 – 0163 [i.e. determining a maximum, reference energy consumption forecast value in an environment of the electric vehicle according to the GPS location using the map data].
Regarding Claim 25:
A non-transitory computer-readable storage medium containing instructions which cause at least one processor to implement a method as claimed in claim 15 when the instructions are executed by the at least one processor.
Wray discloses in at least Paragraphs 0006 & 0043 wherein the method may be implemented by a non-transitory computer-readable storage medium and associated processor [i.e. a non-transitory computer-readable storage medium containing instructions which cause at least one processor to implement the method when the instructions are executed by the at least one processor].
Regarding Claim 26:
Wray discloses: A system for generating map data with energy consumption forecast values for use in energy management of an electric vehicle, the system comprising: (Wray discloses in at least Paragraphs 0005 & 0006 an apparatus for route planning for a hybrid electric vehicle, including the optimization of the control of a vehicle along a route from origin to destination for optimal energy consumption [i.e. a system for generating map data with energy consumption forecast values for use in energy management of an electric vehicle])
at least one memory containing an energy consumption forecast model and/or base map data which comprises a network of road sections; a backend computing device which is designed to carry out the following actions: (Wray discloses in at least Paragraphs 0006 & 0043 wherein the method may be implemented by a non-transitory computer-readable storage medium and associated processor [i.e. at least one memory and a backend computing device]. At least Paragraphs 0144 & 0149 of Wray disclose wherein map data may be recorded and stored on a cloud server or similar database [i.e. at least one memory containing an energy consumption forecast model and/or base map data which comprises a network of road sections])
a) loading the base map data; (Wray discloses in at least Paragraph 0097 wherein a navigation map may be obtained or otherwise learned, from which a state space may be derived from the road segments in the map as disclosed in at least Paragraphs 0105 & 0106 [i.e. loading base map data which comprises a network of road sections])
b) carrying out the following actions for each road section: b1) determining a set of neighboring road sections using the base map data; (Wray discloses in at least Paragraphs 0112 & 0187 wherein neighboring successor edges corresponding to a next road to traverse may be determined, with the transition function determining a probability of transition onto the next segment, which may be based in part on the connections between road segments, such that if the road segments are not connected, the probability of transition between the two is zero [i.e. determining a set of neighboring road sections using the base map data])
b2) calculating transition vectors for each combination of the road section and a neighboring road section, using the base map data; (Wray discloses in at least Paragraphs 0076 & 0109 – 0112 wherein a transition function may be defined that includes a cost vector for transitioning from one state to another, which may include traveling from one road to another [i.e. transition vectors for each combination of the road section and a neighboring road section are calculated])
b3) calculating an energy consumption value for each transition vector using the energy consumption forecast model; (Wray discloses in at least Paragraphs 0122 & 0123 wherein the cost vector may include an expected energy expenditure, including the contribution from engine switching/activation required when traversing a road segment as disclosed in at least Paragraphs 0133 & 0158 – 0161, the engine switching being based on a model as disclosed in at least Paragraphs 0165 & 0166 [i.e. calculating an energy consumption value for each transition vector using an energy consumption forecast model])
b4) calculating an energy consumption forecast value of the road section using the calculated energy consumption values; and (Wray discloses in at least Paragraphs 0122 & 0123 wherein the cost vector may include an expected energy expenditure, including the contribution from engine switching/activation required when traversing a road segment as disclosed in at least Paragraphs 0133 & 0158 – 0161, the engine switching being based on a model as disclosed in at least Paragraphs 0165 & 0166, as well as an expected regeneration for the segment as disclosed in at least Paragraphs 0124 – 0127 [i.e. calculating an energy consumption forecast value of the road section using the calculated energy consumption value for each transition vector]. Wray further discloses in at least Paragraphs 0156 – 0163 wherein a total energy cost for a route may be determined, the route being based on a plurality of road segments)
c) providing map data comprising the network of road sections and the calculated energy consumption forecast values. (Wray discloses in at least Paragraphs 0047 & 0201 wherein the route planner output may be displayed to the driver on the display of a vehicle, which may be based on a learned map output as disclosed in at least Paragraphs 0204 – 0206 & 0212 – 0214 of Wray [i.e. map data comprising the network of road sections and the calculated energy consumption forecast values is provided])
Regarding Claim 27:
The system as claimed in claim 26, wherein the system further comprises an electric vehicle comprising: a GPS sensor; a communication device; and a vehicle computing device; wherein the vehicle computing device is configured to: - determine a position of the electric vehicle using the GPS sensor; - transmit the position to the backend computing device by means of the communication device; and - receive a reference energy consumption forecast value from the backend computing device by means of the communication device; and wherein the backend computing device is further designed to determine the reference energy consumption forecast value based on the position and the map data and to transmit said value to the communication device.
Wray discloses in at least Paragraph 0046 wherein a location unit of a vehicle may include a GPS unit to determine a current vehicle position and orientation, which may be transmitted to a cloud based server as disclosed in at least Paragraph 0144 [i.e. a GPS sensor; a communication device; and a vehicle computing device; wherein the vehicle computing device is configured to: determine a position of the electric vehicle using the GPS sensor and transmit the position to the backend computing device by means of the communication device] which may be used to determine an optimized route of the vehicle based on the costs of a plurality of road segments as disclosed in at least Paragraphs 0054, 0112, & 0156 – 0163 [i.e. receive a reference energy consumption forecast value from the backend computing device by means of the communication device wherein the backend computing device is further designed to determine the reference energy consumption forecast value based on the position and the map data and to transmit said value to the communication device].
Regarding Claim 29:
The system as claimed in claim 26, wherein the set of neighboring road sections contains all road sections that are directly connected to the road section.
Wray discloses in at least Paragraph 0187 wherein neighboring successor edges corresponding to a next road to traverse may be determined, with the transition function determining a probability of transition onto the next segment, which may be based in part on the connections between road segments, such that if the road segments are not connected, the probability of transition between the two is zero [i.e. the set of neighboring road sections contains all road sections that are directly connected to the road section].
Regarding Claim 30:
The system as claimed in claim 26, wherein the set of neighboring road sections contains all road sections that are connected to the road section by at least one other road section.
Wray discloses in at least Paragraphs 0154 – 0160 and Figure 6A, below, wherein road segments [such as 550D] connected to the road section by at least one other road section [such as 550B] are evaluated for required engine activation actions [i.e. the set of neighboring road sections contains all road sections that are connected to the road section by at least one other road section].
Regarding Claim 31:
The system as claimed in claim 26, wherein the base maps for at least some of the road sections have one or more of the following values: speed limit, road type, gradient, length.
Wray discloses in at least Paragraphs 0094 & 0096 wherein the map data for the road may include road slope [i.e. road gradient].
Regarding Claim 32:
The system as claimed in claim 26, wherein the transition vectors have one or more of the following values: difference in speed limit, change of road type, gradient difference.
Wray discloses in at least Paragraphs 0034, 0102, 0134 wherein based on a change in uphill or downhill state for an upcoming road, an engine activation action may be determined and performed [i.e. the transition vector may include a gradient difference of the road].
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.
Claim(s) 20, 21, 33, & 34 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wray (US 2022/0205796 A1) as applied to claims 15 & 26 above, and further in view of Vincent (US 2023/0169874 A1).
Regarding Claim 20:
The computer-implemented method as claimed in claim 15 wherein the energy consumption forecast model comprises at least one trained regression model which contains a mapping rule from transition vectors to energy consumption values.
Wray does not appear to specifically disclose wherein the energy consumption forecast model comprises at least one trained regression model which contains a mapping rule from transition vectors to energy consumption values.
However Vincent teaches in at least Paragraphs 0108 & 0111 wherein a machine learning algorithm may include a regression model [i.e. a trained regression model], which may be used to determine an optimized flight plan as taught in at least Paragraphs 0074 – 0076, based on required power during different flight phases, which may be based on flight parameters as taught in at least Paragraphs 0033, 0044, & 0065 of Vincent [i.e. a trained regression model which contains a mapping rule from transition vectors to energy consumption values].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Wray by incorporating the use of a trained regression model as taught by Vincent.
The motivation to do so is that, as acknowledged by Vincent in at least Paragraphs 0108 & 0111, a machine learning model may determine a relationship between inputs and outputs, improving the learning of an energy consumption forecast.
Regarding Claim 21:
The computer-implemented method as claimed in claim 20 wherein the at least one regression model comprises at least one decision tree provided by a random forest.
Wray does not appear to specifically disclose wherein the at least one regression model comprises at least one decision tree provided by a random forest.
However Vincent teaches in at least Paragraphs 0089 & 0106 wherein a random forest classifier may be used as a regression model, with a decision tree being utilized as an algorithm as taught in at least Paragraph 0112 [i.e. the at least one regression model comprises at least one decision tree provided by a random forest].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Wray by incorporating the use of a decision tree provided by a random forest as taught by Vincent.
The motivation to do so is that, as acknowledged by Vincent in at least Paragraphs 0108 & 0111, a machine learning model may determine a relationship between inputs and outputs, improving the learning of an energy consumption forecast.
Regarding Claim 33:
The system as claimed in claim 26, wherein the energy consumption forecast model comprises at least one trained regression model which contains a mapping rule from transition vectors to energy consumption values.
Wray does not appear to specifically disclose wherein the energy consumption forecast model comprises at least one trained regression model which contains a mapping rule from transition vectors to energy consumption values.
However Vincent teaches in at least Paragraphs 0108 & 0111 wherein a machine learning algorithm may include a regression model [i.e. a trained regression model], which may be used to determine an optimized flight plan as taught in at least Paragraphs 0074 – 0076, based on required power during different flight phases, which may be based on flight parameters as taught in at least Paragraphs 0033, 0044, & 0065 of Vincent [i.e. a trained regression model which contains a mapping rule from transition vectors to energy consumption values].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Wray by incorporating the use of a trained regression model as taught by Vincent.
The motivation to do so is that, as acknowledged by Vincent in at least Paragraphs 0108 & 0111, a machine learning model may determine a relationship between inputs and outputs, improving the learning of an energy consumption forecast.
Regarding Claim 34:
The system as claimed in claim 33, wherein the at least one regression model comprises at least one decision tree provided by a random forest.
Wray does not appear to specifically disclose wherein the at least one regression model comprises at least one decision tree provided by a random forest.
However Vincent teaches in at least Paragraphs 0089 & 0106 wherein a random forest classifier may be used as a regression model, with a decision tree being utilized as an algorithm as taught in at least Paragraph 0112 [i.e. the at least one regression model comprises at least one decision tree provided by a random forest].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Wray by incorporating the use of a decision tree provided by a random forest as taught by Vincent.
The motivation to do so is that, as acknowledged by Vincent in at least Paragraphs 0108 & 0111, a machine learning model may determine a relationship between inputs and outputs, improving the learning of an energy consumption forecast.
Claim(s) 24 & 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wray (US 2022/0205796 A1) as applied to claims 23 & 27 above, and further in view of Wang (CN 115817282 A).
Regarding Claim 24:
The computer-implemented method as claimed in claim 23, the method further comprising the following actions: f) adjusting an energy management configuration of the electric vehicle for preheating a battery of the electric vehicle based on a reference energy consumption forecast value.
Wray does not appear to specifically disclose wherein a battery of the vehicle is preheated based on a reference energy consumption forecast value.
However Wang teaches in at least Paragraphs 0038 & 0062 wherein a rechargeable battery of an electric vehicle may be preheated prior to an arrival at a charging station based on a predicted remaining available power when the vehicle arrives at the charging destination [i.e. adjusting an energy management configuration of the electric vehicle for preheating a battery of the electric vehicle based on a reference energy consumption forecast value].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Wray by incorporating the preheating of the vehicle battery based on a predicted amount of energy as taught by Wang.
The motivation to do so is that, as acknowledged by Wang in at least Paragraphs 0004 & 0038, the charging time of the battery at the destination may be saved by preheating the battery if energy is available to do so.
Regarding Claim 28:
The system as claimed in claim 27, wherein the electric vehicle further comprises: -an energy management device; and -a heater for heating a battery of the electric vehicle, wherein the energy management device is designed to control the heater depending on the reference energy consumption forecast value.
Wray does not appear to specifically disclose wherein a battery of the vehicle is preheated based on a reference energy consumption forecast value.
However Wang teaches in at least Paragraphs 0038 & 0062 wherein a rechargeable battery of an electric vehicle may be preheated prior to an arrival at a charging station based on a predicted remaining available power when the vehicle arrives at the charging destination, through the use of heating sources [i.e. an energy management device and heater which is controlled for preheating a battery of the electric vehicle based on a reference energy consumption forecast value].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Wray by incorporating the preheating of the vehicle battery based on a predicted amount of energy as taught by Wang.
The motivation to do so is that, as acknowledged by Wang in at least Paragraphs 0004 & 0038, the charging time of the battery at the destination may be saved by preheating the battery if energy is available to do so.
Conclusion
The following prior art made of record but not relied upon is considered pertinent to the Applicant’s disclosure:
Calins (US 2012/0232783 A1): Callkins recites a system for determining energy efficient routes, including the prediction of energy consumption data while traversing a route. The prediction may be based on physical attributes of a route, with a comparison being made to previously traversed similar attributed roads.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER RYAN CARDIMINO whose telephone number is (571)272-2759. The examiner can normally be reached M-Th 8:30-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ramya Burgess can be reached at (571)272-6011. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHRISTOPHER R CARDIMINO/Examiner, Art Unit 3661
/RAMYA P BURGESS/Supervisory Patent Examiner, Art Unit 3661
