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 06/22/2023 was filed before the mailing of the first office action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 13 and 18 are objected to because of the following informalities:
Claim 13 recites “The computer program product of claim 9, wherein the trained artificial intelligence model uses a plurality of inputs to output the expected battery consumption” and depended upon Claim 9 introduces “a first trained artificial intelligence model” and “a second trained artificial intelligence model.” Although it is understood through the context of “output[ting] the expected battery consumption,” that “the trained artificial intelligence model” is referencing the “first trained artificial intelligence model,” the deviation from the explicit form of analogous limitation in Claim 5 creates a difficulty in readability such that the scope of the claim could be misconstrued to an inattentive reader.
Claim 18 recites “The computer system of claim 16, wherein the trained artificial intelligence model uses a plurality of inputs to output the expected battery consumption,” and depended upon Claim 16 introduces “a first trained artificial intelligence model” and “a second trained artificial intelligence model.” Although it is understood through the context of “output[ting] the expected battery consumption,” that “the trained artificial intelligence model” is referencing the “first trained artificial intelligence model,” the deviation from the explicit form of analogous limitation in Claim 5 creates a difficulty in readability such that the scope of the claim could be misconstrued to an inattentive reader.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-20 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “performing, by the one or more processors, one or multiple simulations of the electric vehicle completing the parcel delivery instructions along the delivery route by inputting outputs of the first trained artificial intelligence model into a second trained artificial intelligence model; and configuring, by the one or more processors, a size of a battery pack to be used with the electric vehicle at a start of the delivery route at the first location, and a battery pack service schedule for servicing the battery pack along the delivery route, as a function of the second artificial intelligence model performing the multiple simulations” (emphasis added) at the end of the claim. This limitation recites performing “one or multiple” simulations by a second AI model, and then references the second AI model performing “the multiple” simulations. Examiner notes that dependent Claim 8 recites a similar reference to “the multiple simulations.” It is unclear as to whether the second recitation (1) is intended to limit the performing of the simulations to “multiple” simulations, i.e. excluding “one” simulation embodiments, or (2) is a typographical error, intended to reference “the one or multiple simulations.” Therefore, Claim 1 is rejected as indefinite under 35 U.S.C. 112(b). Solely for purposes of compact prosecution, the scope of Claim 1 is limited to the performance of “multiple simulations,” i.e. excluding the embodiment of performing “one” simulation, in accordance with the explicit language of the claim and context from the dependent claim.
Claim 2-20 are rejected as indefinite under similar justification as Claim 1, either via dependency or recitation of similar limitations.
Claim 6 recites “The computer-implemented method of claim 1, wherein the road profile conditions include a curvature of a road, elevation changes of the road along a potential route, and an intensity of the elevation changes.” Although “a road profile” is introduced in Claim 5, this term is not introduced in depended upon Claim 1. Thus, “the road profile” lacks an antecedent basis. Therefore, Claim 6 is rejected under 35 U.S.C. 112(b) as indefinite. Solely for purposes of compact prosecution, Claim 6 will be interpreted as if depending on Claim 5 herein.
Claim 14 recites “The computer program product of claim 9, wherein the road profile conditions include a curvature of a road, elevation changes of the road along a potential route, and an intensity of the elevation changes.” Although “a road profile” is introduced in Claim 13, this term is not introduced in depended upon Claim 9. Thus, “the road profile” lacks an antecedent basis. Therefore, Claim 14 is rejected under 35 U.S.C. 112(b) as indefinite. Solely for purposes of compact prosecution, Claim 14 will be interpreted as if depending on Claim 13 herein.
Claim 19 recites “The computer program product of claim 9, wherein the road profile conditions include a curvature of a road, elevation changes of the road along a potential route, and an intensity of the elevation changes.” Although “a road profile” is introduced in Claim 18, this term is not introduced in depended upon Claim 16. Thus, “the road profile” lacks an antecedent basis. Therefore, Claim 19 is rejected under 35 U.S.C. 112(b) as indefinite. Solely for purposes of compact prosecution, Claim 19 will be interpreted as if depending on Claim 18 herein.
Claim Interpretation
Applicant has clearly set forth a special definition in the specification.
Specification Paragraph 18 states “A computer program product embodiment ("CPP embodiment" or "CPP") is a term used in the present disclosure to describe any set of one, or more, storage media (also called "mediums") collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions and/or data for performing computer operations specified in a given CPP claim. A "storage device" is any tangible device that can retain and store instructions for use by a computer processor. Without limitation, the computer readable storage medium may be an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, a mechanical storage medium, or any suitable combination of the foregoing. Some known types of storage devices that include these mediums include: diskette, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), static random access memory (SRAM), compact disc read only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanically encoded device (such as punch cards or pits / lands formed in a major surface of a disc) or any suitable combination of the foregoing. A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media. As will be understood by those of skill in the art, data is typically moved at some occasional points in time during normal operations of a storage device, such as during access, de-fragmentation or garbage collection, but this does not render the storage device as transitory because the data is not transitory while it is stored.” (Emphasis added).
Claim 9 recites “A computer program product for configuring battery packs that minimize a carbon footprint of a parcel, the computer program product comprising a computer readable hardware storage medium having program instructions embodied therewith, the program instructions readable by one or more processors of a computer system to cause the one or more processors to: . . .” (Emphasis added). Thus, the “computer readable hardware storage medium” of Claim 9 excludes transitory (i.e. signal per se) memory.
The interpretation of Claim 9 is extended to dependent Claims 10-15, as well as to similar limitations of Claims 16-20.
Claim 1 recites “receiving, by one or more processors of a computer system, parcel delivery instructions including parcel level information, at least one electric vehicle property, and a first location and one or more destinations” in the second paragraph and “[extracting] an expected battery consumption of the electric vehicle for each of the plurality of potential routes . . .” (emphasis added) in the third paragraph. Because a singular vehicle (i.e. “the” electric vehicle) is referenced in the third paragraph, the broadest reasonable interpretation of “at least one electric vehicle property” in the second paragraph is analogous to “at least one property of an electric vehicle.” (Emphasis added). Put plainly, the received “parcel delivery instructions” of the second paragraph does not includes information regarding a plurality of vehicles. Therefore, (1) “the electric vehicle” of the third paragraph has sufficient antecedent basis, and (2) the battery consumption analysis and identification of a delivery route in the third paragraph is based on the single vehicle associated with the received parcel delivery instructions.
Claim 1 recites “mapping, by the one or more processors, battery service options along the delivery route output by the first trained artificial intelligence model” at the fourth paragraph and “using, by the one or more processors, a first trained artificial intelligence model to extract, from a plurality of potential routes connecting the first location to the one or more destinations, an expected battery consumption of the electric vehicle for each of the plurality of potential routes, and to identify, based on the expected battery consumption, a delivery route to be used by the electric vehicle that has a lowest expected battery consumption” at the third paragraph. Because the fourth paragraph limits the “mapping [of] battery service options” to the mapping of battery service options “options along the delivery route output by the first trained artificial intelligence model,” the fourth paragraph excludes the mapping of battery service options from the use of the “first trained artificial intelligence model” in the third paragraph to identify the “delivery route” based on “expected battery consumption.” Put plainly, the third paragraph of Claim 1 identifies the delivery route from a plurality of routes based on battery consumption, without mapping battery service options along the routes, then the third paragraph of Claim 1 maps the battery service options to the identified (i.e. selected singular) delivery route.
Claim 1 recites “performing, by the one or more processors, one or multiple simulations of the electric vehicle completing the parcel delivery instructions along the delivery route by inputting outputs of the first trained artificial intelligence model into a second trained artificial intelligence model” in the fifth paragraph. Because the fifth paragraph explicitly states that the outputs of the “first trained artificial intelligence model” are input into the “second trained artificial intelligence model,” the broadest reasonable interpretation of Claim 1 limits the “first trained artificial intelligence model” and the “second trained artificial intelligence model” to being separate, mutually exclusive models.
Claims 1 recites “configuring, by the one or more processors, a size of a battery pack to be used with the electric vehicle at a start of the delivery route at the first location, and a battery pack service schedule for servicing the battery pack along the delivery route, as a function of the second artificial intelligence model performing the multiple simulations” in the last paragraph. As discussed above, the “first trained artificial intelligence model” that identifies the delivery route based on expected battery consumption and the “second trained artificial intelligence model” are separate, mutually exclusive models, such that the outputs of the “first trained artificial intelligence model” are input into the “second trained artificial intelligence model.” Thus, because the size of battery pack and schedule are configured “as a function of the second artificial intelligence model performing the multiple simulations,” the broadest reasonable interpretation of “using, by the one or more processors, a first trained artificial intelligence model . . .” in the third paragraph of Claim 1 is limited to excluding the configured “size of a battery pack” and “battery pack service schedule.” Put plainly, the “first trained artificial intelligence model” cannot use or determine the size of the battery pack or battery service schedule used by the electric vehicle along the route because that occurs with the subsequent use of the “second trained artificial intelligence model.”
The claim interpretation of Claim 1 discussed above is extended to Claims 2-20, via dependency or analogous recited limitations.
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.
Step 1
Claims 1-8 recite a method (i.e. a process), Claims 9-15 recite a product (i.e. a machine or manufacture), and Claims 16-20 recite a system (i.e. a machine or manufacture). Therefore, Claims 1-20 all fall within the one of the four statutory categories of invention of 35 U.S.C. 101.
Step 2A, Prong One
Independent Claim 1 recites the abstract idea of:
“. . . receiving, . . . , parcel delivery instructions including parcel level information, at least one electric vehicle property, and a first location and one or more destinations;
using, . . . , a first . . . model
to extract, from a plurality of potential routes connecting the first location to the one or more destinations, an expected battery consumption of the electric vehicle for each of the plurality of potential routes, and
to identify, based on the expected battery consumption, a delivery route to be used by the electric vehicle that has a lowest expected battery consumption;
mapping, . . . , battery service options along the delivery route output by the first . . . model;
performing, . . . , one or multiple simulations of the electric vehicle completing the parcel delivery instructions along the delivery route by inputting outputs of the first . . . model into a second . . . model; and
configuring, . . . , a size of a battery pack to be used with the electric vehicle at a start of the delivery route at the first location, and a battery pack service schedule for servicing the battery pack along the delivery route, as a function of the second . . . model performing the multiple simulations.”
The limitations stated above are processes/ functions that under broadest reasonable interpretation covers (1) receiving delivery instructions including a property of the vehicle, a first location, and destinations, (2a) using a first model to extract (i.e. calculate) expected battery consumption for a plurality of potential delivery routes, (2b) using a first model to identify a delivery route based on the lowest expected battery consumption, (3) mapping battery service option along the identified delivery route, (4) simulating completion of the delivery instructions along the identified delivery route by inputting the outputs of the first model into the second model (Examiner notes that the broadest reasonable interpretation of a “simulation” includes solving a representative mathematical equation), and (5) configuring a size of a battery pack and service schedule along a delivery route as a function of the second model performing simulations (Examiner notes that the configuration of the size of the battery pack does not include the physical installation of batteries, but is analogous to the mere determination of the size of the battery pack.), all of which are:
mathematical calculations (i.e. calculating expected battery consumption for a plurality of routes, simulating completion of delivery route, and configuring battery size and service schedule as a function of simulation) and mathematical relationship (i.e. the first and second models, inputting the output of the first model into the second model, and selecting the delivery route based on the lowest calculated battery consumption), which are methods of mathematical concepts, an abstract idea, under MPEP 2106.04(a)(2)I;
commercial or legal interactions (i.e. optimizing battery size and delivery route and schedule based on delivery instructions is at least “sales activities and behaviors”), and
managing personal behavior by following rules and interacting between people (i.e. parcel delivery instructions, a delivery route, selecting lowest battery consumption route, are at least “following rules or instructions.”), which are certain methods of organizing human activity, an abstract idea, under MPEP 2106.04(a)(2)II; and
observation (i.e. receiving parcel delivery instructions, extracting expected battery consumption, identifying the lowest battery consumption route), evaluation (i.e. determining battery consumption for each of the plurality of routes to select the lowest route, mapping service options along the route, and simulating delivery along the route), judgment (i.e. selecting the route with the lowest battery consumption), opinion (i.e. configuring the battery size and service schedule based on predicted information), which are mental processes, an abstract idea, under MPEP 2106.04(a)(2)III.
The mere the recitation of generic computer components (i.e., the “one or more processors of a computer system” and “trained artificial intelligence model[s]”) implementing the identified abstract idea does not take the claim out of the mathematical concepts, certain methods of organizing human activity, and mental processes groupings. MPEP 2106.04(d). The claim affirmatively recites, under its broadest reasonable interpretation, “mathematical calculations,” “mathematical relationships,” “commercial or legal interactions,” “managing personal behavior or relationships or interactions between people,” “observation,” “evaluation,” “judgment,” and “opinion,” despite the recitation of additional elements, and therefore recites mathematical concepts, certain methods of organizing human activity, and mental processes groupings of abstract ideas under MPEP 2106.04. Thus, Claim 1 recites an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claim 1 as a whole amounts to: (i) merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent) and (ii) generally links the use of a judicial exception to a particular technological environment or field of use. The claim recites the additional elements of:
(i) “one or more processors of a computer system” and
(ii) “trained artificial intelligence model[s].”
The additional elements of (i) one or more processors of a computer system (Paragraph 21 states “PROCESSOR SET 110 includes one, or more, computer processors of any type now known or to be developed in the future.”) and (ii) trained artificial intelligence models (Fig. 1 and Paragraph 19 shows “computer 101” includes “code 200.” Paragraph 69 states “A training module of code 200 trains the first AI model using supervised machine learning or unsupervised machine learning.” Paragraph 71 states “The second AI model may be trained by supervised machine learning or unsupervised machine learning, implanted via a training module of code 200.”), are recited at a high-level of generality, such that, when viewed as whole/ordered combination (Fig. 1 and Paragraph 19, 21, 69, and 71 shows elements together.), they amount to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)). Additionally, when viewed with the abstract idea in the claim as a whole, the additional elements do not provide a patent eligible improvement to technology per MPEP 2106.05(a).
The (i) one or more processors of a computer system and (ii) trained artificial intelligence models, when viewed as whole/ordered combination (Fig. 1 and 4-6. ¶45 shows that the user interfaces may display information, include a searching element, and receive information.), does no more than generally link the use of the judicial exception to a particular technological environment or field of use (i.e. computer environment) (See MPEP 2106.05(h)).
Accordingly, these additional elements, when viewed as a whole/ordered combination (Fig. 1 and Paragraph 19, 21, 69, and 71 shows elements together.), do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: (i) “apply it” (or an equivalent) and (ii) generally link the use of a judicial exception to a particular technological environment or field of use, and are not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., (i) merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)) and (ii) generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements of the (i) one or more processors of a computer system and (ii) trained artificial intelligence models, do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination (Fig. 1 and Paragraph 19, 21, 69, and 71 shows elements together.), nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claim is ineligible.
Dependent Claims 2-8 recite the abstract idea of:
“. . . converting, . . . , an energy consumed by the electric vehicle along the delivery route to carbon emissions; allocating, . . . , the carbon emissions to each parcel based on a weight of the parcel and distance the electric vehicle travels with the parcel; calculating, . . . , a carbon footprint per unit of parcel; and displaying, . . . , a value corresponding to the carbon footprint per unit of parcel . . .” (Claim 2).
“. . . wherein the at least one electric vehicle property includes a total weight of the electric vehicle, an initial weight of a battery pack, and an initial capacity of the battery pack.” (Claim 3).
“. . . wherein the parcel level information includes a total number of parcels being transported by the electric vehicle and a weight of each parcel.” (Claim 4).
“. . . wherein the first . . . model uses a plurality of inputs to output the expected battery consumption, the plurality of inputs including a total weight of the parcels, a road profile of the plurality of potential routes, a vehicle speed, weather conditions, and traffic conditions.” (Claim 5).
“. . . wherein the road profile conditions include a curvature of a road, elevation changes of the road along a potential route, and an intensity of the elevation changes.” (Claim 6).
“. . . wherein the battery service options include wireless power transmission services, battery swapping services, and wired charging stations.” (Claim 7).
“. . . wherein the multiple simulations include simulating a recharging time associated with using one or more battery services along the delivery route, and simulating whether a battery swap is possible without causing a time delay.” (Claim 8).
Dependent Claims 2-8 have been given the full two-prong analysis including analyzing the further elements and limitations, both individually and in combination. When analyzed individually and in combination, these claims are also held to be patent ineligible under 35 U.S.C. 101. The further limitation of Claims 2-8 fail to establish claims that are not directed to an abstract idea because the further limitations (1) convert energy consumption value to carbon emissions, allocate carbon emission to each parcel, and calculate and display carbon footprint per unit of parcel (Claim 2), (2) limit the vehicle properties to certain types of information (Claim 3), (3) limit the parcel information to certain types of information (Claim 4), (4) limit the inputs of the model to certain types of information (Claim 5), (5) limit the road profile to certain types of information (Claim 6), (6) limit the battery service options to certain types (Claim 7), and (7) simulating recharging time and whether a battery swap is possible (Claim 8). The further elements of Claims 2-8 (i.e. “one or more processors” in Claim 2, “display” in Claim 2, and “trained artificial intelligence model” in Claim 5) fails to establish claims that are not directed to an abstract idea because the elements merely recite additional generic computer components similar to the generic computer components of Claim 1 and generally link the abstract idea to a particular technology or field of use (i.e. computer environment) just as in Claim 1. The organization of the further limitations of Claims 2-8 fail to integrate an abstract idea into a practical application just as discussed above for Claim 1. Additionally, performing the abstract idea of Claim 1 as recited in each of the further limitations of Claims 2-8, individually or in combination, does not (1) impose any meaningful limits on practicing the abstract ideas, or (2) provide improvements to the functioning of computing systems or to another technology or technical field, just as discussed above regarding Claim 1. Therefore, Claims 2-8 amount to mere instructions to implement the abstract idea (1) using generic computer components—using the computer, in its ordinary capacity, as a tool to perform the abstract idea, and (2) generally linked to a particular technology or field of use. Because the claims merely use a computer, in its ordinary capacity in a particular field of use, as a tool to perform the abstract idea cannot provide an inventive concept, the elements and limitations of Claims 2-8 fail to establish that the claims provide an inventive concept, just as in Claim 1. Therefore, Claims 2-8 fails the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
Step 2A, Prong One
Independent Claim 9 recites the abstract idea of:
“ . . . receive parcel delivery instructions including parcel level information, at least one electric vehicle property, and a first location and one or more destinations;
use a first . . . model to extract, from a plurality of potential routes connecting the first location to the one or more destinations, an expected battery consumption of the electric vehicle for each of the plurality of potential routes, and to identify, based on the expected battery consumption, a delivery route to be used by the electric vehicle that has a lowest expected battery consumption;
map battery service options along the delivery route output by the first . . . model;
perform one or multiple simulations of the electric vehicle completing the parcel delivery instructions along the delivery route by inputting outputs of the first . . . model into a second . . . model; and
configure a size of a battery pack to be used with the electric vehicle at a start of the delivery route at the first location, and a battery pack service schedule for servicing the battery pack along the delivery route, as a function of the second . . . model performing the multiple simulations.”
The limitations stated above are processes/ functions that under broadest reasonable interpretation covers (1) receiving delivery instructions including a property of the vehicle, a first location, and destinations, (2a) using a first model to extract (i.e. calculate) expected battery consumption for a plurality of potential delivery routes, (2b) using a first model to identify a delivery route based on the lowest expected battery consumption, (3) mapping battery service option along the identified delivery route, (4) simulating completion of the delivery instructions along the identified delivery route by inputting the outputs of the first model into the second model (Examiner notes that the broadest reasonable interpretation of a “simulation” includes solving a representative mathematical equation), and (5) configuring a size of a battery pack and service schedule along a delivery route as a function of the second model performing simulations (Examiner notes that the configuration of the size of the battery pack does not include the physical installation of batteries, but is analogous to the mere determination of the size of the battery pack.), all of which are:
mathematical calculations (i.e. calculating expected battery consumption for a plurality of routes, simulating completion of delivery route, and configuring battery size and service schedule as a function of simulation) and mathematical relationship (i.e. the first and second models, inputting the output of the first model into the second model, and selecting the delivery route based on the lowest calculated battery consumption), which are methods of mathematical concepts, an abstract idea, under MPEP 2106.04(a)(2)I;
commercial or legal interactions (i.e. optimizing battery size and delivery route and schedule based on delivery instructions is at least “sales activities and behaviors”), and
managing personal behavior by following rules and interacting between people (i.e. parcel delivery instructions, a delivery route, selecting lowest battery consumption route, are at least “following rules or instructions.”), which are certain methods of organizing human activity, an abstract idea, under MPEP 2106.04(a)(2)II; and
observation (i.e. receiving parcel delivery instructions, extracting expected battery consumption, identifying the lowest battery consumption route), evaluation (i.e. determining battery consumption for each of the plurality of routes to select the lowest route, mapping service options along the route, and simulating delivery along the route), judgment (i.e. selecting the route with the lowest battery consumption), opinion (i.e. configuring the battery size and service schedule based on predicted information), which are mental processes, an abstract idea, under MPEP 2106.04(a)(2)III.
The mere the recitation of generic computer components (i.e., “A computer program product for configuring battery packs that minimize a carbon footprint of a parcel, the computer program product comprising a computer readable hardware storage medium having program instructions embodied therewith, the program instructions readable by one or more processors of a computer system to cause the one or more processors to [perform operations]” and “trained artificial intelligence model[s]”) implementing the identified abstract idea does not take the claim out of the mathematical concepts, certain methods of organizing human activity, and mental processes groupings. MPEP 2106.04(d). The claim affirmatively recites, under its broadest reasonable interpretation, “mathematical calculations,” “mathematical relationships,” “commercial or legal interactions,” “managing personal behavior or relationships or interactions between people,” “observation,” “evaluation,” “judgment,” and “opinion,” despite the recitation of additional elements, and therefore recites mathematical concepts, certain methods of organizing human activity, and mental processes groupings of abstract ideas under MPEP 2106.04. Thus, Claim 9 recites an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claim 9 as a whole amounts to: (i) merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent) and (ii) generally links the use of a judicial exception to a particular technological environment or field of use. The claim recites the additional elements of:
(i) computer program product comprising
(ii) a computer readable hardware storage medium having program instructions embodied therewith, the program instructions readable by one or more processors of a computer system to cause the one or more processors to perform operations, and
(iii) trained artificial intelligence models
The additional elements of (i) computer program product comprising (ii) computer readable hardware (Paragraph 18 states “A computer program product embodiment ("CPP embodiment" or "CPP") is a term used in the present disclosure to describe any set of one, or more, storage media (also called "mediums") collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions and/or data for performing computer operations specified in a given CPP claim.” Fig. 1 and Paragraph 22 states “[0022] Computer readable program instructions are typically loaded onto computer 101 to cause a series of operational steps to be performed by processor set 110 of computer 101 and thereby effect a computer-implemented method, . . . These computer readable program instructions are stored in various types of computer readable storage media, such as cache 121 and the other storage media discussed below. . . In computing environment 100, at least some of the instructions for performing the inventive methods may be stored in block 200 in persistent storage 113.”) and (iii) trained artificial intelligence models (Fig. 1 and Paragraph 19 shows “computer 101” includes “code 200.” Paragraph 69 states “A training module of code 200 trains the first AI model using supervised machine learning or unsupervised machine learning.” Paragraph 71 states “The second AI model may be trained by supervised machine learning or unsupervised machine learning, implanted via a training module of code 200.”), are recited at a high-level of generality, such that, when viewed as whole/ordered combination (Fig. 1 and Paragraphs 18-22, 69, and 71 shows elements together.), they amount to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)). Additionally, when viewed with the abstract idea in the claim as a whole, the additional elements do not provide a patent eligible improvement to technology per MPEP 2106.05(a).
The (i) computer program product, (ii) computer readable hardware, and (iii) trained artificial intelligence models, when viewed as whole/ordered combination (Fig. 1 and Paragraphs 18-22, 69, and 71 shows elements together.), does no more than generally link the use of the judicial exception to a particular technological environment or field of use (i.e. computer environment) (See MPEP 2106.05(h)).
Accordingly, these additional elements, when viewed as a whole/ordered combination (Fig. 1 and Paragraphs 18-22, 69, and 71 shows elements together.), do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: (i) “apply it” (or an equivalent) and (ii) generally link the use of a judicial exception to a particular technological environment or field of use, and are not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., (i) merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)) and (ii) generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements of the (i) computer program product, (ii) computer readable hardware, and (iii) trained artificial intelligence models, do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination (Fig. 1 and Paragraphs 18-22, 69, and 71 shows elements together.), nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claim is ineligible.
Dependent Claims 10-15 recite the abstract idea of:
“. . . convert an energy consumed by the electric vehicle along the delivery route to carbon emissions; allocate the carbon emissions to each parcel based on a weight of the parcel and distance the electric vehicle travels with the parcel; calculate a carbon footprint per unit of parcel; and display a value corresponding to the carbon footprint per unit of parcel . . .” (Claim 10).
“. . . wherein the at least one electric vehicle property includes a total weight of the electric vehicle, an initial weight of a battery pack, and an initial capacity of the battery pack.” (Claim 11).
“. . . wherein the parcel level information includes a total number of parcels being transported by the electric vehicle and a weight of each parcel.” (Claim 12).
“. . . wherein the . . . model uses a plurality of inputs to output the expected battery consumption, the plurality of inputs including a total weight of the parcels, a road profile of the plurality of potential routes, a vehicle speed, weather conditions, and traffic conditions.” (Claim 13).
“. . . wherein the road profile conditions include a curvature of a road, elevation changes of the road along a potential route, and an intensity of the elevation changes.” (Claim 14).
“. . . wherein the multiple simulations include simulating a recharging time associated with using one or more battery services along the delivery route, and simulating whether a battery swap is possible without causing a time delay.” (Claim 15).
Dependent Claims 10-15 have been given the full two-prong analysis including analyzing the further elements and limitations, both individually and in combination. When analyzed individually and in combination, these claims are also held to be patent ineligible under 35 U.S.C. 101. The further limitation of Claims 10-15 fail to establish claims that are not directed to an abstract idea because the further limitations (1) convert energy consumption value to carbon emissions, allocate carbon emission to each parcel, and calculate and display carbon footprint per unit of parcel (Claim 10), (2) limit the vehicle properties to certain types of information (Claim 11), (3) limit the parcel information to certain types of information (Claim 12), (4) limit the inputs of the model to certain types of information (Claim 13), (5) limit the road profile to certain types of information (Claim 14), and (6) simulating recharging time and whether a battery swap is possible (Claim 15). The further elements of Claims 10-15 (i.e. the “one or more processors” and “display” in Claim 10, and “trained artificial intelligence model” in Claim 13) fails to establish claims that are not directed to an abstract idea because the elements merely recite additional generic computer components similar to the generic computer components of Claim 9 and generally link the abstract idea to a particular technology or field of use (i.e. computer environment) just as in Claim 9. The organization of the further limitations of Claims 10-15 fail to integrate an abstract idea into a practical application just as discussed above for Claim 9. Additionally, performing the abstract idea of Claim 9 as recited in each of the further limitations of Claims 10-15, individually or in combination, does not (1) impose any meaningful limits on practicing the abstract ideas, or (2) provide improvements to the functioning of computing systems or to another technology or technical field, just as discussed above regarding Claim 9. Therefore, Claims 10-15 amount to mere instructions to implement the abstract idea (1) using generic computer components—using the computer, in its ordinary capacity, as a tool to perform the abstract idea, and (2) generally linked to a particular technology or field of use. Because the claims merely use a computer, in its ordinary capacity in a particular field of use, as a tool to perform the abstract idea cannot provide an inventive concept, the elements and limitations of Claims 10-15 fail to establish that the claims provide an inventive concept, just as in Claim 9. Therefore, Claims 10-15 fails the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
Step 2A, Prong One
Independent Claim 16 recites the abstract idea of:
“. . . receiving, . . . , parcel delivery instructions including parcel level information, at least one electric vehicle property, and a first location and one or more destinations;
using, . . . , a first . . . model
to extract, from a plurality of potential routes connecting the first location to the one or more destinations, an expected battery consumption of the electric vehicle for each of the plurality of potential routes, and
to identify, based on the expected battery consumption, a delivery route to be used by the electric vehicle that has a lowest expected battery consumption;
mapping, . . . , battery service options along the delivery route output by the first . . . model;
performing, . . . , one or multiple simulations of the electric vehicle completing the parcel delivery instructions along the delivery route by inputting outputs of the first . . . model into a second . . . model; and
configuring, . . . , a size of a battery pack to be used with the electric vehicle at a start of the delivery route at the first location, and a battery pack service schedule for servicing the battery pack along the delivery route, as a function of the second . . . model performing the multiple simulations.”
The limitations stated above are processes/ functions that under broadest reasonable interpretation covers (1) receiving delivery instructions including a property of the vehicle, a first location, and destinations, (2a) using a first model to extract (i.e. calculate) expected battery consumption for a plurality of potential delivery routes, (2b) using a first model to identify a delivery route based on the lowest expected battery consumption, (3) mapping battery service option along the identified delivery route, (4) simulating completion of the delivery instructions along the identified delivery route by inputting the outputs of the first model into the second model (Examiner notes that the broadest reasonable interpretation of a “simulation” includes solving a representative mathematical equation), and (5) configuring a size of a battery pack and service schedule along a delivery route as a function of the second model performing simulations (Examiner notes that the configuration of the size of the battery pack does not include the physical installation of batteries, but is analogous to the mere determination of the size of the battery pack.), all of which are:
mathematical calculations (i.e. calculating expected battery consumption for a plurality of routes, simulating completion of delivery route, and configuring battery size and service schedule as a function of simulation) and mathematical relationship (i.e. the first and second models, inputting the output of the first model into the second model, and selecting the delivery route based on the lowest calculated battery consumption), which are methods of mathematical concepts, an abstract idea, under MPEP 2106.04(a)(2)I;
commercial or legal interactions (i.e. optimizing battery size and delivery route and schedule based on delivery instructions is at least “sales activities and behaviors”), and
managing personal behavior by following rules and interacting between people (i.e. parcel delivery instructions, a delivery route, selecting lowest battery consumption route, are at least “following rules or instructions.”), which are certain methods of organizing human activity, an abstract idea, under MPEP 2106.04(a)(2)II; and
observation (i.e. receiving parcel delivery instructions, extracting expected battery consumption, identifying the lowest battery consumption route), evaluation (i.e. determining battery consumption for each of the plurality of routes to select the lowest route, mapping service options along the route, and simulating delivery along the route), judgment (i.e. selecting the route with the lowest battery consumption), opinion (i.e. configuring the battery size and service schedule based on predicted information), which are mental processes, an abstract idea, under MPEP 2106.04(a)(2)III.
The mere the recitation of generic computer components (i.e., the “computer system,” “one or more processors,” “one or more computer readable storage media,” “computer readable code,” and “trained artificial intelligence model[s]”) implementing the identified abstract idea does not take the claim out of the mathematical concepts, certain methods of organizing human activity, and mental processes groupings. MPEP 2106.04(d). The claim affirmatively recites, under its broadest reasonable interpretation, “mathematical calculations,” “mathematical relationships,” “commercial or legal interactions,” “managing personal behavior or relationships or interactions between people,” “observation,” “evaluation,” “judgment,” and “opinion,” despite the recitation of additional elements, and therefore recites mathematical concepts, certain methods of organizing human activity, and mental processes groupings of abstract ideas under MPEP 2106.04. Thus, Claim 16 recites an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claim 16 as a whole amounts to: (i) merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent) and (ii) generally links the use of a judicial exception to a particular technological environment or field of use. The claim recites the additional elements of:
(i) “computer system,”
(ii) “one or more processors,”
(iii) “one or more computer readable storage media,”
(iv) “computer readable code,” and
(v) “trained artificial intelligence model[s].”
The additional elements of (i) computer system (Fig. 1 and Paragraph 20 states “COMPUTER 101 may take the form of a desktop computer, laptop computer, tablet computer, smart phone, smart watch or other wearable computer, mainframe computer, quantum computer or any other form of computer or mobile device now known or to be developed in the future . . .”), (ii) one or more processors (Paragraph 21 states “PROCESSOR SET 110 includes one, or more, computer processors of any type now known or to be developed in the future.”), (iii) one or more computer readable storage media (Fig. 1 and Paragraph 22 shows “cache 121” and “persistent storage 113” store the executable instructions.), (iv) computer readable code (Fig. 1 and Paragraph 19 shows “computer 101” includes “code 200.” See also Paragraphs 22 and 25 discussing “block 200.”), and (v) trained artificial intelligence models (Paragraph 69 states “A training module of code 200 trains the first AI model using supervised machine learning or unsupervised machine learning.” Paragraph 71 states “The second AI model may be trained by supervised machine learning or unsupervised machine learning, implanted via a training module of code 200.”), are recited at a high-level of generality, such that, when viewed as whole/ordered combination (Fig. 1 and Paragraph 19-22, 69, and 71 shows elements together.), they amount to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)). Additionally, when viewed with the abstract idea in the claim as a whole, the additional elements do not provide a patent eligible improvement to technology per MPEP 2106.05(a).
The (i) computer system, (ii) one or more processors, (iii) one or more computer readable storage media, (iv) computer readable code, and (v) trained artificial intelligence models, when viewed as whole/ordered combination (Fig. 1 and Paragraph 19-22, 69, and 71 shows elements together.), does no more than generally link the use of the judicial exception to a particular technological environment or field of use (i.e. computer environment) (See MPEP 2106.05(h)).
Accordingly, these additional elements, when viewed as a whole/ordered combination (Fig. 1 and Paragraph 19-22, 69, and 71 shows elements together.), do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: (i) “apply it” (or an equivalent) and (ii) generally link the use of a judicial exception to a particular technological environment or field of use, and are not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., (i) merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)) and (ii) generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements of the (i) one or more processors of a computer system and (ii) trained artificial intelligence models, do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination (Fig. 1 and Paragraph 19-22, 69, and 71 shows elements together.), nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claim is ineligible.
Dependent Claims 17-20 recite the abstract idea of:
“. . . converting, . . . , an energy consumed by the electric vehicle along the delivery route to carbon emissions; allocating, . . . , the carbon emissions to each parcel based on a weight of the parcel and distance the electric vehicle travels with the parcel; calculating, . . . , a carbon footprint per unit of parcel; and displaying, . . . , a value corresponding to the carbon footprint per unit of parcel . . .” (Claim 17).
“. . . wherein the . . . model uses a plurality of inputs to output the expected battery consumption, the plurality of inputs including a total weight of the parcels, a road profile of the plurality of potential routes, a vehicle speed, weather conditions, and traffic conditions.” (Claim 18).
“. . . wherein the road profile conditions include a curvature of a road, elevation changes of the road along a potential route, and an intensity of the elevation changes.” (Claim 19).
“. . . wherein the multiple simulations include simulating a recharging time associated with using one or more battery services along the delivery route, and simulating whether a battery swap is possible without causing a time delay.” (Claim 20).
Dependent Claims 17-20 have been given the full two-prong analysis including analyzing the further elements and limitations, both individually and in combination. When analyzed individually and in combination, these claims are also held to be patent ineligible under 35 U.S.C. 101. The further limitation of Claims 17-20 fail to establish claims that are not directed to an abstract idea because the further limitations (1) convert energy consumption value to carbon emissions, allocate carbon emission to each parcel, and calculate and display carbon footprint per unit of parcel (Claim 17), (2) limit the inputs of the model to certain types of information (Claim 18), (3) limit the road profile to certain types of information (Claim 19), and (4) simulating recharging time and whether a battery swap is possible (Claim 20). The further elements of Claims 17-20 (i.e. “one or more processors” in Claim 17, “display” in Claim 17, and “trained artificial intelligence model” in Claim 18) fails to establish claims that are not directed to an abstract idea because the elements merely recite additional generic computer components similar to the generic computer components of Claim 16 and generally link the abstract idea to a particular technology or field of use (i.e. computer environment) just as in Claim 16. The organization of the further limitations of Claims 17-20 fail to integrate an abstract idea into a practical application just as discussed above for Claim 16. Additionally, performing the abstract idea of Claim 16 as recited in each of the further limitations of Claims 17-20, individually or in combination, does not (1) impose any meaningful limits on practicing the abstract ideas, or (2) provide improvements to the functioning of computing systems or to another technology or technical field, just as discussed above regarding Claim 16. Therefore, Claims 17-20 amount to mere instructions to implement the abstract idea (1) using generic computer components—using the computer, in its ordinary capacity, as a tool to perform the abstract idea, and (2) generally linked to a particular technology or field of use. Because the claims merely use a computer, in its ordinary capacity in a particular field of use, as a tool to perform the abstract idea cannot provide an inventive concept, the elements and limitations of Claims 17-20 fail to establish that the claims provide an inventive concept, just as in Claim 16. Therefore, Claims 17-20 fails the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
Reasons for No Art Rejection
Claims 1-20 are not rejected over the prior art of record.
The Closest prior art of record is:
US-20200254901-A1 (“Leger”);
US-20210323439-A1 (“Sivertsson”);
US-20210232134-A1 (“Chantz”);
US-20200406780-A1 (“Hassounah”);
US-20220027838-A1 (“Kataoka”);
US-12560446-B2 (“Narayanan”);
US-20190277647-A1 (“Adetola”);
CN-115099543-B (“Li”);
WO-2022069795-A1 (“Ahtikari”);
US-8907629-B2 (“Kelty-629”);
US-8054038-B2 (“Kelty-038”);
US-20220107193-A1 (“Mehra”);
US-20220297546-A1 (“Bennett”);
US-20110251935-A1 (“German”);
EP-4140841-A1 (“Gesang”);
CN-114819305-B (“Yu”);
“Influence of Batteries Weight on Electric Automobile Performance” (“Berjoza” 05/26/2017, ENGINEERING FOR RURAL DEVELOPMENT https://www.tf.llu.lv/conference/proceedings2017/Papers/N316.pdf, DOI: 10.22616/ERDev2017.16.N316);
“What Fleets Need to Know About Electric-Truck Batteries” (“Park” 04/11/2022, HDT Truckinginfo, https://www.truckinginfo.com/10166691/what-fleets-need-to-know-about-electic-truck-batteries);
“World's first electrified road for charging vehicles opens in Sweden” (“Boffey” 04/12/2018, https:/www.theguardian.com/environment/2018/apr/12/words-first-electrified-road-for-cherging-vehicles-opens-in-sweden);
“Battery capacity and recharging needs for electric buses in city transit service” (“Gao” 01/27/2017, https://www.osti.gov/pages/biblio/1342660);
“FedEx deploys 30 Tata Ace EVs for Delhi operations” (“Express Mobility Desk” 01/10/2023, https://www.financialexpress.com/express-mobility/fedex-deploys-30-tata-ace-evsfor-delhi-operations/2942459/);
“Optimal Battery Sizing for Electric Truck Delivery” (“Baek” 02/06/2020, Thermal and Energy Management of Battery-Operated Systems, https://doi.org/10.3390/en13030709); and
“Integrating Machine Learning Into Vehicle Routing Problem: Methods and Applications” (“Shahbazian” 07/15/2024 – NOT PRIOR ART, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10583875).
The Following is an examiner’s statement of reasons for no art rejection:
Leger teaches an electric vehicle used to travel a delivery route. In building a truck load and delivery plan for a specific vehicle, a computer system collects relevant information including delivery points, delivery characteristics, and vehicle characteristics. Then, based on the delivery needs, the computer system calculates the shortest (or otherwise optimal) delivery route. Finally, the computer system optimizes the batteries based on the selected route. However, Leger teaches that the batteries are included in the delivery container, linking the delivery of a container with the elimination of the battery within that container. This teaches away from the broadest reasonable interpretation of the instant claims, which separates the (1) delivery route determination and (2) battery pack size and service schedule.
Sivertsson teaches estimating battery consumption of a vehicle traveling a route using a trained artificial intelligence model. However, the battery consumption estimation is based on the vehicle’s current conditions (i.e. not mere planning of the route) including the battery size (i.e. as efficiency of battery can be dependent on percentage of total capacity). Thus, the battery consumption model of Sivertsson teaches away from the instant claims.
Chantz teaches a computer system that plans a product being transported in a container. The system uses machine learning to predict energy usage and necessary size of energy source. However, Chantz integrates the energy source into the container. Integrating the battery with delivery container teaches away from the instant claims because the container, and adjoining battery size, will be unloaded at the delivery point, i.e. the determination of the delivery route would include determining battery pack size and service schedule.
Hassounah teaches an electric vehicle with modular battery packs that are individually replaceable. A computer system determines the minimum required electric capacity needed for a given, planned, or intended use of the EV. Hassounah further teaches that a machine learning system can determine the minimum capacity based on historical data or learned performance. Additionally, a mapping or route planning software may provide an input of route length and type to calculate the necessary battery capacity. Broadly, Hassounah primarily determines energy needs at the charging station that replaces the batteries, so the vehicle can make it to the next charging station along the route. Thus, Hassounah teaches away from using the calculating battery capacity to determine the route, as the route is provided as an input to determine the needed capacity.
Kataoka teaches an iterative computer algorithm used to allocate batteries to a specified delivery EV and determine a route from the delivery base to the deliver destinations. However, the algorithm combines the battery allocation, charging plan, and route selection in each iteration for each delivery destination, i.e. the process iterates for each delivery destination until there is not another (yet-to-be-planned) delivery destination in the request. Thus, Kataoka teaches away from the two separate models claimed, which separates (1) the route determination and (2) the charging and battery size determinations.
Narayanan teaches a computer system that plans a route of an electric vehicle that includes multiple deliveries and on-route energy replenishment. The computer system uses trained learning agents to generate a route map including waypoint locations (nodes) from a starting location. However, the route is generated to minimize trip cost of an entire fleet of vehicles, and the computer considers energy replenishment as part of the route development. Thus, Narayanan teaches away from the instant claims, which separates (1) the route determination and (2) the charging and battery size determinations.
Adetola teaches a computer system that receives route parameters that includes delivery needs for containers. The computer determines potential routes, predicts vehicle operations along each of the routes, and then determines a predicted energy consumption along each of the potential routes. However, the energy storage of the vehicle is integrated with the delivered container, e.g. the primary embodiment is for refrigerated containers. Thus, the battery needs of the vehicle and battery capacity of the vehicle changes as each delivery is made. Therefore, the energy consumption of the route and size of battery are determined coincidently with the route determination, which teaches away from the instant claims.
Li teaches a computer system that uses delivery vehicle parameters including state information, e.g. vehicle weight or current charge, to estimate battery consumption and update an initial path to include a charging station, utilizing probability factor representing the risk of running out of electricity. Because Li teaches that the path is updated based on the predicted battery consumption and location of charging stations, Li teaches away from using predicted battery consumption to determine route, then mapping charging stations to the selected route. Additionally, Examiner notes that Li is primarily directed to single take-out order delivery.
Ahtikari teaches a computer system that optimizes a charging schedule given a delivery plan and vehicle battery parameters. Ahtikari acknowledges that the given plan can be optimized by a preceding system. The optimized charging schedule includes times and locations for charging stations and delivery locations. However, Ahtikari teaches that the resulting schedule is optimized based on the locations of the destinations and charging stations, together. Additionally, Ahtikari optimizes a charging schedule given an optimized route and vehicle constraints that include battery capacity, which teaches away from the instant limitation of the second trained model configuring the battery size and the charging schedule.
Kelty-629 teaches optimizing the use and charging of an electric vehicle battery by, for example, adjusting driving modes. Although Kelty-629 teaches the modeling and prediction of EV battery performance, Kelty-629 does not teach delivery path selection, mapping charging stations along a route, and determining battery size and charging schedule.
Kelty-038 teaches inputing destination locations and vehicle parameters to output required battery power and charging schedule. However, Kelty-038 does not explicitly teach (1) a configurable battery pack size or (2) use of a trained model. Therefore, the charging schedule output by Kelty-038, would be incompatible with the limitations claimed.
Mehra teaches use of a trained model to predict a carbon footprint for a specified journey, and recommend journey modifications to reduce carbon emissions. However, Mehra does not teach using the calculated carbon footprint of potential journeys to select a journey that satisfies delivery requirements, and then determine battery size and charging schedule based on the selected journey.
Bennett teaches determining carbon footprint of a transport vehicle and providing different modes of transportation to reduce carbon emissions.
German teaches the physical configuration of replacing modular batteries in an electric vehicle. However, German does not teach the determination of battery size and charging schedule based on a delivery route optimized for energy usage.
Gesang teaches optimizing drive modes of a vehicle to minimize fuel consumption.
Yu teaches generating several driving routes, measuring the predicted carbon emissions based on vehicle fuel consumption parameters and road parameters input to a trained model, and selecting the route with the least carbon emissions. However, Yu teaches a travel route for a single destination, and does not explicitly teach a second model that determines battery size and charging schedule.
Berjoza teaches modeling vehicle performance based on vehicle weight and battery capacity. However, Berjoza does not explicitly teach configuring battery pack size on a per route basis, as claimed.
Park teaches the value in replaceable battery units in a delivery fleet.
Boffey teaches that electrified roads can charge electric vehicle while driving.
Gao teaches that transit electric busses can benefit from flexible battery configurations to better service multiple routes.
Express Mobility Desk teaches that switching to electric delivery vehicles can reduce carbon emissions.
Baek teaches performing multiple delivery simulations to determine the optimal battery size for an electric vehicle to perform a delivery route. However, Baek performs the simulations at a fleet-level, meaning the battery size is not determined based on a given delivery route, but in conjunction with the determination of the delivery route of that vehicle. Additionally, Baek assumes charging occurs at the distribution depot, and therefore teaches away from mapping charging along the route.
Shahbazian provides a survey of the machine learning techniques used to optimize vehicle delivery routes and reduce carbon emission, but does not explicitly teach the claimed configuration of trained models.
Generally, the closest prior art teaches portions of the claimed features, but (1) relates to batteries integrated with the delivery containers (Leger, Chantz, and Adetola), (2) do not utilize the two trained models as claimed (Sivertsson, Hassounah, Kataoka, Narayanan, Li, Ahtikari, Kelty-038, Yu, Baek, and Shahbazian), or (3) is incidental to the claim as a whole (Kelty-629, Mehra, Bennett, German, Gesang, Berjoza, Park, Boffey, Gao, and Express Mobility Desk).
With respect to independent Claims 1, 9, and 16, the closest prior art, taken individually and in an ordered combination, does not explicitly or implicitly disclose the specific ordered combination of elements of the claim as a whole. As discussed in the claim interpretation section, the claim is limited to specific functionalities, which, although may be taught by the prior art individually, would not be obvious to combine the prior art of record to teach the claim as a whole. Thus, independent Claims 1, 9, and 16 are novel and non-obvious over the prior art of record.
Dependent Claims 2-8, 10-15, and 17-20 depend on Claims 1, 9, and 16, and therefore are also not rejected by the prior art of record via dependency.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW PARKER GOODMAN whose telephone number is (571) 272-5698. The examiner can normally be reached on Monday-Thursday from 9:30 AM ET to 6:00 PM ET. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Zimmerman, can be reached at telephone number (571) 272-4602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW PARKER GOODMAN/Examiner, Art Unit 3628
/JEFF ZIMMERMAN/Supervisory Patent Examiner, Art Unit 3628
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