DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the amendment filed on 12/09/2025.
Claims 1, 6-8, 12, 16, 18, 22, 24-25, and 30 have been amended and are hereby entered.
Claims 1-31 are currently pending and have been examined.
This action is made FINAL.
Information Disclosure Statement
All references listed in the IDS dated 12/08/2025 have been considered.
Response to Applicant’s Arguments
Claim Interpretation
In the present Remarks, Applicant asserts that the claims have been amended to avoid the 112(f) interpretations set forth in the previous Office Action. The present amendments succeed in removing the necessity for such 112(f) interpretations in relation to some previously identified terms but fail to do so in relation to others. See updated Claim Interpretation section below for more information.
Objections
The previous objections to Claims 6, 16, and 24 are obviated by the present amendments thereto; therefore, these objections are withdrawn.
Claim Rejections – 35 USC § 112
The present amendments obviate the previous 112(a) rejection of Claim 24; therefore, this rejection is withdrawn. Examiner notes that the present amendments to Claim 24 also require the new 112(a) rejection thereto below regarding a separate issue.
The present amendments fail to address or correct the indefiniteness issues set forth in the previous 112(b) rejections of Claims 30-31; therefore, these rejections are maintained or modified in view of present amendments and maintained as appropriate. All other previous 112(b) rejections are obviated; therefore, such rejections are withdrawn.
Claim Rejections – 35 USC § 101
Applicant’s arguments regarding the 101 analysis have been considered and are unpersuasive.
Regarding Step 2A, Prong One, Applicant’s assertion that “amended claim 1 does not recite a mental process or method of organizing human activity” is untrue and unpersuasive for the same reasons explained to Applicant in the Interview of 12/08/2025. Specifically, while Examiner agrees that the argued discrete limitation of “a digital twin platform providing a digital representation of electric vehicles and/or charging stations in a virtual environment” constitutes a non-abstract additional element, this is not the sole content present in the claims, which continue to recite abstract ideas in other claimed elements such as those described in the 101 rejection of the previous Office Action. Even as presently amended (and, in some cases, moreso as presently amended), at least the claimed solving of an optimization problem and assignment of “blocks of work” recite abstract ideas. See updated 101 rejections below for more information. The mere presence of additional elements such as the argued limitation does not negate the recitation of abstract ideas. Indeed, if this were true, Steps 2A, Prong Two and 2B would be entirely superfluous, which is clearly not in keeping with the current state of patent law regarding subject matter eligibility.
Regarding Step 2A, Prong Two, Applicant asserts that the claims are integrated into a practical application by way of embodying an improvement to a technology. Particularly, Applicant argues that the claimed process of vehicle assignments “clearly provide an improved efficiency for electric vehicle fleets, representative of an improvement in the technological field of electric vehicle logistics,” specifically by way of doing so “based on a solution to the optimization problem to increase a key performance indicator of the fleet [sic] electric vehicles.” Examiner disagrees. Regarding such improvements to a technology, MPEP 2106.05(a) states the following: “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” This is precisely what is present here: an improvement to an abstract concept rather than a technology. Modeling, optimization, and assignment of tasks/re-charging in a fleet of service vehicles (or “vehicle logistics,” as articulated by Applicant) is an abstract business-related concern rather than a technological one. Merely calling this a “technological field” does not make it so. Additionally, limiting such vehicles to electric vehicles does not make this otherwise, nor does determining modeling inputs by way of the non-abstract claimed digital twin simulation, nor does the high-level computer-based implementation of such steps in Claim 1 (see, e.g., the seminal Alice case).
Further still, integration into a practical application under Step 2A, Prong Two (including by way of the argued improvement to a technology consideration) may only be achieved by way of any recited additional elements or the combination thereof (see, e.g., MPEP 2106.04(d), 2106.05), and as the modeling, optimization, and assignment-based functionalities recite abstract ideas rather than additional elements, these functionalities are part of the recited abstract idea which must be integrated into a practical application. Abstract ideas may not integrate themselves into a practical application. What scant additional elements are present in the claims are insufficient to show such integration into a practical application, either by way of the improvement to a technology consideration or any other consideration listed in MPEP 2106.04(d).
Regarding Step 2B, Applicant merely references the Step 2A, Prong One arguments already addressed and refuted above. Applicant presents no substantive argument unique to Step 2B standards.
Claim Rejections – 35 USC § 102/103
Applicant’s arguments regarding the 102 and 103 analyses have been considered and are unpersuasive.
Applicant respectively argues against the Usman and Ayman references disclosing particular features by way of either conclusory statements or assertions supported by selective quotes taken out of context, and reductive summaries of some limited elements of these references. Examiner disagrees with these arguments.
Regarding Usman, Applicant first argues that Usman solely relates to simulation, modeling, and scheduling of individual vehicles rather than fleet-wide implementations. This is untrue, as such fleet-based optimizations regarding a plurality of vehicles are discussed throughout the reference, including in passages quoted in previous citations thereto found in the previous Office Action. Indeed, the very title of the reference is “Optimal recharging framework and simulation for electric vehicle fleet” (Examiner’s emphasis). That particular discrete steps of this optimization are, in some instances, explained in relation to individual vehicles does not limit the disclosure thereof to individual vehicles, particularly in view of the explicitly stated intent that these techniques be applied to a fleet of vehicles. Particularly regarding the assignment of “blocks of work” to vehicles, Usman discloses vehicles with assigned routes, said routes being modified by assigned charging locations and timings, which reads upon this claim language. A reference need not use the same language to disclose the same concept.
Applicant’s second argument regarding Usman is that Usman does not disclose any “bus-to-block” assignments. This is irrelevant, as the claims do not require such “bus-to-block” assignments. Rather, the claims specify that “bus-to-block constraints” are one potential input parameter to be considered when solving an optimization problem, with this input parameter being part of a Markush group listing several other potential input parameters which might instead by considered. While Examiner agrees that Usman does not disclose consideration of “bus-to-block constraints” in the manner claimed, it does disclose consideration of other input parameters listed in this Markush group, and as such continues to read upon this claim language. Applicant may wish to review at least MPEP 2117 on the topic of Markush groups.
As Usman is not deficient in the ways argued by Applicant, Ayman need not cure such deficiencies. Regardless, Examiner notes that Applicant’s arguments regarding Ayman are untrue for essentially the same reasons discussed above regarding Usman, e.g., Ayman discusses fleet-based optimization throughout. Applicant’s further assertions regarding Ayman are either untrue or irrelevant. Particularly regarding the models of Ayman not constituting “digital-twin behavioral representation in a virtual environment,” Examiner notes that (a) Ayman is not cited against this limitation, thus this is irrelevant; (b) relatedly, as cited in both previous and present rejections, Ayman is cited as modifying Usman, which does disclose such digital twin-based simulation, and Applicant improperly considers Ayman in isolation rather than in this combination; and (c) as claimed, the models themselves are not digital twins, but merely utilize input data derived from digital twins, which is not the same thing. Particularly regarding the purported “different optimization goal” of Ayman, Examiner notes that “minimiz[ing] the energy use of the vehicles” reads upon the claimed “goal” of improving a KPI, simply in a narrower form than claimed, thus rendering Applicant’s assertion untrue, at least as presently drafted. Particularly regarding the assertion that “Ayman does not teach grouping trips into ‘blocks of work’ and assigning those blocks to each EV,” Examiner notes that this is not what is captured by the present claim language, which contains no such “grouping” of trips.
Claim Objections
Claim 1 is objected to because of the following informalities: In Claim 1, both instances of “fleet electric vehicles” should read “fleet of electric vehicles.” Appropriate correction is required.
Claim Interpretation
Claim 12 discloses the following limitation: “determining an optimized performance output of the electric vehicles and/or vehicle charging stations based at least in part on the behavior models and on input parameters including one or more of…” The optimized performance output of electric vehicle and/or charging station performances may be interpreted in multiple ways. The scope of the claims are interpreted as only including those interpretations for which proper support is found in the original disclosure, e.g., optimized performance of the scheduling or usage of electric vehicles and/or charging stations is included within the claim scope, while optimizing the mechanical performance of electric vehicles and/or charging stations is not. Further, while this “optimized performance output” would normally be considered subjective and thus indefinite in the same way as similar language as discussed in the 112(b) rejections of the previous Office Action (e.g., what might be considered an “optimized performance” of these elements may reasonably differ from person to person), this optimization is interpreted as increasing a key performance indicator of the transit fleet in view of claim language subsequent to the above-quoted limitation, which avoids such indefiniteness. Further references to this optimized performance output in claims depending upon Claim 12 are interpreted in the same manner.
While Claims 9 and 10 contain various terms which constitute generic placeholders/nonce terms (e.g., a replanning platform, a predictive module, an adaptive optimization module, a deep reinforcement learning-based cost prediction module), these terms are not modified by functional language as presently claimed. As such, these terms have not been given 112(f) interpretation. Rather, these terms are given their broadest reasonable interpretation in view of the original disclosure.
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a digital twin platform for generating…” of Claim 1;
“an assignment and strategy module for…” of Claim 1;
“a behavior model module for…” of Claim 2;
“an input module…configured to…” of Claim 5;
“a third-party input module…for…” of Claim 6; and
“…transmitted…to a data pull module” of Claim 25.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. These terms are interpreted in view of at least Paragraphs 0018, 0025, 0056, 0072, 0075, 0082, 0087, and 0132-0133; and Figs. 2-3, 5-6, and 12 as published.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections – 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 24-31 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 24 contains the following limitation: “updating the performance models with the behavior models.” This limitation constitutes new matter, and thus violates the written description requirement of 112(a). Applicant makes no attempt to provide citations of support for this presently amended language (the content and meaning of which significantly differs from its original form), and Examiner can find nothing in the original disclosure which would do so. Indeed, this limitation appears to violate the original disclosure in that performance models are described as an embodiment of behavior models (see, e.g., Paragraph 0059 as published). In accordance with this, the remainder of the original disclosure appears to treat these terms as largely interchangeable (e.g., use of the term “behavior/performance models” in various places in the original disclosure; language such as “generate behavior models to predict ZEV and charger performance” in Paragraph 0047 as published). Ignoring the specificity of the types of models asserted, the original disclosure additionally appears to be devoid of any disclosure of the updating of one model with another model. Instead, the original disclosure solely appears to disclose the updating of models with new data and/or constraints (see, e.g., Paragraphs 0025, 0028, 0056, 0059, 0108-0109, 0123, 0134). As such, this limitation fails to find proper written description support in the original disclosure. Claims 25-31 are rejected due to their dependence upon Claim 24.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7-8, 22, and 30-31 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 7 contains the following limitation: “wherein the assignment and strategy module is configured to solve the optimization problem based on initial and final state constraints for the electric vehicles and/or safety constraints for the electric vehicles.” Claim 1, upon which Claim 7 depends, contains the following limitation: “an assignment and strategy module solving an optimization problem based on the behavior models and on input parameters including one or more of state of charge for electric batteries for the electric vehicles, initial and final state constraints for the electric vehicles, safety constraints for the electric vehicles, charging constraints for chargers at a depot, and bus-to-block constraints.” This above-quoted limitation of Claim 7 is indefinite for multiple reasons, particularly in relation to the above-quoted limitation of Claim 1. Firstly, Claim 1 already specifies that the optimization problem is to be solved “based on” the behavior models and on one or more of the listed input parameters. The above-quoted limitation of Claim 7 specifies that the same optimization problem is to be solved “based on” a (largely) different set of parameters. It is unclear as drafted how this limitation of Claim 7 is to function in conjunction with the limitation of Claim 1, e.g., are the additionally listed constraints of Claim 7 to be used in addition to those of Claim 1 to solve the optimization problem, is the optimization problem to be solved once according to Claim 1 constraints and a second time based on Claim 7 constraints, are the Claim 7 constraints intended to overwrite those of Claim 1 (which would violate 112(d) standards), or in some other way. Secondly, it is unclear as drafted whether the “initial and final state constraints for the electric vehicles” listed in Claim 7 intended to be the same as/relate back to the “initial and final state constraints for the electric vehicles” listed in Claim 1. Thirdly and relatedly, it is unclear if these listed constraints of Claim 7 are intended to constitute “input parameters” such as those listed in Claim 1 (this is particularly important as Claim 1 lists “initial and final state constraints for the electric vehicles” as an input parameter and Claim 7 lists the same without specifying whether it is an input parameter). For the purposes of this examination, this limitation of Claim 7 will be interpreted as “wherein the assignment and strategy module is configured to solve the optimization problem additionally based on one or more of the following input variables: the initial and final state constraints for the electric vehicles and/or safety constraints for the electric vehicles.”
Claim 8 contains the following limitation: “wherein the assignment and strategy module is further configured to assign the blocks of work and/or solve the optimization problem based on a price of electricity, a maximum power supply, the availability of chargers, or any combination thereof.” Claim 1 contains the following limitations: “an assignment and strategy module solving an optimization problem based on the behavior models and on input parameters including one or more of state of charge for electric batteries for the electric vehicles, initial and final state constraints for the electric vehicles, safety constraints for the electric vehicles, charging constraints for chargers at a depot, and bus-to-block constraints” and “the assignment and strategy module configured to assign blocks of work to the electric vehicles based on a solution to the optimization problem to increase a key performance indicator of the fleet electric vehicles.” It is unclear how this limitation of Claim 8 is to function in conjunction with these limitations of Claim 1 in the same way as explained above in relation to Claim 7. For the purposes of this examination, this limitation of Claim 8 will be interpreted as “wherein the assignment and strategy module is further configured to assign the blocks of work and/or solve the optimization problem additionally based on a price of electricity, a maximum power supply, the availability of chargers, or any combination thereof.”
Claim 22 contains the following limitation: “further comprising generating a plan to navigate one or more of the electric vehicles through a charging depot based on improvement to the key performance indicator.” It is unclear as drafted whether this “improvement” to the key performance indicator is intended to relate back to/indicate the same thing as the “increase” to a key performance indicator claimed in Claim 12 (upon which Claim 22 depends). For the purposes of this examination, this language of Claim 22 is interpreted as relating back to the language of Claim 12.
Claim 30 contains the following limitation: “identifying features between landmarks to correlate real-time vehicle data with planned data.” It is unclear as drafted whether “landmarks,” “real-time vehicle data,” and “planned data” in this limitation are intended to relate back to “landmarks,” “real-time electric vehicle data”/“the real-time vehicle data” and “planned data”/“the planned data” respectively previously disclosed in this claim. It is further unclear as drafted whether “the real-time vehicle data” is intended to relate back to “real-time electric vehicle data” previously disclosed in this claim. For the purposes of this examination, this limitation will be interpreted as “identifying features between the landmarks and the planned landmarks to correlate the real-time data with the planned data.”
Claim 31 contains the following limitation: “further comprising using the features, time, and location data to update the models.” It is unclear as drafted whether “time, and location data” are intended to relate back to “time and location planned data” of Claim 30 (upon which Claim 31 depends). Additionally, it is unclear as drafted whether “the models” is intended to relate back to “performance models” of Claim 24, “behavior models” of Claim 24, or neither. For the purposes of this examination, this limitation will be interpreted as “further comprising using the features and the time and location planned data to update the performance models.
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 1-31 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1, the limitations of generating data for use in behavior models for the electric vehicles and/or the charging stations for the electric vehicles; solving an optimization problem based on the behavior models and on input parameters including one or more of state of charge for electric batteries for the electric vehicles, initial and final state constraints for the electric vehicles, safety constraints for the electric vehicles, charging constraints for chargers at a depot, and bus-to-block constraints; assign blocks of work to the electric vehicles based on a solution to the optimization problem to increase a key performance indicator of the fleet electric vehicles, wherein each of the blocks of work includes an electric vehicle trip with one or more destinations; and implement the blocks of work for the electric vehicles according to solution to the optimization problem, as drafted, are processes that, under their broadest reasonable interpretations, cover certain methods of organizing human activity. For example, these limitations fall at least within the enumerated categories of commercial or legal interactions and/or managing personal behavior or relationships or interactions between people (see MPEP 2106.04(a)(2)(II)).
Additionally, the limitations of generating data for use in behavior models for the electric vehicles and/or the charging stations for the electric vehicles; solving an optimization problem based on the behavior models and on input parameters including one or more of state of charge for electric batteries for the electric vehicles, initial and final state constraints for the electric vehicles, safety constraints for the electric vehicles, charging constraints for chargers at a depot, and bus-to-block constraints; assign blocks of work to the electric vehicles based on a solution to the optimization problem to increase a key performance indicator of the fleet electric vehicles, wherein each of the blocks of work includes an electric vehicle trip with one or more destinations; and implement the blocks of work for the electric vehicles according to solution to the optimization problem, as drafted, are processes that, under their broadest reasonable interpretations, cover mental processes. For example, these limitations recite activity comprising observations, evaluations, judgments, and opinions (see MPEP 2106.04(a)(2)(III)).
Additionally, the limitation of solving an optimization problem based on the behavior models and on input parameters including one or more of state of charge for electric batteries for the electric vehicles, initial and final state constraints for the electric vehicles, safety constraints for the electric vehicles, charging constraints for chargers at a depot, and bus-to-block constraints, as drafted, is a process that, under its broadest reasonable interpretation, covers mathematical concepts. For example, these limitations recite mathematical relationships and/or calculations (see MPEP 2106.04(a)(2)(I)).
If a claim limitation, under its broadest reasonable interpretation, covers fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships, or managing interactions between people, it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper but for recitation of generic computer components, it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, mathematical formulae or equations, or mathematical calculations, it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a system, a fleet of electric vehicles, a digital twin platform providing a digital representation of electric vehicles and/or charging stations in a virtual environment, charging stations, an assignment and strategy module, and a depot parking and management module including a processor. A system, a digital twin platform providing a digital representation of electric vehicles and/or charging stations in a virtual environment, an assignment and strategy module, and a depot parking and management module including a processor amount to no more than mere instructions to apply a judicial exception (see MPEP 2106.05(f)). A fleet of electric vehicles and charging stations amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Accordingly, these additional elements do not integrate the abstract ideas into a practical application because they do not, individually or in combination, impose any meaningful limits on practicing the abstract ideas. The claim is therefore directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the judicial exception into a practical application, the additional elements amount to no more than mere instructions to apply a judicial exception, and generally linking the use of a judicial exception to a particular technological environment or field of use for the same reasons as discussed above in relation to integration into a practical application. These cannot provide an inventive concept. Therefore, when considering the additional elements alone and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible.
Claims 2-11, describing various additional limitations to the system of Claim 1, amount to substantially the same unintegrated abstract idea as Claim 1 (upon which these claims depend, directly or indirectly) and are rejected for substantially the same reasons.
Claim 2 discloses wherein the digital twin platform comprises a behavior model module for generating the behavior models, the behavior model module comprising an automated learning element to update the behavior models (mere instructions to apply a judicial exception), which does not integrate the claim into a practical application.
Claim 3 discloses wherein the automated learning element updates the behavior models in real time (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 4 discloses wherein the models comprise physics-based models, neural network-based models, or any combination of both (further defining the abstract idea already set forth in Claim 1), which does not integrate the claim into a practical application.
Claim 5 discloses further comprising an input module (mere instructions to apply a judicial exception) coupled to the digital twin platform, the input module configured to transmit to the digital twin platform telematics information associated with the electric vehicles, the chargers, or any combination thereof (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 6 discloses further comprising a third-party input module coupled to the digital twin platform for transmitting traffic data, weather data, and maps data to the digital twin platform (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 7 discloses wherein the assignment and strategy module is configured to solve the optimization problem based on initial and final state constraints for the electric vehicles and/or safety constraints for the electric vehicles (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which does not integrate the claim into a practical application.
Claim 8 discloses wherein the assignment and strategy module is further configured to assign the blocks of work and/or solve the optimization problem based on a price of electricity, a maximum power supply, the availability of chargers, or any combination thereof(an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which does not integrate the claim into a practical application.
Claim 9 discloses further comprising a replanning platform, the replanning platform comprising a prediction module coupled to an adaptive optimization module (mere instructions to apply a judicial exception), which does not integrate the claim into a practical application.
Claim 10 discloses wherein the prediction module comprises a Deep Reinforcement Learning-Based Cost Prediction module with real-time feedback coupled to an Adaptive Optimization System using a real-time trigger (mere instructions to apply a judicial exception), which does not integrate the claim into a practical application.
Claim 11 discloses wherein the digital twin platform, the assignment and strategy module, the depot parking and management module, or any combination thereof is hosted on one or more cloud platforms (mere instructions to apply a judicial exception), which does not integrate the claim into a practical application.
Regarding Claim 12, the limitations of generating behavior models for the electric vehicles and/or vehicle charging stations; determining an optimized performance output of the electric vehicles and/or vehicle charging stations based at least in part on the behavior models and on input parameters including one or more of state of charge for electric batteries for the electric vehicles, initial and final state constraints for the electric vehicles, safety constraints for the electric vehicles, charging constraints for chargers at a depot, and bus-to-block constraints; and controlling an assignment of blocks of work to each of the electric vehicles based on the optimized performance output to increase a key performance indicator of the transit fleet, wherein each of the blocks of work include an electric vehicle trip with one or more destinations, as drafted, are processes that, under their broadest reasonable interpretations, cover certain methods of organizing human activity. For example, these limitations fall at least within the enumerated categories of commercial or legal interactions and/or managing personal behavior or relationships or interactions between people (see MPEP 2106.04(a)(2)(II)).
Additionally, the limitations of generating behavior models for the electric vehicles and/or vehicle charging stations; determining an optimized performance output of the electric vehicles and/or vehicle charging stations based at least in part on the behavior models and on input parameters including one or more of state of charge for electric batteries for the electric vehicles, initial and final state constraints for the electric vehicles, safety constraints for the electric vehicles, charging constraints for chargers at a depot, and bus-to-block constraints; and controlling an assignment of blocks of work to each of the electric vehicles based on the optimized performance output to increase a key performance indicator of the transit fleet, wherein each of the blocks of work include an electric vehicle trip with one or more destinations, as drafted, are processes that, under their broadest reasonable interpretations, cover mental processes. For example, these limitations recite activity comprising observations, evaluations, judgments, and opinions (see MPEP 2106.04(a)(2)(III)).
Additionally, the limitation of determining an optimized performance output of the electric vehicles and/or vehicle charging stations based at least in part on the behavior models and on input parameters including one or more of state of charge for electric batteries for the electric vehicles, initial and final state constraints for the electric vehicles, safety constraints for the electric vehicles, charging constraints for chargers at a depot, and bus-to-block constraints, as drafted, is a process that, under its broadest reasonable interpretation, covers mathematical concepts. For example, these limitations recite mathematical relationships and/or calculations (see MPEP 2106.04(a)(2)(I)).
If a claim limitation, under its broadest reasonable interpretation, covers fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships, or managing interactions between people, it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper but for recitation of generic computer components, it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, mathematical formulae or equations, or mathematical calculations, it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of electric vehicles in a transit fleet, generating a digital representation of the electric vehicles and/or the vehicle charging stations in a virtual environment, and vehicle charging stations. Generating a digital representation of the electric vehicles and/or the vehicle charging stations in a virtual environment amounts to no more than mere instructions to apply a judicial exception (see MPEP 2106.05(f)). Electric vehicles in a transit fleet and vehicle charging stations amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Accordingly, these additional elements do not integrate the abstract ideas into a practical application because they do not, individually or in combination, impose any meaningful limits on practicing the abstract ideas. The claim is therefore directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the judicial exception into a practical application, the additional elements amount to no more than mere instructions to apply a judicial exception, and generally linking the use of a judicial exception to a particular technological environment or field of use for the same reasons as discussed above in relation to integration into a practical application. These cannot provide an inventive concept. Therefore, when considering the additional elements alone and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible.
Claims 13-23, describing various additional limitations to the method of Claim 12, amount to substantially the same unintegrated abstract idea as Claim 12 (upon which these claims depend, directly or indirectly) and are rejected for substantially the same reasons.
Claim 13 discloses further comprising generating a vehicle behavior model for each individual electric vehicle (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 14 discloses further comprising generating a vehicle behavior model for each type of vehicle (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 15 discloses wherein the behavior models are learning based (mere instructions to apply a judicial exception), which does not integrate the claim into a practical application.
Claim 16 discloses wherein determining the optimized performance output is based at least in part on Deep Reinforcement Learning (DRL)-based (mere instructions to apply a judicial exception) cost prediction models (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which does not integrate the claim into a practical application.
Claim 17 discloses wherein the DRL-based cost prediction models dynamically predict dispatching costs by considering vehicle states, operational parameters, and environmental conditions (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which does not integrate the claim into a practical application.
Claim 18 discloses wherein determining the optimized performance output comprises solving one or more optimization problems (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which does not integrate the claim into a practical application.
Claim 19 discloses wherein solving the optimization problem is triggered by the occurrence of one or more events (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 20 discloses wherein the one or more events comprise any one or more of a low vehicle state of charge, scheduled intervals, and a detected discrepancy between planned and executed trips (further defining the abstract idea already set forth in Claim 19), which does not integrate the claim into a practical application.
Claim 21 discloses further comprising using artificial intelligence (mere instructions to apply a judicial exception) for real-time identification of trip execution status to generate vehicle operational adjustments in real time (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 22 discloses further comprising generating a plan to navigate one or more of the electric vehicles through a charging depot based on improvement to the key performance indicator (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 23 discloses wherein the DRL model is trained on at least historical vehicle data, General Transit Feed Specification (GTFS) Data, or both (mere instructions to apply a judicial exception), which does not integrate the claim into a practical application.
Regarding Claim 24, the limitations of collecting sets of real-time electrical vehicle data and/or real-time electrical charger data for generating performance models for electric vehicles in a fleet of electric vehicles and/or electrical vehicle chargers; inputting the sets of electrical vehicle data and/or the electrical charger data into behavior models; updating the performance models with the behavior models; using the performance models to predict vehicle performance and/or electrical charger performance; storing a predicted vehicle performance and a predicted electrical charge performance; determining a key performance index (KPI) for the utilizing the electric fleet; and assigning blocks of work to each of the electric vehicles based on the performance models, contexts, and/or charging schedules to improve the KPI to within a predetermined range, wherein each of the blocks of work include an electric vehicle trips with one or more destinations, as drafted, are processes that, under their broadest reasonable interpretations, cover certain methods of organizing human activity. For example, these limitations fall at least within the enumerated categories of commercial or legal interactions and/or managing personal behavior or relationships or interactions between people (see MPEP 2106.04(a)(2)(II)).
Additionally, the limitations of collecting sets of real-time electrical vehicle data and/or real-time electrical charger data for generating performance models for electric vehicles in a fleet of electric vehicles and/or electrical vehicle chargers; inputting the sets of electrical vehicle data and/or the electrical charger data into behavior models; updating the performance models with the behavior models; using the performance models to predict vehicle performance and/or electrical charger performance; storing a predicted vehicle performance and a predicted electrical charge performance; determining a key performance index (KPI) for the utilizing the electric fleet; and assigning blocks of work to each of the electric vehicles based on the performance models, contexts, and/or charging schedules to improve the KPI to within a predetermined range, wherein each of the blocks of work include an electric vehicle trips with one or more destinations, as drafted, are processes that, under their broadest reasonable interpretations, cover mental processes. For example, these limitations recite activity comprising observations, evaluations, judgments, and opinions (see MPEP 2106.04(a)(2)(III)).
Additionally, the limitations of updating the performance models with the behavior models; using the performance models to predict vehicle performance and/or electrical charger performance; and assigning blocks of work to each of the electric vehicles based on the performance models, contexts, and/or charging schedules to improve the KPI to within a predetermined range, wherein each of the blocks of work include an electric vehicle trips with one or more destinations, as drafted, are processes that, under their broadest reasonable interpretations, cover mathematical concepts. For example, these limitations recite mathematical relationships and/or calculations (see MPEP 2106.04(a)(2)(I)).
If a claim limitation, under its broadest reasonable interpretation, covers fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships, or managing interactions between people, it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper but for recitation of generic computer components, it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, mathematical formulae or equations, or mathematical calculations, it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of electric vehicles in a transit fleet, electric vehicle chargers, the behavior models including a digital representation of the electric vehicles and/or the vehicle charging stations in a virtual environment, and a prediction store. The behavior models including a digital representation of the electric vehicles and/or the vehicle charging stations in a virtual environment, and a prediction store amount to no more than mere instructions to apply a judicial exception (see MPEP 2106.05(f)). Electric vehicles in a transit fleet and electric vehicle chargers amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Accordingly, these additional elements do not integrate the abstract ideas into a practical application because they do not, individually or in combination, impose any meaningful limits on practicing the abstract ideas. The claim is therefore directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the judicial exception into a practical application, the additional elements amount to no more than mere instructions to apply a judicial exception, and generally linking the use of a judicial exception to a particular technological environment or field of use for the same reasons as discussed above in relation to integration into a practical application. These cannot provide an inventive concept. Therefore, when considering the additional elements alone and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible.
Claims 25-31, describing various additional limitations to the method of Claim 24, amount to substantially the same unintegrated abstract idea as Claim 24 (upon which these claims depend, directly or indirectly) and are rejected for substantially the same reasons.
Claim 25 discloses wherein the sets of real-time electrical vehicle data are collected, at least in part, by onboard electronics on the vehicles (mere instructions to apply a judicial exception), and transmitted by the onboard electronics to a data pull module (insignificant extra-solution activity), which does not integrate the claim into a practical application. The limitation found to recite insignificant extra-solution activity is further found to be well-understood, routine, and conventional as representing the judicially recognized functionality of receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d).
Claim 26 discloses wherein the collecting and transmitting are both performed in real time (further defining the mere instructions to apply a judicial exception and insignificant extra-solution activity already set forth in Claim 25), which does not integrate the claim into a practical application. This transmitting is well-understood, routine, and conventional as already described in relation to Claim 25.
Claim 27 discloses wherein at least some of the electric vehicles are autonomous vehicles (generally linking the use of a judicial exception to a particular technological environment or field of use); translating the charging schedules into instructions for navigating the autonomous vehicles within a charging station (an abstract idea in the form of a certain method of organizing human activity and a mental process); and transmitting the instructions to the autonomous vehicles (insignificant extra-solution activity), which do not integrate the claim into a practical application. The limitation found to recite insignificant extra-solution activity is further found to be well-understood, routine, and conventional as representing the judicially recognized functionality of receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d).
Claim 28 discloses further comprising receiving traffic data, weather data, maps data, or any combination thereof, for generating the performance models (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 29 discloses wherein the contexts comprise weather along a route, traffic, maps, and state of charge of a vehicle (further defining the abstract idea already set forth in Claim 24), which does not integrate the claim into a practical application.
Claim 30 discloses determining planned landmarks from planned data (an abstract idea in the form of a certain method of organizing human activity and a mental process); pairing time and location planned data with real-time electrical vehicle data (an abstract idea in the form of a certain method of organizing human activity and a mental process); determining landmarks in real time and adjusting the planned data to correspond to the real-time vehicle data (an abstract idea in the form of a certain method of organizing human activity and a mental process); and identifying features between landmarks to correlate real-time vehicle data with planned data (an abstract idea in the form of a certain method of organizing human activity and a mental process), which do not integrate the claim into a practical application.
Claim 31 discloses further comprising using the features, time, and location data to update the models (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which does not integrate the claim into a practical application.
Claim Rejections – 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claims 1-2, 5, 7-9, 12-13, 18-22, and 24-26 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Usman et al, “Optimal recharging framework and simulation for electric vehicle fleet,” Future Generation Computer Systems, Vol. 107 (hereafter, “Usman”) (copy attached in IDS of 9/24/2024).
Regarding Claim 1, Usman discloses:
a digital twin platform providing a digital representation of electric vehicles and/or charging stations in a virtual environment for generating data for use in behavior models for the electric vehicles and/or the charging stations for the electric vehicles (Abstract; pgs. 3-4, 13, 16; a planning simulation model is presented which evaluates the feasibility of electric vehicles driving range when recharging is considered; a simulation model is mapped from the proposed framework for evaluation and testing; representing the real systems via simulation allows exploring system behavior in an articulated way; the performance of the proposed strategies was validated by simulations for a charging station of 50 electric vehicles in real time conditions; an optimization algorithm to coordinate the charging of EV s that was developed and implemented using a genetic algorithm which results in an optimal schedule for charging EV batteries throughout the day; the realistic behavior of drivers was used to obtain the mobility patterns and parking availability aspect of EVs to improve the charging and discharging process; Venkatesh and Guan [11] proposed a globally optimal scheduling scheme and a locally optimal scheduling scheme for EV charging and discharging);
an assignment and strategy module solving an optimization problem based on the behavior models and on input parameters including one or more of state of charge for electric batteries for the electric vehicles, initial and final state constraints for the electric vehicles, safety constraints for the electric vehicles, charging constraints for chargers at a depot, and bus-to-block constraints (pgs. 4, 7; a special EV network composed of fixed routes for an EV fleet, where each EV moves along its own cyclic tour of depots; by setting up a recharging station at a depot, an EV can recharge its battery for no longer than a pre-specified duration constraint; authors presented an optimal deployment of recharging stations and an optimal recharging schedule for each EV such that all EV s can continue their tours in the planning horizon with minimum total costs; the planner first determines the energy that can charged during the slot; it depends upon the power of the charger that will be used for charging at the found time slot; therefore, the planner first determines the location where car will be parked at the found time slot (i.e. either at home or at work); then, it determines the vehicle presence time at this location overlapped with period of found slot; using the presence time and charger power it calculates the maximum energy Emax that can be charged at this particular slot; then it calculates the effective energy that is planned to charge at this slot by taking the minimum of required energy to meet minimum energy level constraint, the amount of energy that can be charged during this slot and the amount of the energy that can be charged before the battery gets full during already planned charging in successive slots; this effective energy is added to the battery SOC; if this optimization process successfully iterates over all trips to keep the battery SOC above minimum level at each point in time, this schedule is marked as "feasible");
the assignment and strategy module configured to assign blocks of work to the electric vehicles based on a solution to the optimization problem to increase a key performance indicator of the fleet electric vehicles, wherein each of the blocks of work includes an electric vehicle trip with one or more destinations (pgs. 3-5, 7, 11; special EV network composed of fixed routes for an EV fleet, where each EV moves along its own cyclic tour of depots; by setting up a recharging station at a depot, an EV can recharge its battery for no longer than a pre-specified duration constraint [i.e. controlling the deployment and charging of the electric vehicles based on the behavior models]; the service registers the requesting vehicle at a given charging station by registering the assignment of charging place at given QCS to the vehicle; a new charging activity is added in the schedule after assigning location to specified trip; authors presented an optimal deployment of recharging stations and an optimal recharging schedule for each EV such that all EV s can continue their tours in the planning horizon with minimum total costs; using the selected QCS the EV agent updates the schedule to compensate the lost time due to charging activity; Wang et al [21] considered a special EV network composed of fixed routes for an EV fleet, where each EV moves along its own cyclic tour of depots; by setting up a recharging station at a depot, an EV can recharge its battery for no longer than a pre-specified duration constraint; the planner first determines the energy that can charged during the slot; it depends upon the power of the charger that will be used for charging at the found time slot; therefore, the planner first determines the location where car will be parked at the found time slot (i.e. either at home or at work); then, it determines the vehicle presence time at this location overlapped with period of found slot; using the presence time and charger power it calculates the maximum energy Emax that can be charged at this particular slot; then it calculates the effective energy that is planned to charge at this slot by taking the minimum of required energy to meet minimum energy level constraint, the amount of energy that can be charged during this slot and the amount of the energy that can be charged before the battery gets full during already planned charging in successive slots; this effective energy is added to the battery SOC; if this optimization process successfully iterates over all trips to keep the battery SOC above minimum level at each point in time, this schedule is marked as "feasible"); and
a depot parking and management module including a processor configured to implement the blocks of work for the electric vehicles according to solution to the optimization problem (pg. 4; a special EV network composed of fixed routes for an EV fleet, where each EV moves along its own cyclic tour of depots. By setting up a recharging station at a depot, an EV can recharge its hattery for no longer than a pre-specified duration constraint [i.e. parking and charging the electric vehicles according to optimal charging strategies]; authors presented an optimal deployment of recharging stations and an optimal recharging schedule for each EV such that all EV s can continue their tours in the planning horizon with minimum total costs).
Regarding Claim 2, Usman discloses the limitations of Claim 1. Usman further discloses wherein the digital twin platform comprises a behavior model module for generating the behavior models, the behavior model module comprising an automated learning element to update the behavior models (pgs. 4, 7; an optimization algorithm to coordinate the charging of EVs that was developed and implemented using a genetic algorithm which results in an optimal schedule for charging EV batteries throughout the day; the realistic behavior of drivers was used to obtain the mobility patterns and parking availability aspect of EV s to improve the charging and discharging process [i.e. a behavior model module for generating the behavior models]; Venkatesh and Guan [11] proposed a globally optimal scheduling scheme and a locally optimal scheduling scheme for EV charging and discharging; the planner first determines the energy that can charged during the slot. It depends upon the power of the charger that will be used for charging at the found time slot; therefore, the planner first determines the location where car will be parked at the found time slot (i.e. either at home or at work); then, it determines the vehicle presence time at this location overlapped with period of found slot; using the presence time and charger power it calculates the maximum energy Emax that can be charged at this particular slot; then it calculates the effective energy that is planned to charge at this slot by taking the minimum of required energy to meet minimum energy level constraint, the amount of energy that can be charged during this slot and the amount of the energy that can be charged before the battery gets full during already planned charging in successive slots; this effective energy is added to the battery SOC; if this optimization process successfully iterates over all trips to keep the battery SOC above minimum level at each point in time, this schedule is marked as "feasible" [i.e. automated learning element to update the behavior models]).
Regarding Claim 5, Usman discloses the limitations of Claim 1. Usman further discloses further comprising an input module coupled to the digital twin platform, the input module configured to transmit to the digital twin platform telematics information associated with the electric vehicles, the chargers, or any combination thereof (pgs. 4, 9, 13, 16; the performance of the proposed strategies was validated by simulations for a charging station of 50 electric vehicles in real time conditions; QCS selector being part of the EV agent already contains the daily schedule S of the user; it also contains the information of the SOC level [i.e. to transmit to the digital twin platform telematics information associated with the electric vehicles] at the start of each home-based tour (HH tour); each BEV vehicle has attributes of the current state of charge (SOC), its energy consumption rate per unit distance, battery capacity, and its deepest charging depletion level; information about location of quick charging stations is taken from [37] which provides independent and real-time view of all charging stations in specified region).
Regarding Claim 7, Usman discloses the limitations of Claim 1. Usman further discloses wherein the assignment and strategy module is configured to solve the optimization problem based on initial and final state constraints for the electric vehicles and/or safety constraints for the electric vehicles (pg. 7; the planner first determines the energy that can charged during the slot; it depends upon the power of the charger that will be used for charging at the found time slot; therefore, the planner first determines the location where car will be parked at the found time slot (i.e. either at home or at work); then, it determines the vehicle presence time at this location overlapped with period of found slot; using the presence time and charger power it calculates the maximum energy Emax that can be charged at this particular slot; then it calculates the effective energy that is planned to charge at this slot by taking the minimum of required energy to meet minimum energy level constraint, the amount of energy that can be charged during this slot and the amount of the energy that can be charged before the battery gets full during already planned charging in successive slots; this effective energy is added to the battery SOC; if this optimization process successfully iterates over all trips to keep the battery SOC above minimum level at each point in time, this schedule is marked as "feasible").
Regarding Claim 8, Usman discloses the limitations of Claim 1. Usman further discloses wherein the assignment and strategy module is further configured to assign the blocks of work and/or solve the optimization problem based on a price of electricity, a maximum power supply, the availability of chargers, or any combination thereof (pg. 7; the planner first determines the energy that can charged during the slot; it depends upon the power of the charger that will be used for charging at the found time slot; therefore, the planner first determines the location where car will be parked at the found time slot (i.e. either at home or at work); then, it determines the vehicle presence time at this location overlapped with period of found slot; using the presence time and charger power it calculates the maximum energy Emax that can be charged at this particular slot; then it calculates the effective energy that is planned to charge at this slot by taking the minimum of required energy to meet minimum energy level constraint, the amount of energy that can be charged during this slot and the amount of the energy that can be charged before the battery gets full during already planned charging in successive slots; this effective energy is added to the battery SOC; if this optimization process successfully iterates over all trips to keep the battery SOC above minimum level at each point in time, this schedule is marked as "feasible").
Regarding Claim 9, Usman discloses the limitations of Claim 1. Usman further discloses further comprising a replanning platform, the replanning platform comprising a prediction module coupled to an adaptive optimization module (pgs. 7, 18; the planner first determines the energy that can charged during the slot; it depends upon the power of the charger that will be used for charging at the found time slot; therefore, the planner first determines the location where car will be parked at the found time slot (i.e. either at home or at work); then, it determines the vehicle presence time at this location overlapped with period of found slot; using the presence time and charger power it calculates the maximum energy Emax that can be charged at this particular slot; then it calculates the effective energy that is planned to charge at this slot by taking the minimum of required energy to meet minimum energy level constraint, the amount of energy that can be charged during this slot and the amount of the energy that can be charged before the battery gets full during already planned charging in successive slots; this effective energy is added to the battery SOC; if this optimization process successfully iterates over all trips to keep the battery SOC above minimum level at each point in time, this schedule is marked as "feasible;" DCD (deepest charge depletion) level of the battery is used as the minimum threshold for battery SOC for all trips; in case of battery SOC goes below to the minimum level after a particular trip, a time slot is selected between two time periods t0 and t1, where t1 is the last period before the trip and t0 is the last period when the battery was full; simulations performed on a machine with the following specifications: Processor type: Intel Xeon X5670, Number of processors 2, Cores/processor 12, Threads/processor 24, Clock frequency 2.95 GHz, Memory 128 GB).
Regarding Claim 12, Usman discloses:
generating behavior models for the electric vehicles and/or vehicle charging stations, via generating a digital representation of the electric vehicles and/or the vehicle charging stations in a virtual environment (Abstract; pgs. 3-4, 13, 16; a planning simulation model is presented which evaluates the feasibility of electric vehicles driving range when recharging is considered; a simulation model is mapped from the proposed framework for evaluation and testing; representing the real systems via simulation allows exploring system behavior in an articulated way; the performance of the proposed strategies was validated by simulations for a charging station of 50 electric vehicles in real time conditions; an optimization algorithm to coordinate the charging of EV s that was developed and implemented using a genetic algorithm which results in an optimal schedule for charging EV batteries throughout the day; the realistic behavior of drivers was used to obtain the mobility patterns and parking availability aspect of EVs to improve the charging and discharging process; Venkatesh and Guan [11] proposed a globally optimal scheduling scheme and a locally optimal scheduling scheme for EV charging and discharging);
determining an optimized performance output of the electric vehicles and/or vehicle charging stations based at least in part on the behavior models and on input parameters including one or more of state of charge for electric batteries for the electric vehicles, initial and final state constraints for the electric vehicles, safety constraints for the electric vehicles, charging constraints for chargers at a depot, and bus-to-block constraints (pgs. 4, 7; a special EV network composed of fixed routes for an EV fleet, where each EV moves along its own cyclic tour of depots; by setting up a recharging station at a depot, an EV can recharge its battery for no longer than a pre-specified duration constraint; authors presented an optimal deployment of recharging stations and an optimal recharging schedule for each EV such that all EV s can continue their tours in the planning horizon with minimum total costs; the planner first determines the energy that can charged during the slot; it depends upon the power of the charger that will be used for charging at the found time slot; therefore, the planner first determines the location where car will be parked at the found time slot (i.e. either at home or at work); then, it determines the vehicle presence time at this location overlapped with period of found slot; using the presence time and charger power it calculates the maximum energy Emax that can be charged at this particular slot; then it calculates the effective energy that is planned to charge at this slot by taking the minimum of required energy to meet minimum energy level constraint, the amount of energy that can be charged during this slot and the amount of the energy that can be charged before the battery gets full during already planned charging in successive slots; this effective energy is added to the battery SOC; if this optimization process successfully iterates over all trips to keep the battery SOC above minimum level at each point in time, this schedule is marked as "feasible"); and
controlling an assignment of blocks of work to each of the electric vehicles based on the optimized performance output to increase a key performance indicator of the transit fleet, wherein each of the blocks of work include an electric vehicle trip with one or more destinations (pgs. 3-5, 7, 11; special EV network composed of fixed routes for an EV fleet, where each EV moves along its own cyclic tour of depots; by setting up a recharging station at a depot, an EV can recharge its battery for no longer than a pre-specified duration constraint [i.e. controlling the deployment and charging of the electric vehicles based on the behavior models]; the service registers the requesting vehicle at a given charging station by registering the assignment of charging place at given QCS to the vehicle; a new charging activity is added in the schedule after assigning location to specified trip; authors presented an optimal deployment of recharging stations and an optimal recharging schedule for each EV such that all EV s can continue their tours in the planning horizon with minimum total costs; using the selected QCS the EV agent updates the schedule to compensate the lost time due to charging activity; Wang et al [21] considered a special EV network composed of fixed routes for an EV fleet, where each EV moves along its own cyclic tour of depots; by setting up a recharging station at a depot, an EV can recharge its battery for no longer than a pre-specified duration constraint; the planner first determines the energy that can charged during the slot; it depends upon the power of the charger that will be used for charging at the found time slot; therefore, the planner first determines the location where car will be parked at the found time slot (i.e. either at home or at work); then, it determines the vehicle presence time at this location overlapped with period of found slot; using the presence time and charger power it calculates the maximum energy Emax that can be charged at this particular slot; then it calculates the effective energy that is planned to charge at this slot by taking the minimum of required energy to meet minimum energy level constraint, the amount of energy that can be charged during this slot and the amount of the energy that can be charged before the battery gets full during already planned charging in successive slots; this effective energy is added to the battery SOC; if this optimization process successfully iterates over all trips to keep the battery SOC above minimum level at each point in time, this schedule is marked as "feasible").
Regarding Claim 13, Usman discloses the limitations of Claim 12. Usman further discloses further comprising generating a vehicle behavior model for each individual electric vehicle (pgs. 3-4, 6, 9; a simulation model is mapped from the proposed framework for evaluation and testing; this model can be utilized to optimally manage the PEV fleet to: (1) deliver contracted grid services, (2) supply drivers with sufficiently charged PEVs, and (3) minimize charging costs; a special EV network composed of fixed routes for an EV fleet, where each EV moves along its own cyclic tour of depots; by setting up a recharging station at a depot, an EV can recharge its battery for no longer than a pre-specified duration constraint; authors presented an optimal deployment of recharging stations and an optimal recharging schedule for each EV [i.e. generating a vehicle behavior model for each individual electric vehicle] such that all EVs can continue their tours in the planning horizon with minimum total costs; daily travel schedules planned by an activity-based model; each car C is modelled so that it has its unique energy consumption rate).
Regarding Claim 18, Usman discloses the limitations of Claim 12. Usman further discloses wherein determining the optimized performance output comprises solving one or more optimization problems (pg. 7; the planner first determines the energy that can charged during the slot; it depends upon the power of the charger that will be used for charging at the found time slot; therefore, the planner first determines the location where car will be parked at the found time slot (i.e. either at home or at work); then, it determines the vehicle presence time at this location overlapped with period of found slot; using the presence time and charger power it calculates the maximum energy Emax that can be charged at this particular slot; then it calculates the effective energy that is planned to charge at this slot by taking the minimum of required energy to meet minimum energy level constraint, the amount of energy that can be charged during this slot and the amount of the energy that can be charged before the battery gets full during already planned charging in successive slots; this effective energy is added to the battery SOC; if this optimization process successfully iterates over all trips to keep the battery SOC above minimum level at each point in time, this schedule is marked as "feasible").
Regarding Claim 19, Usman discloses the limitations of Claim 18. Usman further discloses wherein solving the optimization problem is triggered by the occurrence of one or more events (pg. 7; the planner first determines the energy that can charged during the slot; it depends upon the power of the charger that will be used for charging at the found time slot; therefore, the planner first determines the location where car will be parked at the found time slot (i.e. either at home or at work); then, it determines the vehicle presence time at this location overlapped with period of found slot; using the presence time and charger power it calculates the maximum energy Emax that can be charged at this particular slot; then it calculates the effective energy that is planned to charge at this slot by taking the minimum of required energy to meet minimum energy level constraint, the amount of energy that can be charged during this slot and the amount of the energy that can be charged before the battery gets full during already planned charging in successive slots; this effective energy is added to the battery SOC; if this optimization process successfully iterates over all trips to keep the battery SOC above minimum level at each point in time, this schedule is marked as "feasible;" DCD (deepest charge depletion) level of the battery is used as the minimum threshold for battery SOC for all trips; in case of battery SOC goes below to the minimum level after a particular trip, a time slot is selected between two time periods t0 and t1, where t1 is the last period before the trip and t0 is the last period when the battery was full).
Regarding Claim 20, Usman discloses the limitations of Claim 19. Usman further discloses wherein the one or more events comprise any one or more of a low vehicle state of charge, scheduled intervals, and a detected discrepancy between planned and executed trips (pg. 7; the planner first determines the energy that can charged during the slot; it depends upon the power of the charger that will be used for charging at the found time slot; therefore, the planner first determines the location where car will be parked at the found time slot (i.e. either at home or at work); then, it determines the vehicle presence time at this location overlapped with period of found slot; using the presence time and charger power it calculates the maximum energy Emax that can be charged at this particular slot; then it calculates the effective energy that is planned to charge at this slot by taking the minimum of required energy to meet minimum energy level constraint, the amount of energy that can be charged during this slot and the amount of the energy that can be charged before the battery gets full during already planned charging in successive slots; this effective energy is added to the battery SOC; if this optimization process successfully iterates over all trips to keep the battery SOC above minimum level at each point in time, this schedule is marked as "feasible"; DCD (deepest charge depletion) level of the battery is used as the minimum threshold for battery SOC for all trips; in case of battery SOC goes below to the minimum level after a particular trip, a time slot is selected between two time periods t0 and t1, where t1 is the last period before the trip and t0 is the last period when the battery was full).
Regarding Claim 21, Usman discloses the limitations of Claim 20. Usman further discloses using artificial intelligence for real-time identification of trip execution status to generate vehicle operational adjustments in real time (pgs. 4, 7, 9, 13, 16; an optimization algorithm to coordinate the charging of EVs that was developed and implemented using a genetic algorithm which results in an optimal schedule for charging EV batteries throughout the day; the realistic behavior of drivers was used to obtain the mobility patterns and parking availability aspect of EV s to improve the charging and discharging process; the planner first determines the energy that can charged during the slot. It depends upon the power of the charger that will be used for charging at the found time slot; therefore, the planner first determines the location where car will be parked at the found time slot (i.e. either at home or at work); then, it determines the vehicle presence time at this location overlapped with period of found slot; using the presence time and charger power it calculates the maximum energy Emax that can be charged at this particular slot; then it calculates the effective energy that is planned to charge at this slot by taking the minimum of required energy to meet minimum energy level constraint, the amount of energy that can be charged during this slot and the amount of the energy that can be charged before the battery gets full during already planned charging in successive slots; this effective energy is added to the battery SOC; if this optimization process successfully iterates over all trips to keep the battery SOC above minimum level at each point in time, this schedule is marked as "feasible" [i.e. automated learning element to update the behavior models]; DCD (deepest charge depletion) level of the battery is used as the minimum threshold for battery SOC for all trips; in case of battery SOC goes below to the minimum level after a particular trip, a time slot is selected between two time periods t0 and t1, where t1 is the last period before the trip and t0 is the last period when the battery was full).
Regarding Claim 22, Usman discloses the limitations of Claim 12. Usman further discloses generating a plan to navigate one or more of the electric vehicles through a charging depot based on improvement to the key performance indicator (pgs. 4, 7; Wang et al [21] considered a special EV network composed of fixed routes for an EV fleet, where each EV moves along its own cyclic tour of depots; by setting up a recharging station at a depot, an EV can recharge its battery for no longer than a pre-specified duration constraint; the planner first determines the energy that can charged during the slot; it depends upon the power of the charger that will be used for charging at the found time slot; therefore, the planner first determines the location where car will be parked at the found time slot (i.e. either at home or at work); then, it determines the vehicle presence time at this location overlapped with period of found slot; using the presence time and charger power it calculates the maximum energy Emax that can be charged at this particular slot; then it calculates the effective energy that is planned to charge at this slot by taking the minimum of required energy to meet minimum energy level constraint, the amount of energy that can be charged during this slot and the amount of the energy that can be charged before the battery gets full during already planned charging in successive slots; this effective energy is added to the battery SOC; if this optimization process successfully iterates over all trips to keep the battery SOC above minimum level at each point in time, this schedule is marked as "feasible").
Regarding Claim 24, Usman discloses:
collecting sets of real-time electrical vehicle data and/or real-time electrical charger data for generating performance models for electric vehicles in a fleet of electric vehicles and/or electrical vehicle chargers (pgs. 4, 9, 13, 16; the performance of the proposed strategies was validated by simulations for a charging station of 50 electric vehicles in real time conditions; QCS selector being part of the EV agent already contains the daily schedule S of the user; it also contains the information of the SOC level at the start of each home-based tour (HH tour); each BEV vehicle has attributes of the current state of charge (SOC), its energy consumption rate per unit distance, battery capacity, and its deepest charging depletion level; information about location of quick charging stations is taken from [37] which provides independent and real-time view of all charging stations in specified region);
inputting the sets of electrical vehicle data and/or the electrical charger data into behavior models, the behavior models including a digital representation of the electric vehicles and/or the vehicle charging stations in a virtual environment (Abstract; pgs. 3-4, 13, 16; a planning simulation model is presented which evaluates the feasibility of electric vehicles driving range when recharging is considered; a simulation model is mapped from the proposed framework for evaluation and testing; representing the real systems via simulation allows exploring system behavior in an articulated way; the performance of the proposed strategies was validated by simulations for a charging station of 50 electric vehicles in real time conditions; an optimization algorithm to coordinate the charging of EV s that was developed and implemented using a genetic algorithm which results in an optimal schedule for charging EV batteries throughout the day; the realistic behavior of drivers was used to obtain the mobility patterns and parking availability aspect of EVs to improve the charging and discharging process; Venkatesh and Guan [11] proposed a globally optimal scheduling scheme and a locally optimal scheduling scheme for EV charging and discharging);
updating the performance models with the behavior models (Abstract; pgs. 3-4, 13, 16; a planning simulation model is presented which evaluates the feasibility of electric vehicles driving range when recharging is considered; a simulation model is mapped from the proposed framework for evaluation and testing; representing the real systems via simulation allows exploring system behavior in an articulated way; the performance of the proposed strategies was validated by simulations for a charging station of 50 electric vehicles in real time conditions; an optimization algorithm to coordinate the charging of EV s that was developed and implemented using a genetic algorithm which results in an optimal schedule for charging EV batteries throughout the day; the realistic behavior of drivers was used to obtain the mobility patterns and parking availability aspect of EVs to improve the charging and discharging process; Venkatesh and Guan [11] proposed a globally optimal scheduling scheme and a locally optimal scheduling scheme for EV charging and discharging);
using the performance models to predict vehicle performance and/or electrical charger performance (pg. 7; the planner first determines the energy that can charged during the slot; it depends upon the power of the charger that will be used for charging at the found time slot; therefore, the planner first determines the location where car will be parked at the found time slot (i.e. either at home or at work); then, it determines the vehicle presence time at this location overlapped with period of found slot; using the presence time and charger power it calculates the maximum energy Emax that can be charged at this particular slot; then it calculates the effective energy that is planned to charge at this slot by taking the minimum of required energy to meet minimum energy level constraint, the amount of energy that can be charged during this slot and the amount of the energy that can be charged before the battery gets full during already planned charging in successive slots; this effective energy is added to the battery SOC; if this optimization process successfully iterates over all trips to keep the battery SOC above minimum level at each point in time, this schedule is marked as "feasible");
storing a predicted vehicle performance and a predicted electrical charge performance in a prediction store (pgs. 4, 9, 18; QCS selector being part of the EV agent already contains the daily schedule S of the user; it also contains the information of the SOC level at the start of each home-based tour (HH tour); assume that schedule S contains h number of HH tours, where each tour H starts with a unique INITSOC[H]; each tour H consists of t trips where departure location of first trip is always same as arrival location of last trip which is home; each trip T in tour H has its departure location, arrival location, distance, starting time of the trip and its travel duration; if QCSList contains all QCS in study area, then QCS selector module selects one qcs station using following steps: Starting the tour with initial SOC, it lists determines all reachable (candidate) QCS stations during each trip; assume that candidateList[T] contains the reachable QCS during trip T in tour H; each car C is modelled so that it has its unique energy consumption rate [kWh/km]; simulations performed on a machine with the following specifications: Processor type: Intel Xeon X5670, Number of processors 2, Cores/processor 12, Threads/processor 24, Clock frequency 2.95 GHz, Memory 128 GB; Vayá and Andersson [20] aimed to minimize charging costs while satisfying PEVs’ flexible demand; to take driver end-use constraints into account the fleet is modeled as a virtual storage resource with power and energy characteristics that depend on vehicle behavior);
determining a key performance index (KPI) for the utilizing the electric fleet (pgs. 4, 7; Wang et al [21] considered a special EV network composed of fixed routes for an EV fleet, where each EV moves along its own cyclic tour of depots; by setting up a recharging station at a depot, an EV can recharge its battery for no longer than a pre-specified duration constraint; the planner first determines the energy that can charged during the slot; it depends upon the power of the charger that will be used for charging at the found time slot; therefore, the planner first determines the location where car will be parked at the found time slot (i.e. either at home or at work); then, it determines the vehicle presence time at this location overlapped with period of found slot; using the presence time and charger power it calculates the maximum energy Emax that can be charged at this particular slot; then it calculates the effective energy that is planned to charge at this slot by taking the minimum of required energy to meet minimum energy level constraint, the amount of energy that can be charged during this slot and the amount of the energy that can be charged before the battery gets full during already planned charging in successive slots; this effective energy is added to the battery SOC; if this optimization process successfully iterates over all trips to keep the battery SOC above minimum level at each point in time, this schedule is marked as "feasible"); and
assigning blocks of work to each of the electric vehicles based on the performance models, contexts, and/or charging schedules to improve the KPI to within a predetermined range, wherein each of the blocks of work include an electric vehicle trips with one or more destinations (pgs. 3-5, 7, 11; special EV network composed of fixed routes for an EV fleet, where each EV moves along its own cyclic tour of depots; by setting up a recharging station at a depot, an EV can recharge its battery for no longer than a pre-specified duration constraint [i.e. controlling the deployment and charging of the electric vehicles based on the behavior models]; the service registers the requesting vehicle at a given charging station by registering the assignment of charging place at given QCS to the vehicle; a new charging activity is added in the schedule after assigning location to specified trip; authors presented an optimal deployment of recharging stations and an optimal recharging schedule for each EV such that all EV s can continue their tours in the planning horizon with minimum total costs; using the selected QCS the EV agent updates the schedule to compensate the lost time due to charging activity; Wang et al [21] considered a special EV network composed of fixed routes for an EV fleet, where each EV moves along its own cyclic tour of depots; by setting up a recharging station at a depot, an EV can recharge its battery for no longer than a pre-specified duration constraint; the planner first determines the energy that can charged during the slot; it depends upon the power of the charger that will be used for charging at the found time slot; therefore, the planner first determines the location where car will be parked at the found time slot (i.e. either at home or at work); then, it determines the vehicle presence time at this location overlapped with period of found slot; using the presence time and charger power it calculates the maximum energy Emax that can be charged at this particular slot; then it calculates the effective energy that is planned to charge at this slot by taking the minimum of required energy to meet minimum energy level constraint, the amount of energy that can be charged during this slot and the amount of the energy that can be charged before the battery gets full during already planned charging in successive slots; this effective energy is added to the battery SOC; if this optimization process successfully iterates over all trips to keep the battery SOC above minimum level at each point in time, this schedule is marked as "feasible").
Regarding Claim 25, Usman discloses the limitations of Claim 24. Usman further discloses wherein the sets of real-time electrical vehicle data are collected, at least in part, by onboard electronics on the vehicles, and transmitted by the onboard electronics to a data pull module (pgs. 4, 7, 9, 13, 16; the performance of the proposed strategies was validated by simulations for a charging station of 50 electric vehicles in real time conditions; QCS selector being part of the EV agent already contains the daily schedule S of the user; it also contains the information of the SOC level at the start of each home-based tour (HH tour); each BEV vehicle has attributes of the current state of charge (SOC), its energy consumption rate per unit distance, battery capacity, and its deepest charging depletion level; information about location of quick charging stations is taken from [37] which provides independent and real-time view of all charging stations in specified region; reachable QCS stations are those which can be reached by the EV during a trip with given the SOC at the start of the tour; knowing the current SOC of the vehicles necessarily requires collection and transmission to the remote computer performing the optimization and planning steps by way of onboard electronics).
Regarding Claim 26, Usman discloses the limitations of Claim 25. Usman further discloses wherein the collecting and transmitting are both performed in real time (pgs. 4, 7, 9, 13, 16; the performance of the proposed strategies was validated by simulations for a charging station of 50 electric vehicles in real time conditions; QCS selector being part of the EV agent already contains the daily schedule S of the user; it also contains the information of the SOC level at the start of each home-based tour (HH tour); each BEV vehicle has attributes of the current state of charge (SOC), its energy consumption rate per unit distance, battery capacity, and its deepest charging depletion level; information about location of quick charging stations is taken from [37] which provides independent and real-time view of all charging stations in specified region; reachable QCS stations are those which can be reached by the EV during a trip with given the SOC at the start of the tour; knowing the current SOC of the vehicles necessarily requires real time collection and transmission of battery data).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 3-4, 6, 14-17, 23, and 28-31 are rejected under 35 U.S.C. 103 as being unpatentable over Usman in view of Ayman et al, “Data-driven prediction and optimization of energy use for transit fleets of electric and ICE vehicles,” ACM Transactions on Internet Technology (TOIT) 22.1 (hereafter, “Ayman”) (copy attached in IDS of 9/24/2024).
Regarding Claim 3, Usman discloses the limitations of Claim 2. Usman does not explicitly disclose but Ayman does disclose wherein the automated learning element updates the behavior models in real time (pgs. 7:1, 4-6, 26-27; we train and evaluate machine learning models for energy prediction, demonstrating that deep neural networks attain the highest accuracy; we use this dataset to train machine learning models for energy use prediction (deep neural networks, linear regression, and decision trees) and study their performance; real-time data streams are stored for the purpose of providing easy access for model training and updates as well as real-time access for system monitoring; a real-time charge scheduling system, called bCharge, for electric bus fleets; to evaluate their system, they use a real-world streaming dataset from Shenzhen, China, which includes OPS location, bus stop, bus transaction, charging station, and electricity rate data; their approach is based on Markov decision processes and considers both energy costs and bus fare revenues; artificial neural networks perform best for predicting energy use).
One of ordinary skill in the art would have been motivated to include the fleet optimization machine learning techniques of Ayman with the vehicle fleet modeling and optimization system of Usman to find near-optimal solutions efficiently (see at least pg. 7:4 of Ayman).
Regarding Claim 4, Usman discloses the limitations of Claim 2. Usman does not explicitly disclose but Ayman does disclose wherein the models comprise physics-based models, neural network-based models, or any combination of both (pgs. 7:1, 22, 27; we train and evaluate machine learning models for energy prediction, demonstrating that deep neural networks attain the highest accuracy; we train neural-network-based energy consumption as described in Sections 4 and 5; then, we use these predictors to estimate the energy cost of each transit and non-service trip for both EVs and ICEVs; artificial neural networks perform best for predicting energy use).
One of ordinary skill in the art would have been motivated to include the fleet optimization modeling techniques of Ayman with the vehicle fleet modeling and optimization system of Usman to attain the highest accuracy models (see at least the Abstract of Ayman).
Regarding Claim 6, Usman discloses the limitations of Claim 1. Usman does not explicitly disclose but Ayman does disclose further comprising a third-party input module coupled to the digital twin platform for transmitting traffic data, weather data, and maps data to the prediction system (pgs. 7:3, 5-6; we collect and combine vehicle telemetry data, elevation and street-level maps, weather data, and traffic data; we collect weather data from multiple weather stations in Chattanooga at 5-minute intervals using the DarkSky API [11]; this data includes rea-ltime temperature, humidity, air pressure, wind speed, wind direction, and precipitation; we collect traffic data at 1-minute intervals using the HERE APT [20], which provides speed recordings for segments of major roads; every road segment is identified by a unique Traffic Message Channel identifier (TMC ID) [1]; each TMC ID is also associated with a list of latitude and longitude coordinates, which describe the geometry of the road segment; real-time data streams (e.g., ViriCiti, HERE, and DarkSky) are stored for the purpose of providing easy access for model training and updates as well as real-time access for system monitoring). Usman additionally discloses wherein the prediction system is the digital twin platform (Abstract; pgs. 3-4, 13, 16; a planning simulation model is presented which evaluates the feasibility of electric vehicles driving range when recharging is considered; a simulation model is mapped from the proposed framework for evaluation and testing; representing the real systems via simulation allows exploring system behavior in an articulated way; the performance of the proposed strategies was validated by simulations for a charging station of 50 electric vehicles in real time conditions; an optimization algorithm to coordinate the charging of EV s that was developed and implemented using a genetic algorithm which results in an optimal schedule for charging EV batteries throughout the day; the realistic behavior of drivers was used to obtain the mobility patterns and parking availability aspect of EVs to improve the charging and discharging process; Venkatesh and Guan [11] proposed a globally optimal scheduling scheme and a locally optimal scheduling scheme for EV charging and discharging).
One of ordinary skill in the art would have been motivated to include the fleet optimization third-party input data structure and techniques of Ayman with the vehicle fleet modeling and optimization system of Usman to derive accurate energy estimates from energy predictors using a variety of features (see at least pg. 7:3 of Ayman).
Regarding Claim 14, Usman discloses the limitations of Claim 12. Usman does not explicitly disclose but Ayman does disclose further comprising generating a vehicle behavior model for each type of vehicle (pg. 7:2; predictions must be contextualized with a variety of factors, including the type of vehicle, traffic and weather conditions, road gradient, and type of road (e.g., highway vs. residential area) since these factors can have significant impact on energy use).
One of ordinary skill in the art would have been motivated to include the fleet optimization modeling techniques of Ayman with the vehicle fleet modeling and optimization system of Usman to accurately predict electricity and fuel consumption of transit vehicles in the fleet (see at least pg. 7:2 of Ayman).
Regarding Claim 15, Usman discloses the limitations of Claim 12. Usman does not explicitly disclose but Ayman does disclose wherein the behavior models are learning based (pgs. 7:1-2, 22, 27; we train and evaluate machine learning models for energy prediction, demonstrating that deep neural networks attain the highest accuracy; we train neural-network-based energy consumption as described in Sections 4 and 5; then, we use these predictors to estimate the energy cost of each transit and non-service trip for both EVs and ICEVs; artificial neural networks perform best for predicting energy use).
One of ordinary skill in the art would have been motivated to include the fleet optimization machine learning techniques of Ayman with the vehicle fleet modeling and optimization system of Usman to build data-driven predictors of route-level energy usage (see at least pg. 7:2 of Ayman).
Regarding Claim 16, Usman discloses the limitations of Claim 12. Usman does not explicitly disclose but Ayman does disclose wherein the determining the optimized performance output is based at least in part on Deep Reinforcement Learning (DRL)-based cost prediction models (pgs. 7:1, 4-6, 18-21, 26-27; we train and evaluate machine learning models for energy prediction, demonstrating that deep neural networks attain the highest accuracy; we use this dataset to train machine learning models for energy use prediction (deep neural networks, linear regression, and decision trees) and study their performance; real-time data streams are stored for the purpose of providing easy access for model training and updates as well as real-time access for system monitoring; a real-time charge scheduling system, called bCharge, for electric bus fleets; to evaluate their system, they use a real-world streaming dataset from Shenzhen, China, which includes OPS location, bus stop, bus transaction, charging station, and electricity rate data; their approach is based on Markov decision processes and considers both energy costs and bus fare revenues; artificial neural networks perform best for predicting energy use; our goal is to assign a bus to each transit trip; we represent a solution as a set of assignments A; our objective is to minimize the energy use of the vehicles; this objective can minimize both environmental impact and operating costs by imposing the appropriate cost factors on the energy use of liquid-fuel and electric vehicles; formula for predicting energy costs for liquid-fuel and electric vehicles for each assignment A; system builds heuristic algorithms to compute the energy cost of each solution in the current population Pi and then chooses the N lowest-cost solutions as the basis for the next generation of the population; to create the next generation, the algorithm performs mutation and crossover).
The rationale to combine remains the same as for Claim 15.
Regarding Claim 17, Usman in view of Ayman discloses the limitations of Claim 16. Usman does not explicitly disclose but Ayman does disclose wherein the DRL-based cost prediction models dynamically predict dispatching costs by considering vehicle states, operational parameters, and environmental conditions (pgs. 7:1, 4-6, 18-21, 26-27; we train and evaluate machine learning models for energy prediction, demonstrating that deep neural networks attain the highest accuracy; we use this dataset to train machine learning models for energy use prediction (deep neural networks, linear regression, and decision trees) and study their performance; Table 1 illustrates various data considerations utilized in the models, including traffic data, weather data, vehicle parameters such as fuel level/SOC and charging status, etc.; model formulae consider several other parameters as well; real-time data streams are stored for the purpose of providing easy access for model training and updates as well as real-time access for system monitoring; a real-time charge scheduling system, called bCharge, for electric bus fleets; to evaluate their system, they use a real-world streaming dataset from Shenzhen, China, which includes OPS location, bus stop, bus transaction, charging station, and electricity rate data; their approach is based on Markov decision processes and considers both energy costs and bus fare revenues; artificial neural networks perform best for predicting energy use; our goal is to assign a bus to each transit trip; we represent a solution as a set of assignments A; our objective is to minimize the energy use of the vehicles; this objective can minimize both environmental impact and operating costs by imposing the appropriate cost factors on the energy use of liquid-fuel and electric vehicles; formula for predicting energy costs for liquid-fuel and electric vehicles for each assignment A; system builds heuristic algorithms to compute the energy cost of each solution in the current population Pi and then chooses the N lowest-cost solutions as the basis for the next generation of the population; to create the next generation, the algorithm performs mutation and crossover).
The rationale to combine remains the same as for Claim 15.
Regarding Claim 23, Usman in view of Ayman discloses the limitations of Claim 16. Usman does not explicitly disclose but Ayman does disclose wherein the DRL model is trained on at least historical vehicle data, General Transit Feed Specification (GTFS) Data, or both (pgs. 7:1, 22, 27; we train and evaluate machine learning models for energy prediction, demonstrating that deep neural networks attain the highest accuracy; we train neural-network-based energy consumption as described in Sections 4 and 5; then, we use these predictors to estimate the energy cost of each transit and non-service trip for both EVs and ICEVs; artificial neural networks perform best for predicting energy use; we obtain the schedule of the transit agency in GTFS format, which includes all trips, time schedules, bus stop locations, and so forth).
The rationale to combine remains the same as for Claim 15.
Regarding Claim 28, Usman discloses the limitations of Claim 24. Usman does not explicitly disclose but Ayman does disclose further comprising receiving traffic data, weather data, maps data, or any combination thereof, for generating the performance models (pgs. 7:3, 5-6; we collect and combine vehicle telemetry data, elevation and street-level maps, weather data, and traffic data; we collect weather data from multiple weather stations in Chattanooga at 5-minute intervals using the DarkSky API [11]; this data includes rea-ltime temperature, humidity, air pressure, wind speed, wind direction, and precipitation; we collect traffic data at 1-minute intervals using the HERE APT [20], which provides speed recordings for segments of major roads; every road segment is identified by a unique Traffic Message Channel identifier (TMC ID) [1]; each TMC ID is also associated with a list of latitude and longitude coordinates, which describe the geometry of the road segment; real-time data streams (e.g., ViriCiti, HERE, and DarkSky) are stored for the purpose of providing easy access for model training and updates as well as real-time access for system monitoring).
The rationale to combine remains the same as for Claim 6.
Regarding Claim 29, Usman discloses the limitations of Claim 24. Usman additionally discloses wherein the contexts comprise state of charge of a vehicle (pgs. 3-5, 7; special EV network composed of fixed routes for an EV fleet, where each EV moves along its own cyclic tour of depots; by setting up a recharging station at a depot, an EV can recharge its battery for no longer than a pre-specified duration constraint.; authors presented an optimal deployment of recharging stations and an optimal recharging schedule for each EV such that all EV s can continue their tours in the planning horizon with minimum total costs; using the selected QCS the EV agent updates the schedule to compensate the lost time due to charging activity; Wang et al [21] considered a special EV network composed of fixed routes for an EV fleet, where each EV moves along its own cyclic tour of depots; by setting up a recharging station at a depot, an EV can recharge its battery for no longer than a pre-specified duration constraint; the planner first determines the energy that can charged during the slot; it depends upon the power of the charger that will be used for charging at the found time slot; therefore, the planner first determines the location where car will be parked at the found time slot (i.e. either at home or at work); then, it determines the vehicle presence time at this location overlapped with period of found slot; using the presence time and charger power it calculates the maximum energy Emax that can be charged at this particular slot; then it calculates the effective energy that is planned to charge at this slot by taking the minimum of required energy to meet minimum energy level constraint, the amount of energy that can be charged during this slot and the amount of the energy that can be charged before the battery gets full during already planned charging in successive slots; this effective energy is added to the battery SOC; if this optimization process successfully iterates over all trips to keep the battery SOC above minimum level at each point in time, this schedule is marked as "feasible;" DCD (deepest charge depletion) level of the battery is used as the minimum threshold for battery SOC for all trips; in case of battery SOC goes below to the minimum level after a particular trip, a time slot is selected between two time periods t0 and t1, where t1 is the last period before the trip and t0 is the last period when the battery was full). Usman does not explicitly disclose but Ayman does disclose wherein the contexts comprise weather along a route, traffic, maps, and state of charge of a vehicle (pgs. 7:3-6; Table 1 illustrates various data considerations utilized in the models, including traffic data, weather data, vehicle parameters such as fuel level/SOC and charging status, etc.; we collect and combine vehicle telemetry data, elevation and street-level maps, weather data, and traffic data; we collect weather data from multiple weather stations in Chattanooga at 5-minute intervals using the DarkSky API [11]; this data includes rea-ltime temperature, humidity, air pressure, wind speed, wind direction, and precipitation; we collect traffic data at 1-minute intervals using the HERE APT [20], which provides speed recordings for segments of major roads; every road segment is identified by a unique Traffic Message Channel identifier (TMC ID) [1]; each TMC ID is also associated with a list of latitude and longitude coordinates, which describe the geometry of the road segment; real-time data streams (e.g., ViriCiti, HERE, and DarkSky) are stored for the purpose of providing easy access for model training and updates as well as real-time access for system monitoring).
The rationale to combine remains the same as for Claim 6.
Regarding Claim 30, Usman discloses the limitations of Claim 24. Usman does not explicitly disclose but Ayman does disclose:
determining planned landmarks from planned data (pg. 7:18; Trips. During the day, the agency has to serve a given set of transit trips T using its buses; based on discussions with our partner agency, CARTA, we assume that all the locations and time schedules are fixed for all the trips; a bus serving trip t ϵ T leaves from trip origin ƛt-origin ϵ L at time τt-start and arrives to destination ƛt-destination ϵ L at time τt-end; between ƛt-origin and ƛt-destination, the bus must pass through a series of stops at fixed times);
pairing time and location planned data with real-time electric vehicle data (pg. 7:26; a real-time charge scheduling system, called bCharge, for electric bus fleets; to evaluate their system, they use a real-world streaming dataset from Shenzhen, China, which includes GPS location, bus stop, bus transaction, charging station, and electricity rate data [i.e. pairing time and location planned data with real-time data]; their approach is based on Markov decision processes and considers both energy costs and bus fare revenues; however, it is limited to bus transit networks operating only EVs, and it modifies the existing schedule);
determining landmarks in real time and adjusting the planned data to correspond to the real-time vehicle data (pgs. 7:8-9, 26; to train the regression model, we create a training set of locations with ground truth for the correct mapping to road segments. First, we generate routes using a street-level map and select traces of locations along these routes, recording for each location the corresponding, correct road; then, we add random noise to the locations using a two-dimensional Gaussian distribution with zero mean to simulate the noisiness of GPS-based locations; once we have mapped each location to an OSM segment, we add the corresponding OSM Feature ID to each datapoint, which we use to generate samples (Section 3.4) and later to calculate accurate travel distances (Section 3.5); we also add information from OpenStreetMap regarding the road, such as the type of the road, whether the road is one-way or two-way, whether it is a tunnel, and so forth. In our dataset, we encounter 14 different road types in total, which include primary, residential, motorway, and so on; for some roads, the type is “unknown” on OpenStreetMap, which we treat as a distinct type; a real-time charge scheduling system, called bCharge, for electric bus fleets; to evaluate their system, they use a real-world streaming dataset from Shenzhen, China, which includes GPS location, bus stop, bus transaction, charging station, and electricity rate data; their approach is based on Markov decision processes and considers both energy costs and bus fare revenues; however, it is limited to bus transit networks operating only EVs, and it modifies the existing schedule); and
identifying features between landmarks to correlate real-time vehicle data with planned data (pg. 7:26; a real-time charge scheduling system, called bCharge, for electric bus fleets; to evaluate their system, they use a real-world streaming dataset from Shenzhen, China, which includes GPS location, bus stop, bus transaction, charging station, and electricity rate data; their approach is based on Markov decision processes and considers both energy costs and bus fare revenues; however, it is limited to bus transit networks operating only EVs, and it modifies the existing schedule).
The rationale to combine remains the same as for Claim 24.
Regarding Claim 31, Usman in view of Ayman discloses the limitations of Claim 30. Usman does not explicitly disclose but Ayman does disclose further comprising using the features, time, and location data to update the models (pgs. 7:3, 5-6; we collect and combine vehicle telemetry data, elevation and street-level maps, weather data, and traffic data; we collect weather data from multiple weather stations in Chattanooga at 5-minute intervals using the DarkSky API [11]; this data includes rea-ltime temperature, humidity, air pressure, wind speed, wind direction, and precipitation; we collect traffic data at 1-minute intervals using the HERE APT [20], which provides speed recordings for segments of major roads; every road segment is identified by a unique Traffic Message Channel identifier (TMC ID) [1]; each TMC ID is also associated with a list of latitude and longitude coordinates, which describe the geometry of the road segment; real-time data streams (e.g., ViriCiti, HERE, and DarkSky) are stored for the purpose of providing easy access for model training and updates as well as real-time access for system monitoring).
The rationale to combine remains the same as for Claim 6.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Usman in view of Taylor et al (PGPub 20230206762) (hereafter, “Taylor”).
Regarding Claim 10, Usman discloses the limitations of Claim 2. Usman additionally discloses wherein the prediction module comprises a Learning-Based Cost Prediction module coupled to an Adaptive Optimization System using a real-time trigger (pg. 7; the planner first determines the energy that can charged during the slot; it depends upon the power of the charger that will be used for charging at the found time slot; therefore, the planner first determines the location where car will be parked at the found time slot (i.e. either at home or at work); then, it determines the vehicle presence time at this location overlapped with period of found slot; using the presence time and charger power it calculates the maximum energy Emax that can be charged at this particular slot; then it calculates the effective energy that is planned to charge at this slot by taking the minimum of required energy to meet minimum energy level constraint, the amount of energy that can be charged during this slot and the amount of the energy that can be charged before the battery gets full during already planned charging in successive slots; this effective energy is added to the battery SOC; if this optimization process successfully iterates over all trips to keep the battery SOC above minimum level at each point in time, this schedule is marked as "feasible;" DCD (deepest charge depletion) level of the battery is used as the minimum threshold for battery SOC for all trips; in case of battery SOC goes below to the minimum level after a particular trip, a time slot is selected between two time periods t0 and t1, where t1 is the last period before the trip and t0 is the last period when the battery was full). Usman does not explicitly disclose but Taylor does disclose wherein the prediction module comprises a Deep Reinforcement Learning-Based Cost Prediction module with real-time feedback (¶ 0094-0095; the controller may learn from and make decisions on a set of data (including data provided by the various sensors), by making data-driven predictions and adapting according to the set of data; machine learning may involve performing a plurality of machine learning tasks by machine learning systems, such as supervised learning, unsupervised learning, and reinforcement learning; the many types of machine learning algorithms may include decision tree based learning, association rule learning, deep learning, artificial neural networks, genetic learning algorithms, inductive logic programming, support vector machines (SVMs), Bayesian network, reinforcement learning, representation learning, rule-based machine learning, sparse dictionary learning, similarity and metric learning, learning classifier systems (LCS), logistic regression, random forest, K-Means, gradient boost, K-nearest neighbors (KNN), a priori algorithms, and the like; the parameters of the neural network may generate a value at the output node designating that action as the desired action; this action may translate into a signal that causes the equipment to operate; this may be accomplished via back-propagation, feed forward processes, closed loop feedback, or open loop feedback; the machine learning system of the controller may use evolution strategies techniques to tune various parameters of the artificial neural network).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the fleet management machine learning techniques of Taylor with the vehicle fleet modeling and optimization system of Usman because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Taylor are applicable to the base device (Usman), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Usman in view of Rimal et al, “Smart Electric Vehicle Charging in the Era of Internet of Vehicles, Emerging Trends, and Open Issues,” Energies 15.5 (hereafter, “Rimal”) (copy attached in IDS of 9/24/2024).
Regarding Claim 11, Usman discloses the limitations of Claim 2. Usman does not explicitly disclose but Rimal does disclose wherein the digital twin platform, the assignment and strategy module, the depot parking and management module, or any combination thereof is hosted on one or more cloud platforms (pg. 6; each EV is equipped with a Li-ion battery that enables them to store energy, whereby different QoS requirements of EVs are taken into account; EVs in both highway or parking lot scenarios communicate with the edge or remote cloud to semi their information (e.g., spatial location, charging demand) to and receive messages from the cloud (e.g., price, discount, charging capacity of nearby CSs, charging schedule); Multi-class CSs (DC fast charge and Level 2) are considered).
One of ordinary skill in the art would have been motivated to include the fleet management structural arrangements of Rimal with the vehicle fleet modeling and optimization system of Usman to mitigate management complexity, computing and storing big data, and supply resources to P2P direct energy trading between consumers and producers (see at least pg. 2 of Rimal).
Claim 27 is rejected under 35 U.S.C. 103 as being unpatentable over Usman in view of Peng (PGPub 20210203177) (hereafter, “Peng”).
Regarding Claim 27, Usman discloses the limitations of Claim 26. Usman does not explicitly disclose but Peng does disclose:
wherein at least some of the electric vehicles are autonomous vehicles (¶ 0091, 0123; the remote operating system connectivity manager facilitates communications between the vehicle and any one or more autonomous vehicle systems; the driving condition sensor(s) may include one or many sensors that help detect the way in which a vehicle is being driven (e.g., via manual input, autonomously, semi-autonomously, etc.));
translating the charging schedules into instructions for navigating the autonomous vehicles within a charging station (¶ 0040, 0173; receiving, at a scheduler and over a communications network, a set of power provider attributes; reading, by the scheduler, a set of home power usage information from one or more databases; reading, by the scheduler, a set of user charging preferences from one or more databases; reading, by the scheduler, a set of user schedule information from one or more databases; reading, by the scheduler, a set of vehicle information from one or more databases; determining, by the scheduler, an amount of power required to charge a rechargeable energy storage of a vehicle to a desired state of charge (SOC) based on the set of vehicle information and the set of user schedule information and determining, by the scheduler, one or more charging time periods to charge the rechargeable energy storage based on the set of power provider attributes, the set of home power usage information, the set of user charging preferences, the user schedule information, the set of vehicle information, and the amount of power required to charge the vehicle; a vehicle running low on power may automatically determine that pulling over to a rest area, emergency lane, and removing, or "dropping off," at least one power source may reduce enough weight of the vehicle to allow the vehicle to navigate to the closest power source replacement and/or charging area); and
transmitting the instructions to the autonomous vehicles (¶ 0091; remote operating system connectivity manager facilitates communications between the vehicle and any one or more autonomous vehicle systems; these communications can include one or more of navigation information, vehicle information, other vehicle information, weather information, occupant information, or in general any information related to the remote operation of the vehicle).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the charge scheduling vehicle control techniques of Peng with the vehicle fleet modeling and optimization system of Usman because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Peng are applicable to the base device (Usman), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Discussion of Prior Art Cited but Not Applied
For additional information on the state of the art regarding the claims of the present application, please see the following documents not applied in this Office Action (all of which are prior art to the present application):
PGPub 20180373268 – “Systems and Methods for Managing Fleets of Autonomous Vehicles to Optimize Electric Budget,” Antunes et al, disclosing a system for managing the scheduling and charging of a fleet of autonomous vehicles
PGPub 20200258018 – “System and Methods for Maintaining a Vehicle Availability Report with Respect to a Location,” Brady, disclosing a system for using machine learning to manage the schedule of a fleet of vehicles
Viziteu et al, Smart Scheduling of Electric Vehicles based on Reinforcement Learning, Sensors (Basel, Switzerland), 22(10), 3718, disclosing methods for using reinforcement learning techniques to determine a schedule for a fleet of electric vehicles
Liang et al, Mobility-Aware Charging Scheduling for Shared On-Demand Electric Vehicle Fleet Using Deep Reinforcement Learning, IEEE Transactions on Smart Grid (Volume: 12, Issue: 2, 2021, Page(s): 1380-1393), disclosing methods for using reinforcement learning techniques to determine a schedule for a fleet of electric vehicles
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MARK C CLARE/Examiner, Art Unit 3628 /MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628