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 RCE filed on 5/12/2026.
Claims 1, 7-8, 12, 22, 24, and 30-31 have been amended and are hereby entered.
Claim 27 has been canceled.
Claims 1-26 and 28-31 are currently pending and have been examined.
Request for Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/12/2026 has been entered.
Response to Applicant’s Arguments
Objections
The present amendments to Claim 1 obviate the previous objection thereto; therefore, this objection is withdrawn.
Claim Interpretation
Applicant asserts that Claims 1 and 24 are presently amended to avoid the previous interpretation of terms under 112(f) standards. The present amendments fail to do so. Further, several other terms previously interpreted under 112(f) exist in other claims, and the present claim amendments do not avoid these interpretations either.
Applicant presents arguments against the 112(f) interpretations of the particular terms “a digital twin platform” and “an assignment and strategy module.” Firstly, Applicant argues that the term “means” is not used in the claims. While this is true, and while it is also true that the lack of “means” does create a rebuttable presumption that 112(f) does not apply, this is not dispositive that 112(f) does not apply. Indeed, the lack of “means” and the rebuttable presumption related thereto is and has been acknowledged in the 112(f) interpretations themselves at every stage of prosecution thus far, ie: “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.”
Secondly, Applicant argues that Claim 1, “particularly as amended, recites a [sic] specific system components in a defined architecture and further recites how these components operate within the claimed system,” and relatedly that the use of these terms as claimed “is not an unbounded black-box recital. Rather, the claim ties the module to particular inputs, a particular optimization operation, and a particular output used by the claimed depot parking and management module.” Examiner disputes any “specific system components in a defined architecture” as would be understood in the context of 112(f) standards is present in relation to these terms; indeed, the only components recited are the nonce terms themselves, which are not “specific” as they have no generally understood structure. Other than this assertion, Applicant appears to entirely misapprehend the standards of 112(f), as no other piece of Applicant’s argument has any baring on avoiding interpretation under 112(f). Indeed, everything else argued by Applicant here is entirely functional, which is one of the requirements for interpretation under 112(f) rather than something which might refute it. As such, the previous 112(f) interpretations are maintained. Applicant is strongly encouraged to review at least MPEP 2181 regarding the standards of interpretation under 112(f), particularly discussion of the three-prong analysis thereof.
Claim Rejections – 35 USC § 112
The present amendments to Claim 24 obviate the previous 112(a) rejection thereto; therefore, this rejection is withdrawn.
The present amendments to the claims obviate the previous 112(b) rejections thereto; therefore, these rejections are withdrawn.
Claim Rejections – 35 USC § 101
Applicant’s arguments regarding the 101 analysis have been considered and are unpersuasive.
Applicant argues that the claims should be eligible under the streamlined 101 subject matter eligibility analysis. Applicant supports this argument by way of a vague attempted analogy between the presently claimed invention and the language “[a]s an example, a robotic arm assembly having a control system that operates using certain mathematical relationships is clearly not an attempt to tie up use of the mathematical relationships and would not require a full analysis to determine eligibility” of MPEP 2106.06, asserting that “when viewed as a whole, claims 1-31 recite elements such that it is clear that the claims do not seek to tie up all techniques for assigning work and charging vehicles such that others cannot assign work to and/or charge electric vehicles.” Here, Applicant is comparing apples to oranges, as the exemplary language discusses a clearly non-abstract invention (a robotic arm assembly) whose operation is at some level based on mathematical relationships, whereas the present invention is at its core an abstract invention (the determination and assignment of optimized work and charging for a reservable resource) which is merely claimed at a high level as being carried out via computer implementation. There is no reasonable analogy to be made between this exemplary mechanical product and the present invention’s sequence of abstract steps which achieve an abstract end. Even when viewed as a whole (e.g., considering the computer implementation of this abstract sequence of steps),
In addition to the unreasonable analogy of the inventions themselves, Examiner notes that nothing in Applicant’s cited passages nor anything else in MPEP 2106.06 specifies that this or any 101 standard requires the tying up of “all techniques” for using a judicial exception. Indeed, as stated in MPEP 2106.04(I), “[w]hile preemption is the concern underlying the judicial exceptions, it is not a standalone test for determining eligibility. Rapid Litig. Mgmt. v. CellzDirect, Inc., 827 F.3d 1042, 1052, 119 USPQ2d 1370, 1376 (Fed. Cir. 2016). Instead, questions of preemption are inherent in and resolved by the two-part framework from Alice Corp. and Mayo (the Alice/Mayo test referred to by the Office as Steps 2A and 2B).” As is further stated in MPEP 2106.04(I), “[w]hile preemption may signal patent ineligible subject matter, the absence of complete preemption does not demonstrate patent eligibility.”
For these reasons, it is far from “clear” that the present claims are eligible under 101 and therefore due a streamlined analysis rather than a full 101 analysis. Further still, based on a full and proper 101 subject matter eligibility analysis (see 101 rejections and further responses to arguments below), the claims remain subject matter ineligible.
Applicant next begins substantive arguments related to the full 101 subject matter eligibility analysis. As a preliminary observation, Examiner notes that these arguments reference various outdated versions of Eligibility Guidances (e.g., 2014 and 2019 versions). Applicant also presents citations to such Guidance (e.g., the first instance thereof) which does not specify to which year/version the citation relates. Regardless, each of these cited versions have since been superseded by the July 2024 PEG. While some aspects of the previously cited Guidances remain accurate, others do not. In the future, Applicant should only cite to current Guidance.
Regarding Step 2A, Applicant argues that “the Revised PTO Eligibility Guidance states that the concepts ‘identified by the courts as abstract ideas’ include only ‘mathematical concepts,’ ‘certain methods of organizing human activity,’ ‘and ‘mental processes.’ Applicant’s amended claim 1 is not directed to any of these concepts ‘identified by the courts as abstract ideas.’” This argument is a misstatement and misapprehension of 101 subject matter eligibility standards, potentially stemming from Applicant’s failure to consider Step 2A in terms of the sequentially performed Prongs One and Two thereof in the present Remarks. Particularly, while a claim limitation must be found to recite at least one of mental processes, certain methods of organizing human activity, or mathematical concepts to recite an abstract idea under the Prong One analysis (with few exceptions – see MPEP 2106.04(a)(3)), a claim need not be “directed to” one of these individual abstract idea categories (as argued) under Prong Two to be directed to an abstract idea. Rather, as explained in MPEP 2106.04(II)(B), “multiple abstract ideas, which may fall in the same or different groupings, or multiple laws of nature may be recited. In these cases, examiners should not parse the claim. For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A Prong One to make the analysis clear on the record. However, if possible, the examiner should consider the limitations together as a single abstract idea for Step 2A Prong Two and Step 2B (if necessary) rather than as a plurality of separate abstract ideas to be analyzed individually.”
Further, Applicant asserts that rather than being directed to one of these categories of abstract idea (again, an erroneous understanding of 101 standards), it is instead “directed to a system for generating charging schedules based on a digital twin platform and translating the charging schedules into machine executable instructions for autonomous vehicles.” What a claim is “directed to” is not based on some subjective summary of some elements of a claim. Instead, what a claim is “directed to” has particular meaning within the 101 subject matter eligibility analysis (ie: the determination of Step 2A, Prong Two). So far as Examiner can determine, Applicant makes no attempt to perform this analysis to determine what the claims are actually “directed to.” Examiner maintains that a proper application of Step 2A, Prongs One and Two standards to the present claims result in a finding that the claims are instead directed to an abstract idea, as laid out in the updated 101 rejections below.
Still regarding Step 2A, Applicant lists the considerations for integration into a practical application listed in MPEP 2106.04(d); Applicant then argues that the claims are integrated into a practical application by way of embodying an improvement to computer functionality or other technology, but makes no cogent argument to this effect. Rather, Applicant references various elements of Claim 1 (some of which are additional elements but others of which are abstract ideas which cannot integrate themselves into a practical application – see the Final Rejection of 1/21/2026), and argues that such elements provide improvements which are solely abstract in nature rather.
Regarding the consideration of improvements to a technology, MPEP 2106.05(a) states in relevant part that such improvements constitute “a technological solution to a technological problem,” and further that “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.” In particular, Applicant asserts that various claim elements result in “decreasing cost of ownership, optimizing fleet utilization, or decreasing operational expenses,” citing to pg. 9 of the specification as support. None of these constitute an improvement to computer functionality or other technology, as “decreasing cost of ownership, optimizing fleet utilization, [and] decreasing operational expenses” are all purely abstract business concerns rather than technological functions. Merely claiming the almost entirely abstract sequence of steps of Claim 1 generally being effectuated by computer elements does not somehow transform these entirely abstract considerations into technological ones.
Similarly, Applicant also asserts that the claims “can provide an improvement to scheduling, coordination, and navigation of autonomous vehicles at a charging station.” Ignoring the use of “can” which at least rhetorically undermines the argument that an improvement is actually achieved by the claims, each of these similarly recite abstract improvements rather than technological ones. Particularly regarding purported improvements to “navigation of autonomous vehicles at a charging station,” while Applicant couches this improvement in technological language, this improvement is really a broader, abstract improvement, e.g., providing an improved navigation instructions/plan for traversing a charging station. Such instructions might be followed by a human operator of a vehicle in the same way to achieve the same results (e.g., as discussed throughout the original disclosure, which makes clear that the content thereof may be applied to either human drivers or autonomous vehicles), making it abundantly clear that this “improvement” is not limited to autonomous vehicles as argued. These improvements are not improvements to any recited technological element (see, e.g., the seminal Diamond v. Diehr) or a technological functioning thereof 1014 (see, e.g., the Enfish and McRO cases), but rather the present claims merely use such technological elements as tools to perform an abstract process to achieve abstract results/improvements.
Moving to Step 2B, Applicant merely reiterates the same considerations and purported improvements already addressed above in relation to Step 2A, Prong Two. These arguments are unpersuasive for the same reasons explained above.
Claim Rejections – 35 USC § 103
Applicant’s arguments regarding the 103 analysis have been considered and are unpersuasive.
Applicant’s only substantive arguments regarding the 103 rejection is to argue against the previous citations against presently cancelled Claim 27 (varying contents of which are presently rolled up into independent Claims 1, 12, and 24). Regarding the translation of charging schedule into navigation instructions for an autonomous vehicle to follow, Peng discloses the determination of charging schedules at charging stations to be followed by autonomous vehicles (e.g., in the form of “control instructions”). As would be readily apparent to anyone of ordinary skill in the art at the time of filing, this necessarily implies the translation of such a charging schedule into instructions which can be followed by these autonomous vehicles. While it is not entirely clear to Examiner from the arguments why and how Applicant disagrees that the previous citations to Peng disclose this feature, Examiner’s best attempt to steelman Applicant’s reasoning would be an unreasonably narrow reading of the content of Peng, solely based on a lack of identical language and ignoring what one of ordinary skill in the art at the time of filing would read Peng as indicating. Indeed, Examiner finds this argument particularly puzzling, as if the standards for disclosure of such functions were as narrow as Applicant contends here (e.g., ignoring what would be understood by one of ordinary skill in the art), Examiner notes that this rolled up limitation for translating charging schedules into navigation instructions would have been rejected under 112(a) as failing to meet written description standards thereof based on the lack of any explanation of this translation in the present invention’s original disclosure (see, e.g., Paragraphs 0023, 0026, and 0077 as published, which do no more than mention this concept in extremely vague and high-level manner), requiring the removal of this functionality from the claims.
Applicant’s further entirely unsupported argument that “Peng is silent as to ‘a processor to translate’” the above-discussed navigation instructions is similarly untrue. For clarity, Examiner notes that the “processor” of this argument is something which was not claimed in presently cancelled Claim 27, at present is only found in Claim 1, and which Applicant misquotes as “a processor” rather than “the processor.” The scheduler of the previous Peng citations may be effectuated on the remote systems/servers/devices illustrated in Figs. 10 and 12 thereof, with such remote systems incorporating a processor as per at least Paragraphs 0102-0102 and Fig. 11. See updated 103 rejections below for more information regarding this newly claimed feature.
While this addresses the 103 arguments presented in these Remarks, Examiner notes that there appears to be content missing between pages 14 and 15 thereof, as pg. 14 ends with a completed paragraph yet pg. 15 begins mid-sentence. If Applicant mistakenly left out content intended to be argued, Applicant may provide such content in a future round of prosecution.
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 § 101
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 12-23 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 12 contains the following language: “translating the charging schedules into instructions for navigating the autonomous vehicles within a charging station.” In this language, the term “the charging schedules” lacks antecedent basis. For the purposes of this examination, “the charging schedules” will be interpreted as “charging schedules.” Claims 13-23 are rejected due to their dependence upon Claim 12.
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 1-26 and 28-31 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 a charging schedule and blocks of work to the electric vehicles based on a solution to the optimization problem to increase a key performance indicator of the fleet of 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; translating the charging schedule into instructions for navigating the autonomous vehicles within a charging station, 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 a charging schedule and blocks of work to the electric vehicles based on a solution to the optimization problem to increase a key performance indicator of the fleet of electric vehicles, wherein each of the blocks of work includes an electric vehicle trip with one or more destinations; implement the blocks of work for the electric vehicles according to solution to the optimization problem; and implement the blocks of work for the electric vehicles according to solution to the optimization problem; translating the charging schedule into instructions for navigating the autonomous vehicles within a charging station, 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, a depot parking and management module including a processor, and wherein at least some of the electric vehicles are autonomous vehicles. 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, in the context of the claim as a whole, amount to no more than mere instructions to apply a judicial exception (see MPEP 2106.05(f)). A fleet of electric vehicles, wherein at least some of the electric vehicles are autonomous vehicles, and charging stations, in the context of the claim as a whole, 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 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 (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 additionally 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; 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; and translating the charging schedules into instructions for navigating the autonomous vehicles within a charging station, 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; 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; and translating the charging schedules into instructions for navigating the autonomous vehicles within a charging station, 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, wherein at least some of the electric vehicles are autonomous vehicles, generating a digital representation of the electric vehicles and/or the vehicle charging stations in a virtual environment, vehicle charging stations, and transmitting the instructions to the autonomous vehicles. Generating a digital representation of the electric vehicles and/or the vehicle charging stations in a virtual environment, in the context of the claim as a whole, amounts to no more than mere instructions to apply a judicial exception (see MPEP 2106.05(f)). Transmitting the instructions to the autonomous vehicles, in the context of the claim as a whole, amounts to no more than insignificant extra-solution activity (see MPEP 2106.05(g)). Electric vehicles in a transit fleet, wherein at least some of the electric vehicles are autonomous vehicles, and vehicle charging stations, in the context of the claim as a whole, 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. 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). 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 the increase 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; 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; 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; and translating the charging schedules into instructions for navigating the autonomous vehicles within a charging station, 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; 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; 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; and translating the charging schedules into instructions for navigating the autonomous vehicles within a charging station, 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 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, wherein at least some of the electric vehicles are autonomous vehicles, electric vehicle chargers, the behavior models including a digital representation of the electric vehicles and/or the vehicle charging stations in a virtual environment, a prediction store, and transmitting the instructions to the autonomous vehicles. 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, in the context of the claim as a whole, amount to no more than mere instructions to apply a judicial exception (see MPEP 2106.05(f)). Transmitting the instructions to the autonomous vehicles, in the context of the claim as a whole, amount to no more than insignificant extra-solution activity (see MPEP 2106.05(g)). Electric vehicles in a transit fleet, wherein at least some of the electric vehicles are autonomous vehicles, and electric vehicle chargers, in the context of the claim as a whole, 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. 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). 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 25-26 and 28-31, 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 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 the planned data with real-time electric 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 electric vehicle data (an abstract idea in the form of a certain method of organizing human activity and a mental process); and identifying features between the landmarks and the planned landmarks to correlate real-time electric vehicle data with the 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, the time, and the real-time electric vehicle data to update the performance 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 § 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 1-2, 5, 7-9, 12-13, 18-22, and 24-26 are rejected under 35 U.S.C. 103 as being unpatentable over 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) in view of Peng (PGPub 20210203177) (hereafter, “Peng”).
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 a charging schedule and blocks of work to the electric vehicles based on a solution to the optimization problem to increase a key performance indicator of the fleet of 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).
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.)); and
the processor configured to translate the charging schedule into instructions for navigating the autonomous vehicles within a charging station (¶ 0040, 0042, 0097, 0102-0103, 0108, 0117, 0173; Figs. 10-12; 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).
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.
Regarding Claim 2, Usman in view of Peng 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 in view of Peng 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 in view of Peng discloses the limitations of Claim 1. Usman further discloses wherein the assignment and strategy module is configured to solve the optimization problem additionally based on one or more of the following variables: the 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 in view of Peng 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 additionally 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 in view of Peng 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").
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, 0042, 0097, 0102-0103, 0108, 0117, 0173; Figs. 10-12; 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 (¶ 0042, 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).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 13, Usman in view of Peng 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 in view of Peng 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 in view of Peng 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 in view of Peng 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 in view of Peng 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 in view of Peng 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 the increase 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);
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").
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, 0042, 0097, 0102-0103, 0108, 0117, 0173; Figs. 10-12; 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 (¶ 0042, 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).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 25, Usman in view of Peng 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 in view of Peng 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).
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 Peng and 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 in view of Peng 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 and Peng 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 and Peng to attain the highest accuracy models (see at least the Abstract of Ayman).
Regarding Claim 6, Usman in view of Peng 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 and Peng 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 in view of Peng 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 and Peng 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 in view of Peng 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 and Peng to build data-driven predictors of route-level energy usage (see at least pg. 7:2 of Ayman).
Regarding Claim 16, Usman in view of Peng 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 Peng and 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 Peng and 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 in view of Peng 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 in view of Peng 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 in view of Peng 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 the 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 electric 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 the landmarks and the planned landmarks to correlate real-time electric vehicle data with the 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 Peng and Ayman discloses the limitations of Claim 30. Usman does not explicitly disclose but Ayman does disclose further comprising using the features, the time, and real-time electric vehicle data to update 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.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Usman in view of Peng and Taylor et al (PGPub 20230206762) (hereafter, “Taylor”).
Regarding Claim 10, Usman in view of Peng 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 and Peng 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 and Peng), 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 in view of Peng 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 and Peng 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).
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
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/MARK C CLARE/Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628
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