DETAILED ACTION
Notice to Applicant
The following is a FINAL Office action upon examination of application number 18/896,131 filed on 09/25/2024. Claims 1 and 3-7 are pending in this application, and have been examined on the merits discussed below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Application 18/896,131 filed 09/25/2024 claims foreign priority to 23200999.3, filed 09/29/2023.
Response to Amendment
In the response filed February 23, 2026, Applicant amended claims 1, 3-4, and 6-7, and cancelled claims 2 and 8. No new claims were presented for examination.
Applicant's amendments to claim 1 are hereby acknowledged. The amendments are sufficient to overcome the previously issued claim objections; accordingly, these objections have been removed.
Applicant's amendments to claim 7 are hereby acknowledged. The amendments are sufficient to overcome the previously issued claim rejection under 35 U.S.C. 112(b); accordingly, this rejection has been withdrawn.
Claim 8 was cancelled; accordingly, the previously issued claim rejection under 35 U.S.C. 112(b) has been withdrawn.
Applicant's amendments to the claims are hereby acknowledged. The amendments are not sufficient to overcome the previously issued claim rejection under 35 U.S.C. 101; accordingly, this rejection has been maintained.
Response to Arguments
Applicant's arguments filed February 23, 2026, have been fully considered.
Applicant submits “Step 2A - Prong 1 - As indicated in the 2019 Guidance, a claim limitation recites abstract ideas when such abstract ideas are "recited on their own or per se." Here, the Examiner has identified claim limitations that allegedly include abstract ideas; however, none of these limitations recite abstract ideas on their own or per se-they do not recite mathematical concepts, methods of organizing human activity, or mental processes. Rather, the claims recite an improved method for route planning of electric vehicles using specific novel and non-obvious steps. The Examiner alleges that method steps could represent certain methods of organizing human activities or mental processes. However, the claims as a whole are not directed to such concepts.” [Applicant’s Remarks, 02/23/2026, page 7]
The Examiner respectfully disagrees. Under Step 2A Prong One of the eligibility inquiry, Applicant argues that “none of these limitations recite abstract ideas on their own or per se-they do not recite mathematical concepts, methods of organizing human activity, or mental processes.” In this instance, claim 1 has been found to recite an abstract idea that falls into the “Certain methods of organizing human activity” and “Mental Processes” abstract idea groupings. The recited steps, such as receiving a plurality of parameters, determining a ratio, determining a set of EVs based on validity, and assigning or mapping resources to a task, fall squarely with categories of abstract ideas. First, these steps correspond to methods organizing human activity because they describe the same type of planning and scheduling operations that have long been perfumed manually in logistics and fleet management. A human planner can receive information about available resources, evaluate constraints, rank or sort tasks based on priorities, and assign resources to tasks.
Additionally, the steps can be performed as mental processes with the aid of pen and paper. Each operation including limitations related to determining a ratio, comparing values to thresholds, sorting based on criteria, and assigning based on rules, can be carried out in the human mind with aids like pen and paper. Therefore, the claims are reasonably understood as reciting one or more abstract ideas when evaluated under Step 2A Prong One of the eligibility inquiry.
Applicant submits “Step 2A - Prong 2 - Under Prong Two, the Examiner must evaluate whether the alleged exception is integrated into a practical application. Thus, even assuming the result of Step 2A - Prong 1 was YES, the pending claims are still eligible under Step 2A - Prong 2 because the claims include elements that integrate into a practical application. For example, the claimed methods recite an additional element that applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.” [Applicant’s Remarks, 02/23/2026, pages 7-8]
In response, the Examiner first points out that as noted in the previous Office Action [12/04/2025], the steps recited in claim 1 are disembodied steps (i.e., the steps are disembodied since no device/hardware is relied on for performing the claimed limitations).
In response to Applicant’s argument that “Under Prong Two, the Examiner must evaluate whether the alleged exception is integrated into a practical application. Thus, even assuming the result of Step 2A - Prong 1 was YES, the pending claims are still eligible under Step 2A - Prong 2 because the claims include elements that integrate into a practical application,” the Examiner respectfully disagrees. Under Step 2A Prong Two of the eligibility inquiry, any additional elements are evaluated individually and in combination to determine whether they integrate the judicial exception into a practical application, with consideration of the following exemplary considerations that may be indicative of a practical application: an additional element that reflects an improvement to the functioning of a computer or to any other technology or technical field, applying the exception with a particular machine, applying the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, effecting a transformation of a particular article to a different state or thing, and applying or using the judicial exception some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. (pg. 55 of Fed. Reg. / Vol. 84, No. 4 – published Jan. 7, 2019).
In this instance, there are no additional claim elements besides the judicial exception. As noted in the previous Office Action, dated 10/01/2024, because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) (“Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract”); Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility “cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself.”). For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must “transform the nature of the claim” into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981, either at Prong Two or in Step 2B. If there are no additional elements in the claim, then it cannot be eligible. Accordingly, this argument is found unpersuasive.
With respect to Applicant’s argument that “the claimed methods recite an additional element that applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception,” it is noted that preemption is not a standalone test for patent eligibility. Preemption concerns have been addressed by the Examiner through the application of the two-step framework. Applicant’s attempt to show that the recited abstract idea is a specific one is not persuasive. A specific abstract idea is still an abstract idea and is not eligible for patent protection without significantly more recited in the claim. See the July 2015 Update: Subject Matter Eligibility that explains that questions of preemption are inherent in the two-part framework from Alice Corp and Mayo and are resolved by using this framework to distinguish between preemptive claims, and “those that integrate the building blocks into something more…the latter pose no comparable risk of preemption, and therefore remain eligible.” The absence of complete preemption does not guarantee the claim is eligible. Therefore, “[w]here a patent’s claims are deemed only to disclose patent ineligible subject matter under the Mayo framework, as they are in this case, preemption concerns are fully addressed and made moot.” Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371, 1379 (Fed. Cir. 2015). See also OIP Tech., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1362-63 (Fed Cir. 2015). For the reasons above, this argument is found unpersuasive.
Applicant submits “Applicant's claimed embodiments provide just such an improvement in the relevant technological field and thus demonstrate integration into a practical application.” [Applicant’s Remarks, 02/23/2026, page 8]
In response to Applicant’s assertion that “Applicant's claimed embodiments provide just such an improvement in the relevant technological field and thus demonstrate integration into a practical application,” the Examiner respectfully disagrees. Under Step 2A Prong Two of the eligibility inquiry, any additional elements are evaluated individually and in combination to determine whether they integrate the judicial exception into a practical application, with consideration of the following exemplary considerations that may be indicative of a practical application: an additional element that reflects an improvement to the functioning of a computer or to any other technology or technical field, applying the exception with a particular machine, applying the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, effecting a transformation of a particular article to a different state or thing, and applying or using the judicial exception some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In this instance, the additional elements recited in exemplary claim 6 are: an interface configured to, a processor and a memory coupled to the processor, and wherein the memory comprises instructions. These elements have been considered individually and in combination, however these computing elements amount to using a generic computer programmed with computer-executable instructions/software to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment, which is not sufficient to amount to a practical application, as noted in MPEP 2106. See also MPEP 2106.05(f) and 2106.05(h). Furthermore, these additional elements fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Instead, the interface, processor, memory amount to using generic computing devices as tools to implement the abstract idea, which does not amount to a technological improvement or otherwise indicate a practical application. See MPEP 2106.05(f). Notably, Applicant’s Specification acknowledges that the invention may be implement with generic computing devices. See Specification, paragraph 0032.
Even assuming arguendo that an improvement was achieved, improving the method for route planning using only generic computing devices does not improve the computing devices or any technology, but instead any incidental improvement achieved by automating the claim steps would come from the capabilities of a general-purpose computer rather than the sequence of steps/activities recited in the method itself, which does not materially alter the patent eligibility of the claim. See Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.”) (cited in the Federal Circuit's FairWarning decision). Accordingly, the generic computing elements do not integrate the judicial exception into a practical application.
Applicant submits “the claims are directed to an improvement. Furthermore, the claims "clearly do[] not seek to tie up the abstract idea.”” [Applicant’s Remarks, 02/23/2026, pages 8-9]
The Examiner respectfully disagrees. In response to Applicant’s argument that “the claims are directed to an improvement,” it is noted that to establish an improvement, the claim must clearly recite a specific technological advancement or a particular solution to a technological problem, rather than simply applying known abstract technique in a computer environment. In this instance, the recited steps describe generic workflow of data processing and decision making. These operations do not reflect a specific improvement in computer functionality, or any other technological field. Accordingly, this argument is not persuasive.
With respect to Applicant’s argument that “the claims "clearly do[] not seek to tie up the abstract idea”,” it is noted that preemption is not a standalone test for patent eligibility. Preemption concerns have been addressed by the Examiner through the application of the two-step framework. Applicant’s attempt to show that the recited abstract idea is a specific one is not persuasive. A specific abstract idea is still an abstract idea and is not eligible for patent protection without significantly more recited in the claim. See the July 2015 Update: Subject Matter Eligibility that explains that questions of preemption are inherent in the two-part framework from Alice Corp and Mayo and are resolved by using this framework to distinguish between preemptive claims, and “those that integrate the building blocks into something more…the latter pose no comparable risk of preemption, and therefore remain eligible.” The absence of complete preemption does not guarantee the claim is eligible. Therefore, “[w]here a patent’s claims are deemed only to disclose patent ineligible subject matter under the Mayo framework, as they are in this case, preemption concerns are fully addressed and made moot.” Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371, 1379 (Fed. Cir. 2015). See also OIP Tech., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1362-63 (Fed Cir. 2015). For the reasons above, this argument is found unpersuasive.
Applicant submits “Applicant also contends that the claims contain something significantly more than the abstract idea as is required by part 2B of the Alice/Mayo Test.” [Applicant’s Remarks, 02/23/2026, page 9]
Applicant alludes to Step 2B of the eligibility inquiry by suggesting “the claims contain something significantly more than the abstract idea as is required by part 2B of the Alice/Mayo Test.” The Examiner respectfully disagrees and notes that the claims merely product a result in the form of a “set of routes to be mapped to a set of electric vehicles”, which is not an improvement to the interface, processor, memory. These elements have been considered individually and in combination, or any other system or technology. The claims have not been shown to modify, reconfigure, manipulate, or transform interface, processor, memory, or any technology in any discernible manner, much less yield an improvement thereto. There is no indication that any of the additional elements or the combination of elements amount to an improvement to the computer or to any technology. Their individual and collective functions merely provide generic computer implementation. Therefore, these additional claim elements do not amount to significantly more than the abstract idea itself. For the reasons above, thus argument is found unpersuasive.
For the reasons above, in addition to the reasons provided in the updated §101 rejection below, Applicant’s amendment and supporting arguments are not sufficient to overcome the §101 rejection.
Applicant submits “that claims 1 and 6-8 are not obvious and unpatentable over Rogge in view of Meroux et al., because the combination of cited references does not teach or render obvious each and every element of the claims.” [Applicant’s Remarks, 02/23/2026, page 11]
In response to the Applicant’s argument “that claims 1 and 6-8 are not obvious and unpatentable over Rogge in view of Meroux et al., because the combination of cited references does not teach or render obvious each and every element of the claims,” it is noted that this argument is a mere allegation of patentability by the Applicant with no supporting rationale or explanation. Merely stating that the claims do not teach a feature does not offer any insight as to why the specific sections of the prior art relied upon by the Examiner fail to disclose the claimed features. Applicant's arguments amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Applicant submits “Rogge fails to disclose the determination of a distance to time ratio (dttr) for each route of a plurality of routes based on the route information associated with each EV of the EV fleet.” [Applicant’s Remarks, 02/23/2026, page 12]
The Examiner respectfully disagrees. With respect to the §103 rejection of independent claim 1, Applicant argues that “Rogge fails to disclose the determination of a distance to time ratio (dttr) for each route of a plurality of routes based on the route information associated with each EV of the EV fleet.” Rogge explicitly teaches using route characteristics such as distance, average speed, and time related constraints in evaluating and planning routes. The discussion of average speed (km/h) directly corresponds to a distance-to-time relationship, which is equivalent to a distance-to-time ratio as claimed. Additionally, Rogge computes energy consumption and evaluates routing decisions based on these route characteristics, which require consideration of the relationship between distance and time. Even if Rogge does not explicitly use the term “distance-to-time ratio,” it teaches using the same underlying parameter’s (distance and time related metrics such as speed) to evaluate and compare routes. Thus, given the broadest reasonable interpretation consistent with the specification in construing the claimed invention, it is Examiner’s position that the disclosure of Rogge teaches the disputed limitation. Accordingly, this argument is found unpersuasive.
Applicant submits “Rogge does not disclose the determination of a set of EVs from the EV fleet based on a validity of each EV of the EV fleet, and mapping a set of EVs to a set of the routes of the plurality of routes based on the dttr for each route of the plurality of routes.” [Applicant’s Remarks, 02/23/2026, page 12]
In response to Applicant’s argument that “Rogge does not disclose the determination of a set of EVs from the EV fleet based on a validity of each EV of the EV fleet,” it is noted that Rogge was not asserted as teaching the claimed limitation “determining a set of EVs from the EV fleet based on a validity of each EV of the EV fleet.” Accordingly this argument is deemed moot.
In response to Applicant’s argument that Rogge does not disclose “mapping a set of EVs to a set of the routes of the plurality of routes based on the dttr for each route of the plurality of routes,” it is maintained that Rogge does indeed teaches the disputed limitation. Rogge discloses assigning vehicles (i.e. buses) to service trips and constructing schedules in which each trip is matched with a specific vehicle as part of an optimized fleet and scheduling solution. This assignment of vehicles to route/trips is equivalent to mapping a set of EVs to a set of routes. Further, Rogge teaches that the route evaluation and assignment are based on route characteristics, such as distance, speed, and energy consumption. Since energy consumption in Rogge is directly influences by average speed, the vehicle to route assignment depends on the same parameters. Thus, the mapping is not arbitrary, it is driven by route characteristics that correspond to the claimed distance-to-time ratio. Thus, given the broadest reasonable interpretation consistent with the specification in construing the claimed invention, it is Examiner’s position that the disclosure of Rogge teaches the disputed limitation. Accordingly, this argument is found unpersuasive.
Applicant submits “no explanation of how Meroux teaches dttr is provided. Instead, the secondary reference also fails to teach or suggest features related to dttr.” [Applicant’s Remarks, 02/23/2026, page 12]
In response to Applicant’s argument that “no explanation of how Meroux teaches dttr is provided,” it is noted that Meroux was not asserted as teaching the claimed limitation “determining a distance to time ratio (dttr) for each route of a plurality of routes based on the route information associated with each EV of the EV fleet.” Accordingly this argument is deemed moot.
Applicant submits “Further, the Examiner relies on the additional reference to Mason as allegedly teaching the features wherein mapping the set of EVs to the set of the routes based on the dttr for each route of the plurality of routes comprises: determining the plurality of routes comprising the dttr greater than a dttr threshold. The Examiner points to 1 [0021] of Mason; however, this paragraph does not teach or suggest a distance to time ratio.” [Applicant’s Remarks, 02/23/2026, page 13]
With respect to the §103 rejection of independent claim 1, Applicant argues that “Mason; however, this paragraph does not teach or suggest a distance to time ratio.” In response, the Examiner respectfully disagrees. Although Mason does not explicitly use the term “distance-to-time ratio”, paragraph 0021 of Mason teaches selecting routes based on distance and transit time considerations, including reducing travel distance and time. These are the same underlying parameters that define a distance to time relationship. Moreover, Mason discloses narrowing candidate routes using criteria such as a distance and estimates transit time and excluding routes that do not meet predefined constraints. This correspond to the claimed limitation of determining the plurality of routes comprising the dttr greater than a dttr threshold. Thus, given the broadest reasonable interpretation consistent with the specification in construing the claimed invention, it is Examiner’s position that the disclosure of Mason teaches the disputed limitation. Accordingly, this argument is found unpersuasive.
Applicant submits “The Examiner relies on Mason as allegedly teaching the feature of sorting the plurality of routes comprising the dttr greater than the dttr threshold in an order and cites many of the same paragraphs discussed above. Again, the reference fails to teach or suggest any dttr or dttr threshold and fails to teach or suggest the pertinent limitations of the claim.” [Applicant’s Remarks, 02/23/2026, page 14]
With respect to the §103 rejection of independent claim 1, Applicant argues that Mason fails to teach or suggest “any dttr or dttr threshold.” In response, the Examiner respectfully disagrees. Although Mason does not explicitly use the term “distance-to-time ratio”, Mason teaches selecting, prioritizing, and ranking routes based on distance and transit time factors, as well as other cost related metrics. This suggest that routes are compare against a criterion and that routes are arranged in an order of preference based on those criteria. Such operations correspond directly to “sorting the plurality of routes comprising the dttr greater than the dttr threshold in an order. Additionally, Mason discloses generating candidate routes using criteria such as distance, estimated transit time, and cost. The act of filtering routes above or below certain criteria and ranking the remaining route suggest a thresholding step and an ordered evaluation. Thus, given the broadest reasonable interpretation consistent with the specification in construing the claimed invention, it is Examiner’s position that the disclosure of Mason teaches the disputed limitation. Accordingly, this argument is found unpersuasive.
Applicant’s remaining arguments either logically depend from the above-rejected arguments, in which case they too are unpersuasive for the reasons set forth above, or they are
directed to features which have been newly added via amendment. Therefore, this is now the Examiner's first opportunity to consider these limitations and as such any arguments regarding these limitations would be inappropriate since they have not yet been examined. A full rejection of these limitations will be presented later in this Office Action.
Claim Rejections - 35 USC § 101
22. 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.
23. Claims 1 and 3-7 are rejected under 35 U.S.C. 101 because the claims are directed to an abstract idea without significantly more.
24. Claims 1 and 3-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1, 3-5), system (claim 6), computer program product (claim ) are directed to at least one potentially eligible category of subject matter (i.e., process, machine, and article of manufacture, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1 and 3-7 is satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls under the “Certain method of organizing human activities” and “Mental Processes” abstract idea groupings set forth in MPEP 2106 since the claims set forth steps for managing personal behavior or relationships or interactions (e.g., social activities, following rules or instructions) and also sets forth “commercial interactions,” and thus fall under “Certain Methods of Organizing Human Activity,” and steps that can be performed in the human mind (including observation, evaluation, judgment, opinion), and therefore fall under the “Mental Processes” abstract idea grouping. With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below: receiving a plurality of parameters associated with an electric vehicle (EV) fleet, wherein the plurality of parameters comprises types of EVs in the EV fleet, route information associated with each EV of the EV fleet, time slot required for covering a route by each EV of the EV fleet and battery information associated with each EV of the EV fleet; determining a distance to time ratio (dttr) for each route of a plurality of routes based on the route information associated with each EV of the EV fleet; determining a set of EVs from the EV fleet based on a validity of each EV of the EV fleet; mapping the set of EVs to a set of the routes of the plurality of routes based on the dttr for each route of the plurality of routes; and scheduling the set of EVs to the set of the routes based on the mapping, wherein mapping the set of EVs to the set of the routes based on the dttr for each route of the plurality of routes comprises: determining the plurality of routes comprising the dttr greater than a dttr threshold; sorting the plurality of routes comprising the dttr greater than the dttr threshold in an order; determining the set of the routes from the sorted order to be mapped to the set of EVs; and mapping each EV of the set of EVs to the set of the routes based on the plurality of parameters associated with the EV fleet. Considered together, these steps involve collecting information, analyzing data, and performing a scheduling process, such activities set forth an abstract idea of scheduling, which falls under the “Certain methods of organizing human activity.” The claim recites steps such as receiving parameters related to an electric vehicle fleet, determining distance to time ratio, identifying valid vehicles, filtering based on criteria, mapping vehicles to routes, and scheduling vehicles. These steps include tasks that could be performed in the human mind or with the aid or pen and paper, including calculating ratios and applying selection criteria, and therefore falls under the “Mental Processes” abstract idea grouping. Independent claim 6 recites similar limitations as those discussed above and is therefore found to recite the same or substantially the same abstract idea as claim 1.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. With respect to independent claim 1, it is noted that the claim does not recite additional elements (i.e., claim 1 is a method that recites several disembodied steps). Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) (“Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract”); Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility “cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself.”). For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must “transform the nature of the claim” into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981, either at Prong Two or in Step 2B. If there are no additional elements in the claim, then it cannot be eligible. The electric vehicles in claim 1 are not considered additional elements because they are recited only as data entries whose parameters are analyzed for scheduling, and the method does not command, control, or alter the operation of any vehicle. The electric vehicles (EVs) serve merely as resources within an abstract routing and allocation process rather than as physical components involved in any concrete technological operation.
Independent claim 6 recites the additional elements of: an interface configured to, a processor and a memory coupled to the processor, and wherein the memory comprises instructions (claim 6). These elements have been considered individually and in combination, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment (network computing environment). See MPEP 2106.05(f) and 2106.05(h). With respect to the receiving step, when evaluated under Step 2A Prong Two, this step amounts to insignificant extra-solution activity, which does not amount to a practical application (MPEP 2106.05(g)). Furthermore, these additional elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to independent claim 1, it is noted that the claim does not recite additional elements (i.e., claim 1 is a method that recites several disembodied steps). Accordingly, the subject matter encompassed by independent claim 1 fails to amount to significantly more than the abstract idea itself. Independent claim 6 recites the additional elements of: an interface configured to, a processor and a memory coupled to the processor, and wherein the memory comprises instructions (claim 6). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment (network computing environment) and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification acknowledges that the claimed invention relies on nothing more than a general purpose computer executing instructions to implement the invention (Specification at paragraph [0032]: “Generally, as used herein, the term “processor” refers to a computational element that is operable to respond to and processes instructions that drive the system 200. Optionally, the processor includes, but is not limited to, a microprocessor, a microcontroller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or any other type of processing circuit..”). Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). With respect to the receiving step, when evaluated under Step 2A Prong Two and Step 2B, this step amounts to insignificant extra-solution activity, which does not add significantly more because such activity has been recognized as well-understood, routine, and conventional and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 3-5 and 7 recite the same abstract ideas as recited in the independent claims by reciting steps/details for managing commercial interactions and managing personal behavior or relationships or interactions (e.g., social activities, following rules or instructions) and steps that can be performed in the human mind (including observation, evaluation, judgment, opinion). For example, dependent claims 3-5 and 7 recite “wherein the order comprises the plurality of routes sorted in an ascending order based on the dttr of each of the plurality of routes,” “wherein the validity of each of the plurality of EVs is determined based on a set of validity parameters,” “further comprising: generating a notification indicating the scheduling of the set of EVs to the corresponding route of the plurality of routes; and sending the notification to each EV of the set of EVs indicating the schedule,” “implement the method of claim 1,” which recite details/steps that merely refine the same abstract ideas recited in the independent claims. While the claims recite the additional elements of: sending the notification to each EV of the set of EVs (claim 5), the “sending” activity in claim 5 may be considered as insignificant extra-solution activity, which is not enough to amount to a practical application (MPEP 2106.05(g)), and such extra-solution activity has also been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)), a computer readable hardware storage device having computer readable program code stored therein,]the computer readable program code executable by a processor of a computer system (claim 7), these additional elements, although not part of the abstract idea itself does not amount to a practical application (under Step 2A Prong Two) or significantly more (under Step 2B) because when evaluated under Step 2A Prong Two and Step 2B, the additional elements rely on generic computing elements or software for generally linking the judicial exception to a particular technological environment, which does not amount to a practical application. MPEP 2106.05(g)/(h). Accordingly, the additional elements tying the abstract idea to a computer-based operating environment are not sufficient to amount to significantly more.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Claim Rejections - 35 USC § 103
25. 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 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.
26. 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 of this title, 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.
27. 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.
28. 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.
29. Claims 1 and 3-7 are rejected under 35 U.S.C. 103 as being unpatentable over Rogge, Matthias, et al. "Electric bus fleet size and mix problem with optimization of charging infrastructure." Applied Energy 211 (2018): 282-295, [hereinafter Rogge], in view of Meroux et al., Pub. No.: US 2022/0252415 A1, [hereinafter Meroux], in further view of Mason et al., Pub. No.: US 2016/0258770 A1, [hereinafter Mason].
As per claim 1, Rogge teaches a computer-implemented method for route planning of electric vehicles (EVs), the method (page 286: “3. Solution approach: The developed approach aims to provide a straightforward toolchain for the strategic planning of electric public transport bus fleets. It is based on the input data of bus operators, which is enriched by additional information and finally utilized within a genetic algorithm that evaluates alternatives regarding fleet composition and scheduling...The route determination ensures that all distances of potential deadhead trips between service trips and between a service trip and charging station at the depot are realistic. The data prepared in the preprocessing is used within the GGA (C), which generates a population in which each individual describes a feasible assignment of an entire service trip set to of sub schedules (blocks) starting and ending at the depot.”) comprising:
receiving a plurality of parameters associated with an electric vehicle (EV) fleet, wherein the plurality of parameters comprises types of EVs in the EV fleet (page 283: “The paper addresses strategic electric bus planning by focusing on the “Electric Vehicle Scheduling Fleet Size and Mix Problem with Optimization of Charging Infrastructure” (EVS-FMC), minimizing the total cost of ownership (TCO) of electric vehicle fleets...Provided a set of service trips and a candidate set of vehicle types, the EVS-FMC proposes a fleet-composition investment, in terms of number of vehicles to by per vehicle type, as well as a vehicle schedule that serves all service trips, and a set of chargers to buy per depot, that all together minimize TCO.”; page 286, Fig. 3: Input – Vehicle Types), route information associated with each EV of the EV fleet (page 285: “Each service trip in the EVS-FMC is defined by a starting time and location, an ending time and location, and a set of route characteristics”; page 286, Fig. 3: Input - Route characteristics; page 287: “The energy consumption depends on the bus type as well as on several trip characteristics: the length of the route, the average speed, the expected passenger load per trip, and the slope of the route.”), time slot required for covering a route by each EV of the EV fleet (page 285: “Each service trip in the EVS-FMC is defined by a starting time and location, an ending time and location, and a set of route characteristics. The trip has to be operated by exactly one bus of the given bus type.”; page 286: Fig. 3: Input – Service Trips; page 286: “In vehicle scheduling, the start time of service trips is pre-defined by the timetable.”) and battery information associated with each EV of the EV fleet (page 285: “The selection of bus types and quantities has to be done jointly with the scheduling of service trips to ensure that the particularities of each bus type are considered properly. Buses with a small battery capacity, for example, require several charging events during the daytime, whereas high-battery-capacity buses are charged mainly at night, which directly influences the fleet size and the number of required chargers...Each bus type has a limited battery capacity, a specific energy consumption depending on the operational conditions, a fixed cost for the purchase of the bus, and energy-related costs for the battery usage, which represent the replacements costs for a degraded battery, or battery leasing…”; page 285: Formal problem formulation – “Let V be the set of bus types. Each bus type k V ∈ is represented by a usable battery capacity Ek in the range from SOCmax to SOCmin…”);
determining a distance to time ratio (dttr) for each route of a plurality of routes based on the route information associated with each EV of the EV fleet (page 287, discussing that the energy consumption depends on the bus type as well as on several trip characteristics: the length of the route, the average speed, the expected passenger load per trip, and the slope of the route. Therefore, the energy consumption is computed in a simulation…; page 290, discussing that the scenarios analyzed in the following represent two different cities and modes of operation. Scenario A, in the German city of Aachen, represents a constant-frequency operation in an urban environment; and scenario B, in the Danish city of Roskilde, concerns an operation with different frequencies in the peak hours and operation on a more regional environment, in that distances are generally larger and the average speed of operation is slightly higher compared to the Aachen scenario. The scenarios thereby cover two typical modes of operation encountered by bus operators. In terms of fleet size, ranging from 12 to 14 diesel buses, the scenarios’ scope corresponds to the yearly vehicle replacement of a mid-size bus operator; page 290 -4.1..2 – Operational Characteristics, discussing that the average speeds reveal that scenario A has a dominating urban character, whereas scenario B reflects suburban operation. The reported runtime represents the accumulated time across all buses away from the depot and a handling offset of 15 min for every incoming or outgoing trip; page 290, Table 1: “average speed [km/h]” [i.e., the average speed corresponds to the claimed distance to time ratio – See instant application Spec at paragraph 0043: “The dttr is measured in kilometre per hour (km/h).”]);
determining a set of EVs from the EV fleet (page 286, discussing that the developed approach aims to provide a straightforward toolchain for the strategic planning of electric public transport bus fleets [i.e., EV fleet]. It is based on the input data of bus operators; page 287, discussing that the total number of required buses is computed in the fitness function by combining blocks and charging events to construct full-day vehicle schedules; pages 288-289, discussing that the fitness function evaluates the solution with regard to the TCO (total cost of ownership), consisting of operational costs, number of required buses, and number of required chargers; page 285);
mapping the set of EVs to a set of the routes of the plurality of routes based on the dttr for each route of the plurality of routes (pages 286-287: Solution Approach, discussing that the developed approach aims to provide a straightforward toolchain for the strategic planning of electric public transport bus fleets. It is based on the input data of bus operators, which is enriched by additional information and finally utilized within a genetic algorithm that evaluates alternatives regarding fleet composition and scheduling…The route determination ensures that all distances of potential deadhead trips between service trips and between a service trip and charging station at the depot are realistic. The data prepared in the preprocessing is used within the GGA (C), which generates a population in which each individual describes a feasible assignment of an entire service trip set to of sub schedules starting and ending at the depot…Individuals in the population are evaluated regarding their TCO; page 287, discussing that the energy consumption depends on the bus type as well as on several trip characteristics: the length of the route, the average speed, the expected passenger load per trip, and the slope of the route...Thus, the energy consumption values
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of servicing trip i and deadheading from trip i to trip j are computed for every individual bus type, and serve as input to the algorithm. Varying energy consumptions, for instance caused by weight differences between vehicle types, can be taken into account, and their influence on the TCO can be evaluated; pages 289-290, discussing that the fitness function evaluates the solution with regard to the TCO, consisting of operational costs, number of required buses, and number of required chargers [the route planning is based on the TCO, the TCO is based on the energy consumption of a trip, the energy consumption of a trip is based on its average speed which corresponds to the dttr]); and
scheduling the set of EVs to the set of the routes based on the mapping (page 283, discussing strategic electric bus planning by focusing on the “Electric Vehicle Scheduling Fleet Size and Mix Problem with Optimization of Charging Infrastructure” (EVS-FMC), minimizing the total cost of ownership (TCO) of electric vehicle fleets; page 284 – Fig.1 “Outcomes are highlighted on the right by exemplary vehicle schedules and the corresponding number of required chargers.”; page 285: “Output, as depicted on the top right, assigns a sub set of trips to a bus of a specific type, together referred to as the vehicle schedule, and includes charging time. Furthermore, the number of chargers is determined as well, and the usage of these charges over time follows directly from the vehicle schedule. The selection of bus types and quantities has to be done jointly with the scheduling of service trips to ensure that the particularities of each bus type are considered properly; page 286: “Individual vehicle schedules are obtained”; page 287, discussing delivering the required fleet size and mix and the corresponding schedules);
wherein mapping the set of EVs to the set of the routes based on the dttr for each route of the plurality of routes comprises: determining the plurality of routes comprising the dttr greater than a dttr (page 290, discussing that the scenarios analyzed in the following represent two different cities and modes of operation. Scenario A, in the German city of Aachen, represents a constant-frequency operation in an urban environment; and scenario B, in the Danish city of Roskilde, concerns an operation with different frequencies in the peak hours and operation on a more regional environment, in that distances are generally larger and the average speed of operation is slightly higher compared to the Aachen scenario…The average speeds reveal that scenario A has a dominating urban character, whereas scenario B reflects suburban operation [i.e., the average speed corresponds to the claimed distance to time ratio – See instant application Spec at paragraph 0043: “The dttr is measured in kilometre per hour (km/h)”]); and
mapping each EV of the set of EVs to the set of the routes based on the plurality of parameters associated with the EV fleet (pages 286-287: Solution Approach, discussing that the developed approach aims to provide a straightforward toolchain for the strategic planning of electric public transport bus fleets. It is based on the input data of bus operators, which is enriched by additional information and finally utilized within a genetic algorithm that evaluates alternatives regarding fleet composition and scheduling…The route determination ensures that all distances of potential deadhead trips between service trips and between a service trip and charging station at the depot are realistic. The data prepared in the preprocessing is used within the GGA (C), which generates a population in which each individual describes a feasible assignment of an entire service trip set to of sub schedules starting and ending at the depot…Individuals in the population are evaluated regarding their TCO; page 287, discussing that the energy consumption depends on the bus type as well as on several trip characteristics: the length of the route, the average speed, the expected passenger load per trip, and the slope of the route...Thus, the energy consumption values
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of servicing trip i and deadheading from trip i to trip j are computed for every individual bus type, and serve as input to the algorithm. Varying energy consumptions, for instance caused by weight differences between vehicle types, can be taken into account, and their influence on the TCO can be evaluated; pages 289-290, discussing that the fitness function evaluates the solution with regard to the TCO, consisting of operational costs, number of required buses, and number of required chargers; page 290, discussing that the scenarios analyzed in the following represent two different cities and modes of operation. Scenario A, in the German city of Aachen, represents a constant-frequency operation in an urban environment; and scenario B, in the Danish city of Roskilde, concerns an operation with different frequencies in the peak hours and operation on a more regional environment, in that distances are generally larger and the average speed of operation is slightly higher compared to the Aachen scenario…The average speeds reveal that scenario A has a dominating urban character, whereas scenario B reflects suburban operation).
While Rogge teaches determining a set of EVs from the EV fleet , Rogge does not explicitly teach that the determining is based on a validity of each EV of the EV fleet; wherein mapping the set of EVs to the set of the routes based on the dttr for each route of the plurality of routes comprises: determining the plurality of routes comprising the dttr greater than a dttr threshold; sorting the plurality of routes comprising the dttr greater than the dttr threshold in an order; and determining the set of the routes from the sorted order to be mapped to the set of EVs. Meroux in the analogous art of fleet management systems teaches:
determining a set of EVs from the EV fleet based on a validity of each EV of the EV fleet (paragraph 0017, discussing assigning various vehicles of the fleet to various travel routes; paragraph 0035, discussing an optimized vehicle assignment procedure that is based on evaluating various factors prior to assigning various vehicles to various travel routes…The assignments may be based on first determining whether any of the electric vehicles can complete the longest route (Route D) based on the actual battery capacities shown in table 200. The third electric vehicle has an actual battery capacity of 69.6 kWh, which satisfies the estimated 68 kWh required for Route D. The other two electric vehicles are unsuitable for Route D. Consequently, the third electric vehicle may be assigned to Route D. The next longest route (Route B) requires an estimated 65 kWh and the second electric vehicle having an actual battery capacity of 66.5 kWh is suitable for this route. Hence, the second electric vehicle may be assigned to Route B [i.e., This shows determining a set of EVs from the EV fleet based on a validity of each EV of the EV fleet]. The next longest route (Route A) requires an estimated 55 kWh and the first electric vehicle having an actual battery capacity of 58.1 kWh is suitable for this route. Hence, the first electric vehicle may be assigned to Route A…; paragraph 0043, discussing that a determination is made whether the travel range of the vehicle is adequate to complete the travel route without an energy replenishment operation such as, for example, a battery recharging operation; paragraph 0044, discussing that if the determination indicates that the travel range of the electric vehicle is adequate to complete the travel route, deployment costs associated with deploying the electric vehicle on the travel route are calculated. In an example scenario, the deployment cost of the electric vehicle may be calculated as a part of calculating deployment costs of some or all vehicles of a fleet of vehicles. Some or all of the vehicles of the fleet may be deployed based on factors such as, for example, predicted feasibility for deployment of a type of vehicle, lowest expected operating cost, cost savings due to eliminating use of gasoline…; paragraph 0055).
Examiner notes that, Meroux in addition to Rogge as cited above, also teaches mapping each EV of the set of EVs to the set of the routes based on the plurality of parameters associated with the EV fleet (paragraph 0017, discussing assigning various vehicles of the fleet to various travel routes; paragraph 0035, discussing an optimized vehicle assignment procedure that is based on evaluating various factors prior to assigning various vehicles to various travel routes…The assignments may be based on first determining whether any of the electric vehicles can complete the longest route (Route D) based on the actual battery capacities shown in table 200. The third electric vehicle has an actual battery capacity of 69.6 kWh, which satisfies the estimated 68 kWh required for Route D. The other two electric vehicles are unsuitable for Route D. Consequently, the third electric vehicle may be assigned to Route D. The next longest route (Route B) requires an estimated 65 kWh and the second electric vehicle having an actual battery capacity of 66.5 kWh is suitable for this route. Hence, the second electric vehicle may be assigned to Route B. The next longest route (Route A) requires an estimated 55 kWh and the first electric vehicle having an actual battery capacity of 58.1 kWh is suitable for this route. Hence, the first electric vehicle may be assigned to Route A…; paragraph 0043, discussing that a determination is made whether the travel range of the vehicle is adequate to complete the travel route without an energy replenishment operation such as, for example, a battery recharging operation; paragraph 0044, discussing that if the determination indicates that the travel range of the electric vehicle is adequate to complete the travel route, deployment costs associated with deploying the electric vehicle on the travel route are calculated. In an example scenario, the deployment cost of the electric vehicle may be calculated as a part of calculating deployment costs of some or all vehicles of a fleet of vehicles. Some or all of the vehicles of the fleet may be deployed based on factors such as, for example, predicted feasibility for deployment of a type of vehicle, lowest expected operating cost, cost savings due to eliminating use of gasoline…).
Rogge is directed towards strategic planning of electric bus fleets. Meroux is directed to systems and methods for assigning travel routes to vehicles of a fleet of vehicles. Therefore they are deemed to be analogous as they both are directed towards fleet management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Rogge with Meroux because the references are analogous art because they are both directed to solutions for fleet management systems, which falls within applicant’s field of endeavor (method for route planning of a fleet), and because modifying Rogge to include Meroux’s feature for including determining a set of EVs from the EV fleet based on a validity of each EV of the EV fleet, in the manner claimed, would serve the motivation of implementing short term operational strategies associated with assigning various types of vehicles to various travel routes in a cost-effective manner and also supporting long-term fleet strategies such as vehicle purchases, vehicle deployment strategies, and stress-testing (Meroux at paragraph 0073); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The Rogge-Meroux combination does not explicitly teach wherein mapping the set of EVs to the set of the routes based on the dttr for each route of the plurality of routes comprises: determining the plurality of routes comprising the dttr greater than a dttr threshold; sorting the plurality of routes comprising the dttr greater than the dttr threshold in an order; and determining the set of the routes from the sorted order to be mapped to the set of EVs. However, Mason in the analogous art of route selection systems teaches these concepts. Mason teaches
wherein mapping the set of EVs to the set of the routes based on the dttr for each route of the plurality of routes comprises: determining the plurality of routes comprising the dttr greater than a dttr threshold (paragraph 0021, discussing that route selection for a fleet of vehicles is often performed by considering shortest distance or least amount of transit time. For each vehicle in the fleet, it can be desirable to reduce the travel distance or transit time…; paragraph 0027, discussing that for example, given two equally feasible routes that would result in on-time deliveries for a delivery company, the routing module may select the route with lowest energy cost based on one route having more level elevation than the other route. Due to the more level elevation, the chosen route can result in less energy consumption for some types of vehicles; paragraph 0028, discussing that the type of vehicle used can also factor into route selection. Many different types of vehicles can be used in a fleet managed by the vehicle management system. Some examples include electric vehicles, hybrid vehicles, and alternative fuel vehicles. Electric-vehicles can include battery-operated or solar power vehicles; paragraph 0048, discussing that the generation of feasible, or candidate, routes can provide an initial narrowing down of the universe of possible routes between waypoints to further be selected based on additional criteria or constraints; paragraph 0049, discussing that the initial reduction can narrow down the possible routes to exclude highly improbable routes (e.g., based on distance and estimated transit time factors). The initial search algorithms can include, for example, Dijkstra's algorithm, Munkres (Hungarian) algorithm, breadth-first algorithms, depth-first algorithms, best-first algorithms, and/or the like…; paragraph 0053, discussing that the route with the lowest energy cost is selected as long as the distances of the feasible routes are within a predetermined or user-defined threshold…; paragraph 0052);
sorting the plurality of routes comprising the dttr greater than the dttr threshold in an order (paragraph 0026, discussing that the routing module can select at least some routes for the vehicles that reduce energy use costs, improve operational efficiencies, improve customer service, and/or reduce vehicle emissions. The routing module can automatically select routes that take into account factors that affect energy usage, such as terrain or elevation, vehicle characteristics, driver characteristics, road conditions, traffic, speed limits, stop time, turn information, traffic information, and weather information, and the like; paragraph 0038, discussing that the route calculation module can determine one or more alternative feasible, or candidate, routes from a starting waypoint to a destination waypoint. The feasible routes can be determined using one or more initial searching algorithms based on one or more initial criteria, factors or variables (e.g., distance and/or estimated transit time) to trim down the search space to exclude unreasonable routes...; paragraph 0054, discussing that routes are selected based on energy, time, and/or distance concerns, but the routes are prioritized for one of these concerns; paragraph 0060); and
determining the set of the routes from the sorted order to be mapped to the set of EVs (paragraph 0032, discussing selecting particular vehicles for a route based on their energy usage characteristics; paragraph 0052, discussing that the route calculation module identifies a preferred route from the feasible routes based on the overlaid energy use costs, thereby selecting a route based on energy use in addition to shortest distance and/or shortest estimated transit time; paragraph 0054, discussing that routes are selected based on energy, time, and/or distance concerns, but the routes are prioritized for one of these concerns; paragraph 0060, discussing that the distance cost D1 of route R1 (on a scale of 1 to 10) can be 4 and the distance cost of D1 of route R2 can be 6. The distance costs can be weighted at 40% and the energy use costs can be weighted at 60%. Thus, the total overall cost of route R1 would be 5.2 and the total overall cost of route R2 would be 4.2. Therefore, route R2 could be selected as the preferred route; paragraph 0022).
The Rogge-Meroux combination describes features related to fleet management and routing. Mason is directed towards selecting routes for fleet vehicles. Therefore they are deemed to be analogous as they both are directed towards fleet management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Rogge-Meroux combination with Mason because the references are analogous art because they are both directed to solutions for fleet management systems, which falls within applicant’s field of endeavor (method for route planning of a fleet), and because modifying the Rogge-Meroux combination to include Mason’s features for including wherein mapping the set of EVs to the set of the routes based on the dttr for each route of the plurality of routes comprises: determining the plurality of routes comprising the dttr greater than a dttr threshold, sorting the plurality of routes comprising the dttr greater than the dttr threshold in an order, and determining the set of the routes from the sorted order to be mapped to the set of EVs, in the manner claimed, would serve the motivation of selecting routes for the vehicles that reduce costs and improve operational efficiencies (Mason at paragraph 0026); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 3, the Rogge-Meroux-Mason combination teaches the method according to claim 1. Although not explicitly taught by the Rogge-Meroux combination, Mason in the analogous art of route selection systems teaches wherein the order comprises the plurality of routes sorted in an ascending order based on the dttr of each of the plurality of routes (paragraph 0005, discussing that the computer system can further rank the candidate transportation routes; paragraph 0026, discussing that the routing module can select at least some routes for the vehicles that reduce energy use costs, improve operational efficiencies, improve customer service, and/or reduce vehicle emissions; paragraph 0038, discussing that the route calculation module can determine one or more alternative feasible, or candidate, routes from a starting waypoint to a destination waypoint. The feasible routes can be determined using one or more initial searching algorithms based on one or more initial criteria, factors or variables (e.g., distance and/or estimated transit time) to trim down the search space to exclude unreasonable routes...; paragraph 0054, discussing that routes are selected based on energy, time, and/or distance concerns, but the routes are prioritized for one of these concerns; paragraph 0060, discussing that the distance cost D1 of route R1 (on a scale of 1 to 10) can be 4 and the distance cost of D1 of route R2 can be 6. The distance costs can be weighted at 40% and the energy use costs can be weighted at 60%. Thus, the total overall cost of route R1 would be 5.2 and the total overall cost of route R2 would be 4.2. Therefore, route R2 could be selected as the preferred route).
The Rogge-Meroux combination describes features related to fleet management and routing. Mason is directed towards selecting routes for fleet vehicles. Therefore they are deemed to be analogous as they both are directed towards fleet management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Rogge-Meroux combination with Mason because the references are analogous art because they are both directed to solutions for fleet management systems, which falls within applicant’s field of endeavor (method for route planning of a fleet), and because modifying the Rogge-Meroux combination to include Mason’s feature for including wherein the order comprises the plurality of routes sorted in an ascending order based on the dttr of each of the plurality of routes, in the manner claimed, would serve the motivation of selecting routes for the vehicles that reduce costs and improve operational efficiencies (Mason at paragraph 0026); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 4, the Rogge-Meroux-Mason combination teaches the method according to claim 1. Although not explicitly taught by Rogge, Meroux in the analogous art of fleet management systems teaches wherein the validity of each of the plurality of EVs is determined based on a set of validity parameters (paragraph 0035, discussing an optimized vehicle assignment procedure that is based on evaluating various factors prior to assigning various vehicles to various travel routes…The assignments may be based on first determining whether any of the electric vehicles can complete the longest route (Route D) based on the actual battery capacities shown in table 200. The third electric vehicle has an actual battery capacity of 69.6 kWh, which satisfies the estimated 68 kWh required for Route D. The other two electric vehicles are unsuitable for Route D. Consequently, the third electric vehicle may be assigned to Route D. The next longest route (Route B) requires an estimated 65 kWh and the second electric vehicle having an actual battery capacity of 66.5 kWh is suitable for this route. Hence, the second electric vehicle may be assigned to Route B. The next longest route (Route A) requires an estimated 55 kWh and the first electric vehicle having an actual battery capacity of 58.1 kWh is suitable for this route. Hence, the first electric vehicle may be assigned to Route A…; paragraph 0043, discussing that a determination is made whether the travel range of the vehicle is adequate to complete the travel route without an energy replenishment operation such as, for example, a battery recharging operation; paragraph 0044, discussing that if the determination indicates that the travel range of the electric vehicle is adequate to complete the travel route, deployment costs associated with deploying the electric vehicle on the travel route are calculated. In an example scenario, the deployment cost of the electric vehicle may be calculated as a part of calculating deployment costs of some or all vehicles of a fleet of vehicles. Some or all of the vehicles of the fleet may be deployed based on factors such as, for example, predicted feasibility for deployment of a type of vehicle, lowest expected operating cost, cost savings due to eliminating use of gasoline…; paragraphs 0038, 0055).
Rogge is directed towards strategic planning of electric bus fleets. Meroux is directed to systems and methods for assigning travel routes to vehicles of a fleet of vehicles. Therefore they are deemed to be analogous as they both are directed towards fleet management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Rogge with Meroux because the references are analogous art because they are both directed to solutions for fleet management systems, which falls within applicant’s field of endeavor (method for route planning of a fleet), and because modifying Rogge to include Meroux’s feature for including determining a set of EVs from the EV fleet based on a validity of each EV of the EV fleet, in the manner claimed, would serve the motivation of implementing short term operational strategies associated with assigning various types of vehicles to various travel routes in a cost-effective manner and also supporting long-term fleet strategies such as vehicle purchases, vehicle deployment strategies, and stress-testing (Meroux at paragraph 0073); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 5, the Rogge-Meroux-Mason combination teaches the method according to claim 1. Although not explicitly taught by Rogge, Meroux in the analogous art of fleet management systems teaches further comprising: generating a notification (paragraph 0059, discussing that a vehicle dispatch system may inform the vehicle computer in the electric vehicle of the depleted battery charge in the battery and/or transmit a command to perform a battery recharging operation. The vehicle computer may issue a driver alert regarding the low battery charge condition when the electric vehicle is a driver-operated vehicle. The vehicle computer may also advise the driver to travel to a recharging station and may provide directions to reach the charging station).
While the Rogge-Meroux combination teaches generating a notification and Meroux describes assigning various vehicles of the fleet to various travel routes (paragraph 0017), the Rogge-Meroux combination does not explicitly teach that the notification indicates the scheduling of the set of EVs to the corresponding route of the plurality of routes; and sending the notification to each EV of the set of EVs indicating the schedule. However, Mason in the analogous art of route selection systems teaches these concepts. Mason teaches:
generating a notification indicating the scheduling of the set of EVs to the corresponding route of the plurality of routes (paragraph 0040, discussing that the calculated route output module can output the one or more routes identified by the route calculation module. The routes can be output to a vehicle-based display unit, a handheld mobile device, and/or to a remote location over the network. In some embodiments, the calculated route output module can output feedback to a driver (e.g., directions, instructions, warnings, alerts, alarms). For example, the calculated route output module can output a real-time suggested driving speed that can minimize or reduce energy use. The output feedback can include voice commands, audible alerts, and/or on-screen text or graphics; paragraph 0110, discussing that in scenarios where routes of multiple vehicles of a large enterprise fleet are being selected, re-routing information may be generated at a centralized dispatch center or routing center and transmitted to the vehicles…The centralized dispatch center can include a display that shows where some or all of the vehicles of a fleet are currently located and where there are issues of traffic congestion, construction, detours, accidents, etc. The centralized dispatch center may advantageously be able to determine if vehicles have sufficient fuel or battery capacity to complete the route if re-routed. In some embodiments, the vehicles can receive re-routing information from a third-party application accessible over a communications network and not from a centralized company routing center); and
sending the notification to each EV of the set of EVs indicating the schedule (paragraph 0110, discussing that in scenarios where routes of multiple vehicles of a large enterprise fleet are being selected, re-routing information may be generated at a centralized dispatch center or routing center and transmitted to the vehicles…The centralized dispatch center can include a display that shows where some or all of the vehicles of a fleet are currently located and where there are issues of traffic congestion, construction, detours, accidents, etc. The centralized dispatch center may advantageously be able to determine if vehicles have sufficient fuel or battery capacity to complete the route if re-routed. In some embodiments, the vehicles can receive re-routing information from a third-party application accessible over a communications network and not from a centralized company routing center; paragraph 0120, discussing that the routing module can generate control instructions and transmit those control instructions directly to a vehicle's engine).
The Rogge-Meroux combination describes features related to fleet management and routing. Mason is directed towards selecting routes for fleet vehicles. Therefore they are deemed to be analogous as they both are directed towards fleet management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Rogge-Meroux combination with Mason because the references are analogous art because they are both directed to solutions for fleet management systems, which falls within applicant’s field of endeavor (method for route planning of a fleet), and because modifying the Rogge-Meroux combination to include Mason’s features for including generating a notification indicating the scheduling of the set of EVs to the corresponding route of the plurality of routes; and sending the notification to each EV of the set of EVs indicating the schedule, in the manner claimed, would serve the motivation of selecting routes for the vehicles that reduce costs and improve operational efficiencies (Mason at paragraph 0026); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 6 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 1, as discussed above. Further, as per claim 6, although not explicitly taught by Rogge, Meroux in the analogous art of fleet management systems teaches a system for route planning of electric vehicles, the system comprising: an interface configured to receive a plurality of parameters associated with an EV fleet (paragraph 0017: “The vehicle dispatch system 105 may include one or more computers that are communicatively coupled to a network 140, such as, for example, a computer 106 that is communicatively coupled to the network 140. The computer 106 may be any of various types of computers such as, for example, a desktop computer, a laptop computer, a tablet computer, or a handheld device such as a smartphone...”; paragraph 0020, discussing that a travel route assignment procedure in may be executed by launching the software application 107 in the computer 106. In an example procedure, the software application 107 may obtain information from various sources via the network 140. For example, information pertaining to a battery provided in an electric vehicle in the fleet may be obtained from the vehicle battery database; paragraph 0042, discussing that route information may be obtained about a travel route that is being considered for assigning to the electric vehicle…; paragraph 0063, discussing an example procedure to assign an electric vehicle to a travel route...At block 605, a query may be originated for obtaining data about a vehicle. In an example scenario, the query may be originated by the computer 106 in order to obtain data about the first electric vehicle 120 from sources such as, for example, the vehicle computer 121…The computer 106 may also obtain data pertaining to the first electric vehicle 120 from other sources such as, for example, the vehicle battery database 110 and the vehicle maintenance records system; paragraph 0064, discussing that the query may be originated by the computer in order to obtain data such as, for example, a distance of a first travel route, a distance of a second travel route, an average speed profile, grade characteristics of the first and/or second travel route, weather conditions along the first and/or second travel route...In some cases, data may be obtained by use of a real-time traffic application programming interface (API) and/or a weather API. In some other cases, data may be obtained from other vehicle computers.);
a processor (paragraph 0017: “The computer 106 generally includes a processor 111 and a memory 109.”); and
a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, configures the processor (paragraph 0017: “The computer 106 generally includes a processor 111 and a memory 109. The memory 109, which is one example of a non-transitory computer-readable medium, may be used to store an operating system (OS) 108 and various other code modules such as, for example, a software application 107 that may be downloaded into the memory 109. The software application 107 can be executed by the processor 111 for performing various operations in accordance with disclosure such as, for example, assigning various vehicles of the fleet to various travel routes...”; paragraph 0036: “In the context of software, the operations represent computer-executable instructions stored on one or more non-transitory computer-readable media, such as the memory 109 in the computer 106, that, when executed by one or more processors, such as the processor 111 in the computer 106, perform the recited operations. One example of a software containing such computer-executable instructions is the software application 107 provided in the computer 106.”; paragraph 0077).
Rogge is directed towards strategic planning of electric bus fleets. Meroux is directed to systems and methods for assigning travel routes to vehicles of a fleet of vehicles. Therefore they are deemed to be analogous as they both are directed towards fleet management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Rogge with Meroux because the references are analogous art because they are both directed to solutions for fleet management systems, which falls within applicant’s field of endeavor (method for route planning of a fleet), and because modifying Rogge to include Meroux’s features for including a system for route planning of electric vehicles, the system comprising: an interface configured to receive a plurality of parameters associated with an EV fleet, a processor, and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, configures the processor, in the manner claimed, would serve the motivation of implementing short term operational strategies associated with assigning various types of vehicles to various travel routes in a cost-effective manner and also supporting long-term fleet strategies such as vehicle purchases, vehicle deployment strategies, and stress-testing (Meroux at paragraph 0073) and it would have been obvious to implement Rogge’s route planning method on the computerized platform of Meroux in order to automated the processing and scheduling operations; and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 7 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 1, as discussed above. As per claim 7, the Rogge-Meroux-Mason combination teaches the method of claim 1. Although not explicitly taught by Rogge, Meroux in the analogous art of fleet management systems teaches a computer program product, comprising a computer readable hardware storage device having computer readable program code stored therein, the computer readable program code executable by a processor of a computer system to implement the method of claim 1 (paragraph 0017, discussing that the vehicle dispatch system 105 may include one or more computers that are communicatively coupled to a network 140, such as, for example, a computer 106 that is communicatively coupled to the network 140. The computer 106 may be any of various types of computers such as, for example, a desktop computer, a laptop computer, a tablet computer, or a handheld device such as a smartphone containing a processor and a memory. The computer 106 generally includes a processor 111 and a memory 109. The memory 109, which is one example of a non-transitory computer-readable medium, may be used to store an operating system (OS) 108 and various other code modules such as, for example, a software application 107 that may be downloaded into the memory 109. The software application 107 can be executed by the processor 111 for performing various operations in accordance with disclosure such as, for example, assigning various vehicles of the fleet to various travel routes. Some operational aspects of the software application are described below in the form of various methods and procedures to assign various vehicles to various travel routes; paragraph 0075, discussing that implementations of the systems, apparatuses, devices, and methods disclosed may comprise or utilize one or more devices that include hardware, such as, for example, one or more processors and system memory...; paragraph 0076, discussing that computer-executable instructions comprise, for example, instructions and data which, when executed at a processor, cause the processor to perform a certain function or group of functions…; paragraph 0077, discussing that a memory device, such as the memory 109 provided in the computer 106 of the vehicle dispatch system 105 or in a vehicle computer, can include any one memory element or a combination of volatile memory elements and non-volatile memory elements. Moreover, the memory device may incorporate electronic, magnetic, optical, and/or other types of storage media. In the context of this document, a “non-transitory computer-readable medium” can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device..).
Rogge is directed towards strategic planning of electric bus fleets. Meroux is directed to systems and methods for assigning travel routes to vehicles of a fleet of vehicles. Therefore they are deemed to be analogous as they both are directed towards fleet management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Rogge with Meroux because the references are analogous art because they are both directed to solutions for fleet management systems, which falls within applicant’s field of endeavor (method for route planning of a fleet), and because modifying Rogge to include Meroux’s features for including a computer program product, comprising a computer readable hardware storage device having computer readable program code stored therein, the computer readable program code executable by a processor of a computer system to implement the method of claim 1, in the manner claimed, would serve the motivation of implementing short term operational strategies associated with assigning various types of vehicles to various travel routes in a cost-effective manner and also supporting long-term fleet strategies such as vehicle purchases, vehicle deployment strategies, and stress-testing (Meroux at paragraph 0073) and it would have been obvious to implement Rogge’s route planning method on the computerized platform of Meroux in order to automated the processing and scheduling operations; and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ferguson, Pub. No.: US 2019/0362629 A1 – describes systems and methods for designing a preferred route for a vehicle.
Cerna, Fernando V., et al. "Optimal delivery scheduling and charging of EVs in the navigation of a city map." IEEE Transactions on Smart Grid 9.5 (2017): 4815-4827 presents a mixed integer linear programming model to optimize the costs of maintenance and extra hours for scheduling a fleet of battery electric vehicles (BEVs) so that the products are delivered to prespecified delivery points along a route.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625