DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the amendment filed on 01/13/2026.
Claims 1, 10, 16, and 20 have been amended and are hereby entered.
Claims 1-20 are currently pending and have been examined.
This action is made FINAL.
Response to Arguments
Applicant’s arguments, see page 9, filed 01/13/2026, with respect to the objection to claim 10 have been fully considered and are persuasive. Applicant’s amendments to claim 10 have corrected the informality. The objection to claim 10 has been withdrawn.
Applicant’s arguments, see pages 9-12, filed 01/13/2026, with respect to the 35 U.S.C. 101 rejections of claims 1-20 have been fully considered but are not persuasive. The 35 U.S.C. 101 rejections of claims 1-20 have been maintained.
Applicant begins on pages 9-10 that the claims do not recite an abstract idea. Applicant argues that the claimed invention is instead addressed to a technologically rooted problem of optimizing EV charging in light of electrical cost/availability constraints to ensure the EV has sufficient charge for its routine travel. Applicant argues that the involvement of “real-world sensors and devices” precludes the claims from reciting a mental process, and argues that the intelligent scheduling of vehicle charging is not “organizing behavior” but is “inextricably linked to vehicle battery technology”. Examiner respectfully disagrees. Firstly, Examiner notes that the claims as drafted do not recite the variety of “real-world sensors and devices” to which Applicant is directing their arguments. Claim 1 recites “a transceiver configured to receive historical inputs associated with a vehicle” and “the processor is configured to: determine a routine travel behavior of the vehicle based on the historical inputs, wherein the routine travel behavior comprises a charging pattern, a parking pattern, and a travel pattern associated with the vehicle”. No particular sensors or devices used to measure/quantify the historical inputs are claimed. Accordingly, the transceiver “receiving” the historical inputs has a broadest reasonable interpretation covering the receipt of a data table indicating a vehicle’s parking locations, travel, charging times/amounts, battery levels, etc. A human could, at least with the aid of pen and paper, review such a data table and observe and identify patterns in the vehicle travel, parking, and battery consumption (i.e. a vehicle typically travels from a home to an office location and back on Mon-Fri, the battery charge tends to drop by X% each trip to/from the office, etc.). A human user could then determine a parking/charging location from the routine travel behavior (i.e. home location/garage), estimate a departure time from the location based on the routine travel behavior (i.e. 7:30 A.M. for the morning commute), estimate a future arrival time at the location (i.e. 6 P.M. arrival back from the evening commute), and estimate an amount of energy required to make the trip (i.e. sum the average battery consumption for each leg between 7:30 A.M. departure and 6 P.M. arrival). MPEP 2106.04(a)(2) III.C. recites “examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process”. Paragraph [0046] of Applicant’s disclosure recites “The processor 116 may then output a notification to the user interface (e.g., on the user device or the vehicle HMI) to charge the vehicle 102 at the scheduled time slots or may cause the vehicle 102 to automatically charge at the scheduled time slots”. The predetermined action of beginning charging is using a computer as a tool to perform the notification to begin charging, and thus the performance of the predetermined action also falls under Mental Process.
Regarding the managing of behavior, the claimed invention is performing the abstract data manipulation discussed above to determine the optimal time to charge a user’s vehicle, then sending a notification to begin charging at the appropriate time. Determining an optimal time for a user to charge their electric vehicle still recites the management of personal behavior, as the invention is still controlling when a user is and is not charging their vehicle.
Next, Applicant argues that the judicial exceptions of the claims are integrated into a practical application at Step 2A Prong Two. Applicant argues that the claims improve a technological field by using “specific computational techniques” to ensure a vehicle has sufficient charge for a trip with minimal cost or user intervention. Applicant argues that the claims are analogous to McRo and is also eligible for similar reasoning as found in Diamond v. Diehr. Examiner respectfully disagrees.
First, Examiner notes that instead of “specific computational techniques”, claim 1 recites high-level steps of “determining a routine travel behavior based on the historical inputs”, “estimating a future departure and arrival times at a parking and charging location”, and “estimating an amount of energy required by the vehicle to travel between the future departure and arrival times”. Instead of claiming a particular, specific computational technique, claim 1 is reciting an intended solution (i.e. taking the broad “historical inputs” of a vehicle and turning them into a routine travel behavior). MPEP 2106.05(a) recites, “An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome”. MPEP 2106.05(f) recites, “When determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners may consider the following:(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".” Therefore, the claimed computation steps of the invention, instead of reflecting an improvement to technology, amounts to no more than mere instructions to apply the exception using generic computing components.
Examiner also notes that McRo addressed 3D animation, which is not analogous to determining vehicle charging patterns, and that the steps claimed in McRo were not able to be automated previously. While this new capability of automation provided a technical improvement in McRo, the steps of the instant invention are not indicated in Applicant’s specification as being unable to have been automated previously. Accordingly, Applicant’s arguments regarding McRo are not persuasive.
Applicant also argues at Step 2A Prong Two that the outcome of the process has been amended to recite the automatic charging of the battery and that this amendment makes the claim integrate its judicial exception into a practical application similar to Diamond v. Diehr. Examiner respectfully disagrees. Examiner notes that the instant specification [0048] recites “the processor 116 may output a command signal/notification to the vehicle 102 at the scheduled time slots, to cause automatic vehicle charging”. The claimed limitation of “the predetermined action includes automatically initiating charging the vehicle” therefore covers a processor sending a command to a vehicle that results in the vehicle charging. However, like the “specific computational techniques” discussion above, how this command results in the vehicle charging is not recited in the claims or the specification. Instead, the idea of a solution of a processor sending a command that results in the vehicle charging is recited. Consequently, the automatic initiation of charging as recited in the claim amounts to no more than mere instructions to apply an exception. Furthermore, in contrast with Diamond v. Diehr in which the limitations transformed raw rubber into cured molded rubber, the claimed invention is not transforming the vehicle or battery into a materially different form. Turning to the factors of MPEP 2106.05(c), the change of a battery from “not charging” to “charging” is change with a high level of generality (see above in which the specification recites that a command is sent to charge without reciting how the charging takes place), the vehicle/battery are not particularly recited, the switch to charging a battery does not result in the battery/vehicle having a different function or use (storing energy for transportation), and the automatic charging contributes nominally to the execution of the method (see instant specification [0046] for the process being directed to determining optimal charging times, with the actual initiation of the charging being satisfactorily performed by either a human alerted to optimal times or by a processor sending a signal automatically). Accordingly, Applicant’s arguments based on Diamond v. Diehr and the automatic charging are not persuasive.
Applicant then argues on pages 10-11 again that the data complexity and amount of data being processed in the claimed invention precludes the claimed invention from being a mere application of a judicial exception and instead is a technical solution to a technical problem. Examiner respectfully disagrees. Particularly, Examiner notes that some of the data manipulations particularly argued by Applicant (the continuous monitoring of vehicle use patterns, dynamic calculations of energy requirements from battery models, calculating precise kWh of energy needed for a trip, smart meter readings) are not recited in the claims nor in the specification as filed. Historical inputs are received once in claim 1 and step 504 in the specification. The energy requirement estimation is not recited in the claims as being performed in any particular manner, and is just based on the routine travel behavior. In the specification, regression models and summation may be used, but nothing in the specification indicates that they are battery-specific models. The calculation of kWh is not recited in the claims nor in the specification. In the specification, SOC of the battery is measured as a percentage, not an absolute energy amount, and the difference in percentages is used to determine the estimated SOC required for a trip. Smart meter readings of a home are not recited in the claims, and smart meters are not recited in the specification as filed. Applicant’s arguments regarding these features are not persuasive.
Argued features that are present in the specification (consideration of home energy usage/utility rates, determining routine charging/parking/traveling behavior, weather forecasts) are not recited in the claims in such a manner as to be a technical solution to a technical problem. Regarding the utility rates, claim 10 merely recites “determine a plurality of charging rates” in a plurality of timeslots without reciting any further detail as to how the rates are calculated. The specification recites receiving utility rates as an input and only considering when the rate is below a threshold, an abstract determination that can be performed by a human. Regarding the detection of routine travel behavior and travel patterns, as discussed above the recited limitations of claim 1 merely recite that the routine and patterns are determined from the historical input data without any particulars as to how such pattern recognition needs to be accomplished in the claimed invention. In the specification, “any analytical method” can be used to determine the probability of parking during a timeslot (paragraph [0031]), and odometer readings can be used as trip patterns (paragraph [0053]). Fig. 1 of the specification shows an example pattern that would be detectable by a human using pen and paper, and even if the pattern detection were to be considered beyond the capability of the human mind, “any analytical method” being used to determine probabilities does not indicate to one of ordinary skill in the art that a technical improvement is being made. Finally, weather forecasts are not recited in the claimed invention, and in specification [0047] the idea of a solution to use weather conditions to estimate energy is recited without any detail as to how that solution is accomplished. Applicant’s arguments regarding these features are also unpersuasive.
Regarding Applicant’s Thales arguments, Examiner finds these arguments unpersuasive. Thales’s claim of a particular set of sensors is not analogous to the instant invention, as the instant invention has no particular structure of sensors that integrates its judicial exception into a practical application. Instead, historical inputs are merely “received by the system”.
While the specification does point to the benefit of ensuring that the user’s vehicle is ready and prevents disruption to their routine as Applicant argues, Examiner notes that this improvement is an improvement to the abstract idea (i.e. an improvement to the user’s behavior/routine by ensuring that the user is properly charging their vehicle to be ready for use by the user). MPEP 2106.05(a) II. recites, “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology”. Applicant’s argument about balancing energy sources is not persuasive because the balancing of energy sources is not recited or reflected in the claimed invention. Accordingly, Applicant’s arguments at Step 2A Prong Two that the claimed invention is a technical improvement are not persuasive.
Last, Applicant argues on pages 11-12 that the claimed invention is not well-understood, routine, and conventional and is thus patent eligible at Step 2B. Applicant argues that the number of references in the 35 U.S.C. 103 rejections indicate that the features were not well-understood, routine, and conventional. Examiner respectfully disagrees. First, Examiner notes that MPEP 2106.05 I. recites “Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101.” Accordingly, 35 U.S.C. 103 arguments, which will be addressed later, are not indicative of eligibility. Additionally, the additional elements of the claimed invention, for at least the reasoning discussed above, are no more than mere instructions to apply the judicial exception. Per MPEP 2106.05(f)(1) “The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it”” and MPEP 2106.05 II. states that conclusions from MPEP 2106.05(f) (the “apply it” consideration) are carried over from Step 2A Prong Two into Step 2B. MPEP 2106.05 II. further states that the elements that need to be re-evaluated to determine whether they are well-understood, routine, and conventional are those determined to be insignificant extra-solution activity. Because the additional elements of the claimed invention, as a whole, are instructions to apply a judicial exception using generic computing components, the claimed invention does not amount to significantly more than the judicial exception at Step 2B. Applicant’s arguments are not persuasive, and claims 1-20 are ineligible and still stand rejected under 35 U.S.C. 101.
Applicant’s arguments, see pages 12-16, filed 01/13/2026, with respect to the 35 U.S.C. 103 rejections of claims 1-20 have been fully considered but are not persuasive. The 35 U.S.C. 103 rejections of claims 1-20 have been maintained.
After summarizing the rejections on page 12, Applicant argues that the use of eight references throughout the obviousness rejections “strongly indicates non-obviousness”. However, Examiner respectfully notes that, while eight references are used across the rejections for claims 1-20, no more than four references are used to reject any single claim. Applicant’s argument appears to be that all eight references are being combined together in the 35 U.S.C. 103 rejections, but from the rejection in the previous and current Office Actions, only a maximum of four references (to teach claims 9 and 12) are being combined. Applicant then appears to turn their arguments to the combination used to teach claims 14 and 15 as an example of how the references are allegedly “not naturally compatible”. In the interest of clarity, Examiner will respond to the later-appearing arguments regarding claim 1 on pages 13-15 of Remarks before responding to claim 14-15 arguments alongside arguments regarding other dependent claims.
Applicant argues on pages 13-14 that the combination of Payne and O’Gorman does not teach all of the limitations of claim 1. Applicant argues that Payne does not teach analyzing historical data to derive a persistent “routine travel behavior” encompassing daily patterns, and does not use “long-term historical averages or patterns”. Examiner respectfully disagrees. In paragraph Payne [0040], that was explicitly cited in the rejection, Payne teaches “For example, if the driver drives to work at 8:00 a.m. every weekday and then drives home at 5:00 p.m. every weekday, the ECU 106 may predict that the vehicle 100 is going to travel to the driver's work place at 8:00 a.m. and return to the driver's house at 5:00 p.m.”. Payne [0058], also explicitly cited as determining routine travel behavior, recites that predicted routes are based on a route history. Paragraph [0042], also cited, teaches “The ECU 106 may also be capable of predicting an amount of time spent at each location (and may thus predict a time of day that the vehicle 100 will be at each location based on the predicted amount of time spent at each location and a current time). For example, if a driver typically stays at his workplace for eight hours on weekdays, the ECU 106 may predict that the user will stay at his work place for eight hours on future trips. In some embodiments, the ECU 106 can average an amount of time spent at each location. For example, if the driver stays at his workplace for 8.5 hours one day and 8 hours the next day, the ECU 106 may use the value of 8.25 hours as the expected time that the driver will stay at his workplace. In some embodiments, the ECU 106 may remove outliers. For example, if the driver stays at his workplace for 8.5 hours one day, 8 hours the next day and 12 hours the next day, the ECU 106 may still use the value of 8.25 hours as the expected time that the driver will stay at his workplace. The ECU 106 can also use another method for predicting an amount of time spent at a destination, such as selecting a median amount of time”. Accordingly, Payne is analyzing travel history to determine a persistent routine behavior, particularly during weekdays. Examiner notes that Applicant’s specification explicitly calls out weekday behavior as an example of routine travel behavior in at least [0022]. Accordingly, Payne teaches determining persistent travel behavior.
Accordingly, the only deficiency of Payne regarding claim 1 is that Payne does not explicitly teach determining a charging routine as part of the routine travel behavior (although Payne does teach vehicles being able to be charged at the parking locations). O’Gorman paragraphs [0014] and [0045] cure this deficiency by determining a vehicle routine charge locations using historical data. O’Gorman [0045] further teaches “plug-in routines” based on how regularly a user charges at a location and teaches tracking the duration of plug-in time for charging at a location. Thus, O’Gorman cures the deficiency of Payne, and the combination of Payne and O’Gorman teaches all of the limitations of claim 1. O’Gorman does not need to teach the entire “multi-step predictive process” argued by Applicant as those limitations are taught by Payne as discussed above.
Applicant next argues on page 14 that the motivation to combine Payne and O’Gorman is “tenuous and hindsight-driven” and “circular”. Applicant argues that there is no expressed shortcoming in Payne that would motivate the incorporation of the charging pattern determination of O’Gorman, and that the only motivation to combine the references is Applicant’s own disclosure. Examiner respectfully disagrees. First, as Examiner states in the motivation to combine, both Payne and O’Gorman teach the determination of a vehicle’s travel routine. Payne and O’Gorman are also both directed to managing the recharging of an electric vehicle across multiple travel stops. Accordingly, one of ordinary skill in the art, upon viewing the Payne and O’Gorman references, would have recognized that, as the field of endeavor and problem to be solved are analogous between the references, the gathering and analyzing of historical vehicle data of Payne could be modified to include the charging routine detection of O’Gorman with predictable results as a part of the Payne’s required operations of gathering historical vehicle data. Accordingly, the motivation to combine is that of using a known technique (charging pattern recognition of O’Gorman) to improve similar devices (Payne charging determination method) in the same way. See MPEP 2141 III. Applicant’s own invention would not be required for one of ordinary skill in the art to have motivation to combine the charging patterns of O’Gorman to the teachings of Payne. Applicant’s arguments against the motivation to combine Payne and O’Gorman are not persuasive.
Applicant further argues on pages 14-15 that neither Payne nor O’Gorman teach the newly added limitation of automatically initiating charging of the vehicle. Applicant appears to admit that the system of Payne causes charging at a charging station, but Applicant argues that this automatic charging is not based “on a comprehensive routine-based energy shortfall calculation” as in claim 1. Applicant also argues that claim 1 requires initiating the charge at the current location in advance based on a computed energy gap. Examiner respectfully disagrees. First, Examiner notes that claim 1 does not prescribe a location at which the predetermined action including initiating charging of the vehicle must take place. The limitation recites “perform a predetermined action based on the amount of energy, wherein the predetermined action includes automatically initiating charging of the vehicle” and is silent regarding particular timing or location constraints that Applicant is arguing. Payne [0081]-[0082], cited as teaching the predetermined action in the previous Office Action, explicitly teach the Tuesday/Thursday routine route that is being taken by the vehicle from the vehicle user’s home to work and back. Paragraphs [0081] and [0082] further recite a shortfall for returning to the house, as each direction requires 60% of the battery charge. Payne therefore teaches this “routine-based energy shortfall calculation” as the user will be short on charge for their Tues/Thurs routine travel. The travel routine for Tuesday and Thurs is obtained in Payne via the methodology discussed above, which covers the “multi-variable assessment” argued by Applicant. Finally, based on the routine energy shortfall on Tues/Thurs for a vehicle traveling from home to work and back, [0082] explicitly teaches automatically causing the internal electric vehicle charger to add charge at the work location to remedy the predetermined shortfall. Therefore, the combination of Payne and O’Gorman teaches the new limitation of claim 1. Applicant arguments against the combination of Payne and O’Gorman teaching claim 1 are not persuasive. Claim 1 still stands rejected over Payne and O’Gorman.
Applicant also argues that dependent claims are distinguished over the prior art for additional reasons. Returning to Applicant’s arguments on pages 12-13 against the combination of Payne, O’Gorman, and Sudarsan, presumably about claims 14 and 15, Applicant argues that the references operate on different timescales and data and merging them would require “significant coordination”. Applicant also argues that Examiner’s reasoning never articulates how or why one of ordinary skill in the art would arrive at the combination other than asserting the combination would be beneficial. Applicant also argues that Examiner is using Applicant’s own disclosure for motivation to combine. Examiner respectfully disagrees. First, Examiner notes that the combination of Payne and O’Gorman has been addressed in the preceding paragraphs. Regarding the incorporation of Sudarsan into the combination of Payne and O’Gorman, Examiner notes that Sudarsan itself provides the cited motivation to combine in [0052] in that determining a battery failure timeline and scheduling maintenance accordingly prevents inconveniencing the vehicle owner by reducing waits for maintenance or parts to service the vehicle. While Applicant might also have user convenience in mind with their invention, because Sudarsan explicitly provides the motivation on its own, one of ordinary skill in the art would have recognized the motivation to combine references without viewing Applicant’s disclosure. Therefore, the combination of Payne, O’Gorman, and Sudarsan has the motivation to combine preventative vehicle battery maintenance present in the references themselves, and are not arbitrary combinations as Applicant is arguing. Examiner further notes that the driver characterization data used to construct the battery failure timelines includes distances traveled, charging behavior of the driver, and other features (paragraph [0057]) that dovetail with the historical vehicle data collected by the combination of Payne and O’Gorman. Accordingly, Applicant’s arguments that the combination of Payne, O’Gorman, and Sudarsan uses impermissible hindsight reasoning is not persuasive.
Finally, Applicant argues on pages 15-16 that the claim 13 combination of Payne, O’Gorman, and Shpati also improperly relies on Applicant’s disclosure to find a motivation to combine. Applicant argues that Shpati is in a “relatively separate” domain from Payne and O’Gorman, and that an EV maker would not necessarily be looking to optimize for both charge scheduling and cold-weather battery preconditioning. Accordingly, Applicant argues that one of ordinary skill in the art would not find the combination obvious, and that Shpati’s indication of improved range and user experience from preconditioning is not a sufficient motivation to render the combination obvious. Examiner respectfully disagrees. First, the motivation by Shpati is [0007] “Other attendant benefits may include enhanced vehicle operation, improved customer experience, and reduced range anxiety. In addition to improved charging capabilities and customer experience, disclosed concepts may help to increase driving range and battery pack performance for electric-drive vehicles”. MPEP 2141 III. states “Examples of rationales that may support a conclusion of obviousness include…(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention”. As Payne and O’Gorman are directed towards the operation of electric vehicles, and in particular managing the charge of the battery of the electric vehicle throughout a routine journey, the motivations/benefits of Shpati would be recognized as benefits that would directly improve the operation of Payne and O’Gorman. One of ordinary skill in the art viewing the references, even absent of Applicant’s disclosure, would recognize the benefits and motivation to incorporate battery preconditioning of Shpati.
This motivation from Shpati further aligns with the other references. Taking “reduced range anxiety” as an example, Payne considers such “range anxiety” by explicitly calling for a charge buffer/margin to be added to any charging operation to ensure that the vehicle can complete its journey (see [0075] “A buffer may be added to the SOC of the battery to ensure that the vehicle can travel from the first destination to the second destination”). Especially in view of Payne and Shpati, one of ordinary skill in the art would have recognized that the preconditioning of Shpati could potentially lower the required charge buffer by reducing a user’s range anxiety, resulting in a more efficient system (i.e. less charging time due to a reduced buffer, greater ability to charge at cheaper locations in Payne instead of charging extra at a more expensive location just to be sure the trip can be completed, etc.).
Lastly, regarding Applicant’s argument that the charging scheduling and battery preconditioning may have been pursued separately, Examiner notes that MPEP 2143.01 I. states “The disclosure of desirable alternatives does not necessarily negate a suggestion for modifying the prior art to arrive at the claimed invention… In affirming the Board’s obviousness rejection, the court held that the prior art as a whole suggested the desirability of the combination of shoe sole limitations claimed, thus providing a motivation to combine, which need not be supported by a finding that the prior art suggested that the combination claimed by the applicant was the preferred, or most desirable combination over the other alternatives [regarding In re Fulton, 391 F.3d 1195, 73 USPQ2d 1141 (Fed. Cir. 2004)]”. Therefore, just because an EV may pursue an alternative in which battery preconditioning is not considered for whatever reason does not negate the motivation provided by Shpati that battery preconditioning would provide additional benefits to the combination of Payne and O’Gorman. Applicant’s arguments regarding the dependent claims have been considered but are not persuasive. Claims 1-20 still stand rejected under 35 U.S.C. 103.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite estimating when a vehicle will be charging and estimating an amount of energy required by the vehicle for its future travel.
As an initial matter, claims 1-15 fall into at least the machine category of statutory subject matter. Claims 16-19 fall into at least the process category of statutory subject matter. Finally, claim 20 falls into at least the manufacture category of statutory subject matter. Therefore, all claims fall into at least one of the statutory categories. Eligibility analysis proceeds to Step 2A.
In claim 1, the limitation of “determine a routine travel behavior of the vehicle based on the historical inputs, wherein the routine travel behavior comprises a charging pattern, a parking pattern, and a travel pattern associated with the vehicle”, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a system”, “a transceiver”, and “a processor communicatively coupled to the transceiver, wherein the processor is configured to,” nothing in the claim element precludes the step from practically being performed in the mind. Similarly, the limitations of “determine a parking and charging location associated with the vehicle based on the routine travel behavior; estimate a future departure time from the parking and charging location and a future arrival time at the parking and charging location of the vehicle based on the routine travel behavior; estimate an amount of energy required by the vehicle to travel between the future departure time and the future arrival time, based on the routine travel behavior; and perform a predetermined action based on the amount of energy, wherein the predetermined action includes automatically initiating charging of the vehicle”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Additionally, claim 1 recites the concept of instructing a user when to charge a vehicle which is a certain method of organizing human activity including Managing Personal Behavior or Relationships or Interactions Between People. Receive historical inputs associated with a vehicle; determine a routine travel behavior of the vehicle based on the historical inputs, wherein the routine travel behavior comprises a charging pattern, a parking pattern, and a travel pattern associated with the vehicle; determine a parking and charging location associated with the vehicle based on the routine travel behavior; estimate a future departure time from the parking and charging location and a future arrival time at the parking and charging location of the vehicle based on the routine travel behavior; estimate an amount of energy required by the vehicle to travel between the future departure time and the future arrival time, based on the routine travel behavior; and perform a predetermined action based on the amount of energy all, as a whole, fall under the category of Managing Personal Behavior or Relationships or Interactions Between People. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a system; a transceiver; a processor communicatively coupled to the transceiver, wherein the processor is configured to perform the recited steps; and automatically initiating charging of the vehicle. The recited additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a system; a transceiver; a processor communicatively coupled to the transceiver, wherein the processor is configured to perform the recited steps; and automatically initiating charging of the vehicle amounts to no more than mere instructions to apply the exception using generic computer components. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claims 2-15 further limit the abstract idea of claim 1 without adding any new additional elements. Therefore, by the analysis of claim 1 above these claims, individually and as an ordered combination, do not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
In claim 16, the limitation of “A method comprising: determining, by a processor, a routine travel behavior of a vehicle based on historical inputs associated with the vehicle, wherein the routine travel behavior comprises a charging pattern, a parking pattern, and a travel pattern associated with the vehicle”, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a processor,” nothing in the claim element precludes the step from practically being performed in the mind. Similarly, the limitations of “determining, by the processor, a parking and charging location associated with the vehicle based on the routine travel behavior; estimating, by the processor, a future departure time from the parking and charging location and a future arrival time at the parking and charging location of the vehicle based on the routine travel behavior; estimating, by the processor, an amount of energy required by the vehicle to travel between the future departure time and the future arrival time, based on the routine travel behavior; and performing, by the processor, a predetermined action based on the amount of energy, wherein the predetermined action includes automatically initiating charging of the vehicle”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Additionally, claim 16 recites the concept of instructing a user when to charge a vehicle which is a certain method of organizing human activity including Managing Personal Behavior or Relationships or Interactions Between People. A method comprising: determining a routine travel behavior of a vehicle based on historical inputs associated with the vehicle, wherein the routine travel behavior comprises a charging pattern, a parking pattern, and a travel pattern associated with the vehicle; determining a parking and charging location associated with the vehicle based on the routine travel behavior; estimating a future departure time from the parking and charging location and a future arrival time at the parking and charging location of the vehicle based on the routine travel behavior; estimating an amount of energy required by the vehicle to travel between the future departure time and the future arrival time, based on the routine travel behavior; and performing a predetermined action based on the amount of energy all, as a whole, fall under the category of Managing Personal Behavior or Relationships or Interactions Between People. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a processor and automatically initiating charging of the vehicle. The recited additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a processor and automatically initiating charging of the vehicle amounts to no more than mere instructions to apply the exception using generic computer components. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claims 17-19 further limit the abstract idea of claim 16 without adding any new additional elements. Therefore, by the analysis of claim 16 above these claims, individually and as an ordered combination, do not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
In claim 20, the limitation of “determine a routine travel behavior of a vehicle based on historical inputs associated with the vehicle, wherein the routine travel behavior comprises a charging pattern, a parking pattern, and a travel pattern associated with the vehicle”, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a non-transitory computer-readable storage medium having instructions stored thereupon” and “a processor,” nothing in the claim element precludes the step from practically being performed in the mind. Similarly, the limitations of “determine a parking and charging location associated with the vehicle based on the routine travel behavior; estimate a future departure time from the parking and charging location and a future arrival time at the parking and charging location of the vehicle based on the routine travel behavior; estimate an amount of energy required by the vehicle to travel between the future departure time and the future arrival time, based on the routine travel behavior; and perform a predetermined action based on the amount of energy, wherein the predetermined action includes automatically initiating charging of the vehicle”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Additionally, claim 20 recites the concept of instructing a user when to charge a vehicle which is a certain method of organizing human activity including Managing Personal Behavior or Relationships or Interactions Between People. Determine a routine travel behavior of a vehicle based on historical inputs associated with the vehicle, wherein the routine travel behavior comprises a charging pattern, a parking pattern, and a travel pattern associated with the vehicle; determine a parking and charging location associated with the vehicle based on the routine travel behavior; estimate a future departure time from the parking and charging location and a future arrival time at the parking and charging location of the vehicle based on the routine travel behavior; estimate an amount of energy required by the vehicle to travel between the future departure time and the future arrival time, based on the routine travel behavior; and perform a predetermined action based on the amount of energy all, as a whole, fall under the category of Managing Personal Behavior or Relationships or Interactions Between People. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a non-transitory computer-readable storage medium having instructions stored thereupon, a processor, and automatically initiating charging of the vehicle. The recited additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a non-transitory computer-readable storage medium having instructions stored thereupon, a processor, and automatically initiating charging of the vehicle amounts to no more than mere instructions to apply the exception using generic computer components. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 16-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Payne et al. (U.S. Pre-Grant Publication No. 2017/0088000, hereafter known as Payne) in view of O’Gorman et al. (U.S. Pre-Grant Publication No. 2022/0072975, hereafter known as O’Gorman).
Regarding claim 1, Payne teaches:
A system comprising: a transceiver configured to receive historical inputs associated with a vehicle; and a processor communicatively coupled to the transceiver, wherein the processor is configured to (see Fig. 1 and [0023] "With reference to FIG. 1, a system 101 for cost-efficient charge planning of a vehicle 100 based on route prediction includes the vehicle 100, a shared charge station database 138, at least one charging station and at least one utility company" for the overall system. See Network Access Device 122, [0037] "When the navigation unit 120 is separate from the vehicle, it can communicate with the vehicle 100 via the network access device 122", [0039], and [0059] using previously measured times, destinations for the transceiver . See ECU 106 and [0033] "The ECU 106 can include one or more processors or controllers specifically designed for automotive systems" for the processor)
determine a routine travel behavior of the vehicle based on the historical inputs, wherein the routine travel behavior comprises (see [0022] "At least one of the navigation unit or the ECU can predict a route set that includes at least two predicted routes and predicted amounts of time spent at each destination. The route set can be predicted based on previously detected data, the current location, the current time of day and/or the current day of the week", [0040], [0058]-[0059] for detecting traveling and parking patterns based on previously detected data)
determine a parking and charging location associated with the vehicle based on the routine travel behavior (see [0066] "At block 254, the ECU may predict a route set based on one or more of a current location of the vehicle, a current time of day, a current day of the week, a current date or the like. In some embodiments, alternative or additional factors may be used to predict the route set. The ECU may also predict an amount of time spent at each location in block 256" and [0072]-[0074] for the determination of parking destinations in a route and the parking destinations have energy available to charge the vehicle)
estimate a future departure time from the parking and charging location and a future arrival time at the parking and charging location of the vehicle based on the routine travel behavior (see [0066] "The ECU may also predict an amount of time spent at each location in block 256. The ECU can also determine what times the vehicle will be at each location based on the predicted amount of time spent at each location and a current time of day" for the estimation of the times the vehicle will be at each location, in other words, the arrival and departure times at the parking and charging locations. See [0040] "if the driver drives to work at 8:00 a.m. every weekday and then drives home at 5:00 p.m. every weekday, the ECU 106 may predict that the vehicle 100 is going to travel to the driver's work place at 8:00 a.m. and return to the driver's house at 5:00 p.m." and [0042] for estimating departure and arrival times at work and home locations)
estimate an amount of energy required by the vehicle to travel between the future departure time and the future arrival time, based on the routine travel behavior (see Fig. 4 Route Set 1 and [0060] "the ECU can determine an amount of electrical energy required to reach the first destination from the current location...The ECU can then use a measured amount of energy from a previous trip along the route or an average of measured amounts of energy from two or more previous trips along the route", [0072], and [0081] "The charge required to reach the next destination, i.e., returning to the house 302, is 60% of the available SOC of the battery 110 (i.e., 60% of the stored energy between a lower SOC limit and an upper SOC limit)...After the vehicle 100 reaches the workplace 304, the battery 110 will have 40% of the available SOC remaining. Because the SOC of the battery 110 is at 40% at the workplace 304 and because it will take 60% SOC to return to the house 302, the battery 110 will require an additional 20% SOC to reach the second destination (the house 302)" for estimating an amount of charge required to travel from home to work and back to home, which is 120% of the maximum charge of the vehicle battery in the cited example)
and perform a predetermined action based on the amount of energy, wherein the predetermined action includes automatically initiating charging of the vehicle (see [0081]-[0082] "After the vehicle 100 reaches the workplace 304, the battery 110 will have 40% of the available SOC remaining. Because the SOC of the battery 110 is at 40% at the workplace 304 and because it will take 60% SOC to return to the house 302, the battery 110 will require an additional 20% SOC to reach the second destination (the house 302) plus any buffer if desired...the ECU 106 can cause the internal electric vehicle charger 118 to add 30% SOC while the vehicle 100 is at the workplace 304. The 30% corresponds to the 20% additional energy required to reach the house 302 and a 10% buffer" for a predetermined action being to cause the internal charger to automatically charge the vehicle sufficiently to make the next leg of the predicted route back to the starting home charging location)
While Payne explicitly teaches the determination of traveling and parking patterns as discussed above, and Payne implies the tracking of charging at these parking locations in [0044]-[0045], Payne does not explicitly teach determining a routine charging pattern as part of the routine travel behavior. O’Gorman teaches determining a charging pattern in [0014] ("a controller system may identify an EV user's routine charge locations using historical vehicle data" and [0045]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine charging patterns of an electric vehicle as in O’Gorman in the system executing the method of Payne. As in O’Gorman, it is within the capabilities of one of ordinary skill in the art to incorporate determining charging patterns of an electric vehicle to Payne' s invention with the predictable result of determining a vehicle’s travel routine as needed in Payne. See O’Gorman [0043] in particular for determining a vehicle routine based on routine charging similar to Payne’s determination of a routine for a vehicle as discussed above.
Regarding claim 2, the combination of Payne and O’Gorman teaches all of the limitations of claim 1 above. Payne further teaches:
wherein the processor is further configured to: determine most visited locations and respective visited time slots associated with the vehicle based on the routine travel behavior, wherein the most visited locations comprise most visited parking and charging locations (see [0079], [0081] "the first route set is performed on Tuesdays and Thursdays and includes a route from the house 302 to the workplace 304 and a route from the workplace 304 to the house 302. The ECU 106 may learn that the driver typically spends about eight hours at his workplace" and [0083] "The second route set occurs on Saturdays and includes a route from the house 302 to the park 308 and a route from the park 308 to the house 302. The ECU can determine that the vehicle 100 typically spends three hours at the park 308" and Fig. 4 for determining the most visited locations on each weekday. See [0040] and [0066] for determining timeslots that are spent at each location)
and determine the parking and charging location from the most visited locations (see [0085] "Because the ECU 106 predicts that the vehicle 100 will only be at the school 306 for a quarter of an hour, the ECU 106 may determine that there is not sufficient time to charge the battery 110 while the vehicle 100 is at the school 306. Thus, the ECU 106 may cause the internal electric vehicle charger 118 to charge the battery 110 with enough energy to reach the house 302 while the vehicle 100 is at the workplace 304" for determining which locations are parking and charging locations from the most visited locations by ruling out the school due to time limitations)
Regarding claim 16, Payne teaches:
A method comprising (see Figs. 2A and 2B and [0056]-[0078] for the overall method)
Regarding the remaining limitations of claim 16, see the rejection of claim 1 above.
Regarding claim 17, the combination of Payne and O’Gorman teaches all of the limitations of claim 16 above. Regarding the limitations introduced in claim 17, see the rejection of claim 2 above.
Regarding claim 20, Payne teaches:
A non-transitory computer-readable storage medium having instructions stored thereupon which, when executed by a processor, cause the processor to (see [0034] "The memory 108 may be a non-transitory memory or a data storage device, such as a hard disk drive, a solid state disk drive, a hybrid disk drive, or other appropriate data storage, and may further store machine readable instructions which may be loaded and executed by the ECU 106")
Regarding the remaining limitations of claim 20, see the rejection of claim 1 above.
Claims 3-8 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Payne in view of O’Gorman and Hettrich et al. (U.S. Pre-Grant Publication No. 2016/0059733, hereafter known as Hettrich).
Regarding claim 3, the combination of Payne and O’Gorman teaches all of the limitations of claim 2 above. Payne teaches determining confidence values that a particular route is being taken as discussed regarding claim 1 above. O’Gorman further teaches confidence intervals for locations at which the user regularly plugs their vehicle in to charge in at least [0014], [0016], and [0036]. However, the combination of Payne and O’Gorman does not explicitly teach the estimation of a plurality of a plurality of probabilities of parking the vehicle at the parking/charging location at a plurality of future time slots based on the routine travel behavior. Hettrich teaches:
wherein the processor is further configured to estimate a plurality of probabilities of parking the vehicle at the parking and charging location at a plurality of future time slots based on the routine travel behavior (see Fig. 5E and [0129] "the basic input parameters include historical drive start information, combined with location (home), and day of the week (Sunday). These inputs result in a determination that the confidence level of driving at the relevant time is less than the probability threshold, so pre-warming does not occur. FIG. 5E shows the confidence that a drive will start over the time period shown based on the historical drive start information and based on the day of the week being Sunday" for the probability a route will start across a plurality of time periods with rough peaks around 8am 12 pm and 5pm. In combination with Payne’s determination of times a vehicle will be at parking/charging locations based on the predicted route, the probability that the vehicle will be parked according to the route at differing time slots is determined based on the routine travel behavior)
One of ordinary skill in the art would have recognized that applying the known technique of estimating a plurality of probabilities that the vehicle begin a route (and subsequently park at the charging locations) over a plurality of future timeslots of Hettrich to the combination of Payne and O’Gorman would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Hettrich to the teaching of the combination of Payne and O’Gorman would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such estimating a plurality of probabilities that the vehicle begin a route (and subsequently park at the charging locations) over a plurality of future timeslots. Further, applying estimating a plurality of probabilities that the vehicle begin a route (and subsequently park at the charging locations) over a plurality of future timeslots to the combination of Payne and O’Gorman would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more flexibility to the route prediction of Payne alone. By incorporating the chance that a vehicle will begin a route at different times throughout the day as discussed above in Hettrich, the resulting combination would allow for the identification of and rection to routes that may be delayed or offset by a period of time. For example, the incorporating of Hettrich probabilities would allow the resulting combination to recognize the Payne user’s workday route even if it does not start exactly at 8 a.m. as described in Payne [0040].
Regarding claim 4, the combination of Payne, O’Gorman, and Hettrich teaches all of the limitations of claim 3 above. Payne further teaches:
wherein the processor is further configured to: set a first threshold to ascertain a parking event at the parking and charging location (see [0043] "Stated differently, the ECU 106 may perform certain operations if the confidence value that the predicted route set is the correct route set is greater than the threshold confidence value. The threshold confidence value may be set to any threshold amount. For example, it may be a 60% confidence, an 80% confidence or the like")
determine that a probability, of the plurality of probabilities, at a first future time slot, of the plurality of future time slots, is greater than the first threshold; and determine that the vehicle is expected to park at the parking and charging location during the first future time slot based on a determination that the probability is greater than the first threshold (see [0043] "To account for potential inaccuracies, the ECU 106 can determine a confidence value corresponding to a certainty that the predicted route or route set is correct. The ECU 106 may then compare the determined confidence value to a threshold confidence value. The ECU 106 may act as though no route has been predicted if the confidence value of the prediction is less than the threshold confidence value" and [0044] "The ECU 106 can determine when to charge the battery 110 based on a predicted route set and an amount of time spent at each location of the route set. The ECU 106 may be configured to do so only when the determined confidence value is above the threshold confidence value" and [0046] "the ECU 106 may use a second threshold confidence value and act as though confidence values above the second threshold confidence value correspond to 100% confidence value. For example, a lower threshold confidence value may be 60% and a higher threshold confidence value may be 85%. If the determined confidence value is 90%, the ECU 106 may control the charging of the battery 110 as if the determined confidence value is 100%" for the second threshold of Payne corresponding to the first threshold of the present invention, above which the ECU performs charging based on the confidence in the route being above the threshold. See [0066] for the time slot being spent at the location being determined based on the route)
Regarding claim 5, the combination of Payne, O’Gorman, and Hettrich teaches all of the limitations of claim 4 above. Payne further teaches:
wherein the processor is further configured to detect a travel frequency associated with the vehicle in the plurality of future time slots, and wherein the travel frequency is part of the routine travel behavior (see [0058] "the route set can be predicted based on various factors including a route history, a current location, a current time of day, a current day of the week or the like" and Fig. 4 and [0079] for different travel frequencies associated with the vehicle based on the day of the week)
Regarding claim 6, the combination of Payne, O’Gorman, and Hettrich teaches all of the limitations of claim 5 above. While Payne teaches a travel frequency as discussed above regarding claim 5 and a threshold confidence value as discussed above regarding claim 4, the combination of Payne and O’Gorman does not explicitly teach the confidence threshold being based on the travel frequency. Hettrich further teaches:
wherein the processor sets the first threshold based on the travel frequency (see [0064] "The amount of time before an upcoming predicted drive may also affect the probability threshold. If it is determined that the next likely drive will not occur for a day or more, for example, the probability threshold may be determined to be relatively higher in order to avoid wasting energy pre-heating the battery again and again. Conversely, if it is determined that the next likely drive will occur within a shorter period, e.g., hours, the probability threshold may be determined to be relatively lower" for the threshold that a route will occur, and the parking of the vehicle at the locations that are part of the route, based on the expected travel frequency)
It would have been obvious to one of ordinary skill in the art to incorporate the setting of the threshold confidence that a user will depart on a drive (and park at the determined parking and charging locations at a future timeslot) based on the travel frequency of the user of Hettrich into the combination of Payne, O’Gorman, and Hettrich. As Hettrich states, in [0064] above, if the expected travel frequency is lower, then a higher threshold should be used to avoid unnecessarily preparing a battery for a route the user will likely not wind up taking. Accordingly, one of ordinary skill in the art would have recognized that adjusting the threshold confidence level based on the travel frequency of the vehicle would reduce wasting of energy for unrealized drives.
Regarding claim 7, the combination of Payne, O’Gorman, and Hettrich teaches all of the limitations of claim 6 above. Payne further teaches:
wherein the processor is further configured to estimate the future departure time and the future arrival time at the parking and charging location based on the first threshold (see [0066] "The ECU may also predict an amount of time spent at each location in block 256. The ECU can also determine what times the vehicle will be at each location based on the predicted amount of time spent at each location and a current time of day" and [0046] "the ECU 106 may use a second threshold confidence value and act as though confidence values above the second threshold confidence value correspond to 100% confidence value. For example, a lower threshold confidence value may be 60% and a higher threshold confidence value may be 85%. If the determined confidence value is 90%, the ECU 106 may control the charging of the battery 110 as if the determined confidence value is 100%" for determining the route based on the threshold confidence and the departure and arrival times based on the route)
Regarding claim 8, the combination of Payne, O’Gorman, and Hettrich teaches all of the limitations of claim 7 above. Payne further teaches:
wherein to perform the predetermined action, the processor is further configured to: estimate a first State of Charge (SOC) level of a vehicle battery at the future departure time from the parking and charging location based on the routine travel behavior (see [0055] "When the user returns to his house, the ECU 106 may instruct the internal electric vehicle charger 118 to charge the battery 110 to the maximum SOC limit" and Fig. 4 showing the user fills up the battery at the home location for the estimate of a SOC when departing home to be 100%)
and predict a second SOC level of the vehicle battery at the future arrival time based on the first SOC and the amount of energy (see [0081] "The charge required to reach the next destination, i.e., returning to the house 302, is 60% of the available SOC of the battery 110 (i.e., 60% of the stored energy between a lower SOC limit and an upper SOC limit)...After the vehicle 100 reaches the workplace 304, the battery 110 will have 40% of the available SOC remaining. Because the SOC of the battery 110 is at 40% at the workplace 304 and because it will take 60% SOC to return to the house 302, the battery 110 will require an additional 20% SOC to reach the second destination (the house 302)" for predicting that the battery will not have sufficient energy to power the vehicle back to the house. In other words, SOC on arrival would be 0%. A further 20% SOC is added at work to allow the vehicle to arrive back at home)
Regarding claim 18, the combination of Payne and O’Gorman teaches all of the limitations of claim 17 above. Regarding the limitations introduced in claim 18, see the rejection of claim 3 above.
Regarding claim 19, the combination of Payne, O’Gorman, and Hettrich teaches all of the limitations of claim 18 above. Regarding the limitations introduced in claim 19, see the rejection of claim 4 above.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Payne in view of O’Gorman, Hettrich, and Zauli (U.S. Patent No. 11,532,943; hereafter known as Zauli).
Regarding claim 9, the combination of Payne, O’Gorman, and Hettrich teaches all of the limitations of claim 8 above. As discussed above regarding claim 8, Payne teaches determining an estimated SOC of the vehicle battery at both the departure and arrival from a charging location. Payne further references a “minimum SOC limit” in [0044] that the vehicle can be in danger of falling below if not sufficiently charged. However, the combination of Payne, O’Gorman, and Hettrich does not explicitly teach comparing the SOC estimated upon arrival with a second threshold, determining that the vehicle needs additional energy when the estimated arrival SOC is below a second threshold, and outputting a first notification comprising an indication of the need for additional energy. Zauli teaches:
wherein to perform the predetermined action, the processor is further configured to: compare the second SOC with a second threshold (see Col. 40 line 66 thru Col. 41 line 5 "6) The net energy from stay determined in step 5, is subtracted from the present state of charge of the battery...This represents the state of charge the battery would reach by the end of the stay...if no charging occurred before the arrival event (note this could be a negative number at this point)" for determining a predicted SOC of a battery after being in use based on the current state of the battery and energy needed during the in-use period. In combination with Payne, the "stay/in-use" period of Zauli corresponds to the time the vehicle will be driven in Payne (i.e. to work and back on T/Th). Col. 41 lines 5-14 "7) The amount of additional energy required before the arrival event is determined as the difference between the amount of energy available in the battery in the present charge state (or target state of charge of the maintain mode, e.g. 50% absolute state of charge) and the result of step 6. Note: the goal of this step is to manage the battery to an end state of 50% absolute state of charge, but this could optionally by some other number as set by the maintain mode range specified by the user (e.g., based on battery chemistry type)" for the comparison of the predicted remaining SOC with a 50% target SOC)
determine that the vehicle requires additional energy to travel between the future departure time and the future arrival time when the second SOC is less than the second threshold (see Col. 41 lines 14-16 "8) If the amount of energy required, as determined in step 7 is a positive number, the charge start algorithm, as previously described, is applied" for determining that additional charge is needed when it is determined that the difference between the 50% target SOC and the predicted SOC after the in-use period is a positive number. In other words, determining additional charge is required when the predicted SOC after the in-use period is less than the target 50% threshold)
and output a first notification comprising an indication of a requirement of additional energy (see Col. 49 lines 53-54 "the user interface module is configured to display a determined charge start time" and Col. 50 lines 24-67 for the determination of a charge start time to provide the determined additional energy and displaying the charge start time on the user interface indicating additional energy is required and being acquired)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the comparison of a predicted arrival SOC of the vehicle battery to a threshold, determining that additional energy is required when the estimated arrival SOC is below the threshold, and generating a notification indicating the need for additional energy of Zauli into the combination of Payne, O’Gorman, and Hettrich. As Zauli states in Col. 41 lines 19-26 “it is possible for the preparation mode to target a state of charge that is below a full state of charge, wherein the target is determined via the sum of the battery's present state of charge and the additional state of charge determined in step 7, thereby providing a state of charge that can satisfy the predicted net energy deficit of the stay while avoiding a full state of charge to increase longevity of the battery” and Col. 27 lines 5-7 “discharging the battery to the lifespan-saving charge state (e.g. 50%), while at the same time providing a practical use of the energy being consumed”. Accordingly, by ensuring that the estimated SOC upon arrival is at the threshold percentage, the combined system can increase the longevity of the vehicle battery.
Additionally, one of ordinary skill in the art would have recognized that applying the known technique of the comparison of a predicted arrival SOC of the vehicle battery to a threshold, determining that additional energy is required when the estimated arrival SOC is below the threshold, and generating a notification indicating the need for additional energy of Zauli to the combination of Payne, O’Gorman, and Hettrich would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Zauli to the teaching of the combination of Payne, O’Gorman, and Hettrich would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such comparison of a predicted arrival SOC of the vehicle battery to a threshold, determining that additional energy is required when the estimated arrival SOC is below the threshold, and generating a notification indicating the need for additional energy. Further, applying the comparison of a predicted arrival SOC of the vehicle battery to a threshold, determining that additional energy is required when the estimated arrival SOC is below the threshold, and generating a notification indicating the need for additional energy to the combination of Payne, O’Gorman, and Hettrich would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow for a safety buffer of charge to allow for deviations from the expected route/routine of the vehicle while minimizing the risk of the user running out of charge mid-route. See Payne [0075].
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Payne in view of O’Gorman and Heinrich (U.S. Pre-Grant Publication No. 2024/0100983, hereafter known as Heinrich).
Regarding claim 10, the combination of Payne and O’Gorman teaches all of the limitations of claim 1. Payne further teaches:
wherein the processor is further configured to: determine a time duration for which the vehicle is at the parking and charging location, based on the future departure time and the future arrival time (see [0042] "if a driver typically stays at his workplace for eight hours on weekdays, the ECU 106 may predict that the user will stay at his work place for eight hours on future trips. In some embodiments, the ECU 106 can average an amount of time spent at each location. For example, if the driver stays at his workplace for 8.5 hours one day and 8 hours the next day, the ECU 106 may use the value of 8.25 hours as the expected time that the driver will stay at his workplace. In some embodiments, the ECU 106 may remove outliers. For example, if the driver stays at his workplace for 8.5 hours one day, 8 hours the next day and 12 hours the next day, the ECU 106 may still use the value of 8.25 hours as the expected time that the driver will stay at his workplace" and [0040] "if the driver drives to work at 8:00 a.m. every weekday and then drives home at 5:00 p.m. every weekday, the ECU 106 may predict that the vehicle 100 is going to travel to the driver's work place at 8:00 a.m. and return to the driver's house at 5:00 p.m." for predicting the amount of time the user will be at a charging location based on how when the user vehicle will be arriving at and departing from a location)
Payne further teaches determining charging cost rates at various charging locations and minimizing the costs of charging accordingly. O’Gorman also teaches the determination of a cost profile for charging locations and minimizing charging costs accordingly. However, the combination of Payne and O’Gorman still does not explicitly teach determining a plurality of charging rates at a plurality of time slots in the time duration of charging and scheduling vehicle charging based on the plurality of charging rates. Heinrich teaches:
determine a plurality of charging rates of charging the vehicle at a plurality of time slots in the time duration (see [0062] "FIG. 3 shows a prediction 22 being made for the electricity price trend for the future time period T. The computing unit 20 receives price information 24 from the energy market for this purpose. Weather data 23 are additionally taken into consideration and an improved electricity price prediction 22 is made for the time period T" and Fig. 3 for determining electricity rates for a plurality of periods in the future, see the high and low price periods throughout period T. Examiner notes that "rates" as used in Applicant's disclosure covers the price for electricity (see at least specification [0014]))
and perform the predetermined action based on the plurality of charging rates, wherein the predetermined action comprises scheduling vehicle charging based on the plurality of charging rates (see [0063] "Said computing unit takes the user and vehicle data and also the information about past charging processes already gathered and also weather data 23 as a basis for drawing up a total energy requirement of the N vehicles in the time period T, a temporal distribution of the charging requirement over the time period T and a prediction for the electricity price trend in the time period T. An optimized total charging power profile POPT, 25 is calculated therefrom, which, for example, is optimized for a low electricity price or for a proportion of renewable energy sources. Energy may then be purchased 27 on the electricity exchange 26 in accordance with this optimized total charging power profile 25" and Fig. 4 for determining an optimized charging profile based on the charging rates)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the determination of a plurality of charging rates at different time slots during the parking duration and scheduling charging based on the plurality of charging rates of Heinrich into the combination of Payne and O’Gorman. As Heinrich states in [0004] “More and more electrical power becomes necessary for charging electrically operated vehicles with an electrical traction machine that is fed from a vehicle battery, such as pure electric vehicles or what are known as plug-in hybrids. It can therefore be beneficial for a user of an electric vehicle or a plug-in hybrid to charge the vehicle battery as much as possible when the electricity is particularly cheap and/or when as much energy from renewable energy sources as possible is available”. Accordingly, one of ordinary skill in the art would have recognized that scheduling charging based on lower charging rates would be beneficial as it would allow a user to save money on the large amount of energy required for the vehicle.
Regarding claim 11, the combination of Payne, O’Gorman, and Heinrich teach all of the limitations of claim 10 above. While Payne does not explicitly consider the presence of renewable energy and performing charging when renewable energy is available, O’Gorman teaches in at least [0054], [0064], and [0068] determining that the charging locations are equipped with renewable energy generating devices and preferring to charge at locations that can generate renewable energy. However, the combination of Payne and O’Gorman does not explicitly teach that the inputs associated with renewable energy availability are indicative of energy availability during the charging timeframe as claimed in claim 11. However, Heinrich further teaches:
wherein the processor is further configured to: obtain inputs associated with a renewable energy availability during the time duration (see [0038] "appropriate weather data are a weather forecast. A prediction about the availability of wind or solar energy in the future time period can be derived from the weather forecast. The availability of renewable energy sources such as solar or wind energy can in turn influence the price trend on the energy market. The price trend on the energy market can therefore preferably be predicted by taking weather data or a weather forecast into consideration")
and perform the predetermined action based on the renewable energy availability, wherein the predetermined action comprises scheduling vehicle charging when renewable energy is available (see [0063] "Said computing unit takes the user and vehicle data and also the information about past charging processes already gathered and also weather data 23 as a basis for drawing up a total energy requirement of the N vehicles in the time period T, a temporal distribution of the charging requirement over the time period T and a prediction for the electricity price trend in the time period T. An optimized total charging power profile POPT, 25 is calculated therefrom, which, for example, is optimized for a low electricity price or for a proportion of renewable energy sources. Energy may then be purchased 27 on the electricity exchange 26 in accordance with this optimized total charging power profile 25" and [0039] for scheduling charging to maximize the proportion of renewable resources)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the determination of a plurality of charging rates at different time slots during the parking duration and scheduling charging based on the plurality of charging rates of Heinrich into the combination of Payne and O’Gorman. As Heinrich states in [0004] “More and more electrical power becomes necessary for charging electrically operated vehicles with an electrical traction machine that is fed from a vehicle battery, such as pure electric vehicles or what are known as plug-in hybrids. It can therefore be beneficial for a user of an electric vehicle or a plug-in hybrid to charge the vehicle battery as much as possible when the electricity is particularly cheap and/or when as much energy from renewable energy sources as possible is available”. Accordingly, one of ordinary skill in the art would have recognized that scheduling charging based on lower charging rates would be beneficial as it would allow a user to save money on the large amount of energy required for the vehicle.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Payne in view of O’Gorman, Heinrich, and Kobayashi (U.S. Pre-Grant Publication No. 2015/0155715, hereafter known as Kobayashi).
Regarding claim 12, the combination of Payne, O’Gorman, and Heinrich teaches all of the limitations of claim 10 above. While Payne and O’Gorman teach charging vehicles at home and workplaces, the combination of Payne, O’Gorman, and Heinrich does not explicitly teach considering the historical energy consumption associated with a charging location, estimating an energy consumption of the charging location during the vehicle charging period, and scheduling vehicle charging based on the energy consumption at the charging location. Kobayashi teaches:
wherein the processor is further configured to: obtain inputs associated with historical energy consumption associated with the parking and charging location (see [0073] "The storage 316 stores, as knowledge, the usage and supply route of electric power in the home based on user's schedule information and life style" and Fig. 7B and [0105] "FIG. 7B is a table showing power consumption (KWh) corresponding to each user's action status. FIG. 7B shows a table 750 that stores a date, season, day of week, weather, and average temperature (.degree. C.). The action statuses are classified on a date basis into, for example, going out, one person at home, . . . , by going out, going out, and the number of persons at home. In addition, a date, season, day of week, weather, and average temperature (.degree. C.) are included as attributes to be used when obtaining a power consumption")
estimate an energy consumption at the parking and charging location during the time duration based on the inputs (see [0106] "The determining unit 312 obtains a past power consumption based on the same action status and predicts, based on the past power consumption of the home appliances 250, energy necessary to use the home appliances 250 installed in the home" for estimating energy consumption at the user's home during the time duration (Saturday, Sunday, etc.). See 7A and [0098] "In step S701, the cloud server 210 acquires the driving schedule of the electric vehicle 220. In step S703, the determining unit 312 determines the necessary energy of the car battery 221 based on the driving schedule. In step S705, the detector 313 detects the charge energy of the car battery 221" for the determination of car battery charging protocol being dependent on the charging/driving schedule of the vehicle)
and perform the predetermined action based on the energy consumption at the parking and charging location, wherein the predetermined action comprises scheduling vehicle charging based on the energy consumption at the parking and charging location (see [0102] "Upon determining in step S707 that no surplus energy exists in the car battery 221, the process advances to step S717 to determine whether the charge energy with which the home battery 240 is charged is larger than the scheduled power usage in the home" and [0103] "If the charge energy with which the home battery 240 is charged is smaller than the scheduled power usage in the home, the process advances to step S721 to determine to directly charge the car battery 221 using electric power supplied from the power company" for scheduling vehicle charging based on the power usage of the home)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the obtaining of historical energy usage at a charging location, estimating energy consumption at the charging location during the vehicle’s time at the charging location, and schedule the vehicle charging based on the energy consumption at the charging location of Kobayashi into the combination of Payne, O’Gorman, and Heinrich. As Kobayashi states at [0136] “the information processing apparatus according to this embodiment switches the power supply route in the home in consideration of the action schedule of the user. It is therefore possible to store electric energy of an appropriate amount at an appropriate timing and efficiently use electricity”. Accordingly, one of ordinary skill in the art would have recognized that the incorporation of using additional power sources local to the charging location (in other words, the home battery of Kobayashi) to charge the vehicle depending on the energy needs of the user’s home during the charging period would allow the user to more effectively and efficiently use electricity.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Payne in view of O’Gorman and Shpati et al. (U.S. Pre-Grant Publication No. 2023/0347777, hereafter known as Shpati).
Regarding claim 13, the combination of Payne and O’Gorman teaches all of the limitations of claim 1 above. However, the combination of Payne and O’Gorman does not explicitly teach determining a need to precondition the battery and outputting a notification indicating the need to precondition the vehicle battery. Shpati teaches:
wherein the processor is further configured to: determine a requirement of preconditioning of a vehicle battery when the vehicle is located at the parking and charging location based on the routine travel behavior (see [0046]-[0047] for predicting a departure time and trip range for an upcoming trip for a vehicle. See [0035] for the departure time being an average departure time for the vehicle/based on routine driving behavior. See [0048]-[0049] for determining the departure time and ambient driving conditions will impact battery range enough to require preconditioning of the battery)
and perform the predetermined action based on the requirement of preconditioning, wherein the predetermined action comprising outputting a second notification comprising an indication of the requirement of preconditioning (see [0049] "Process block 125 may include alerting a user that preconditioning of the vehicle battery is recommended based on the predicted departure time and the predicted impact of existing driving conditions on battery performance")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the determination of whether a vehicle battery needs to be preconditioned based on the routine travel behavior and sending a second notification indicating the need for preconditioning of Shpati into the combination of Payne and O’Gorman. As Shpati states in [0007] “Other attendant benefits may include enhanced vehicle operation, improved customer experience, and reduced range anxiety. In addition to improved charging capabilities and customer experience, disclosed concepts may help to increase driving range and battery pack performance for electric-drive vehicles”. Accordingly, one of ordinary skill in the art would have recognized that incorporating battery preconditioning based on routine travel behavior and ambient conditions into Payne and O’Gorman would allow for longer driving range and improved battery performance over Payne and O’Gorman alone.
Claims 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Payne in view of O’Gorman and Sudarsan et al. (U.S. Pre-Grant Publication No. 2022/0065940, hereafter known as Sudarsan).
Regarding claim 14, the combination of Payne and O’Gorman teaches all of the limitations of claim 1 above. However, the combination of Payne and O’Gorman does not explicitly teach the predicting a vehicle battery health based on the routine travel behavior and scheduling a vehicle maintenance based on the battery health. Sudarsan teaches:
wherein the processor is further configured to: predict a vehicle battery health based on the routine travel behavior (see [0057]-[0059] for obtaining routine travel and charging behavior of a driver/vehicle, inputting the routine behavior through a trained model, and obtaining a battery failure timeline based on the behavior. Also see [0050]-[0052])
and schedule a vehicle maintenance based on the vehicle battery health (see [0060] "the method 200 includes, at 208, scheduling maintenance for the vehicle based on the battery failure timeline. For example, when the battery failure timeline 154 indicates that battery failure is imminent, the computing device(s) 120 may notify the driver, update the maintenance resource schedule 156, or both")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate predicting a vehicle battery failure timeline and scheduling maintenance for the vehicle based on the routine travel behavior of a vehicle of Sudarsan into the combination of Payne and O’Gorman. As Sudarsan states in [0052] “when the battery failure timeline 154 for a particular vehicle 104 indicates a likely impending failure for the battery pack 108 or a cell of the battery pack 108 of the particular vehicle 104, the data post-processing instructions 144 can update the maintenance resources schedule 156 to indicate that particular spare parts, test equipment, technical manuals, technicians, or other maintenance resources will be needed to service the particular vehicle 104. Scheduling maintenance resources in advance can be especially beneficial for a maintenance location that is associated with multiple vehicles, such as a vehicle dealership… the maintenance location can order spare parts or equipment in advance, which allows the maintenance location to keep a lower stock of parts or equipment on hand without inconveniencing to vehicle owners by making them wait for parts or equipment to arrive”. Accordingly, one of ordinary skill in the art would have recognized that incorporating to predictively schedule vehicle battery maintenance for a vehicle would benefit both the vehicle owner and the provider of vehicle maintenance.
Regarding claim 15, the combination of Payne, O’Gorman, and Sudarsan teaches all of the limitations of claim 14 above. As discussed above, the combination of Payne and O’Gorman does not explicitly teach scheduling the vehicle maintenance based on the routine travel behavior. Sudarsan further teaches:
wherein the processor is further configured to schedule the vehicle maintenance based on the routine travel behavior (see [0057]-[0060] for scheduling the vehicle maintenance based on the routine travel behavior's impact on battery life determined by the machine learning model)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate predicting a vehicle battery failure timeline and scheduling maintenance for the vehicle based on the routine travel behavior of a vehicle of Sudarsan into the combination of Payne and O’Gorman. As Sudarsan states in [0052] “when the battery failure timeline 154 for a particular vehicle 104 indicates a likely impending failure for the battery pack 108 or a cell of the battery pack 108 of the particular vehicle 104, the data post-processing instructions 144 can update the maintenance resources schedule 156 to indicate that particular spare parts, test equipment, technical manuals, technicians, or other maintenance resources will be needed to service the particular vehicle 104. Scheduling maintenance resources in advance can be especially beneficial for a maintenance location that is associated with multiple vehicles, such as a vehicle dealership… the maintenance location can order spare parts or equipment in advance, which allows the maintenance location to keep a lower stock of parts or equipment on hand without inconveniencing to vehicle owners by making them wait for parts or equipment to arrive”. Accordingly, one of ordinary skill in the art would have recognized that incorporating to predictively schedule vehicle battery maintenance for a vehicle would benefit both the vehicle owner and the provider of vehicle maintenance.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ramezani et al. (U.S. Pre-Grant Publication No. 2013/0054045) teaches selecting an electric vehicle schedule based on multiple weighted charging objectives
Tsuda et al. (U.S. Pre-Grant Publication No. 2021/0380012) teaches predicting power consumption of a vehicle based on a future traveling schedule
Tseng et al. (U.S. Patent No. 9,731,617) teaches scheduling charging of a vehicle battery based on a learned key-on pattern
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL C MORONEY whose telephone number is (571)272-4403. The examiner can normally be reached Mon-Fri 8:30-5:30.
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/M.C.M./Examiner, Art Unit 3628
/EMMETT K. WALSH/Primary Examiner, Art Unit 3628
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