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 06/05/2026 and Request for Continued Examination filed on 06/10/2026.
Claims 1, 5, 16, and 20 have been amended and are hereby entered.
Claims 3-4, and 18-19 have been canceled.
Claims 1-2, 5-17, and 20 are currently pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/05/2026 has been entered.
Response to Arguments
Applicant’s arguments, see pages 8-12, filed 06/05/2026, 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 The 35 U.S.C. 101 rejections of claims 3, 4, 18, and 19 have been withdrawn in light of the claims’ cancellation. The 35 U.S.C. 101 rejections of claims 1-20 have been maintained.
Applicant begins on pages 8-9 that the claims do not recite an abstract idea. Applicant particularly argues across pages 8-9 that the claims do not recite a Mental Process because the amended claims recite “specific computational techniques” that cannot practically be performed in the human mind. Examiner respectfully disagrees. Particularly, Examiner disagrees with Applicant that the amended limitations regarding the parking probability profile, setting a parking threshold, and determining the future depart and arrival time based on the intersection of the probability profile and threshold go beyond the capabilities of the human mind. Specifically, under broadest reasonable interpretation, the creation of a probability profile covers observing routine travel behavior and dividing the number of times the vehicle is parked at the location for a time period by the number of occurrences of said time period (i.e. vehicle is parked between 4-5 A.M. 9 out of 10 days observed leads to a 90% parking probability during the 4-5 A.M. interval in the future). Setting a threshold as recited in the claims does not have any particular requirements/methodology and therefore covers a human mind choosing a threshold value based on its own judgment. Finally, determining the departure and arrival time based on the intersection of the parking probability and threshold covers, under broadest reasonable interpretation, determining the first period of time in the morning in which a parking probability drops below the threshold and the first period of time in the evening in which a parking probability surpasses the threshold. Even if one were to consider some form of interpolation to obtain a more precise estimate, a human with the aid of pen and paper could perform linear interpolation between the values of two periods that bridge the threshold to determine a depart/arrive time. This comparison of a probability value to a threshold value is one again within the capability of a human mind. While Applicant argues that computational processing beyond the scope of the human mind is required, the broadest reasonable interpretation of the argued parking probability profile does not support such an argument.
Regarding the sending of a command signal to begin the charging, Examiner again refers to MPEP 2106.04(a)(2) III.C.: “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 transmitting a command signal to the vehicle at a schedule time is using a computer as a tool to begin charging, and thus the performance of the predetermined action also falls under Mental Process.
Regarding the managing of behavior argued on Page 9, the claimed invention is performing the abstract data manipulation discussed above to determine the optimal time to charge a user’s vehicle. 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. Examiner refers to MPEP 2106.04(a)(2) II. “It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.” In the claimed invention, the recited system is in control over when and how the user charges their vehicle. Even though the user’s choice to begin charging or potentially ignore a notification to charge has been amended out of the claims with the command to begin automated charging at the particular scheduled time, the behavior of when the user’s vehicle is charged for later is being managed by the claimed invention.
Next, Applicant argues across pages 9-11 that the judicial exceptions of the claims are integrated into a practical application at Step 2A Prong Two. Applicant’s arguments regarding the parking probability profile at this step in eligibility analysis revolve around the profile generation and comparison to a probability threshold are “specific computational techniques” that should be found eligible in light of MPEP 2106.05(a), Desjardins, and McRo. Examiner respectfully disagrees. As shown above, the generation and use of the parking probability profile to estimate a future departure and arrival time still falls under the judicial exception. While the generation and usage of the profile is more specific than the previously presented claim 1, the amendments still cover Mental Processes and certain methods of organizing human activities being performed in a computing environment. Per MPEP 2106.04 I. “The Court has held that a claim may not preempt abstract ideas, laws of nature, or natural phenomena, even if the judicial exception is narrow (e.g., a particular mathematical formula such as the Arrhenius equation). See, e.g., Mayo, 566 U.S. at 79-80, 86-87, 101 USPQ2d at 1968-69, 1971 (claims directed to ‘narrow laws that may have limited applications’ held ineligible); Flook, 437 U.S. at 589-90, 198 USPQ at 197 (claims that did not ‘wholly preempt the mathematical formula’ held ineligible). This is because such a patent would ‘in practical effect [] be a patent on the [abstract idea, law of nature or natural phenomenon] itself’” and MPEP 2106.05(f) “Requiring more than mere instructions to apply an exception does not mean that the claim must be narrow in order to be eligible. The courts have identified some broad claims as eligible see, e.g., McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 120 USPQ2d 1091 (Fed. Cir. 2016); Thales Visionix Inc. v. United States, 850 F.3d. 1343, 121 USPQ2d 1898 (Fed. Cir. 2017), and some narrow claims as ineligible see e.g., Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 112 USPQ2d 1750 (Fed. Cir. 2014); Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)”. In the fact pattern of the amended claims, the limitations that Applicant argues provide “specific computational techniques” that render the claims eligible are instead narrowing the abstract idea itself. As shown in the MPEP, an abstract idea does not become eligible simply because it is narrow. Amending the claim to recite a more detailed data processing algorithm to be applied using a computer does not improve the functioning of the computer. At most, the improvement is to the abstract idea itself. As MPEP 2106.05(a) II. recites “However, 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.”
Regarding Desjardins, Examiner respectfully disagrees with Applicant’s assertion that these amended claims are being improperly dismissed and evaluated at too high a level of generality. Examiner is applying the broadest reasonable interpretation of these amended limitations in light of the specification as required per MPEP 2106 II. As shown above regarding Prong One, the broadest reasonable interpretation of the generation and use of the profile falls into the judicial exception of the claims. Therefore, even setting aside the disparity in machine learning usage between Desjardins and the claimed invention, the decision in Desjardins is not applicable to the currently presented claims because the limitations argued by Applicant fall into the judicial exception and are not improving technology.
Regarding McRo, Applicant appears to be arguing that McRO shows that specific rules or techniques are necessarily sufficient to show technological improvement. MPEP 2106.05(a) states “in McRO, the court relied on the specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans, when determining that the claims were directed to improvements in computer animation instead of an abstract idea. McRO, 837 F.3d at 1313-14, 120 USPQ2d at 1100-01”. Therefore, while the automation of a previously manual process is not the only way to show an improvement in technology, McRO demonstrated its technical improvement in the specification and claim in this manner. As discussed above, the argued limitations surrounding the probability profile are further narrowing the abstract idea and not an improvement to the functioning of the computer or other technology.
As for the improvements recited in the specification that Applicant points to in [0015], the improvements in vehicle energy usage/rates and the enhanced user experience of operating the vehicle are improvements to the abstract idea. The determination of the timing to charge the vehicle resulting in the improved user experience and lower rates is determined by the abstract idea of the claims determining optimal charging behavior of the user, with the improvements merely being applied using generic computing components. Per MPEP 2106.05(a), “It is important to note, the judicial exception alone cannot provide the improvement” and 2106.05(a) II. “However, 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 arguments at Step 2A Prong Two regarding the transmission of the command signals centers around the “tangible, real-world outcome” caused by the transmission and the claim’s allegedly similarity to Diamond v Diehr. Examiner respectfully disagrees. First, the claim recites “transmitting a command signal to the vehicle at a scheduled time slot to cause the vehicle to charge”. While the command’s intended result is to cause the vehicle to charge, the claim stops at the transmission of the command signal. Accordingly, the claim does not positively recite the vehicle beginning to charge and instead leaves the commencing of charging as the intended result of the command once received by the vehicle outside the scope of the claim. Furthermore, instead of reciting a particular action or modification to the process overall like in Diamond v Diehr (see MPEP 2106.05(a) II. “Particular computerized method of operating a rubber molding press, e.g., a modification of conventional rubber-molding processes to utilize a thermocouple inside the mold to constantly monitor the temperature and thus reduce under- and over-curing problems common in the art, Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 8 and 10 (1981)” (emphasis added)), the claimed invention is taking a result from an abstract algorithm, the start time for charging, and applying the result to begin charging. The claimed invention does not recite any modification to the charging connection or how charging is begun for the vehicle. Therefore, in contrast to Diamond v Diehr, the claimed invention is not improving a physical process using its judicial exception. Instead, the claimed invention is taking an abstract idea and applying the result of the abstract idea using computing components.
As shown above, the claimed invention does not fall into the “improvements” analysis of MPEP 2106.05(a). Instead, the claim’s additional elements fall into the “apply it” analysis of MPEP 2106.05(f). Applicant’s arguments at Step 2A Prong Two are not persuasive, and eligibility analysis proceeds to Step 2B.
Applicant argues across pages 11-12 of Remarks that the “combination of computational techniques and automated vehicle control provides an inventive concept that amounts to significantly more than the judicial exception. Examiner respectfully disagrees. First, Applicant lists limitations including the probability profile which fall into the abstract idea itself and therefore cannot be significantly more than the judicial exception. Furthermore, MPEP 2106.05 II. states that at Step 2B, conclusions are carried over from Step 2A Prong Two for considerations including the “apply it” consideration from MPEP 2106.05(f) which the additional elements of the claims fall into. Per MPEP 2106.05(f), “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more”. Therefore, Applicant’s arguments that the claims amount to significantly more at Step 2B are not persuasive. The pending claims still stand rejected under 35 U.S.C. 101.
Applicant’s arguments, see pages 12-14, filed 06/01/2026, with respect to the 35 U.S.C. 103 rejections of claims 1-20 have been fully considered but are either moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument or are not persuasive. While the 35 U.S.C. 103 rejections of claims 3-4 and 18-19 have been withdrawn in light of the cancellation of these claims, claims 1-2, 5-17, and 20 still stand rejected under 35 U.S.C. 103.
First, Applicant argues across pages 12-13 that Payne, O’Gorman, and Hettrich do not teach the newly added limitations of “wherein to estimate the future departure time and the future arrival time, the processor is configured to: generate a parking probability profile comprising 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; set a threshold to ascertain a parking event at the parking and charging location; and determine the future departure time and the future arrival time based on intersection points between the parking probability profile and the threshold” in claim 1. This argument is moot, as the Krumm reference (U.S. Pre-Grant Publication No. 2012/0310376, hereafter known as Krumm) is used to teach these features in the rejection of claim 1 below.
Applicant also argues on Page 13 of Remarks that Payne and O’Gorman do not teach the newly added limitation of automatically initiating charging of the vehicle “by transmitting a command signal to the vehicle at a scheduled time slot to cause the vehicle to charge”. Applicant argues that because the ECU of Payne is within the vehicle Payne cannot teach transmitting a command signal to the vehicle. Examiner respectfully disagrees. Specifically, Applicant is correct Payne teaches an internal ECU, but the amended claim recites that the vehicle must receive the command signal without reciting that the signal must necessarily come from outside the vehicle. That is, Applicant is improperly importing structure from paragraphs [0021]-[0022] of the specification into the claims. Payne [0033] recites “The ECU 106 can also control the operation of the internal electric vehicle charger 118 to start or stop charging of the battery 110” for transmitting a control signal to the vehicle’s internal charger to start/stop charging on schedule. The vehicle’s internal charger is receiving the control command, which is what is required by the broadest reasonable interpretation of “by transmitting a command signal to the vehicle at a scheduled time slot to cause the vehicle to charge” because the processor is transmitting the control command to the vehicle. The claimed invention does not preclude the processor from being located within the vehicle itself because claim 1 makes no mention of the location of the processor. As currently constructed, as long as the processor sends a control command that is received by the vehicle, the broadest reasonable interpretation of the limitation is met. Applicant’s argument that Payne does not teach the transmission to the vehicle is not persuasive.
Furthermore, even assuming Payne failed to teach the transmission to the vehicle, which Examiner does not concede, O’Gorman would still teach the embodiment argued by Applicant. O’Gorman teaches in [0029] and [0062] that the controllers determining the charging schedule can either be located on the vehicle or as an external server. Accordingly, O’Gorman explicitly teaches that the transmission of the vehicle charging schedule comes from a device external to the vehicle and is sent to the vehicle, with the combination of O’Gorman with Payne to teach this feature merely requiring a simple substitution of an external device for an internal device as is already discussed in O’Gorman [0029]. Therefore, Applicant’s arguments that the combination of Payne and O’Gorman fail to teach the transmission to the vehicle are additionally unpersuasive.
Finally, Applicant’s arguments on page 14 of Remarks that the other independent claims are allowable over the cited references are not persuasive for similar reasoning as discussed above regarding claim 1. Applicant’s arguments regarding the dependent claims being patentable by virtue of their dependence on the independent claims are not persuasive because the independent claims are not allowable over the prior art and because the prior art teaches the limitations of the dependent claims as shown in the rejections below. Accordingly, claims 1-2, 5-17, and 20 still stand rejected under 35 U.S.C. 103.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 6-9 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 6 recites the limitation "the first threshold". There is insufficient antecedent basis for this limitation in the claim. Specifically, “a first threshold” is not introduced earlier in claim 6 and it is unclear if “the first threshold” refers back to “a threshold” introduced in claim 1 because of the last of the “first” descriptor. Accordingly, the scope of claim 6 is indefinite and the claim is rejected under 35 U.S.C. 112(b).
Claim 7 is rejected by virtue of its dependency on indefinite claim 6 as well as the claim’s own recitation of “the first threshold” that lacks antecedent basis. Claims 8-9 are rejected by virtue of their dependency on indefinite claims 6 and 7.
For the purposes of examination, Examiner will be interpreting “the first threshold” in claims 6 and 7 as “the threshold”.
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 1-2, 5-17, and 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 1-2 and 5-15 fall into at least the machine category of statutory subject matter. Claims 16 and 17 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 , wherein to estimate the future departure time and the future arrival time, the processor is configured to: generate a parking probability profile comprising 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; set a threshold to ascertain a parking event at the parking and charging location; and determine the future departure time and the future arrival time based on intersection points between the parking probability profile and the threshold; 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 by transmitting a command signal to the vehicle at a scheduled time slot to cause the vehicle to charge”, 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, wherein to estimate the future departure time and the future arrival time, the processor is configured to: generate a parking probability profile comprising 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; set a threshold to ascertain a parking event at the parking and charging location; and determine the future departure time and the future arrival time based on intersection points between the parking probability profile and the threshold; 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 by transmitting a command signal to the vehicle at a scheduled time slot to cause the vehicle to charge. 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 by transmitting a command signal to the vehicle at a scheduled time slot to cause the vehicle to charge 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 and 5-15 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, wherein estimating the future departure time and the future arrival time comprises: generating a parking probability profile comprising 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; setting a threshold to ascertain a parking event at the parking and charging location; and determining the future departure time and the future arrival time based on intersection points between the parking probability profile and the threshold; 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 by transmitting a command signal to the vehicle at a scheduled time slot to cause the vehicle to charge”, 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, wherein estimating the future departure time and the future arrival time comprises: generating a parking probability profile comprising 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; setting a threshold to ascertain a parking event at the parking and charging location; and determining the future departure time and the future arrival time based on intersection points between the parking probability profile and the threshold; 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 by transmitting a command signal to the vehicle at a scheduled time slot to cause the vehicle to charge. 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 by transmitting a command signal to the vehicle at a scheduled time slot to cause the vehicle to charge 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 17 further limits the abstract idea of claim 16 without adding any new additional elements. Therefore, by the analysis of claim 16 above, claim 17 does not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claim is 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, wherein to estimate the future departure time and the future arrival time, the instructions further cause the processor to: generate a parking probability profile comprising 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; set a threshold to ascertain a parking event at the parking and charging location; and determine the future departure time and the future arrival time based on intersection points between the parking probability profile and the threshold; 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 by transmitting a command signal to the vehicle at a scheduled time slot to cause the vehicle to charge”, 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, wherein to estimate the future departure time and the future arrival time, the instructions further cause the processor to: generate a parking probability profile comprising 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; set a threshold to ascertain a parking event at the parking and charging location; and determine the future departure time and the future arrival time based on intersection points between the parking probability profile and the threshold; 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 by transmitting a command signal to the vehicle at a scheduled time slot to cause the vehicle to charge. 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 by transmitting a command signal to the vehicle at a scheduled time slot to cause the vehicle to charge 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, 5, 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) and Krumm et al. (U.S. Pre-Grant Publication No. 2012/0310376, hereafter known as Krumm).
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 by transmitting a command signal to the vehicle at a scheduled time slot to cause the vehicle to charge (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. Also see [0033] “The ECU 106 can also control the operation of the internal electric vehicle charger 118 to start or stop charging of the battery 110” for transmitting a control signal to the vehicle’s internal charger to start/stop charging on schedule. The ECU’s control command reads on the broadest reasonable interpretation of transmitting a command to the vehicle, as it is the vehicle’s internal charger that receives the command to start/stop charging)
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. While Payne teaches the estimation of departure and arrival times based on routine travel behavior, Payne does not explicitly teach the estimation being performed by generating a parking probability profile comprising 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; setting a threshold to ascertain a parking event at the parking and charging location; and determining the future departure time and the future arrival time based on intersection points between the parking probability profile and the threshold.
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.
The combination of Payne and O’Gorman still does not explicitly teach generating a parking probability profile comprising 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; setting a threshold to ascertain a parking event at the parking and charging location; and determining the future departure time and the future arrival time based on intersection points between the parking probability profile and the threshold. Krumm teaches:
wherein to estimate the future departure time and the future arrival time, the processor is configured to: generate a parking probability profile comprising 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 [0056] “Having identified one or more similar sequences using any combination of the techniques described above, an occupancy probability for the future 212 (i.e. for time after time t) can then be calculated using those identified similar occupancy sequences (block 108). In the example shown in FIG. 2, the prediction uses the average bit values in corresponding time slots in the similar sequences, such that in the first slot after time t, the computed occupancy probability is only 0.2 (i.e. only one of the five similar sequences 210 showed occupancy during the corresponding time slot) and in the fifth and sixth time slots after time t, there is a probability of 1.0, as all five similar sequences showed occupancy in the corresponding time slot” and element 212 in Fig. 2 for a probability profile generated for a plurality of future timeslots for a user being home. See [0058] for probability predictions going beyond the current day. Also see [0067] “Charging of electric vehicles (e.g. ahead of when it is predicted that someone will leave the building) or other vehicle related processes (e.g. pre-heating or pre-cooling vehicles or vehicle interiors, batteries or engines in harsh climates)” for the occupancy probability being used to determine when to charge/precondition a vehicle. In combination with Payne and O’Gorman, the home occupancy probability profile informing the charging of electric vehicle would represent the parking probability of the electric vehicle at the user’s home)
set a threshold to ascertain a parking event at the parking and charging location (see Once an occupancy probability has been computed, a threshold may be applied to provide a binary prediction that a space is going to be either occupied or not occupied (which may be referred to as an occupancy prediction) and this is shown in FIG. 5 (block 502). In an example, a threshold of 0.5 may be used (e.g. if probability >0.5 then space will be occupied).”)
and determine the future departure time and the future arrival time based on intersection points between the parking probability profile and the threshold (see [0059] for determining whether a user will be home based on applying the threshold to the probability that the user is home for each timeslot. See [0060] – [0067] for the prediction of a user’s arrival or departure based on applying the threshold to the probability of the user being home in future timeslots being used to trigger automated actions. See [0071] for determining arrival time based on an intersection of the probability with the threshold. In combination with Payne and O’Gorman, the intersections of the probability with the threshold would be for the arrival and departure of the electric vehicle associated with the user)
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 occupancy and arrival and departure time prediction method as disclosed in Krumm in the combination executing the method of Payne and O’Gorman. As in Krumm, it is within the capabilities of one of ordinary skill in the art to generate a parking probability profile comprising 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 and determine the future departure time and the future arrival time based on intersection points between the parking probability profile and a set threshold in the combination of Payne and O’Gorman with the predictable result of scheduling electric vehicle charging before a user’s predicted departure as needed in the combination of Payne and O’Gorman.
Specifically, Krumm teaches in [0065] that the occupancy prediction can be used for “Scheduling tasks, such as tasks… which are energy consuming (e.g. clothes in a tumble dryer need to be dry before a person arrives home such that occupancy prediction sets a deadline for scheduling the task but where energy costs vary across the day, this can also be taken into consideration, and similarly for electric vehicle charging, the vehicle needs to be charged before the next predicted departure from the home but there is some flexibility within this to select a time based on electricity rates)” and [0067] “Charging of electric vehicles (e.g. ahead of when it is predicted that someone will leave the building)”. Payne (see at least Abstract) and O’Gorman (see at least [0058]) both prioritize cost effective vehicle charging. By incorporating the occupancy prediction of the vehicle at home of Krumm into the combination of Payne and O’Gorman, the routine vehicle usage can used to better time the vehicle charging to make the charging more cost efficient.
Regarding claim 2, the combination of Payne, O’Gorman, and Krumm 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 5, the combination of Payne, O’Gorman, and Krumm teaches all of the limitations of claim 2 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 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, O’Gorman, and Krumm 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 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Payne in view of O’Gorman, Krumm, and Hettrich et al. (U.S. Pre-Grant Publication No. 2016/0059733, hereafter known as Hettrich).
Regarding claim 6, the combination of Payne, O’Gorman, and Krumm teaches all of the limitations of claim 5 above. Krumm further teaches the threshold being based on a desired sensitivity for the predictions in [0059]. However, the combination of Payne, O’Gorman, and Krumm does not explicitly teach the confidence threshold being based on the travel frequency. Hettrich 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 Krumm. 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, Krumm, 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, Krumm, 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)
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Payne in view of O’Gorman, Krumm, Hettrich, and Zauli (U.S. Patent No. 11,532,943; hereafter known as Zauli).
Regarding claim 9, the combination of Payne, O’Gorman, Krumm, 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, Krumm, 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, Krumm, 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, Krumm, 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, Krumm, 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, Krumm, 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, Krumm, and Heinrich (U.S. Pre-Grant Publication No. 2024/0100983, hereafter known as Heinrich).
Regarding claim 10, the combination of Payne, O’Gorman, and Krumm 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. Krumm further teaches:
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 [0060] “the occupancy prediction generated in block 108 (and to which a threshold may have been applied in block 502) may be used to trigger an automatic action (block 504). Examples of such automatic actions which may be triggered based on an occupancy prediction include, but are not limited to:” and [0065] “Scheduling tasks, such as tasks… where energy costs vary across the day, this can also be taken into consideration, and similarly for electric vehicle charging, the vehicle needs to be charged before the next predicted departure from the home but there is some flexibility within this to select a time based on electricity rates” for scheduling the vehicle charging based on varying electricity 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 occupancy and arrival and departure time prediction method as disclosed in Krumm in the combination executing the method of Payne and O’Gorman. As in Krumm, it is within the capabilities of one of ordinary skill in the art to generate a parking probability profile comprising 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 and determine the future departure time and the future arrival time based on intersection points between the parking probability profile and a set threshold in the combination of Payne and O’Gorman with the predictable result of scheduling electric vehicle charging before a user’s predicted departure as needed in the combination of Payne and O’Gorman.
Specifically, Krumm teaches in [0065] that the occupancy prediction can be used for “Scheduling tasks, such as tasks… which are energy consuming (e.g. clothes in a tumble dryer need to be dry before a person arrives home such that occupancy prediction sets a deadline for scheduling the task but where energy costs vary across the day, this can also be taken into consideration, and similarly for electric vehicle charging, the vehicle needs to be charged before the next predicted departure from the home but there is some flexibility within this to select a time based on electricity rates)” and [0067] “Charging of electric vehicles (e.g. ahead of when it is predicted that someone will leave the building)”. Payne (see at least Abstract) and O’Gorman (see at least [0058]) both prioritize cost effective vehicle charging. By incorporating the occupancy prediction of the vehicle at home of Krumm into the combination of Payne and O’Gorman, the routine vehicle usage can used to better time the vehicle charging to make the charging more cost efficient.
However, while Krumm teaches considering electricity rates where scheduling charging, the combination of Payne, O’Gorman, and Krumm 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, O’Gorman, and Krumm. 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, Krumm, 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, O’Gorman, and Krumm 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, O’Gorman, and Krumm. 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, Krumm, Heinrich, and Kobayashi (U.S. Pre-Grant Publication No. 2015/0155715, hereafter known as Kobayashi).
Regarding claim 12, the combination of Payne, O’Gorman, Krumm, and Heinrich teaches all of the limitations of claim 10 above. While Payne, O’Gorman, and Krumm teach charging vehicles at home and workplaces, the combination of Payne, O’Gorman, Krumm, 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, Krumm, 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, Krumm, and Shpati et al. (U.S. Pre-Grant Publication No. 2023/0347777, hereafter known as Shpati).
Regarding claim 13, the combination of Payne, O’Gorman, and Krumm teaches all of the limitations of claim 1 above. Krumm further contemplates the need to precondition batteries in harsh climates before a user is predicted to depart in [0067]. However, the combination of Payne, O’Gorman, and Krumm 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, O’Gorman, and Krumm. 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, O’Gorman, and Krumm would allow for longer driving range and improved battery performance over Payne, O’Gorman, and Krumm alone.
Claims 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Payne in view of O’Gorman, Krumm, and Sudarsan et al. (U.S. Pre-Grant Publication No. 2022/0065940, hereafter known as Sudarsan).
Regarding claim 14, the combination of Payne, O’Gorman, and Krumm teaches all of the limitations of claim 1 above. However, the combination of Payne, O’Gorman, and Krumm 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, O’Gorman, and Krumm. 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, Krumm, and Sudarsan teaches all of the limitations of claim 14 above. As discussed above, the combination of Payne, O’Gorman, and Krumm 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, O’Gorman, and Krumm. 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Umeno et al. (U.S. Pre-Grant Publication No. 2016/0267547) teaches determining “at home” probabilities for a user over predefined time intervals
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/M.C.M./Examiner, Art Unit 3626
/EMMETT K. WALSH/Primary Examiner, Art Unit 3626
