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 .
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.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
A claim that recites an abstract idea, a law of nature, or a natural phenomenon is directed to a judicial exception. Abstract ideas include the following groupings of subject matter, when recited as such in a claim limitation: (a) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; (b) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and (c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See the 2019 Revised Patent Subject Matter Eligibility Guidance.
Even when a judicial element is recited in the claim, an additional claim element(s) that integrates the judicial exception into a practical application of that exception renders the claim eligible under §101. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The following examples are indicative that an additional element or combination of elements may integrate the judicial exception into a practical application:
the additional element(s) reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
the additional element(s) that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
the additional element(s) implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
the additional element(s) effects a transformation or reduction of a particular article to a different state or thing; and
the additional element(s) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Examples in which the judicial exception has not been integrated into a practical application include:
the additional element(s) merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
the additional element(s) adds insignificant extra-solution activity to the judicial exception; and
the additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
See the 2019 Revised Patent Subject Matter Eligibility Guidance.
Claims 1, 9, & 16 recite predicting when an EV needs to be charged, where the EV needs to be charged, and for how long the EV needs to be charged based on individualized characteristics of the driver or one or more passengers, weather conditions, and geospatial characteristics, evaluating an availability of one or more EV charging stations located within a given radius of the EV, comparing a location of the one or more available EV charging stations, located within the given radius of the EV, to one or more desired locations of the driver of the EV, determining an estimated waiting time at the one or more EV charging stations, scheduling an EV charging time at the one or more EV charging stations, based on the location and duration, as drafted, is a device & process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer elements. The claim is practically able to be performed in the mind. For example, but for the “A computer-implemented method for predicting electric vehicle (EV) charging behavior of a driver, the method comprising, training a machine learning model, A computer program product, comprising a non-transitory tangible storage device having program code embodied therewith, the program code executable by a processor of a computer to perform a method, the method comprising, A computer system, comprising: one or more computer devices each having one or more processors and one or more tangible storage devices; and a program embodied on at least one of the one or more storage devices, the program having a plurality of program instructions for execution by the one or more processors, the program instructions comprising instructions for,” language, “predicting when an EV needs to be charged, where the EV needs to be charged, and for how long the EV needs to be charged based on individualized characteristics of the driver or one or more passengers, weather conditions, and geospatial characteristics, evaluating an availability of one or more EV charging stations located within a given radius of the EV, comparing a location of the one or more available EV charging stations, located within the given radius of the EV, to one or more desired locations of the driver of the EV, determining an estimated waiting time at the one or more EV charging stations, scheduling an EV charging time at the one or more EV charging stations, based on the location and duration” in the context of this claim encompasses the user determining which charging station is best to use to charge their vehicle. 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 claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – using “A computer-implemented method for predicting electric vehicle (EV) charging behavior of a driver, the method comprising, training a machine learning model, A computer program product, comprising a non-transitory tangible storage device having program code embodied therewith, the program code executable by a processor of a computer to perform a method, the method comprising, A computer system, comprising: one or more computer devices each having one or more processors and one or more tangible storage devices; and a program embodied on at least one of the one or more storage devices, the program having a plurality of program instructions for execution by the one or more processors, the program instructions comprising instructions for,”. The devices are recited at a high-level of generality (i.e., device configured to determine a charging reservation) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using “A computer-implemented method for predicting electric vehicle (EV) charging behavior of a driver, the method comprising, training a machine learning model, A computer program product, comprising a non-transitory tangible storage device having program code embodied therewith, the program code executable by a processor of a computer to perform a method, the method comprising, A computer system, comprising: one or more computer devices each having one or more processors and one or more tangible storage devices; and a program embodied on at least one of the one or more storage devices, the program having a plurality of program instructions for execution by the one or more processors, the program instructions comprising instructions for,”, amounts to 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.
Similarly for claims 2-8, 10-15, & 17-20 in the context of this claim encompasses the user figuring the best factors or attributes to find the best charging station for the vehicle. 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 claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The devices are recited at a high-level of generality (i.e., device configured to determine a charging reservation) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. 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 § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 5-6, 9-11, 13-14,16-18, & 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2019/0308510A1 (“Beaurepaire 510`”).
As per claim 1 Beaurepaire 510` discloses
A computer-implemented method for predicting electric vehicle (EV) charging behavior of a driver (see at least Beaurepaire 510`, para. [0007]: computer-implemented method comprises recording a usage history, a usage pattern, or combination thereof associated with an operation of a vehicle or device by a user. The method also comprises generating a representation of a remaining energy level of the vehicle or device.), the method comprising:
predicting when an EV needs to be charged, where the EV needs to be charged, and for how long the EV needs to be charged based on individualized characteristics of the driver or one or more passengers, weather conditions, and geospatial characteristics (see at least Beaurepaire 510`, para. [0042]: In step 401, the data module 301 records a usage history, a usage pattern, or combination(e.g., usage data) thereof associated with an operation of a vehicle 101 or a device (e.g., UE 113) by a user. The usage data can be stored in the user database 111. In one embodiment, the usage history can include data records recording when, where, how long, energy consumption, etc. used during an operational instance of the vehicle 101 or UE 113 (e.g., a trip made in the vehicle 101 by the user).Additional contextual parameters (e.g., weather, traffic conditions, road conditions, number of passengers, etc.) can also be collected and analyzed by the data module 301. & para. [0060-0061]: In one embodiment, the recommendation module 305 can use the same or similar predictive or statistical models as described above with respect to the process 400 of FIG. 4. For example, the trained prediction models can be used in combination with the digital map data of the geographic database to identify energy station facilities 121 (e.g., recharging facilities, refueling facilities, etc.) that can be recommended to the user. In one embodiment, the recommendation module 305 recommends when and where to refuel/recharge (e.g., replenish energy reserves) based on the predicted evolution of energy levels (e.g., predicted according to the embodiments of the process 400 of FIG. 4), and the proximity of such fuel/charge stations (e.g., energy station facilities 121) along the predicted or planned routes.);
evaluating an availability of one or more EV charging stations located within a given radius of the EV (see at least Beaurepaire 510`, para. [0060]: para. [0060]:In one embodiment, the recommendation module 305 recommends when and where to refuel/recharge (e.g., replenish energy reserves) based on the predicted evolution of energy levels (e.g., predicted according to the embodiments of the process 400 of FIG. 4), and the proximity of such fuel/charge stations (e.g., energy station facilities 121) along the predicted or planned routes. & para. [0064: For example, the recommendation module 305 can query the digital map data of the geographic database 109 to determine nearby charging or refueling stations that have the request charging modes (e.g., fast charging) with the charging connectors that are compatible with the user's vehicle 101 or UE 113.);
comparing a location of the one or more available EV charging stations, located within the given radius of the EV, to one or more desired locations of the driver of the EV (see at least Beaurepaire 510`, para. [0067]: For example, the recommendation module 305 can suggest that the user stop or remain at a charging location (e.g., when recharging is expected to take more than a threshold amount of time), and use a shared car, ride sharing service, public transport, or other alternative modes of transportation to continue on the user's trip while the vehicle 101 reaches a desired energy level. For example, the recommendation module 305 can interface the geographic database 109and/or the services platform 117 or any of the services 119 to determine whether there are any alternate modes of transportation near a recommended recharging/refueling location or otherwise suitable to continue the user's trip.);
determining an estimated waiting time at the one or more EV charging stations (see at least Beaurepaire 510`, para. [0066-0068]:The recommendation module 305 can create several entries in the calendar data if several recharging/refueling options were initially recommended. In one embodiment, the replenishment time (e.g., recharging or refueling time) can be with respect to reaching a full recharge/refueling or to reach a desired day (e.g., charge 40 mins to reach the weekend)…For example, the user can request the energy management module 103 send the user a notification when the charge level is enough for specified period of time. For example, if the user requests a notification for when the energy level is enough for a week, the energy management module 103 can respond by confirming the notification request and/or presenting an estimated time to reach the request energy level (e.g., “Charge for week should take approximately 1.5 hours”).);
scheduling an EV charging time at the one or more EV charging stations, based on the location and duration (see at least Beaurepaire 510`, para. [0066-0069]: In one embodiment, batteries for electric vehicles 101 have optimal charging cycles that can be considered for optimal use and maintenance, particularly in light of the high costs of such batteries. Accordingly, the recommendation module 305 can consider these optimal charging cycles when recommending charging times, locations, and/or charge levels. For example, if it is recommended that the battery level should ideally never fall below a minimum percentage, then the recommendation module 305 should take this into account when recommending when and where to charge.).
As per claim 2 Beaurepaire 510` discloses
further comprising: training a machine learning model to predict potential "range anxiety" for the driver, or the one or more passengers, of the EV (see at least Beaurepaire 510`, para. [0047-0048]: For example, during training of such a model, the prediction module 303 uses a learner module that feeds feature sets from each individual training data set (e.g., ground truth labeled feature sets that annotate and observed set of remaining energy level related features with a known operating time) into the feature detection model to compute a predicted matching feature using an initial set of model parameters (e.g., an initial set of model weights)…If the accuracy or level of performance does not meet a threshold or configured level, the learner module incrementally adjusts the model parameters or weights until the model generates predictions at a desired or configured level of accuracy with respect to the ground truth data…In one embodiment, the predicted time that the vehicle can be operated can further account for an energy reserve level, an energy buffer level, or a combination thereof associated with the user. In one embodiment, the energy reserve represents a user's comfort level with respect to how much energy (e.g., charge or fuel) remains before the user typically replenishes (e.g., recharges or refuels).A collected usage history may indicate, for instance, that a particular user usually recharges or refuels when the remaining energy level reaches 25% of absolute capacity (e.g., battery capacity, fuel tank size, etc.).); and
establishing that the "range anxiety" is a causal relation with health or behavioral concerns of the driver, or the one or more passengers, of the EV (see at least Beaurepaire 510`, para. [0033-0034]:For example, one source of anxiety or cognitive load on drivers is “range anxiety” (e.g., when driving in an electric vehicle 101 in particular, but also in any vehicle in general). This range anxiety refers, for instance, to when the drivers worry over when they will runout of battery charge or fuel. As a result, drivers may tend to act conservatively and unnecessarily recharge/refuel “just in case” to relieve range anxiety. However, this unnecessarily recharging/refueling can have potential negative sides effects. For example, particularly with respect electric car recharging stations, recharging stations are still relatively rare compared to traditional fueling stations, making recharging spots these stations also relatively rare. Accordingly, vehicles 101 that unnecessarily charge or charge for unnecessarily long times can occupy valuable charging spaces that would otherwise be better used by other vehicles 101that have actual charging needs (e.g., vehicles 101 with almost depleted batteries, vehicles with planned trips that exceed their currently available range, etc.).).
As per claim 3 Beaurepaire 510` discloses
further comprising: triggering an amelioration action when the predicted "range anxiety" is above a given threshold, wherein the amelioration action comprises generating an actionable alert to the driver, or the one or more passengers, to charge the EV at a specific location, at a specific time, and for a specific duration. (see at least Beaurepaire 510`, para. [0048-0051]: Accordingly, the prediction module 303 can also make a refueling prediction based the current absolute fuel/charge level and the user's comfort or reserve level (e.g., the battery is 30% full, but the user usually recharges before the battery reaches 25%). para. [0066-0068]:The recommendation module 305 can create several entries in the calendar data if several recharging/refueling options were initially recommended. In one embodiment, the replenishment time (e.g., recharging or refueling time) can be with respect to reaching a full recharge/refueling or to reach a desired day (e.g., charge 40 mins to reach the weekend)…For example, the user can request the energy management module 103 send the user a notification when the charge level is enough for specified period of time. For example, if the user requests a notification for when the energy level is enough for a week, the energy management module 103 can respond by confirming the notification request and/or presenting an estimated time to reach the request energy level (e.g., “Charge for week should take approximately 1.5 hours”).).
As per claim 5 Beaurepaire 510` discloses
wherein the predicted charging information of the EV automatically creates a calendar event, for the driver, with detailed metadata of the predicted charging (see at least Beaurepaire 510`, para. [0066]: In addition, the recommendation module 305 can automatically update the user's calendar data based on the reservation request by, for instance, making an entry in the user's calendar to make sure the user remembers and sees the entry. The recommendation module 305 can create several entries in the calendar data if several recharging/refueling options were initially recommended.).
As per claim 6 Beaurepaire 510` discloses
further comprising: optimizing the predicted charging of the EV based on economic costs and EV charging factors (see at least Beaurepaire 510`, para. [0063-0064]: In one embodiment, the recommended time, the recommended location, or a combination thereof is further based on an energy replenishment cost. In some countries or jurisdictions, energy costs (e.g., electricity costs for recharging) can vary between different times of the day (e.g., between day versus night, with night time usually being cheaper because of less demand).); and
recommending a cost-effective EV charging, wherein the EV charging factors comprise a charging time, a location, a charger type and emission, a route, and health and behavioral concerns of the driver, or the one or more passengers (see at least Beaurepaire 510`, para. [0063-0064]: In one embodiment, the recommended time, the recommended location, or a combination thereof is further based on an energy replenishment mode of the vehicle, an energy replenishment connector of the vehicle, or a combination thereof. For example, the recommendation module 305 can query the digital map data of the geographic database 109 to determine nearby charging or refueling stations that have the request charging modes (e.g., fast charging) with the charging connectors that are compatible with the user's vehicle 101 or UE 113.).
As per claim 9 Beaurepaire 510` discloses
A computer program product, comprising a non-transitory tangible storage device having program code embodied therewith, the program code executable by a processor of a computer to perform a method (see at least Beaurepaire 510`, para. [0007]: computer-implemented method comprises recording a usage history, a usage pattern, or combination thereof associated with an operation of a vehicle or device by a user. The method also comprises generating a representation of a remaining energy level of the vehicle or device.), the method comprising:
predicting when an EV needs to be charged, where the EV needs to be charged, and for how long the EV needs to be charged based on individualized characteristics of the driver or one or more passengers, weather conditions, and geospatial characteristics (see at least Beaurepaire 510`, para. [0042]: In step 401, the data module 301 records a usage history, a usage pattern, or combination(e.g., usage data) thereof associated with an operation of a vehicle 101 or a device (e.g., UE 113) by a user. The usage data can be stored in the user database 111. In one embodiment, the usage history can include data records recording when, where, how long, energy consumption, etc. used during an operational instance of the vehicle 101 or UE 113 (e.g., a trip made in the vehicle 101 by the user).Additional contextual parameters (e.g., weather, traffic conditions, road conditions, number of passengers, etc.) can also be collected and analyzed by the data module 301. & para. [0060-0061]: In one embodiment, the recommendation module 305 can use the same or similar predictive or statistical models as described above with respect to the process 400 of FIG. 4. For example, the trained prediction models can be used in combination with the digital map data of the geographic database to identify energy station facilities 121 (e.g., recharging facilities, refueling facilities, etc.) that can be recommended to the user. In one embodiment, the recommendation module 305 recommends when and where to refuel/recharge (e.g., replenish energy reserves) based on the predicted evolution of energy levels (e.g., predicted according to the embodiments of the process 400 of FIG. 4), and the proximity of such fuel/charge stations (e.g., energy station facilities 121) along the predicted or planned routes.);
evaluating an availability of one or more EV charging stations located within a given radius of the EV (see at least Beaurepaire 510`, para. [0060]: para. [0060]:In one embodiment, the recommendation module 305 recommends when and where to refuel/recharge (e.g., replenish energy reserves) based on the predicted evolution of energy levels (e.g., predicted according to the embodiments of the process 400 of FIG. 4), and the proximity of such fuel/charge stations (e.g., energy station facilities 121) along the predicted or planned routes. & para. [0064: For example, the recommendation module 305 can query the digital map data of the geographic database 109 to determine nearby charging or refueling stations that have the request charging modes (e.g., fast charging) with the charging connectors that are compatible with the user's vehicle 101 or UE 113.);
comparing a location of the one or more available EV charging stations, located within the given radius of the EV, to one or more desired locations of the driver of the EV (see at least Beaurepaire 510`, para. [0067]: For example, the recommendation module 305 can suggest that the user stop or remain at a charging location (e.g., when recharging is expected to take more than a threshold amount of time), and use a shared car, ride sharing service, public transport, or other alternative modes of transportation to continue on the user's trip while the vehicle 101 reaches a desired energy level. For example, the recommendation module 305 can interface the geographic database 109and/or the services platform 117 or any of the services 119 to determine whether there are any alternate modes of transportation near a recommended recharging/refueling location or otherwise suitable to continue the user's trip.);
determining an estimated waiting time at the one or more EV charging stations (see at least Beaurepaire 510`, para. [0066-0068]:The recommendation module 305 can create several entries in the calendar data if several recharging/refueling options were initially recommended. In one embodiment, the replenishment time (e.g., recharging or refueling time) can be with respect to reaching a full recharge/refueling or to reach a desired day (e.g., charge 40 mins to reach the weekend)…For example, the user can request the energy management module 103 send the user a notification when the charge level is enough for specified period of time. For example, if the user requests a notification for when the energy level is enough for a week, the energy management module 103 can respond by confirming the notification request and/or presenting an estimated time to reach the request energy level (e.g., “Charge for week should take approximately 1.5 hours”).);
scheduling an EV charging time at the one or more EV charging stations, based on the location and duration (see at least Beaurepaire 510`, para. [0066-0069]: In one embodiment, batteries for electric vehicles 101 have optimal charging cycles that can be considered for optimal use and maintenance, particularly in light of the high costs of such batteries. Accordingly, the recommendation module 305 can consider these optimal charging cycles when recommending charging times, locations, and/or charge levels. For example, if it is recommended that the battery level should ideally never fall below a minimum percentage, then the recommendation module 305 should take this into account when recommending when and where to charge.).
As per claim 10 Beaurepaire 510` discloses
further comprising: training a machine learning model to predict potential "range anxiety" for the driver, or the one or more passengers, of the EV (see at least Beaurepaire 510`, para. [0047-0048]: For example, during training of such a model, the prediction module 303 uses a learner module that feeds feature sets from each individual training data set (e.g., ground truth labeled feature sets that annotate and observed set of remaining energy level related features with a known operating time) into the feature detection model to compute a predicted matching feature using an initial set of model parameters (e.g., an initial set of model weights)…If the accuracy or level of performance does not meet a threshold or configured level, the learner module incrementally adjusts the model parameters or weights until the model generates predictions at a desired or configured level of accuracy with respect to the ground truth data…In one embodiment, the predicted time that the vehicle can be operated can further account for an energy reserve level, an energy buffer level, or a combination thereof associated with the user. In one embodiment, the energy reserve represents a user's comfort level with respect to how much energy (e.g., charge or fuel) remains before the user typically replenishes (e.g., recharges or refuels).A collected usage history may indicate, for instance, that a particular user usually recharges or refuels when the remaining energy level reaches 25% of absolute capacity (e.g., battery capacity, fuel tank size, etc.).); and
establishing that the "range anxiety" is a causal relation with health or behavioral concerns of the driver, or the one or more passengers, of the EV (see at least Beaurepaire 510`, para. [0033-0034]:For example, one source of anxiety or cognitive load on drivers is “range anxiety” (e.g., when driving in an electric vehicle 101 in particular, but also in any vehicle in general). This range anxiety refers, for instance, to when the drivers worry over when they will runout of battery charge or fuel. As a result, drivers may tend to act conservatively and unnecessarily recharge/refuel “just in case” to relieve range anxiety. However, this unnecessarily recharging/refueling can have potential negative sides effects. For example, particularly with respect electric car recharging stations, recharging stations are still relatively rare compared to traditional fueling stations, making recharging spots these stations also relatively rare. Accordingly, vehicles 101 that unnecessarily charge or charge for unnecessarily long times can occupy valuable charging spaces that would otherwise be better used by other vehicles 101that have actual charging needs (e.g., vehicles 101 with almost depleted batteries, vehicles with planned trips that exceed their currently available range, etc.).).
As per claim 11 Beaurepaire 510` discloses
further comprising: triggering an amelioration action when the predicted "range anxiety" is above a given threshold, wherein the amelioration action comprises generating an actionable alert to the driver, or the one or more passengers, to charge the EV at a specific location, at a specific time, and for a specific duration (see at least Beaurepaire 510`, para. [0048-0051]: Accordingly, the prediction module 303 can also make a refueling prediction based the current absolute fuel/charge level and the user's comfort or reserve level (e.g., the battery is 30% full, but the user usually recharges before the battery reaches 25%). para. [0066-0068]:The recommendation module 305 can create several entries in the calendar data if several recharging/refueling options were initially recommended. In one embodiment, the replenishment time (e.g., recharging or refueling time) can be with respect to reaching a full recharge/refueling or to reach a desired day (e.g., charge 40 mins to reach the weekend)…For example, the user can request the energy management module 103 send the user a notification when the charge level is enough for specified period of time. For example, if the user requests a notification for when the energy level is enough for a week, the energy management module 103 can respond by confirming the notification request and/or presenting an estimated time to reach the request energy level (e.g., “Charge for week should take approximately 1.5 hours”).).
As per claim 13 Beaurepaire 510` discloses
wherein the predicted charging information of the EV automatically creates a calendar event, for the driver, with detailed metadata of the predicted charging (see at least Beaurepaire 510`, para. [0066]: In addition, the recommendation module 305 can automatically update the user's calendar data based on the reservation request by, for instance, making an entry in the user's calendar to make sure the user remembers and sees the entry. The recommendation module 305 can create several entries in the calendar data if several recharging/refueling options were initially recommended.).
As per claim 14 Beaurepaire 510` discloses
further comprising: optimizing the predicted charging of the EV based on economic costs and EV charging factors (see at least Beaurepaire 510`, para. [0063-0064]: In one embodiment, the recommended time, the recommended location, or a combination thereof is further based on an energy replenishment cost. In some countries or jurisdictions, energy costs (e.g., electricity costs for recharging) can vary between different times of the day (e.g., between day versus night, with night time usually being cheaper because of less demand).); and
recommending a cost-effective EV charging, wherein the EV charging factors comprise a charging time, a location, a charger type and emission, a route, and health and behavioral concerns of the driver, or the one or more passengers (see at least Beaurepaire 510`, para. [0063-0064]: In one embodiment, the recommended time, the recommended location, or a combination thereof is further based on an energy replenishment mode of the vehicle, an energy replenishment connector of the vehicle, or a combination thereof. For example, the recommendation module 305 can query the digital map data of the geographic database 109 to determine nearby charging or refueling stations that have the request charging modes (e.g., fast charging) with the charging connectors that are compatible with the user's vehicle 101 or UE 113.).
As per claim 16 Beaurepaire 510` discloses
A computer system, comprising: one or more computer devices each having one or more processors and one or more tangible storage devices; and a program embodied on at least one of the one or more storage devices, the program having a plurality of program instructions for execution by the one or more processors, the program instructions comprising instructions for (see at least Beaurepaire 510`, para. [0004-0007]: According to another embodiment, an apparatus comprises at least one processor, and at least one memory including computer program code for one or more computer programs, the at least one memory and the computer program code configured to, with the at least one processor, cause, at least in part, the apparatus to determine a remaining energy level of a vehicle or device. ):
predicting when an EV needs to be charged, where the EV needs to be charged, and for how long the EV needs to be charged based on individualized characteristics of the driver or one or more passengers, weather conditions, and geospatial characteristics (see at least Beaurepaire 510`, para. [0042]: In step 401, the data module 301 records a usage history, a usage pattern, or combination(e.g., usage data) thereof associated with an operation of a vehicle 101 or a device (e.g., UE 113) by a user. The usage data can be stored in the user database 111. In one embodiment, the usage history can include data records recording when, where, how long, energy consumption, etc. used during an operational instance of the vehicle 101 or UE 113 (e.g., a trip made in the vehicle 101 by the user).Additional contextual parameters (e.g., weather, traffic conditions, road conditions, number of passengers, etc.) can also be collected and analyzed by the data module 301. & para. [0060-0061]: In one embodiment, the recommendation module 305 can use the same or similar predictive or statistical models as described above with respect to the process 400 of FIG. 4. For example, the trained prediction models can be used in combination with the digital map data of the geographic database to identify energy station facilities 121 (e.g., recharging facilities, refueling facilities, etc.) that can be recommended to the user. In one embodiment, the recommendation module 305 recommends when and where to refuel/recharge (e.g., replenish energy reserves) based on the predicted evolution of energy levels (e.g., predicted according to the embodiments of the process 400 of FIG. 4), and the proximity of such fuel/charge stations (e.g., energy station facilities 121) along the predicted or planned routes.);
evaluating an availability of one or more EV charging stations located within a given radius of the EV (see at least Beaurepaire 510`, para. [0060]: para. [0060]:In one embodiment, the recommendation module 305 recommends when and where to refuel/recharge (e.g., replenish energy reserves) based on the predicted evolution of energy levels (e.g., predicted according to the embodiments of the process 400 of FIG. 4), and the proximity of such fuel/charge stations (e.g., energy station facilities 121) along the predicted or planned routes. & para. [0064: For example, the recommendation module 305 can query the digital map data of the geographic database 109 to determine nearby charging or refueling stations that have the request charging modes (e.g., fast charging) with the charging connectors that are compatible with the user's vehicle 101 or UE 113.);
comparing a location of the one or more available EV charging stations, located within the given radius of the EV, to one or more desired locations of the driver of the EV (see at least Beaurepaire 510`, para. [0067]: For example, the recommendation module 305 can suggest that the user stop or remain at a charging location (e.g., when recharging is expected to take more than a threshold amount of time), and use a shared car, ride sharing service, public transport, or other alternative modes of transportation to continue on the user's trip while the vehicle 101 reaches a desired energy level. For example, the recommendation module 305 can interface the geographic database 109and/or the services platform 117 or any of the services 119 to determine whether there are any alternate modes of transportation near a recommended recharging/refueling location or otherwise suitable to continue the user's trip.);
determining an estimated waiting time at the one or more EV charging stations (see at least Beaurepaire 510`, para. [0066-0068]:The recommendation module 305 can create several entries in the calendar data if several recharging/refueling options were initially recommended. In one embodiment, the replenishment time (e.g., recharging or refueling time) can be with respect to reaching a full recharge/refueling or to reach a desired day (e.g., charge 40 mins to reach the weekend)…For example, the user can request the energy management module 103 send the user a notification when the charge level is enough for specified period of time. For example, if the user requests a notification for when the energy level is enough for a week, the energy management module 103 can respond by confirming the notification request and/or presenting an estimated time to reach the request energy level (e.g., “Charge for week should take approximately 1.5 hours”).);
scheduling an EV charging time at the one or more EV charging stations, based on the location and duration (see at least Beaurepaire 510`, para. [0066-0069]: In one embodiment, batteries for electric vehicles 101 have optimal charging cycles that can be considered for optimal use and maintenance, particularly in light of the high costs of such batteries. Accordingly, the recommendation module 305 can consider these optimal charging cycles when recommending charging times, locations, and/or charge levels. For example, if it is recommended that the battery level should ideally never fall below a minimum percentage, then the recommendation module 305 should take this into account when recommending when and where to charge.).
As per claim 17 Beaurepaire 510` discloses
further comprising: training a machine learning model to predict potential "range anxiety" for the driver, or the one or more passengers, of the EV (see at least Beaurepaire 510`, para. [0047-0048]: For example, during training of such a model, the prediction module 303 uses a learner module that feeds feature sets from each individual training data set (e.g., ground truth labeled feature sets that annotate and observed set of remaining energy level related features with a known operating time) into the feature detection model to compute a predicted matching feature using an initial set of model parameters (e.g., an initial set of model weights)…If the accuracy or level of performance does not meet a threshold or configured level, the learner module incrementally adjusts the model parameters or weights until the model generates predictions at a desired or configured level of accuracy with respect to the ground truth data…In one embodiment, the predicted time that the vehicle can be operated can further account for an energy reserve level, an energy buffer level, or a combination thereof associated with the user. In one embodiment, the energy reserve represents a user's comfort level with respect to how much energy (e.g., charge or fuel) remains before the user typically replenishes (e.g., recharges or refuels).A collected usage history may indicate, for instance, that a particular user usually recharges or refuels when the remaining energy level reaches 25% of absolute capacity (e.g., battery capacity, fuel tank size, etc.).); and
establishing that the "range anxiety" is a causal relation with health or behavioral concerns of the driver, or the one or more passengers, of the EV (see at least Beaurepaire 510`, para. [0033-0034]:For example, one source of anxiety or cognitive load on drivers is “range anxiety” (e.g., when driving in an electric vehicle 101 in particular, but also in any vehicle in general). This range anxiety refers, for instance, to when the drivers worry over when they will runout of battery charge or fuel. As a result, drivers may tend to act conservatively and unnecessarily recharge/refuel “just in case” to relieve range anxiety. However, this unnecessarily recharging/refueling can have potential negative sides effects. For example, particularly with respect electric car recharging stations, recharging stations are still relatively rare compared to traditional fueling stations, making recharging spots these stations also relatively rare. Accordingly, vehicles 101 that unnecessarily charge or charge for unnecessarily long times can occupy valuable charging spaces that would otherwise be better used by other vehicles 101that have actual charging needs (e.g., vehicles 101 with almost depleted batteries, vehicles with planned trips that exceed their currently available range, etc.).).
As per claim 18 Beaurepaire 510` discloses
further comprising: triggering an amelioration action when the predicted "range anxiety" is above a given threshold, wherein the amelioration action comprises generating an actionable alert to the driver, or the one or more passengers, to charge the EV at a specific location, at a specific time, and for a specific duration (see at least Beaurepaire 510`, para. [0048-0051]: Accordingly, the prediction module 303 can also make a refueling prediction based the current absolute fuel/charge level and the user's comfort or reserve level (e.g., the battery is 30% full, but the user usually recharges before the battery reaches 25%). para. [0066-0068]:The recommendation module 305 can create several entries in the calendar data if several recharging/refueling options were initially recommended. In one embodiment, the replenishment time (e.g., recharging or refueling time) can be with respect to reaching a full recharge/refueling or to reach a desired day (e.g., charge 40 mins to reach the weekend)…For example, the user can request the energy management module 103 send the user a notification when the charge level is enough for specified period of time. For example, if the user requests a notification for when the energy level is enough for a week, the energy management module 103 can respond by confirming the notification request and/or presenting an estimated time to reach the request energy level (e.g., “Charge for week should take approximately 1.5 hours”).).
As per claim 20 Beaurepaire 510` disclose
wherein the predicted charging information of the EV automatically creates a calendar event, for the driver, with detailed metadata of the predicted charging (see at least Beaurepaire 510`, para. [0066]: In addition, the recommendation module 305 can automatically update the user's calendar data based on the reservation request by, for instance, making an entry in the user's calendar to make sure the user remembers and sees the entry. The recommendation module 305 can create several entries in the calendar data if several recharging/refueling options were initially recommended.).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 4, 12, & 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beaurepaire 510`, in view of US 2023/0051766A1 (“Beaurepaire 766`).
As per claim 4 Beaurepaire 510` discloses
further comprising: determining optimal charging parameters (location, duration, and time) while minimizing operation downtime of the EV (see at least Beaurepaire, para. [0072]: the embodiments for providing a time-based representation of remaining energy levels described herein are particularly applicable to electric vehicles due to: (1) the relatively long charging times for these vehicles; (2) the range anxiety some drivers face; and (3) relatively low number of charging stations compared to fuel stations. For at least those reasons, the system 100 faces several technical challenges and provides solutions. For example, with respect to relatively long electric vehicle charging times, the system 100 optimizes the charging times by recommending charging times that are sufficient to cover a user's normal vehicle usage but are not more than what is needed to minimize charging times. With respect to range anxiety, the system 100 surfaces how many days the user can drive for using a remaining charge level. Providing days can reduce range anxiety because it is a more intuitive representation that can be more easily understood than an abstract charge level. ).
However Beaurepaire 510` does not explicitly disclose
training a machine learning model to predict possible idle time of the EV at a given time of day.
Beaurepaire 766` teaches
training a machine learning model to predict possible idle time of the EV at a given time of day (see at least Beaurepaire 766`, para. [0054]: The location data, such as in location database 208 and/or map database 108 includes a comprehensive database of existing EV charge points, other relevant points-of-interest (POI, e.g., retail stores, entertainment venues, restaurants, tourist attractions, or any other location that people may seek out), traffic patterns, and a map of the road network. The data associated with charge points can further include location specifics, charging modes (e.g., direct current, single/multi-phase alternating current, etc.), plug types including wireless induction charging, number of connectors, etc. Charging point utilization information may not be available through the location database, but may be available through a charge point service provider or information service. The charging point utilization information can include usage time windows, kilowatt-hours delivered, number of vehicles connected, plug standards in use, idle/inactive time windows, delivered charge mode, delivered voltages, vehicle charge states, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Beaurepaire to incorporate the teaching of training a machine learning model to predict possible idle time of the EV at a given time of day of Beaurepaire 510`, with a reasonable expectation of success, in order to build new charge points where they are needed most (see at least Beaurepaire 766`, para. [0004]).
As per claim 12 Beaurepaire 510` discloses
further comprising: determining optimal charging parameters (location, duration, and time) while minimizing operation downtime of the EV (see at least Beaurepaire, para. [0072]: the embodiments for providing a time-based representation of remaining energy levels described herein are particularly applicable to electric vehicles due to: (1) the relatively long charging times for these vehicles; (2) the range anxiety some drivers face; and (3) relatively low number of charging stations compared to fuel stations. For at least those reasons, the system 100 faces several technical challenges and provides solutions. For example, with respect to relatively long electric vehicle charging times, the system 100 optimizes the charging times by recommending charging times that are sufficient to cover a user's normal vehicle usage but are not more than what is needed to minimize charging times. With respect to range anxiety, the system 100 surfaces how many days the user can drive for using a remaining charge level. Providing days can reduce range anxiety because it is a more intuitive representation that can be more easily understood than an abstract charge level.).
However Beaurepaire 510` does not explicitly disclose
training a machine learning model to predict possible idle time of the EV at a given time of day.
Beaurepaire 766` teaches
training a machine learning model to predict possible idle time of the EV at a given time of day (see at least Beaurepaire 766`, para. [0054]: The location data, such as in location database 208 and/or map database 108 includes a comprehensive database of existing EV charge points, other relevant points-of-interest (POI, e.g., retail stores, entertainment venues, restaurants, tourist attractions, or any other location that people may seek out), traffic patterns, and a map of the road network. The data associated with charge points can further include location specifics, charging modes (e.g., direct current, single/multi-phase alternating current, etc.), plug types including wireless induction charging, number of connectors, etc. Charging point utilization information may not be available through the location database, but may be available through a charge point service provider or information service. The charging point utilization information can include usage time windows, kilowatt-hours delivered, number of vehicles connected, plug standards in use, idle/inactive time windows, delivered charge mode, delivered voltages, vehicle charge states, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Beaurepaire to incorporate the teaching of training a machine learning model to predict possible idle time of the EV at a given time of day of Beaurepaire 510`, with a reasonable expectation of success, in order to build new charge points where they are needed most (see at least Beaurepaire 766`, para. [0004]).
As per claim 19 Beaurepaire 510` discloses
further comprising: determining optimal charging parameters (location, duration, and time) while minimizing operation downtime of the EV (see at least Beaurepaire, para. [0072]: the embodiments for providing a time-based representation of remaining energy levels described herein are particularly applicable to electric vehicles due to: (1) the relatively long charging times for these vehicles; (2) the range anxiety some drivers face; and (3) relatively low number of charging stations compared to fuel stations. For at least those reasons, the system 100 faces several technical challenges and provides solutions. For example, with respect to relatively long electric vehicle charging times, the system 100 optimizes the charging times by recommending charging times that are sufficient to cover a user's normal vehicle usage but are not more than what is needed to minimize charging times. With respect to range anxiety, the system 100 surfaces how many days the user can drive for using a remaining charge level. Providing days can reduce range anxiety because it is a more intuitive representation that can be more easily understood than an abstract charge level.).
However Beaurepaire 510` does not explicitly disclose
training a machine learning model to predict possible idle time of the EV at a given time of day.
Beaurepaire 766` teaches
training a machine learning model to predict possible idle time of the EV at a given time of day (see at least Beaurepaire 766`, para. [0054]: The location data, such as in location database 208 and/or map database 108 includes a comprehensive database of existing EV charge points, other relevant points-of-interest (POI, e.g., retail stores, entertainment venues, restaurants, tourist attractions, or any other location that people may seek out), traffic patterns, and a map of the road network. The data associated with charge points can further include location specifics, charging modes (e.g., direct current, single/multi-phase alternating current, etc.), plug types including wireless induction charging, number of connectors, etc. Charging point utilization information may not be available through the location database, but may be available through a charge point service provider or information service. The charging point utilization information can include usage time windows, kilowatt-hours delivered, number of vehicles connected, plug standards in use, idle/inactive time windows, delivered charge mode, delivered voltages, vehicle charge states, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Beaurepaire to incorporate the teaching of training a machine learning model to predict possible idle time of the EV at a given time of day of Beaurepaire 510`, with a reasonable expectation of success, in order to build new charge points where they are needed most (see at least Beaurepaire 766`, para. [0004]).
Claim(s) 7 & 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beaurepaire 510`, in view of US 2023/0402845A1 (“Albanna”).
As per claim 7 Beaurepaire 510` does not explicitly disclose
further comprising: training a modular neural network for a joint analysis of predicting an EV charging station and output of a weather-impact analysis to determine optimal EV charging factors.
Albanna teaches
further comprising: training a modular neural network for a joint analysis of predicting an EV charging station and output of a weather-impact analysis to determine optimal EV charging factors (see at least Albanna, para. [0091]: A profile source may also be environment profile 1228 providing data such as past weather conditions, predicted future weather conditions, historical power outage data or otherwise other environmental data. para. [0095]: Further, based on predictive analytics about inclement weather or certain seasons of the year during which renewable energy sources may not be readily available, the power delivery module 1216 may propose decreasing the use of certain home loads to meet a predicted DC fast charging demand and vice versa. & para. [0097] & para. [0103]: In a particular embodiment, the feature profile includes each feature (e.g., 1. a load voltage, 2. a load power, 3. a weather forecast, 4. connected secondary energy flow managers 5. a source power, 6. A source power 7. an EV charging time, 8. an operator preference, 9. remaining life cycles of an EV battery and 10. weights given to each feature). Using the extracted features and a trained M/L model 1206 that has been trained using a large number of different datasets, power delivery module 1216 may determine a power delivery proposal 1212 for the system.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Beaurepaire to incorporate the teaching of training a modular neural network for a joint analysis of predicting an EV charging station and output of a weather-impact analysis to determine optimal EV charging factors of Albanna, with a reasonable expectation of success, in order to control energy use and production to benefit from lower energy tariffs (see at least Albanna, para. [0047]).
As per claim 15 Beaurepaire 510` does not explicitly disclose
further comprising: training a modular neural network for a joint analysis of predicting an EV charging station and output of a weather-impact analysis to determine optimal EV charging factors .
Albanna teaches
further comprising: training a modular neural network for a joint analysis of predicting an EV charging station and output of a weather-impact analysis to determine optimal EV charging factors (see at least Albanna, para. [0091]: A profile source may also be environment profile 1228 providing data such as past weather conditions, predicted future weather conditions, historical power outage data or otherwise other environmental data. para. [0095]: Further, based on predictive analytics about inclement weather or certain seasons of the year during which renewable energy sources may not be readily available, the power delivery module 1216 may propose decreasing the use of certain home loads to meet a predicted DC fast charging demand and vice versa. & para. [0097] & para. [0103]: In a particular embodiment, the feature profile includes each feature (e.g., 1. a load voltage, 2. a load power, 3. a weather forecast, 4. connected secondary energy flow managers 5. a source power, 6. A source power 7. an EV charging time, 8. an operator preference, 9. remaining life cycles of an EV battery and 10. weights given to each feature). Using the extracted features and a trained M/L model 1206 that has been trained using a large number of different datasets, power delivery module 1216 may determine a power delivery proposal 1212 for the system.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Beaurepaire to incorporate the teaching of training a modular neural network for a joint analysis of predicting an EV charging station and output of a weather-impact analysis to determine optimal EV charging factors of Albanna, with a reasonable expectation of success, in order to control energy use and production to benefit from lower energy tariffs (see at least Albanna, para. [0047]).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beaurepaire 510`, in view of US 2012/0032637A1 (“Kotooka”).
As per claim 8 Beaurepaire 510` does not explicitly disclose
further comprising: stopping the EV charging when an actual charge level exceeds a predicted charge level required, by a predetermined amount, to reach a desired location.
Kotooka teaches
further comprising: stopping the EV charging when an actual charge level exceeds a predicted charge level required, by a predetermined amount, to reach a desired location (see at least Kotooka, para. [0068]: In a step S53, the controller 25 determines whether or not to stop power generation. More specifically, the controller 25 decides to stop power generation when the actual SOC is larger than the SOC upper limit value. & para. [0097]: In a step S15, the controller 23 sets a route from a current location of the host vehicle to a destination on the basis of the host vehicle position information (longitude, latitude, altitude), the host vehicle advancement direction information, the map data (route, altitude, road gradient, road curvature, and so on) recorded in the reception buffer, and destination information set by the driver. The controller 23 then guides the driver to travel along the route using images and voice. & para. [0130]: In a step S1608, the controller 23 calculates the SOC upper limit value and the SOC lower limit value. The SOC upper limit value and SOC lower limit value are a target upper limit value and a target lower limit value for managing the battery SOC so that the battery SOC does not become either excessive or deficient during travel in the battery energy management area. The step S1608 corresponds to a management target value calculation unit.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Beaurepaire 510` to incorporate the teaching of stopping the EV charging when an actual charge level exceeds a predicted charge level required, by a predetermined amount, to reach a desired location of Kotooka, with a reasonable expectation of success, in order for an improvement in efficiency and a reduction in cost (see at least Kotooka, para. [0047]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABDO ALGEHAIM whose telephone number is (571)272-3628. The examiner can normally be reached Monday-Friday 8-5PM EST.
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/MOHAMED ABDO ALGEHAIM/Primary Examiner, Art Unit 3668