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/response filed on 2 Jan 2026.
Claims 1, 6, 8, 13, 15 and 20 were amended.
Claim 1-20 are pending.
Response to Amendment
Applicant amended claims 1, 15 and 20 to recite:
identifying an effect on the EV of a condition external to the EV;
generating a modified transfer rate at which energy is to be transfer4red from the EV to a location based on the condition; and
controlling the EV to transfer energy to the location through a bidirectional charger at the modified transfer rate.
The Examiner notes that support for the above can be found in:
[0121] In one embodiment, a location such as a building, a residence, or the like (not depicted), communicably coupled to one or more of the electric grid 404B, the vehicle 402B, and/or the charging station(s) 406B. The rate of electric flow to one or more of the location, the vehicle 402B, the other vehicle(s) 408B is modified, depending on external conditions, such as weather. For example, when the external temperature is extremely hot or extremely cold, raising the chance for an outage of electricity, the flow of electricity to a connected vehicle 402B/408B is slowed to help minimize the chance for an outage.
Continued Examination Under 37 CFR 1.114
Receipt is acknowledged of a request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e) and a submission, filed on 22 June 2026.
Claim Rejections - 35 USC § 101, software per se
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 therefore, subject to the conditions and requirements of this title.
Claim 15-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
When nonfunctional descriptive material is recorded on some computer-readable medium, in a computer or on an electromagnetic carrier signal, it is not statutory since no requisite functionality is present to satisfy the practical application requirement. Merely claiming nonfunctional descriptive material, i.e., abstract ideas, stored on a computer-readable medium, in a computer, or on an electromagnetic carrier signal, does not make it statutory (see Diamond v. Diehr, 450 U.S. *175, 185-86, 209 USPQ). In contrast, a claimed computer-readable medium encoded with a data structure defines structural and functional interrelationships between the data structure and the computer software and hardware components which permit the data structure’s functionality to be realized, and is thus statutory. Please refer to MPEP 2106.01. However, a computer program can be eligible for patent protection if it is tangibly embodied on a computer readable medium and, when executed by a computer, performs the steps of the invention. The claims as written are directed to non- statutory subject matter, appropriate correction is required.
Please note that the specification is open-ended with “may be” and/or “may include” in at least [0130] … The memory 422E may be a dynamic random-access memory (DRAM) device, a static random-access memory (SRAM) device, flash memory, or another memory device. In some embodiments, the memory 422E also may include non-volatile memory or a similar permanent storage device and media, which may include a hard disk drive, a floppy disk drive, a compact disc read only memory (CD-ROM) device, a digital versatile disk read only memory (DVD-ROM) device, a digital versatile disk random access memory (DVD-RAM) device, a digital versatile disk rewritable (DVD-RW) device, a flash memory device, or some other mass storage device for storing information on a permanent basis.
Open-ended definitions therefore include “signal”.
Appropriate correction is required.
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.
Claim(s) 1, 8 and 15 are/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Slutzky et al (US 2020/0282855).
Claim 1: A method comprising (see at least [0005], [0019] of Slutzky et al.)
Claim 8: An apparatus comprising: a memory; and a processor coupled to the memory, the processor configured to: (see at least [0015], [0019]-[0020] of Slutzky et al.)
Claim 15: A computer-readable storage medium comprising instructions stored therein which when executed by a processor cause the processor to perform: (see at least [0015], [0019]-[0020] of Slutzky et al.)
(Claims 1, 8 and 15)
determining a future energy need at a location; (see at least [0023] (a vehicle-to-building operator may analyze historic electricity load or historic temperature data for a particular building site. Using that historic data, the operator may identify past building load peak events and determine when peak events are likely to occur at the building in the future (i.e., predict or anticipate future energy needs for the building)); [0027] (The V2X system also may use historic, forecast, and/or real-time building load data (e.g., for an electric vehicle battery, for a building, etc.) to make such decisions. Examples include using such data to preserve battery health and to predict future peak events to ensure an electric vehicle is available to use in revenue generating or conserving activities); [0062] (each of the foregoing applications, the V2X system 400, 500 may use historic and forecasted temperature data to anticipate a building 406, 506's energy needs, including major peaking events for the building 406, 506's electric load); [0063] (The V2X system 400, 500 also may use historic load data for the building to identify load patterns and past peak events and predict or anticipate a building 406, 506's energy needs based on that analysis); [0065] historic or forecasted weather data is analyzed by a V2X system, such as the V2X system 400, 500 described above, to identify patterns. The weather data may include temperature or humidity or any other suitable weather metric that may contribute to a peak electric load); [0067] of Slutzky et al.)
responsive to the future energy need exceeding a threshold, recommending an electric vehicle (EV) to provide energy to the location at a transfer rate to lower the future energy need below the threshold; (see at least [0062] the V2X system 400, 500 may use historic or forecasted temperature data to predict or anticipate when a major peaking event will occur and ensure an electric vehicle 402, 502a:f is available for discharge at the building 406, 506 during that time identifying an effect on the EV of a condition external to the EV); [0063] (The V2X system 400,500 ensures that an electric vehicle 402, 502a:f is available during the building 406, 506's peak energy needs by dispatching a vehicle 402, 502a:f from a fleet or issuing a command or other instructions… the V2X system 400, 500 may control the discharge of the electric vehicle batteries ….while also offsetting the peak load to the building); [0068] (the V2X system determines the number of electric vehicles, such as electric vehicles 402, 502a:f described above, that will be needed at the day and time of an anticipated peak load event to discharge enough electricity to offset the building load enough to prevent the anticipated peak load event.) of Slutzky et al.);
identifying an effect on the EV of a conditional external to the EV; (see at least [0027] The V2X system may use weather data, including temperature and humidity, to make strategic decisions and control these interactions); [0033] Ambient air temperature also effects battery health but can be mitigated. By tracking ambient air temperature relative to battery health…); [0040] The V2X system also may use any other suitable inputs (e.g., humidity), alone or in combination with each other, to estimate or predict the battery temperature or temperature range at step 306.); [0041] (The V2X system may log and track this temperature data for use in predicting battery temperature. For example, battery temperature may be determined at step 306 at a particular date and time, and the weather (e.g., temperature, humidity, etc.) for that same day and time for the same location as the battery may also be recorded. of Slutzsky et al.)
generating a modified transfer rate at which energy is to be transferred from the EV to the location based on the condition; (see at least [0033] (Ambient air temperature also effects battery health but can be mitigated. By tracking ambient air temperature relative to battery health, the V2X system determines if mitigation actions are needed, such as lowering kW power commands.); [0045] (The V2X system also may determine to limit charging and/or discharging activity to lower kW power levels based on ambient temperature); [0075] (V2X system may predict both battery temperature and peak load events. By logging and tracking battery temperature data at step 306, high battery temperatures may be predicted at step 308…) of Slutzky et al.) and
controlling the EV to transfer energy to the location through a bidirectional charger at the modified transfer rate. (see at least [0005] (temperature data to protect battery health during bidirectional charging… such as in vehicle-to-grid, vehicle-to building, and related activities); [0044] (rather than determining not to discharge a battery at 25 kW based on battery temperature, the V2X system may determine to let the electric vehicle discharge it battery at something below 25 kW (e.g., 13.5 kW)); [0045] (The V2X system may determine that, because the air temperature is quite high when the electric vehicle is connected to the charger in the afternoon and because the electric vehicle was just driven, the vehicle battery is likely to be relatively hot. To preserve battery health, charging and discharging would be limited as much as possible and would be done only if needed, and only at a low power level.); [0073] (the V2X system may determine at step 308 that certain electric vehicles may only be discharged below a certain level (e.g., below 25 kW) based on their battery temperature) of Slutzky et al.)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 2-3, 5, 9-10, 12, 16-17, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Slutzky et al (US 2020/0282855) in view of Roy et al. (US2022/0261715).
Claims 2, 9, 16:
Slutzky et al. teaches claims 1, 8 and 15 above.
Slutzky et al. further teach:
receiving energy consumption data from an energy-consuming system at the location, wherein the determining of the future energy need comprises: (see at least [0064] (A building 406, 506 load can be predicted based on historical load data, historical weather data, and/or other site data, such as building size, building age, equipment ( e.g., cooling, heating, lighting, and other systems), usage and program (e.g., office, hospital, 24/7 operations, server farm, etc.), number of windows, automated building controls, and any service by separate energy plants or chillers); [0066] (historic or forecasted building or site load data is analyzed by the V2X system to identify peak load events. The peak load data may include actual loads measured at the meter, utility bills with identifiable peak load charges, or any other suitable data that may be used to correlate a peak load event to a particular day.); [0067] (peak load event can then be predicted to occur on days and times at which the same or similar temperature and/or humidity is occurring or expected to occur. Other metrics also may be considered instead or in addition to temperature and/or humidity, such as the number of people and/or employees occupying a building, the occurrence of certain events, etc.) of Slutzky et al.)
Slutzky et al. does not explicitly disclose:
determining the future energy need based on an execution of an artificial intelligence (AI) model on the energy consumption data.
Roy et al. disclose determining the future energy need based on an execution of an artificial intelligence (AI) model on the energy consumption data (see at least [0072] (steps 1102a-1102b involve ingesting grid telemetry data, utility company data, infrastructure data, historical outage data, and other data relevant to the electrical grid and using that data in a neural network to determine a grid risk score for that region. Steps 1103a-103b involve ingesting historical and predicted local weather data, historical and predicted climate change data, historical and predicted natural disasters, and other data relevant to climate/weather and using that data in a neural network) of Roy et al.). One of Ordinary skill in the art would have been motivated to expand the system/method/CRM of Slutzky et al. to include determining the future energy need based on an execution of an artificial intelligence (AI) model on the energy consumption data of Roy et al. since the machine learning/energy storage approach mitigates such an attack by providing as-fast-as-can-be reactions to changes in the grid and having energy stores in place when current power generation fails (see at least [0033] of Roy et al.)
Claims 3, 10, 17:
Slutzky et al. teaches claims 1, 8 and 15 above.
Slutzky et al. further teach:
receiving historical weather data for the location, (see at least [0064] (A building 406, 506 load can be predicted based on historical load data, historical weather data,) of Slutzky et al.)
Slutzky et al. does not explicitly disclose:
wherein the determining of the future energy need comprises: determining the future energy need based on an execution of an artificial intelligence (AI) model on the historical weather data.
Roy et al. disclose wherein the determining of the future energy need comprises: determining the future energy need based on an execution of an artificial intelligence (AI) model on the historical weather data (see at least [0072] (steps 1102a-1102b involve ingesting grid telemetry data, utility company data, infrastructure data, historical outage data, and other data relevant to the electrical grid and using that data in a neural network to determine a grid risk score for that region. Steps 1103a-103b involve ingesting historical and predicted local weather data, historical and predicted climate change data, historical and predicted natural disasters, and other data relevant to climate/weather and using that data in a neural network) of Roy et al.). One of Ordinary skill in the art would have been motivated to expand the system/method/CRM of Slutzky et al. to include wherein the determining of the future energy need comprises: determining the future energy need based on an execution of an artificial intelligence (AI) model on the historical weather data of Roy et al. since the machine learning/energy storage approach mitigates such an attack by providing as-fast-as-can-be reactions to changes in the grid and having energy stores in place when current power generation fails (see at least [0033] of Roy et al.)
Claims 5, 12, 19:
Slutzky et al. teaches claims 1, 8 and 15 above.
Slutzky et al. further teach:
receiving identifiers of types of energy storage systems at the location, (see at least [0018] (The electric vehicle in which the present disclosure may be implemented may be any vehicle with a battery that may be utilized as an energy storage asset, including an electric truck, electric bus, electric car, electric forklift, electric motorcycle, electric scooter, electric wheelchair, electric bicycle, etc.); [0026] (The disclosed V2X system enables the battery or batteries in an electric vehicle or vehicles to provide energy storage services when the battery of the vehicle is not being used, such as when the vehicle is stationary and/or turned off. In the V2X system, stored energy in the electric vehicle batteries may provide valuable services to virtually anyone in need of additional electricity ( e.g., grid operators, utilities, building owners, homeowners, etc.)….); [0033] ( V2X system is battery calendar life loss, which occurs whether the battery is active or not and is primarily affected by the ambient air temperature and average state of charge experienced throughout the battery's life…. y tracking ambient air temperature relative to battery health, the V2X system determines if mitigation actions are needed, such as lowering kW power commands); [0035] (temperature metrics tracked on various scales of the battery 100, such as the cell level 102, module level 104 and/or pack level 106…. Vehicles generate such battery temperature data and preferably allow the data to be share with the V2X system… the V2X system may be configured to measure this data using sensors… the V2X system uses the temperature data to identify, control and/or facilitate… activities in a manner to optimize the health, live and/or useful capacity of the electric vehicle batteries); [0037] (V2X system determine appropriate usage of the battery while preserving battery health at step 206.); [0039] (Fig.3, the V2X system receives one or more input data at step 302 regarding at least one of the factors that impact battery temperature… the V2X system may receive information or data indication the EV had just been charged a significant amount…V2X system may receive information or data that there currently is a high ambient air temperature… V2X system may receive information or data about ambient air, such as a building site… V2X system may receive information regarding other factors that impact battery temperature communicated to operations management software from EV’s BMS, from observing and tracking EV’s activities, sensors…) of Slutzky et al.)
Slutzky et al. does not explicitly disclose:
wherein the determining of the future energy need comprises: determining the future energy need based on an execution of an artificial intelligence (AI) model on the identifiers.
Roy et al. disclose wherein the determining of the future energy need comprises: determining the future energy need based on an execution of an artificial intelligence (AI) model on the identifiers (see at least [0031] (with energy stores in place, cloud-base neural networks begin learning about the grid and patterns thereof. The neural networks learn this by ingested data such as telemetry already available from devices on the grid, the energy storage stations…); [0032] (the neural networks provide a rank-score of the electrical demand and electrical vulnerability of regions… the knowledge of regional electrical demand/vulnerability with regional climate and socio-economic information along with strategically placed energy stores… optimization of stored/released energy to the grid is performed via the neural networks but controlled from an optimization core which sends updated parameters to energy stores to change or maintain the amount of energy stored); [0035](a high voltage battery pack capable of rapid charge-discharge rates to facilitate extreme fast charging (XFC) … to support grid resource management by providing supplemental power distribution to a local grid during periods of time when grid energy is high demand); [0040] (battery packs comprise different battery technologies… may be connected in series, parallel, or a combination, where batteries…); [0053] The high-voltage battery pack 120 may consist of one or more of a plurality of individual batteries configured in a series, parallel, or combination of series and parallel connections) of Roy et al.). One of Ordinary skill in the art would have been motivated to expand the system/method/CRM of Slutzky et al. to include wherein the determining of the future energy need comprises: determining the future energy need based on an execution of an artificial intelligence (AI) model on the identifiers of Roy et al. since the machine learning/energy storage approach mitigates such an attack by providing as-fast-as-can-be reactions to changes in the grid and having energy stores in place when current power generation fails (see at least [0033] of Roy et al.)
Claim(s) 4, 11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Slutzky et al. in view of Hancock et al. (WO 2023/049998) further in view of Cheung et al. (US 2021/0382501).
Claims 4, 11, 18:
Slutzky et al. teaches claims 1, 8 and 15 above.
Slutzky et al. does not explicitly disclose:
determining a size of a rechargeable battery based on the future energy need,
wherein the recommending of the EV comprises: recommending the EV based on the size of the rechargeable battery
Slutzky et al. [0050] teach many variables impact charge and discharge cycles of electric vehicle batteries, including the number of miles driven per vehicle per year, size of building or other load, building or other load characteristics (e.g., flat vs. peaky, duration of peak, predictability of peaks, etc.), vehicle battery size (e.g., 60 kWh vs. 30 kWh), temperature and humidity profile for location of electric vehicles and chargers, minimum battery state of charge, maximum throughput, site electricity tariff schedule, distribution grid peaks, transmission grid peaks and congestion, and times of high kWh pricing.
Hancock et al. disclose determining a size of a rechargeable battery based on the future energy need, (see at least [0016] (the vehicle operation data comprises, for at least one of the electric vehicles, at least one of a battery capacity, a battery charge level, a rate of battery charge, a rate of battery discharge, a battery age, a battery temperature, a historical battery discharge rate, a distance to recharge, an expected vehicle weight, or data related to an expected vehicle route, a driving schedule, a driving distance, or driver); [0115] (while reducing facility energy use 2310 through the use of stored EV capacity during the peak 2302,); [0143] (vehicle data used in the optimisation calculation may vary, and may include known variables, non-limiting examples of which may include battery capacity, state of charge or charge levels, rate of charge/discharge, weight, age, temperature, available use or life remaining based on current use (in driving time or distance or both), or other relevant historical battery information.); [0151] (Individual EV data 004 may comprise a data feed that contains specific information that comes from the actual electric vehicle, and contains information including vehicle location data in the form of GPS, battery information including current battery state (charge status) that is the amount that the battery is currently charged, battery temperature and age, battery capacity (e.g. the max charge the battery may hold, which may change with age and temperature), battery charge and discharge rates, and general vehicle information, such as if the EV is autonomous or non-autonomous.); [0248] determine the facility's energy profile 1632 using past data to determine energy needs within the facility or geographically spread entity to create a predictive model of future energy needs including possible peaks and downtimes. Next it will determine the charge station of each vehicle and the current battery capacity 1634.) of Hancock et al. et al.). One of Ordinary skill in the art would have been motivated to expand the system/method/CRM of Slutzky et al. to include determining a size of a rechargeable battery based on the future energy need, of Hancock since monitoring energy across diverse needs and across different applications (see at least [80] of Hancock et al.).
Cheung et al. discloses wherein the recommending of the EV comprises: recommending the EV based on the size of the rechargeable battery (see at least Fig. 7; [0074] The fleet management system 120 (e.g., the estimation engine 670) determines the estimated amount of energy based on the timing information and data describing energy usage at the location.); [0075] (The fleet management system 120 selects 730 an AEV from a fleet of AEVs to fulfill the request. For example, the power source dispatch engine 660 selects an AEV based on the current location of the selected AEV, the requested location, a battery level of the selected AEV, and the estimated amount of energy for servicing the request.) of Cheung et al.). One of Ordinary skill in the art would have been motivated to expand the system/method/CRM of Slutzky et al. to include wherein the recommending of the EV comprises: recommending the EV based on the size of the rechargeable battery of Cheung et al. since any individuals do not have the financial resources to purchase or the space to store seldom used backup power systems, making it difficult for those who experience a sudden power outage and do not have a backup system, the use of electric vehicles are a good alternative to conventical backup systems and can be dispatched on demand (see at least [0013]-[0014] of Cheung et al.).
Claim(s) 6, 13 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Slutzky further in view of Dicker et al. (US 2019/0347754)
Claims 6, 13, 20:
Slutzky et al. teaches claims 1 (method), 8 (apparatus) and 15 (CRM) above.
Slutzky et al. further teach:
predicting an optimal future time to start using the EV based on the future energy need; (see at least (historic temperature data for Las Vegas, Nev. may indicate that a building with an electric vehicle charger or charging station in Las Vegas may likely experience a peak electric load on July 2 from 1-1:30 pm due to extremely hot temperatures on that date and time historically. Accordingly, the V2X system will ensure that a particular number of electric vehicles are available at the building on July 2 from 1-1:30 pm) of Slutzky et al.) and
Slutzky et al. does not explicitly disclose:
displaying the optimal future time via a user interface.
Dicker et al. disclose displaying the optimal future time via a user interface (see at Fig. 1 (Provider app 187(transport invite); Designated app 195, ETA data, requests), [0022] (on-demand transportation arrangement platform, in which ETA data may be utilized to invite drivers or proximate SDVs to service a scheduled ride as the start time approaches); [0034] (submitting the on-demand start request 197…user can scroll through different transport service types on the designated application 195. system 100 can provide dynamic ETA data 140 to the user device 190 indicating an estimated time of arrival for one or more of the available transport providers… the selection engine 135 can utilize map data 179 and/or traffic data 177 from a mapping engine 175 and the provider locations 181 of various vehicles 185 operating throughout the given region to provide the user with the ETA data 1498 for each scrolled service type. Thus if the start request 197 indicates a service type, the selection engine 135 can filter through the vehicles 185 proximate to the start location in order to transmit one or more transport invitations or directives 182 to only those vehicles that satisfy the characteristics of the specified service type); [0036] (provide a scheduled transport service option through the designated application 195. As provided herein, the scheduled transport feature can be selected by a user to input a set of data corresponding to a user request 196 in the form of a scheduled transport request 198); [0039] Upon receiving a scheduled transport request 198, the scheduling engine 140 can input data indicating a scheduled transport 142 into the scheduling logs 132 for the requesting user. In certain implementations, the scheduled transport 142 can include a start location, a destination, and a start time and start date.); [0041] If the start time is within a certain time range of a typical on-duty start time of the driver (e.g., within thirty minutes), then the selection engine 135 can provide the claim offer 186 to the driver at any time prior to the start time ( or a predetermined time prior to the start time, as described herein); [0044] (Based on such weightings and the characteristics of the scheduled transport 142 (e.g., start location, destination, start time, service type, etc.), the selection engine 135 can converge on one or more most optimal drivers to which the claim offer 186 is to be provided. The driver(s) can either accept or decline the claim offer 186 accordingly.); [68] Fig. 3 (illustrating a driver device executing a designated driver application for transport service) of Dicker et al.) One of Ordinary skill in the art would have been motivated to expand the system/method/CRM of Slutzky et al. to include displaying the optimal future time via a user interface of Dicker et al. since on-demand transport services can provide a platform connecting available transport providers with requesting users using designated applications executing on mobile devices. (see at least [0002] of Dicker et al.).
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Slutzky et al. further in view of Lewin et al. (US2022/0060017).
Claims 7, 14:
Slutzky et al. teaches claims 1 (method), 8 (apparatus) and 15 (CRM) above.
Slutzky et al. do not explicitly disclose:
receiving historical power outage data for the location from one or more external servers, wherein the determining comprises determining the future energy need of the energy infrastructure at the location based on the historical power outage data.
Lewin et al. disclose receiving historical power outage data for the location from one or more external servers, wherein the determining comprises determining the future energy need of the energy infrastructure at the location based on the historical power outage data (see at least Fig. 1 (grid infrastructure reports; weather, outage info) (energy consumption habits) (power management application); [0055] (The management server 122 may further collect information from third parties, including grid infrastructure reports 128, weather (reports) data 130, reports of vegetation management data 132, energy pricing data 134, grid emissions 136, outage information aggregators 138, and regional energy sources 140.); [0084] (Power regulation data from third-party data sources 126 may include grid infrastructure reports 128, current or predicted weather data 130, vegetation management data 132, energy pricing data 134, and data from outage information aggregators 138); [0067] (management server 122 may analyze the usage data and determine patterns based on other data. For example, the user device 104 may include location tracking features.); [0068] (management server 122 may further use the collected information to predict power interrupting events, such as power outages and power surges. As an example, historical power outage data may indicate that the power utility pre-emptively shuts power off to prevent creating wildfires from downed power lines. Weather data may indicate that the fire danger is increased when it is hot, dry, and windy. Therefore, when the weather is hot, dry, and windy, the management server 122 may predict that a power outage will occur. The management server 122 may further predict the length of the power outage by analyzing previous outages and their associated conditions. Predicting the time and duration of a power outage may be used to help an individual system prepare for the outage. For example, a user may ensure that supplemental power systems such as local power supply 154 (e.g., batteries) are charged. Charged local power supplies may allow the user to continue to use electronic devices throughout the power interrupting event. In this manner, a power interrupting event does not need to be as disruptive.); Fig. 4 (predicting power interruption event based on historical power generation information…) of Lewin et al.) One of Ordinary skill in the art would have been motivated to expand the system/method/CRM of Slutzky et al. to include receiving historical power outage data for the location from one or more external servers, wherein the determining comprises determining the future energy need of the energy infrastructure at the location based on the historical power outage data of Lewin et al. since there is a need for a supplemental power supply that protects against power interruption events (see at least [0008] of Lewin et al.).
Response to Arguments
101 Rejection:
The currently amended limitations of independent claims 1, 7 and 15 — specifically “controlling the EV to transfer energy to the location through a bidirectional charger at the modified transfer rate” — integrate the abstract idea into a practical application at Step 2A, Prong 2. The claims go beyond mere recommendation or data analysis; they require adaptive physical control of energy transfer through specific hardware (a bidirectional charger) at a modified rate. The 101 rejection has therefore been withdrawn.
103 Rejections:
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are 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.
Conclusion
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
SARAH M. MONFELDT
Supervisory Patent Examiner
Art Unit 3629
/SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
Prosecution Insights