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. 2006.
Claims 1-20 were amended.
Claim 1-20 are pending.
Filing Date
The instant application does claim priority to another application and therefore has an effective filing date of 13 March 2024.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture or composition of matter? MPEP 2106.03.
Per Step 1, claim 8-14 is to an apparatus (i.e., a machine/system), claim 1-7 to a method (i.e., a process), and claim 15-20 to a non-transitory computer-readable medium (i.e., a manufacture or machine). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1, 8 15 is:
determining a future energy need of an energy infrastructure at a location;
responsive to the future energy need being above a threshold, recommending an electric vehicle (EV) to provide energy to the location to lower the future energy need below the threshold
predicting an optimal future time to start using the EV based on the future energy need
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, determining an energy need of an infrastructure and recommending an electric vehicle that could provide energy to that infrastructure [0002]. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the claim is directed to determining an energy need of an infrastructure and recommending an electric vehicle that could provide energy to that infrastructure [0002], which constitutes a process that, under its broadest reasonable interpretation, covers commercial activity. This is further supported by [0002] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers commercial interactions, including contracts, legal obligations, advertising, marketing, sales activities or behaviors, and/or business relations, then it falls within the Certain Methods of Organizing Human Activity – Commercial or Legal Interactions grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f). Further, in MPEP 2106.05(f) it is noted that "[use] of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, according to the MPEP, this is not solely limited to computers but includes other technology that, recited in an equivalent to “apply it,” is a mere instruction to perform the abstract idea on that technology.
Claims 1, 8 and 15 recite the following additional elements:
user interface,
memory,
processor coupled to the memory,
computer-readable storage medium
Dependent claims 2-3, 5, 9-10, 12, 16-17 and 19 recite the following additional element:
artificial intelligence (AI) model
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [0032]-[0033] of applicant’s specification as filed.
Examiner interprets the artificial intelligence (AI) model/Machine Learning described in para. [0056], [0070], [0087] of applicant’s specification as filed as additional elements. MPEP 2106.05(f) is explicit that simply using other machinery as a tool also amounts to no more than merely applying the abstract idea to a computer, especially when claimed in a solution-oriented manner:
(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
[…]
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
In this case, the artificial intelligence (AI) model is merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system with machine learning model(s). Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f), they do not integrate the abstract idea into a practical application.
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitates the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system with machine learning models. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
Further, the analysis takes into consideration all dependent claims as well:
As noted above, claims 2-3, 5, 9-10, 12, 16-17 and 19 further recite an AI model, which is generically applied to the claims and is an off the shelf software. Beyond the recitation of an AI model the dependent claims do not recite any further additional elements, they merely narrow the abstract idea.
Accordingly, claims 1-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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) 1-3, 5, 8-10, 12, 15-17, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cheung et al. (US 2021/0382501) in view of Roy et al. (US2022/0261715).
Claim 1: A method comprising (see at least [16]; [19] of Cheung et al.)
Claim 8: An apparatus comprising: a processor that executes instructions stored in a memory to configure the processor to: (see at least [19]; [99] of Cheung et al.)
Claim 15: A computer-readable storage medium comprising instructions that when executed by a processor, cause the processor to perform: (see at least [19]; [99] of Cheung et al.)
(Claims 1, 8 and 15)
determining a future energy need at a location; (see at least [15] (the fleet management system can receive power service status information from a utility and/or from in-home monitoring systems…); [17] (power source manager is configured to determine an estimated amount of energy for servicing the request based at least in part on a duration associated with the request and data describing energy usage at the location); [0036] (a user makes a request for backup power on the user device 130, which transmits the request to the fleet management system 120. The user provides a location at which the power supply is requested. e.g. the address of the user’s home 180. .. the home 180 may include equipment for alerting the fleet management system 120 when backup power is requested… the fleet management system 120 may select an AEV 110 to drive to the home 180 … other types of buildings may be powered by an AEV 110 or set of AEVs110, including multi-unit residential buildings, stores, restaurants, office buildings, other commercial buildings, etc.); [0037] (the fleet management system 120 also received data from a utility company 190 that supplies power to customers. The utility company 190 may provide information describing planned and/or unplanned power outages to the fleet management system 120, or the fleet management system may obtain publicly available data provided by the utility company 190 describing such outages. The fleet management system 120 may use the utility data to identify locations for providing power supplies and identify times or predicted times during which power is to be supplied); [65] (The power outage information includes data describing areas affected ( e.g. , a set of locations , or geographic boundaries of an outage ), a start time of the power outage , and an expected time that the utility company 190 will restore power. The power outage information may include information describing both planned and unplanned outages) of Cheung et al.)
responsive to the future energy need exceeding a threshold, recommending an electric vehicle (EV) to provide energy to the location to lower the future energy need [[below the threshold]]; (see at least [0015] (fleet management system can receive power service status information from a utility and/or from in-home monitoring systems, and the fleet management system automatically dispatches AEVs in the event of an unexpected power outage, or in advance of a planned outage…); [0016] (receiving a request for a power source, the request including a location and timing data; determining an estimated amount of energy for servicing the request based at least in part on the timing data and the data describing the energy usage at the location; selecting an AEV from a fleet of AEVs to fulfill the request, the AEV selected based on a current location of the AEV, the location, a battery level of a battery of the AEV, and the estimated amount of energy for the servicing request; and instructing the AEV to drive to the location, where the AEV is configured to distribute electric power from the battery upon reaching the location…) of Cheung et al.); and
predicting an optimal future time to start using the EV based on the future energy need. (see at least [37] fleet management system 1200 may use the utility data to identify locations for providing power supplies and identify times or predicted times during which power is to be supplied); [40] the fleet management system 120 receives a power request from a user via the user device 130. The user may input parameters of the power request 210 into the user device 130 via a user interface provided by the fleet management system 120. For example, the user may indicate a start time of the power request (e.g. as soon as possible because power is currently out, or at an expected start time of the power outage). The user may indicate an end time for the power request if an estimated end time of the power outage is known to the user or if the user is requesting power for a fixed duration of time….); [67] (The power source dispatch engine 660 selects and dispatches AEVs 110 for servicing power requests. For requests for power at a given time and location, the power source dispatch engine 660 selects an AEV 110 based on the parameters of the power request (e.g. , location, duration, estimated power consumption rate ) and based on data describing the available AEVs 110 ( e.g. location, charge levels) of Cheung et al.)
Cheung et al. does not explicitly disclose:
[provide energy to the location to lower the future energy need] below the threshold
Cheung et al. does teach [65]
In some embodiments, the UI server 610 receives a request from a user device 130 for power at a given location, e.g. the user's home, business, a park, or another location. The UI server 610 may receive a request to be fulfilled immediately or at a particular time in the future e.g. a request submitted by a user in response to an unexpected outage at their home or a request submitted by a user to obtain power at an off-grid location, such as a park, a campsite, or a home not connected to a power grid ( e.g. an off-grid solar-powered home with an empty or malfunctioning battery backup system).
Roy et al. disclose [provide energy to the location to lower the future energy need] below the threshold (see at least [37] (allows the charging station to provide the local grid with stored energy when grid conditions warrant the use of additional power reserves); [38] (store energy in their battery packs that was purchased … grid reserves… energy purchased and stored during off-peak hours can be used during peak hours to provide lower cost power for consumers… and utilities…); [39] (ensure there are available energy reserves … event condition which triggers the system to begin energy storage when a specific event occurs…);[41] (keep track of the proportion of renewable energy used to charge the high-voltage battery pack of the charging station withing a time frame such as hour, daily, weekly, monthly, yearly, total lifespan …); [65] (power reserve stations 930a-n is to store energy in strategic locations for use in distributing power when and where needed… any power need) of Roy et al.). One of Ordinary skill in the art would have been motivated to expand the system/method/CRM of Cheung et al. to include [provide energy to the location to lower the future energy need] below the threshold 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 [33] of Roy et al.)
Claims 2, 9, 16:
Cheung et al. in view of Roy et al. teaches claims 1, 8 and 15 above.
Cheung 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 [44] (…the usage information 330 may include, for example, a total amount of energy consumed by the house 180, a current consumption rate of the home 180, data showing the energy consumption rate over time…) of Cheung et al.)
Cheung 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 [72] (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 Cheung 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 [33] of Roy et al.)
Claims 3, 10, 17:
Cheung et al. in view of Roy et al. teaches claims 1, 8 and 15 above.
Cheung 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 [72] (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 Cheung 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 [33] of Roy et al.)
Claims 5, 12, 19:
Cheung et al. in view of Roy et al. teaches claims 1, 8 and 15 above.
Cheung et al. does not explicitly disclose:
receiving identifiers of types of energy storage systems at the location, 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 receiving identifiers of types of energy storage systems at the location, 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 [31] (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…); [32] (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); [35](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); [40] (battery packs comprise different battery technologies… may be connected in series, parallel, or a combination, where batteries…); [53] 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 Cheung et al. to include receiving identifiers of types of energy storage systems at the location, 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 [33] of Roy et al.)
Claim(s) 4, 11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cheung et al. in view of Roy et al. further in view of Hancock et al.
Claims 4, 11, 18:
Cheung et al. in view of Roy et al. teaches claims 1, 8 and 15 above.
Cheung et al. teaches:
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.)
Cheung et al. does not explicitly disclose:
determining a size of a rechargeable battery based on the future energy need,
Hancock et al. disclose determining a size of a rechargeable battery based on the future energy need, (see at least [16] (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.); [0284] 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 Cheung 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.)
Claim(s) 6, 13 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cheung et al. in view of Roy et al. further in view of Dicker et al. (US 2019/0347754)
Claims 6, 13, 20:
Cheung et al. in view of Roy et al. teaches claims 1 (method), 8 (apparatus) and 15 (CRM) above.
Cheung et al. in view of Roy et al. do not explicitly disclose:
displaying the optimal future time via a user interface.
Cheung et al. [33] teaches that the fleet management system 120 may further provide a service for providing rides to users with the fleet of AEVs 110, a service that delivers items using AEVs 110 (e.g. prepared foods, groceries, packages, etc.) or other services hat use the fleet of AEVs 110. A single AEV may be able to perform multiple services, e.g. at certain times, AEV 110a provides rides to users, and at other times , the AEV 110a is used to supply power. The fleet management system 120 may assign an AEV 110 to a particular task depending on demand for the various service of the AEV availability.
[27] of Cheung et al. teaches that the AEV 110 may be a vehicle that switches between a semi-autonomous state and fully autonomous state.
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), [22] (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); [34] (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); [36] (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); [39] 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.); [41] 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); [44] (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 Cheung 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 [2] of Dicker et al.).
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cheung et al. in view of Roy et al. further in view of Lewin et al. (US2022/0060017).
Claims 7, 14:
Cheung et al. in view of Roy et al. teaches claims 1 (method), 8 (apparatus) and 15 (CRM) above.
Cheung et al. in view of Roy et al. do not explicitly disclose:
wherein the method further comprises 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 wherein the method further comprises 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 Cheung et al. to include wherein the method further comprises 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:
Applicant's arguments filed 6 Jan 2026 have been fully considered but they are not persuasive. Applicant argues that claim 6 was not rejected under the 101, this is however incorrect, claim 6 was included in the header, and it was stated in the body “Beyond the recitation of an AI model the dependent claims do not recite any further additional element, they merely narrow the abstract idea. Accordingly, claims 1-10 are rejected under 35 USC 101 as being directed to non-statutory subject matter.”
Step 2A, Prong 1:
Claims 1-20 recite an abstract idea, primarily in the form of:
mental processes: observations, evaluations, judgments, recommendations, and predictions that can be performed in the human mind or with pencil-and-paper reasoning; and for claims 2, 3, and 5, also mathematical concepts, because the claims invoke an unspecified AI model to process input data and generate a prediction/determination without reciting any technical implementation details.
Regarding the independent claims, “determining a future energy need at a location” “responsive to the future energy need exceeding a threshold, recommending an electric vehicle (EV)” “predicting an optimal future time to start using the EV based on the future energy need.” This is evaluation + judgment + recommendation. No particular machine action is required by the claim language.
Also, the “recommending” and “optimal time” limitations are results-oriented informational outputs, not technological operations.
Prong 2A, Prong 2: Integration into a practical application:
None of the claims integrate the abstract idea into a practical application. The claims stop at recommendation/prediction, with no technological action. For example, the claims do not control an EV, command a bidirectional charger, manage inverter operation, alter charging/discharging current, modify a home energy management controller, reconfigure a battery system, actuate a breaker/panel/load shed device, improve network communications, improve AI inference performance, or otherwise cause a physical or technical change. Instead the claims end at determining, recommending, predicting, displaying. Also the EV and “location” are merely context/field of use.
Additionally, even in ordered combination, the claims do not recite a non-conventional technical arrangement. There is no claimed architecture showing that the combination: improves computer functionality, improves energy-system control, improves charger operation, improves battery efficiency, improves communication reliability, or achieves a technological result through specified means.
The specification itself repeatedly describes these computing components at a high level and as conventional, e.g.: generic processor / memory / server / network / UI disclosures, generic AI/ML development and deployment environment, generic receipt of weather/outage/energy data, generic use of models for prediction. The specification does not appear to describe: a specialized inference engine, a particular non-conventional EV/home-energy integration mechanism, a new protocol for V2H/V2G dispatch, or a specific technical improvement in computer or energy infrastructure operation.
Claims 1-20 remain rejected under 35 USC 101.
102/103 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. Specifically the independent claims were amended to now recite to provide energy to the location to lower the future energy need below the threshold. Previously the claim recited
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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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SARAH M. MONFELDT
Supervisory Patent Examiner
Art Unit 3629
/SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629