Information processing device

Abstract

To provide an information processing device capable of accurately identifying an electricity rate plan tailored to a user and presenting the identified plan to the user.SOLUTION: A server device 30 generates a trained algorithm by performing machine learning using training data where the data declared or entered by a plurality of users using a user terminal 10 and the data extracted from a user DB device 20 for each of the plurality of users serve as explanatory variable and the electricity rate plan each user has contracted with each electric power company serves as an objective variable. The server 30 identifies an electric rate plan tailored to a user using the trained algorithm, and presents the identified plan to the user.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a technique for presenting an electricity rate plan suited to a user. [Background technology]

[0002] As the need for saving electricity in homes and the like increases, it is becoming important to select an electricity rate plan that matches the amount of electricity used and the time period during which electricity is used in each home and the like. For example, Patent Document 1 discloses a system in which user information including information on the living environment of a target user, such as the number of people in the household, address, occupation, and installation of new energy equipment (photovoltaic power generation equipment, storage batteries, fuel cells, etc.), is input, a comparison user group with similar user information and past power consumption is identified, and the cheapest electricity rate plan among the electricity rate plans that the identified comparison user group actually has contracts with is proposed to the target user. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2017-72915 A Summary of the Invention [Problem to be solved by the invention]

[0004] The present invention has been made in view of the above-mentioned background, and has an object to accurately identify an electricity rate plan suited to a user and present it to the user. [Means for solving the problem]

[0005] In order to solve the above problem, the present invention provides an information processing device comprising: a memory unit that stores a learned algorithm generated by machine learning using training data in which data affecting each user's electricity usage is used as an explanatory variable and the electricity rate plan to which each user has subscribed is used as a target variable; an identification unit that inputs data affecting a certain user's electricity usage into the learned algorithm to identify the electricity rate plan; and a presentation unit that presents information related to the identified electricity rate plan to the certain user. Effect of the Invention

[0006] According to the present invention, it is possible to accurately identify an electricity rate plan suited to a user and present it to the user. [Brief description of the drawings]

[0007] [Figure 1] 1 is a diagram showing an example of an overall configuration of an information processing system 1 according to an embodiment of the present invention. [Diagram 2] FIG. 4 is a block diagram showing a hardware configuration of a server device 30 according to the embodiment. [Diagram 3] FIG. 2 is a block diagram showing a hardware configuration of a user terminal 10 according to the embodiment. [Figure 4] FIG. 4 is a block diagram showing a functional configuration of a server device 30 according to the embodiment. [Diagram 5] FIG. 11 is a diagram illustrating explanatory variables in the embodiment. [Figure 6] FIG. 2 is a diagram illustrating an example of a response variable in the embodiment. [Figure 7] 13 is a flowchart showing an example of an operation performed by the server device 30 according to the embodiment when performing machine learning. [Figure 8] 13 is a flowchart showing an example of an operation performed by the server device 30 according to the embodiment when identifying an electricity rate plan to be presented to a user. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] [Present embodiment] FIG. 1 is a diagram showing an example of an information processing system 1 according to the present embodiment of the present invention. The information processing system 1 includes a plurality of user terminals 10 used by a plurality of users, a user database (DB) device 20 that stores data related to the plurality of users in association with each user, a server device 30 that performs processing to identify an electricity rate plan suited to each user and present it to the user, and a network 2 including a wireless communication network or a wired communication network that communicatively connects the user terminals 10, the user DB device 20, and the server device 30. The user terminals 10 are computers such as smartphones, wearable devices, or tablets. The user DB device 20 is a computer that functions as a storage device according to the present invention. The server device 30 is a computer that functions as an information processing device according to the present invention. Note that the numbers of the user terminals 10, the user DB devices 20, and the server devices 30 shown in FIG. 1 are merely examples and are not limited to the numbers shown in the figure.

[0009] In the information processing system 1 configured as above, the server device 30 generates a trained algorithm by performing machine learning using teacher data in which the data reported or input by a plurality of users using the user terminal 10 and the data extracted for each of the plurality of users from the user DB device 20 are used as explanatory variables, and the electricity rate plans that each of the plurality of users has contracted with each electric power company are used as objective variables. Then, the server device 30 uses this trained algorithm to identify an electricity rate plan suitable for a certain user and present it to the user.

[0010] 2 is a diagram illustrating an example of a hardware configuration of the server device 30. The server device 30 is physically configured as a computer including a processor 3001, a memory 3002, a storage 3003, and a bus connecting these. In the following description, the word "apparatus" can be interpreted as a circuit, a device, a unit, and the like. The hardware configuration of the server device 30 may be configured to include one or more of the devices shown in the figure, or may be configured to exclude some of the devices.

[0011] Each function in the server device 30 is realized by loading a specific software (program) onto hardware such as a processor 3001 and a memory 3002, so that the processor 3001 performs calculations, controls communications via a communication device 3004, and controls at least one of reading and writing of data in the memory 3002 and the storage 3003.

[0012] The processor 3001 controls the entire computer by running, for example, an operating system. The processor 3001 may be configured with a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, and the like.

[0013] The processor 3001 reads out a program (program code), a software module, data, etc. from at least one of the storage 3003 and the communication device 3004 into the memory 3002, and executes various processes according to the read out programs. The program used is a program that causes a computer to execute at least a part of the operations described below. The functional blocks of the server device 30 may be realized by a control program stored in the memory 3002 and operated in the processor 3001. The various processes may be executed by one processor 3001, or may be executed simultaneously or sequentially by two or more processors 3001. The processor 3001 may be implemented by one or more chips. The program may be transmitted to the server device 30 from the network 2 via a telecommunication line.

[0014] The memory 3002 is a computer-readable recording medium, and may be composed of at least one of, for example, a Read Only Memory (ROM), an Erasable Programmable ROM (EPROM), an Electrically Erasable Programmable ROM (EEPROM), a Random Access Memory (RAM), etc. The memory 3002 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 3002 can store a program (program code), a software module, etc. that can be executed to implement the method according to this embodiment.

[0015] Storage 3003 is a computer-readable recording medium, and may be constituted by at least one of, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, etc. Storage 3003 may be referred to as an auxiliary storage device.

[0016] The communication device 3004 is hardware (transmission / reception device) for performing communication between computers via the network 2, and is also called, for example, a network device, a network controller, a network card, or a communication module.

[0017] Each device, such as the processor 3001 and the memory 3002, is connected by a bus for communicating information. The bus may be configured using a single bus, or may be configured using different buses between each device.

[0018] Furthermore, the server device 30 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), etc., and some or all of the functional blocks may be realized by the hardware. For example, the processor 3001 may be implemented using at least one of these pieces of hardware.

[0019] Fig. 3 is a diagram showing an example of the hardware configuration of the user terminal 10. The hardware configuration of the user terminal 10 may be configured to include one or more of the devices shown in Fig. 3, or may be configured to exclude some of the devices. Furthermore, the user terminal 10 may be configured by communicating with a plurality of devices having different housings.

[0020] The user terminal 10 is physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a positioning device 1007, and a bus connecting these. Each function of the user terminal 10 is realized by loading a predetermined software (program) onto hardware such as the processor 1001 and the memory 1002, causing the processor 1001 to perform calculations, control communication by the communication device 1004, and control at least one of reading and writing data from and to the memory 1002 and the storage 1003. The processor 1001, the memory 1002, the storage 1003, the communication device 1004, and the bus connecting these are similar in terms of hardware to the processor 3001, the memory 3002, the storage 3003, the communication device 3004, and the bus connecting these described for the server device 30, and therefore description thereof will be omitted.

[0021] The input device 1005 is an input device (e.g., a key, a microphone, a switch, a button, etc.) that accepts input from the outside. The output device 1006 is an output device (e.g., a display, an LED lamp, a speaker, etc.) that outputs to the outside. The input device and the output device may be integrated into one device (e.g., a touch panel).

[0022] The positioning device 1007 is hardware for performing positioning using a known method such as GPS, base station positioning, etc. This positioning method may be any method that performs positioning with the required accuracy.

[0023] 4 is a block diagram illustrating a functional configuration of the server device 30. In the server device 30, a processor 3001, a memory 3002, a storage 3003, and a communication device 3004 cooperate with each other to realize the functions of an acquisition unit 31, a storage unit 32, a machine learning unit 33, an identification unit 34, and a presentation unit 35.

[0024] The acquisition unit 31 acquires various data related to the user from the user terminal 10 or the user DB device 20 via the network 2. The data acquired by the acquisition unit 31 from the user terminal 10 is data declared or input by each user using the user terminal 10, and includes, for example, the user's behavioral pattern (information related to lifestyle such as tendency to get up early in the morning or to be active until late at night, which corresponds to data related to the user's behavioral pattern), the electricity rate plan with which the user has a contract (including the name of the electric power company with which the contract is made), the contract capacity of the electricity rate, information related to the house in which the user lives (area or layout, etc.), the number of household members in the house, the usage status of electrical appliances in the house (types and number of electrical appliances, etc.), the user's location history, etc. In addition, the data that the acquisition unit 31 acquires from the user DB device 20 includes past electricity usage detected in each user's home (i.e., equivalent to data related to the user's past electricity usage), the location or area where the user uses electricity (which can be identified by a supply point identification number in Japan, and equivalent to data related to the location where the user uses electricity), the user's transportation usage history, the user's schedule, etc.

[0025] The machine learning unit 33 performs machine learning using teacher data in which data that affects the amount of electricity used by the user among the data acquired by the acquisition unit 31 is used as an explanatory variable, and the electricity rate plan that the user has contracted with the electric power company is used as an objective variable. More specifically, the explanatory variables in this machine learning include, as exemplified in FIG. 5, the behavioral patterns of each user, information about the house in which the user lives (size or layout, etc.), the number of household members in the house, the usage status of electrical appliances in the house (types and number of electrical appliances, etc.), the electrical capacity currently used in the house under contract with the electric power company, the location history of the user, the history of electrical usage detected in each user's house, the location or area where the user uses electricity (which can be identified by a supply point identification number), the user's use history of transportation, the user's schedule, etc. In addition, the objective variable in this machine learning is the electrical rate plan that each user has contracted with (including the name of the electric power company with which the contract is made and the contract capacity), as exemplified in FIG. 6.

[0026] Among the explanatory variables, the user's location history, the user's transportation usage history, or the user's schedule are data for estimating the time when the user is out of the house. In other words, the period when the user's location is at a location other than the house where the user lives can be said to be the time when the user is out of the house, the period when the user uses transportation continuously within a certain period can be said to be the time when the user is out of the house, and if the user's schedule is other than a schedule implemented at the house, the period of such a schedule can be said to be the time when the user is out of the house. In other words, data that affects the user's electricity usage, that is, the explanatory variables, include data for estimating the time when the user is in a location other than the location where electricity is used, and more specifically, include location history data of the user terminal 10 carried by the user, or usage history data of the transportation used by the user, and data related to the user's schedule. The storage unit 32 stores the learned algorithm generated by the machine learning unit 33 in addition to the explanatory variables and objective variables described above.

[0027] The identification unit 34 inputs data that affects the electricity usage of a user who wishes to know the electricity rate plan that suits him or her (i.e., data corresponding to the explanatory variables mentioned above) into the learned algorithm stored in the memory unit 32, and thereby identifies the electricity rate plan output from the learned algorithm.

[0028] The presentation unit 35 transmits information about the electricity rate plan identified by the identification unit 34 to the user terminal 10 of the certain user. In the user terminal 10, this information is output by a method such as display or audio output.

[0029] Next, the operation of the server device 30 when performing machine learning will be described with reference to FIG. 7. In the server device 30, the acquisition unit 31 acquires various data related to a plurality of users from the user terminal 10 or the user DB device 20 via the network 2. The machine learning unit 33 generates teacher data using data that affects the user's electricity usage amount among the data acquired by the acquisition unit 31 as explanatory variables and the electricity rate plan that the user has contracted with the power company as a target variable (step S11), and performs so-called supervised machine learning (step S12). The storage unit 32 stores the generated trained algorithm (step S13).

[0030] Next, the operation of the server device 30 when identifying an electricity rate plan to present to a user will be described with reference to Fig. 8. A user who wishes to know an electricity rate plan that suits him / her operates his / her user terminal 10 to request the presentation of an electricity rate plan. In response to this request, the acquisition unit 31 acquires data corresponding to explanatory variables for the user from the user terminal 10 or the user DB device 20 via the network 2 (step S21).

[0031] The identification unit 34 inputs data corresponding to the explanatory variables described above into the learned algorithm stored in the memory unit 32 (step S22), thereby identifying the electricity rate plan output from the learned algorithm (step S23).

[0032] The presentation unit 35 transmits information about the electricity rate plan identified by the identification unit 34 to the user terminal 10 of the certain user (step S24). In the user terminal 10, this content is output by a method such as display or audio output.

[0033] According to the embodiment described above, by performing machine learning using training data for a large number of users, it is possible to accurately identify an electricity rate plan that suits a user and present it to the user.

[0034] [Variations] The present invention is not limited to the above-described embodiment. The above-described embodiment may be modified as follows. In addition, two or more of the following modifications may be combined and implemented.

[0035] It is expected that the user's electricity usage will change depending on the user's level of interest in clean energy. Clean energy generally refers to energy that poses little or no risk of burden on the environment. Therefore, data that influences the user's electricity usage may include data indicating the user's level of interest in clean energy.

[0036] The block diagrams used in the description of the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized by using one device that is physically or logically combined, or may be realized by using two or more devices that are physically or logically separated and directly or indirectly connected (for example, by wire, wirelessly, etc.). The functional blocks may be realized by combining the one device or the multiple devices with software.

[0037] Functions include, but are not limited to, judgment, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs the function of transmission is called a transmitting unit or a transmitter. As described above, there is no particular limitation on the method of realization of either of these.

[0038] For example, the server device 30 in one embodiment of the present disclosure may function as a computer that performs the processing of the present disclosure.

[0039] Each aspect / embodiment described in the present disclosure may be applied to at least one of systems using LTE (Long Term Evolution), LTE-A (LTE-Advanced), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), FRA (Future Radio Access), NR (new Radio), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, UWB (Ultra-WideBand), Bluetooth (registered trademark), and other suitable systems, and next-generation systems extended based on these. In addition, a combination of multiple systems (e.g., a combination of at least one of LTE and LTE-A and 5G, etc.) may be applied.

[0040] The order of the steps, sequences, flow charts, etc. of each aspect / embodiment described in this disclosure may be changed unless inconsistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0041] The input and output information may be stored in a specific location (e.g., memory) or may be managed using a management table. The input and output information may be overwritten, updated, or added to. The output information may be deleted. The input information may be transmitted to another device.

[0042] The determination may be based on a value represented by a single bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0043] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described in the present disclosure. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0044] Software, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise, should be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc. Also, software, instructions, information, etc. may be transmitted or received over a transmission medium. For example, if the software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave, etc.), then such wired and / or wireless technologies are included within the definition of a transmission medium.

[0045] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof. In addition, terms explained in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings.

[0046] Furthermore, the information, parameters, etc. described in this disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information.

[0047] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0048] Any reference to an element using a designation such as "first," "second," etc., used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must precede the second element in some way.

[0049] The "part" in the configuration of each of the above devices may be replaced with "means," "circuit," "device," etc.

[0050] When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Further, when used in this disclosure, the term "or" is not intended to be an exclusive or.

[0051] In this disclosure, where articles have been added due to translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0052] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different." [Explanation of symbols]

[0053] 1: Information processing system, 2: Network, 10: User terminal, 1001: Processor, 1002: Memory, 1003: Storage, 1004: Communication device, 1005: Input device, 1006: Output device, 1007: Positioning device, 20: User database device, 30: Server device, 31: Acquisition unit, 32: Machine learning unit, 33: Memory unit, 34: Identification unit 34: Presentation unit, 3001: Processor, 3002: Memory, 3003: Storage, 3004: Communication device.

Claims

1. A storage unit that stores a learned algorithm generated by machine learning using training data in which data affecting the amount of electricity usage of each user is an explanatory variable and an electricity rate plan subscribed to by each user is an objective variable; and An identification unit that inputs data that affects a certain user's electricity usage into the learned algorithm to identify the electricity rate plan; a presentation unit that presents information about the identified electricity rate plan to the certain user; An information processing device comprising:

2. The data that affects the electricity usage of each user includes data reported or input by each user and data associated with each user and stored in a storage device.

2. The information processing apparatus according to claim 1,

3. The data affecting the electricity usage of the user includes data regarding the behavior of the user.

3. The information processing apparatus according to claim 2.

4. The data affecting the user's electricity usage includes data regarding the user's past electricity usage.

3. The information processing apparatus according to claim 2.

5. The data affecting the user's electricity usage includes data regarding the location where the user uses electricity.

3. The information processing apparatus according to claim 2.

6. The data affecting the electricity usage of the user includes data for estimating a time when the user is in a location other than a location where the user uses electricity.

3. The information processing apparatus according to claim 2.

7. The data for estimating the time when the user is in a location other than the location where the electricity is used includes historical data of the location of a user terminal carried by the user.

7. The information processing apparatus according to claim 6.

8. The data for estimating the time when the user is in a location other than the location where electricity is used includes the usage history data of the transportation means used by the user.

7. The information processing apparatus according to claim 6.

9. The data for estimating the time when the user is in a location other than the location where the electricity is used includes data regarding the schedule of the user.

7. The information processing apparatus according to claim 6.

10. The data influencing the user's electricity usage includes data indicative of the user's level of interest in clean energy.

2. The information processing apparatus according to claim 1,

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