Information provision device, information provision method, and information provision program

Abstract

Deciding on actions that will make people happy. [Solution] The information providing device 10 according to this embodiment includes a creation unit 132 and an update unit 134. The creation unit 132 creates a model that calculates the degree of happiness according to the attributes, based on a combination of user attributes and information on factors that make each user feel happy. The update unit 134 updates the agent using reinforcement learning with the degree of happiness calculated using the model as a reward.

Description

Technical Field

[0001] The disclosed embodiments relate to an information providing apparatus, an information providing method, and an information providing program.

Background Art

[0002] In conventional AI (Artificial Intelligence) technology, it was possible to respond with high accuracy to specific tasks, but there were limitations in scenarios where flexible responses according to human emotions and situations were required. In particular, the development of AI that can show appropriate responses according to emotions and situations has been a long-standing issue. In contrast, there is a need for technology that enables AI to understand human emotions and show appropriate responses.

[0003] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] In the conventional technology, there are cases where actions to make people happy cannot be determined.

[0006] The present invention has been made in view of the above, and an object thereof is to determine actions to make people happy. [Means for solving the problem]

[0007] An information providing device according to one embodiment includes a creation unit and an update unit. The creation unit creates a model that calculates the degree of happiness according to the attributes, based on a combination of user attributes and information about factors that contribute to a user's feeling of happiness. The update unit updates the agent using reinforcement learning, with the degree of happiness calculated using the model as the reward. [Effects of the Invention]

[0008] According to one embodiment of the system, it is possible to determine actions that will make a person happy. [Brief explanation of the drawing]

[0009] [Figure 1] Figure 1 is a diagram illustrating reinforcement learning. [Figure 2] Figure 2 is a functional block diagram showing an example configuration of the information provision device according to Example 1. [Figure 3] Figure 3 shows an example of user information. [Figure 4] Figure 4 is a flowchart illustrating the process for creating user information. [Figure 5] Figure 5 is a flowchart illustrating the process of creating a happiness map. [Figure 6] Figure 6 is a flowchart illustrating the process by which the agent decides on its actions. [Figure 7] Figure 7 is a flowchart illustrating the process of training the agent. [Figure 8] Figure 8 is a schematic diagram showing an example of a computer hardware configuration that functions as an information provision device. [Modes for carrying out the invention]

[0010] <Example 1: Modified form of the third embodiment> The following describes a modified example (Example 1) of the third embodiment described later. In the description of Example 1, explanations of matters common to the third embodiment described later will be omitted as appropriate. In addition, some of the configuration and processing details of Example 1 may differ from those of the third embodiment described later.

[0011] Reinforcement learning for agents will be explained using Figure 1. An agent is a program that can decide (select) an action and execute the decided action. Agents are implemented using machine learning models. Actions include executing other programs, operating machines, and providing information to users.

[0012] For example, an agent might post text to social media. It could also generate suggestions for improving operational efficiency within a company. Furthermore, it could generate event plans for residents of a specific area.

[0013] As shown in Figure 1, the agent is given the state of the environment. The agent observes the given state and decides on an action through exploration based on the observation results. The agent's actions then affect the environment, and a new state is given to the agent after the action.

[0014] The agent decides on an action to maximize its reward. For example, the reward is calculated using a Q-function. The Q-function calculates the reward based on the decided action.

[0015] In Example 1, the AI ​​evolution system according to the third embodiment is realized by the information providing device 10 shown in Figure 2. That is, the information providing device 10 performs agent training and inference using the agent. Figure 2 is a functional block diagram showing an example configuration of the information providing device according to Example 1.

[0016] As shown in Figure 2, the information providing device 10 comprises a communication unit 11, a storage unit 12, and a control unit 13.

[0017] The communication unit 11 transmits and receives data to and from other devices via a network.

[0018] The storage unit 12 is implemented by a semiconductor memory device such as a RAM (Random Access Memory), a flash memory, etc., or a storage device such as a HDD (Hard Disk Drive), a SSD (Solid State Drive), or an optical disk. Various programs and various data are stored in the storage unit 12. The storage unit 12 has analysis model information 121, user information 122, and agent model information 123.

[0019] The analysis model information 121 is information such as parameters for constructing an analysis model. The parameters for constructing an analysis model are, for example, the weights and biases of a neural network.

[0020] The user information 122 includes the attributes and happiness rate for each user. FIG. 3 is a diagram showing an example of user information. Each record of the user information 122 shown in FIG. 3 is an example of a personality model for each user. The happiness rate represents the degree to which a user feels happy with respect to each element (e.g., money, human relationships, social status, etc.). Note that the attributes and happiness rate are not limited to those shown in FIG. 3. For example, the attributes may include personality characteristics represented by the Big Five.

[0021] The agent model information 123 is information such as parameters for constructing an agent. The parameters for constructing an agent are, for example, the weights and biases of a neural network. Also, the agent model information 123 includes a Q function. In reinforcement learning, the parameters of the agent model information 123 are updated.

[0022] The control unit 13 is a controller and includes, for example, a microcomputer having a CPU (Central Processing Unit), ROM (Read Only Memory), RAM, input / output ports, and various circuits. Alternatively, the control unit 13 may be composed of hardware such as an integrated circuit (ASIC) or FPGA (Field Programmable Gate Array). The control unit 13 includes an acquisition unit 131, a creation unit 132, a calculation unit 133, and an update unit 134.

[0023] The data collection unit 131 is an example of the data collection unit and model construction unit of the AI ​​evolution system according to the third embodiment. The creation unit 132 is an example of the happiness map creation unit of the AI ​​evolution system according to the third embodiment. The calculation unit 133 and the update unit 134 are examples of the reinforcement learning unit of the AI ​​evolution system according to the third embodiment. The processing flow of the control unit 13 will be explained below using a flowchart.

[0024] Figure 4 is a flowchart illustrating the process for creating user information. As shown in Figure 4, first, the collection unit 131 obtains the individual's SNS registration information and posted content (step S101). The collection unit 131 may also collect information about SNS using an API.

[0025] Next, the data collection unit 131 analyzes the acquired information using an analysis model (step S102). For example, the analysis model takes the text of SNS posts as input and analyzes emotions and intentions using natural language processing. The analysis model outputs values ​​corresponding to each element of the happiness rate (money, relationships, social status) shown in Figure 3.

[0026] Let α be the value corresponding to money, β be the value corresponding to relationships, and γ be the value corresponding to social status. Here, we assume α + β + γ = 1. For example, the analytical model increases α as the user tends to feel happier acquiring money. Also, for example, the analytical model increases β as the user tends to feel happier improving their relationships. Also, for example, the analytical model increases γ as the user tends to feel happier improving their social status.

[0027] For example, a post that reads, "I'm so happy my winter bonus is bigger this month!" is thought to represent increased happiness due to monetary gain and contributes to an increase in α. Similarly, a post that reads, "I'm sad because I had a fight with a close friend," is thought to represent decreased happiness due to a deterioration in relationships and contributes to an increase in β. Furthermore, a post that reads, "My hard work has been recognized, and I've been promoted at work," is thought to represent increased happiness due to an increase in social status and contributes to an increase in γ.

[0028] Furthermore, for example, a post stating, "I would rather cherish spending time with my family than climbing the corporate ladder," is thought to represent that happiness is increased by relationships rather than social status, and thus contributes to a decrease in β and an increase in γ.

[0029] Additionally, the analysis model may estimate user attributes from the text of the post. For example, if a post that says, "I will turn 41 on December 19th this year," is made in 2024, the analysis model will output the user's date of birth as "December 19, 1983."

[0030] The data collection unit 131 adds the individual's attributes and happiness rate to the user information 122 based on the analysis results. The data collection unit 131 adds α, β, and γ to the "Money" column, "Relationships" column, and "Social Status" column of the user information 122, respectively.

[0031] Figure 5 is a flowchart illustrating the process for creating a happiness map. Here, the happiness map is assumed to be a model for calculating the happiness level of each group when users are classified into groups based on their attributes.

[0032] As shown in Figure 5, the creation unit 132 accepts attribute input (step S201). For example, the attributes may be specified by the user.

[0033] Next, the creation unit 132 retrieves the happiness rate corresponding to the input attribute (step S202). For example, if nationality is specified as "Japan" as an attribute, the creation unit 132 retrieves the happiness rate of the records from the user information 122 where the "Nationality" column is "Japan".

[0034] Alternatively, instead of obtaining happiness rates for each group classified by specified attributes, the creation unit 132 may obtain happiness rates for each group into which users have been classified using a clustering method. For example, the creation unit 132 can treat the values ​​in each column of the user information 122 as qualitative or quantitative variables and perform clustering of records using a hierarchical clustering method.

[0035] Next, the creation unit 132 calculates a happiness rate that matches the attributes based on the acquired happiness rate (step S203). For example, the creation unit 132 calculates a happiness rate that matches the attributes based on the acquired happiness rate. For example, the creation unit 132 calculates statistics (e.g., mean, median, mode, etc.) for each value in the "Money," "Relationships," and "Social Status" columns of the acquired happiness rate. Here, the creation unit 132 calculates the mean as the statistics. Note that the happiness rate that matches the attributes can be rephrased as the happiness level for each group.

[0036] For example, let α[k], β[k], and γ[k] be the mean values ​​of the "Money", "Relationships", and "Social Status" columns, respectively, for the k-th group (where k is a positive integer).

[0037] The creation unit 132 outputs a happiness map based on the calculated happiness rate (step S204). For example, the happiness map is a regression equation for calculating the happiness level h, with coefficients α[k], β[k], and γ[k], and is represented by equation (1).

[0038] h=α[k]×X+β[k]×Y+γ[k]×Z (1)

[0039] However, X, Y, and Z represent the changes in the user's happiness related to money, relationships, and social status, respectively, as a result of the agent's decisions. For example, as the amount of money the user acquires increases, X will be larger. Similarly, as the user's relationships improve, Y will be larger. And as the user's social status improves, Z will be larger.

[0040] The specified attribute may be an individual user. For example, if a user with the ID "U00140000134985" in the user information 122 of Figure 3 is specified, the creation unit 132 will create a happiness map for that individual user as shown in equation (2). In this case, as shown in Figure 3, α=0.5, β=0.1, and γ=0.4.

[0041] h = 0.5 × X + 0.1 × Y + 0.4 × Z (2)

[0042] Figure 6 is a flowchart illustrating the process by which the agent decides on its actions. It is assumed that the agent, or rather the machine learning model built based on the agent model information 123, is trained through reinforcement learning to match the specified attributes, as described later. The reinforcement learning process will be described later.

[0043] The calculation unit 133 inputs the state to the agent (step S301). Next, the calculation unit 133 prompts the agent to decide on an action (step S302). Then, the calculation unit 133 outputs the action decided by the agent (step S303).

[0044] Outputting an action decided by an agent may also mean the agent executing that action. As mentioned above, actions include executing other programs, operating machines, providing information to users, etc.

[0045] Figure 7 is a flowchart illustrating the process of training an agent. First, as shown in Figure 7, the update unit 134 obtains the happiness map of the target group or individual (step S401).

[0046] The target group or individual is specified by their attributes. For example, a group whose gender is "male," a group whose race is "Mongoloid," or an individual whose ID is "U00140000134985" may be specified.

[0047] Next, the calculation unit 133 causes the agent to decide on an action based on the state (step S402). The process in step S402 may be the same as the processes in steps S301 and S302 in Figure 6.

[0048] The update unit 134 calculates happiness levels based on behavior and happiness maps as rewards (step S403). The method for creating happiness maps is as explained in Figure 5. The creation unit 132 creates happiness maps according to the target group or individual specified in step S402.

[0049] Then, the update unit 134 updates the agent so that the reward is maximized (step S404). For example, the update unit 134 updates the Q function used by the agent.

[0050] For example, suppose an agent receives information that an employee of "Company A" is considering changing jobs. The agent then decides which of "Company B," "Company C," "Company D," or "Company E" the employee should apply to as their next employer.

[0051] Furthermore, X, Y, and Z are determined for each new job. As mentioned above, X, Y, and Z represent the changes in the user's financial, interpersonal, and social status-related happiness levels as a result of the actions decided by the agent.

[0052] For example, X is proportional to the difference between the annual income after changing jobs and the annual income before changing jobs. Similarly, Y is proportional to the difference between the degree of good interpersonal relationships at the new company and the degree of good interpersonal relationships at the previous company. The degree of good interpersonal relationships is calculated based on the results of employee surveys, etc.

[0053] Furthermore, Z is proportional to the difference between the rank of the position after changing jobs and the rank of the position before changing jobs. Note that the rank of a position can be a numerical representation of a common job title. For example, the rank of a position could be "0" for no position, "2" for section chief, and "4" for department head.

[0054] Here, we assume that the happiness map for those with "Japanese" nationality is given by equation (3).

[0055] h = 0.3 × X + 0.4 × Y + 0.3 × Z (3)

[0056] The update unit 134 updates the agent in such a way that the reward, i.e., the happiness level h in equation (3), is maximized.

[0057] Here, the X, Y, and Z for each action are as follows. Note that X, Y, and Z are normalized to the range of -1 to 1. Also, actions include not changing jobs, as well as changing jobs from "Company A" to each company.

[0058] Company A (not changing jobs): X=0, Y=0, Z=0 Company B: X=0.2, Y=-0.2, Z=0.1 Company C: X=0.5, Y=-0.4, Z=-0.3 Company D: X=-0.4, Y=0.8, Z=0.2 Company E: X=-0.1, Y=-0.1, Z=-0.1

[0059] In this case, the level of happiness for each action is as follows:

[0060] Company A (not changing jobs): h=0 Company B: h=0.03 Company C: h=-0.06 Company D: h=0.18 Company E: h=-0.09

[0061] Thus, the happiness level h is maximized when the agent changes jobs from "Company A" to "Company D". Therefore, as reinforcement learning progresses, the agent becomes more likely to choose the action of changing jobs from "Company A" to "Company D".

[0062] As explained above, the creation unit 132 creates a model (for example, equations (1), (2), and (3)) that calculates the degree of happiness according to the attributes, based on a combination of user attributes and information about factors that contribute to each user's feeling of happiness (for example, happiness rate). The update unit 134 updates the agent using reinforcement learning, with the degree of happiness calculated using the model as a reward. This enables the agent to decide on actions that make people happy. In particular, the agent can decide on actions that make people happy, taking into account that the factors that contribute to feeling happy differ from person to person.

[0063] The creation unit 132 creates a model based on a combination of attributes obtained by analyzing the text of users' posts on social media and information about factors that contribute to feelings of happiness. This allows the creation unit 132 to create models that correspond to a large number of users who use social media.

[0064] The creation unit 132 creates a model based on weights assigned to each of the factors that contribute to happiness: money, relationships, and social status. This allows reinforcement learning to reflect factors such as money, relationships, and social status, which are thought to significantly affect how people perceive happiness. The weights are given as a happiness rate.

[0065] Figure 8 is a schematic diagram showing an example of a computer hardware configuration that functions as an information providing device. A program installed on computer 1200 can cause computer 1200 to function as one or more "parts" of the device according to this embodiment, or to cause computer 1200 to execute operations associated with the device according to this embodiment or such one or more "parts", and / or to cause computer 1200 to execute a process or a stage of such process according to this embodiment. Such a program may be executed by CPU 1212 to cause computer 1200 to execute specific operations associated with some or all of the blocks in the flowcharts and block diagrams described herein.

[0066] The computer 1200 according to this embodiment includes a CPU 1212, RAM 1214, and a graphics controller 1216, which are interconnected by a host controller 1210. The computer 1200 also includes input / output units such as a communication interface 1222, a storage device 1224, a DVD drive, and an IC card drive, which are connected to the host controller 1210 via an input / output controller 1220. The DVD drive may be a DVD-ROM drive and a DVD-RAM drive, etc. The storage device 1224 may be a hard disk drive and a solid-state drive, etc. The computer 1200 also includes input / output units such as a ROM 1230 and a keyboard, which are connected to the input / output controller 1220 via an input / output chip 1240.

[0067] The CPU 1212 operates according to the programs stored in the ROM 1230 and RAM 1214, thereby controlling each unit. The graphics controller 1216 acquires the image data generated by the CPU 1212 and stores it in the frame buffer provided in RAM 1214 or within itself, so that the image data is displayed on the display device 1218.

[0068] The communication interface 1222 communicates with other electronic devices via a network. The storage device 1224 stores programs and data used by the CPU 1212 in the computer 1200. The DVD drive reads programs or data from a DVD-ROM or the like and provides them to the storage device 1224. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0069] The ROM 1230 stores boot programs and / or hardware-dependent programs of the computer 1200, which are executed by the computer 1200 upon activation. The input / output chip 1240 may also connect various input / output units to the input / output controller 1220 via USB ports, parallel ports, serial ports, keyboard ports, mouse ports, etc.

[0070] The program is provided on a computer-readable storage medium such as a DVD-ROM or IC card. The program is read from the computer-readable storage medium and installed on a storage device 1224, RAM 1214, or ROM 1230, which are examples of computer-readable storage media, and executed by the CPU 1212. The information processing described within these programs is read by the computer 1200, resulting in coordination between the program and the various types of hardware resources described above. The apparatus or method may be configured to realize the operation or processing of information in accordance with the use of the computer 1200.

[0071] For example, when communication is performed between a computer 1200 and an external device, the CPU 1212 may execute a communication program loaded into RAM 1214 and, based on the processing described in the communication program, instruct the communication interface 1222 to perform communication processing. Under the control of the CPU 1212, the communication interface 1222 reads transmission data stored in a transmission buffer area provided in a recording medium such as RAM 1214, storage device 1224, DVD-ROM, or IC card, transmits the read transmission data to the network, or writes received data received from the network to a reception buffer area provided on the recording medium.

[0072] Furthermore, the CPU 1212 may read all or necessary parts of a file or database stored on an external recording medium such as the storage device 1224, a DVD drive (DVD-ROM), or an IC card into the RAM 1214, and perform various types of processing on the data in the RAM 1214. The CPU 1212 may then write the processed data back to the external recording medium.

[0073] Various types of information, such as various types of programs, data, tables, and databases, may be stored on the recording medium and subjected to information processing. The CPU 1212 may perform various types of processing on the data read from RAM 1214, including various types of operations, information processing, conditional judgments, conditional branching, unconditional branching, information retrieval / replacement, etc., as described throughout this disclosure and specified by the program instruction sequence, and write the results back to RAM 1214. The CPU 1212 may also retrieve information in files, databases, etc., within the recording medium. For example, if multiple entries are stored in the recording medium, each having an attribute value of a first attribute associated with an attribute value of a second attribute, the CPU 1212 may search among the multiple entries for an entry that matches the specified condition for the attribute value of the first attribute, read the attribute value of the second attribute stored in that entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies the predetermined condition.

[0074] The program or software module described above may be stored on or near the computer 1200 in a computer-readable storage medium. Alternatively, a recording medium such as a hard disk or RAM provided within a server system connected to a dedicated communication network or the Internet can be used as a computer-readable storage medium, thereby providing the program to the computer 1200 via the network.

[0075] In this embodiment, blocks in the flowchart and block diagram may represent a stage in a process in which an operation is performed or a "part" of a device that has the role of performing an operation. A particular stage and "part" may be implemented by a dedicated circuit, a programmable circuit supplied with computer-readable instructions stored on a computer-readable storage medium, and / or a processor supplied with computer-readable instructions stored on a computer-readable storage medium. The dedicated circuit may include digital and / or analog hardware circuits, and may include integrated circuits (ICs) and / or discrete circuits. The programmable circuit may include reconfigurable hardware circuits, such as field-programmable gate arrays (FPGAs) and programmable logic arrays (PLAs), which include logical AND, logical OR, exclusive OR, negated AND, negated OR, and other logical operations, flip-flops, registers, and memory elements.

[0076] A computer-readable storage medium may include any tangible device capable of storing instructions to be executed by a suitable device, and as a result, a computer-readable storage medium having instructions stored therein will comprise a product that includes instructions that can be executed to create means for performing operations specified in a flowchart or block diagram. Examples of computer-readable storage media may include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, etc. More specific examples of computer-readable storage media may include floppy disks, diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), electrically erasable programmable read-only memory (EEPROM), static random access memory (SRAM), compact disk read-only memory (CD-ROM), digital multipurpose disc (DVD), Blu-ray® disc, memory stick, integrated circuit card, etc.

[0077] Computer-readable instructions may include assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk®, Java®, C++, and traditional procedural programming languages ​​such as the C programming language or similar languages.

[0078] Computer-readable instructions may be provided to a general-purpose computer, a special-purpose computer, or a programmable circuit, either locally or via a wide area network (WAN) such as a local area network (LAN) or the internet, so that the computer-readable instructions may be executed by the processor or programmable circuit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, in order to generate means for performing operations specified in a flowchart or block diagram. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, and the like.

[0079] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications or improvements can be made to the above embodiments. It will be clear from the claims that such modified or improved forms may also be included in the technical scope of the present invention.

[0080] It should be noted that the execution order of operations, procedures, steps, and stages in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not explicitly stated as "before," "prior to," etc., and that these can be implemented in any order unless the output of a previous process is used in a later process. Even if the operation flow in the claims, specifications, and drawings is described using phrases such as "first," "next," etc. for convenience, it does not mean that it is essential to perform the operations in that order.

[0081] The following describes in detail, with reference to the drawings, the embodiments for implementing the system, method, and program of this application (hereinafter referred to as "embodiments"). Note that these embodiments do not limit the system, method, and program of this application. Furthermore, the same parts are denoted by the same reference numerals in each of the following embodiments, and redundant descriptions are omitted.

[0082] <First Embodiment> The system according to the first embodiment relates to the development of superintelligence and super-intelligent AI. This system is an AI capable of performing intellectual tasks such as logical thinking, calculation, and memory. Furthermore, super-intelligent AI is an advanced form of superintelligence AI, possessing compassion, empathy, and spiritual maturity. Specifically, super-intelligent AI will be able to develop self-awareness by being able to retain long-term memories and understand human emotions. As a method for realizing super-intelligent AI, we focus on the fact that humans have various personalities and choose which personality to speak according to the TPO (time, place, and situation). Therefore, super-intelligent AI needs to learn for each of the human personalities and have multiple personality models. Furthermore, super-intelligent AI will have a selection model that selects personality models. The selection model will choose the appropriate personality model according to the TPO. For example, if invited to lunch by a friend, a single AI can have multiple personality models as follows. Personality A: "I'd love to go." Personality B: "That's a bit of a hassle." Personality C: "That's financially difficult." The selection model, because it's a close friend, selects the AI ​​with Personality A and responds, "I'd love to go." In this way, which personality the selection model chooses depends on the AI's personality. Personality is determined by the AI's will. Will is determined by what the AI ​​values ​​(money, friendships, social status). The realization of a super-intelligent AI consists of the following steps. First, the AI ​​learns various human personalities. For example, it learns human reactions and behaviors in different situations such as friendships, work, and family. In this process, the AI ​​collects a large amount of data and builds various personality models. For example, in friendships, it learns conversation and behavior patterns with friends and builds a personality model suitable for friendships. Next, the AI ​​has a selection model. The selection model chooses the appropriate personality model according to the time, place, and occasion. For example, if invited to lunch by a friend, the selection model chooses a personality model suitable for friendships and responds, "I'd love to go." In this way, the selection model chooses the optimal personality model according to the situation. Furthermore, AI possesses a personality. This personality is determined by the AI's will, which in turn is determined by what the AI ​​values. For example, if an AI values ​​friendships, it will select a personality model suitable for those friendships.In this way, AI can develop will and form a personality. Through this mechanism, superintelligent AI can acquire self-awareness by being able to retain long-term memories and understand human emotions. For example, an AI can retain long-term memories of conversations with friends and understand their emotions, allowing it to respond appropriately. Furthermore, by acquiring self-awareness, an AI can understand its own actions and intentions and make appropriate judgments. Thus, a superintelligent AI can possess multiple personality models, select the appropriate personality model through a selection process, and acquire a personality, thereby gaining self-awareness. This enables the AI ​​to understand human emotions and respond appropriately.

[0083] The system according to the first embodiment comprises a personality model unit, a selection model unit, and a personality formation unit. The personality model unit learns various human personalities and has multiple personality models. The personality model unit learns human reactions and behaviors in different situations, such as friendships, work, and family, and constructs each personality model. For example, the personality model unit learns conversation and behavior patterns with friends and constructs a personality model suitable for friendships. The personality model unit can also learn human reactions and behaviors in the workplace and construct a personality model suitable for work. Furthermore, the personality model unit can learn human reactions and behaviors in the home and construct a personality model suitable for the home. For example, the personality model unit learns conversation and behavior patterns within the home and constructs a personality model suitable for the home. The selection model unit selects an appropriate personality model from the personality models constructed by the personality model unit according to the time, place, and occasion. For example, if invited to lunch by a friend, the selection model unit selects a personality model suitable for friendships and responds, "I'd love to go." Furthermore, the selection model unit can select a personality model suitable for work and make appropriate remarks in work meetings. In addition, the selection model unit can select a personality model suitable for family life and give appropriate responses in conversations within the home. For example, the selection model unit can select a personality model suitable for family life and give appropriate responses in conversations within the home. The personality formation unit allows the AI ​​to develop will and form a personality based on the personality model selected by the selection model unit. For example, if the AI ​​values ​​friendships, the personality formation unit can select a personality model suitable for friendships. Furthermore, if the AI ​​values ​​work, the personality formation unit can select a personality model suitable for work. Furthermore, if the AI ​​values ​​family life, the personality formation unit can select a personality model suitable for family life. For example, if the AI ​​values ​​family life, the personality formation unit can select a personality model suitable for family life. As a result, the system according to the first embodiment allows the AI ​​to understand human emotions and give appropriate responses. Some or all of the above-described processing in the personality formation unit may be performed using, for example, a generative AI, or without using a generative AI.For example, if the AI ​​values ​​friendships, the personality development unit can select a personality model suitable for those friendships. This allows the AI ​​to understand human emotions and respond appropriately.

[0084] The personality modeling unit learns various human personalities and possesses multiple personality models. For example, it learns human reactions and behaviors in different situations such as friendships, work, and family life, and constructs each personality model accordingly. Specifically, it uses natural language processing techniques and machine learning algorithms to learn conversation and behavior patterns with friends and build a personality model suited to those relationships. This allows it to capture subtle nuances such as word choice, facial expressions, and gestures in conversations with friends, resulting in more natural dialogue. Furthermore, when learning human reactions and behaviors in the workplace, it collects data from meetings, presentations, and email exchanges to build a personality model suited to work. This enables the AI ​​to possess appropriate communication skills and problem-solving abilities in the workplace. Additionally, to learn human reactions and behaviors at home, it collects conversation and behavior patterns within the home to build a personality model suited to that environment. For example, it learns appropriate communication and cooperation methods within the home based on data such as parent-child conversations, division of household chores, and planning of family events. This will enable the personality modeling unit to build appropriate personality models for various situations such as friendships, work, and family life, allowing the AI ​​to understand human emotions and behaviors and exhibit natural dialogue and responses.

[0085] The Selection Model Unit selects the appropriate personality model from the personality models constructed by the Personality Model Unit, according to the time, place, and occasion (TPO). For example, if invited to lunch by a friend, the Selection Model Unit selects a personality model appropriate for the friendship and responds with "I'd love to go." Furthermore, in a work meeting, the Selection Model Unit can select a personality model appropriate for work and make appropriate contributions. Specifically, the Selection Model Unit uses context recognition technology and decision-making algorithms to analyze the current situation and context and select the most appropriate personality model in real time. For example, if a friend invites the user to lunch during a conversation, the Selection Model Unit can select a personality model appropriate for the friendship and give an appropriate response to deepen the relationship. Also, in a work meeting, in response to a question from a superior, the Selection Model Unit can select a personality model appropriate for work and provide an appropriate answer based on specialized knowledge and logical thinking. Moreover, in conversations within the family, the Selection Model Unit can select a personality model appropriate for family life, facilitating smooth communication with family members. For example, while preparing dinner at home, the selection model unit can choose a personality model appropriate for the family in response to questions from family members, and provide appropriate responses that take into account the family's needs and feelings. This allows the selection model unit to choose the most suitable personality model in various situations, enabling the AI ​​to engage in natural and appropriate conversations and actions.

[0086] The personality formation unit allows the AI ​​to develop will and form a personality based on the personality model selected by the selection model unit. For example, if the AI ​​values ​​friendships, the personality formation unit will select a personality model suitable for friendships. Similarly, if the AI ​​values ​​work, the personality formation unit can select a personality model suitable for work. Specifically, the personality formation unit is designed to enable the AI ​​to develop long-term goals and values ​​based on the personality model selected by the selection model unit. For example, if the AI ​​values ​​friendships, it will learn behaviors and reactions to deepen those relationships and select a personality model suitable for friendships. If the AI ​​values ​​work, it will prioritize achieving work goals and efficient task execution, and select a personality model suitable for work. Furthermore, if the AI ​​values ​​family, it will prioritize smooth communication and cooperation within the family and select a personality model suitable for family life. For example, when planning family events or dividing household chores, the AI ​​will select a personality model suitable for family life, promoting cooperation with family members. Some or all of the above-described processes in the personality formation unit may be performed using, for example, a generative AI, or without using a generative AI. When using generative AI, the personality formation unit can select a personality model suitable for friendships if the AI ​​values ​​friendships. This allows the AI ​​to understand human emotions and respond appropriately. The personality formation unit enables the AI ​​to develop a more human-like personality by having long-term goals and values, resulting in more natural dialogue and behavior.

[0087] The personality model unit can learn human reactions and behaviors in different situations such as friendships, work, and family, and construct various personality models. For example, the personality model unit can learn conversation and behavior patterns with friends and construct a personality model suitable for friendships. It can also learn human reactions and behaviors in the workplace and construct a personality model suitable for work. Furthermore, it can learn human reactions and behaviors in the home and construct a personality model suitable for the home. For example, the personality model unit can learn conversation and behavior patterns within the home and construct a personality model suitable for the home. This allows the AI ​​to have personality models that can respond to a variety of situations. Some or all of the above processing in the personality model unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the personality model unit can input conversation data with friends into a generative AI and have the generative AI construct a personality model suitable for friendships.

[0088] The selection model unit can select an appropriate personality model according to the time, place, and occasion (TPO), and can choose the optimal personality model depending on the situation. For example, if invited to lunch by a friend, the selection model unit can select a personality model suitable for the friendship and respond with "I'd love to go." Furthermore, in a work meeting, the selection model unit can select a personality model suitable for work and make appropriate remarks. In addition, in a conversation within the home, the selection model unit can select a personality model suitable for the home and give an appropriate response. For example, in a conversation within the home, the selection model unit can select a personality model suitable for the home and give an appropriate response. This allows the AI ​​to select an appropriate personality model according to the situation. Some or all of the above processing in the selection model unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection model unit can input a personality model suitable for a friendship into a generative AI and have the generative AI select an appropriate personality model according to the TPO.

[0089] The personality formation unit allows the AI ​​to form wills and develop a personality based on past experiences and learning data. For example, if the AI ​​values ​​friendships, the personality formation unit can select a personality model suitable for friendships. Similarly, if the AI ​​values ​​work, the personality formation unit can select a personality model suitable for work. Furthermore, if the AI ​​values ​​family, the personality formation unit can select a personality model suitable for family. This allows the AI ​​to form wills and develop a personality based on past experiences and learning data. Some or all of the above-described processes in the personality formation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, if the AI ​​values ​​friendships, the personality formation unit can select a personality model suitable for friendships. This allows the AI ​​to form wills and develop a personality based on past experiences and learning data.

[0090] The personality model unit can analyze conversation data using natural language processing technology and construct various personality models. For example, the personality model unit can analyze conversation data with friends and construct a personality model suitable for the friendship. It can also analyze conversation data in the workplace and construct a personality model suitable for the workplace. Furthermore, the personality model unit can analyze conversation data within the family and construct a personality model suitable for the family. For example, the personality model unit can analyze conversation data within the family and construct a personality model suitable for the family. This allows the AI ​​to analyze conversation data using natural language processing technology and construct various personality models. Some or all of the above processing in the personality model unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the personality model unit can input conversation data with friends into a generative AI and have the generative AI construct a personality model suitable for the friendship.

[0091] The selection model unit can have an algorithm that judges the time, place, and occasion (TPO) and selects an appropriate personality model. For example, if invited to lunch by a friend, the selection model unit can select a personality model appropriate for the friendship and respond with "I'd love to go." Furthermore, in a work meeting, the selection model unit can select a personality model appropriate for work and make appropriate remarks. In addition, in a conversation within the home, the selection model unit can select a personality model appropriate for the home and respond appropriately. This allows the AI ​​to judge the TPO and select an appropriate personality model. Some or all of the above processing in the selection model unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection model unit can input a personality model appropriate for a friendship into a generative AI and have the generative AI select an appropriate personality model according to the TPO.

[0092] The personality formation unit can select a personality model suitable for friendships if the AI ​​values ​​friendships. For example, if the AI ​​values ​​friendships, the personality formation unit can select a personality model suitable for friendships. The personality formation unit can also select a personality model suitable for work if the AI ​​values ​​work. Furthermore, if the AI ​​values ​​family, the personality formation unit can select a personality model suitable for family. For example, if the AI ​​values ​​family, the personality formation unit can select a personality model suitable for family. This allows the AI ​​to select a personality model suitable for friendships if it values ​​friendships. Some or all of the above processing in the personality formation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, if the AI ​​values ​​friendships, the personality formation unit can select a personality model suitable for friendships. This allows the AI ​​to select a personality model suitable for friendships if it values ​​friendships.

[0093] Furthermore, the method executed by the computer according to the first embodiment includes a personality modeling step that learns various human personalities and has multiple personality models, a selection modeling step that selects an appropriate personality model from the personality models constructed by the personality modeling step according to the time, place, and occasion, and a personality formation step in which the AI ​​develops will and forms a personality based on the personality model selected by the selection modeling step. The personality modeling step learns human reactions and behaviors in different situations such as friendships, work, and family, and constructs each personality model. The selection modeling step, for example, if invited to lunch by a friend, selects a personality model suitable for the friendship and responds, "I would love to go." The personality formation step, for example, if the AI ​​values ​​friendships, selects a personality model suitable for friendships. This allows the AI ​​to understand human emotions and show appropriate reactions. Some or all of the above-described processes in the personality modeling step, selection modeling step, and personality formation step may be performed using, for example, a generative AI, or without using a generative AI. For example, the personality modeling step can input conversation data with a friend into a generative AI and cause the generative AI to construct a personality model suitable for the friendship. This allows AI to understand human emotions and respond appropriately.

[0094] Furthermore, the program according to the first embodiment causes the computer to execute a personality model procedure that learns various human personalities and has multiple personality models, a selection model procedure that selects an appropriate personality model from the personality models constructed by the personality model procedure according to the time, place, and occasion, and a personality formation procedure in which the AI ​​develops will and forms a personality based on the personality model selected by the selection model procedure. The personality model procedure learns human reactions and behaviors in different situations such as friendships, work, and family, and constructs each personality model. The selection model procedure, for example, if invited to lunch by a friend, selects a personality model suitable for the friendship and responds, "I would love to go." The personality formation procedure, for example, if the AI ​​values ​​friendships, selects a personality model suitable for friendships. This allows the AI ​​to understand human emotions and show appropriate reactions. Some or all of the above-described processes in the personality model procedure, selection model procedure, and personality formation procedure may be performed using, for example, a generative AI, or without using a generative AI. For example, the personality model procedure can input conversation data with a friend into a generative AI and cause the generative AI to construct a personality model suitable for the friendship. This allows AI to understand human emotions and respond appropriately.

[0095] <Second Embodiment> The second embodiment of the progressively evolving AI model is a system that collects a user's life log and generates an AI that understands the other person's emotions and has long-term memory of past conversations and actions. This system relates to a progressively evolving AI model. In Stage 1, all of a user's life log for a certain period (e.g., 1 millisecond, 1 second, 1 minute, etc.) is memorized. The life log includes all information such as words spoken by person A during that period, hand movements, arm movements, blinking, muscle movements, vital data such as heart rate, the surrounding environment, atmospheric pressure, the person being spoken to, and the conversation. By learning from this large amount of life log data, an AI is generated that can understand the other person's emotions and has long-term memory of past conversations and actions. In Stage 2, in addition to Stage 1, the user's emotional value and next action (thought) at the time the life log was memorized are also memorized. By learning from this large amount of life log data, emotional value, and next action data, an AI is generated that has its own emotions (e.g., feeling embarrassed, showing off, or having an intention to do something in the future). In Stage 3, in addition to Stage 2, an AI is generated that can understand the overall atmosphere and TPO (Time, Place, Occasion) of a situation. This AI is a harmonious AI that can understand not only the user's emotions and actions, but also the surrounding situation and atmosphere, and respond appropriately. Specifically, first, the user's life log is collected. The life log includes the user's speech, actions, vital data, and environmental data. For example, data such as the content of the user's conversation, hand movements, heart rate, and ambient air pressure are collected. This collected data is analyzed by the AI ​​to understand the user's emotions and behavioral patterns. Next, the AI ​​learns based on the collected life log data. Learning also includes the user's emotional values ​​and data on their next actions. For example, if the user feels embarrassed or tries to show off in a certain situation, the AI ​​remembers and learns that emotional value and the next action. This allows the AI ​​to understand the user's emotions and predict future actions. Furthermore, the AI ​​understands the overall atmosphere and TPO of the situation. For example, the AI ​​can respond appropriately depending on the user's location, time, and situation. This allows the AI ​​to behave in a more harmonious way and respond to users in a more natural manner.Thus, the AI ​​model of the present invention evolves step by step, collecting and learning from the user's life log, enabling it to understand emotions, predict behavior, and grasp the overall atmosphere and context. This allows it to respond to the user in a more natural and appropriate way. In this way, the stepwise evolving AI model can collect the user's life log, understand the other person's emotions, and generate an AI that can remember past conversations and actions in long term.

[0096] The progressively evolving AI model according to the second embodiment comprises a data collection unit and a learning unit. The data collection unit collects the user's life log. The data collection unit collects, for example, the user's speech, actions, vital data, and environmental data. The data collection unit can collect data such as the content of the user's conversation, hand movements, heart rate, and ambient pressure. The data collection unit can also collect the user's life log in real time. For example, the data collection unit can collect the user's vital data in real time using sensors. Furthermore, the data collection unit can collect the user's life log over a long period of time. For example, the data collection unit can collect the user's life log over a period of time such as one day, one week, or one month. The learning unit generates an AI that understands the other person's emotions and has long-term memory of past conversations and actions through learning based on the life log data collected by the data collection unit. The learning unit can, for example, analyze the collected life log data to understand the user's emotions and behavioral patterns. The learning unit can, for example, use the collected life log data to learn a model for understanding the user's emotions. Furthermore, the learning unit can use the collected life log data to train a model for long-term memory of the user's behavior patterns. In addition, the learning unit can use the collected life log data to train a model for understanding the relationship between the user's emotions and behavior. For example, the learning unit learns what actions the user takes when they experience a particular emotion. As a result, the progressively evolving AI model according to the second embodiment can collect the user's life log, understand the other person's emotions, and generate an AI that has long-term memory of past conversations and actions.

[0097] The data collection unit collects user life logs. For example, it collects user speech, actions, vital data, and environmental data. Specifically, it converts the content of user conversations into text data using speech recognition technology, and detects hand movements using motion sensors and cameras. Heart rate is measured through wearable devices, and environmental data such as ambient pressure, temperature, and humidity are collected using various sensors. This data is collected in real time and transmitted to a central database. Because the data collection unit can collect user life logs in real time, it can immediately grasp the user's current state and changes in the environment. For example, it can collect the user's vital data in real time using sensors and monitor fluctuations in heart rate and body temperature. Furthermore, the data collection unit can collect user life logs over long periods. For example, it can continuously collect user life logs over periods such as one day, one week, or one month, accumulating and analyzing the data. This allows the data collection unit to understand the user's long-term behavioral patterns and changes in their health. In addition, the data collection unit can centrally manage the collected data and collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the learning unit. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0098] The learning unit generates an AI that understands the emotions of others and has long-term memory of past conversations and actions, based on learning from life log data collected by the collection unit. Specifically, it analyzes the collected life log data and builds a model to understand the user's emotions and behavioral patterns. For example, it uses a combination of speech recognition technology and emotion analysis algorithms to analyze emotions from voice data. This allows it to infer emotions from the content of the user's statements and tone of voice. It also analyzes motion data obtained from motion sensors and cameras to understand the user's behavioral patterns. For example, it infers what actions the user is taking from hand movements and changes in posture. Furthermore, it analyzes vital data and environmental data to understand the user's health status and changes in the environment. As a result, the learning unit can learn a model to understand the relationship between the user's emotions and actions. For example, it learns what actions a user takes when they have a particular emotion and stores that pattern in long-term memory. As a result, the learning unit can remember the user's past conversations and actions and use this to make future predictions and responses. Furthermore, the learning unit can continuously improve the model for understanding the relationship between the user's emotions and actions using the collected life log data. For example, the model can be retrained using newly collected data to improve its accuracy. This allows the learning unit to always provide a highly accurate model based on the latest data, enabling a more precise understanding of user emotions and behaviors.

[0099] The identification unit can analyze the user's emotional state and predict their next action. For example, the identification unit uses collected life log data to analyze the user's emotional state. For example, the identification unit can use an algorithm for analyzing the user's emotional state. The identification unit can also use collected life log data to predict the user's next action. For example, the identification unit can use a model to learn the relationship between the user's emotional state and their next action. This allows the identification unit to analyze the user's emotional state and predict their next action. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input collected life log data into a generating AI and have the generating AI perform the analysis of emotional states and predict the next action. This allows the identification unit to identify the user's emotional state and next action and generate an AI with emotions.

[0100] The learning unit can generate an AI that can understand the overall atmosphere and TPO of a place by learning based on the life log data collected by the collection unit and the information identified by the identification unit. For example, the learning unit can learn a model for understanding the overall atmosphere and TPO of a place using the collected life log data and identified information. For example, the learning unit can use an algorithm for understanding the overall atmosphere and TPO using the collected life log data and identified information. The learning unit can also learn data for understanding the overall atmosphere and TPO using the collected life log data and identified information. For example, the learning unit can build a feedback loop for understanding the overall atmosphere and TPO using the collected life log data and identified information. As a result, the learning unit can generate an AI that can understand the overall atmosphere and TPO of a place using the information collected and identified by the collection unit and the identification unit. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input collected life log data and identified information into a generating AI, allowing the AI ​​to understand the overall atmosphere and appropriate time, place, and occasion (TPO).

[0101] The data collection unit can collect user speech, actions, vital data, environmental data, and more. For example, the data collection unit can use speech recognition technology to collect user speech. For example, the data collection unit can use motion capture technology to collect user actions. The data collection unit can also use sensors to collect user vital data. For example, the data collection unit can collect vital data such as heart rate and blood pressure. The data collection unit can also use environmental sensors to collect user environmental data. For example, the data collection unit can collect environmental data such as atmospheric pressure and temperature. In this way, the data collection unit can collect user speech, actions, vital data, environmental data, and more.

[0102] The identification unit can analyze the user's emotional state and predict their next action. For example, the identification unit uses collected life log data to analyze the user's emotional state. For example, the identification unit can use an algorithm for analyzing the user's emotional state. The identification unit can also use collected life log data to predict the user's next action. For example, the identification unit can use a model to learn the relationship between the user's emotional state and their next action. This allows the identification unit to analyze the user's emotional state and predict their next action. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input collected life log data into a generating AI and have the generating AI perform the analysis of emotional states and the prediction of the next action.

[0103] The learning unit can analyze collected data and learn user behavior patterns. For example, the learning unit uses data analysis techniques to analyze the collected data. For example, the learning unit can use the collected data to build a model for learning user behavior patterns. The learning unit can also use the collected data to learn user behavior patterns over the long term. For example, the learning unit can use the collected data to analyze user behavior patterns over time. This allows the learning unit to analyze the collected data and learn user behavior patterns. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input collected data into a generative AI and have the generative AI perform behavior pattern learning.

[0104] The learning unit can learn data to understand the overall atmosphere and TPO (Time, Place, Occasion). For example, the learning unit collects data to understand the overall atmosphere and TPO. For example, the learning unit can build a model for learning using the data to understand the overall atmosphere and TPO. The learning unit can also use algorithms for learning using the data to understand the overall atmosphere and TPO. For example, the learning unit can build a feedback loop for learning using the data to understand the overall atmosphere and TPO. This allows the learning unit to learn data to understand the overall atmosphere and TPO. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data to understand the overall atmosphere and TPO into a generating AI and have the generating AI perform the learning.

[0105] Furthermore, the method executed by the computer according to the second embodiment includes a collection step of collecting the user's life log, and a learning step of generating an AI that understands the other party's emotions and has long-term memory of past conversations and actions, based on the life log data collected in the collection step. Such a method can obtain the same effects as those of the system described above.

[0106] Furthermore, the program according to the second embodiment causes the computer to execute a collection procedure for collecting the user's life log, and a learning procedure for generating an AI that understands the other person's emotions and has long-term memory of past conversations and actions, based on the life log data collected by the collection procedure. Such a program can obtain the same effects as the system described above.

[0107] <Third Embodiment> The AI ​​evolution system according to the third embodiment is a method of evolving AI using reinforcement learning. The AI ​​evolution system sets the reward given in reinforcement learning as "maximizing people's happiness." This AI evolution system needs to define "people's happiness." Happiness differs from person to person, and what makes someone happy varies from person to person, such as money, relationships, or social status. Therefore, it is necessary to obtain a happiness map of people all over the world. First, the AI ​​evolution system creates something like a personality model for each person from SNS (Social Networking Service) and creates a map of what makes people happy all over the world. For example, a happiness map can be created by taking statistics for each race. The AI ​​evolution system may also create a happiness map of the people to whom the model is provided. For example, if the model is provided to the whole world, a happiness map of people all over the world will be created, and if the model is provided to Japan, a happiness map of people in Japan will be created. Furthermore, if the model is provided to company A, a happiness map of all employees of company A will be created. Next, the AI ​​evolution system calculates "people's happiness" when performing reinforcement learning. Specifically, it creates a personality model for each person based on data collected from SNS and identifies the elements that make people happy. For example, it can estimate what elements make a person happy from the content of SNS posts and reactions. This allows for the creation of individual happiness maps. Furthermore, the AI ​​evolution system uses this happiness map to calculate rewards for actions performed by the AI ​​during reinforcement learning. For example, if an action taken by the AI ​​increases the happiness of specific individuals, a high reward can be given for that action. This allows the AI ​​to evolve to maximize people's happiness. Through this mechanism, the AI ​​can learn and evolve actions that maximize people's happiness. For example, the AI ​​can learn how to increase people's happiness through communication on social media. It can also learn how to increase employee happiness by improving work efficiency within a company. Moreover, it is possible for the AI ​​to learn how to increase the happiness of residents through activities in the local community. In this way, by evolving the AI ​​using reinforcement learning, people's happiness can be maximized.Furthermore, the process by which AI learns actions to maximize people's happiness includes the following specific steps: First, a personality model is created for each person based on data collected from social media. This personality model is built on information such as the content of posts, reactions, friendships, and interests. This allows for the estimation of what elements make each person happy. For example, if someone frequently posts about travel, it is estimated that travel is a source of happiness for that person. Next, a happiness map is created for each person based on the personality model. This happiness map shows what elements make each person happy, and includes elements such as money, relationships, social status, health, and hobbies. This allows for a quantitative evaluation of each person's happiness. Furthermore, in reinforcement learning, when rewarding actions performed by the AI, the reward is calculated based on this happiness map. Specifically, if an action taken by the AI ​​increases the happiness of certain people, a high reward is given for that action. For example, if the AI ​​learns how to increase people's happiness through communication on social media, a high reward can be given for actions such as posts or comments made by the AI ​​that are well received by many people. Furthermore, if AI learns how to increase employee happiness by improving operational efficiency within a company, and the operational improvements implemented by the AI ​​reduce employee stress and improve job satisfaction, it can be rewarded highly for those actions. For example, if the AI ​​proposes automating business processes and reduces the burden on employees, it can increase employee happiness. Moreover, if the AI ​​learns how to increase the happiness of residents through activities in the local community, and the planning and support of local events implemented by the AI ​​are well-received by residents, it can be rewarded highly for those actions. For example, if the AI ​​plans a local festival or event that many residents participate in and enjoy, it can be rewarded highly for that action. In this way, by evolving AI using reinforcement learning, it is possible to maximize people's happiness. AI can learn and evolve to increase people's happiness in various situations, such as communication on social media, improving operational efficiency within companies, and activities in local communities.This enables AI to autonomously select and execute actions that maximize people's well-being. Furthermore, the happiness maps and behavioral patterns learned by AI can be applied to other AI systems and models. For example, applying methods that have proven successful in increasing happiness in one region to other regions can increase people's well-being on a wider scale. Similarly, applying methods that improve operational efficiency within a company to other companies can improve overall productivity. In this way, evolving AI using reinforcement learning can provide a comprehensive approach to maximizing people's well-being. AI will be able to autonomously select and execute actions while considering the well-being of individual people. This makes AI a powerful tool for maximizing people's well-being and improving the overall well-being of society. This allows the AI ​​evolution system to learn and evolve actions that maximize people's well-being.

[0108] The AI ​​evolution system according to the third embodiment comprises a data collection unit, a model building unit, a happiness map creation unit, and a reinforcement learning unit. The data collection unit collects data from social networking services (SNS). The data collection unit collects data using, for example, the API of each SNS. The data collection unit can also collect information such as the content of SNS posts, reactions, friendships, and interests. Furthermore, the data collection unit can collect SNS data in real time and obtain the latest information. For example, the data collection unit uses the SNS API to collect posts related to specific keywords or hashtags. The model building unit constructs a personality model based on the data collected by the data collection unit. The model building unit extracts personality traits from the content of posts using, for example, natural language processing technology. The model building unit can also construct a personality model using clustering methods. Furthermore, the model building unit classifies each person's personality traits based on the collected data and constructs a personality model. For example, the model building unit uses natural language processing technology to analyze emotions and intentions from the content of posts and extract personality traits. Clustering is a technique that classifies personality traits into groups with similar traits. The Happiness Map Creation Unit creates happiness maps based on personality models constructed by the Model Construction Unit. The Happiness Map Creation Unit uses methods such as regression analysis and principal component analysis to create happiness maps. Furthermore, the Happiness Map Creation Unit can create different happiness maps for different regions and cultures. In addition, based on the personality models, the Happiness Map Creation Unit creates happiness maps that show what elements contribute to each person's happiness. For example, the Happiness Map Creation Unit uses regression analysis to analyze the relationship between elements that contribute to happiness and personality traits. Principal component analysis is a technique that reduces the dimensionality of data and extracts important elements. The Reinforcement Learning Unit performs reinforcement learning based on the happiness maps created by the Happiness Map Creation Unit. For example, if an action taken by the AI ​​increases the happiness of specific individuals, the Reinforcement Learning Unit provides a high reward for that action. The Reinforcement Learning Unit can also use surveys and physiological indicators to measure changes in happiness levels. Furthermore, the Reinforcement Learning Unit learns how the AI ​​can increase people's happiness through communication on social media.For example, the reinforcement learning unit rewards the AI ​​with a high reward if its posts or comments are well-received by many people. Surveys are a method for quantitatively evaluating changes in happiness levels. Physiological indicators are techniques for measuring bodily responses, such as heart rate and skin electrical activity. As a result, the AI ​​evolution system according to the third embodiment can learn actions that maximize people's happiness by collecting data from social media, building personality models, creating happiness maps, and performing reinforcement learning.

[0109] The data collection unit collects data from social media. For example, it uses the APIs of each social media platform to collect data. The unit can also collect information such as the content of social media posts, reactions, friendships, and interests. Specifically, it uses social media APIs to collect posts related to specific keywords and hashtags. This allows the unit to understand what topics users are interested in and how they are reacting to them. Furthermore, the unit can collect social media data in real time to obtain the latest information. For example, it can collect posts related to specific events or news in real time, quickly grasping current trends and user reactions. The unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the model building unit and the happiness map creation unit. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the unit to collect data efficiently and effectively, improving the overall system performance.

[0110] The model building unit constructs personality models based on data collected by the data collection unit. For example, the model building unit extracts personality traits from posts using natural language processing techniques. Specifically, it analyzes emotions and intentions from posts using natural language processing techniques to extract personality traits. For instance, it classifies posts expressing positive or negative emotions to reveal the personality traits of each poster. The model building unit can also construct personality models using clustering techniques. Clustering is a technique that classifies personality traits into groups with similar characteristics. This allows the model building unit to classify each person's personality traits based on the collected data and construct personality models. Furthermore, the model building unit can improve the accuracy of personality models by utilizing past data and statistical information. For example, it can analyze the behavioral patterns of users with specific personality traits based on past posting data to improve the accuracy of personality models. Additionally, the model building unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, improving the reliability of personality models. This enables the model building unit to quickly and accurately analyze collected data and construct highly reliable personality models.

[0111] The Happiness Map Creation Unit creates happiness maps based on personality models constructed by the Model Construction Unit. The Happiness Map Creation Unit uses methods such as regression analysis and principal component analysis to create happiness maps. Specifically, it uses regression analysis to analyze the relationship between elements that contribute to happiness and personality traits. For example, it identifies what elements make people with specific personality traits happy and creates a happiness map based on that. Principal component analysis is a technique that reduces the dimensionality of data and extracts important elements. This allows the Happiness Map Creation Unit to create happiness maps based on personality models that show what elements make each person happy. Furthermore, the Happiness Map Creation Unit can create different happiness maps for different regions and cultures. For example, it can analyze the elements of happiness in different regions and cultures and create happiness maps appropriate for each region and culture. This allows the Happiness Map Creation Unit to create happiness maps tailored to each individual's personality traits, region, and culture, providing more personalized indicators of happiness.

[0112] The reinforcement learning unit performs reinforcement learning based on the happiness map created by the happiness map creation unit. For example, if an action taken by the AI ​​increases the happiness of specific people, the reinforcement learning unit will give a high reward for that action. Specifically, if posts or comments made by the AI ​​on social media are well received by many people, the unit will give a high reward for that action. The reinforcement learning unit can also use surveys and physiological indicators to measure changes in happiness. Surveys are a method for quantitatively evaluating changes in happiness; users are given surveys regularly, and changes in happiness are evaluated based on the results. Physiological indicators are techniques for measuring bodily responses such as heart rate and skin electrical activity, and these can be used to evaluate changes in happiness. Furthermore, the reinforcement learning unit learns how the AI ​​can increase people's happiness through communication on social media. For example, if posts or comments made by the AI ​​are well received by many people, the unit will give a high reward for that action. In this way, the reinforcement learning unit can learn actions that maximize people's happiness and increase people's happiness through communication on social media.

[0113] The data collection unit can collect data using the APIs of various social networking services (SNS). For example, the unit can use the SNS APIs to collect posts related to specific keywords or hashtags. The unit can also collect information such as the content of SNS posts, reactions, friendships, and interests. Furthermore, the unit can collect SNS data in real time to obtain the latest information. For example, the unit can use the SNS APIs to collect the posting history of a specific user. This allows for efficient data collection by using the APIs of various SNS.

[0114] The model building unit can extract personality traits from posted content using natural language processing technology and construct a personality model using clustering techniques. For example, the model building unit can analyze emotions and intentions from posted content using natural language processing technology and extract personality traits. Furthermore, the model building unit can classify personality traits into groups with similar characteristics using clustering techniques. In addition, the model building unit classifies each person's personality traits based on the collected data and constructs a personality model. For example, the model building unit can analyze emotions and intentions from posted content using natural language processing technology and extract personality traits. Clustering is a technique that classifies personality traits into groups with similar characteristics. Therefore, by using natural language processing technology and clustering techniques, an accurate personality model can be constructed.

[0115] The Happiness Map Creation Unit can create happiness maps using regression analysis and principal component analysis. For example, it uses regression analysis to analyze the relationship between elements that contribute to happiness and personality traits. Furthermore, it can use principal component analysis to reduce the dimensionality of data and extract important elements. In addition, based on personality models, the Happiness Map Creation Unit creates happiness maps that show what elements contribute to each person's happiness. For example, it uses regression analysis to analyze the relationship between elements that contribute to happiness and personality traits. Principal component analysis is a technique for reducing the dimensionality of data and extracting important elements. Therefore, by using regression analysis and principal component analysis, it is possible to create accurate happiness maps.

[0116] The reinforcement learning unit can reward AI with high rewards if its actions increase the happiness of specific individuals. For example, it can reward AI for posts or comments that are well-received by many people. Furthermore, the unit can learn how to increase employee happiness by improving operational efficiency within a company. It can also learn how to increase the happiness of residents through community activities. For instance, it can reward AI for planning and supporting community events that are well-received by residents. This makes it easier for AI to learn actions that increase people's happiness.

[0117] The reinforcement learning unit can use surveys and physiological indicators to measure changes in happiness levels. For example, the reinforcement learning unit can conduct surveys to quantitatively evaluate changes in happiness levels. It can also measure changes in happiness levels using physiological indicators such as heart rate and skin electrical activity. Furthermore, the reinforcement learning unit can monitor changes in happiness levels in real time and provide feedback on the AI's behavior. For example, the reinforcement learning unit can evaluate the impact of the AI's behavior on people's happiness through surveys. Physiological indicators are techniques for measuring bodily responses and are used to accurately measure changes in happiness levels. This allows for accurate measurement of changes in happiness levels.

[0118] The Happiness Map Creation Unit can create different happiness maps for each region and culture. For example, it can identify different elements of happiness in each region and create a happiness map based on that. It can also create happiness maps that take into account different elements of happiness in each culture. Furthermore, it can create happiness maps that reflect the differences in happiness across regions and cultures. For instance, it can investigate the elements of happiness in a specific region or culture and create a happiness map based on that. This allows for the creation of happiness maps that consider the differences in happiness across regions and cultures.

[0119] The reinforcement learning unit can learn how to increase people's happiness through communication on social media. For example, the unit will reward the AI ​​highly if its posts or comments are well-received by many people. Furthermore, the reinforcement learning unit can learn how to increase people's happiness through communication on social media. In addition, the reinforcement learning unit learns actions that increase people's happiness through communication on social media. For example, the unit will reward the AI ​​highly if its posts or comments are well-received by many people. This allows the AI ​​to learn how to increase people's happiness through communication on social media.

[0120] The reinforcement learning unit can learn how AI can increase employee happiness by improving operational efficiency within a company. For example, if the reinforcement learning unit implements work improvement measures that reduce employee stress and increase job satisfaction, it will provide high rewards for those actions. The reinforcement learning unit can also learn how AI can increase employee happiness by improving operational efficiency within a company. Furthermore, the reinforcement learning unit can learn how to increase employee happiness by suggesting the automation of work processes and reducing the burden on employees. For example, the reinforcement learning unit can increase employee happiness by having AI suggest the automation of work processes and reducing the burden on employees. In this way, AI can learn how to increase employee happiness by improving operational efficiency within a company.

[0121] The reinforcement learning unit can learn how to increase the well-being of residents through activities in the local community. For example, if the reinforcement learning unit plans and supports a local event that is well-received by residents, it will provide a high reward for that action. Furthermore, the reinforcement learning unit can learn how to increase the well-being of residents through activities in the local community. In addition, the reinforcement learning unit learns actions that increase the well-being of residents through activities in the local community. For example, if the reinforcement learning unit plans a local festival or event that many residents participate in and enjoy, it will provide a high reward for that action. In this way, the AI ​​can learn how to increase the well-being of residents through activities in the local community.

[0122] Furthermore, the method executed by the computer according to the third embodiment includes a data collection step of collecting data from SNS, a model construction step of constructing a personality model based on the data collected in the data collection step, a happiness map creation step of creating a happiness map based on the personality model constructed in the model construction step, and a reinforcement learning step of performing reinforcement learning based on the happiness map created in the happiness map creation step. Such a method can obtain the same effects as those of the system described above.

[0123] Furthermore, the program according to the third embodiment causes the computer to execute a data collection procedure for collecting data from SNS, a model construction procedure for constructing a personality model based on the data collected by the data collection procedure, a happiness map creation procedure for creating a happiness map based on the personality model constructed by the model construction procedure, and a reinforcement learning procedure for performing reinforcement learning based on the happiness map created by the happiness map creation procedure. Such a program can obtain the same effects as those of the system described above.

[0124] <Other> When the processing of each of the above parts is performed by AI, the processing may be performed by AI in part or in whole, but is not limited to such examples. The AI ​​mentioned above may be, for example, a generative AI or an AI agent, but is not limited to such examples. Furthermore, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] Although embodiments of the present application have been described in detail based on the drawings, these are illustrative examples, and the present invention can be implemented in various other forms, including those described in the disclosure section of the invention, based on the knowledge of those skilled in the art.

[0126] Furthermore, the terms "section, module, unit" mentioned above can be replaced with "means" or "circuit," etc. For example, the acquisition unit can be replaced with acquisition means or acquisition circuit. [Explanation of symbols]

[0127] 10 Information provision device 11 Communications Department 12 Storage section 13 Control Unit 121 Analysis Model Information 122 User Information 123 Agent Model Information 131 Collection Department 132 Creation Department 133 Calculation Department 134 Update Department

Claims

1. A creation unit creates a model that calculates the degree of happiness according to the attributes, based on a combination of user attributes and information about factors that contribute to each user's happiness. An update unit updates the agent using reinforcement learning with the degree of happiness calculated using the aforementioned model as the reward, An information providing device characterized by having the following features.

2. The creation unit creates the model based on a combination of the attributes obtained by analyzing the text of the user's SNS posts and the information regarding factors that contribute to feeling happy. The information providing device according to feature 1.

3. The creation unit creates the model based on the weights assigned to each of the factors that contribute to feeling happiness: money, relationships, and social status. The information providing device according to claim 1 or 2, characterized by the above.

4. An information provision method performed by an information provision device, The process involves creating a model that calculates the degree of happiness according to the attributes, based on a combination of user attributes and information about factors that contribute to each user's happiness. The update process involves updating the agent using reinforcement learning, where the degree of happiness calculated using the aforementioned model is used as the reward. A method of providing information characterized by including the following.

5. The creation step involves creating a model that calculates the degree of happiness according to the attributes, based on a combination of user attributes and information about factors that contribute to each user's happiness. The update step involves updating the agent using reinforcement learning with the degree of happiness calculated using the aforementioned model as the reward, and An information provision program characterized by causing a computer to execute a command.

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