system

Abstract

AI understands human emotions and responds appropriately. [Solution] The system according to the present invention is characterized by comprising: a personality model generation unit that generates multiple personality models corresponding to each personality by performing learning based on the life logs of each person's personality; a long-term memory model generation unit that generates a long-term memory model that, when a person's life log is input, is trained to output a memory vector indicating the emotions that the person has towards the subject in the life log; and a learning unit that performs learning of the personality model using a memory vector based on the person's life log as input.

Description

Technical Field

[0001] The present invention relates to a system.

Background Art

[0002] Conventionally, technologies for making responses to humans using AI (Artificial Intelligence) have been provided. As an example of such a technology, a technology related to a chatbot that makes responses to user utterances has been provided.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the conventional technology, there are cases where it cannot be said that AI can make flexible responses according to human emotions and situations, and there is room for improvement.

[0005] The system according to the embodiment aims to enable AI to understand human emotions and show appropriate responses.

Means for Solving the Problems

[0006] The system according to the embodiment includes a personality model generation unit that generates a plurality of personality models corresponding to each personality by performing learning based on the life log for each human personality, a long-term memory model generation unit that generates a long-term memory model that is learned to output a memory vector indicating the emotion that the human has for the target in the life log when the life log of the human is input, and a learning unit that performs learning of the personality model with the memory vector based on the life log of the human as an input.

Effects of the Invention

[0007] The system according to this embodiment allows the AI ​​to understand human emotions and respond appropriately. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 shows an example of information processing according to the embodiment. [Figure 2] Figure 2 shows an example of the evolution of AI. [Figure 3] Figure 3 shows an example of the configuration of the information processing device 10 according to the embodiment. [Figure 4] Figure 4 shows an example of a life log database 31. [Figure 5] Figure 5 is a flowchart showing an example of the information processing procedure according to the embodiment. [Modes for carrying out the invention]

[0009] The following describes in detail, with reference to the drawings, the embodiments for implementing the system according to the present application (hereinafter referred to as "embodiments"). Note that these embodiments do not limit the system according to the present application. Furthermore, the same parts are denoted by the same reference numerals in each of the following embodiments, and redundant descriptions are omitted.

[0010] [1. Embodiments] The information processing realized by the system of this embodiment will be explained using Figure 1. Figure 1 is a diagram showing an example of information processing according to the embodiment. In Figure 1, it is assumed that the information processing according to the embodiment is realized by System 1, which is an example of the system according to the present application.

[0011] System 1 according to this embodiment includes an information processing device 10 and a user terminal. The information processing device 10 and the user terminal are connected to each other via a network N, either by wire or wireless, enabling communication between them. Network N is, for example, a Wide Area Network (WAN) such as the Internet. Note that System 1 may include multiple information processing devices 10 and multiple user terminals.

[0012] In System 1, the information processing device 10 stores the life logs of users (people) collected from each user terminal. The information processing device 10 then uses the users' life logs to train AI (various models).

[0013] In System 1, the user terminal is an information processing device used by the user. For example, the user terminal records the user's life log. Specifically, the user terminal records a life log that shows information such as words spoken by the user, actions (e.g., hand movements, arm movements), vital data (e.g., blinking, muscle movements, heart rate, brain waves), the surrounding environment, atmospheric pressure, information about the person the user is talking to (e.g., relationship with the user, actions, vital data, etc.), the content of the conversation, and information posted on a designated service (e.g., SNS (Social Networking Service)) at regular intervals (e.g., 1 ms (millisecond), 1 second, 1 minute, etc.).

[0014] The user terminal may be an IoT (Internet of Things) device such as a smartphone, or a wearable device such as a smartwatch. The user terminal records the user's life log using the device's camera, microphone, and various sensors (e.g., accelerometer, gyroscope, vital signs sensor, etc.). The user terminal may also collect and record the user's life log from a wearable device connected to the device.

[0015] Below, using Figure 1, we will explain an example of the information processing performed by System 1.

[0016] First, the information processing device 10 collects the user's life log and clusters the life log according to the user's personality (step S1). For example, the information processing device 10 estimates the user's personality based on the user's statements and posted information shown in the life log, and clusters the user's life log according to that user's personality.

[0017] To give a specific example, the information processing device 10 collects information about user U1 at each point in time from the user terminal used by user U1 as lifelog #1 when user U1 converses with user U2. For example, at time t1, the information processing device 10 collects information about user U2 with whom user U1 is conversing (e.g., statements and actions), user U1's actions, and user U1's vital data as lifelog #1. Furthermore, at time t2, which follows time t1, the information processing device 10 collects information about user U2 with whom user U1 is conversing, user U1's actions, user U1's vital data, and user U1's statements as lifelog #1.

[0018] Similarly, the information processing device 10 collects information about user U3 at each point in time (for example, time points t3, t4, ...) from the user terminal used by user U3, as a life log #2 of when user U3 converses with user U4.

[0019] Then, the information processing device 10 clusters the collected life logs. For example, when a person is invited to lunch by a friend, the information processing device 10 values the friendship and estimates which of the multiple personalities, such as personality A that gives an answer (utterance) like "I really want to go", personality B that values social status and gives an answer like "It's a bit of a hassle", and personality C that values money and gives an answer like "Financially it's tough", the user belongs to (in other words, estimates what kind of personality the user had in the collected life logs). Then, the information processing device 10 clusters the user's life logs according to the personality to which the user belongs.

[0020] In the following description, the life logs clustered into personality A may be described as "personality A life logs", the life logs clustered into personality B may be described as "personality B life logs", and the life logs clustered into personality C may be described as "personality C life logs".

[0021] Subsequently, the information processing device 10 generates various models based on the collected life logs (step S2). For example, the information processing device 10 generates a plurality of personality models corresponding to each personality based on the life logs clustered for each personality (step S2-1). To give a specific example, the information processing device 10 performs learning using the personality A life logs, and thus generates a personality model (hereinafter, may be described as "personality A personality model") that has been learned to output an answer of the user with personality A to the life log when any life log is input. Similarly, the information processing device 10 generates a personality B personality model that has been learned using the personality B life logs, a personality C personality model that has been learned using the personality C life logs, and so on.

[0022] To give a more concrete example, suppose that in the example in Figure 1, lifelog #1 is clustered to personality A. In other words, suppose that in lifelog #1, the personality selected by user U1 was personality A. In such a case, when the information processing device 10 receives an action (input) performed on user U1 as input, it trains the personality A personality model so that it outputs user U1's reaction (output) to that action. For example, when the information processing device 10 receives lifelog #1 at time t1 (for example, a statement by user U2) as input, it trains the personality A personality model so that it outputs lifelog #1 at time t2 (for example, a statement by user U1).

[0023] Furthermore, the extraction of inputs and outputs from the life log can be performed using any method; for example, it may be done using a rule-based approach.

[0024] Furthermore, when a life log is input, the information processing device 10 generates a long-term memory model that has been trained to output a memory vector indicating the emotion that user has towards the target of the user's action in the life log (step S2-2). For example, when life log #1 is input, the information processing device 10 trains its long-term memory model to output a memory vector #1 indicating the emotion that user U1 has towards user U2 with whom user U1 is conversing (for example, an emotion estimated based on life log #1). Also, when life log #2 is input, the information processing device 10 trains its long-term memory model to output a memory vector #2 indicating the emotion that user U3 has towards user U4 with whom user U3 is conversing (for example, an emotion estimated based on life log #2). Here, the information processing device 10 trains its long-term memory model so that the more the emotion that user U1 has towards user U2 differs from the emotion that user U3 has towards user U4, the more different memory vectors #1 and #2 are.

[0025] Furthermore, the information processing device 10 generates a self-will model that has been trained to output an emotion vector indicating the user's emotions in a life log when it receives a life log and a memory vector based on that life log as input (step S2-3). For example, the information processing device 10 trains its self-will model to output an emotion vector #1 indicating the emotions that user U1 has in life log #1 (for example, emotions estimated based on life log #1) when it receives life log #1 and memory vector #1 as input. The information processing device 10 also trains its self-will model to output an emotion vector #2 indicating the emotions that user U3 has in life log #2 (for example, emotions estimated based on life log #2) when it receives life log #2 and memory vector #2 as input. Here, the information processing device 10 trains its self-will model so that the more the emotions that user U1 has in life log #1 differ from the emotions that user U3 has in life log #2, the more different emotion vectors #1 and #2 are.

[0026] Furthermore, the information processing device 10 generates an estimation model that, when inputting a life log and a user's action on the life log (e.g., a reaction), outputs an estimation vector representing the emotion estimated by the user as the emotion of the target of the action (step S2-4). For example, when inputting life log #1 and user U1's action on life log #1 (e.g., a statement to user U2), the information processing device 10 learns the estimation model to output estimation vector #1 representing the emotion estimated by user U1 (e.g., an emotion estimated based on life log #1) as the emotion of user U2, the target of the action. Also, when inputting life log #2 and user U3's action on life log #2 (e.g., a statement to user U4), the information processing device 10 learns the estimation model to output estimation vector #2 representing the emotion estimated by user U3 (e.g., an emotion estimated based on life log #2) as the emotion of user U4, the target of the action. Here, the information processing device 10 learns an estimation model such that the more the emotion estimated by user U1 in lifelog #1 differs from the emotion estimated by user U3 in lifelog #2, the more the estimation vector #1 and estimation vector #2 differ.

[0027] Next, the information processing device 10 takes the memory vector, emotion vector, and estimation vector as input and trains the personality model (step S3). For example, when the information processing device 10 inputs life log #1 (e.g., an action taken against user U1), memory vector #1, emotion vector #1, and estimation vector #1 into the personality A personality model, it trains the personality A personality model so that it outputs user U1's reaction to life log #1. To give a specific example, when the information processing device 10 inputs life log #1 (e.g., a statement made by user U2), memory vector #1, emotion vector #1, and estimation vector #1 at time t1 into the personality A personality model, it trains the personality A personality model so that it outputs life log #1 at time t2 (e.g., a statement made by user U1).

[0028] By generating and learning models as described above, AI can understand human emotions and evolve further. Here, we will explain the stages of AI evolution using Figure 2. Figure 2 is a diagram illustrating an example of AI evolution.

[0029] As shown in Figure 2, AI evolves through stages 1 ("Natural conversation with humans (chatbot)") to 5 ("Performing tasks for the entire organization"). Since stages 1 through 5 are the same as conventionally conceived stages of AI evolution, a detailed explanation will be omitted.

[0030] Here, humans have various personalities. And humans, for example, choose a personality and speak according to their emotions in the situation (life log) at that time. Focusing on this point, the information processing device 10 according to the embodiment gives the AI ​​a personality model corresponding to each of the multiple personalities and has it select the output from the personality model corresponding to the personality that matches the life log at that time (for example, the AI's emotions). Furthermore, the information processing device 10 according to the embodiment has the AI ​​learn the correlation between the answer to the subject (for example, the person being talked to) and the emotions (memory vector) it has towards the subject in various life logs. As a result, the information processing device 10 according to the embodiment enables the AI ​​to understand emotions and select a personality, and the AI ​​to have emotions (in other words, long-term memory) towards the subject of the answer output and make answers based on that. That is, as shown in step 6 of Figure 2, the information processing device 10 according to the embodiment can evolve the AI ​​to be able to understand emotions and have long-term memory.

[0031] Furthermore, the information processing device 10 according to this embodiment can learn the user's emotions (emotion vector) in various life logs, taking into account the emotions (memory vector) the user has towards the subject, and output a response. As a result, the information processing device 10 according to this embodiment can take into account the emotions the AI ​​has towards the subject, and the AI ​​can have emotions (self-will) such as feeling embarrassed or showing off, and output a response. In other words, as shown in step 7 of Figure 2, the information processing device 10 according to this embodiment can evolve the AI ​​so that it can have its own will. To put it another way, a superintelligence AI can evolve into a superintelligence that can have self-awareness by being able to retain long-term memories and understand human emotions and thoughts.

[0032] Furthermore, the information processing device 10 according to this embodiment allows the AI ​​to learn the emotions (estimated vectors) estimated by the user as the emotions possessed by the subject in various life logs, and output a response. As a result, the information processing device 10 according to this embodiment allows the AI ​​to understand and harmonize with the emotions of the subject before outputting a response. In other words, as shown in stage 8 of Figure 2, the information processing device 10 according to this embodiment allows the AI ​​to evolve into a harmonious superintelligence.

[0033] Based on the above, the information processing device 10 according to this embodiment allows the AI ​​to understand human emotions and respond appropriately.

[0034] [2. Configuration of the Information Processing Device] Next, the configuration of the information processing device 10 will be described using Figure 3. Figure 3 is a diagram showing an example of the configuration of the information processing device 10 according to the embodiment. As shown in Figure 3, the information processing device 10 has a communication unit 20, a storage unit 30, and a control unit 40.

[0035] (Regarding Communications Section 20) The communication unit 20 is implemented, for example, by a NIC (Network Interface Card). The communication unit 20 is connected to the network N by wire or wireless connection and transmits and receives information with user terminals, etc.

[0036] (Regarding memory unit 30) The storage unit 30 is implemented by, for example, semiconductor memory elements such as RAM (Random Access Memory) and flash memory, or by storage devices such as hard disks and optical discs. As shown in Figure 3, the storage unit 30 has a life log database 31 and a model database 32.

[0037] (Regarding Lifelog Database 31) The life log database 31 stores various types of information related to life logs. Here, an example of the information stored in the life log database 31 is explained using Figure 4. Figure 4 is a diagram showing an example of the life log database 31. In the example in Figure 4, the life log database 31 has items such as "life log ID," "user information," "target information," "conversation information," "action information," "physical information," and "environmental information."

[0038] "Lifelog ID" indicates identification information used to identify the lifelog. "User Information" indicates information about the user as shown in the lifelog. "Target Information" indicates information about the subject (person, etc.) with which the user is having a conversation, as shown in the lifelog. "Conversation Information" indicates the content of the user's conversation as shown in the lifelog (for example, the content of the conversation with the subject shown in "Target Information"). "Action Information" indicates information about actions as shown in the lifelog. "Physical Information" indicates information about the body as shown in the lifelog (for example, vital data). "Environmental Information" indicates the environment around the user as shown in the lifelog.

[0039] In other words, Figure 4 shows an example where the user information related to the user indicated by the life log identified by the life log ID "LID#1" is "User Information #1", the target information related to the object with which the user is having a conversation is "Target Information #1", the user's conversation information is "Conversation Information #1", the user's action information is "Action Information #1", the user's physical information is "Physical Information #1", and so on, with the user's environmental information being "Environmental Information #1".

[0040] (Regarding Model Database 32) The model database 32 stores personality models that have been trained to output responses corresponding to a predetermined personality when a life log is input. The model database 32 also stores long-term memory models that have been trained to output memory vectors indicating the emotions a person has towards the subject in a life log when a person's life log is input. The model database 32 also stores self-will models that have been trained to output emotion vectors indicating the emotions of a person in a life log when a person's life log and a memory vector based on that life log are input. The model database 32 also stores estimation models that have been trained to output estimation vectors indicating the emotions estimated by a person as the emotions of the subject of an action, when a person's life log and the action taken by that person in relation to that life log are input.

[0041] (Regarding the control unit 40) The control unit 40 is a controller, and is realized, for example, by a CPU (Central Processing Unit) or MPU (Micro Processing Unit) executing various programs stored in the memory device inside the information processing device 10 using RAM as the working area. Alternatively, the control unit 40 is a controller, and is realized, for example, by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array). As shown in Figure 3, the control unit 40 according to this embodiment has a personality model generation unit 41, a long-term memory model generation unit 42, a self-will model generation unit 43, an estimation model generation unit 44, and a learning unit 45, and realizes or executes the information processing functions and operations described below.

[0042] (Regarding the personality model generation unit 41) The personality model generation unit 41 generates multiple personality models corresponding to each personality by learning based on the life logs of each individual personality. For example, in the example in Figure 1, the personality model generation unit 41 refers to the memory unit 30 (e.g., the life log database 31) and generates multiple personality models corresponding to each personality based on the life logs clustered for each personality, and stores them in the memory unit 30 (e.g., the model database 32).

[0043] (Regarding the long-term memory model generation unit 42) The long-term memory model generation unit 42 generates a long-term memory model that, when a human life log is input, outputs a memory vector indicating the emotions the person has towards the object in the life log. For example, in the example in Figure 1, the long-term memory model generation unit 42 refers to the memory unit 30 (e.g., the life log database 31) and, when a life log is input, generates a long-term memory model that has been trained to output a memory vector indicating the emotions the user has towards the object of the user's action in the life log, and stores it in the memory unit 30 (e.g., the model database 32).

[0044] Furthermore, the long-term memory model generation unit 42 may train the long-term memory model so that it outputs different memory vectors as the emotions that humans have towards the objects in their life logs differ. For example, in the example in Figure 1, the long-term memory model generation unit 42 trains the long-term memory model so that memory vector #1 and memory vector #2 differ as the emotions that user U1 has towards user U2 differ from the emotions that user U3 has towards user U4.

[0045] (Regarding the self-will model generation unit 43) The self-will model generation unit 43 generates a self-will model that, when given a human life log and a memory vector based on that life log as input, outputs an emotion vector indicating the human's emotions in that life log. For example, in the example in Figure 1, the self-will model generation unit 43 refers to the memory unit 30 (e.g., the life log database 31) and, when given a life log and a memory vector based on that life log as input, generates a self-will model that, when given, outputs an emotion vector indicating the user's emotions in that life log, and stores it in the memory unit 30 (e.g., the model database 32).

[0046] Furthermore, the self-will model generation unit 43 may train the self-will model so that the more different the emotions, the more different the emotion vectors it outputs. For example, in the example in Figure 1, the self-will model generation unit 43 trains the self-will model so that the more different the emotion that user U1 has in life log #1 is from the emotion that user U3 has in life log #2, the more different emotion vectors #1 and #2 are.

[0047] (Regarding the estimation model generation unit 44) The estimation model generation unit 44 generates an estimation model that, when inputting a person's life log and the person's actions toward the life log, outputs an estimation vector representing the emotion estimated by the person as the emotion of the target of the action. For example, in the example in Figure 1, the estimation model generation unit 44 refers to the memory unit 30 (for example, the life log database 31) and, when inputting a life log and the user's actions toward the life log, generates an estimation model that, when inputting the life log and the user's actions toward the life log, outputs an estimation vector representing the emotion estimated by the user as the emotion of the target of the action.

[0048] Furthermore, the estimation model generation unit 44 may train the estimation model so that it outputs different estimation vectors the more different the emotions estimated by humans are. For example, in the example in Figure 1, the estimation model generation unit 44 trains the long-term memory model so that the more different the emotion estimated by user U1 in lifelog #1 is from the emotion estimated by user U3 in lifelog #2, the more different estimation vector #1 and estimation vector #2 are.

[0049] (Regarding Learning Section 45) The learning unit 45 takes memory vectors based on human life logs as input and trains the personality model. For example, in the example in Figure 1, the learning unit 45 refers to the memory unit 30 (for example, the life log database 31 or the model database 32) and trains the personality A personality model so that when life log #1 or memory vector #1 is input to the personality A personality model, it outputs the user U1's reaction to life log #1.

[0050] Furthermore, the learning unit 45 may also train the personality model using an emotion vector based on a human life log as input. For example, in the example in Figure 1, when life log #1 and emotion vector #1 are input to the personality A personality model, the learning unit 45 trains the personality A personality model so that it outputs the user U1's reaction to life log #1.

[0051] Furthermore, the learning unit 45 may also train the personality model using an estimated vector based on a human life log as input. For example, in the example in Figure 1, when life log #1 and estimated vector #1 are input to the personality A personality model, the learning unit 45 trains the personality A personality model so that it outputs the user U1's reaction to life log #1.

[0052] [3. Information Processing Flow] The information processing procedure of the information processing device 10 according to the embodiment will be explained using Figure 5. Figure 5 is a flowchart showing an example of the information processing procedure according to the embodiment.

[0053] As shown in Figure 5, the information processing device 10 generates multiple personality models corresponding to each personality by learning the life logs of each individual personality (step S101). Next, the information processing device 10 generates a long-term memory model that, when a human life log is input, is trained to output a memory vector indicating the emotions the person has towards the subject in the life log (step S102). Subsequently, the information processing device 10 uses the memory vector based on the human life log as input to train the personality model (step S103) and then terminates the process.

[0054] [4. Variations] The above-described embodiment is merely an example, and various modifications and applications are possible.

[0055] [4-1. Other Embodiments] The system relating to this application may be implemented as follows.

[0056] <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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] <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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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).

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] <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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] <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.

[0099] [4-2. Regarding the processing method] Of the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, and conversely, all or part of the processes described as being performed manually can be performed automatically by known methods. In addition, the processing procedures, specific names, and information including various data and parameters shown in the above text and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each figure is not limited to the information shown.

[0100] Furthermore, the components of each illustrated device are functionally conceptual and do not necessarily need to be physically configured as shown. In other words, the specific forms of distribution and integration of each device are not limited to those shown, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads and usage conditions.

[0101] Furthermore, the embodiments described above can be combined as appropriate, provided that the processing content is not contradictory. [5. Other] Although some embodiments of the present invention have been described in detail above with reference to the drawings, these are illustrative examples, and the present invention can be implemented in various other forms with modifications and improvements based on the knowledge of those skilled in the art, starting with the embodiments described in the disclosure section of the invention.

[0102] Furthermore, the configuration of the aforementioned information processing device 10 can be flexibly changed, for example, by calling external platforms, etc., via APIs (Application Programming Interfaces) or network computing, depending on the function.

[0103] Furthermore, the term "part" in the claims can be replaced with "means," "circuit," etc. For example, the personality model generation unit can be replaced with a personality model generation means or a personality model generation circuit. [Explanation of Symbols]

[0104] 1 System 10 Information Processing Devices 20 Communications Department 30 Storage section 31 Life Log Database 32 Model Databases 40 Control Unit 41 Personality Model Generation Unit 42 Long-term memory model generation unit 43 Self-will model generation unit 44 Estimation Model Generation Unit 45. Learning Department

Claims

1. A personality model generation unit generates multiple personality models corresponding to each personality by learning based on the life logs of each individual personality, A long-term memory model generation unit generates a long-term memory model that, when a human life log is input, is trained to output a memory vector indicating the emotions that the person has towards the subject in the life log. A learning unit that learns the personality model using memory vectors based on a human life log as input. A system characterized by having the following features.

2. The long-term memory model generation unit, The long-term memory model is trained so that it outputs different memory vectors depending on the different emotions a person has towards the subject in their life log. The system according to feature 1.

3. Self-will model generation unit: When a person's life log and a memory vector based on that life log are input, a self-will model is generated that has been trained to output an emotion vector representing the emotions of the person in that life log. It further possesses, The aforementioned learning unit, Furthermore, the personality model is trained using emotional vectors based on human life logs as input. The system according to feature 1.

4. The self-will model generation unit, The self-will model is trained so that different emotions output different emotional vectors. The system according to claim 3.

5. An estimation model generation unit generates an estimation model that, when given a person's life log and the person's actions in response to that life log as input, outputs an estimation vector representing the emotion estimated by the person as the emotion of the target of that action. It further possesses, The aforementioned learning unit, Furthermore, the personality model is trained using estimated vectors based on human life logs as input. The system according to feature 1.

6. The estimation model generation unit, The estimation model is trained so that it outputs different estimation vectors the more different the emotions estimated by humans are. The system according to claim 5, characterized in that it is the same as described in claim 5.

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