Information processing device, information processing program, and information processing method

Abstract

To change the mindset of users who are in a negative state. [Solution] The server includes an estimation unit (113) that estimates the user's mental state based on predetermined information, an extraction unit (114) that, if the mental state is estimated to be negative, extracts the user's past positive data from the user's life log data stored in storage (13), and an output unit (115) that outputs the past positive data to a predetermined device.

Description

Technical Field

[0001] The present disclosure relates to an information processing apparatus, an information processing program, and an information processing method.

Background Art

[0002] Conventionally, a technique for changing information provided to a user according to the mental state of the user has been known (for example, Patent Documents 1 and 2). The invention described in Patent Document 1 is an invention related to welcome control of a vehicle that entertains a user. When the mental state of the user, such as anxiety, sadness, or apathy, is not good, an image related to healing is displayed on the road surface, window glass, etc. to relieve the negative emotions of the user.

[0003] The invention described in Patent Document 2 attempts to improve the state of the user by estimating whether the user is positive or negative and providing a positive interpretation of the situation in which the user is placed when the user is estimated to be negative.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0005] The techniques described in Patent Documents 1 and 2 can improve the state of the user, but it is only a temporary improvement. Therefore, with the techniques described in Patent Documents 1 and 2, it is not possible to have a mindset of being positive and wanting to grow oneself more, for example, improving one's abilities through continuous efforts and learning.

[0006] One aspect of this disclosure aims to change the mindset of users in a negative state. [Means for solving the problem]

[0007] To solve the above problems, an information processing device according to one aspect of the present disclosure includes: an estimation unit that estimates the mental state of a user based on predetermined information; an extraction unit that, if the estimation unit estimates that the mental state is negative, extracts past positive data of the user from the user's life log data stored in a storage unit; and an output unit that outputs the past positive data to a predetermined device.

[0008] To solve the above problems, an information processing method according to one aspect of the present disclosure is an information processing method used in an information processing device, comprising: an estimation step of estimating the mental state of a user based on predetermined information; an extraction step of extracting past positive data of the user from the user's life log data stored in a memory unit if the mental state is estimated to be negative; and an output step of outputting the past positive data to a predetermined device.

[0009] Each aspect of the information processing device described herein may be implemented by a computer. In this case, an information processing program that enables the computer to implement the information processing device by operating the computer as each part (software element) of the information processing device, and a computer-readable recording medium on which the program is recorded, also fall within the scope of this disclosure. [Effects of the Invention]

[0010] According to one aspect of this disclosure, it is possible to change the mindset of users who are in a negative state. [Brief explanation of the drawing]

[0011] [Figure 1] This is a diagram illustrating the outline of the information processing system according to Embodiment 1 of this disclosure. [Figure 2] This is a block diagram showing an example of the server hardware configuration according to Embodiment 1 of this disclosure. [Figure 3] This is a block diagram showing an example of the functions of the processor according to Embodiment 1 of this disclosure. [Figure 4] This flowchart shows an example of the processing performed by the server according to Embodiment 1 of this disclosure. [Figure 5] This block diagram shows an example of the functions of the processor according to Embodiment 2 of this disclosure. [Figure 6] This block diagram shows an example of the functions of the processor according to Embodiment 3 of this disclosure. [Figure 7] This is a block diagram showing an example of the functions of the processor according to Embodiment 4 of this disclosure. [Figure 8] This flowchart shows an example of the processing performed by the server according to Embodiment 5 of this disclosure. [Modes for carrying out the invention]

[0012] [Embodiment 1] Embodiment 1 of this disclosure will be described in detail below with reference to the drawings. In the drawings, identical or substantially identical components are denoted by the same reference numerals and will not be repeated in the description.

[0013] The terms "First," "Second," "Third," etc., used in this disclosure are used to distinguish one component from another, and are not intended to limit the number, order, or priority of such components. For example, the presence of "First Element" and "Second Element" does not mean that only two elements, "First Element" and "Second Element," are adopted, nor does it mean that "First Element" must precede "Second Element."

[0014] (Overview of Information Processing System 100) FIG. 1 is a diagram for explaining the outline of the information processing system 100. In the information processing system 100 shown in FIG. 1, the server 10 deploys a service that provides predetermined information (positive data 30 described later) to the user 20.

[0015] The installation location of the server 10 is not particularly limited. For example, the server 10 may be installed in the management center of an operator that deploys a service for providing positive data 30. Also, the server 10 may be installed within Japan or outside Japan. Further, the server 10 may be a physical server or a cloud server.

[0016] The server 10 has two main functions. The first function is to acquire various information about the user 20. The second function is to process the acquired information. First, the first function will be described.

[0017] (First function) The information about the user 20 includes, for example, data related to the user 20's life, actions, experiences, etc. Such data is hereinafter referred to as "lifestyle log data". The server 10 periodically acquires the user 20's lifestyle log data from the smartphone 21 possessed by the user 20, various sensors with unrestricted installation locations, various devices with unrestricted installation locations, etc. via the communication network NW. The communication network NW is not particularly limited, and for example, the Internet may be used.

[0018] As an example of the life log data transmitted from the smartphone 21 to the server 10, there are image data (including still images and videos) captured by the camera of the smartphone 21, call history, posted messages to SNS (Social Networking Service), and the like. Further, the life log data transmitted from the smartphone 21 to the server 10 may include the schedule information of the user 20. The schedule information is, for example, information on the schedule related to the work of the user 20 and the schedule related to the private life. Further, the life log data transmitted from the smartphone 21 to the server 10 may include acceleration data. Such acceleration data can be used for analyzing the number of steps, walking speed, walking rhythm (walking cycle, tempo) of the user 20, body movement during sleep (turning over, small movements), and the like. By analyzing the body movement during sleep, the sleep time, the quality of sleep, and the like can be obtained.

[0019] Note that since the image data captured by the camera of the smartphone 21 includes the shooting date and time, location information, etc., by analyzing the image data, stores and facilities such as restaurants visited by the user 20, the visit time, the stay time, the ordered menu information, and the like can be obtained. Therefore, the life log data may include stores and facilities such as restaurants visited by the user 20, the visit time, the stay time, the ordered menu information, and the like.

[0020] Further, when the user 20 wears a smartwatch, vital information such as the heart rate, blood pressure, body temperature, etc. of the user 20 may be transmitted from the smartwatch to the server 10. Such vital information is also an example of the life log data.

[0021] The "various sensors whose installation locations are not limited" may include, for example, surveillance cameras, surveillance microphones, and the like. The server 10 may acquire image data and audio data related to the user 20 from surveillance cameras, surveillance microphones, and the like. As an acquisition method, a well-known face recognition system and voice recognition system may be adopted.

[0022] "Various devices whose installation location is not limited" may include, for example, an external server. Server 10 may acquire weather information, traffic information, etc., from the "external server". Although such weather information, traffic information, etc., is not data directly related to user 20, it can be used to estimate whether user 20's mental state is negative or not, as will be described later, and can therefore be considered a type of "life log data".

[0023] Furthermore, if user 20 is wearing an HMD (Head Mounted Display), server 10 may acquire image data of the direction of user 20's gaze and audio data collected by a microphone positioned near user 20's ear. This allows server 10 to acquire real-time life log data of user 20's experience.

[0024] Furthermore, the life log data acquired by server 10 may be limited to only the data permitted by user 20.

[0025] (Second function) Next, the second function of server 10 will be described. Based on user 20's life log data, server 10 estimates whether user 20's mental state is negative or not. If the estimation results in a negative mental state, server 10 provides user 20 with positive data 30 to alleviate the stress user 20 feels and change their mindset. In this embodiment, the positive data 30 is data showing user 20's past successes, happy memories, and enjoyable memories. In Figure 1, the positive data 30 is shown as images (still images or moving images). Such images represent user 20's own special successes. By recalling their own special successes as if watching a life review, user 20 can dispel negative emotions and switch to positive thinking. In other words, such images can make them feel more positive and enable them to adopt a mindset of wanting to grow more, for example, by continuously making efforts and learning to improve their abilities. This provides a sense of mental liberation and mood stability similar to that experienced during therapy with a professional therapist, giving the user a feeling of rediscovering their inner self and being reborn. Note that the three positive data 30 shown in Figure 1 may be different videos or the same video. Also, while Figure 1 shows three, or multiple, positive data simultaneously provided to the user 20, the user 20 may be provided with only one positive data.

[0026] (Definition of a successful experience) In this embodiment, "successful experiences" may include the following: success in starting a business, completing a project at work, success in negotiations and business deals at work, promotion or advancement at a company, obtaining qualifications, success in dieting, success in creative activities (publishing a book, holding a solo exhibition, etc.), marriage and childbirth, winning awards in contests and competitions, acquiring new skills, volunteer activities, success in raising children, recovery from illness or injury, achieving goals in sports or hobbies, getting a dream job (politician, professional baseball player, etc.), buying a home, etc.

[0027] Happy and enjoyable memories may overlap with some or all of successful experiences.

[0028] Figure 1 shows a scenario in which, after the user 20 returns home, an image (positive data 30) is displayed on a display installed in the user's home. However, the medium on which the image is displayed and the location where the image is provided are not limited to this. The medium on which the image is displayed may be a smartphone 21, a tablet device, a PC (Personal Computer), a television, etc. Alternatively, the server 10 may project the image onto a dedicated screen, wall, etc., using a projector. Such control can be realized, for example, by configuring the projector as an IoT appliance (Internet of Things) and connecting it to the server 10 in a communicative manner.

[0029] The location where the video is provided may be a dedicated private room in the city. That is, a dedicated private room may be set up in the city, and the video may be provided to the user 20 in that private room. As another example, the server 10 may provide the video when the user 20 is driving the vehicle. For example, the server 10 may display the video on a car navigation system, or it may project the video onto the windshield using a HUD (Head-Up Display). Such control can be realized, for example, by configuring the vehicle as a connected car and enabling communication with the server 10. The vehicle may also be equipped with an autonomous driving function. The server 10 may provide the video to the user 20 only when the vehicle is driving autonomously.

[0030] Furthermore, the positive data 30 provided to user 20 is not limited to video (still images or videos). Audio data may also be provided as positive data 30. Even with audio data, user 20 can recall their own special success experience through hearing. Of course, both video and audio may be provided.

[0031] Furthermore, when providing video to user 20, server 10 may control a vibration device (e.g., a reclining chair) to provide user 20 with vibrations that user 20 finds comfortable. By providing user 20 with both video and vibration in this way, a more effective mindset can be achieved. Such control can be realized, for example, by configuring the reclining chair as an IoT appliance and connecting it to server 10 in a communicative manner.

[0032] Furthermore, when providing video to user 20, server 10 may control an aroma diffuser (e.g., an aroma diffuser) to provide user 20 with a scent that user 20 finds pleasant. By providing user 20 with both video and scent in this way, a more effective mindset can be achieved. Such control can be realized, for example, by configuring the aroma diffuser as an IoT appliance and connecting it to server 10 in a communicative manner.

[0033] Furthermore, when providing video to user 20, server 10 may control lighting fixtures (e.g., ceiling lights) to provide user 20 with light and color that user 20 finds comfortable. By providing user 20 with video, light, and color in this way, a more effective mindset can be achieved. Such control can be realized, for example, by configuring the ceiling light as an IoT appliance and connecting it to server 10 in a communicative manner.

[0034] Server 10 may provide users 20 with a combination of video, audio, vibration, scent, light, and color as desired. However, either video or audio must be included in the provided data.

[0035] As described above, the information processing system 100 according to this embodiment is a system that provides positive data 30 to relieve the stress felt by the user 20 and to improve their mindset. Such a service may be available through a "subscription model" where a monthly or annual fee is paid, or it may be available through a "purchase model" where it can be used permanently after purchase.

[0036] (Hardware configuration of Server 10) Next, an example of the hardware configuration of server 10 will be described with reference to Figure 2. Figure 2 is a block diagram showing an example of the hardware configuration of server 10.

[0037] As shown in Figure 2, the server 10 comprises a processor 11, memory 12, storage 13, and a communication interface 14. Each of these components is connected to communicate with the others via a bus 15.

[0038] The processor 11 reads a predetermined program from the storage 13, loads it into the memory 12, and executes processing according to the program. Such a program may be one that causes the computer to execute at least a part of the functions described below. The program may also function in combination with other programs already stored in the storage 13, or in combination with other programs implemented in other devices. Furthermore, the program may be delivered to the server 10 via wireless communication. In this case, the processor 11 loads the delivered program into the memory 12 and executes processing. In other words, the program does not necessarily have to be stored in the storage 13.

[0039] The processor 11 is not particularly limited, but can be implemented as, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an MPU (Micro Processor Unit), or an FPGA (Field-Programmable Gate Array). Although Figure 2 shows one processor, it is not limited to this, and multiple processors may be provided.

[0040] Memory 12 is a computer-readable recording medium and consists of at least one of the following: RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Registered Trademark) (Electrically Erasable Programmable ROM), etc. Such memory 12 may also be called registers, cache, main memory, etc.

[0041] Storage 13 is a computer-readable recording medium. The lifelog data 131 described above is stored in storage 13. Such storage 13 is composed of, for example, an HDD (Hard Disk Drive), an SSD (Solid State Drive), etc. Storage 13 may also be a portable recording medium such as a flexible disk, optical disk, compact disk, or Blu-ray® disc. Storage 13 is also sometimes called an auxiliary storage device.

[0042] Communication I / F14 is an interface for communicating with any external device. Communication I / F14 is implemented, for example, as hardware such as a network adapter, various communication software, or a combination thereof, and is configured to enable wireless communication via a communication network NW.

[0043] (Examples of processor 11 functions) Next, an example of the functions of the processor 11 of the server 10 will be described with reference to Figure 3. Figure 3 is a block diagram showing an example of the functions of the processor 11. The processor 11 reads a program from the storage 13 and executes the program using the memory 12 as a working area, thereby functioning as an acquisition unit 111, a classification unit 112, an estimation unit 113, an extraction unit 114, and an output unit 115. What is described as "~unit" here may be replaced with "~circuit," "~device," or "~equipment," or with "~step," "~procedure," or "~process." In other words, what is described as "~unit" may be implemented by a program stored in the storage 13 as described above, or by hardware such as elements, devices, boards, and wiring only, or by a combination of software and hardware. These functions will be described below.

[0044] The acquisition unit 111 acquires user 20's life log data via a communication network NW from the user 20's smartphone 21, smartwatch or HMD worn by the user 20, and surveillance cameras and microphones that are not limited to a specific location. The acquisition unit 111 outputs the acquired life log data to the classification unit 112.

[0045] The classification unit 112 classifies the life log data. The classification method is not particularly limited, but for example, the classification unit 112 may classify the life log data using a machine learning model. Through such classification, the life log data is divided into two types: estimation data for estimating the mental state of the user 20 and positive data 30 (see Figure 1) for relieving the stress of the user 20. Note that the classification items are not limited to these two types and may be further subdivided. The classified life log data is labeled as appropriate and stored in the storage 13.

[0046] In addition to machine learning models, rule-based classification, which uses pre-defined rules, may also be employed as a classification method.

[0047] The estimation unit 113 obtains estimation data (lifelog data) for estimating the mental state of user 20 by referring to the storage 13, and uses the estimation data to estimate the mental state of user 20. Specifically, the estimation unit 113 estimates whether user 20's mental state is positive or negative based on the estimation data.

[0048] The estimation method is not particularly limited, but for example, the estimation unit 113 may estimate whether the user 20's mental state is positive or negative based on heart rate, heart rate variability, sleep duration, weight, facial expression, etc.

[0049] Regarding heart rate, it is known that when stress levels rise, the sympathetic nervous system becomes more active, and heart rate tends to increase. Therefore, the estimation unit 113 may estimate that user 20's mental state is negative if the heart rate is higher than normal. Regarding heart rate variability, an index called LF / HF (Low Frequency, High Frequency), which represents the overall balance between the sympathetic and parasympathetic nervous systems, is known as a stress index based on heart rate variability. When LF / HF is high, the sympathetic nervous system is dominant, and it is said that this indicates a high level of stress. Therefore, the estimation unit 113 may estimate that user 20's mental state is negative if LF / HF is higher than normal.

[0050] Regarding sleep duration, high stress levels can lead to shorter sleep durations. As is well known, chronic stress can result in insomnia or intermittent sleep. Therefore, the estimation unit 113 may estimate that user 20's mental state is negative if their sleep duration is shorter than usual. Regarding weight, stress can cause appetite to increase or decrease. Therefore, the estimation unit 113 may estimate that user 20's mental state is negative if their weight has increased or decreased significantly in a short period of time.

[0051] Regarding facial expressions, it is said that if many expressions indicating negative emotions, such as frown lines or drooping corners of the mouth, appear, the user is under stress. Therefore, if the estimation unit 113 analyzes the facial expressions and finds many expressions indicating negative emotions, it may estimate that the user 20's mental state is negative.

[0052] Furthermore, the estimation unit 113 may estimate whether the user 20's mental state is positive or negative based on keywords included in posts on social media, sent emails, schedules, etc.

[0053] The estimation unit 113 may estimate the mental state of the user 20 using the above-mentioned indicators individually, or it may estimate the mental state of the user 20 from a comprehensive perspective by arbitrarily combining multiple indicators.

[0054] The estimation unit 113 outputs the estimation result to the extraction unit 114.

[0055] If the estimation unit 113 estimates that the user 20's mental state is negative, the extraction unit 114 refers to the storage 13 and extracts positive data 30 from the life log data that will help the user 20 alleviate stress and change their mindset. The extraction unit 114 outputs the extracted positive data 30 to the output unit 115.

[0056] The output unit 115 provides positive data 30 (video in Figure 1) to the user 20 by outputting positive data 30 to a predetermined device. This allows the user 20 to recall their own special success experiences as if watching a kaleidoscope, thereby dispelling negative emotions and switching to positive thinking. In other words, such video can make one feel more positive and foster a mindset of striving for further personal growth, such as improving abilities through continuous effort and learning. The "predetermined device" here may be a display, PC, television, projector, etc., installed in the user 20's home, as described above, or it may be a smartphone 21, tablet device, etc. Also, as described above, the "predetermined device" may be a car navigation system, HUD, etc.

[0057] (Process flow) Next, with reference to Figure 4, the flow of processing performed by server 10 will be described. Figure 4 is a flowchart showing an example of processing performed by server 10. This flowchart may be executed repeatedly at a predetermined interval.

[0058] In step S101, the server 10 acquires user 20's life log data via the communication network NW from the user 20's smartphone 21, smartwatch or HMD worn by the user 20, and surveillance cameras or microphones that are not limited to a specific location. As mentioned above, the server 10 may be configured to acquire only the data permitted by the user 20.

[0059] In step S102, the server 10 classifies the lifelog data acquired in step S101. As described above, the server 10 may classify the lifelog data using a machine learning model, or it may classify the lifelog data according to pre-configured rules.

[0060] In step S103, the server 10 obtains estimation data for estimating the mental state of user 20 by referring to the storage 13, and uses the estimation data to estimate the mental state of user 20. As mentioned above, estimation methods include using heart rate, heart rate variability, sleep duration, weight, facial expressions, content of SNS posts, sent emails, schedule, etc. These indicators may be used individually or in any combination.

[0061] If user 20's mental state is estimated to be negative (YES in step S104), the process proceeds to step S105. On the other hand, if user 20's mental state is estimated to be positive (NO in step S104), the series of processes ends.

[0062] In step S105, the server 10 refers to the storage 13 and extracts positive data 30 from the life log data to alleviate the stress felt by the user 20 and change their mindset.

[0063] In step S106, the server 10 outputs positive data 30 to a predetermined device. This allows the server 10 to provide positive data 30 to the user 20.

[0064] As described above, the information processing method according to Embodiment 1 includes an estimation step (S103) for estimating the mental state of user 20 based on predetermined information, an extraction step (S105) for extracting past positive data 30 of user 20 from the user 20's life log data stored in the storage unit (storage 13) if it is estimated that the mental state of user 20 is negative, and an output step (S106) for outputting the past positive data 30 to a predetermined device.

[0065] Note that the processing flow shown in the flowchart in Figure 4 is just one example, and you may delete steps, add new steps, or change the processing order as long as it does not deviate from the main point.

[0066] (Effects and Benefits) As described above, the following effects and advantages can be obtained according to Embodiment 1.

[0067] The information processing device (for example, server 10) includes an estimation unit 113 that estimates the mental state of user 20 based on predetermined information, an extraction unit 114 that, if the estimation unit 113 estimates that the mental state of user 20 is negative, extracts past positive data 30 of user 20 from the user 20's life log data stored in the storage unit, and an output unit 115 that outputs the positive data 30 extracted by the extraction unit 114 to a predetermined device. An example of the "storage unit" here is the storage 13 described above. An example of the "predetermined information" here is the estimation data stored in the storage 13.

[0068] In recent years, with the advancement of digital devices and AI (Artificial Intelligence) technology, a "surveillance society" is emerging where our actions are constantly monitored for purposes such as improving public safety and economic development. While there are benefits to such a surveillance society, some individuals may find themselves unable to cope with mental stress in a constantly negative state due to the difficulty of living in a monitored society. In this regard, according to this embodiment, positive data 30 can be provided to a negative user 20 to relieve stress and change their mindset. As a result, the user 20 can recall their own special success experiences, happy memories, and enjoyable memories as if they were watching a kaleidoscope, thereby dispelling negative emotions and switching to positive thinking. In other words, with such positive data 30, they can become more optimistic and develop a mindset of wanting to grow themselves further, for example, by improving their abilities through continuous effort and learning. This results in a sense of liberation and mood stability similar to that obtained after receiving therapy from a professional therapist, and they can regain a richer sense of self and feel reborn.

[0069] Lifelog data may include data about user 20's life, activities, and experiences. Furthermore, past positive data may represent at least one of user 20's past successes, happy memories, or enjoyable memories.

[0070] [Embodiment 2] Next, Embodiment 2 of the present disclosure will be described with reference to Figure 5. Figure 5 is a block diagram showing an example of the functions of the processor 11. The difference between Embodiment 2 and Embodiment 1 is that, as shown in Figure 5, the classification unit 112a is included as a function of the classification unit 112. Components that overlap with Embodiment 1 will be referred to by their reference numerals and their descriptions will not be repeated. The differences will be described below.

[0071] The scene classification unit 112a labels and distinguishes multiple positive data points according to their respective scenes. These scenes include, for example, "work," "family," "learning," and "leisure activities."

[0072] The scene classification unit 112a may classify positive data such as "starting a business," "completing a project at work," "successful negotiations / business deals at work," "promotion / advancement at the company," and "getting a dream job" under the scene "work."

[0073] Furthermore, the scene classification unit 112a may classify positive data such as "marriage and childbirth," "successful parenting," and "recovery from illness or injury" under the scene "family."

[0074] Furthermore, the scene classification unit 112a may classify the positive data indicating "acquiring qualifications," "winning awards in contests and competitions," and "acquiring new skills" under the scene "learning."

[0075] Furthermore, the scene classification unit 112a may classify the above-mentioned "successful dieting," "successful creative activities," "volunteer activities," and "achieving goals in sports or hobbies" under the scene "leisure activities."

[0076] In this way, the scene classification unit 112a labels each of the multiple positive data for each scene and classifies them in a distinguishable manner. The classified positive data is stored in the storage 13.

[0077] In Embodiment 2, if the estimation unit 113 estimates that the user 20's mental state is negative, it further estimates the cause of that negativity. For example, if the estimation unit 113 looks at the user 20's schedule and finds an item called "funeral," it can estimate that the cause of the user 20's depression is the death of a loved one. Upon receiving such estimation results, the extraction unit 114 extracts positive data that can effectively alleviate the stress the user 20 is feeling. For example, the extraction unit 114 may extract positive data labeled "family." Since the user 20 is provided with positive data related to "family," they can develop a mindset to live more positively.

[0078] As another example, the estimation unit 113 can estimate from image data showing user 20's sales performance that the reason for user 20's decline is poor sales performance. Upon receiving such an estimation result, the extraction unit 114 may extract positive data labeled "work." Since user 20 is provided with positive data related to "work," they can recall the glorious past achievements of being the top salesperson, as if seeing a kaleidoscope of memories, thereby dispelling negative emotions and switching to positive thinking.

[0079] As described above, in Embodiment 2, past positive data is classified for each specific scene and stored in the storage unit (storage 13). The estimation unit 113 may estimate the cause of the user 20's negative mental state. The extraction unit 114 may extract positive data related to the cause from the past positive data classified for each scene. The output unit 115 may output the positive data related to the cause. With this configuration, the above-described effects can be obtained.

[0080] [Embodiment 3] Next, Embodiment 3 of the present disclosure will be described with reference to Figure 6. Figure 6 is a block diagram showing an example of the functions of the processor 11. The difference between Embodiment 3 and Embodiment 1 is that, as shown in Figure 6, the weighting unit 112b is included as a function of the classification unit 112. Components that overlap with Embodiment 1 will be referred to by their reference numerals and their descriptions will not be repeated. The differences will be described below.

[0081] The weighting unit 112b assigns weights to each of the multiple positive data. For example, the weighting unit 112b may assign greater weights to positive data that represent successful experiences, happy memories, and enjoyable memories from when the user 20 was younger. Alternatively, the weighting unit 112b may assign greater weights to positive data that represent successful experiences, happy memories, and enjoyable memories from when the user 20 was older.

[0082] Furthermore, the weighting unit 112b may assign weights to positive data according to the user 20's values. For example, if there are many images indicating "volunteer activities" in past image data, it is presumed that user 20 is a user who is enthusiastic about volunteer activities. In this case, the weighting unit 112b may assign weights to positive data indicating volunteer activities.

[0083] In Embodiment 3, if the estimation unit 113 estimates that the user 20's mental state is negative, it further estimates the stress level. The method for estimating the stress level is not particularly limited, but well-known evaluation scales, such as the PSS (Perceived Stress Scale) or STAI (State-Trait Anxiety Inventory), may be used. By using such well-known evaluation scales, the stress level can be classified into categories such as "mild," "moderate," and "severe."

[0084] When stress levels are severe, effective positive data is needed to alleviate stress. Therefore, if the estimation unit 113 estimates that the stress level is severe, the extraction unit 114 may extract positive data with a higher weight compared to when the stress level is estimated to be mild. For example, if a user 20 who is enthusiastic about volunteer activities is experiencing significant depression, positive data with a higher weight (positive data indicating volunteer activities) will be provided, allowing for more effective stress relief and fostering a positive mindset.

[0085] As described above, in Embodiment 3, past positive data is stored in the storage unit (storage 13) weighted according to predetermined conditions. If the estimation unit 113 estimates that the user 20's mental state is negative, it may further estimate the stress level. The extraction unit 114 may extract positive data corresponding to the stress level from the weighted past positive data. The output unit 115 may output positive data corresponding to the stress level. With this configuration, the effects described above can be obtained. An example of the "predetermined conditions" here is, as mentioned above, the user 20's age, values, etc.

[0086] [Embodiment 4] Next, Embodiment 4 of the present disclosure will be described with reference to Figure 7. Figure 7 is a block diagram showing an example of the functions of the processor 11. The difference between Embodiment 4 and Embodiment 1 is that, as shown in Figure 7, the determination unit 116 is included as a function of the processor 11. Components that overlap with Embodiment 1 will be referred to by their reference numerals and their descriptions will not be repeated. The following will focus on the differences.

[0087] The determination unit 116 determines what kind of place or situation is suitable for providing positive data to the user 20. As an example, let's assume that the user 20 is a user who commutes by car. After finishing work, the user 20 will drive home. Now, let's assume that the user 20 stops at a restaurant before going home. In this case, there are three possible places or situations for providing positive data to the user 20: (1) when driving the car, (2) when eating at the restaurant, and (3) when relaxing at home. If (1) the user is driving the car, for example, positive data will be provided to the car navigation system or HUD. If (2) the user is eating at the restaurant, for example, positive data will be provided to the smartphone 21. If (3) the user is relaxing at home, for example, positive data will be provided to a display installed at home.

[0088] The determination method used by the determination unit 116 is not particularly limited, but for example, the determination unit 116 may determine, based on past stress relief trends, which location or situation would be most effective in providing positive data to user 20 to relieve stress. For example, if providing positive data while eating at a restaurant tends to relieve stress more quickly compared to when driving or at home, the determination unit 116 may determine that user 20 is at a restaurant when providing positive data. The output unit 115, upon receiving the determination result from the determination unit 116, provides positive data when user 20 is at a restaurant. In this way, the server 10 can provide positive data to user 20 at a more effective timing by appropriately selecting the location or situation for providing positive data. The server 10 can obtain user 20's location information from the smartphone 21.

[0089] As described above, in Embodiment 4, the server 10 may further include a determination unit 116 that determines a location or situation in which to provide past positive data to the user 20 based on predetermined conditions. The output unit 115 may output past positive data in the location or situation determined by the determination unit 116. With such a configuration, the effects described above can be obtained. An example of the "predetermined conditions" here is, as described above, a condition that indicates the tendency for stress relief, such as what kind of location or situation in which to provide positive data to the user 20 will effectively relieve stress.

[0090] [Embodiment 5] Next, Embodiment 5 of the present disclosure will be described with reference to Figure 8. Figure 8 is a flowchart showing an example of processing performed by the server 10. However, the processing in steps S201 to S206 is the same as the processing in steps S101 to S106 shown in Figure 4, so its explanation will be omitted.

[0091] In the flowchart shown in Figure 4 above, it was explained that positive data is provided to alleviate user 20's stress. However, there may be cases where the provided positive data (hereinafter referred to as the first positive data) does not contribute to user 20's stress relief.

[0092] Therefore, as shown in Figure 8, in step S207, the server 10 determines whether the stress has been relieved after a predetermined time has elapsed since providing the first positive data. Here, "stress has been relieved" means that the user 20's mental state has changed from negative to positive. If the stress is relieved (YES in step S207), the series of processes ends. On the other hand, if the stress is not relieved even after the predetermined time has elapsed (NO in step S207), that is, if the user 20's mental state remains negative, the process proceeds to step S208.

[0093] In step S208, the server 10 switches the positive data it provides to the user 20. Specifically, the server 10 switches the positive data it provides to the user 20 from the first positive data to the second positive data. The server 10 may continue to switch to the third positive data, the fourth positive data, and so on, until the user 20's stress is relieved.

[0094] Thus, in Embodiment 5, if the negative state is not resolved even after a predetermined time has elapsed, the extraction unit 114 may extract other positive data (second positive data, third positive data, fourth positive data, etc.). The output unit 115 may switch the positive data to be output to other positive data and output it. With the above configuration, it is possible to find positive data that is effective in relieving the stress of the user 20 while relieving the stress of the user 20.

[0095] Furthermore, if multiple positive data points are provided to the user 20 simultaneously, it is also possible to switch one of the multiple positive data points. As another example, it is also possible to switch any combination of the multiple positive data points. As yet another example, it is also possible to switch all of the multiple positive data points. In any of these modes, the same effect as described above can be obtained.

[0096] [Other Embodiments] While mental states were described above in terms of positive or negative, they are not limited to this. Mental states may be subdivided based on Russell's Circle of Affect model. For example, Server 10 may subdivide and estimate negative, or unfavorable, mental states into categories such as tension, anger, irritation, anxiety, sadness, lethargy, and fatigue. Server 10 may then provide positive data appropriate to each of these subdivided categories. This allows for the development of an effective mindset.

[0097] [Examples of implementation using software] The function of an information processing device is a program (information processing program) that causes the computer to function as an information processing device, and can be realized by a program that causes the computer to function as each control block of the information processing device (acquisition unit 111, classification unit 112, estimation unit 113, extraction unit 114, output unit 115, and determination unit 116).

[0098] In this case, the information processing device includes a computer having at least one device (e.g., a processor) and at least one storage device (e.g., memory) as hardware for executing a program. The computer executes the program, thereby realizing each of the functions described in each embodiment.

[0099] The program may be recorded on one or more computer-readable recording media, not temporary ones. These recording media may or may not be present in the information processing device. In the latter case, the program may be supplied to the information processing device via any wired or wireless transmission medium.

[0100] Furthermore, some or all of the functions of each control block can be implemented by logic circuits. For example, an integrated circuit in which logic circuits functioning as each control block are formed is also included in the scope of this disclosure. In addition, it is also possible to implement the functions of each control block using, for example, a quantum computer.

[0101] Furthermore, each process described in each embodiment may be executed by an AI. In this case, the AI ​​may operate on an information processing device, or it may operate on another device (for example, an edge computer or a cloud server).

[0102] This disclosure is not limited to the embodiments described above, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of this disclosure. [Explanation of Symbols]

[0103] 100 Information Processing Systems 10 servers 13 Storage 30 Positive Data 111 Acquisition Department 112 Classification Department 112a Scene Classification Section 112b Weighting section 113 Estimation Department 114 Extraction part 115 Output section 116 Judgment section 131 Life Log Data

Claims

1. An estimation unit that estimates the user's mental state based on predetermined information, If the estimation unit estimates that the mental state is negative, the extraction unit extracts past positive data of the user from the user's life log data stored in the memory unit. The system includes an output unit that outputs the aforementioned past positive data to a predetermined device. Information processing device.

2. The aforementioned past positive data is classified by specific scene and stored in the memory unit. The estimation unit estimates the cause of the user's negative mental state, The extraction unit extracts positive data related to the cause from the past positive data classified by scene, The output unit outputs positive data related to the cause. The information processing apparatus according to claim 1.

3. The aforementioned past positive data is stored in the storage unit, weighted according to predetermined conditions. If the estimation unit estimates that the user's mental state is negative, it further estimates the stress level. The extraction unit extracts positive data corresponding to the stress level from the weighted past positive data. The output unit outputs positive data corresponding to the stress level. The information processing apparatus according to claim 1.

4. The system further includes a determination unit that determines the location or situation in which the user is provided with the past positive data based on predetermined conditions. The output unit outputs the past positive data at the location or scene determined by the determination unit. The information processing apparatus according to claim 1.

5. If the negative state is not resolved after a predetermined time has elapsed, the extraction unit extracts other positive data. The output unit switches the positive data to be output to the other positive data and outputs it. The information processing apparatus according to claim 1.

6. The aforementioned life log data includes data relating to the user's life, activities, and experiences. The aforementioned past positive data is data that represents at least one of the user's past successes, happy memories, or enjoyable memories. The information processing apparatus according to any one of claims 1 to 5.

7. An information processing program for causing a computer to function as an information processing device according to claim 1, wherein the computer functions as the estimation unit, the extraction unit, and the output unit.

8. An information processing method used in an information processing device, An estimation step that estimates the user's mental state based on predetermined information, If the aforementioned mental state is estimated to be negative, the extraction step involves extracting past positive data of the user from the user's life log data stored in the memory unit. The output step includes outputting the aforementioned past positive data to a predetermined device, Information processing methods.

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