Mental information input system, program and method

Abstract

This invention provides a mental information input system, method, and program that enables the interpretation of the true feelings and subconscious minds of individuals receiving support, thereby realizing advanced feedback and behavioral therapy in cognitive behavioral therapy, which involves recording self-observation based on the actions or actions of the individuals being supported. [Solution] In a cognitive behavioral therapy feedback system, the management server includes an observation record unit 331 which includes a decision operation acquisition unit that acquires actions or operations by the person being supported as an incomplete operation history and input information determined by the actions or operations by the person being supported as a decision operation history; an observation record database 35d which stores self-observation records including the incomplete operation history and the decision operation history; an analysis unit 332 which analyzes the correlation between the behavioral information and cognitive information of the person being supported based on the self-observation records; and a response generation unit which generates response information to be provided to the person being supported according to the analysis results in the correlation feature analysis unit.

Description

Technical Field

[0001] The present invention relates to a mental information input system, program, and method for a client to perform self-observation recording in psychotherapy or psychological therapy such as cognitive behavioral therapy and behavioral therapy.

Background Art

[0002] In recent years, self-monitoring has been proposed as a management technique used in psychotherapy or psychological therapy such as cognitive behavioral therapy and behavioral therapy. According to this self-monitoring, effects such as strengthening the target behavior and boosting one's own motivation can be expected by recording one's own behavior and activities.

[0003] Here, cognitive behavioral therapy (CBT) is a scientific treatment method for systematically improving problem behaviors and symptoms by using behavioral therapy techniques based on learning theory and cognitive approaches based on cognitive therapy or logic therapy. In the treatment of cognitive behavioral therapy, the aim is to enable the client to self-care for problems and symptoms. The therapist and the client share the problems and jointly set treatment goals to solve the problems.

[0004] By the way, in recent years, services and systems for supporting the above-mentioned self-monitoring for cognitive behavioral therapy have been developed through communication networks (see Patent Document 1). In this system, behavioral therapy is implemented based on behavioral records such as text input by the client himself / herself.

Prior Art Documents

Non-Patent Documents

[0005]

Non-Patent Document 1

Patent Document 1

[0006] However, the system disclosed in Patent Document 1 had a problem in that, for example, information where the client initially entered an action record but hesitated or canceled the final decision operation such as sending it—so-called "incomplete operations"—was not properly tracked within the system, making it impossible to provide adequate psychotherapy. More specifically, these incomplete operations include content that the client canceled as a result of hesitation or deep thought, and this can be important information that reflects the client's true feelings and deep psychology. However, conventional systems lacked the functionality to track and analyze such incomplete actions, making it impossible to interpret important psychological indicators such as what the client was unsure about or intentionally modified. As a result, it was difficult to provide in-depth behavioral therapy based on the client's true intentions and emotions.

[0007] Therefore, the present invention aims to provide a mental information input system, program, and method that enables the interpretation of a client's true feelings and subconscious mind by recording and tracking data, including information that the client entered but later canceled, and a history of actions in which the client hesitated, in cognitive behavioral therapy that involves recording self-observation records based on the client's actions or actions. This enables advanced feedback and behavioral therapy that could not be provided by conventional technologies. [Means for solving the problem]

[0008] To solve the above problems, the present invention provides a system for inputting mental information, including self-observation records based on the actions or operations of a person receiving support, An incomplete operation acquisition unit that acquires the actions or operations performed by the person receiving support as an incomplete operation history, A decision operation acquisition unit acquires input information determined by the actions or operations of the person receiving support as a decision operation history, An observation record storage unit that stores self-observation records including the incomplete operation history and the decision operation history, Based on the aforementioned self-observation records, a correlation feature analysis unit analyzes the correlation between the behavioral information and cognitive information of the person being supported, A response generation unit generates response information to be provided to the person to be supported, in accordance with the analysis results in the correlation feature analysis unit. It is equipped with.

[0009] Furthermore, the present invention relates to a method for inputting mental information, including self-observation records based on the actions or operations of a person receiving support, using a computer. The computer acquires actions or operations performed by the person being supported as an incomplete operation history, and acquires input information determined by the actions or operations performed by the person being supported as a determined operation history. An observation record storage step in which a self-observation record including the incomplete operation history and the decision operation history is stored in the observation record storage unit, The correlation feature analysis unit of the computer analyzes the correlation between the behavioral information and cognitive information of the person being supported, based on the self-observation record, in a correlation feature analysis step. The response generation step involves the computer's response generation unit generating response information to be provided to the person being supported, in accordance with the analysis results in the correlation feature analysis unit. Includes.

[0010] In the above invention, the pattern calculation unit analyzes the correlation between the behavioral and cognitive information of the person being supported, based on the comparison result between the incomplete operation history and the decision operation history, and calculates a characteristic pattern that characterizes the analyzed correlation. A natural language model, trained through machine learning to calculate response information corresponding to the comparison result between the incomplete operation history and the decision operation history and the feature pattern, receives the comparison result between the incomplete operation history and the decision operation history and the feature pattern, and the computer's response request unit generates and executes a prompt for the natural language model to generate response information. It is preferable.

[0011] In the above invention, it is preferable that the system further includes an operation pattern analysis unit that analyzes operation patterns including at least one of the following in the input operations of the person being supported: time delay, frequency of re-input, occurrence of deletion operations, and time period of occurrence. The response generation unit estimates the potential psychological state of the person being supported based on the analysis results by the operation pattern analysis unit and determines the content and timing of the response.

[0012] In the above invention, it is preferable that, in both the incomplete operation history and the decided operation history, the computer's operation identification unit uses a machine learning model to estimate the level of awareness of an operation in order to distinguish between the conscious and unconscious operations of the person being supported, and evaluates the cognitive and emotional tendencies of the person being supported based on the identified level of awareness.

[0013] Furthermore, the systems and methods according to the present invention described above can be realized by executing a program of the present invention written in a predetermined language on a computer. That is, by installing the program of the present invention on the IC chip or memory device of a mobile terminal device, smartphone, wearable device, mobile PC or other information processing terminal, or a general-purpose computer such as a personal computer or server computer, and executing it on the CPU, a system having the above-described functions can be constructed, or a method according to the present invention can be implemented.

[0014] Furthermore, the program of the present invention can be distributed, for example, via a communication line, and can also be transferred as a package application that runs on a standalone computer by recording it on a computer-readable recording medium. Specifically, this recording medium can be a variety of recording media, such as magnetic recording media like flexible disks and cassette tapes, optical discs like CD-ROMs and DVD-ROMs, and RAM cards. With a computer-readable recording medium on which this program is recorded, the above-described system and method can be easily implemented using a general-purpose computer or a dedicated computer, and the program can be easily stored, transported, and installed. [Effects of the Invention]

[0015] As described above, according to the present invention, by acquiring and accumulating a history of incomplete operations and a history of decided operations based on the actions and operations of the person being supported, and by analyzing the correlation between behavioral information and cognitive information, it is possible to grasp the client's behavioral and cognitive tendencies in detail. This makes it possible to generate appropriate responses, provide more individualized support, and reduce the psychological burden on the client.

[0016] Furthermore, the above invention compares the history of incomplete operations with the history of decided operations, calculates feature patterns based on the comparison results, and utilizes a natural language model to generate responses through machine learning, thereby providing appropriate response information tailored to the client's state. This enables rapid response to changes in the client's behavior and cognition, and by using a machine learning model, it is possible to automatically generate responses that are optimal for individual needs, resulting in more accurate support.

[0017] In the above invention, by analyzing the client's operation patterns (such as time delays, frequency of re-entry, and deletion operations) and estimating the client's psychological state based on these patterns, a more effective response can be provided by adjusting the content and timing of the response based on the results of this analysis.

[0018] In addition, in the above invention, conscious and unconscious operations in the unfinished operation history and the decision operation history are identified, and based on this, the cognitive and emotional tendencies of the client are evaluated, so that an appropriate evaluation based on the level of consciousness for the operation becomes possible, and the accuracy of treatment can be improved.

Brief Description of Drawings

[0019] [Figure 1] It is an explanatory diagram conceptually showing the overall configuration of the cognitive behavioral therapy feedback system S according to an embodiment. [Figure 2] It is a block diagram showing the internal configuration of the management server 3 according to an embodiment. [Figure 3] It is a block diagram showing the configuration of the analysis unit in the management server 3 according to an embodiment. [Figure 4] It is a block diagram showing the configuration of the feedback processing unit in the management server 3 according to an embodiment. [Figure 5] It is a block diagram showing the internal configuration of the smartphone 1 for the client according to an embodiment. [Figure 6] It is a flowchart showing the overall operation of the cognitive behavioral therapy feedback system according to an embodiment. [Figure 7] It is a flowchart showing the operation of the cognitive behavioral therapy feedback system at the time of feedback execution (self-counseling mode) according to an embodiment. [Figure 8] It is an explanatory diagram showing the situation of monitoring user operations according to an embodiment. <舍 [Figure 9] It is an explanatory diagram showing the analysis situation by monitoring the unfinished operations and decision operations of user operations according to an embodiment. [Figure 10] It is an explanatory diagram showing the pattern of response selection according to an embodiment.

Modes for Carrying Out the Invention

[0020] The following describes in detail embodiments of the mental information input system, method, and program of the present invention when applied to a cognitive behavioral therapy feedback system S, with reference to the attached drawings. It should be noted that the present invention is not limited to the embodiments described above, and the components can be modified and implemented in practice without departing from the gist of the invention. Furthermore, various inventions can be formed by appropriate combinations of the multiple components disclosed in the above embodiments. For example, some components may be deleted from all the components shown in the embodiments.

[0021] In this embodiment, the mental information input system, method, and program of the present invention are described as being applied to cognitive behavioral therapy, but the present invention is not limited thereto. The present invention can be applied not only to psychotherapies such as cognitive behavioral therapy, but also to fields such as mental care and learning support in companies, educational institutions, and other non-medical environments. For example, it is useful in general mental care, learning support, mental care for students in educational institutions, psychological support for employees in companies, and improving mental health in the workplace environment.

[0022] (Overview of Cognitive Behavioral Therapy) In the cognitive behavioral therapy according to this embodiment, the goal is for the client, who is the recipient of support, to be able to self-care for their problems and symptoms. The therapist and the client share the problems, and together they set therapeutic goals and work to solve the problems.

[0023] In the cognitive behavioral therapy according to this embodiment, self-monitoring is performed on the client's smartphone through a dedicated application. Through this self-monitoring, the client records their own actions and events, and also records their thoughts, feelings, and insights regarding those actions and events as mental information, including "automatic thoughts." This employs cognitive behavioral therapy and cognitive restructuring techniques.

[0024] This method of recording mental information such as automatic thoughts provides feedback that encourages self-awareness in the client by showing the causal relationship between automatic thoughts and emotion estimation, such as which words in the client's own "automatic thoughts" (i.e., the client's own "thoughts") were focused on to estimate emotions. This gives the client an opportunity to think for themselves, and a significant psychological effect can be expected.

[0025] Furthermore, in this embodiment, self-monitoring through the application allows for an assessment of cognitive aspects, such as observing what kinds of thoughts frequently arise in what situations, how confident the client is in the actions required in that situation, how they perceive the results of their actions, and how they think about future prospects. In this embodiment, self-observation records are assigned as homework, instructing clients to record what kinds of thoughts arise in actual situations. Based on these records, analysis is performed to identify common thoughts observed in many situations and what kinds of cognitions strongly influence behavior and mood.

[0026] Based on the analysis results of the self-monitoring, parents, doctors, educators, therapists, etc., send messages such as advice and counseling. The psychologist's (Dr1) smartphone (1d) has a dedicated application with a counseling interface installed, which allows for viewing necessary information, entering documents, and sending messages (such as feedback to the client). Furthermore, in sending counseling, advice, and progress reports as feedback to the client via message, this system accumulates and analyzes the client's self-observation records and flexibly selects a response from the generative AIGa1 or the psychologist (Dr1) according to the client's situation, thereby reducing the human burden on the psychologist (Dr1) while enabling appropriate support tailored to the psychological state of the client (CLa~c).

[0027] (Overall structure of a cognitive behavioral therapy feedback system) Figure 1 shows the overall configuration of the cognitive behavioral therapy feedback system S according to this embodiment. The system according to this embodiment is for providing cognitive behavioral therapy to multiple clients CL (CLa to c), and as shown in Figure 1, it is generally composed of smartphones 1 (1a to c) used by each client CL and a management server 3 installed on the internet 2. In this embodiment, smartphone 1 is described as an example of an information processing terminal device, but any information processing terminal device such as a personal computer or tablet PC can be used.

[0028] Furthermore, the mental information input system and method according to the present invention can be realized by executing the program of the present invention, which is written in a predetermined language, on a computer. In this embodiment, the mental information input system of the present invention can be constructed by executing the mental information input program of the present invention on the CPUs of the smartphone 1 (1a~c) and the management server 3.

[0029] In this embodiment, the management server 3 is a server that processes the progress of cognitive behavioral therapy. It can be implemented as a single server device or a group of multiple server devices. By executing software, it virtually constructs multiple functional modules on the CPU, and each functional module works together to perform processing. Furthermore, the management server 3 can send and receive data via the Internet 2 using its communication function, and can also display web pages via browser software using its web server function.

[0030] Smartphone 1 (1a, 1b) is a portable information processing terminal device that utilizes wireless communication. It communicates wirelessly with relay points such as wireless base stations 22, allowing users to receive communication services such as voice calls and data communication while on the move. Examples of communication methods for this mobile phone include 3G (3rd Generation), LTE (Long Term Evolution), and 5G. In addition to communication functions, Smartphone 1 is equipped with various other functions, including a digital camera, application software execution capabilities, and location information acquisition capabilities such as GPS (Global Positioning System). Such information processing terminal devices include mobile computers such as tablet PCs, as well as IoT devices such as smartwatches and smart speakers.

[0031] Furthermore, this smartphone 1 is equipped with a liquid crystal display as a display unit for displaying information, and also with operation devices such as operation buttons for the user to perform input operations. These operation devices include a touch panel, which is superimposed on the liquid crystal display and is an input unit that acquires operation signals such as touch operations that specify the coordinate position on the liquid crystal display. Specifically, this touch panel is an input device that receives operation signals by pressure, electrostatic detection, etc., from touch operations using the user's fingertip or a pen, and is configured by superimposing a liquid crystal display that displays graphics and a touch sensor that receives operation signals corresponding to the coordinate position of the graphics displayed on the liquid crystal display.

[0032] In this embodiment, the client CL (CLa~c) uses a smartphone 1 (1a~c). The client's smartphone 1 has a client application installed and functions as a dedicated client terminal. When a guardian or other person monitors the mental health treatment of the client CL, the guardian runs a guardian application on their smartphone. The guardian application is equipped with functions such as receiving and viewing alerts from the client application. For example, in addition to a dedicated application for receiving and viewing alerts, it is equipped with communication applications such as an email application, a browser application, and a messaging application.

[0033] (Internal structure of each device) Next, the internal structure of each device constituting the cognitive behavioral therapy feedback system S described above will be explained. Figures 2 to 4 show the internal configuration of the management server 3 according to this embodiment, and Figure 5 shows the internal configuration of the smartphone 1 according to this embodiment. In this explanation, the term "module" refers to a functional unit composed of hardware such as devices or equipment, software that has the same function, or a combination thereof, for achieving a predetermined operation.

[0034] (1) Management Server First, let me explain the internal configuration of the management server 3. The management server 3 is a server device located on the Internet 2, and is capable of sending and receiving data with each smartphone 1 via the Internet 2. Specifically, the management server 3 includes a communication interface 31 for data communication via the Internet 2, an authentication unit 32 for authenticating the user and user terminal's authority, a cognitive behavioral therapy execution unit 33 and a response generation unit 34 for executing the overall progress processing of cognitive behavioral therapy, an information distribution unit 36 ​​for distributing various content data to each client, a billing processing unit 37, and various database groups 35 (35a to 35d).

[0035] The database group 35 includes an AI learning database 35a for storing the learning results of artificial intelligence, a user database 35b for storing information about users such as clients and their guardians, a cognitive behavioral therapy database 35c for storing worksheets executed during work mode related to cognitive behavioral therapy, serial content delivered in lesson mode, scenario data related to lesson progress processing, etc., and an observation record database 35d as an observation record storage unit for storing data related to monitoring for each client. Each of these databases may be a single database, or it may be divided into multiple databases and relationships may be set up to link the data together, creating a relational database.

[0036] The Cognitive Behavioral Therapy Database 35c is a storage device that stores a series of interconnected content, such as teaching materials and videos related to psychotherapy or psychological therapy, used in lesson mode. This Database 35c also stores data such as scenario data and worksheets.

[0037] The information stored in the user database 35b includes identification information that identifies clients and guardians, or identifiers (user ID, device ID) that identify the mobile device used by the client, and authentication information linked to passwords, etc. It also includes the user's personal information linked to the user ID, as well as the model of the device. In addition, the user database 35b stores authentication history (access history) for each user or user device, observation results regarding the progress of each client's lessons and work, status, points, usage history, etc., in relation to the cognitive behavioral therapy database 35c, as well as payment information.

[0038] The observation record database 35d is primarily a database that stores observation data by associating occurrence record information and automated thought information for each client. The information stored in this observation record database 35d consists of self-observation records collected from numerous clients through worksheets. In this embodiment, each client's actions or operations are included in the self-observation record as an incomplete operation history, and the input information determined by each client's actions or operations is included as a decision operation history. In addition, occurrence record information, behavioral information, cognitive information, and characteristic keywords are also stored in the self-observation record for each client, all of which are interconnected.

[0039] The authentication unit 32 is a module that establishes a communication session with each smartphone 1 via the communication interface 31 and performs authentication processing for each established communication session. This authentication process involves obtaining authentication information from the smartphones 1 (1a-c) of the client or their guardian who is accessing the system, referring to the user database 35b to identify the accessor, and authenticating their authority. The authentication results from the authentication unit 32 (user ID, authentication time, session ID, etc.) are sent to the cognitive behavioral therapy execution unit 33 and are also stored as authentication history in the user database 35b via the authentication unit 32. In addition, the authentication unit 32 also performs authentication for access from supporters participating in the operation of cognitive behavioral therapy, such as staff and SEs on the management side, as well as smartphones 1 and counselors, and grants the access rights assigned to each accessor.

[0040] Furthermore, the authentication unit 32 is equipped with a location information management unit 32a, which acquires the current time and current location of the authenticated client from the smartphone 1 and records it in the user database 35b, as well as recording it in the observation record database 35d via the observation record unit 331 as an observation record of the client.

[0041] The Cognitive Behavioral Therapy Execution Unit 33 is a module that performs the necessary processing for cognitive behavioral therapy and conducts treatment. It collects and analyzes various observation records through an application installed on the smartphone used by each client, and controls the generation system AIGa1 to provide information necessary for treatment. The Cognitive Behavioral Therapy Execution Unit 33 also has the function of conducting cognitive behavioral therapy by generating various event processing to develop work mode and lesson mode according to the cognitive behavioral therapy scenario data. It executes a script that includes certain rules, logic, and algorithms, and generates event processing such as lessons, work, mini-games, and movie playback.

[0042] The cognitive behavioral therapy scenarios performed in this cognitive behavioral therapy execution unit 33 include: • Self-monitoring to enable you to view yourself objectively. • Cognitive restructuring to correct cognitive distortions Behavioral activation is an approach to boost mood by activating behavior, in order to recover from a decline in activity levels that occurs when one is depressed or feeling down. Sleep care as a means of addressing insomnia that often occurs when one is depressed or feeling down (specifically, avoiding naps to increase sleep pressure and maintaining a regular sleep schedule, such as waking up at a set time in the morning even if you can't sleep). The majority of depression and stress stem from interpersonal relationships. To alleviate the stress caused by being unable to express one's opinions to others, learn assertion skills (communication skills) to effectively communicate your opinions to others. • Problem-solving skills to learn how to deal with troubles and worries that cause stress. This includes things like:

[0043] In this embodiment, the cognitive behavioral therapy execution unit 33 on the management server 3 collaborates with the cognitive behavioral therapy execution unit 141 on the smartphone 1 through the synchronization processing unit 361 and the synchronization processing unit 145 on the smartphone 1. The management server 3 performs some of the cognitive behavioral therapy progress processing and work analysis processing, while the cognitive behavioral therapy execution unit 141 on the smartphone 1 performs some of the graphic processing and event processing.

[0044] For example, on the management server 3 side, event processing that may occur is predicted based on the scenario's progress, the client's lesson progress level, as well as the user's classification, current location, and current time. The conditions for these events are generated on the management server 3 side, and these conditions are sent to the smartphone 1 side. Based on the conditions received from the management server 3, the actual event processing and the corresponding graphic processing can be executed on the smartphone 1 side.

[0045] Furthermore, the cognitive behavioral therapy execution unit 33 according to this embodiment is equipped with an alert monitoring function, which constantly monitors the alert signal transmitted from the smartphone 1, and when an alert signal is detected, it can send an alert message to a registered contact (telephone number, email address, etc.). In particular, the cognitive behavioral therapy execution unit 33 includes an observation recording unit 331, an analysis unit 332, and an AI control unit 333 as modules related to cognitive behavioral therapy.

[0046] (Observation Record Section 331) The observation recording unit 331 is a module that collects self-observation records based on the actions or operations of each client through the smartphones 1a to c used by each client. The observation records recorded by this observation recording unit 331 include not only text data entered by the client, but also video footage of the client's facial expressions, or the client's blood flow, voice, and location information history, as well as records of the occurrence of external factors and automated thought information regarding the client's actions and perceptions in response to those external factors.

[0047] In this embodiment, the observation recording unit 331 has a function to collect self-observation records based on the actions or operations of each client through the smartphones 1a to c used by each client. In this embodiment, the module for collecting these self-observation records includes an input receiving unit 331a, modules 331b to 331d that perform various information acquisition processing, and an information control unit 331e.

[0048] The input receiving unit 331a is a module that receives input from the client and selects the appropriate processing based on the type of input. The input received by the input receiving unit 331a is classified and stored as history, and as a result the response classification unit classifies the client's response patterns and further classifies the client's attributes according to the combination of response patterns for each external factor. In accordance with the classification by the input receiving unit 331a, the response classification unit 332b performs appropriate analysis processing.

[0049] Furthermore, this input receiving unit 331a includes an incomplete operation acquisition unit 331b, a decision operation acquisition unit 331c, and a sensor information acquisition unit 331d, which are modules for executing each acquisition process.

[0050] The incomplete operation acquisition unit 331b is a module that acquires actions or operations by each client as an incomplete operation history, and the decision operation acquisition unit 331c is a module that acquires input information determined by actions or operations by each client as a decision operation history.

[0051] The incomplete operation acquisition unit 331b has a mechanism to trace incomplete operations, such as input errors, rewriting, and deletion, before the user presses the send or confirm button when performing operations such as text input on the client's smartphone, as shown in Figures 8 and 9. The functions and processes for acquiring these incomplete operations are as follows.

[0052] • Real-time event listening The system monitors touch operation events on the client's application (text input, gesture operations, deletion, focus movement, etc.) in real time. This information is sent to the incomplete operation acquisition unit 331b at regular intervals or after specific actions (such as stopping after input) and acquired as an incomplete operation history. • Asynchronous communication To synchronize client input with the server in real time, each input is sent to the incomplete operation acquisition unit 331b via asynchronous communication, and the server continuously traces what the user is inputting. • Retention and analysis of input logs The incomplete operation acquisition unit 331b records the content entered by the client, deleted characters, and rewritten parts as input logs in the incomplete operation history, records each operation with timestamped data, and tracks the changes from the content initially entered by the user in chronological order.

[0053] Furthermore, the decision operation acquisition unit 331c acquires and records the data included in the aforementioned incomplete operation history as a decision operation history when the client finally performs a completion operation such as clicking the send button, making a decision operation, or saving operation, by adding a completion flag to the data and distinguishing it from the incomplete operation history.

[0054] The sensor information acquisition unit 331d is a module that traces operations other than text input, and acquires sensor signals detected on the client terminal as one of the incomplete operations. For example, operations such as attempting to select an option from a pull-down menu or checkbox on the client terminal but ultimately interrupting the selection without completing it, operations where the user started dragging a file or object but stopped the operation before dropping it, operations where the user started scrolling a page or content but stopped the operation before reaching a specific area, and operations where the user attempted to click a link or button but the transition was not actually completed, are recorded in the incomplete operation history by the sensor information acquisition unit 331d as behavioral data when the user hesitated or stopped their actions midway.

[0055] The information control unit 331e is a module that acquires data and signals as information, calculates characteristic patterns, and records them in various databases according to the calculated patterns. It integrally acquires data and signals received from multiple sensors and input devices as information, and uses algorithms to analyze and extract fluctuations, trends, or other characteristic elements of the signals and data. The resulting patterns are recorded in various predefined databases, forming a foundation for utilizing the data for purposes such as feedback control or report generation.

[0056] (Analysis Department 332) The analysis unit 332 is a module that analyzes the client's behavior based on self-observation records acquired and recorded in accordance with the progress of cognitive behavioral therapy by the cognitive behavioral therapy execution unit 33. For example, it functions as a correlation feature analysis unit that analyzes the correlation between each client's behavioral information and cognitive information. The analysis unit 332 also has the function of collecting work entered by the client in accordance with the progress of the cognitive behavioral therapy lessons and work, and performing an evaluation of the user according to the cognitive behavioral therapy written in that work.

[0057] The analysis of this client can be performed on the management server 3, on the smartphone 1, or jointly by both the management server 3 and the smartphone 1. The analysis unit 332 manages the analysis results performed or stored on the management server 3 and the analysis results performed and stored on the smartphone 1, compares the results from both, and, if necessary, delivers all or part of the analysis results to the smartphone 1 to synchronize them. To elaborate on the analysis unit 332, as shown in Figure 4, the analysis unit 332 is equipped with an automatic thought acquisition unit 332a, an emotion generation unit 332c, a cognitive distortion evaluation unit 332f, and an evaluation execution unit 332g.

[0058] The automated thought acquisition unit 332a is a module that acquires various types of information input by the client via the smartphone 1. The automated thoughts acquired here include not only behavioral records entered by the client themselves, but also biometric information such as blood flow, body temperature, and heart rate input from the biometric information analysis unit 332l. The means of acquiring information in the automated thought acquisition unit 332a include reading data recorded by the observation recording unit 331 and receiving data directly from the smartphone 1 via the communication interface 31.

[0059] The automatic thought acquisition unit 332a is equipped with a response classification unit 332b, which is a module that classifies the data acquired by the automatic thought acquisition unit 332a and sends it to the emotion generation unit 332c and the cognitive distortion evaluation unit 332f.

[0060] The response classification unit 332b also has the function of classifying the client's response patterns based on occurrence record information, behavioral information, cognitive information, and characteristic keywords stored in the observation record database 35d. In particular, in this embodiment, when the client inputs self-monitoring text, the response classification unit 332b acquires actions or operations by the client that have not yet resulted in a return, transmission, or decision operation as "incomplete operations," and acquires completed operations and their input information as "decision operations."

[0061] In this embodiment, the response classification unit 332b comprises a comparison unit 334a, a pattern calculation unit 334b, and an operation pattern analysis unit 334c. The comparison unit 334a is a module that compares the incomplete operation history with the decision operation history, and the pattern calculation unit 334b ​​is a module that analyzes the correlation between the client's behavioral information and cognitive information according to the comparison result by the comparison unit 334a, and calculates a characteristic pattern that characterizes the analyzed correlation. The operation pattern analysis unit 334c is a module that analyzes operation patterns that include at least one of the following in the client's input operations: time delay, frequency of re-input, occurrence of deletion operations, or time period of occurrence.

[0062] The emotion generation unit 332c is a module that generates emotion data by classifying observation data stored in the statistical data storage unit according to a pre-learned model which is a correlation pattern between the occurrence record information and the automatic thought information. The emotion generation unit 332c according to this embodiment includes a pre-learned model management unit 332d. The pre-learned model management unit 332d is a module that generates and manages a learning model in advance by the generation system AIGa1. The emotion data generated by this emotion generation unit 332c is input to the judgment unit 332e.

[0063] The cognitive distortion evaluation unit 332f is a module that compares the automated thought information of a specific client with the emotional data generated by the generation system AIGa1 and evaluates the client's cognitive distortion based on the degree of agreement between them. In this embodiment, it includes a determination unit 332e. The determination unit 332e is a module that compares the automated thought information of a specific client with the emotional data, classifies the client's automated thought information based on the degree of agreement between them, and calculates the deviation rate from the emotional data related to that classification. The deviation rate calculated by the cognitive distortion evaluation unit 332f is input to the evaluation execution unit 332g.

[0064] The evaluation execution unit 332g is a module that performs comprehensive evaluation processing based on the results of evaluation and analysis of acquired automatic thoughts, emotions, and cognitive distortions. Specifically, it comprises a causal relationship analysis unit 332h, a change extraction unit 332i, a qualitative evaluation calculation unit 332j, and a self-counseling management unit 332k.

[0065] The causal relationship analysis unit 332h is a module that extracts keywords included in the occurrence record information and emotion data used to calculate the deviation rate as feature keywords, weights the extracted feature keywords according to the deviation rate calculated by the comparison judgment unit, and records the feature keywords as causal relationship data based on the frequency of that weighting.

[0066] Furthermore, the causal relationship analysis unit 332h has the function of analyzing the correlation between external factors and the client's behavior and cognition regarding those external factors, and extracting characteristic keywords that characterize the analyzed correlation. The characteristic keywords extracted here are associated with occurrence record information, behavioral information, and cognitive information and stored in the observation data storage units, databases 35a to d.

[0067] The change extraction unit 332i is a module that extracts statistical numerical changes in a time series for a predetermined item from the information stored in the observation record database 35d. When selecting a response, it automatically generates these statistical numerical changes as response information according to the client's psychological state. Examples of numerical changes extracted by this change extraction unit 332i include the following: • Changes in emotional intensity The emotional intensity recorded by the client through self-monitoring (for example, evaluating emotions such as anxiety and anger on a scale from 0 to 10) is analyzed over time, and the changes in how that intensity decreases or increases are quantified. • Frequency of negative automatic thoughts The frequency of negative automatic thoughts is recorded, and whether that frequency decreases as treatment progresses is evaluated. For example, if the number of negative thoughts per week decreases, the change in the value is evaluated as the treatment outcome. • Frequency of problematic behaviors The frequency of maladaptive behaviors (e.g., overeating or avoidance behaviors) is tracked, and their decrease is numerically confirmed. The number of times a specific behavior occurs in a day is recorded, and the numerical changes in that number over time are analyzed. • Indicators of behavioral activation The client records the frequency of positive behaviors (e.g., socializing with friends or relaxation activities) and quantifies the increase or decrease in these behaviors. These numerical changes serve as important indicators for objectively evaluating the effectiveness of treatment, and by utilizing the AIGa1 generative system for more efficient analysis, they become fundamental data for providing feedback to clients and determining the next treatment steps.

[0068] The qualitative evaluation calculation unit 332j is a module that objectively evaluates the degree to which a client's cognitive distortions have improved. Specifically, it measures the improvement in the client's cognitive distortions based on the change in the deviation rate calculated by the judgment unit 332e. This change in the deviation rate is considered as "improvement" if the deviation rate has decreased, and the degree of change is input as qualitative evaluation data to the user database 35b and the billing processing unit 37. The billing processing unit 37 calculates the degree of improvement for each client based on the qualitative evaluation data and performs billing processing according to that degree.

[0069] The self-counseling management unit 332k is a module that classifies the client's response patterns based on occurrence record information, automated thought information, and deviation rates stored in the observation record database 35d. The feedback processing unit has the function of selecting and presenting comments according to the response patterns classified by the self-counseling management unit 332k.

[0070] (AI control unit 333) The AI ​​control unit 333 is a module that analyzes input from a client and generates an appropriate response by performing natural language processing (NLP) in conjunction with an external AI service. The AI ​​control unit 333 establishes a dialogue with the user in conjunction with an external NLP service and, in order to generate a response corresponding to the characteristics (age, gender, personality, experience level, knowledge, etc.) of AI characters such as doctors, counselors, and teachers realized by NLP, it refers to the AI ​​learning database 35a and the cognitive behavioral therapy database 35c, and performs processes such as generating information requests (prompts, etc.), verifying responses, outputting information, and reflecting the dialogue results in the database. In particular, the AI ​​control unit 333 functions as a response request unit that receives the comparison results and feature patterns of incomplete operation history and decided operation history, generates prompts for the natural language model, and executes them to generate response information.

[0071] Furthermore, the AI ​​control unit 333 also has a function to predict external factors that have occurred in accordance with information on the current time and current location, in cooperation with an external AI service (in this case, the generation system AIGa1) during the response generation process in the response generation unit 34. The analysis unit 332 also has a function to generate feedback information necessary for processing in the feedback processing unit 344, using behavioral information, cognitive information, and related feature keywords associated with the external factors predicted by the AI ​​control unit 333 as intermediate seeds.

[0072] The AI ​​control unit 333 makes predictions by referring to table data that lists the expected timing, time, location, speed of movement, and acceleration for each external factor. Based on the current time, current location, speed of movement, acceleration, and direction, it predicts external factors that are likely to occur at that time, given the client's current location and situation. For example, if the client is moving at a certain speed or higher during commuting hours, there is a high probability that the client is on a train. The AI ​​control unit 333 then refers to table data regarding potential troubles, changes in "thoughts," "emotions," and "physical state" that may occur on trains during commuting hours, and extracts candidate external factors that may occur.

[0073] (Response generation unit 34) The response generation unit 34 is a module that generates response information to be provided to the client in accordance with the analysis results in the cognitive behavioral therapy execution unit 33, including correlation feature analysis by the analysis unit 332. For example, it executes a process to sequentially provide a series of content according to a scenario that describes event processing for advancing a series of consecutive content. In this embodiment, the scenario is advanced based on each client's lesson progress and the number of times worksheets have been created. This scenario data is script data that describes the development of lesson mode and work mode.

[0074] Furthermore, this response generation unit 34 has the function of synchronizing the cognitive behavioral therapy progress processing on the smartphone 1 and the cognitive behavioral therapy progress processing on the management server 3 according to this scenario data. Specifically, the management server 3 predicts event processing that may occur based on the progress of the lesson in the scenario data, generates the conditions for its occurrence on the management server 3 side, and sends those conditions to the smartphones 1a to 1c.

[0075] Furthermore, the response generation unit 34 has functions for generating responses to interact with the client in order to execute a cognitive behavioral therapy scenario, such as character control, chat execution, and worksheet generation.

[0076] The character control function generates feedback based on behavioral information, cognitive information, and characteristic keywords related to external factors predicted by the generation system AIGa1. The feedback generated here, along with other advice, is sent to the output control unit 147 on the smartphone 1 and displayed as dialogue for the psychologist character, etc. This character control function generates display data for displaying animations and CG of people, creatures, etc., on the display 13a on the smartphone 1. The display data is generated by combining graphic data, image data, text data, video data, audio, and other data.

[0077] The chat execution function generates display data for displaying the conversational exchange on the display 13a of the smartphone 1. This chat execution function sends feedback from the generation system AIGa1 and human intervention to the client as messages from the conversation partner, and also receives messages from the client as automated thoughts. Messages received by this chat execution function will continue the conversation according to the prepared scenario if they fall within that scenario. If an irregular response that deviates from the scenario is received, the re-evaluation execution function of the observation recording unit 331 can request re-evaluation and re-analysis to modify or replace the scenario, allowing the conversation to continue.

[0078] The re-evaluation execution function of the observation record unit 331 monitors the interactions in chats and worksheets entered by the client through each module within the observation record unit 331. In this monitoring, if keywords or indirect behaviors listed in the monitoring list stored in the cognitive behavioral therapy database 35c are detected in the interactions, a request for re-evaluation is sent to the analysis unit 332. The analysis unit 332 then analyzes the frequency of occurrence of the relevant keywords and indirect behaviors according to the client's response type, determines whether an alert is necessary, the danger level, and the urgency, and if the danger level exceeds a certain level, it has the function to send an alert to psychologists, advisors, etc., via the human operation unit 343. The alert from this re-evaluation execution function may also be sent directly to the smartphones of the client's guardians, clinical psychologists, assigned advisors, etc.

[0079] The worksheet generation function of the observation recording unit 331 records self-observation records (cognitive behavioral therapy such as behavioral records and cognitive records) entered based on the client's actions, and executes a worksheet as a work mode for analyzing the recorded self-observation records. In this worksheet, information on the occurrence of external factors, behavioral information regarding the client's actions in response to the external factors that occurred, and cognitive information regarding the client's perception of the external factors are recorded based on the client's actions.

[0080] Specifically, the response generation unit 34 includes an automatic response execution unit 341, a response selection unit 342, a human operation unit 343, and a feedback processing unit 344. The automatic response execution unit 341 is a module that automatically executes a response based on the analysis results from the analysis unit 332 using the generation system AIGa1. In this embodiment, the automatic response execution unit 341 also has the function of generating numerical changes in predetermined items according to the time series from the information stored in the observation record database 35d, which has been extracted by the change extraction unit 332i of the analysis unit 332, as response information (feedback) for the automatic response, according to the psychological state of the client.

[0081] On the other hand, the human operation unit 343 is a module that accepts predetermined human operations when a human, such as a psychologist, doctor, or counselor, operates it to generate feedback. It generates feedback that includes content such as text and audio entered by the responder through an information terminal (PC or smartphone) used by the responder, such as a psychologist. In particular, the human operation unit 343 is a module for human responders to provide feedback when it is difficult for the generation system AIGa1 to respond. Through this human operation unit 343, it connects to an information terminal used by the responder, such as a psychologist, and allows advice from the psychologist to be sent to the client.

[0082] In this embodiment, the system also includes a function to send data necessary for intervention from templates and databases to the PC used by the psychologist, for the purpose of standardizing and applying feedback patterns performed by human psychologists. Furthermore, the content of the automated response generated by the AIGa1 generation system when the automated response execution unit 341 responds can be referenced as data necessary for human intervention, such as templates.

[0083] Furthermore, even when the response selection unit 342 selects an automated response by the automated response execution unit 341, the human operation unit 343 also functions as an approval execution unit, sending a request for human approval regarding the content of the automated response, depending on the client's psychological state. The response generation unit 34 generates response information (feedback) reflecting the approval result from this approval execution unit function and notifies the client.

[0084] The response selection unit 342 is a module that selects an automatic response by the generation system AIGa1 in the automatic response execution unit 341 or a human response in the human operation unit 343 based on the analysis results by the analysis unit 332. The response generation unit 34 generates response information to be provided to the client based on the correlation characteristics analyzed by the analysis unit 332 and the selection result by the response selection unit 342. The response selection unit 342 determines the optimal response method according to the client's psychological state, performs an automatic response by the automatic response execution unit 341 if the generation system AIGa1 is able to respond, and sends a request for a human response through the human operation unit 343 if a human response is necessary under specific circumstances. The response generation unit 34 generates response information based on the human response operated in response to this request.

[0085] The aforementioned feedback processing unit 344 is a module that generates comments in the form of questions for the user, based on weighted characteristic keywords and deviation rates included in the causal relationship data, and presents them to the client. The feedback processing unit 344 generates feedback using keywords related to the external factors predicted by the AI ​​control unit 333, and prompts the user to input information about the predicted external factors in the form of questions from the character.

[0086] (Information Distribution Department 36) The information distribution unit 36 ​​is a module that, based on the client's lesson progress and under the control of the cognitive behavioral therapy execution unit 33, distributes the analysis results performed by the analysis unit 332 to each smartphone 1 via the communication interface 31 in order to synchronize them.

[0087] The synchronization processing unit 361 of the information distribution unit 36 ​​is equipped with a trigger generation function that instructs the timing for presenting feedback to the client. This trigger generation function is a module that instructs the timing of event processing, such as automatically launching the application on the smartphone 1, outputting a notification from the application, or having a character automatically start speaking within the application, as a way of presenting feedback. For example, it outputs feedback prompting the client to take the next lesson after a certain amount of time has passed since the end of the previous lesson.

[0088] (2) Smartphone 1 (1a~c) Next, the internal configuration of the client's smartphone 1 will be described. In this embodiment, clients CLa to c each use smartphones 1a to c. The hardware configuration of these smartphones 1a to c is basically the same, and the client's smartphone 1 functions as a dedicated client terminal by having a client application installed. The parent's or guardian's smartphone only needs to have the function to receive alerts from the client application and view their contents. For example, in addition to a dedicated application for receiving and viewing alerts, it may have communication applications such as an email application, a browser application, or a messaging application.

[0089] This section describes the client's smartphone 1. As shown in Figure 5, the client's smartphone 1 is equipped with a communication interface 11, an input interface 12, an output interface 13, an application execution unit 14, and memory 15. The hardware configuration is the same for the psychologist's and guardian's smartphones.

[0090] The communication interface 11 is a communication interface for data communication and has the function of contactless communication via wireless or other means, as well as contact (wired) communication via cables, adapters, etc. The input interface 12 is a device for inputting user operations, such as a mouse, keyboard, operation buttons, or touch panel 12a. The output interface 13 is a device for outputting video and sound, such as a display or speaker. In particular, this output interface 13 includes a display 13a such as an LCD display, and this display unit is superimposed on the touch panel 12a, which is the input interface.

[0091] Memory 15 is a storage device that stores the OS (Operating System), firmware, programs for various applications, and other data. Within Memory 15, user IDs or terminal IDs that identify users or terminals are stored, as well as lesson data and worksheets downloaded from the management server 3, and input data processed by the application execution unit 14. In particular, in this embodiment, Memory 15 stores scenario data obtained from the management server 3, movie data used in lesson mode, and monitoring lists referenced when alerts are issued.

[0092] The application execution unit 14 is a module that executes applications such as general operating systems, game applications, and browser software, and is usually implemented by a processing unit such as a CPU. In particular, in the client's smartphone 1, the cognitive behavioral therapy feedback program according to the present invention is executed in this application execution unit 14, and a display data generation unit 146, an output control unit 147, a location information acquisition unit 148, and a cognitive behavioral therapy execution unit 141 are virtually constructed.

[0093] The cognitive behavioral therapy execution unit 141 is a module that executes lesson mode and work mode as event processing according to the control of the analysis unit 332. In this embodiment, the cognitive behavioral therapy execution unit 141 has the function of sequentially unfolding story mode and game mode according to the rules, logic, and algorithms related to cognitive behavioral therapy described in the scenario, and generating various event processing to advance cognitive behavioral therapy. It synchronizes with the cognitive behavioral therapy execution unit 33 on the management server 3 and generates event processing such as playing lesson movies and inputting into worksheets according to the scenario progress, the user's lesson progress, time, and current location.

[0094] Furthermore, in this embodiment, the cognitive behavioral therapy execution unit 141 is configured to cooperate with the cognitive behavioral therapy execution unit 33 on the management server 3 side. The management server 3 performs part of the cognitive behavioral therapy progress processing, while the cognitive behavioral therapy execution unit 141 on the smartphone 1 side processes part of the graphic processing and event processing. For example, the management server 3 generates the conditions for event occurrence, sends these conditions to the smartphone 1, and the smartphone 1 processes the actual event generation (trigger generation) and the corresponding graphic processing. The cognitive behavioral therapy execution unit 141 includes an automatic thought acquisition unit 142, a biometric information acquisition unit 143, an operation identification unit 144, and a synchronization processing unit 145.

[0095] The automatic thought acquisition unit 142 is a module that acquires text, images, and other biometric information from the client through the input interface 12, camera, and other sensors provided in the smartphone 1. The automatic thoughts acquired here include behavioral records entered by the client themselves, images (client's facial expressions) captured by the camera 12b, location information acquired by the location information acquisition unit 148, signals obtained from various sensors such as the accelerometer, and biometric information such as blood flow, body temperature, and heart rate measured by other sensors and acquired by the biometric information acquisition unit 143.

[0096] The operation identification unit 144 is a module that uses a machine learning model to estimate the level of awareness of an operation in order to identify differences between the client's conscious and unconscious operations in both the incomplete operation history and the decided operation history, and evaluates the client's cognitive and emotional tendencies based on the identified level of awareness.

[0097] The synchronization processing unit 145 is a module that synchronizes and links processing on the smartphone 1 with processing on the management server 3. The display data generation unit 146 is a module that generates display data to be displayed on the display 13a. The display data is generated by combining graphic data, image data, text data, video data, audio, and other data.

[0098] The location information acquisition unit 148 is a module that acquires information regarding the current time and current location as current information. In this embodiment, current location information is input from GPS or the like, and current time information is input from the timing unit. The location information acquisition unit 148 has the function of acquiring and recording location information indicating the position of the device. This location information acquisition function includes, for example, a method of detecting the device's position by signals from satellites, such as GPS, or a method of detecting the position by radio wave strength from a mobile phone base station 22 or a Wi-Fi communication access point, as shown in Figure 1.

[0099] Furthermore, the output control unit 147 is equipped with a trigger function that controls the timing of presenting feedback according to instructions from the management server 3. This trigger function is a module that instructs the timing of event processing, such as automatically starting the application, outputting notifications from the application, or having characters automatically start speaking within the application, as a way of presenting feedback. For example, it outputs feedback prompting the user to take the next lesson after a certain amount of time has passed since the end of the previous lesson.

[0100] In this embodiment, feedback is triggered by input of current location, movement speed, current time, and elapsed time. However, the present invention is not limited to this. For example, external factors may be predicted or trigger data generated based on detection data from detection devices such as a camera, thermometer, heart rate monitor, and microphone connected to the input interface 12. These detection devices can be provided in a wearable terminal connected via wireless communication.

[0101] (Operation of the cognitive behavioral therapy feedback system S) The mental information input method of the present invention can be implemented by operating the cognitive behavioral therapy feedback system S described above. Figures 6 to 8 show the operation of the cognitive behavioral therapy feedback system S. Note that the processing procedure described below is merely an example, and each process may be modified as much as possible. Furthermore, depending on the embodiment, steps in the processing procedure described below can be omitted, replaced, and added as appropriate.

[0102] (1) Overall operation As shown in Figure 6, first, the user terminal, smartphone 1, launches a cognitive behavioral therapy feedback application to input automated thoughts, which are then acquired by the management server 3 (S201, S101). In addition, during this automated thought input process, incomplete operations are monitored and recorded. In this monitoring of incomplete operations, any operational actions such as input errors, rewriting, or deletion before pressing the send or confirm button during the automated thought input process are traced as incomplete operations.

[0103] For example, as shown in Figure 8, in the initial stages of the input operation, the user initially entered a record of behavior based on negative self-perception, such as "I was tired from the morning and couldn't concentrate on work." However, this entry was partially deleted and rewritten to reflect positive self-perception, such as "I felt anxious from the morning, but I was able to make progress on my work." This entry was also later corrected, and ultimately, the user acknowledged the negative mental burden of "feeling anxious," recognized the causal relationship of "losing concentration," and spontaneously adopted the solution of "taking a walk to clear their head," but ultimately arrived at the mental result of "not feeling refreshed." Upon receiving such automated thought input and monitoring and recording of the incomplete operations during that process, the management server 3 references the pre-trained model (S102) and generates emotion data (S103).

[0104] Then, the automatic thoughts entered by the client themselves are compared with the emotional data generated by the AIGa1 generator to calculate the discrepancy rate (S104), and an evaluation of cognitive distortion is performed (S105). Next, based on this evaluation of cognitive distortion, various analyses of the evaluation are performed (S106). In this embodiment, analyses of the correlation and causal relationship between external events and automatic thoughts, qualitative evaluations regarding the recovery or progression of cognitive distortion, and diagnoses for self-counseling are performed.

[0105] For example, in the example shown in Figure 8, the initial negative self-perception input is deleted, then corrected to a positive description, and finally the decision is reached based on the result that the individual attempted to resolve the issue themselves but was unsuccessful. As for the evaluation of this behavior, as shown in Figure 9, although negative automatic thoughts are initially expressed in the initial input, they are later deleted, suggesting that such thoughts are likely to be the root cause of daily stress and anxiety, indicating a high degree of mental dissociation. Subsequently, further corrections are made, and while anxiety and fatigue were initially emphasized, the final description emphasizes positive cognitions such as "I tried to concentrate" and "I tried to change my mood by taking a walk." In this case, human intervention is often necessary, so a positive response should be given depending on the situation and the degree of dissociation, such as "You were able to change your mood by taking a walk. The connection between your actions and emotions is becoming clearer within you, which is a good sign."

[0106] Furthermore, in the example in Figure 8, the description of the behavior "I couldn't concentrate on my work" is ultimately corrected to a self-assessment of "I was able to make progress on my work." Since this shows a positive trend, the AI's automated response may be limited to objective evaluations and numerical values ​​only, such as "It is highly likely that 'cognitive restructuring,' a common process in cognitive behavior, has progressed by about 7.2%." In this case, as shown in Figure 9, the numerical changes related to cognitive restructuring over time may be extracted and returned as response information in the form of a flag diagram showing these numerical changes.

[0107] By referring to this self-monitoring input and the history of incomplete operations, the underlying psychological state is estimated. In the example shown in Figure 8, the initial entries made by the client often express their emotions and thoughts honestly, and if they are deleted, it may reflect the core emotions or problems they are actually experiencing. In response to this, a human intervention might be used to reply, such as, "The act of deletion itself may be part of your self-defense mechanism. When there is resistance to expressing negative emotions or a desire not to show weakness to others, it is possible that there are further emotions or unresolved issues behind it."

[0108] On the other hand, depending on the client's condition, as the description progresses, the AI ​​may diagnose that the client is showing signs of recovery, such as finding solutions or coping behaviors (trying to take a walk) rather than simply being overwhelmed by negative emotions, and then, as shown in Figure 9, it may respond with an automated AI response such as, "As a result of the treatment, there are signs of improvement of approximately 84% compared to last month," along with objective numerical changes such as graphs.

[0109] Based on the results of these various analyses, an appropriate feedback mode is selected (S107), and feedback processing is performed through the selected mode to generate output information (S108). Figure 10 shows the feedback mode patterns of the generation system AIGa1 and smartphone 1.

[0110] Basically, feedback to the client is provided through human intervention by a psychologist or other Dr1, including psychological advice, counseling, encouragement, and reports on the healing process. However, depending on the client's psychological state, for example, if they are showing signs of recovery and it is acceptable for the generative system AIGa1 to respond mechanically and automatically, or if the client is uncomfortable with interpersonal interactions, the response selection unit 342 determines and selects whether to correspond to the generative system AIGa1 or to the psychologist or other Dr1, as shown in Figure 10(a). Here, based on the analysis results by the analysis unit 332, a response selection step is executed in which the response selection unit selects either an automatic response by the generative system AIGa1 or a human response. As a result, based on the correlation characteristics analyzed by the analysis unit 332 and the selection result by the response selection unit 342, the response generation unit 34 generates response information to be provided to the client and notifies the client.

[0111] Furthermore, the response selection unit 342 automatically responds when it is possible to respond using only the generation system AIGa1, depending on the client's psychological state. However, as shown in Figure (b), if a human response is necessary under certain circumstances, the generation system AIGa1 sends a request for a human response, and the response generation unit 34 sends response information generated by the human operation performed in response to this request to the client. In this way, both the efficiency of AI and the emotional support of humans are utilized by intervening with human intervention as needed.

[0112] On the other hand, in cases where the client shows a strong tendency towards recovery, a mechanical and automated response by AI may actually allow the treatment to proceed more smoothly. In such cases, as shown in Figure (c), the AIGa1 generator sends a graph or other data showing numerical changes that succinctly indicate the healing tendency (recovery tendency) to the client as a response. Specifically, the change extraction unit 332i extracts numerical changes in predetermined items according to the time series from observation record information, including numerical values ​​indicating the client's state, stored in the observation record database 35d. This unit generates these numerical changes as response information and sends it to the client as a response from the AIGa1 generator, without human intervention. This allows the client to quantitatively understand changes in their psychological state and healing tendencies, reducing the interpersonal burden on the client while enabling more accurate psychological support.

[0113] Furthermore, as mentioned above, even when automatic responses are possible using only the generation system AIGa1, some clients may not want to rely entirely on the AI's responses. Therefore, as shown in Figure (d), even when an automatic response by the generation system AIGa1 is selected, the response selection unit 342, depending on the client's psychological state, sends a request for human approval to the psychologist or other Dr1 regarding the content of the automatic response through the approval execution function of the automatic response execution unit 341 or the human operation unit 343. The response generation unit 34 generates response information reflecting the approval result of the approval execution function, and the information distribution unit 36 ​​executes the notification in accordance with the human approval. This allows the content of the AIGa1's responses to be confirmed by the human psychologist or other Dr1, and allows for human adjustments if necessary. This increases the client's trust in the AI's automatic responses and provides the client with consistency and a sense of security.

[0114] Furthermore, if the client requires a human response, or if the client strongly desires a human response, a human response by a psychologist or other professional (Dr1) is selected. In this case, as shown in Figure (d), the human operation unit 343 accepts the human operation from the psychologist or other professional (Dr1), and in response to that human operation, it presents a hypothetical automated response as a response proposal, showing what kind of response the generation system AIGa1 would have given, which can then be referenced. As a result, the burden on the psychologist or other professional (Dr1) providing the human response is reduced, human error is avoided, and up-to-date and comprehensive responses can be achieved. Furthermore, in order to analyze the client's psychological state when making a selection using the response selection unit 342, this embodiment analyzes the correlation between behavioral information and cognitive information by considering the client's history of incomplete operations and decision operations in their behavior and actions.

[0115] The response information generated in this manner is delivered to the client's smartphone 1, and after waiting for feedback response processing from smartphone 1, synchronization processing is performed with the application on smartphone 1 (S109 and S202). The communication session progresses while sending and receiving the data necessary for feedback, and feedback is performed in various modes (S204). Subsequently, each of the processes S201-203 and S101-109 described above is repeated until the application terminates ("Y" in S204) ("N" in S204).

[0116] (2) Feedback execution The process related to the execution of feedback in step S203 described above will now be explained in detail. Figure 7 shows the sequence during feedback execution. As shown in the figure, first, the management server 3 performs an evaluation of cognitive distortion (S301), and then it is determined whether the evaluation value (here, the "deviation rate" mentioned above) is greater than the threshold (S302). In step S302, if it is determined that the evaluation value exceeds the threshold ("Y" in S302), it is determined that cognitive distortion has occurred, and this determination result is reflected in the subsequent feedback.

[0117] On the other hand, in step S302, if the evaluation value is determined to be below the threshold ("N" in S302), it is determined that no cognitive distortion has occurred, and this determination is reflected in subsequent feedback. After the evaluation value has been determined, the pattern rules are referenced according to the evaluation value, and scenarios necessary for each mode, such as for characters or chat, are generated according to the feedback mode (S304).

[0118] After this scenario is generated, a response for cognitive reconstruction is initiated for the client (S305), and the smartphone 1 executes event processing for this cognitive reconstruction (S401). In this event processing, a response is executed by the generation system AIGa1, which can be, for example, a dialogue with a character by the generation system AIGa1, or a chat dialogue with the generation system AIGa1. In accordance with this event processing, the management server 3 provides the client with support from the generation system AIGa1 or a human through dialogue with the character or chat (S306). Support here can include in-application functions such as "asking questions," "prompting awareness," and "encouraging messages," as well as outputting messages through other messaging applications, email, smart speakers, etc.

[0119] Then, based on the client's responses during this cognitive reconstruction, the causal relationship between the automatic thought sentences and emotions is estimated, and the relevant sections are highlighted (S403). At this time, if it is necessary to observe the client's facial expressions, biometric information is also acquired through functions such as video chat (S404). In addition, the client's responses during the interaction in this cognitive reconstruction event processing are acquired by the management server 3 as new automatic thoughts and become subject to re-evaluation.

[0120] Subsequently, self-counseling is performed (S405). Here, automatic thoughts and worksheets are entered, and biometric information such as voice, video, blood flow, and heart rate is acquired as needed (S406). These acquired automatic thoughts and biometric information are transmitted to the management server 3 and recorded (S407, S307). Upon receiving this automatic thoughts and biometric information, a decision is made as to whether re-evaluation is necessary (S308). If necessary ("necessary" in S308), the processing from the evaluation of cognitive distortion (S301) onward is executed again. On the other hand, if re-evaluation is not necessary ("unnecessary" in S308), or when ending the mode ("Y" in S408), step S203 is terminated.

[0121] (Effects / Actions) As explained above, according to this embodiment, by accumulating and analyzing the client's self-observation records and flexibly selecting responses from the generation system AIGa1 or the psychologist Dr1, it becomes possible to reduce the human burden on the psychologist Dr1 while providing appropriate support according to the psychological state of the client CLa~c, and to concentrate human resources on clients who truly need human counseling.

[0122] Furthermore, in this embodiment, the system automatically selects a generative AIGa1 or a human response from a psychologist or other professional (Dr1) according to the client's psychological state. By intervening or acknowledging as needed, both the efficiency of AI and the emotional support of humans can be utilized. This facilitates human intervention in situations where AI-based responses are deemed inappropriate, thereby enabling appropriate psychotherapy.

[0123] Furthermore, in automated responses using the AIGa1 generation system, the time-series changes in the client's behavior and psychological state are analyzed and mechanically provided to the client, allowing the client to quantitatively understand their healing progress. This reduces the interpersonal burden on the client while enabling more accurate psychological support. On the other hand, even when a human response is selected, the AI's automated response can be referenced, reducing the burden on psychologists and other Dr1s, avoiding human error, while enabling the execution of up-to-date and comprehensive responses. This allows for rapid and effective human responses based on AI support.

[0124] In this embodiment, by acquiring and accumulating a history of incomplete and decided operations based on the client's behavior and actions, and analyzing the correlation between behavioral and cognitive information, it becomes possible to understand the client's behavioral and cognitive tendencies in detail. This makes it possible to generate appropriate responses, provide more individualized support, and reduce the client's psychological burden.

[0125] Furthermore, in this embodiment, by comparing the history of incomplete operations and the history of decided operations, and calculating feature patterns based on the comparison results, a natural language model is utilized to generate responses through machine learning, thereby providing appropriate response information that matches the client's state. This enables rapid response to changes in the client's behavior and cognition, and by using a machine learning model, it is possible to automatically generate responses that are optimal for individual needs, thereby achieving more accurate support.

[0126] In this embodiment, by analyzing the client's operation patterns (such as time delays, frequency of re-entry, and deletion operations) and estimating the client's psychological state based on these patterns, a more effective response can be provided by adjusting the content and timing of the response based on the analysis results. Furthermore, in this embodiment, by identifying conscious and unconscious operations in the history of incomplete operations and the history of decided operations, and evaluating the client's cognitive and emotional tendencies based on these, it becomes possible to make appropriate evaluations based on the level of awareness regarding operations, thereby improving the accuracy of treatment.

[0127] It should be noted that the present invention is not limited to the embodiments described above, and the components can be modified and implemented in practice without departing from the spirit of the invention. Furthermore, various inventions can be formed by appropriately combining the multiple components disclosed in the above embodiments. For example, some components may be deleted from all the components shown in the embodiments. [Explanation of symbols]

[0128] CLa~c... Client Ga1…Generation AI Dr1…Psychologist etc. S... Cognitive Behavioral Therapy Feedback System 1…Smartphone 1a~c... Smartphone 2…Internet 3…Management Server 11…Communication Interface 12…Input Interface 12a...Touch panel 12b... Camera 13…Output Interface 13a…Display 14…Application execution unit 15…Memory 22… Wireless base stations 31…Communication Interface 32…Authentication Department 32a...Location information management department 33…Cognitive Behavioral Therapy Implementation Department 34...Response generation unit 35…Databases 35a... AI Learning Database 35b...User Database 35c... Cognitive Behavioral Therapy Database 35d... Observation Record Database 36… Information Distribution Department 37…Billing Processing Unit 141... Cognitive Behavioral Therapy Implementation Department 142…Automatic Thought Acquisition Department 143... Biometric Information Acquisition Unit 144... Operation identification unit 145...Synchronization Processing Unit 146...Display data generation unit 147…Output Control Unit 148...Location information acquisition unit 331... Observation Record Department 331a...Input receiving unit 331b…Unfinished operation acquisition unit 331c…Decision operation acquisition unit 331d... Sensor information acquisition unit 331e... Information Control Unit 331f…Comparison section 331g...Pattern calculation unit 331h... Operation Pattern Analysis Department 332…Analysis Department 332a…Automatic thought acquisition part 332b… Reaction Classification Section 332c…Emotion generation part 332d... Pre-trained model management department 332e...Judgment section 332f...Evaluation Department 332g…Evaluation Execution Department 332h... Causal Relationship Analysis Department 332i... Change extraction unit 332j...Qualitative Evaluation Calculation Department 332k...Self-Counseling Management Department 332l…Biological Information Analysis Department 333…AI control unit 334a…Comparison section 334b...Pattern calculation unit 334c... Operation Pattern Analysis Unit 341...Automatic response execution unit 342...Response Selection Section 343...Human operation section 344…Feedback Processing Unit 361...Synchronization Processing Unit

Claims

1. A system for inputting mental information, including self-observation records based on the actions or operations of the person receiving support, An incomplete operation acquisition unit that acquires the actions or operations performed by the person receiving support as an incomplete operation history, A decision operation acquisition unit acquires input information determined by the actions or operations of the person receiving support as a decision operation history, An observation record storage unit that stores self-observation records including the incomplete operation history and the decision operation history, Based on the aforementioned self-observation records, a correlation feature analysis unit analyzes the correlation between the behavioral information and cognitive information of the person being supported, A response generation unit generates response information to be provided to the person to be supported, in accordance with the analysis results in the correlation feature analysis unit. A mental information input system characterized by having the following features.

2. A comparison unit that compares the incomplete operation history with the decision operation history, A pattern calculation unit analyzes the correlation between the behavioral and cognitive information of the person being supported and the comparison results obtained by the comparison unit, and calculates a characteristic pattern that characterizes the analyzed correlation. A natural language model trained to calculate response information corresponding to the comparison result between the incomplete operation history and the decision operation history and the feature pattern, A response request unit receives the comparison result between the incomplete operation history and the decision operation history and the feature pattern, generates and executes a prompt for the natural language model to generate the response information. Furthermore, it is equipped with The mental information input system according to claim 1.

3. The system further includes an operation pattern analysis unit that analyzes operation patterns including at least one of the following in the input operations of the person being supported: time delay, frequency of re-entry, occurrence of deletion operations, and time of occurrence. Based on the analysis results from the operation pattern analysis unit, the response generation unit estimates the potential psychological state of the person being supported and determines the content and timing of the response. A mental information input system according to claim 1 or 2, characterized in that it is the same as described in claim 1 or 2.

4. In both the incomplete operation history and the decided operation history, the system further includes an operation identification unit that uses a machine learning model to estimate the level of awareness of an operation in order to distinguish between conscious and unconscious operations of the person being supported. Based on the level of consciousness identified by the aforementioned operation identification unit, the cognitive and emotional tendencies of the person receiving support are evaluated. A mental information input system according to any one of claims 1 to 3.

5. A program for inputting mental information, including self-observation records based on the actions or actions of the person receiving support, and which uses a computer. An incomplete operation acquisition unit that acquires the actions or operations performed by the person receiving support as an incomplete operation history, A decision operation acquisition unit acquires input information determined by the actions or operations of the person receiving support as a decision operation history, An observation record storage unit that stores self-observation records including the incomplete operation history and the decision operation history, Based on the aforementioned self-observation records, a correlation feature analysis unit analyzes the correlation between the behavioral information and cognitive information of the person being supported, Response generation unit generates response information to be provided to the person to be supported, in accordance with the analysis results in the correlation feature analysis unit. A mental information input program characterized by functioning as such.

6. The aforementioned computer, A comparison unit that compares the incomplete operation history with the decision operation history, A pattern calculation unit analyzes the correlation between the behavioral and cognitive information of the person being supported and the comparison results obtained by the comparison unit, and calculates a characteristic pattern that characterizes the analyzed correlation. A natural language model trained to calculate response information corresponding to the comparison result between the incomplete operation history and the decision operation history and the feature pattern, A response request unit receives the comparison result between the incomplete operation history and the decision operation history, as well as the feature pattern, generates and executes a prompt for the natural language model, and generates the response information. The mental information input program according to claim 5, characterized in that it is further made to function as such.

7. The aforementioned computer, It will further function as an operation pattern analysis unit that analyzes operation patterns including at least one of the following in the input operations of the person being supported: time delay, frequency of re-entry, occurrence of deletion operations, and time of occurrence. Based on the analysis results from the operation pattern analysis unit, the response generation unit estimates the potential psychological state of the person being supported and determines the content and timing of the response. The mental information input program according to claim 5.

8. The aforementioned computer, In both the incomplete operation history and the decided operation history, the operation identification unit further functions as an operation identification unit that estimates the level of awareness of an operation using a machine learning model, in order to distinguish between conscious and unconscious operations of the person being supported. Based on the level of consciousness identified by the aforementioned operation identification unit, the cognitive and emotional tendencies of the person receiving support are evaluated. A mental information input program according to any one of the features of 5.

9. A method for inputting mental information, including self-observation records based on the actions or actions of the person receiving support, The computer acquires actions or operations performed by the person being supported as an incomplete operation history, and acquires input information determined by the actions or operations performed by the person being supported as a determined operation history. An observation record storage step in which a self-observation record including the incomplete operation history and the decision operation history is stored in the observation record storage unit, The correlation feature analysis unit of the computer analyzes the correlation between the behavioral information and cognitive information of the person being supported, based on the self-observation record, in a correlation feature analysis step. The response generation step involves the computer's response generation unit generating response information to be provided to the person being supported, in accordance with the analysis results in the correlation feature analysis unit. A method for inputting mental information, characterized by including [a specific element].

10. The pattern calculation unit of the computer analyzes the correlation between the behavioral and cognitive information of the person being supported, based on the comparison result between the incomplete operation history and the decision operation history, and calculates a characteristic pattern that characterizes the analyzed correlation. A response request step in which a natural language model trained to calculate response information corresponding to the comparison result between the incomplete operation history and the decision operation history and the feature pattern receives the comparison result between the incomplete operation history and the decision operation history and the feature pattern, and the computer's response request unit generates and executes a prompt for the natural language model to generate response information. The mental information input method according to claim 9, further comprising the above.

11. The process further includes an operation pattern analysis step in which the operation pattern analysis unit analyzes operation patterns that include at least one of the following in the user's input operations: time delay, frequency of re-entry, occurrence of deletion operations, or time period of occurrence. In the response generation step, the potential psychological state of the person being supported is estimated based on the analysis results by the operation pattern analysis unit, and the content and timing of the response are determined. The mental information input method according to feature 9.

12. In both the incomplete operation history and the decided operation history, the computer's operation identification unit further includes an operation identification step in which it estimates the level of awareness of an operation using a machine learning model in order to distinguish between conscious and unconscious operations of the person being supported. Based on the level of awareness identified in the aforementioned operation identification step, the cognitive and emotional tendencies of the person receiving support are evaluated. The mental information input method according to feature 9.

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