system

Abstract

We provide the system. [Solution] A means for converting audio data acquired using an audio input device into text data, An artificial intelligence means for analyzing the user's emotional state based on the text data, A means for predicting future behavioral tendencies based on the aforementioned emotional state and generating specific action suggestions for the user, A system including a display device for presenting the aforementioned action suggestions to the user.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Understanding one's own emotions and behavior patterns is a difficult task for many users, which hinders self-growth and improvement of the quality of life. Also, when making a future action plan, it is difficult to choose an appropriate action due to the lack of specific guidelines. In such a situation, there is a need to provide support for users to better understand their own emotions and take effective actions in future life.

Means for Solving the Problems

[0005] This invention provides a system that converts voice data acquired using a voice input device into text data, and then uses artificial intelligence to analyze the user's emotions based on this data. Furthermore, this system predicts future behavioral tendencies based on the analysis results and generates and presents specific action suggestions to the user, thereby promoting user understanding and supporting the formulation of appropriate action plans. In addition, this system refers to past activity records to clearly identify the user's behavioral patterns. As a result, users can gain deeper insights into their own emotions and behaviors, promoting self-growth.

[0006] A "voice input device" is a device used to acquire voice data as digital data.

[0007] "Voice data" refers to digital data based on a speaker's utterance, acquired by a voice input device.

[0008] "Text data" refers to string data generated by analyzing audio data, and it represents linguistic information.

[0009] "Artificial intelligence tools" are computer programs that analyze text data to understand and evaluate the emotional state of users.

[0010] "Emotional state" refers to data that represents the user's psychological and emotional state, including the type and intensity of emotions identified through analysis.

[0011] "Future behavioral tendencies" refer to data that predicts the user's potential future actions and choices, and are inferred based on past behavioral patterns and current emotional states.

[0012] "Action suggestions" refer to advice that indicates specific actions or choices that users should take, based on predicted future behavioral trends.

[0013] A "display device" is a device used to visually present information generated by a system, and includes displays and screens.

[0014] An "activity log" is a collection of data about a user's past actions and their results, and serves as a source of information for identifying behavioral patterns.

[0015] "Behavioral patterns" refer to certain tendencies or characteristics observed in users' behavior, and are derived from past data. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the language used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This system, centered around a voice input device, acquires voice data and performs emotion analysis of the user, as well as suggests future actions. A specific implementation of the system is described below.

[0038] First, the user launches the application using their smartphone or a compatible device and accesses the voice diary function. At this time, the device activates the voice input device and records the user's voice data. The recorded voice data is acquired in real time and sent to the server while maintaining quality.

[0039] The server utilizes speech recognition technology to convert received audio data into text data. In this conversion process, the server uses the latest learning algorithms to convert audio information into text with high accuracy. Subsequently, based on the generated text data, the server uses artificial intelligence to analyze the user's emotional state.

[0040] After the emotion analysis is complete, the server refers to past activity records and combines them with the obtained emotional states to predict the user's future behavioral tendencies. Based on these predictions, the server generates specific action suggestions and organizes their content. The generated action suggestions are provided as specific and practical advice that the user can incorporate into their own action plan.

[0041] Finally, the refined action suggestions are presented to the user via the device, communicated in an easy-to-understand format through visual displays. Users can use these suggestions to set specific actions for personal growth and life improvement. This entire system enables users to gain a deeper understanding of their own emotions and develop constructive action plans for the future.

[0042] For example, if a user records in voice, "Today was a fulfilling day. My new project was a success," the device sends this to the server. Based on voice recognition and emotion analysis, the server identifies emotional states such as "joy" and "sense of accomplishment." Based on this, the server suggests the next step, "It's time to use your sense of accomplishment to take on a new challenge," and presents this suggestion to the user. Through this process, the user can use their sense of accomplishment as motivation and plan their next actions.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user launches the smartphone application and selects the voice diary function. This starts voice input mode, and the device activates the microphone and prepares to receive voice data.

[0046] Step 2:

[0047] When a user speaks the contents of their diary aloud, the device records the audio in real time. This recorded audio data is temporarily stored on the device.

[0048] Step 3:

[0049] Once recording is complete, the device sends the audio data to the server. This data transmission takes place over the internet using a secure communication protocol.

[0050] Step 4:

[0051] The server uses a speech recognition engine to convert the received audio data into text data. During this process, measures are taken to ensure appropriate conversion based on the speaker's language and speaking style.

[0052] Step 5:

[0053] The server uses artificial intelligence to perform sentiment analysis on the converted text data. This analysis extracts emotional expressions within the text and identifies specific emotional states.

[0054] Step 6:

[0055] The server compares this information with accumulated past activity records and predicts the user's future behavioral tendencies based on identified emotional states. This prediction is made using statistical methods based on past patterns.

[0056] Step 7:

[0057] Based on the prediction, the server generates action suggestions. These suggestions are designed to be specific and actionable for the user.

[0058] Step 8:

[0059] Once the proposal is finalized, the server formats the information and converts it into a user-friendly format.

[0060] Step 9:

[0061] The completed action plan is sent to the device and displayed on the application screen. The user can refer to it and use it as material to plan their next actions.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] Conventional voice input systems often simply convert voice data into text data without further emotion analysis or the development of specific action suggestions. As a result, there is a lack of support for users to fully understand their own emotions and plan concrete actions that lead to improvements in their lives.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes a device that converts voice information into text information, a machine learning device that analyzes emotions based on the text information, and a device that predicts future behavior and generates specific action guidelines. This enables users to gain a deeper understanding of their own emotions and plan their future actions.

[0067] A "voice input device" is a device used to acquire a user's voice as digital data.

[0068] "Textual information" refers to text data obtained by converting audio data collected through voice input devices.

[0069] A "machine learning device" is a device that performs artificial intelligence technology used to analyze textual information and identify the emotions of users.

[0070] "Action guidelines" are information that, based on analyzed emotional data, suggests specific actions that users should take in the future.

[0071] A "display device" is a device that presents behavioral guidelines to users in an easy-to-read format.

[0072] This system provides advanced emotion analysis and behavioral suggestions using a voice input device. Its main components include a voice input device, a voice recognition and emotion analysis system on the server, and a display device for the user. The specific operation of the system is described below.

[0073] The user launches an application on their smartphone or voice-enabled device and uses the voice diary function. This initiates voice input, recording the user's speech. This recording is then presented to the user as a prompt, such as "Please tell us about your emotional state today." The acquired audio data is transmitted from the user's device to the server in high quality.

[0074] The server uses a cloud-based speech recognition service to convert speech data into text data. This process could involve using, for example, a commercial speech recognition API. The text data converted from speech is then subjected to sentiment analysis using a generative AI model. This analyzes the emotional nuances contained in the text and identifies specific emotional states.

[0075] Based on the sentiment analysis results, the server combines this with the user's past behavioral data to predict future behavior. Then, it uses a generative AI model to construct beneficial action suggestions for the user. For example, if a user makes a positive statement such as "Today's project was a success," a suggestion like "Maintain this momentum and take on a new challenge" might be generated.

[0076] Ultimately, the device visually presents this action suggestion to the user. The display is shown on the application screen in an easy-to-understand format. This allows the user to refer to the suggested action plan and use it to improve their daily life and personal growth.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The user launches the application on their smartphone or voice-enabled device and selects the voice diary function. This initiates voice input from the device. The prompt "Please tell us about your emotional state today" is displayed to the user, and voice data input is obtained. The output is a real-time recording of the user's speech.

[0080] Step 2:

[0081] The terminal compresses the recorded audio data into packets. This data is transmitted to the server via the network while maintaining high quality. It takes audio data as input and sends a digitized audio stream as output to the server.

[0082] Step 3:

[0083] The server converts the received audio data into text data using speech recognition software. In this process, it utilizes a cloud-based speech recognition API to perform the conversion from speech to text information. It receives audio data as input and generates text data as output.

[0084] Step 4:

[0085] Based on the generated text data, the server performs sentiment analysis. Utilizing a generative AI model, it analyzes the text content, extracts emotional nuances, and identifies the user's emotional state. The input is text data, and the output is the identified emotional state.

[0086] Step 5:

[0087] The server combines past behavioral history with the results of sentiment analysis to predict future behavior. This process generates specific action suggestions that are helpful to the user based on the generated emotional state. It takes emotional state and historical data as input, and the output is action suggestions.

[0088] Step 6:

[0089] The terminal presents the user with action suggestions received from the server. These suggestions are displayed in a visually easy-to-understand format and appropriately presented on the application screen. The input is the action suggestions from the server, and the output is the information presented to the user.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] In modern purchasing behavior, consumers often make impulsive purchases based on their emotions, which tends to result in financial risks and wasteful spending. Therefore, it is necessary to properly understand consumers' emotional states and propose purchasing behaviors accordingly to support more planned and rational consumption management.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes means for converting voice data acquired using a voice input device into text data, artificial intelligence means for analyzing the user's emotional state based on the text data, and means for analyzing past consumption activities, evaluating the user's purchasing motivations, and managing consumption habits. This makes it possible to provide specific consumption management suggestions and action plans that are tailored to the user's emotions.

[0095] A "voice input device" is a device that has the function of taking in voice data as a digital signal and using it for subsequent processing.

[0096] "Text data" refers to data obtained by converting audio data into a string of characters, and is data that can be processed as digital information.

[0097] "Artificial intelligence methods" refer to techniques that use machine learning and data analysis to analyze the emotional state of users from text data.

[0098] "Action suggestions" refer to specific actions recommended to the user based on their analyzed emotional state and behavioral tendencies.

[0099] A "display device" is hardware used to provide information to users in a visual format, and includes screens and monitors.

[0100] "Consumption activity" refers to a series of actions in which users trade economic value, such as purchasing goods or using services.

[0101] "Managing consumer habits" is a method of analyzing users' purchasing behavior to eliminate waste and encourage planned consumption.

[0102] The system that implements this application consists of a terminal equipped with a voice input device and a server connected to it. First, the user inputs voice information about their daily consumption activities and expenses into the terminal. The terminal uses the voice input device to convert the voice data into a digital signal and transmits it to the server.

[0103] The server is responsible for converting received audio data into text data. The speech recognition algorithm used here utilizes speech recognition APIs such as Google® Cloud Speech-to-Text. The converted text data is then analyzed for emotional state using artificial intelligence. This analysis employs IBM Watson® Tone Analyzer and similar sentiment analysis engines.

[0104] By combining analyzed emotional states with records of existing consumer activity, the server predicts future purchasing behavior and generates specific consumer management suggestions for the user. This includes analyzing the user's past consumer activity patterns and spending trends to identify emotion-based purchasing motivations.

[0105] Ultimately, the action suggestions and consumption management suggestions compiled by the server are communicated to the user through the terminal's display device. Based on this information, the user can then engage in planned consumption behavior. For example, if a user voice-inputs, "I was feeling excited today, so I made an impulse purchase," the analysis recognizes "excitement," and a suggestion such as "Enjoy a refreshing experience within your budget" is made.

[0106] Examples of prompt statements include the following:

[0107] User comment: 'I was feeling really excited today, so I made an impulse purchase.'

[0108] Please generate action suggestions for the AI ​​model that take into account emotions and consumer behavior.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The user uses a voice input device to record information about their daily consumption activities in voice. This voice data becomes the input. The terminal captures this data using the microphone of the voice input device and stores it as a digital signal.

[0112] Step 2:

[0113] The terminal transmits the acquired audio data to the server. The input to this operation is digitized audio data, and the output is transmission to the server. This transmission takes place over a network such as Wi-Fi or mobile data communication.

[0114] Step 3:

[0115] The server converts the received audio data into text data. The input for this step is digital audio data, and the text data is generated using speech recognition with the Google Cloud Speech-to-Text API. The output of this process is the converted text data.

[0116] Step 4:

[0117] The server analyzes emotional states based on text data. This input is the text data from step 3, and emotional analysis is performed using tools such as IBM Watson Tone Analyzer. The output is the analyzed emotional information.

[0118] Step 5:

[0119] The server analyzes the analyzed emotional information and past consumption data to evaluate the motivations behind purchasing behavior. The input for this step is emotional information and consumption history, and data mining techniques are applied to identify purchasing motivations. The result is the output.

[0120] Step 6:

[0121] The server predicts future purchasing behavior and generates specific consumption management suggestions. The input for this step is the evaluation of purchasing motivations, and the generated suggestions are the output. A generative AI model may be used in this process.

[0122] Step 7:

[0123] The terminal receives action suggestions and consumption management suggestions sent from the server and communicates them to the user through a display device. The input for this step is the generated suggestions, and the output is a display that the user can visually interpret.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] This system converts voice data acquired using a voice input device into text data and uses an emotion engine to analyze the user's emotional state with high accuracy. A specific embodiment of this system is described below.

[0126] First, the user launches a dedicated application using a compatible device such as a smartphone or tablet to access the voice diary function. The device functions as a voice input device, recording the user's speech in real time. The recorded voice data is sent to the server while maintaining its quality.

[0127] The audio data received by the server is converted into text data by a speech recognition engine. In this conversion process, the spoken content is appropriately interpreted and stored as text information. Next, the server uses an emotion engine to analyze not only the text data but also features such as intonation, speed, and volume of the speech, in order to recognize the user's emotions with higher accuracy.

[0128] Based on the analyzed emotional information, the server refers to past activity records to identify the user's behavioral patterns. This allows the server to form a dataset for predicting future behavioral trends. Based on these predicted trends, it generates specific action suggestions. These suggestions are designed to help the user improve their life and achieve their goals.

[0129] The generated action suggestions are formatted, including visual elements, and presented to the user in an easy-to-understand manner via the device. In this way, users can gain a deeper understanding of their own emotions and develop action plans based on them.

[0130] For example, if a user leaves a voice diary entry stating, "Today was a stressful day, and I faced many problems," the device sends the audio to a server. Through speech recognition and emotion engine analysis, the server recognizes complex emotional states such as "stress" and "the need to address challenges." Based on this, the server generates and provides relaxation-promoting behavioral suggestions to the user, such as "Try to refresh yourself and make time for yourself." This process allows users to choose appropriate actions according to their emotional state, thereby reducing stress in their daily lives.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user launches the voice diary app on their smartphone and taps the button to start the voice input function. The device activates the microphone and prepares to acquire voice data.

[0134] Step 2:

[0135] When a user speaks aloud about daily events or their feelings, the device records the audio in real time and temporarily saves it to local storage.

[0136] Step 3:

[0137] After recording is complete, the device converts the recorded audio data into data packets for transmission to the server and sends them using a secure communication protocol.

[0138] Step 4:

[0139] The server processes the received audio data through a speech recognition engine, converting the audio into text data. During this text conversion, context is taken into consideration when transcribing the text.

[0140] Step 5:

[0141] Along with the converted text data, the server activates an emotion engine to analyze the intonation, speed, volume, and other aspects of the speech to comprehensively evaluate the user's emotions.

[0142] Step 6:

[0143] Based on the acquired emotional information, the server refers to past behavioral history and activity records to identify the user's behavioral patterns. This identification allows for accurate future predictions based on past data.

[0144] Step 7:

[0145] The server predicts future behavioral tendencies based on identified behavioral patterns and sentiment analysis results. Based on these tendencies, it generates optimal action suggestions for the user.

[0146] Step 8:

[0147] The generated action suggestions are organized on the server using visual elements to make them easy for the user to understand.

[0148] Step 9:

[0149] The device presents the user with organized action suggestions. These suggestions are displayed on the screen, allowing the user to plan their next actions based on the suggestions.

[0150] (Example 2)

[0151] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0152] Currently, there are limited systems that accurately analyze users' emotions based on information obtained using voice data, and then automatically generate and present concrete action suggestions to improve their lives based on that analysis. In particular, there is a challenge in improving the accuracy of emotional state analysis while simultaneously presenting suggestions in a way that is easy for users to understand.

[0153] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0154] In this invention, the server includes means for converting voice information acquired using a voice acquisition device into text information, intelligent engine means for analyzing the user's emotional state based on the text information, and means for predicting future behavioral tendencies based on the emotional state and generating specific action suggestions for the user. This makes it possible to analyze the user's emotions with high accuracy and provide personalized action suggestions.

[0155] A "speech acquisition device" is a device for collecting speech information and recording it as an electrical signal.

[0156] "Audio information" refers to the representation of sound waveforms as digital data, obtained by an audio acquisition device.

[0157] "Textual information" refers to data obtained by analyzing audio information and converting it into a corresponding text format.

[0158] An "intelligent engine system" is a system that uses artificial intelligence to analyze textual information and related information in order to infer the emotional state of the user.

[0159] "Behavioral tendencies" refer to patterns of behavior that a user may potentially take in the future, based on their past behavioral history and current emotional state.

[0160] "Action suggestions" are specific recommended actions presented to the user based on their analyzed emotional state and behavioral tendencies.

[0161] A "visualization device" is a device that has a display function to visually present generated action suggestions to the user.

[0162] This invention provides a system that analyzes a user's emotional state based on their voice and generates specific action suggestions. This system operates in conjunction with a voice acquisition device, a server, and a terminal.

[0163] Voice acquisition device

[0164] Users utilize smartphones or tablets that function as voice acquisition devices. These devices collect voice information by launching a dedicated application and recording speech. The recorded voice information is processed appropriately in real time and transmitted to a server.

[0165] server

[0166] When the server receives audio information, it converts it into text information using a speech recognition engine. This conversion process can utilize commercially available speech recognition software, such as Amazon Transcribe or the Google Speech-to-Text API. The converted text information is then input into an intelligent engine.

[0167] The intelligent engine utilizes natural language processing and acoustic analysis technologies to analyze textual information and associated features, thereby accurately predicting the user's emotional state. This analysis employs, for example, natural language processing libraries and machine learning models, which are commonly known AI technologies.

[0168] Based on the analysis of the user's emotional state, the server predicts future behavioral tendencies and generates specific action suggestions for the user. These suggestions are structured as advice and guidance aimed at improving the user's daily life and achieving their goals.

[0169] terminal

[0170] The generated action suggestions are presented to the user in an easy-to-understand manner through a visualization device on the terminal. This allows the user to create an appropriate action plan based on their emotional state.

[0171] For example, if a user records a voice diary entry saying, "I was busy and tired today," the server analyzes this text information, detects that the user is feeling stressed, and generates action suggestions such as "Take a rest." These suggestions are then displayed graphically on the device screen.

[0172] An example of a prompt when using a generative AI model might be, "Please tell me how to analyze voice data, identify the user's emotions, and generate specific action suggestions." This prompt will be used as reference during the system's learning process.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The user launches a dedicated application installed on their smartphone or tablet. Within the app, the user selects the voice diary function and begins speaking to input information. The device records this speech in real time, acting as a voice acquisition device. During this recording process, the audio is recorded as digital data, which then serves as input data for subsequent processing.

[0176] Step 2:

[0177] The terminal sends the recorded audio data to the server. In this process, the terminal appropriately compresses the audio data and transmits it to the server efficiently and securely via the communication line. Thus, compressed audio data is formed as output, and the server receives this data as input.

[0178] Step 3:

[0179] The server sends the received audio data to the speech recognition engine, where it is converted into text. In this conversion step, the speech recognition engine analyzes the audio signal using an acoustic model and a language model, and outputs the corresponding text. As a result, the audio data is saved as text data.

[0180] Step 4:

[0181] The server uses the converted text data to perform sentiment analysis using an intelligent engine. This involves integrated analysis using both natural language processing techniques based on the text data and features such as intonation and volume extracted from the audio data. The output of this process is data indicating the user's emotional state.

[0182] Step 5:

[0183] The server predicts future behavioral trends based on analyzed emotional state data and referencing past behavioral history. This process utilizes machine learning algorithms to extract behavioral patterns and generate predictive models based on input emotional state and historical data. The output provides the foundational data for future behavioral suggestions.

[0184] Step 6:

[0185] The server uses behavioral trend data and a generative AI model to create specific action suggestions. This includes a process that uses prompts to the generative AI model to automatically generate suggestions. The output is specific and personalized action suggestions for the user.

[0186] Step 7:

[0187] The terminal presents the action suggestions received from the server to the user using a visualization device. In this step, graphic elements and interfaces are adjusted to make the content of the action suggestions visually easy to understand, and finally output to the user. The user then adjusts their daily actions based on this.

[0188] (Application Example 2)

[0189] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0190] In customer interactions, there is a challenge in quickly understanding the emotional state of customers and providing an optimal customer service experience based on their individual needs. Furthermore, there is a need for a system that can provide highly accurate and appropriate action suggestions without relying on direct customer feedback.

[0191] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0192] In this invention, the server includes means for converting voice information obtained using voice acquisition means into text information, machine learning means for analyzing the user's emotional state based on the text information and the intonation, speed, and volume of the voice, and communication means for analyzing the voice information in real time and feeding the analysis results back to the user. This makes it possible to provide a fast and accurate customer service experience based on the emotional needs of the customer.

[0193] "Voice acquisition means" refers to devices or methods that acquire voice information from users in real time and convert it into data in a format suitable for analysis.

[0194] "Textual information" refers to data in text format generated using speech recognition technology based on audio information.

[0195] "Machine learning methods" refer to artificial intelligence technologies that use algorithms based on acquired data to analyze and predict a user's emotional state.

[0196] "Emotional state" refers to the state of a user's emotions and mood, as judged from characteristic quantities such as intonation, speed, and volume of their voice.

[0197] "Communication means" refers to the technologies and protocols used to send and receive analysis results between a server and a terminal in real time.

[0198] A "behavioral suggestion" is a proposal that indicates specific actions or behaviors that the user should take, based on their analyzed emotional state.

[0199] "Customer service experience" refers to the entire process of how sales staff interact with customers when explaining or providing information about products and services.

[0200] A "server" is a data processing device that receives, analyzes, and generates action suggestions for voice information.

[0201] The system that realizes this application example is equipped with an advanced voice analysis and feedback mechanism using voice acquisition means, machine learning means, communication means, and display means. First, as the voice acquisition means, a microphone mounted on smart glasses acquires conversations with customers as voice information in real time. The voice information is transmitted to a server via the communication means.

[0202] The server utilizes speech recognition technology to convert audio information into text. Common speech recognition software includes Google Cloud Speech-to-Text. The converted text information and parameters such as intonation, speed, and volume are processed using machine learning to analyze the customer's emotional state. Here, an emotion analysis engine such as IBM Watson Tone Analyzer is used.

[0203] The analyzed emotional state is transmitted back to the smart glasses via communication and displayed as visual information. This allows service staff to instantly grasp the customer's emotional needs and provide the optimal service experience.

[0204] For example, if a customer service representative receives an analysis result through smart glasses that says, "You seem a little tired today," they can immediately offer a suggestion to the customer such as, "We'll be happy to assist you as soon as possible." An example of such a prompt might be, "Generate advice on how to approach a customer who is seeking relaxation."

[0205] These features enable the system to contribute to improving the customer experience in the service industry and to provide more personalized services.

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The terminal uses a microphone built into smart glasses as a means of voice acquisition to capture conversations with customers in real time as voice information. The acquired voice information is converted into a digital signal and subjected to noise reduction processing. This voice information is transmitted to a server via a communication means. The input is the customer's voice, and the output is clean, digitized voice data.

[0209] Step 2:

[0210] The server converts the received audio data into text using speech recognition technology. A speech recognition engine like Google Cloud Speech-to-Text is used here. The text data obtained through speech recognition becomes the basis for further analysis. The input is digitized audio data, and the output is text data.

[0211] Step 3:

[0212] The server uses an emotion analysis engine to evaluate the emotional state based on the converted text information and parameters such as intonation, speed, and volume of the speech. IBM Watson Tone Analyzer is used for this analysis. As a result of the analysis, tags and scores indicating the customer's emotional state are generated. The input is text information and speech features, and the output is the analyzed emotional state and its evaluation data.

[0213] Step 4:

[0214] The server uses the analysis results to generate behavioral suggestions based on the customer's emotional state using a generative AI model. In this process, it references past data and patterns, and utilizes the learning results of the generated model. The generated suggestions are presented in a customer-centric format. The input is the analyzed emotional state and past data, and the output is the behavioral suggestions.

[0215] Step 5:

[0216] The terminal displays action suggestions obtained via communication as visual information on smart glasses. This display provides quick decision-making information for customer service staff and is presented as a prompt. Specifically, the suggested content is visualized on the glasses' display as text or simplified icons. The input is the action suggestion, and the output is the visually represented action suggestion information.

[0217] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0218] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0219] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0223] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0224] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0225] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0226] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0227] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0228] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0229] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0230] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0231] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0232] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0233] This system, centered around a voice input device, acquires voice data and performs emotion analysis of the user, as well as suggests future actions. A specific implementation of the system is described below.

[0234] First, the user launches the application using their smartphone or a compatible device and accesses the voice diary function. At this time, the device activates the voice input device and records the user's voice data. The recorded voice data is acquired in real time and sent to the server while maintaining quality.

[0235] The server utilizes speech recognition technology to convert received audio data into text data. In this conversion process, the server uses the latest learning algorithms to convert audio information into text with high accuracy. Subsequently, based on the generated text data, the server uses artificial intelligence to analyze the user's emotional state.

[0236] After the emotion analysis is complete, the server refers to past activity records and combines them with the obtained emotional states to predict the user's future behavioral tendencies. Based on these predictions, the server generates specific action suggestions and organizes their content. The generated action suggestions are provided as specific and practical advice that the user can incorporate into their own action plan.

[0237] Finally, the refined action suggestions are presented to the user via the device, communicated in an easy-to-understand format through visual displays. Users can use these suggestions to set specific actions for personal growth and life improvement. This entire system enables users to gain a deeper understanding of their own emotions and develop constructive action plans for the future.

[0238] For example, if a user records in voice, "Today was a fulfilling day. My new project was a success," the device sends this to the server. Based on voice recognition and emotion analysis, the server identifies emotional states such as "joy" and "sense of accomplishment." Based on this, the server suggests the next step, "It's time to use your sense of accomplishment to take on a new challenge," and presents this suggestion to the user. Through this process, the user can use their sense of accomplishment as motivation and plan their next actions.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] The user launches the smartphone application and selects the voice diary function. This starts voice input mode, and the device activates the microphone and prepares to receive voice data.

[0242] Step 2:

[0243] When a user speaks the contents of their diary aloud, the device records the audio in real time. This recorded audio data is temporarily stored on the device.

[0244] Step 3:

[0245] Once recording is complete, the device sends the audio data to the server. This data transmission takes place over the internet using a secure communication protocol.

[0246] Step 4:

[0247] The server uses a speech recognition engine to convert the received audio data into text data. During this process, measures are taken to ensure appropriate conversion based on the speaker's language and speaking style.

[0248] Step 5:

[0249] The server uses artificial intelligence to perform sentiment analysis on the converted text data. This analysis extracts emotional expressions within the text and identifies specific emotional states.

[0250] Step 6:

[0251] The server compares this information with accumulated past activity records and predicts the user's future behavioral tendencies based on identified emotional states. This prediction is made using statistical methods based on past patterns.

[0252] Step 7:

[0253] Based on the prediction, the server generates action suggestions. These suggestions are designed to be specific and actionable for the user.

[0254] Step 8:

[0255] Once the proposal is finalized, the server formats the information and converts it into a user-friendly format.

[0256] Step 9:

[0257] The completed action plan is sent to the device and displayed on the application screen. The user can refer to it and use it as material to plan their next actions.

[0258] (Example 1)

[0259] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0260] Conventional voice input systems often simply convert voice data into text data without further emotion analysis or the development of specific action suggestions. As a result, there is a lack of support for users to fully understand their own emotions and plan concrete actions that lead to improvements in their lives.

[0261] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0262] In this invention, the server includes a device that converts voice information into text information, a machine learning device that analyzes emotions based on the text information, and a device that predicts future behavior and generates specific action guidelines. This enables users to gain a deeper understanding of their own emotions and plan their future actions.

[0263] A "voice input device" is a device used to acquire a user's voice as digital data.

[0264] "Textual information" refers to text data obtained by converting audio data collected through voice input devices.

[0265] A "machine learning device" is a device that performs artificial intelligence technology used to analyze textual information and identify the emotions of users.

[0266] "Action guidelines" are information that, based on analyzed emotional data, suggests specific actions that users should take in the future.

[0267] A "display device" is a device that presents behavioral guidelines to users in an easy-to-read format.

[0268] This system provides advanced emotion analysis and behavioral suggestions using a voice input device. Its main components include a voice input device, a voice recognition and emotion analysis system on the server, and a display device for the user. The specific operation of the system is described below.

[0269] The user launches an application on their smartphone or voice-enabled device and uses the voice diary function. This initiates voice input, recording the user's speech. This recording is then presented to the user as a prompt, such as "Please tell us about your emotional state today." The acquired audio data is transmitted from the user's device to the server in high quality.

[0270] The server uses a cloud-based speech recognition service to convert speech data into text data. This process could involve using, for example, a commercial speech recognition API. The text data converted from speech is then subjected to sentiment analysis using a generative AI model. This analyzes the emotional nuances contained in the text and identifies specific emotional states.

[0271] Based on the sentiment analysis results, the server combines this with the user's past behavioral data to predict future behavior. Then, it uses a generative AI model to construct beneficial action suggestions for the user. For example, if a user makes a positive statement such as "Today's project was a success," a suggestion like "Maintain this momentum and take on a new challenge" might be generated.

[0272] Ultimately, the device visually presents this action suggestion to the user. The display is shown on the application screen in an easy-to-understand format. This allows the user to refer to the suggested action plan and use it to improve their daily life and personal growth.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The user launches the application on their smartphone or voice-enabled device and selects the voice diary function. This initiates voice input from the device. The prompt "Please tell us about your emotional state today" is displayed to the user, and voice data input is obtained. The output is a real-time recording of the user's speech.

[0276] Step 2:

[0277] The terminal compresses the recorded audio data into packets. This data is transmitted to the server via the network while maintaining high quality. It takes audio data as input and sends a digitized audio stream as output to the server.

[0278] Step 3:

[0279] The server converts the received audio data into text data using speech recognition software. In this process, it utilizes a cloud-based speech recognition API to perform the conversion from speech to text information. It receives audio data as input and generates text data as output.

[0280] Step 4:

[0281] Based on the generated character data, the server performs sentiment analysis. By making full use of the generation AI model, it analyzes the content of the text to extract the emotional nuances and identify the user's emotional state. The input is the character data, and the output is the identified emotional state.

[0282] Step 5:

[0283] The server fuses the past behavior history and the results of sentiment analysis to predict future actions. In this process, based on the generated emotional state, specific action proposals useful to the user are created. The input is the emotional state and past data, and the output is the action proposal.

[0284] Step 6:

[0285] The terminal presents the action proposal received from the server to the user. The proposal is displayed in a visually understandable form and appropriately presented on the application screen. The input is the action proposal from the server, and the output is the information presented to the user.

[0286] (Application Example 1)

[0287] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] In modern purchasing activities, users often engage in impulsive consumption activities based on emotions, and as a result, there is a tendency to incur economic risks and wasteful expenditures. Therefore, it is necessary to appropriately understand the user's emotional state and provide corresponding proposals for purchasing actions to support more planned and reasonable consumption management.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0290] In this invention, the server includes means for converting voice data acquired using a voice input device into text data, artificial intelligence means for analyzing the user's emotional state based on the text data, and means for analyzing past consumption activities, evaluating the user's purchasing motivations, and managing consumption habits. This makes it possible to provide specific consumption management suggestions and action plans that are tailored to the user's emotions.

[0291] A "voice input device" is a device that has the function of taking in voice data as a digital signal and using it for subsequent processing.

[0292] "Text data" refers to data obtained by converting audio data into a string of characters, and is data that can be processed as digital information.

[0293] "Artificial intelligence methods" refer to techniques that use machine learning and data analysis to analyze the emotional state of users from text data.

[0294] "Action suggestions" refer to specific actions recommended to the user based on their analyzed emotional state and behavioral tendencies.

[0295] A "display device" is hardware used to provide information to users in a visual format, and includes screens and monitors.

[0296] "Consumption activity" refers to a series of actions in which users trade economic value, such as purchasing goods or using services.

[0297] "Managing consumer habits" is a method of analyzing users' purchasing behavior to eliminate waste and encourage planned consumption.

[0298] The system that implements this application consists of a terminal equipped with a voice input device and a server connected to it. First, the user inputs voice information about their daily consumption activities and expenses into the terminal. The terminal uses the voice input device to convert the voice data into a digital signal and transmits it to the server.

[0299] The server is responsible for converting the received audio data into text data. The speech recognition algorithm used here utilizes speech recognition APIs such as Google Cloud Speech-to-Text. The converted text data is then analyzed for emotional state using artificial intelligence. This analysis employs IBM Watson Tone Analyzer and similar sentiment analysis engines.

[0300] By combining analyzed emotional states with records of existing consumer activity, the server predicts future purchasing behavior and generates specific consumer management suggestions for the user. This includes analyzing the user's past consumer activity patterns and spending trends to identify emotion-based purchasing motivations.

[0301] Ultimately, the action suggestions and consumption management suggestions compiled by the server are communicated to the user through the terminal's display device. Based on this information, the user can then engage in planned consumption behavior. For example, if a user voice-inputs, "I was feeling excited today, so I made an impulse purchase," the analysis recognizes "excitement," and a suggestion such as "Enjoy a refreshing experience within your budget" is made.

[0302] Examples of prompt statements include the following:

[0303] User comment: 'I was feeling really excited today, so I made an impulse purchase.'

[0304] Please generate action suggestions for the AI ​​model that take into account emotions and consumer behavior.

[0305] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0306] Step 1:

[0307] The user uses a voice input device to record information about daily consumption activities in voice. This voice data serves as the input. The terminal captures this data using the microphone of the voice input device and stores it as a digital signal.

[0308] Step 2:

[0309] The terminal transmits the acquired voice data to the server. The input for this operation is the digitized voice data, and the transmission to the server is the output. This transmission is carried out via a network such as Wi-Fi or mobile data communication.

[0310] Step 3:

[0311] The server converts the received voice data into text data. The input for this step is the digital voice data, and text data is generated through speech recognition using the Google Cloud Speech-to-Text API. The output of this process is the converted text data.

[0312] Step 4:

[0313] The server analyzes the emotional state based on the text data. The input for this is the text data from Step 3, and emotional analysis is performed using, for example, the IBM Watson Tone Analyzer. The output is the analyzed emotional information.

[0314] Step 5:

[0315] The server analyzes the analyzed emotional information and past consumption data to evaluate the motivation for purchasing behavior. The input for this step is the emotional information and consumption history, and data mining techniques are applied to identify the purchasing motivation. This result is the output.

[0316] Step 6:

[0317] The server predicts future purchasing behavior and generates specific consumption management suggestions. The input for this step is the evaluation of purchasing motivations, and the generated suggestions are the output. A generative AI model may be used in this process.

[0318] Step 7:

[0319] The terminal receives action suggestions and consumption management suggestions sent from the server and communicates them to the user through a display device. The input for this step is the generated suggestions, and the output is a display that the user can visually interpret.

[0320] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0321] This system converts voice data acquired using a voice input device into text data and uses an emotion engine to analyze the user's emotional state with high accuracy. A specific embodiment of this system is described below.

[0322] First, the user launches a dedicated application using a compatible device such as a smartphone or tablet to access the voice diary function. The device functions as a voice input device, recording the user's speech in real time. The recorded voice data is sent to the server while maintaining its quality.

[0323] The audio data received by the server is converted into text data by a speech recognition engine. In this conversion process, the spoken content is appropriately interpreted and stored as text information. Next, the server uses an emotion engine to analyze not only the text data but also features such as intonation, speed, and volume of the speech, in order to recognize the user's emotions with higher accuracy.

[0324] Based on the analyzed emotional information, the server refers to past activity records to identify the user's behavioral patterns. This allows the server to form a dataset for predicting future behavioral trends. Based on these predicted trends, it generates specific action suggestions. These suggestions are designed to help the user improve their life and achieve their goals.

[0325] The generated action suggestions are formatted, including visual elements, and presented to the user in an easy-to-understand manner via the device. In this way, users can gain a deeper understanding of their own emotions and develop action plans based on them.

[0326] For example, if a user leaves a voice diary entry stating, "Today was a stressful day, and I faced many problems," the device sends the audio to a server. Through speech recognition and emotion engine analysis, the server recognizes complex emotional states such as "stress" and "the need to address challenges." Based on this, the server generates and provides relaxation-promoting behavioral suggestions to the user, such as "Try to refresh yourself and make time for yourself." This process allows users to choose appropriate actions according to their emotional state, thereby reducing stress in their daily lives.

[0327] The following describes the processing flow.

[0328] Step 1:

[0329] The user launches the voice diary app on their smartphone and taps the button to start the voice input function. The device activates the microphone and prepares to acquire voice data.

[0330] Step 2:

[0331] When a user speaks aloud about daily events or their feelings, the device records the audio in real time and temporarily saves it to local storage.

[0332] Step 3:

[0333] After recording is complete, the device converts the recorded audio data into data packets for transmission to the server and sends them using a secure communication protocol.

[0334] Step 4:

[0335] The server processes the received audio data through a speech recognition engine, converting the audio into text data. During this text conversion, context is taken into consideration when transcribing the text.

[0336] Step 5:

[0337] Along with the converted text data, the server activates an emotion engine to analyze the intonation, speed, volume, and other aspects of the speech to comprehensively evaluate the user's emotions.

[0338] Step 6:

[0339] Based on the acquired emotional information, the server refers to past behavioral history and activity records to identify the user's behavioral patterns. This identification allows for accurate future predictions based on past data.

[0340] Step 7:

[0341] The server predicts future behavioral tendencies based on identified behavioral patterns and sentiment analysis results. Based on these tendencies, it generates optimal action suggestions for the user.

[0342] Step 8:

[0343] The generated action suggestions are organized on the server using visual elements to make them easy for the user to understand.

[0344] Step 9:

[0345] The device presents the user with organized action suggestions. These suggestions are displayed on the screen, allowing the user to plan their next actions based on the suggestions.

[0346] (Example 2)

[0347] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0348] Currently, there are limited systems that accurately analyze users' emotions based on information obtained using voice data, and then automatically generate and present concrete action suggestions to improve their lives based on that analysis. In particular, there is a challenge in improving the accuracy of emotional state analysis while simultaneously presenting suggestions in a way that is easy for users to understand.

[0349] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0350] In this invention, the server includes means for converting voice information acquired using a voice acquisition device into text information, intelligent engine means for analyzing the user's emotional state based on the text information, and means for predicting future behavioral tendencies based on the emotional state and generating specific action suggestions for the user. This makes it possible to analyze the user's emotions with high accuracy and provide personalized action suggestions.

[0351] A "speech acquisition device" is a device for collecting speech information and recording it as an electrical signal.

[0352] "Audio information" refers to the representation of sound waveforms as digital data, obtained by an audio acquisition device.

[0353] "Textual information" refers to data obtained by analyzing audio information and converting it into a corresponding text format.

[0354] An "intelligent engine system" is a system that uses artificial intelligence to analyze textual information and related information in order to infer the emotional state of the user.

[0355] "Behavioral tendencies" refer to patterns of behavior that a user may potentially take in the future, based on their past behavioral history and current emotional state.

[0356] "Action suggestions" are specific recommended actions presented to the user based on their analyzed emotional state and behavioral tendencies.

[0357] A "visualization device" is a device that has a display function to visually present generated action suggestions to the user.

[0358] This invention provides a system that analyzes a user's emotional state based on their voice and generates specific action suggestions. This system operates in conjunction with a voice acquisition device, a server, and a terminal.

[0359] Voice acquisition device

[0360] Users utilize smartphones or tablets that function as voice acquisition devices. These devices collect voice information by launching a dedicated application and recording speech. The recorded voice information is processed appropriately in real time and transmitted to a server.

[0361] server

[0362] When the server receives audio information, it converts it into text information using a speech recognition engine. This conversion process can utilize commercially available speech recognition software, such as Amazon Transcribe or the Google Speech-to-Text API. The converted text information is then input into an intelligent engine.

[0363] The intelligent engine utilizes natural language processing and acoustic analysis technologies to analyze textual information and associated features, thereby accurately predicting the user's emotional state. This analysis employs, for example, natural language processing libraries and machine learning models, which are commonly known AI technologies.

[0364] Based on the analysis of the user's emotional state, the server predicts future behavioral tendencies and generates specific action suggestions for the user. These suggestions are structured as advice and guidance aimed at improving the user's daily life and achieving their goals.

[0365] terminal

[0366] The generated action suggestions are presented to the user in an easy-to-understand manner through a visualization device on the terminal. This allows the user to create an appropriate action plan based on their emotional state.

[0367] For example, if a user records a voice diary entry saying, "I was busy and tired today," the server analyzes this text information, detects that the user is feeling stressed, and generates action suggestions such as "Take a rest." These suggestions are then displayed graphically on the device screen.

[0368] An example of a prompt when using a generative AI model might be, "Please tell me how to analyze voice data, identify the user's emotions, and generate specific action suggestions." This prompt will be used as reference during the system's learning process.

[0369] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0370] Step 1:

[0371] The user launches a dedicated application installed on their smartphone or tablet. Within the app, the user selects the voice diary function and begins speaking to input information. The device records this speech in real time, acting as a voice acquisition device. During this recording process, the audio is recorded as digital data, which then serves as input data for subsequent processing.

[0372] Step 2:

[0373] The terminal sends the recorded audio data to the server. In this process, the terminal appropriately compresses the audio data and transmits it to the server efficiently and securely via the communication line. Thus, compressed audio data is formed as output, and the server receives this data as input.

[0374] Step 3:

[0375] The server sends the received audio data to the speech recognition engine, where it is converted into text. In this conversion step, the speech recognition engine analyzes the audio signal using an acoustic model and a language model, and outputs the corresponding text. As a result, the audio data is saved as text data.

[0376] Step 4:

[0377] The server uses the converted text data to perform sentiment analysis using an intelligent engine. This involves integrated analysis using both natural language processing techniques based on the text data and features such as intonation and volume extracted from the audio data. The output of this process is data indicating the user's emotional state.

[0378] Step 5:

[0379] The server predicts future behavioral trends based on analyzed emotional state data and referencing past behavioral history. This process utilizes machine learning algorithms to extract behavioral patterns and generate predictive models based on input emotional state and historical data. The output provides the foundational data for future behavioral suggestions.

[0380] Step 6:

[0381] The server uses behavioral trend data and a generative AI model to create specific action suggestions. This includes a process that uses prompts to the generative AI model to automatically generate suggestions. The output is specific and personalized action suggestions for the user.

[0382] Step 7:

[0383] The terminal presents the action suggestions received from the server to the user using a visualization device. In this step, graphic elements and interfaces are adjusted to make the content of the action suggestions visually easy to understand, and finally output to the user. The user then adjusts their daily actions based on this.

[0384] (Application Example 2)

[0385] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0386] In customer interactions, there is a challenge in quickly understanding the emotional state of customers and providing an optimal customer service experience based on their individual needs. Furthermore, there is a need for a system that can provide highly accurate and appropriate action suggestions without relying on direct customer feedback.

[0387] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0388] In this invention, the server includes means for converting voice information obtained using voice acquisition means into text information, machine learning means for analyzing the user's emotional state based on the text information and the intonation, speed, and volume of the voice, and communication means for analyzing the voice information in real time and feeding the analysis results back to the user. This makes it possible to provide a fast and accurate customer service experience based on the emotional needs of the customer.

[0389] "Voice acquisition means" refers to devices or methods that acquire voice information from users in real time and convert it into data in a format suitable for analysis.

[0390] "Textual information" refers to data in text format generated using speech recognition technology based on audio information.

[0391] "Machine learning methods" refer to artificial intelligence technologies that use algorithms based on acquired data to analyze and predict a user's emotional state.

[0392] "Emotional state" refers to the state of a user's emotions and mood, as judged from characteristic quantities such as intonation, speed, and volume of their voice.

[0393] "Communication means" refers to the technologies and protocols used to send and receive analysis results between a server and a terminal in real time.

[0394] A "behavioral suggestion" is a proposal that indicates specific actions or behaviors that the user should take, based on their analyzed emotional state.

[0395] "Customer service experience" refers to the entire process of how sales staff interact with customers when explaining or providing information about products and services.

[0396] A "server" is a data processing device that receives, analyzes, and generates action suggestions for voice information.

[0397] The system that realizes this application example is equipped with an advanced voice analysis and feedback mechanism using voice acquisition means, machine learning means, communication means, and display means. First, as the voice acquisition means, a microphone mounted on smart glasses acquires conversations with customers as voice information in real time. The voice information is transmitted to a server via the communication means.

[0398] The server utilizes speech recognition technology to convert audio information into text. Common speech recognition software includes Google Cloud Speech-to-Text. The converted text information and parameters such as intonation, speed, and volume are processed using machine learning to analyze the customer's emotional state. Here, an emotion analysis engine such as IBM Watson Tone Analyzer is used.

[0399] The analyzed emotional state is transmitted back to the smart glasses via communication and displayed as visual information. This allows service staff to instantly grasp the customer's emotional needs and provide the optimal service experience.

[0400] For example, if a customer service representative receives an analysis result through smart glasses that says, "You seem a little tired today," they can immediately offer a suggestion to the customer such as, "We'll be happy to assist you as soon as possible." An example of such a prompt might be, "Generate advice on how to approach a customer who is seeking relaxation."

[0401] These features enable the system to contribute to improving the customer experience in the service industry and to provide more personalized services.

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The terminal uses a microphone built into smart glasses as a means of voice acquisition to capture conversations with customers in real time as voice information. The acquired voice information is converted into a digital signal and subjected to noise reduction processing. This voice information is transmitted to a server via a communication means. The input is the customer's voice, and the output is clean, digitized voice data.

[0405] Step 2:

[0406] The server converts the received audio data into text using speech recognition technology. A speech recognition engine like Google Cloud Speech-to-Text is used here. The text data obtained through speech recognition becomes the basis for further analysis. The input is digitized audio data, and the output is text data.

[0407] Step 3:

[0408] The server uses an emotion analysis engine to evaluate the emotional state based on the converted text information and parameters such as intonation, speed, and volume of the speech. IBM Watson Tone Analyzer is used for this analysis. As a result of the analysis, tags and scores indicating the customer's emotional state are generated. The input is text information and speech features, and the output is the analyzed emotional state and its evaluation data.

[0409] Step 4:

[0410] The server uses the analysis results to generate behavioral suggestions based on the customer's emotional state using a generative AI model. In this process, it references past data and patterns, and utilizes the learning results of the generated model. The generated suggestions are presented in a customer-centric format. The input is the analyzed emotional state and past data, and the output is the behavioral suggestions.

[0411] Step 5:

[0412] The terminal displays action suggestions obtained via communication as visual information on smart glasses. This display provides quick decision-making information for customer service staff and is presented as a prompt. Specifically, the suggested content is visualized on the glasses' display as text or simplified icons. The input is the action suggestion, and the output is the visually represented action suggestion information.

[0413] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0414] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0415] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0416] [Third Embodiment]

[0417] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0418] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0419] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0420] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0421] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0422] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0423] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0424] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0425] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0426] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0427] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0428] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0429] This system, centered around a voice input device, acquires voice data and performs emotion analysis of the user, as well as suggests future actions. A specific implementation of the system is described below.

[0430] First, the user launches the application using their smartphone or a compatible device and accesses the voice diary function. At this time, the device activates the voice input device and records the user's voice data. The recorded voice data is acquired in real time and sent to the server while maintaining quality.

[0431] The server utilizes speech recognition technology to convert received audio data into text data. In this conversion process, the server uses the latest learning algorithms to convert audio information into text with high accuracy. Subsequently, based on the generated text data, the server uses artificial intelligence to analyze the user's emotional state.

[0432] After the emotion analysis is complete, the server refers to past activity records and combines them with the obtained emotional states to predict the user's future behavioral tendencies. Based on these predictions, the server generates specific action suggestions and organizes their content. The generated action suggestions are provided as specific and practical advice that the user can incorporate into their own action plan.

[0433] Finally, the refined action suggestions are presented to the user via the device, communicated in an easy-to-understand format through visual displays. Users can use these suggestions to set specific actions for personal growth and life improvement. This entire system enables users to gain a deeper understanding of their own emotions and develop constructive action plans for the future.

[0434] For example, if a user records in voice, "Today was a fulfilling day. My new project was a success," the device sends this to the server. Based on voice recognition and emotion analysis, the server identifies emotional states such as "joy" and "sense of accomplishment." Based on this, the server suggests the next step, "It's time to use your sense of accomplishment to take on a new challenge," and presents this suggestion to the user. Through this process, the user can use their sense of accomplishment as motivation and plan their next actions.

[0435] The following describes the processing flow.

[0436] Step 1:

[0437] The user launches the smartphone application and selects the voice diary function. This starts voice input mode, and the device activates the microphone and prepares to receive voice data.

[0438] Step 2:

[0439] When a user speaks the contents of their diary aloud, the device records the audio in real time. This recorded audio data is temporarily stored on the device.

[0440] Step 3:

[0441] Once recording is complete, the device sends the audio data to the server. This data transmission takes place over the internet using a secure communication protocol.

[0442] Step 4:

[0443] The server uses a speech recognition engine to convert the received audio data into text data. During this process, measures are taken to ensure appropriate conversion based on the speaker's language and speaking style.

[0444] Step 5:

[0445] The server uses artificial intelligence to perform sentiment analysis on the converted text data. This analysis extracts emotional expressions within the text and identifies specific emotional states.

[0446] Step 6:

[0447] The server compares this information with accumulated past activity records and predicts the user's future behavioral tendencies based on identified emotional states. This prediction is made using statistical methods based on past patterns.

[0448] Step 7:

[0449] Based on the prediction, the server generates action suggestions. These suggestions are designed to be specific and actionable for the user.

[0450] Step 8:

[0451] Once the proposal is finalized, the server formats the information and converts it into a user-friendly format.

[0452] Step 9:

[0453] The completed action plan is sent to the device and displayed on the application screen. The user can refer to it and use it as material to plan their next actions.

[0454] (Example 1)

[0455] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0456] Conventional voice input systems often simply convert voice data into text data without further emotion analysis or the development of specific action suggestions. As a result, there is a lack of support for users to fully understand their own emotions and plan concrete actions that lead to improvements in their lives.

[0457] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0458] In this invention, the server includes a device that converts voice information into text information, a machine learning device that analyzes emotions based on the text information, and a device that predicts future behavior and generates specific action guidelines. This enables users to gain a deeper understanding of their own emotions and plan their future actions.

[0459] A "voice input device" is a device used to acquire a user's voice as digital data.

[0460] "Textual information" refers to text data obtained by converting audio data collected through voice input devices.

[0461] A "machine learning device" is a device that performs artificial intelligence technology used to analyze textual information and identify the emotions of users.

[0462] "Action guidelines" are information that, based on analyzed emotional data, suggests specific actions that users should take in the future.

[0463] A "display device" is a device that presents behavioral guidelines to users in an easy-to-read format.

[0464] This system provides advanced emotion analysis and behavioral suggestions using a voice input device. Its main components include a voice input device, a voice recognition and emotion analysis system on the server, and a display device for the user. The specific operation of the system is described below.

[0465] The user launches an application on their smartphone or voice-enabled device and uses the voice diary function. This initiates voice input, recording the user's speech. This recording is then presented to the user as a prompt, such as "Please tell us about your emotional state today." The acquired audio data is transmitted from the user's device to the server in high quality.

[0466] The server uses a cloud-based speech recognition service to convert speech data into text data. This process could involve using, for example, a commercial speech recognition API. The text data converted from speech is then subjected to sentiment analysis using a generative AI model. This analyzes the emotional nuances contained in the text and identifies specific emotional states.

[0467] Based on the sentiment analysis results, the server combines this with the user's past behavioral data to predict future behavior. Then, it uses a generative AI model to construct beneficial action suggestions for the user. For example, if a user makes a positive statement such as "Today's project was a success," a suggestion like "Maintain this momentum and take on a new challenge" might be generated.

[0468] Ultimately, the device visually presents this action suggestion to the user. The display is shown on the application screen in an easy-to-understand format. This allows the user to refer to the suggested action plan and use it to improve their daily life and personal growth.

[0469] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0470] Step 1:

[0471] The user launches the application on their smartphone or voice-enabled device and selects the voice diary function. This initiates voice input from the device. The prompt "Please tell us about your emotional state today" is displayed to the user, and voice data input is obtained. The output is a real-time recording of the user's speech.

[0472] Step 2:

[0473] The terminal compresses the recorded audio data into packets. This data is transmitted to the server via the network while maintaining high quality. It takes audio data as input and sends a digitized audio stream as output to the server.

[0474] Step 3:

[0475] The server converts the received audio data into text data using speech recognition software. In this process, it utilizes a cloud-based speech recognition API to perform the conversion from speech to text information. It receives audio data as input and generates text data as output.

[0476] Step 4:

[0477] Based on the generated text data, the server performs sentiment analysis. Utilizing a generative AI model, it analyzes the text content, extracts emotional nuances, and identifies the user's emotional state. The input is text data, and the output is the identified emotional state.

[0478] Step 5:

[0479] The server combines past behavioral history with the results of sentiment analysis to predict future behavior. This process generates specific action suggestions that are helpful to the user based on the generated emotional state. It takes emotional state and historical data as input, and the output is action suggestions.

[0480] Step 6:

[0481] The terminal presents the user with action suggestions received from the server. These suggestions are displayed in a visually easy-to-understand format and appropriately presented on the application screen. The input is the action suggestions from the server, and the output is the information presented to the user.

[0482] (Application Example 1)

[0483] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0484] In modern purchasing behavior, consumers often make impulsive purchases based on their emotions, which tends to result in financial risks and wasteful spending. Therefore, it is necessary to properly understand consumers' emotional states and propose purchasing behaviors accordingly to support more planned and rational consumption management.

[0485] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0486] In this invention, the server includes means for converting voice data acquired using a voice input device into text data, artificial intelligence means for analyzing the user's emotional state based on the text data, and means for analyzing past consumption activities, evaluating the user's purchasing motivations, and managing consumption habits. This makes it possible to provide specific consumption management suggestions and action plans that are tailored to the user's emotions.

[0487] A "voice input device" is a device that has the function of taking in voice data as a digital signal and using it for subsequent processing.

[0488] "Text data" refers to data obtained by converting audio data into a string of characters, and is data that can be processed as digital information.

[0489] "Artificial intelligence methods" refer to techniques that use machine learning and data analysis to analyze the emotional state of users from text data.

[0490] "Action suggestions" refer to specific actions recommended to the user based on their analyzed emotional state and behavioral tendencies.

[0491] A "display device" is hardware used to provide information to users in a visual format, and includes screens and monitors.

[0492] "Consumption activity" refers to a series of actions in which users trade economic value, such as purchasing goods or using services.

[0493] "Managing consumer habits" is a method of analyzing users' purchasing behavior to eliminate waste and encourage planned consumption.

[0494] The system that implements this application consists of a terminal equipped with a voice input device and a server connected to it. First, the user inputs voice information about their daily consumption activities and expenses into the terminal. The terminal uses the voice input device to convert the voice data into a digital signal and transmits it to the server.

[0495] The server is responsible for converting the received audio data into text data. The speech recognition algorithm used here utilizes speech recognition APIs such as Google Cloud Speech-to-Text. The converted text data is then analyzed for emotional state using artificial intelligence. This analysis employs IBM Watson Tone Analyzer and similar sentiment analysis engines.

[0496] By combining analyzed emotional states with records of existing consumer activity, the server predicts future purchasing behavior and generates specific consumer management suggestions for the user. This includes analyzing the user's past consumer activity patterns and spending trends to identify emotion-based purchasing motivations.

[0497] Ultimately, the action suggestions and consumption management suggestions compiled by the server are communicated to the user through the terminal's display device. Based on this information, the user can then engage in planned consumption behavior. For example, if a user voice-inputs, "I was feeling excited today, so I made an impulse purchase," the analysis recognizes "excitement," and a suggestion such as "Enjoy a refreshing experience within your budget" is made.

[0498] Examples of prompt statements include the following:

[0499] User comment: 'I was feeling really excited today, so I made an impulse purchase.'

[0500] Please generate action suggestions for the AI ​​model that take into account emotions and consumer behavior.

[0501] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0502] Step 1:

[0503] The user uses a voice input device to record information about their daily consumption activities in voice. This voice data becomes the input. The terminal captures this data using the microphone of the voice input device and stores it as a digital signal.

[0504] Step 2:

[0505] The terminal transmits the acquired audio data to the server. The input to this operation is digitized audio data, and the output is transmission to the server. This transmission takes place over a network such as Wi-Fi or mobile data communication.

[0506] Step 3:

[0507] The server converts the received audio data into text data. The input for this step is digital audio data, and the text data is generated using speech recognition with the Google Cloud Speech-to-Text API. The output of this process is the converted text data.

[0508] Step 4:

[0509] The server analyzes emotional states based on text data. This input is the text data from step 3, and emotional analysis is performed using tools such as IBM Watson Tone Analyzer. The output is the analyzed emotional information.

[0510] Step 5:

[0511] The server analyzes the analyzed emotional information and past consumption data to evaluate the motivations behind purchasing behavior. The input for this step is emotional information and consumption history, and data mining techniques are applied to identify purchasing motivations. The result is the output.

[0512] Step 6:

[0513] The server predicts future purchasing behavior and generates specific consumption management suggestions. The input for this step is the evaluation of purchasing motivations, and the generated suggestions are the output. A generative AI model may be used in this process.

[0514] Step 7:

[0515] The terminal receives action suggestions and consumption management suggestions sent from the server and communicates them to the user through a display device. The input for this step is the generated suggestions, and the output is a display that the user can visually interpret.

[0516] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0517] This system converts voice data acquired using a voice input device into text data and uses an emotion engine to analyze the user's emotional state with high accuracy. A specific embodiment of this system is described below.

[0518] First, the user launches a dedicated application using a compatible device such as a smartphone or tablet to access the voice diary function. The device functions as a voice input device, recording the user's speech in real time. The recorded voice data is sent to the server while maintaining its quality.

[0519] The audio data received by the server is converted into text data by a speech recognition engine. In this conversion process, the spoken content is appropriately interpreted and stored as text information. Next, the server uses an emotion engine to analyze not only the text data but also features such as intonation, speed, and volume of the speech, in order to recognize the user's emotions with higher accuracy.

[0520] Based on the analyzed emotional information, the server refers to past activity records to identify the user's behavioral patterns. This allows the server to form a dataset for predicting future behavioral trends. Based on these predicted trends, it generates specific action suggestions. These suggestions are designed to help the user improve their life and achieve their goals.

[0521] The generated action suggestions are formatted, including visual elements, and presented to the user in an easy-to-understand manner via the device. In this way, users can gain a deeper understanding of their own emotions and develop action plans based on them.

[0522] For example, if a user leaves a voice diary entry stating, "Today was a stressful day, and I faced many problems," the device sends the audio to a server. Through speech recognition and emotion engine analysis, the server recognizes complex emotional states such as "stress" and "the need to address challenges." Based on this, the server generates and provides relaxation-promoting behavioral suggestions to the user, such as "Try to refresh yourself and make time for yourself." This process allows users to choose appropriate actions according to their emotional state, thereby reducing stress in their daily lives.

[0523] The following describes the processing flow.

[0524] Step 1:

[0525] The user launches the voice diary app on their smartphone and taps the button to start the voice input function. The device activates the microphone and prepares to acquire voice data.

[0526] Step 2:

[0527] When a user speaks aloud about daily events or their feelings, the device records the audio in real time and temporarily saves it to local storage.

[0528] Step 3:

[0529] After recording is complete, the device converts the recorded audio data into data packets for transmission to the server and sends them using a secure communication protocol.

[0530] Step 4:

[0531] The server processes the received audio data through a speech recognition engine, converting the audio into text data. During this text conversion, context is taken into consideration when transcribing the text.

[0532] Step 5:

[0533] Along with the converted text data, the server activates an emotion engine to analyze the intonation, speed, volume, and other aspects of the speech to comprehensively evaluate the user's emotions.

[0534] Step 6:

[0535] Based on the acquired emotional information, the server refers to past behavioral history and activity records to identify the user's behavioral patterns. This identification allows for accurate future predictions based on past data.

[0536] Step 7:

[0537] The server predicts future behavioral tendencies based on identified behavioral patterns and sentiment analysis results. Based on these tendencies, it generates optimal action suggestions for the user.

[0538] Step 8:

[0539] The generated action suggestions are organized on the server using visual elements to make them easy for the user to understand.

[0540] Step 9:

[0541] The device presents the user with organized action suggestions. These suggestions are displayed on the screen, allowing the user to plan their next actions based on the suggestions.

[0542] (Example 2)

[0543] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0544] Currently, there are limited systems that accurately analyze users' emotions based on information obtained using voice data, and then automatically generate and present concrete action suggestions to improve their lives based on that analysis. In particular, there is a challenge in improving the accuracy of emotional state analysis while simultaneously presenting suggestions in a way that is easy for users to understand.

[0545] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0546] In this invention, the server includes means for converting voice information acquired using a voice acquisition device into text information, intelligent engine means for analyzing the user's emotional state based on the text information, and means for predicting future behavioral tendencies based on the emotional state and generating specific action suggestions for the user. This makes it possible to analyze the user's emotions with high accuracy and provide personalized action suggestions.

[0547] A "speech acquisition device" is a device for collecting speech information and recording it as an electrical signal.

[0548] "Audio information" refers to the representation of sound waveforms as digital data, obtained by an audio acquisition device.

[0549] "Textual information" refers to data obtained by analyzing audio information and converting it into a corresponding text format.

[0550] An "intelligent engine system" is a system that uses artificial intelligence to analyze textual information and related information in order to infer the emotional state of the user.

[0551] "Behavioral tendencies" refer to patterns of behavior that a user may potentially take in the future, based on their past behavioral history and current emotional state.

[0552] "Action suggestions" are specific recommended actions presented to the user based on their analyzed emotional state and behavioral tendencies.

[0553] A "visualization device" is a device that has a display function to visually present generated action suggestions to the user.

[0554] This invention provides a system that analyzes a user's emotional state based on their voice and generates specific action suggestions. This system operates in conjunction with a voice acquisition device, a server, and a terminal.

[0555] Voice acquisition device

[0556] Users utilize smartphones or tablets that function as voice acquisition devices. These devices collect voice information by launching a dedicated application and recording speech. The recorded voice information is processed appropriately in real time and transmitted to a server.

[0557] server

[0558] When the server receives audio information, it converts it into text information using a speech recognition engine. This conversion process can utilize commercially available speech recognition software, such as Amazon Transcribe or the Google Speech-to-Text API. The converted text information is then input into an intelligent engine.

[0559] The intelligent engine utilizes natural language processing and acoustic analysis technologies to analyze textual information and associated features, thereby accurately predicting the user's emotional state. This analysis employs, for example, natural language processing libraries and machine learning models, which are commonly known AI technologies.

[0560] Based on the analysis of the user's emotional state, the server predicts future behavioral tendencies and generates specific action suggestions for the user. These suggestions are structured as advice and guidance aimed at improving the user's daily life and achieving their goals.

[0561] terminal

[0562] The generated action suggestions are presented to the user in an easy-to-understand manner through a visualization device on the terminal. This allows the user to create an appropriate action plan based on their emotional state.

[0563] For example, if a user records a voice diary entry saying, "I was busy and tired today," the server analyzes this text information, detects that the user is feeling stressed, and generates action suggestions such as "Take a rest." These suggestions are then displayed graphically on the device screen.

[0564] An example of a prompt when using a generative AI model might be, "Please tell me how to analyze voice data, identify the user's emotions, and generate specific action suggestions." This prompt will be used as reference during the system's learning process.

[0565] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0566] Step 1:

[0567] The user launches a dedicated application installed on their smartphone or tablet. Within the app, the user selects the voice diary function and begins speaking to input information. The device records this speech in real time, acting as a voice acquisition device. During this recording process, the audio is recorded as digital data, which then serves as input data for subsequent processing.

[0568] Step 2:

[0569] The terminal sends the recorded audio data to the server. In this process, the terminal appropriately compresses the audio data and transmits it to the server efficiently and securely via the communication line. Thus, compressed audio data is formed as output, and the server receives this data as input.

[0570] Step 3:

[0571] The server sends the received audio data to the speech recognition engine, where it is converted into text. In this conversion step, the speech recognition engine analyzes the audio signal using an acoustic model and a language model, and outputs the corresponding text. As a result, the audio data is saved as text data.

[0572] Step 4:

[0573] The server uses the converted text data to perform sentiment analysis using an intelligent engine. This involves integrated analysis using both natural language processing techniques based on the text data and features such as intonation and volume extracted from the audio data. The output of this process is data indicating the user's emotional state.

[0574] Step 5:

[0575] The server predicts future behavioral trends based on analyzed emotional state data and referencing past behavioral history. This process utilizes machine learning algorithms to extract behavioral patterns and generate predictive models based on input emotional state and historical data. The output provides the foundational data for future behavioral suggestions.

[0576] Step 6:

[0577] The server uses behavioral trend data and a generative AI model to create specific action suggestions. This includes a process that uses prompts to the generative AI model to automatically generate suggestions. The output is specific and personalized action suggestions for the user.

[0578] Step 7:

[0579] The terminal presents the action suggestions received from the server to the user using a visualization device. In this step, graphic elements and interfaces are adjusted to make the content of the action suggestions visually easy to understand, and finally output to the user. The user then adjusts their daily actions based on this.

[0580] (Application Example 2)

[0581] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0582] In customer interactions, there is a challenge in quickly understanding the emotional state of customers and providing an optimal customer service experience based on their individual needs. Furthermore, there is a need for a system that can provide highly accurate and appropriate action suggestions without relying on direct customer feedback.

[0583] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0584] In this invention, the server includes means for converting voice information obtained using voice acquisition means into text information, machine learning means for analyzing the user's emotional state based on the text information and the intonation, speed, and volume of the voice, and communication means for analyzing the voice information in real time and feeding the analysis results back to the user. This makes it possible to provide a fast and accurate customer service experience based on the emotional needs of the customer.

[0585] "Voice acquisition means" refers to devices or methods that acquire voice information from users in real time and convert it into data in a format suitable for analysis.

[0586] "Textual information" refers to data in text format generated using speech recognition technology based on audio information.

[0587] "Machine learning methods" refer to artificial intelligence technologies that use algorithms based on acquired data to analyze and predict a user's emotional state.

[0588] "Emotional state" refers to the state of a user's emotions and mood, as judged from characteristic quantities such as intonation, speed, and volume of their voice.

[0589] "Communication means" refers to the technologies and protocols used to send and receive analysis results between a server and a terminal in real time.

[0590] A "behavioral suggestion" is a proposal that indicates specific actions or behaviors that the user should take, based on their analyzed emotional state.

[0591] "Customer service experience" refers to the entire process of how sales staff interact with customers when explaining or providing information about products and services.

[0592] A "server" is a data processing device that receives, analyzes, and generates action suggestions for voice information.

[0593] The system that realizes this application example is equipped with an advanced voice analysis and feedback mechanism using voice acquisition means, machine learning means, communication means, and display means. First, as the voice acquisition means, a microphone mounted on smart glasses acquires conversations with customers as voice information in real time. The voice information is transmitted to a server via the communication means.

[0594] The server utilizes speech recognition technology to convert audio information into text. Common speech recognition software includes Google Cloud Speech-to-Text. The converted text information and parameters such as intonation, speed, and volume are processed using machine learning to analyze the customer's emotional state. Here, an emotion analysis engine such as IBM Watson Tone Analyzer is used.

[0595] The analyzed emotional state is transmitted back to the smart glasses via communication and displayed as visual information. This allows service staff to instantly grasp the customer's emotional needs and provide the optimal service experience.

[0596] For example, if a customer service representative receives an analysis result through smart glasses that says, "You seem a little tired today," they can immediately offer a suggestion to the customer such as, "We'll be happy to assist you as soon as possible." An example of such a prompt might be, "Generate advice on how to approach a customer who is seeking relaxation."

[0597] These features enable the system to contribute to improving the customer experience in the service industry and to provide more personalized services.

[0598] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0599] Step 1:

[0600] The terminal uses a microphone built into smart glasses as a means of voice acquisition to capture conversations with customers in real time as voice information. The acquired voice information is converted into a digital signal and subjected to noise reduction processing. This voice information is transmitted to a server via a communication means. The input is the customer's voice, and the output is clean, digitized voice data.

[0601] Step 2:

[0602] The server converts the received audio data into text using speech recognition technology. A speech recognition engine like Google Cloud Speech-to-Text is used here. The text data obtained through speech recognition becomes the basis for further analysis. The input is digitized audio data, and the output is text data.

[0603] Step 3:

[0604] The server uses an emotion analysis engine to evaluate the emotional state based on the converted text information and parameters such as intonation, speed, and volume of the speech. IBM Watson Tone Analyzer is used for this analysis. As a result of the analysis, tags and scores indicating the customer's emotional state are generated. The input is text information and speech features, and the output is the analyzed emotional state and its evaluation data.

[0605] Step 4:

[0606] The server uses the analysis results to generate behavioral suggestions based on the customer's emotional state using a generative AI model. In this process, it references past data and patterns, and utilizes the learning results of the generated model. The generated suggestions are presented in a customer-centric format. The input is the analyzed emotional state and past data, and the output is the behavioral suggestions.

[0607] Step 5:

[0608] The terminal displays action suggestions obtained via communication as visual information on smart glasses. This display provides quick decision-making information for customer service staff and is presented as a prompt. Specifically, the suggested content is visualized on the glasses' display as text or simplified icons. The input is the action suggestion, and the output is the visually represented action suggestion information.

[0609] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0610] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0611] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0612] [Fourth Embodiment]

[0613] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0614] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0615] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0616] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0617] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0618] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0619] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0620] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0621] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0622] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0623] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0624] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0625] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0626] This system, centered around a voice input device, acquires voice data and performs emotion analysis of the user, as well as suggests future actions. A specific implementation of the system is described below.

[0627] First, the user launches the application using their smartphone or a compatible device and accesses the voice diary function. At this time, the device activates the voice input device and records the user's voice data. The recorded voice data is acquired in real time and sent to the server while maintaining quality.

[0628] The server utilizes speech recognition technology to convert received audio data into text data. In this conversion process, the server uses the latest learning algorithms to convert audio information into text with high accuracy. Subsequently, based on the generated text data, the server uses artificial intelligence to analyze the user's emotional state.

[0629] After the emotion analysis is complete, the server refers to past activity records and combines them with the obtained emotional states to predict the user's future behavioral tendencies. Based on these predictions, the server generates specific action suggestions and organizes their content. The generated action suggestions are provided as specific and practical advice that the user can incorporate into their own action plan.

[0630] Finally, the refined action suggestions are presented to the user via the device, communicated in an easy-to-understand format through visual displays. Users can use these suggestions to set specific actions for personal growth and life improvement. This entire system enables users to gain a deeper understanding of their own emotions and develop constructive action plans for the future.

[0631] For example, if a user records in voice, "Today was a fulfilling day. My new project was a success," the device sends this to the server. Based on voice recognition and emotion analysis, the server identifies emotional states such as "joy" and "sense of accomplishment." Based on this, the server suggests the next step, "It's time to use your sense of accomplishment to take on a new challenge," and presents this suggestion to the user. Through this process, the user can use their sense of accomplishment as motivation and plan their next actions.

[0632] The following describes the processing flow.

[0633] Step 1:

[0634] The user launches the smartphone application and selects the voice diary function. This starts voice input mode, and the device activates the microphone and prepares to receive voice data.

[0635] Step 2:

[0636] When a user speaks the contents of their diary aloud, the device records the audio in real time. This recorded audio data is temporarily stored on the device.

[0637] Step 3:

[0638] Once recording is complete, the device sends the audio data to the server. This data transmission takes place over the internet using a secure communication protocol.

[0639] Step 4:

[0640] The server uses a speech recognition engine to convert the received audio data into text data. During this process, measures are taken to ensure appropriate conversion based on the speaker's language and speaking style.

[0641] Step 5:

[0642] The server uses artificial intelligence to perform sentiment analysis on the converted text data. This analysis extracts emotional expressions within the text and identifies specific emotional states.

[0643] Step 6:

[0644] The server compares this information with accumulated past activity records and predicts the user's future behavioral tendencies based on identified emotional states. This prediction is made using statistical methods based on past patterns.

[0645] Step 7:

[0646] Based on the prediction, the server generates action suggestions. These suggestions are designed to be specific and actionable for the user.

[0647] Step 8:

[0648] Once the proposal is finalized, the server formats the information and converts it into a user-friendly format.

[0649] Step 9:

[0650] The completed action plan is sent to the device and displayed on the application screen. The user can refer to it and use it as material to plan their next actions.

[0651] (Example 1)

[0652] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0653] Conventional voice input systems often simply convert voice data into text data without further emotion analysis or the development of specific action suggestions. As a result, there is a lack of support for users to fully understand their own emotions and plan concrete actions that lead to improvements in their lives.

[0654] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0655] In this invention, the server includes a device that converts voice information into text information, a machine learning device that analyzes emotions based on the text information, and a device that predicts future behavior and generates specific action guidelines. This enables users to gain a deeper understanding of their own emotions and plan their future actions.

[0656] A "voice input device" is a device used to acquire a user's voice as digital data.

[0657] "Textual information" refers to text data obtained by converting audio data collected through voice input devices.

[0658] A "machine learning device" is a device that performs artificial intelligence technology used to analyze textual information and identify the emotions of users.

[0659] "Action guidelines" are information that, based on analyzed emotional data, suggests specific actions that users should take in the future.

[0660] A "display device" is a device that presents behavioral guidelines to users in an easy-to-read format.

[0661] This system provides advanced emotion analysis and behavioral suggestions using a voice input device. Its main components include a voice input device, a voice recognition and emotion analysis system on the server, and a display device for the user. The specific operation of the system is described below.

[0662] The user launches an application on their smartphone or voice-enabled device and uses the voice diary function. This initiates voice input, recording the user's speech. This recording is then presented to the user as a prompt, such as "Please tell us about your emotional state today." The acquired audio data is transmitted from the user's device to the server in high quality.

[0663] The server uses a cloud-based speech recognition service to convert speech data into text data. This process could involve using, for example, a commercial speech recognition API. The text data converted from speech is then subjected to sentiment analysis using a generative AI model. This analyzes the emotional nuances contained in the text and identifies specific emotional states.

[0664] Based on the sentiment analysis results, the server combines this with the user's past behavioral data to predict future behavior. Then, it uses a generative AI model to construct beneficial action suggestions for the user. For example, if a user makes a positive statement such as "Today's project was a success," a suggestion like "Maintain this momentum and take on a new challenge" might be generated.

[0665] Ultimately, the device visually presents this action suggestion to the user. The display is shown on the application screen in an easy-to-understand format. This allows the user to refer to the suggested action plan and use it to improve their daily life and personal growth.

[0666] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0667] Step 1:

[0668] The user launches the application on their smartphone or voice-enabled device and selects the voice diary function. This initiates voice input from the device. The prompt "Please tell us about your emotional state today" is displayed to the user, and voice data input is obtained. The output is a real-time recording of the user's speech.

[0669] Step 2:

[0670] The terminal compresses the recorded audio data into packets. This data is transmitted to the server via the network while maintaining high quality. It takes audio data as input and sends a digitized audio stream as output to the server.

[0671] Step 3:

[0672] The server converts the received audio data into text data using speech recognition software. In this process, it utilizes a cloud-based speech recognition API to perform the conversion from speech to text information. It receives audio data as input and generates text data as output.

[0673] Step 4:

[0674] Based on the generated text data, the server performs sentiment analysis. Utilizing a generative AI model, it analyzes the text content, extracts emotional nuances, and identifies the user's emotional state. The input is text data, and the output is the identified emotional state.

[0675] Step 5:

[0676] The server combines past behavioral history with the results of sentiment analysis to predict future behavior. This process generates specific action suggestions that are helpful to the user based on the generated emotional state. It takes emotional state and historical data as input, and the output is action suggestions.

[0677] Step 6:

[0678] The terminal presents the user with action suggestions received from the server. These suggestions are displayed in a visually easy-to-understand format and appropriately presented on the application screen. The input is the action suggestions from the server, and the output is the information presented to the user.

[0679] (Application Example 1)

[0680] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0681] In modern purchasing behavior, consumers often make impulsive purchases based on their emotions, which tends to result in financial risks and wasteful spending. Therefore, it is necessary to properly understand consumers' emotional states and propose purchasing behaviors accordingly to support more planned and rational consumption management.

[0682] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0683] In this invention, the server includes means for converting voice data acquired using a voice input device into text data, artificial intelligence means for analyzing the user's emotional state based on the text data, and means for analyzing past consumption activities, evaluating the user's purchasing motivations, and managing consumption habits. This makes it possible to provide specific consumption management suggestions and action plans that are tailored to the user's emotions.

[0684] A "voice input device" is a device that has the function of taking in voice data as a digital signal and using it for subsequent processing.

[0685] "Text data" refers to data obtained by converting audio data into a string of characters, and is data that can be processed as digital information.

[0686] "Artificial intelligence methods" refer to techniques that use machine learning and data analysis to analyze the emotional state of users from text data.

[0687] "Action suggestions" refer to specific actions recommended to the user based on their analyzed emotional state and behavioral tendencies.

[0688] A "display device" is hardware used to provide information to users in a visual format, and includes screens and monitors.

[0689] "Consumption activity" refers to a series of actions in which users trade economic value, such as purchasing goods or using services.

[0690] "Managing consumer habits" is a method of analyzing users' purchasing behavior to eliminate waste and encourage planned consumption.

[0691] The system that implements this application consists of a terminal equipped with a voice input device and a server connected to it. First, the user inputs voice information about their daily consumption activities and expenses into the terminal. The terminal uses the voice input device to convert the voice data into a digital signal and transmits it to the server.

[0692] The server is responsible for converting the received audio data into text data. The speech recognition algorithm used here utilizes speech recognition APIs such as Google Cloud Speech-to-Text. The converted text data is then analyzed for emotional state using artificial intelligence. This analysis employs IBM Watson Tone Analyzer and similar sentiment analysis engines.

[0693] By combining analyzed emotional states with records of existing consumer activity, the server predicts future purchasing behavior and generates specific consumer management suggestions for the user. This includes analyzing the user's past consumer activity patterns and spending trends to identify emotion-based purchasing motivations.

[0694] Ultimately, the action suggestions and consumption management suggestions compiled by the server are communicated to the user through the terminal's display device. Based on this information, the user can then engage in planned consumption behavior. For example, if a user voice-inputs, "I was feeling excited today, so I made an impulse purchase," the analysis recognizes "excitement," and a suggestion such as "Enjoy a refreshing experience within your budget" is made.

[0695] Examples of prompt statements include the following:

[0696] User comment: 'I was feeling really excited today, so I made an impulse purchase.'

[0697] Please generate action suggestions for the AI ​​model that take into account emotions and consumer behavior.

[0698] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0699] Step 1:

[0700] The user uses a voice input device to record information about their daily consumption activities in voice. This voice data becomes the input. The terminal captures this data using the microphone of the voice input device and stores it as a digital signal.

[0701] Step 2:

[0702] The terminal transmits the acquired audio data to the server. The input to this operation is digitized audio data, and the output is transmission to the server. This transmission takes place over a network such as Wi-Fi or mobile data communication.

[0703] Step 3:

[0704] The server converts the received audio data into text data. The input for this step is digital audio data, and the text data is generated using speech recognition with the Google Cloud Speech-to-Text API. The output of this process is the converted text data.

[0705] Step 4:

[0706] The server analyzes emotional states based on text data. This input is the text data from step 3, and emotional analysis is performed using tools such as IBM Watson Tone Analyzer. The output is the analyzed emotional information.

[0707] Step 5:

[0708] The server analyzes the analyzed emotional information and past consumption data to evaluate the motivations behind purchasing behavior. The input for this step is emotional information and consumption history, and data mining techniques are applied to identify purchasing motivations. The result is the output.

[0709] Step 6:

[0710] The server predicts future purchasing behavior and generates specific consumption management suggestions. The input for this step is the evaluation of purchasing motivations, and the generated suggestions are the output. A generative AI model may be used in this process.

[0711] Step 7:

[0712] The terminal receives action suggestions and consumption management suggestions sent from the server and communicates them to the user through a display device. The input for this step is the generated suggestions, and the output is a display that the user can visually interpret.

[0713] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0714] This system converts voice data acquired using a voice input device into text data and uses an emotion engine to analyze the user's emotional state with high accuracy. A specific embodiment of this system is described below.

[0715] First, the user launches a dedicated application using a compatible device such as a smartphone or tablet to access the voice diary function. The device functions as a voice input device, recording the user's speech in real time. The recorded voice data is sent to the server while maintaining its quality.

[0716] The audio data received by the server is converted into text data by a speech recognition engine. In this conversion process, the spoken content is appropriately interpreted and stored as text information. Next, the server uses an emotion engine to analyze not only the text data but also features such as intonation, speed, and volume of the speech, in order to recognize the user's emotions with higher accuracy.

[0717] Based on the analyzed emotional information, the server refers to past activity records to identify the user's behavioral patterns. This allows the server to form a dataset for predicting future behavioral trends. Based on these predicted trends, it generates specific action suggestions. These suggestions are designed to help the user improve their life and achieve their goals.

[0718] The generated action suggestions are formatted, including visual elements, and presented to the user in an easy-to-understand manner via the device. In this way, users can gain a deeper understanding of their own emotions and develop action plans based on them.

[0719] For example, if a user leaves a voice diary entry stating, "Today was a stressful day, and I faced many problems," the device sends the audio to a server. Through speech recognition and emotion engine analysis, the server recognizes complex emotional states such as "stress" and "the need to address challenges." Based on this, the server generates and provides relaxation-promoting behavioral suggestions to the user, such as "Try to refresh yourself and make time for yourself." This process allows users to choose appropriate actions according to their emotional state, thereby reducing stress in their daily lives.

[0720] The following describes the processing flow.

[0721] Step 1:

[0722] The user launches the voice diary app on their smartphone and taps the button to start the voice input function. The device activates the microphone and prepares to acquire voice data.

[0723] Step 2:

[0724] When a user speaks aloud about daily events or their feelings, the device records the audio in real time and temporarily saves it to local storage.

[0725] Step 3:

[0726] After recording is complete, the device converts the recorded audio data into data packets for transmission to the server and sends them using a secure communication protocol.

[0727] Step 4:

[0728] The server processes the received audio data through a speech recognition engine, converting the audio into text data. During this text conversion, context is taken into consideration when transcribing the text.

[0729] Step 5:

[0730] Along with the converted text data, the server activates an emotion engine to analyze the intonation, speed, volume, and other aspects of the speech to comprehensively evaluate the user's emotions.

[0731] Step 6:

[0732] Based on the acquired emotional information, the server refers to past behavioral history and activity records to identify the user's behavioral patterns. This identification allows for accurate future predictions based on past data.

[0733] Step 7:

[0734] The server predicts future behavioral tendencies based on identified behavioral patterns and sentiment analysis results. Based on these tendencies, it generates optimal action suggestions for the user.

[0735] Step 8:

[0736] The generated action suggestions are organized on the server using visual elements to make them easy for the user to understand.

[0737] Step 9:

[0738] The device presents the user with organized action suggestions. These suggestions are displayed on the screen, allowing the user to plan their next actions based on the suggestions.

[0739] (Example 2)

[0740] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0741] Currently, there are limited systems that accurately analyze users' emotions based on information obtained using voice data, and then automatically generate and present concrete action suggestions to improve their lives based on that analysis. In particular, there is a challenge in improving the accuracy of emotional state analysis while simultaneously presenting suggestions in a way that is easy for users to understand.

[0742] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0743] In this invention, the server includes means for converting voice information acquired using a voice acquisition device into text information, intelligent engine means for analyzing the user's emotional state based on the text information, and means for predicting future behavioral tendencies based on the emotional state and generating specific action suggestions for the user. This makes it possible to analyze the user's emotions with high accuracy and provide personalized action suggestions.

[0744] A "speech acquisition device" is a device for collecting speech information and recording it as an electrical signal.

[0745] "Audio information" refers to the representation of sound waveforms as digital data, obtained by an audio acquisition device.

[0746] "Textual information" refers to data obtained by analyzing audio information and converting it into a corresponding text format.

[0747] An "intelligent engine system" is a system that uses artificial intelligence to analyze textual information and related information in order to infer the emotional state of the user.

[0748] "Behavioral tendencies" refer to patterns of behavior that a user may potentially take in the future, based on their past behavioral history and current emotional state.

[0749] "Action suggestions" are specific recommended actions presented to the user based on their analyzed emotional state and behavioral tendencies.

[0750] A "visualization device" is a device that has a display function to visually present generated action suggestions to the user.

[0751] This invention provides a system that analyzes a user's emotional state based on their voice and generates specific action suggestions. This system operates in conjunction with a voice acquisition device, a server, and a terminal.

[0752] Voice acquisition device

[0753] Users utilize smartphones or tablets that function as voice acquisition devices. These devices collect voice information by launching a dedicated application and recording speech. The recorded voice information is processed appropriately in real time and transmitted to a server.

[0754] server

[0755] When the server receives audio information, it converts it into text information using a speech recognition engine. This conversion process can utilize commercially available speech recognition software, such as Amazon Transcribe or the Google Speech-to-Text API. The converted text information is then input into an intelligent engine.

[0756] The intelligent engine utilizes natural language processing and acoustic analysis technologies to analyze textual information and associated features, thereby accurately predicting the user's emotional state. This analysis employs, for example, natural language processing libraries and machine learning models, which are commonly known AI technologies.

[0757] Based on the analysis of the user's emotional state, the server predicts future behavioral tendencies and generates specific action suggestions for the user. These suggestions are structured as advice and guidance aimed at improving the user's daily life and achieving their goals.

[0758] terminal

[0759] The generated action suggestions are presented to the user in an easy-to-understand manner through a visualization device on the terminal. This allows the user to create an appropriate action plan based on their emotional state.

[0760] For example, if a user records a voice diary entry saying, "I was busy and tired today," the server analyzes this text information, detects that the user is feeling stressed, and generates action suggestions such as "Take a rest." These suggestions are then displayed graphically on the device screen.

[0761] An example of a prompt when using a generative AI model might be, "Please tell me how to analyze voice data, identify the user's emotions, and generate specific action suggestions." This prompt will be used as reference during the system's learning process.

[0762] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0763] Step 1:

[0764] The user launches a dedicated application installed on their smartphone or tablet. Within the app, the user selects the voice diary function and begins speaking to input information. The device records this speech in real time, acting as a voice acquisition device. During this recording process, the audio is recorded as digital data, which then serves as input data for subsequent processing.

[0765] Step 2:

[0766] The terminal sends the recorded audio data to the server. In this process, the terminal appropriately compresses the audio data and transmits it to the server efficiently and securely via the communication line. Thus, compressed audio data is formed as output, and the server receives this data as input.

[0767] Step 3:

[0768] The server sends the received audio data to the speech recognition engine, where it is converted into text. In this conversion step, the speech recognition engine analyzes the audio signal using an acoustic model and a language model, and outputs the corresponding text. As a result, the audio data is saved as text data.

[0769] Step 4:

[0770] The server uses the converted text data to perform sentiment analysis using an intelligent engine. This involves integrated analysis using both natural language processing techniques based on the text data and features such as intonation and volume extracted from the audio data. The output of this process is data indicating the user's emotional state.

[0771] Step 5:

[0772] The server predicts future behavioral trends based on analyzed emotional state data and referencing past behavioral history. This process utilizes machine learning algorithms to extract behavioral patterns and generate predictive models based on input emotional state and historical data. The output provides the foundational data for future behavioral suggestions.

[0773] Step 6:

[0774] The server uses behavioral trend data and a generative AI model to create specific action suggestions. This includes a process that uses prompts to the generative AI model to automatically generate suggestions. The output is specific and personalized action suggestions for the user.

[0775] Step 7:

[0776] The terminal presents the action suggestions received from the server to the user using a visualization device. In this step, graphic elements and interfaces are adjusted to make the content of the action suggestions visually easy to understand, and finally output to the user. The user then adjusts their daily actions based on this.

[0777] (Application Example 2)

[0778] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0779] In customer interactions, there is a challenge in quickly understanding the emotional state of customers and providing an optimal customer service experience based on their individual needs. Furthermore, there is a need for a system that can provide highly accurate and appropriate action suggestions without relying on direct customer feedback.

[0780] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0781] In this invention, the server includes means for converting voice information obtained using voice acquisition means into text information, machine learning means for analyzing the user's emotional state based on the text information and the intonation, speed, and volume of the voice, and communication means for analyzing the voice information in real time and feeding the analysis results back to the user. This makes it possible to provide a fast and accurate customer service experience based on the emotional needs of the customer.

[0782] "Voice acquisition means" refers to devices or methods that acquire voice information from users in real time and convert it into data in a format suitable for analysis.

[0783] "Textual information" refers to data in text format generated using speech recognition technology based on audio information.

[0784] "Machine learning methods" refer to artificial intelligence technologies that use algorithms based on acquired data to analyze and predict a user's emotional state.

[0785] "Emotional state" refers to the state of a user's emotions and mood, as judged from characteristic quantities such as intonation, speed, and volume of their voice.

[0786] "Communication means" refers to the technologies and protocols used to send and receive analysis results between a server and a terminal in real time.

[0787] A "behavioral suggestion" is a proposal that indicates specific actions or behaviors that the user should take, based on their analyzed emotional state.

[0788] "Customer service experience" refers to the entire process of how sales staff interact with customers when explaining or providing information about products and services.

[0789] A "server" is a data processing device that receives, analyzes, and generates action suggestions for voice information.

[0790] The system that realizes this application example is equipped with an advanced voice analysis and feedback mechanism using voice acquisition means, machine learning means, communication means, and display means. First, as the voice acquisition means, a microphone mounted on smart glasses acquires conversations with customers as voice information in real time. The voice information is transmitted to a server via the communication means.

[0791] The server utilizes speech recognition technology to convert audio information into text. Common speech recognition software includes Google Cloud Speech-to-Text. The converted text information and parameters such as intonation, speed, and volume are processed using machine learning to analyze the customer's emotional state. Here, an emotion analysis engine such as IBM Watson Tone Analyzer is used.

[0792] The analyzed emotional state is transmitted back to the smart glasses via communication and displayed as visual information. This allows service staff to instantly grasp the customer's emotional needs and provide the optimal service experience.

[0793] For example, if a customer service representative receives an analysis result through smart glasses that says, "You seem a little tired today," they can immediately offer a suggestion to the customer such as, "We'll be happy to assist you as soon as possible." An example of such a prompt might be, "Generate advice on how to approach a customer who is seeking relaxation."

[0794] These features enable the system to contribute to improving the customer experience in the service industry and to provide more personalized services.

[0795] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0796] Step 1:

[0797] The terminal uses a microphone built into smart glasses as a means of voice acquisition to capture conversations with customers in real time as voice information. The acquired voice information is converted into a digital signal and subjected to noise reduction processing. This voice information is transmitted to a server via a communication means. The input is the customer's voice, and the output is clean, digitized voice data.

[0798] Step 2:

[0799] The server converts the received audio data into text using speech recognition technology. A speech recognition engine like Google Cloud Speech-to-Text is used here. The text data obtained through speech recognition becomes the basis for further analysis. The input is digitized audio data, and the output is text data.

[0800] Step 3:

[0801] The server uses an emotion analysis engine to evaluate the emotional state based on the converted text information and parameters such as intonation, speed, and volume of the speech. IBM Watson Tone Analyzer is used for this analysis. As a result of the analysis, tags and scores indicating the customer's emotional state are generated. The input is text information and speech features, and the output is the analyzed emotional state and its evaluation data.

[0802] Step 4:

[0803] The server uses the analysis results to generate behavioral suggestions based on the customer's emotional state using a generative AI model. In this process, it references past data and patterns, and utilizes the learning results of the generated model. The generated suggestions are presented in a customer-centric format. The input is the analyzed emotional state and past data, and the output is the behavioral suggestions.

[0804] Step 5:

[0805] The terminal displays action suggestions obtained via communication as visual information on smart glasses. This display provides quick decision-making information for customer service staff and is presented as a prompt. Specifically, the suggested content is visualized on the glasses' display as text or simplified icons. The input is the action suggestion, and the output is the visually represented action suggestion information.

[0806] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0807] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0808] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0809] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0810] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0811] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0812] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0813] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0814] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0815] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0816] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0817] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0818] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0819] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0820] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0821] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0822] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0823] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0824] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0825] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0826] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0827] The following is further disclosed regarding the embodiments described above.

[0828] (Claim 1)

[0829] A means for converting audio data acquired using an audio input device into text data,

[0830] An artificial intelligence means for analyzing the user's emotional state based on the text data,

[0831] A means for predicting future behavioral tendencies based on the aforementioned emotional state and generating specific action suggestions for the user,

[0832] A system including a display device for presenting the aforementioned action suggestions to the user.

[0833] (Claim 2)

[0834] The system according to claim 1, wherein the analysis means identifies the user's behavioral patterns by referring to past activity records.

[0835] (Claim 3)

[0836] The system according to claim 1, wherein the display means further includes means for visually arranging action suggestions to aid the user's understanding.

[0837] "Example 1"

[0838] (Claim 1)

[0839] A device that converts voice information acquired using a voice input device into text information,

[0840] A machine learning device for analyzing the user's emotions based on the textual information,

[0841] A device that predicts future behavior based on the aforementioned emotions and generates specific action guidelines for the user,

[0842] A system including a display device for presenting the aforementioned guidelines to users.

[0843] (Claim 2)

[0844] The system according to claim 1, wherein the analysis device includes a process to identify the user's behavioral patterns by referring to past activity information.

[0845] (Claim 3)

[0846] The system according to claim 1, further comprising processing to visually arrange the guidelines for action in order to help the user understand them.

[0847] "Application Example 1"

[0848] (Claim 1)

[0849] A means for converting audio data acquired using an audio input device into text data,

[0850] An artificial intelligence means for analyzing the user's emotional state based on the text data,

[0851] A means for predicting future behavioral tendencies based on the aforementioned emotional state and generating specific action suggestions for the user,

[0852] A means to analyze past consumer activity, evaluate users' purchasing motivations, and manage consumer habits,

[0853] A system including a display device for presenting the aforementioned action suggestions and consumption management suggestions to the user.

[0854] (Claim 2)

[0855] The system according to claim 1, wherein the analysis means identifies the user's behavioral patterns by referring to past activity records.

[0856] (Claim 3)

[0857] The system according to claim 1, wherein the display means further includes means for visually arranging action suggestions to aid the user's understanding.

[0858] "Example 2 of combining an emotion engine"

[0859] (Claim 1)

[0860] A means for converting audio information acquired using an audio acquisition device into text information,

[0861] An intelligent engine means for analyzing the user's emotional state based on the textual information,

[0862] A means for predicting future behavioral tendencies based on the aforementioned emotional state and generating specific action suggestions for the user,

[0863] A system including a visualization device for presenting the aforementioned action suggestions to the user.

[0864] (Claim 2)

[0865] The system according to claim 1, wherein the analysis means identifies the user's behavioral characteristics by referring to past activity history.

[0866] (Claim 3)

[0867] The system according to claim 1, further comprising a mechanism for visually organizing action suggestions to aid user understanding.

[0868] "Application example 2 of combining emotional engines"

[0869] (Claim 1)

[0870] A method for converting audio information obtained using an audio acquisition means into text information,

[0871] A machine learning method for analyzing the user's emotional state based on the text information and the intonation, speed, and volume of the voice,

[0872] A method for predicting future behavioral tendencies based on the aforementioned emotional state and generating specific action suggestions for the user,

[0873] A means of expression for presenting the aforementioned action proposal to the user via a smart device,

[0874] A formatting method for presenting the aforementioned action proposal as visual information to aid user understanding,

[0875] A communication means for analyzing voice information in real time and immediately feeding the analysis results back to the user,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, wherein the analysis means identifies the user's behavioral tendencies by referring to past activity history.

[0879] (Claim 3)

[0880] The system according to claim 1, further comprising means of providing a customer service experience that is natural and based on the emotional needs of the user, using a communication terminal. [Explanation of Symbols]

[0881] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for converting audio data acquired using an audio input device into text data, An artificial intelligence means for analyzing the user's emotional state based on the text data, A means for predicting future behavioral tendencies based on the aforementioned emotional state and generating specific action suggestions for the user, A system including a display device for presenting the aforementioned action suggestions to the user.

2. The system according to claim 1, wherein the analysis means identifies the user's behavioral patterns by referring to past activity records.

3. The system according to claim 1, wherein the display means further includes means for visually organizing action suggestions to aid the user's understanding.

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