system

Abstract

We provide the system. [Solution] A means comprising multiple different artificial intelligence models, each of which generates information based on a different perspective, A means for receiving input from a user and sending information generation requests to the aforementioned multiple artificial intelligence models, A means for integrating the information generated from the aforementioned multiple artificial intelligence models and presenting it to the user, A system that includes this.

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, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003] [[ID=2】

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Currently, in information generation systems using artificial intelligence, many of them aim to provide accurate answers, and there is a problem that it is difficult to draw out original ideas and diverse perspectives. For this reason, obtaining multi-faceted and creative opinions in discussions and decision-making scenarios is restricted. An object of the present invention is to solve this problem and provide a system that broadens the scope of discussions by generating opinions from diverse perspectives.

Means for Solving the Problems

[0005] The system according to the present invention comprises multiple artificial intelligence models with different perspectives. This allows each model to generate information tailored to its specific characteristics based on user input. Furthermore, by integrating the information generated from these models and presenting it to the user, diverse perspectives can be provided, facilitating creative discussion.

[0006] An "artificial intelligence model" is an algorithm or program designed to generate information based on a specific perspective or thought pattern.

[0007] "Information generation" is the process of creating new data and opinions based on user input.

[0008] "User input" refers to text and other forms of data that users provide to the system, which form the basis for the generation AI model when generating information.

[0009] "Perspective" refers to a specific viewpoint or approach for understanding or interpreting things.

[0010] "Integration" is the process of combining multiple different pieces of information or data into a single, coherent form.

[0011] "Presentation" refers to the action a system takes to show generated information or opinions to the user. [Brief explanation of the drawing]

[0012] [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 the data processing device and 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]It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It 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 Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] 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.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the 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), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is implemented by a system whose main components are a server, a terminal, and a user. The server is equipped with multiple artificial intelligence models with diverse perspectives, each model having different properties and thought patterns. This makes it possible to generate information from diverse viewpoints.

[0034] The terminal receives input data provided by the user and sends it to the server as needed. When the user inputs a topic for discussion or decision-making, the terminal analyzes it and sends it to the server. At this time, each artificial intelligence model generates information based on the analyzed data, utilizing predefined characteristics.

[0035] The server integrates information output from multiple generative AI models and sends it back to the terminal in a consistent format. The terminal visualizes the received information for the user and presents it in an easy-to-understand format. This allows the user to gain new ideas and insights based on the various opinions and perspectives generated.

[0036] As a concrete example, consider a scenario where a user uses this system to develop a market strategy for a new product. When the user inputs "New Product Market Launch Strategy" as the topic, multiple AI models on the server generate relevant information from various perspectives (e.g., probability of success, potential risks, customer response, etc.). This information is then integrated and sent back to the terminal for presentation to the user, enabling a multifaceted analysis. In this way, the present invention makes it easy to acquire unprecedented perspectives and ideas.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] Users input the topic they wish to discuss via their terminal. This input should include the discussion theme or specific issues.

[0040] Step 2:

[0041] The terminal receives input data from the user and sends it to the server according to the data format. Meta information about the topic may be included during transmission.

[0042] Step 3:

[0043] The server distributes the received topic data to multiple artificial intelligence models. Each model analyzes the data and generates information based on its own pre-configured perspective.

[0044] Step 4:

[0045] An optimistic AI model generates opinions and information that focus on positive scenarios. For example, it might say, "The new product is likely to be successful in the market."

[0046] Step 5:

[0047] AI models with a critical perspective generate opinions that take potential problems and negative scenarios into account. For example, they might say, "Gaining market share may be difficult due to strong competing products."

[0048] Step 6:

[0049] Risk-focused AI models generate information about specific risk factors and risk concerns. For example, information such as "there is a risk of supply chain problems occurring."

[0050] Step 7:

[0051] The server integrates the information generated from each AI model, maintains a delicate balance of consistency, and packages the aggregated information.

[0052] Step 8:

[0053] The server sends the integrated information back to the terminal, which then displays this information to the user in an easy-to-understand format. When presenting the information, the characteristics of each opinion are clearly indicated, allowing the user to easily compare and consider them.

[0054] Step 9:

[0055] Users consider the diverse opinions presented and use them to make their own judgments and generate new ideas. Through this step, users can obtain results from multiple perspectives.

[0056] (Example 1)

[0057] 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."

[0058] In modern society, there is a need to quickly and comprehensively acquire information from diverse perspectives. However, summarizing information from different viewpoints is time-consuming and laborious, and maintaining consistency is difficult. Therefore, a system is needed that efficiently integrates information from different perspectives and presents it in a user-friendly format.

[0059] 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.

[0060] In this invention, the server includes means for comprising a plurality of different inference devices, each of which generates information based on a different perspective; means for receiving data from an input unit and transmitting information generation requests to the plurality of inference devices; and means for integrating the information generated from the plurality of inference devices and presenting it to the input unit. This makes it possible for the user to acquire diverse information and easily gain new insights.

[0061] A "reasoning device" is an artificial intelligence module that possesses different perspectives and viewpoints and generates information about a specific subject.

[0062] An "input unit" is a device or interface used by users to input data related to discussions and decision-making, and it plays a role in communicating with the server.

[0063] An "information generation request" refers to the process of instructing an inference device to create information based on input provided by the user.

[0064] "Information integration" is the process of combining output information generated by multiple inference devices and consolidating it into a unified and consistent format.

[0065] "Visual visualization" refers to the use of visual representations such as diagrams and tables to display integrated information in a way that is easy for users to understand.

[0066] This invention provides an information processing system whose main components are a server, a terminal, and a user. The server has multiple inference devices (generative AI models), which generate information from different perspectives. Specifically, it is capable of generating optimistic information, critical information, and risk assessment information.

[0067] Users input discussion and decision-making topics in text format via their terminals. For example, a prompt might read, "Please tell me the success factors for a new product launch strategy."

[0068] The terminal parses the input topic, converts it to the required format, and sends it to the server. Based on the received input, the server processes the information generation request using each inference unit. Each inference unit generates information based on a predetermined perspective and returns its output to the server.

[0069] The server integrates information obtained from multiple inference devices, ensures consistency, and then sends it back to the terminal. The terminal visualizes this integrated information for the user. The information is displayed using visual means such as graphs and charts, making it easy for the user to understand and gain new insights.

[0070] In this way, a system is realized that allows users to acquire information based on diverse perspectives and opinions, and effectively support their decision-making.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] Users input topics related to discussion and decision-making into the terminal in text format. Prompts can be used for this input. For example, "Please tell me the success factors for a new product launch strategy." The input topic is received by the terminal.

[0074] Step 2:

[0075] The terminal analyzes the topic entered by the user. Using natural language processing technology, it analyzes the intent and category of the entered text and converts it into data in a format that can be easily processed by the server. This analysis result is then sent to the server.

[0076] Step 3:

[0077] The server receives the analyzed data sent from the terminal. Next, based on the analysis results, it determines and selects which generative AI model is most suitable for generating information. Based on this selection, an information generation request is sent to each AI model.

[0078] Step 4:

[0079] Each selected generative AI model processes data based on requests from the server. Each model utilizes specific algorithms and databases to generate new information from a predetermined perspective (e.g., success factors, potential risks, customer reactions, etc.). This generated information is then returned to the server.

[0080] Step 5:

[0081] The server integrates information provided by multiple generative AI models. It performs data calculations to remove duplicate information and organize it into consistent and non-contradictory data. This integrated information is converted into a consistent report format and sent to the terminal.

[0082] Step 6:

[0083] The terminal receives integrated information sent from the server and visualizes it for the user. At this stage, the information is often displayed using graphs and charts to make it easy to understand. The user gains insights from the visualized information and uses it to support their decision-making.

[0084] (Application Example 1)

[0085] 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."

[0086] In existing consumer goods sales and marketing environments, there is a challenge in analyzing diverse customer behaviors and reactions in real time and visually presenting immediate marketing strategies based on that analysis on-site. To address this problem, there is a need to provide an effective system that enables quicker and more comprehensive situational assessment and appropriate action at the sales site.

[0087] 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.

[0088] In this invention, the server includes means for having multiple different intelligent models, each of which generates data based on a different perspective; means for receiving information from a user and transmitting data generation requests to the multiple intelligent models; means for integrating the data generated from the multiple intelligent models and presenting it to the user; and means for providing strategic suggestions in real time through a visual device based on behavioral data acquired from an image acquisition device. This makes it possible to immediately grasp customer behavior at the sales site and determine and execute the optimal marketing strategy in real time accordingly.

[0089] An "intelligent model" is a form of artificial intelligence that possesses different perspectives and viewpoints and generates information based on those characteristics.

[0090] A "user" is an entity that inputs information, requests data generation from an intelligent model, and receives the resulting output.

[0091] A "data generation request" is the process by which a user requests an intelligent model to generate specific data or information.

[0092] "Integration" is the process of combining data generated from multiple intelligence models and presenting it in a consistent manner.

[0093] "Image acquisition device" is a general term for cameras and sensors used to capture customer movements and reactions within a store or sales environment.

[0094] A "visual device" is a device that presents integrated information and suggestions to users in real time, and includes smart glasses, among others.

[0095] "Real-time strategic proposals" refer to suggestions for appropriate marketing and sales tactics that are implemented immediately in response to customer behavior and circumstances.

[0096] The system implementing this invention consists of a server, a terminal, and a user. The server provides multiple intelligent models, each designed to generate data from a different perspective. This allows the server to analyze user input using generative AI models and provide diverse opinions and data. Examples of intelligent models include a model for calculating the probability of success, a model for evaluating potential risks, and a model for predicting customer reactions.

[0097] The terminal functions as hardware that allows users to input behavioral data and transmit it to a server. Typical devices include smart glasses and smartphones, which process information obtained from in-store image acquisition devices in real time.

[0098] The user is the entity that requests information generation from the system based on the input information. For example, when deciding on specific strategies regarding store operations, the user can refer to data presented from the server through a visual device.

[0099] In this system, the image acquisition device collects data such as customer movement patterns and product attention levels. The collected data is analyzed by various intelligent models on the server, and the results are integrated and presented on the visual device. This real-time information provision allows users to dynamically determine appropriate marketing strategies.

[0100] A concrete example is when introducing a new product to the market. The system analyzes customer reactions in real time and, based on that information, suggests an "effective display method for the new product" to the user. An example of a prompt to the generating AI model in this case would be, "Based on customer in-store behavior data, please suggest an effective marketing strategy for the new product." This allows the user to immediately formulate and implement a concrete market strategy.

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] Users input tasks and information requiring analysis using a terminal. This information may include data obtained from image acquisition devices and sensors. The terminal receives this input information, formats it into a data format, and sends it to the server. The input may include customer movement data and information on products of interest.

[0104] Step 2:

[0105] The server requests data generation from multiple intelligent models based on the received information. Each intelligent model generates information utilizing its own characteristics (success probability, risk assessment, customer response, etc.). Specifically, it generates information on the optimal placement of products based on the received movement data. This process uses a generative AI model, which operates based on specific prompt statements.

[0106] Step 3:

[0107] The server integrates multiple pieces of information generated from the intelligent model. This integration process compiles and outputs the data in a consistent and actionable format. This output may include, for example, suggestions for effective display methods for new products. The server then sends this back to the terminal.

[0108] Step 4:

[0109] The terminal presents the user with integrated data received from the server. Information is provided visually to the user via smart glasses or a display device. This information includes real-time updated marketing strategies and customer response analyses, allowing the user to make quick decisions based on it.

[0110] Through these steps, users can make data-driven strategic decisions in real time.

[0111] 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.

[0112] The present invention is implemented by a system whose main components are a server, a terminal, a user, and an emotion engine. This system enables multifaceted information generation that takes into account the user's emotional state. The server is equipped with multiple different artificial intelligence models, each trained to generate information from a different perspective. These models include optimistic, critical, and risk assessment models.

[0113] The terminal receives input from the user and sends it to the server. During this process, the emotion engine analyzes the user's input and recognizes their emotional state. The emotion engine can evaluate the user's emotions (e.g., joy, sadness, surprise) using text analysis technology.

[0114] The emotion engine optimizes the instructions for information generation to the appropriate artificial intelligence model on the server based on the evaluation of that emotion. As a result, the generated information takes the user's emotional state into account and becomes more personalized.

[0115] The server integrates the information generated by the artificial intelligence model and sends it to the terminal. The terminal visually presents the received information to the user. Here, the information is presented in a way that is tailored to the user's emotional state, allowing the user to easily understand the generated opinions and gain insights.

[0116] For example, when a user provides input regarding the market evaluation of a new product, the emotion engine senses the user's level of interest and concern, and adjusts the information generation process accordingly. If the user has an optimistic sentiment, optimistic opinions will be emphasized and presented more prominently. In this way, a form of improving the user experience and providing richer, more meaningful information is revealed.

[0117] The following describes the processing flow.

[0118] Step 1:

[0119] The user enters a specific topic they want to discuss or analyze into the terminal. This input data is captured by the terminal in text format.

[0120] Step 2:

[0121] The device sends user input to the emotion engine. The emotion engine analyzes the input text and identifies the user's emotional state. This analysis uses natural language processing techniques.

[0122] Step 3:

[0123] The emotion engine identifies the user's emotions (e.g., joy, sadness, surprise) based on the analysis results. This emotion data is crucial to the information generation process.

[0124] Step 4:

[0125] The device sends user sentiment data and topic information to the server.

[0126] Step 5:

[0127] The server transmits the received sentiment data to each artificial intelligence model and instructs each model to generate topic-based information. Based on the sentiment data, the style and perspective of the information generated by the models are adjusted.

[0128] Step 6:

[0129] An optimistic AI model takes advantage of the user's positive emotions and generates information from a positive perspective. For example, it might generate information such as, "The new product will be well-received by many consumers."

[0130] Step 7:

[0131] Critical AI models, when negative sentiment data is present, perform careful analysis while taking it into account, and generate information that points out potential challenges. For example, they might generate information such as, "Due to intense competition, careful consideration is needed regarding your market position."

[0132] Step 8:

[0133] The server integrates the output from all models, taking sentiment data into consideration and constructing aggregated data while ensuring the consistency of the information.

[0134] Step 9:

[0135] The device receives integrated information and presents it in a format appropriate to the user's emotional state. The information is visually distinguishable and displayed in a way that allows the user to quickly gain insights.

[0136] Step 10:

[0137] Users form new ideas and judgments based on the information presented. Through this process, users acquire rich information and can analyze it from multiple perspectives.

[0138] (Example 2)

[0139] 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".

[0140] Conventional information generation systems present information uniformly without considering the user's emotional state, making it difficult for users to obtain personalized information they need. Furthermore, existing systems fail to adequately cover different perspectives, resulting in a lack of multifaceted information. This has led to a problem where users struggle to gain meaningful insights.

[0141] 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.

[0142] In this invention, the server includes a terminal for receiving input from the user, means for analyzing the user's emotional state from the input text, and means for sending information generation requests to multiple different artificial intelligence models based on the emotional analysis. This enables the provision of multifaceted and personalized information tailored to the user's emotional state.

[0143] "User input" refers to text and data provided by the user through their device.

[0144] A "terminal" refers to a device or interface used by a user to provide input data and display the received information.

[0145] "Emotional state" refers to the mental and emotional characteristics analyzed from user input, including joy, sadness, surprise, and so on.

[0146] "Means of analysis" refers to technologies and devices used to process input data and extract useful information.

[0147] An "artificial intelligence model" refers to an algorithm or program trained to perform a specific task and possesses the ability to generate information from different perspectives.

[0148] "Means for sending information generation requests" refers to a function that instructs an artificial intelligence model based on the results of analysis to generate information.

[0149] "Means of integrating information" refers to technologies that combine information generated by multiple artificial intelligence models to form a final output.

[0150] "Means of presenting information" refers to interfaces and display methods used to show integrated results to users.

[0151] To implement this invention, a configuration including a server, a terminal, a user, and an emotion engine is required. The server has multiple artificial intelligence models that generate information based on various different perspectives. These models are trained to take into account specific viewpoints such as optimism, critical thinking, and risk assessment. The server has the ability to receive information generation requests for these models and perform the necessary processing. The emotion engine utilizes natural language processing (NLP) to analyze the user's input as text and evaluate their emotional state.

[0152] The terminal receives text input from the user and sends it to the server. The input is analyzed by an emotion engine to identify the user's emotional state. The emotion engine's analysis results optimize instructions to the artificial intelligence model on the server, generating information appropriate to the user's emotions. At this time, the terminal plays the role of visually presenting the received information to the user.

[0153] As a concrete example, a user inputs "How will this new product perform in the market?" into the device. The device passes this input to the emotion engine, which then transmits the analysis result—that the user is optimistic—to the server. The server selects an optimistic model and sends back the positive market assessment information generated by the model to the device. The device then analyzes this information and presents it to the user using bright colors and appropriate visuals, thereby deepening the user's insights.

[0154] As an example of a prompt, consider the sentence, "Please give me your optimistic view on the likelihood of this new product succeeding in the market." This prompt is used as an instruction to the model, and appropriate information is generated based on the user's input.

[0155] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0156] Step 1:

[0157] The terminal receives text input from the user. The user enters questions or opinions on topics of interest. This input serves as the starting point for the next process.

[0158] Step 2:

[0159] The device passes the user's input to the emotion engine. The emotion engine uses natural language processing techniques to analyze the emotional state from the input text. For example, it analyzes expressions like "I'm really looking forward to it" and identifies an optimistic emotional state. This analysis result is then used to optimize the subsequent information generation process.

[0160] Step 3:

[0161] The terminal sends instructions to the server to select an appropriate artificial intelligence model based on the analysis results of the emotion engine. Here, an optimistic, critical, or risk-assessing model is selected depending on the emotional state. The selection process then begins generating information from each respective perspective.

[0162] Step 4:

[0163] The server generates information using multiple generative AI models in response to instructions from the terminal. Each model provides information based on its own perspective (optimistic, critical, risk assessment). This information generation process aims to form a multifaceted collection of data based on user input.

[0164] Step 5:

[0165] The server integrates the information provided by the generative AI model. During the integration process, information is weighted and emphasized according to the user's emotional state. This results in a collection of personalized information that is most relevant to the user.

[0166] Step 6:

[0167] The server sends the integrated information to the terminal. The terminal then visually organizes and presents this information to the user. For example, optimistic opinions might be highlighted with bright colors and positive icons. This visualization allows the user to easily understand the information and gain insights.

[0168] (Application Example 2)

[0169] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0170] Traditional information delivery systems have a problem in that they provide uniform information without considering the user's emotional state, resulting in a lack of improvement in the user experience. In particular, in shopping scenarios at physical stores, it was difficult for users to select appropriate products according to their emotions at the time. Therefore, there is a need to provide more personalized and emotionally relevant information.

[0171] 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.

[0172] In this invention, the server includes means for providing a plurality of different data processing models, each of which generates information based on different evaluation criteria; means for receiving input from a user and sending information generation requests to the plurality of data processing models; and means for integrating the information generated from the plurality of data processing models and presenting it to the user. This makes it possible to provide personalized information in accordance with the user's emotional state.

[0173] A "data processing model" is a computer program that analyzes data based on various perspectives and criteria to generate information.

[0174] An "information generation request" is a process that instructs a data processing model to generate information based on user input.

[0175] "User emotional state" refers to the emotions and psychological reactions a user experiences in a particular situation, including joy, sadness, surprise, and so on.

[0176] "Emotionally relevant information" refers to information that is highly relevant to the user, generated while taking into account the user's current emotional state.

[0177] "Means of selecting and presenting products" refers to a function that selects appropriate products based on the user's emotional state and presents them to the user visually or otherwise.

[0178] The system of this invention, centered around a server, combines multifaceted data processing and sentiment analysis to provide personalized information to users. The server uses multiple data processing models to evaluate user input from various perspectives and generates information based on these evaluations. This enables the presentation of optimal information that takes into account the user's emotional state. The hardware requires the server itself and a data input terminal, while the software implements a sentiment analysis engine and multiple AI models.

[0179] When users select products in a physical store using smart glasses or a head-mounted display, they provide information about accessories via voice or eye-tracking input. The device collects this information, and an emotion analysis engine performs the analysis. After the analysis, the server selects an appropriate data processing model and adjusts the information generation process. Finally, information tailored to the user's emotional state is visually presented, making product selection easier.

[0180] For example, if a user says, "I'm feeling cheerful today," the sentiment analysis engine will capture that positive emotion, and the server will select a highly novel fashion item. An example of a prompt to the generating AI model would be, "Please recommend new products that are suitable for when the user is feeling positive."

[0181] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0182] Step 1:

[0183] The device accepts voice or eye-tracking input from the user. The input data is used as foundational data to be sent to a server for analysis of the user's current emotional state.

[0184] Step 2:

[0185] The server receives the transmitted user input and analyzes its content using an emotion analysis engine. The emotion analysis engine uses text analysis techniques to determine the user's emotional state (i.e., positive, negative, neutral, etc.) from the input data. The output of this step is data related to the user's emotional state.

[0186] Step 3:

[0187] The server selects the appropriate model from several data processing models based on the output of the sentiment analysis engine. Here, it determines which model to use to generate information that best reflects the user's emotions. The selected model may be optimistic, critical, or risk-assessing. The output of this step is the selected data processing model.

[0188] Step 4:

[0189] The server uses a selected data processing model to generate information tailored to the user's emotional state. Here, the generating AI model generates product information based on the prompt text and processes it into information highly relevant to the user. The output of this step is information to be suggested to the user.

[0190] Step 5:

[0191] The terminal receives information transmitted from the server and presents it visually to the user. The user can then select specific products based on the presented information. The final output is product information displayed on a visual display.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] [Second Embodiment]

[0196] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0197] 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.

[0198] 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).

[0199] 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.

[0200] 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.

[0201] 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).

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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".

[0208] This invention is implemented by a system whose main components are a server, a terminal, and a user. The server is equipped with multiple artificial intelligence models with diverse perspectives, each model having different properties and thought patterns. This makes it possible to generate information from diverse viewpoints.

[0209] The terminal receives input data provided by the user and sends it to the server as needed. When the user inputs a topic for discussion or decision-making, the terminal analyzes it and sends it to the server. At this time, each artificial intelligence model generates information based on the analyzed data, utilizing predefined characteristics.

[0210] The server integrates information output from multiple generative AI models and sends it back to the terminal in a consistent format. The terminal visualizes the received information for the user and presents it in an easy-to-understand format. This allows the user to gain new ideas and insights based on the various opinions and perspectives generated.

[0211] As a concrete example, consider a scenario where a user uses this system to develop a market strategy for a new product. When the user inputs "New Product Market Launch Strategy" as the topic, multiple AI models on the server generate relevant information from various perspectives (e.g., probability of success, potential risks, customer response, etc.). This information is then integrated and sent back to the terminal for presentation to the user, enabling a multifaceted analysis. In this way, the present invention makes it easy to acquire unprecedented perspectives and ideas.

[0212] The following describes the processing flow.

[0213] Step 1:

[0214] Users input the topic they wish to discuss via their terminal. This input should include the discussion theme or specific issues.

[0215] Step 2:

[0216] The terminal receives input data from the user and sends it to the server according to the data format. Meta information about the topic may be included during transmission.

[0217] Step 3:

[0218] The server distributes the received topic data to multiple artificial intelligence models. Each model analyzes the data and generates information based on its own pre-configured perspective.

[0219] Step 4:

[0220] An optimistic AI model generates opinions and information that focus on positive scenarios. For example, it might say, "The new product is likely to be successful in the market."

[0221] Step 5:

[0222] AI models with a critical perspective generate opinions that take potential problems and negative scenarios into account. For example, they might say, "Gaining market share may be difficult due to strong competing products."

[0223] Step 6:

[0224] Risk-focused AI models generate information about specific risk factors and risk concerns. For example, information such as "there is a risk of supply chain problems occurring."

[0225] Step 7:

[0226] The server integrates the information generated from each AI model, maintains a delicate balance of consistency, and packages the aggregated information.

[0227] Step 8:

[0228] The server sends the integrated information back to the terminal, which then displays this information to the user in an easy-to-understand format. When presenting the information, the characteristics of each opinion are clearly indicated, allowing the user to easily compare and consider them.

[0229] Step 9:

[0230] Users consider the diverse opinions presented and use them to make their own judgments and generate new ideas. Through this step, users can obtain results from multiple perspectives.

[0231] (Example 1)

[0232] 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."

[0233] In modern society, there is a need to quickly and comprehensively acquire information from diverse perspectives. However, summarizing information from different viewpoints is time-consuming and laborious, and maintaining consistency is difficult. Therefore, a system is needed that efficiently integrates information from different perspectives and presents it in a user-friendly format.

[0234] 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.

[0235] In this invention, the server includes means for comprising a plurality of different inference devices, each of which generates information based on a different perspective; means for receiving data from an input unit and transmitting information generation requests to the plurality of inference devices; and means for integrating the information generated from the plurality of inference devices and presenting it to the input unit. This makes it possible for the user to acquire diverse information and easily gain new insights.

[0236] A "reasoning device" is an artificial intelligence module that possesses different perspectives and viewpoints and generates information about a specific subject.

[0237] An "input unit" is a device or interface used by users to input data related to discussions and decision-making, and it plays a role in communicating with the server.

[0238] An "information generation request" refers to the process of instructing an inference device to create information based on input provided by the user.

[0239] "Information integration" is the process of combining output information generated by multiple inference devices and consolidating it into a unified and consistent format.

[0240] "Visual visualization" refers to the use of visual representations such as diagrams and tables to display integrated information in a way that is easy for users to understand.

[0241] This invention provides an information processing system whose main components are a server, a terminal, and a user. The server has multiple inference devices (generative AI models), which generate information from different perspectives. Specifically, it is capable of generating optimistic information, critical information, and risk assessment information.

[0242] Users input discussion and decision-making topics in text format via their terminals. For example, a prompt might read, "Please tell me the success factors for a new product launch strategy."

[0243] The terminal parses the input topic, converts it to the required format, and sends it to the server. Based on the received input, the server processes the information generation request using each inference unit. Each inference unit generates information based on a predetermined perspective and returns its output to the server.

[0244] The server integrates information obtained from multiple inference devices, ensures consistency, and then sends it back to the terminal. The terminal visualizes this integrated information for the user. The information is displayed using visual means such as graphs and charts, making it easy for the user to understand and gain new insights.

[0245] In this way, a system is realized that allows users to acquire information based on diverse perspectives and opinions, and effectively support their decision-making.

[0246] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0247] Step 1:

[0248] Users input topics related to discussion and decision-making into the terminal in text format. Prompts can be used for this input. For example, "Please tell me the success factors for a new product launch strategy." The input topic is received by the terminal.

[0249] Step 2:

[0250] The terminal analyzes the topic entered by the user. Using natural language processing technology, it analyzes the intent and category of the entered text and converts it into data in a format that can be easily processed by the server. This analysis result is then sent to the server.

[0251] Step 3:

[0252] The server receives the analyzed data sent from the terminal. Next, based on the analysis results, it determines and selects which generative AI model is most suitable for generating information. Based on this selection, an information generation request is sent to each AI model.

[0253] Step 4:

[0254] Each selected generative AI model processes data based on requests from the server. Each model utilizes specific algorithms and databases to generate new information from a predetermined perspective (e.g., success factors, potential risks, customer reactions, etc.). This generated information is then returned to the server.

[0255] Step 5:

[0256] The server integrates information provided by multiple generative AI models. It performs data calculations to remove duplicate information and organize it into consistent and non-contradictory data. This integrated information is converted into a consistent report format and sent to the terminal.

[0257] Step 6:

[0258] The terminal receives integrated information sent from the server and visualizes it for the user. At this stage, the information is often displayed using graphs and charts to make it easy to understand. The user gains insights from the visualized information and uses it to support their decision-making.

[0259] (Application Example 1)

[0260] 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 glasses 214 will be referred to as the "terminal."

[0261] In existing consumer goods sales and marketing environments, there is a challenge in analyzing diverse customer behaviors and reactions in real time and visually presenting immediate marketing strategies based on that analysis on-site. To address this problem, there is a need to provide an effective system that enables quicker and more comprehensive situational assessment and appropriate action at the sales site.

[0262] 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.

[0263] In this invention, the server includes means for having multiple different intelligent models, each of which generates data based on a different perspective; means for receiving information from a user and transmitting data generation requests to the multiple intelligent models; means for integrating the data generated from the multiple intelligent models and presenting it to the user; and means for providing strategic suggestions in real time through a visual device based on behavioral data acquired from an image acquisition device. This makes it possible to immediately grasp customer behavior at the sales site and determine and execute the optimal marketing strategy in real time accordingly.

[0264] An "intelligent model" is a form of artificial intelligence that possesses different perspectives and viewpoints and generates information based on those characteristics.

[0265] A "user" is an entity that inputs information, requests data generation from an intelligent model, and receives the resulting output.

[0266] A "data generation request" is the process by which a user requests an intelligent model to generate specific data or information.

[0267] "Integration" is the process of combining data generated from multiple intelligence models and presenting it in a consistent manner.

[0268] "Image acquisition device" is a general term for cameras and sensors used to capture customer movements and reactions within a store or sales environment.

[0269] A "visual device" is a device that presents integrated information and suggestions to users in real time, and includes smart glasses, among others.

[0270] "Real-time strategic proposals" refer to suggestions for appropriate marketing and sales tactics that are implemented immediately in response to customer behavior and circumstances.

[0271] The system implementing this invention consists of a server, a terminal, and a user. The server provides multiple intelligent models, each designed to generate data from a different perspective. This allows the server to analyze user input using generative AI models and provide diverse opinions and data. Examples of intelligent models include a model for calculating the probability of success, a model for evaluating potential risks, and a model for predicting customer reactions.

[0272] The terminal functions as hardware that allows users to input behavioral data and transmit it to a server. Typical devices include smart glasses and smartphones, which process information obtained from in-store image acquisition devices in real time.

[0273] The user is the entity that requests information generation from the system based on the input information. For example, when deciding on specific strategies regarding store operations, the user can refer to data presented from the server through a visual device.

[0274] In this system, the image acquisition device collects data such as customer movement patterns and product attention levels. The collected data is analyzed by various intelligent models on the server, and the results are integrated and presented on the visual device. This real-time information provision allows users to dynamically determine appropriate marketing strategies.

[0275] A concrete example is when introducing a new product to the market. The system analyzes customer reactions in real time and, based on that information, suggests an "effective display method for the new product" to the user. An example of a prompt to the generating AI model in this case would be, "Based on customer in-store behavior data, please suggest an effective marketing strategy for the new product." This allows the user to immediately formulate and implement a concrete market strategy.

[0276] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0277] Step 1:

[0278] Users input tasks and information requiring analysis using a terminal. This information may include data obtained from image acquisition devices and sensors. The terminal receives this input information, formats it into a data format, and sends it to the server. The input may include customer movement data and information on products of interest.

[0279] Step 2:

[0280] Based on the received information, the server makes data generation requests to multiple intelligent models. Here, each intelligent model generates information by leveraging its respective characteristics (such as success probability, risk assessment, customer response, etc.). Specifically, it generates information on the optimal placement of products from the received flow line data. In this process, a generation AI model is used, and the model operates based on specific prompt texts.

[0281] Step 3:

[0282] The server integrates the multiple pieces of information generated by the intelligent models. In this integration process, the data is compiled and output in a consistent and practical form. This output includes, for example, proposals for effective display methods of new products. The server returns this to the terminal.

[0283] Step 4:

[0284] The terminal presents the integrated data received from the server to the user. It visually provides information to the user via smart glasses or display devices. This information includes real-time updated marketing strategies and analysis results of customer responses, based on which the user can quickly make decisions.

[0285] Through these steps, the user can make strategic decisions based on data in real time.

[0286] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0287] The present invention is implemented by a system whose main components are a server, a terminal, a user, and an emotion engine. This system enables multifaceted information generation that takes into account the user's emotional state. The server is equipped with multiple different artificial intelligence models, each trained to generate information from a different perspective. These models include optimistic, critical, and risk assessment models.

[0288] The terminal receives input from the user and sends it to the server. During this process, the emotion engine analyzes the user's input and recognizes their emotional state. The emotion engine can evaluate the user's emotions (e.g., joy, sadness, surprise) using text analysis technology.

[0289] The emotion engine optimizes the instructions for information generation to the appropriate artificial intelligence model on the server based on the evaluation of that emotion. As a result, the generated information takes the user's emotional state into account and becomes more personalized.

[0290] The server integrates the information generated by the artificial intelligence model and sends it to the terminal. The terminal visually presents the received information to the user. Here, the information is presented in a way that is tailored to the user's emotional state, allowing the user to easily understand the generated opinions and gain insights.

[0291] For example, when a user provides input regarding the market evaluation of a new product, the emotion engine senses the user's level of interest and concern, and adjusts the information generation process accordingly. If the user has an optimistic sentiment, optimistic opinions will be emphasized and presented more prominently. In this way, a form of improving the user experience and providing richer, more meaningful information is revealed.

[0292] The following describes the processing flow.

[0293] Step 1:

[0294] The user enters a specific topic they want to discuss or analyze into the terminal. This input data is captured by the terminal in text format.

[0295] Step 2:

[0296] The device sends user input to the emotion engine. The emotion engine analyzes the input text and identifies the user's emotional state. This analysis uses natural language processing techniques.

[0297] Step 3:

[0298] The emotion engine identifies the user's emotions (e.g., joy, sadness, surprise) based on the analysis results. This emotion data is crucial to the information generation process.

[0299] Step 4:

[0300] The device sends user sentiment data and topic information to the server.

[0301] Step 5:

[0302] The server transmits the received sentiment data to each artificial intelligence model and instructs each model to generate topic-based information. Based on the sentiment data, the style and perspective of the information generated by the models are adjusted.

[0303] Step 6:

[0304] An optimistic AI model takes advantage of the user's positive emotions and generates information from a positive perspective. For example, it might generate information such as, "The new product will be well-received by many consumers."

[0305] Step 7:

[0306] When there is negative sentiment data, the critical AI model conducts a careful analysis while taking it into account and generates information that points out potential issues. For example, it generates information such as "Due to intense competition, attention needs to be paid to the position in the market."

[0307] Step 8:

[0308] The server integrates the outputs from all models, constructs aggregated data while ensuring the consistency of the information considering sentiment data.

[0309] Step 9:

[0310] The terminal receives the integrated information and presents the information in a format suitable for the user's emotional state. At this time, the information is visually distinguishable and presented so that the user can quickly gain insights.

[0311] Step 10:

[0312] The user forms new ideas or judgments based on the presented information. Through this process, the user obtains rich information and can conduct analysis from multiple perspectives.

[0313] (Example 2)

[0314] Next, Example 2 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".

[0315] In the conventional information generation system, since personalized information required by the user is difficult to obtain because uniform information is presented without considering the user's emotional state, and the existing system cannot fully cover different perspectives and lacks multi-faceted information provision, the user has a problem of being difficult to obtain meaningful insights.

[0316] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0317] In this invention, the server includes a terminal for receiving input from the user, means for analyzing the user's emotional state from the input text, and means for sending information generation requests to multiple different artificial intelligence models based on the emotional analysis. This enables the provision of multifaceted and personalized information tailored to the user's emotional state.

[0318] "User input" refers to text and data provided by the user through their device.

[0319] A "terminal" refers to a device or interface used by a user to provide input data and display the received information.

[0320] "Emotional state" refers to the mental and emotional characteristics analyzed from user input, including joy, sadness, surprise, and so on.

[0321] "Means of analysis" refers to technologies and devices used to process input data and extract useful information.

[0322] An "artificial intelligence model" refers to an algorithm or program trained to perform a specific task and possesses the ability to generate information from different perspectives.

[0323] "Means for sending information generation requests" refers to a function that instructs an artificial intelligence model based on the results of analysis to generate information.

[0324] "Means of integrating information" refers to technologies that combine information generated by multiple artificial intelligence models to form a final output.

[0325] "Means of presenting information" refers to interfaces and display methods used to show integrated results to users.

[0326] To implement this invention, a configuration including a server, a terminal, a user, and an emotion engine is required. The server has multiple artificial intelligence models that generate information based on various different perspectives. These models are trained to take into account specific viewpoints such as optimism, critical thinking, and risk assessment. The server has the ability to receive information generation requests for these models and perform the necessary processing. The emotion engine utilizes natural language processing (NLP) to analyze the user's input as text and evaluate their emotional state.

[0327] The terminal receives text input from the user and sends it to the server. The input is analyzed by an emotion engine to identify the user's emotional state. The emotion engine's analysis results optimize instructions to the artificial intelligence model on the server, generating information appropriate to the user's emotions. At this time, the terminal plays the role of visually presenting the received information to the user.

[0328] As a concrete example, a user inputs "How will this new product perform in the market?" into the device. The device passes this input to the emotion engine, which then transmits the analysis result—that the user is optimistic—to the server. The server selects an optimistic model and sends back the positive market assessment information generated by the model to the device. The device then analyzes this information and presents it to the user using bright colors and appropriate visuals, thereby deepening the user's insights.

[0329] As an example of a prompt, consider the sentence, "Please give me your optimistic view on the likelihood of this new product succeeding in the market." This prompt is used as an instruction to the model, and appropriate information is generated based on the user's input.

[0330] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0331] Step 1:

[0332] The terminal receives text input from the user. The user enters questions or opinions on topics of interest. This input serves as the starting point for the next process.

[0333] Step 2:

[0334] The device passes the user's input to the emotion engine. The emotion engine uses natural language processing techniques to analyze the emotional state from the input text. For example, it analyzes expressions like "I'm really looking forward to it" and identifies an optimistic emotional state. This analysis result is then used to optimize the subsequent information generation process.

[0335] Step 3:

[0336] The terminal sends instructions to the server to select an appropriate artificial intelligence model based on the analysis results of the emotion engine. Here, an optimistic, critical, or risk-assessing model is selected depending on the emotional state. The selection process then begins generating information from each respective perspective.

[0337] Step 4:

[0338] The server generates information using multiple generative AI models in response to instructions from the terminal. Each model provides information based on its own perspective (optimistic, critical, risk assessment). This information generation process aims to form a multifaceted collection of data based on user input.

[0339] Step 5:

[0340] The server integrates the information provided by the generative AI model. During the integration process, information is weighted and emphasized according to the user's emotional state. This results in a collection of personalized information that is most relevant to the user.

[0341] Step 6:

[0342] The server sends the integrated information to the terminal. The terminal then visually organizes and presents this information to the user. For example, optimistic opinions might be highlighted with bright colors and positive icons. This visualization allows the user to easily understand the information and gain insights.

[0343] (Application Example 2)

[0344] 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."

[0345] Traditional information delivery systems have a problem in that they provide uniform information without considering the user's emotional state, resulting in a lack of improvement in the user experience. In particular, in shopping scenarios at physical stores, it was difficult for users to select appropriate products according to their emotions at the time. Therefore, there is a need to provide more personalized and emotionally relevant information.

[0346] 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.

[0347] In this invention, the server includes means for providing a plurality of different data processing models, each of which generates information based on different evaluation criteria; means for receiving input from a user and sending information generation requests to the plurality of data processing models; and means for integrating the information generated from the plurality of data processing models and presenting it to the user. This makes it possible to provide personalized information in accordance with the user's emotional state.

[0348] A "data processing model" is a computer program that analyzes data based on various perspectives and criteria to generate information.

[0349] An "information generation request" is a process that instructs a data processing model to generate information based on user input.

[0350] "User emotional state" refers to the emotions and psychological reactions a user experiences in a particular situation, including joy, sadness, surprise, and so on.

[0351] "Emotionally relevant information" refers to information that is highly relevant to the user, generated while taking into account the user's current emotional state.

[0352] "Means of selecting and presenting products" refers to a function that selects appropriate products based on the user's emotional state and presents them to the user visually or in other ways.

[0353] The system of this invention, centered around a server, combines multifaceted data processing and sentiment analysis to provide personalized information to users. The server uses multiple data processing models to evaluate user input from various perspectives and generates information based on these evaluations. This enables the presentation of optimal information that takes into account the user's emotional state. The hardware requires the server itself and a data input terminal, while the software implements a sentiment analysis engine and multiple AI models.

[0354] When users select products in a physical store using smart glasses or a head-mounted display, they provide information about accessories via voice or eye-tracking input. The device collects this information, and an emotion analysis engine performs the analysis. After the analysis, the server selects an appropriate data processing model and adjusts the information generation process. Finally, information tailored to the user's emotional state is visually presented, making product selection easier.

[0355] For example, if a user says, "I'm feeling cheerful today," the sentiment analysis engine will capture that positive emotion, and the server will select a highly novel fashion item. An example of a prompt to the generating AI model would be, "Please recommend new products that are suitable for when the user is feeling positive."

[0356] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0357] Step 1:

[0358] The device accepts voice or eye-tracking input from the user. The input data is used as foundational data to be sent to a server for analysis of the user's current emotional state.

[0359] Step 2:

[0360] The server receives the transmitted user input and analyzes its content using an emotion analysis engine. The emotion analysis engine uses text analysis techniques to determine the user's emotional state (i.e., positive, negative, neutral, etc.) from the input data. The output of this step is data related to the user's emotional state.

[0361] Step 3:

[0362] The server selects the appropriate model from several data processing models based on the output of the sentiment analysis engine. Here, it determines which model to use to generate information that best reflects the user's emotions. The selected model may be optimistic, critical, or risk-assessing. The output of this step is the selected data processing model.

[0363] Step 4:

[0364] The server uses a selected data processing model to generate information tailored to the user's emotional state. Here, the generating AI model generates product information based on the prompt text and processes it to be highly relevant to the user. The output of this step is information to be suggested to the user.

[0365] Step 5:

[0366] The terminal receives information transmitted from the server and presents it visually to the user. The user can then select specific products based on the presented information. The final output is product information displayed on a visual display.

[0367] 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.

[0368] 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.

[0369] 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.

[0370] [Third Embodiment]

[0371] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0372] 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.

[0373] 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).

[0374] 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.

[0375] 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.

[0376] 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).

[0377] 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.

[0378] 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.

[0379] 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.

[0380] 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.

[0381] 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.

[0382] 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".

[0383] This invention is implemented by a system whose main components are a server, a terminal, and a user. The server is equipped with multiple artificial intelligence models with diverse perspectives, each model having different properties and thought patterns. This makes it possible to generate information from diverse viewpoints.

[0384] The terminal receives input data provided by the user and sends it to the server as needed. When the user inputs a topic for discussion or decision-making, the terminal analyzes it and sends it to the server. At this time, each artificial intelligence model generates information based on the analyzed data, utilizing predefined characteristics.

[0385] The server integrates information output from multiple generative AI models and sends it back to the terminal in a consistent format. The terminal visualizes the received information for the user and presents it in an easy-to-understand format. This allows the user to gain new ideas and insights based on the various opinions and perspectives generated.

[0386] As a concrete example, consider a scenario where a user uses this system to develop a market strategy for a new product. When the user inputs "New Product Market Launch Strategy" as the topic, multiple AI models on the server generate relevant information from various perspectives (e.g., probability of success, potential risks, customer response, etc.). This information is then integrated and sent back to the terminal for presentation to the user, enabling a multifaceted analysis. In this way, the present invention makes it easy to acquire unprecedented perspectives and ideas.

[0387] The following describes the processing flow.

[0388] Step 1:

[0389] Users input the topic they wish to discuss via their terminal. This input should include the discussion theme or specific issues.

[0390] Step 2:

[0391] The terminal receives input data from the user and sends it to the server according to the data format. Meta information about the topic may be included during transmission.

[0392] Step 3:

[0393] The server distributes the received topic data to multiple artificial intelligence models. Each model analyzes the data and generates information based on its own pre-configured perspective.

[0394] Step 4:

[0395] An optimistic AI model generates opinions and information that focus on positive scenarios. For example, it might say, "The new product is likely to be successful in the market."

[0396] Step 5:

[0397] AI models with a critical perspective generate opinions that take potential problems and negative scenarios into account. For example, they might say, "Gaining market share may be difficult due to strong competing products."

[0398] Step 6:

[0399] Risk-focused AI models generate information about specific risk factors and risk concerns. For example, information such as "there is a risk of supply chain problems occurring."

[0400] Step 7:

[0401] The server integrates the information generated from each AI model, maintains a delicate balance of consistency, and packages the aggregated information.

[0402] Step 8:

[0403] The server sends the integrated information back to the terminal, which then displays this information to the user in an easy-to-understand format. When presenting the information, the characteristics of each opinion are clearly indicated, allowing the user to easily compare and consider them.

[0404] Step 9:

[0405] Users consider the diverse opinions presented and use them to make their own judgments and generate new ideas. Through this step, users can obtain results from multiple perspectives.

[0406] (Example 1)

[0407] 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."

[0408] In modern society, there is a need to quickly and comprehensively acquire information from diverse perspectives. However, summarizing information from different viewpoints is time-consuming and laborious, and maintaining consistency is difficult. Therefore, a system is needed that efficiently integrates information from different perspectives and presents it in a user-friendly format.

[0409] 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.

[0410] In this invention, the server includes means for comprising a plurality of different inference devices, each of which generates information based on a different perspective; means for receiving data from an input unit and transmitting information generation requests to the plurality of inference devices; and means for integrating the information generated from the plurality of inference devices and presenting it to the input unit. This makes it possible for the user to acquire diverse information and easily gain new insights.

[0411] A "reasoning device" is an artificial intelligence module that possesses different perspectives and viewpoints and generates information about a specific subject.

[0412] An "input unit" is a device or interface used by users to input data related to discussions and decision-making, and it plays a role in communicating with the server.

[0413] An "information generation request" refers to the process of instructing an inference device to create information based on input provided by the user.

[0414] "Information integration" is the process of combining output information generated by multiple inference devices and consolidating it into a unified and consistent format.

[0415] "Visual visualization" refers to the use of visual representations such as diagrams and tables to display integrated information in a way that is easy for users to understand.

[0416] This invention provides an information processing system whose main components are a server, a terminal, and a user. The server has multiple inference devices (generative AI models), which generate information from different perspectives. Specifically, it is capable of generating optimistic information, critical information, and risk assessment information.

[0417] Users input discussion and decision-making topics in text format via their terminals. For example, a prompt might read, "Please tell me the success factors for a new product launch strategy."

[0418] The terminal parses the input topic, converts it to the required format, and sends it to the server. Based on the received input, the server processes the information generation request using each inference unit. Each inference unit generates information based on a predetermined perspective and returns its output to the server.

[0419] The server integrates information obtained from multiple inference devices, ensures consistency, and then sends it back to the terminal. The terminal visualizes this integrated information for the user. The information is displayed using visual means such as graphs and charts, making it easy for the user to understand and gain new insights.

[0420] In this way, a system is realized that allows users to acquire information based on diverse perspectives and opinions, and effectively support their decision-making.

[0421] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0422] Step 1:

[0423] Users input topics related to discussion and decision-making into the terminal in text format. Prompts can be used for this input. For example, "Please tell me the success factors for a new product launch strategy." The input topic is received by the terminal.

[0424] Step 2:

[0425] The terminal analyzes the topic entered by the user. Using natural language processing technology, it analyzes the intent and category of the entered text and converts it into data in a format that can be easily processed by the server. This analysis result is then sent to the server.

[0426] Step 3:

[0427] The server receives the analyzed data sent from the terminal. Next, based on the analysis results, it determines and selects which generative AI model is most suitable for generating information. Based on this selection, an information generation request is sent to each AI model.

[0428] Step 4:

[0429] Each selected generative AI model processes data based on requests from the server. Each model utilizes specific algorithms and databases to generate new information from a predetermined perspective (e.g., success factors, potential risks, customer reactions, etc.). This generated information is then returned to the server.

[0430] Step 5:

[0431] The server integrates information provided by multiple generative AI models. It performs data calculations to remove duplicate information and organize it into consistent and non-contradictory data. This integrated information is converted into a consistent report format and sent to the terminal.

[0432] Step 6:

[0433] The terminal receives integrated information sent from the server and visualizes it for the user. At this stage, the information is often displayed using graphs and charts to make it easy to understand. The user gains insights from the visualized information and uses it to support their decision-making.

[0434] (Application Example 1)

[0435] 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."

[0436] In existing consumer goods sales and marketing environments, there is a challenge in analyzing diverse customer behaviors and reactions in real time and visually presenting immediate marketing strategies based on that analysis on-site. To address this problem, there is a need to provide an effective system that enables quicker and more comprehensive situational assessment and appropriate action at the sales site.

[0437] 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.

[0438] In this invention, the server includes means for having multiple different intelligent models, each of which generates data based on a different perspective; means for receiving information from a user and transmitting data generation requests to the multiple intelligent models; means for integrating the data generated from the multiple intelligent models and presenting it to the user; and means for providing strategic suggestions in real time through a visual device based on behavioral data acquired from an image acquisition device. This makes it possible to immediately grasp customer behavior at the sales site and determine and execute the optimal marketing strategy in real time accordingly.

[0439] An "intelligent model" is a form of artificial intelligence that possesses different perspectives and viewpoints and generates information based on those characteristics.

[0440] A "user" is an entity that inputs information, requests data generation from an intelligent model, and receives the resulting output.

[0441] A "data generation request" is the process by which a user requests an intelligent model to generate specific data or information.

[0442] "Integration" is the process of combining data generated from multiple intelligence models and presenting it in a consistent manner.

[0443] "Image acquisition device" is a general term for cameras and sensors used to capture customer movements and reactions within a store or sales environment.

[0444] A "visual device" is a device that presents integrated information and suggestions to users in real time, and includes smart glasses, among others.

[0445] "Real-time strategic proposals" refer to suggestions for appropriate marketing and sales tactics that are implemented immediately in response to customer behavior and circumstances.

[0446] The system implementing this invention consists of a server, a terminal, and a user. The server provides multiple intelligent models, each designed to generate data from a different perspective. This allows the server to analyze user input using generative AI models and provide diverse opinions and data. Examples of intelligent models include a model for calculating the probability of success, a model for evaluating potential risks, and a model for predicting customer reactions.

[0447] The terminal functions as hardware that allows users to input behavioral data and transmit it to a server. Typical devices include smart glasses and smartphones, which process information obtained from in-store image acquisition devices in real time.

[0448] The user is the entity that requests information generation from the system based on the input information. For example, when deciding on specific strategies regarding store operations, the user can refer to data presented from the server through a visual device.

[0449] In this system, the image acquisition device collects data such as customer movement patterns and product attention levels. The collected data is analyzed by various intelligent models on the server, and the results are integrated and presented on the visual device. This real-time information provision allows users to dynamically determine appropriate marketing strategies.

[0450] A concrete example is when introducing a new product to the market. The system analyzes customer reactions in real time and, based on that information, suggests an "effective display method for the new product" to the user. An example of a prompt to the generating AI model in this case would be, "Based on customer in-store behavior data, please suggest an effective marketing strategy for the new product." This allows the user to immediately formulate and implement a concrete market strategy.

[0451] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0452] Step 1:

[0453] Users input tasks and information requiring analysis using a terminal. This information may include data obtained from image acquisition devices and sensors. The terminal receives this input information, formats it into a data format, and sends it to the server. The input may include customer movement data and information on products of interest.

[0454] Step 2:

[0455] The server requests data generation from multiple intelligent models based on the received information. Each intelligent model generates information utilizing its own characteristics (success probability, risk assessment, customer response, etc.). Specifically, it generates information on the optimal placement of products based on the received movement data. This process uses a generative AI model, which operates based on specific prompt statements.

[0456] Step 3:

[0457] The server integrates multiple pieces of information generated from the intelligent model. This integration process compiles and outputs the data in a consistent and actionable format. This output may include, for example, suggestions for effective display methods for new products. The server then sends this back to the terminal.

[0458] Step 4:

[0459] The terminal presents the user with integrated data received from the server. Information is provided visually to the user via smart glasses or a display device. This information includes real-time updated marketing strategies and customer response analyses, allowing the user to make quick decisions based on it.

[0460] Through these steps, users can make data-driven strategic decisions in real time.

[0461] 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.

[0462] The present invention is implemented by a system whose main components are a server, a terminal, a user, and an emotion engine. This system enables multifaceted information generation that takes into account the user's emotional state. The server is equipped with multiple different artificial intelligence models, each trained to generate information from a different perspective. These models include optimistic, critical, and risk assessment models.

[0463] The terminal receives input from the user and sends it to the server. During this process, the emotion engine analyzes the user's input and recognizes their emotional state. The emotion engine can evaluate the user's emotions (e.g., joy, sadness, surprise) using text analysis technology.

[0464] The emotion engine optimizes the instructions for information generation to the appropriate artificial intelligence model on the server based on the evaluation of that emotion. As a result, the generated information takes the user's emotional state into account and becomes more personalized.

[0465] The server integrates the information generated by the artificial intelligence model and sends it to the terminal. The terminal visually presents the received information to the user. Here, the information is presented in a way that is tailored to the user's emotional state, allowing the user to easily understand the generated opinions and gain insights.

[0466] For example, when a user provides input regarding the market evaluation of a new product, the emotion engine senses the user's level of interest and concern, and adjusts the information generation process accordingly. If the user has an optimistic sentiment, optimistic opinions will be emphasized and presented more prominently. In this way, a form of improving the user experience and providing richer, more meaningful information is revealed.

[0467] The following describes the processing flow.

[0468] Step 1:

[0469] The user enters a specific topic they want to discuss or analyze into the terminal. This input data is captured by the terminal in text format.

[0470] Step 2:

[0471] The device sends user input to the emotion engine. The emotion engine analyzes the input text and identifies the user's emotional state. This analysis uses natural language processing techniques.

[0472] Step 3:

[0473] The emotion engine identifies the user's emotions (e.g., joy, sadness, surprise) based on the analysis results. This emotion data is crucial to the information generation process.

[0474] Step 4:

[0475] The device sends user sentiment data and topic information to the server.

[0476] Step 5:

[0477] The server transmits the received sentiment data to each artificial intelligence model and instructs each model to generate topic-based information. Based on the sentiment data, the style and perspective of the information generated by the models are adjusted.

[0478] Step 6:

[0479] An optimistic AI model takes advantage of the user's positive emotions and generates information from a positive perspective. For example, it might generate information such as, "The new product will be well-received by many consumers."

[0480] Step 7:

[0481] Critical AI models, when negative sentiment data is present, perform careful analysis while taking it into account, and generate information that points out potential challenges. For example, they might generate information such as, "Due to intense competition, careful consideration is needed regarding your market position."

[0482] Step 8:

[0483] The server integrates the output from all models, taking sentiment data into consideration and constructing aggregated data while ensuring the consistency of the information.

[0484] Step 9:

[0485] The device receives integrated information and presents it in a format appropriate to the user's emotional state. The information is visually distinguishable and displayed in a way that allows the user to quickly gain insights.

[0486] Step 10:

[0487] Users form new ideas and judgments based on the information presented. Through this process, users acquire rich information and can analyze it from multiple perspectives.

[0488] (Example 2)

[0489] 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."

[0490] Conventional information generation systems present information uniformly without considering the user's emotional state, making it difficult for users to obtain personalized information they need. Furthermore, existing systems fail to adequately cover different perspectives, resulting in a lack of multifaceted information. This has led to a problem where users struggle to gain meaningful insights.

[0491] 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.

[0492] In this invention, the server includes a terminal for receiving input from the user, means for analyzing the user's emotional state from the input text, and means for sending information generation requests to multiple different artificial intelligence models based on the emotional analysis. This enables the provision of multifaceted and personalized information tailored to the user's emotional state.

[0493] "User input" refers to text and data provided by the user through their device.

[0494] A "terminal" refers to a device or interface used by a user to provide input data and display the received information.

[0495] "Emotional state" refers to the mental and emotional characteristics analyzed from user input, including joy, sadness, surprise, and so on.

[0496] "Means of analysis" refers to technologies and devices used to process input data and extract useful information.

[0497] An "artificial intelligence model" refers to an algorithm or program trained to perform a specific task and possesses the ability to generate information from different perspectives.

[0498] "Means for sending information generation requests" refers to a function that instructs an artificial intelligence model based on the results of analysis to generate information.

[0499] "Means of integrating information" refers to technologies that combine information generated by multiple artificial intelligence models to form a final output.

[0500] "Means of presenting information" refers to interfaces and display methods used to show integrated results to users.

[0501] To implement this invention, a configuration including a server, a terminal, a user, and an emotion engine is required. The server has multiple artificial intelligence models that generate information based on various different perspectives. These models are trained to take into account specific viewpoints such as optimism, critical thinking, and risk assessment. The server has the ability to receive information generation requests for these models and perform the necessary processing. The emotion engine utilizes natural language processing (NLP) to analyze the user's input as text and evaluate their emotional state.

[0502] The terminal receives text input from the user and sends it to the server. The input is analyzed by an emotion engine to identify the user's emotional state. The emotion engine's analysis results optimize instructions to the artificial intelligence model on the server, generating information appropriate to the user's emotions. At this time, the terminal plays the role of visually presenting the received information to the user.

[0503] As a concrete example, a user inputs "How will this new product perform in the market?" into the device. The device passes this input to the emotion engine, which then transmits the analysis result—that the user is optimistic—to the server. The server selects an optimistic model and sends back the positive market assessment information generated by the model to the device. The device then analyzes this information and presents it to the user using bright colors and appropriate visuals, thereby deepening the user's insights.

[0504] As an example of a prompt, consider the sentence, "Please give me your optimistic view on the likelihood of this new product succeeding in the market." This prompt is used as an instruction to the model, and appropriate information is generated based on the user's input.

[0505] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0506] Step 1:

[0507] The terminal receives text input from the user. The user enters questions or opinions on topics of interest. This input serves as the starting point for the next process.

[0508] Step 2:

[0509] The device passes the user's input to the emotion engine. The emotion engine uses natural language processing techniques to analyze the emotional state from the input text. For example, it analyzes expressions like "I'm really looking forward to it" and identifies an optimistic emotional state. This analysis result is then used to optimize the subsequent information generation process.

[0510] Step 3:

[0511] The terminal sends instructions to the server to select an appropriate artificial intelligence model based on the analysis results of the emotion engine. Here, an optimistic, critical, or risk-assessing model is selected depending on the emotional state. The selection process then begins generating information from each respective perspective.

[0512] Step 4:

[0513] The server generates information using multiple generative AI models in response to instructions from the terminal. Each model provides information based on its own perspective (optimistic, critical, risk assessment). This information generation process aims to form a multifaceted collection of data based on user input.

[0514] Step 5:

[0515] The server integrates the information provided by the generative AI model. During the integration process, information is weighted and emphasized according to the user's emotional state. This results in a collection of personalized information that is most relevant to the user.

[0516] Step 6:

[0517] The server sends the integrated information to the terminal. The terminal then visually organizes and presents this information to the user. For example, optimistic opinions might be highlighted with bright colors and positive icons. This visualization allows the user to easily understand the information and gain insights.

[0518] (Application Example 2)

[0519] 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."

[0520] Traditional information delivery systems have a problem in that they provide uniform information without considering the user's emotional state, resulting in a lack of improvement in the user experience. In particular, in shopping scenarios at physical stores, it was difficult for users to select appropriate products according to their emotions at the time. Therefore, there is a need to provide more personalized and emotionally relevant information.

[0521] 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.

[0522] In this invention, the server includes means for providing a plurality of different data processing models, each of which generates information based on different evaluation criteria; means for receiving input from a user and sending information generation requests to the plurality of data processing models; and means for integrating the information generated from the plurality of data processing models and presenting it to the user. This makes it possible to provide personalized information in accordance with the user's emotional state.

[0523] A "data processing model" is a computer program that analyzes data based on various perspectives and criteria to generate information.

[0524] An "information generation request" is a process that instructs a data processing model to generate information based on user input.

[0525] "User emotional state" refers to the emotions and psychological reactions a user experiences in a particular situation, including joy, sadness, surprise, and so on.

[0526] "Emotionally relevant information" refers to information that is highly relevant to the user, generated while taking into account the user's current emotional state.

[0527] "Means of selecting and presenting products" refers to a function that selects appropriate products based on the user's emotional state and presents them to the user visually or otherwise.

[0528] The system of this invention, centered around a server, combines multifaceted data processing and sentiment analysis to provide personalized information to users. The server uses multiple data processing models to evaluate user input from various perspectives and generates information based on these evaluations. This enables the presentation of optimal information that takes into account the user's emotional state. The hardware requires the server itself and a data input terminal, while the software implements a sentiment analysis engine and multiple AI models.

[0529] When users select products in a physical store using smart glasses or a head-mounted display, they provide information about accessories via voice or eye-tracking input. The device collects this information, and an emotion analysis engine performs the analysis. After the analysis, the server selects an appropriate data processing model and adjusts the information generation process. Finally, information tailored to the user's emotional state is visually presented, making product selection easier.

[0530] For example, if a user says, "I'm feeling cheerful today," the sentiment analysis engine will capture that positive emotion, and the server will select a highly novel fashion item. An example of a prompt to the generating AI model would be, "Please recommend new products that are suitable for when the user is feeling positive."

[0531] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0532] Step 1:

[0533] The device accepts voice or eye-tracking input from the user. The input data is used as foundational data to be sent to a server for analysis of the user's current emotional state.

[0534] Step 2:

[0535] The server receives the transmitted user input and analyzes its content using an emotion analysis engine. The emotion analysis engine uses text analysis techniques to determine the user's emotional state (i.e., positive, negative, neutral, etc.) from the input data. The output of this step is data related to the user's emotional state.

[0536] Step 3:

[0537] The server selects the appropriate model from several data processing models based on the output of the sentiment analysis engine. Here, it determines which model to use to generate information that best reflects the user's emotions. The selected model may be optimistic, critical, or risk-assessing. The output of this step is the selected data processing model.

[0538] Step 4:

[0539] The server uses a selected data processing model to generate information tailored to the user's emotional state. Here, the generating AI model generates product information based on the prompt text and processes it into information highly relevant to the user. The output of this step is information to be suggested to the user.

[0540] Step 5:

[0541] The terminal receives information transmitted from the server and presents it visually to the user. The user can then select specific products based on the presented information. The final output is product information displayed on a visual display.

[0542] 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.

[0543] 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.

[0544] 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.

[0545] [Fourth Embodiment]

[0546] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0547] 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.

[0548] 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).

[0549] 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.

[0550] 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.

[0551] 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).

[0552] 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.

[0553] 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.

[0554] 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.

[0555] 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.

[0556] 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.

[0557] 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.

[0558] 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".

[0559] This invention is implemented by a system whose main components are a server, a terminal, and a user. The server is equipped with multiple artificial intelligence models with diverse perspectives, each model having different properties and thought patterns. This makes it possible to generate information from diverse viewpoints.

[0560] The terminal receives input data provided by the user and sends it to the server as needed. When the user inputs a topic for discussion or decision-making, the terminal analyzes it and sends it to the server. At this time, each artificial intelligence model generates information based on the analyzed data, utilizing predefined characteristics.

[0561] The server integrates information output from multiple generative AI models and sends it back to the terminal in a consistent format. The terminal visualizes the received information for the user and presents it in an easy-to-understand format. This allows the user to gain new ideas and insights based on the various opinions and perspectives generated.

[0562] As a concrete example, consider a scenario where a user uses this system to develop a market strategy for a new product. When the user inputs "New Product Market Launch Strategy" as the topic, multiple AI models on the server generate relevant information from various perspectives (e.g., probability of success, potential risks, customer response, etc.). This information is then integrated and sent back to the terminal for presentation to the user, enabling a multifaceted analysis. In this way, the present invention makes it easy to acquire unprecedented perspectives and ideas.

[0563] The following describes the processing flow.

[0564] Step 1:

[0565] Users input the topic they wish to discuss via their terminal. This input should include the discussion theme or specific issues.

[0566] Step 2:

[0567] The terminal receives input data from the user and sends it to the server according to the data format. Meta information about the topic may be included during transmission.

[0568] Step 3:

[0569] The server distributes the received topic data to multiple artificial intelligence models. Each model analyzes the data and generates information based on its own pre-configured perspective.

[0570] Step 4:

[0571] An optimistic AI model generates opinions and information that focus on positive scenarios. For example, it might say, "The new product is likely to be successful in the market."

[0572] Step 5:

[0573] AI models with a critical perspective generate opinions that take potential problems and negative scenarios into account. For example, they might say, "Gaining market share may be difficult due to strong competing products."

[0574] Step 6:

[0575] Risk-focused AI models generate information about specific risk factors and risk concerns. For example, information such as "there is a risk of supply chain problems occurring."

[0576] Step 7:

[0577] The server integrates the information generated from each AI model, maintains a delicate balance of consistency, and packages the aggregated information.

[0578] Step 8:

[0579] The server sends the integrated information back to the terminal, which then displays this information to the user in an easy-to-understand format. When presenting the information, the characteristics of each opinion are clearly indicated, allowing the user to easily compare and consider them.

[0580] Step 9:

[0581] Users consider the diverse opinions presented and use them to make their own judgments and generate new ideas. Through this step, users can obtain results from multiple perspectives.

[0582] (Example 1)

[0583] 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".

[0584] In modern society, there is a need to quickly and comprehensively acquire information from diverse perspectives. However, summarizing information from different viewpoints is time-consuming and laborious, and maintaining consistency is difficult. Therefore, a system is needed that efficiently integrates information from different perspectives and presents it in a user-friendly format.

[0585] 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.

[0586] In this invention, the server includes means for comprising a plurality of different inference devices, each of which generates information based on a different perspective; means for receiving data from an input unit and transmitting information generation requests to the plurality of inference devices; and means for integrating the information generated from the plurality of inference devices and presenting it to the input unit. This makes it possible for the user to acquire diverse information and easily gain new insights.

[0587] A "reasoning device" is an artificial intelligence module that possesses different perspectives and viewpoints and generates information about a specific subject.

[0588] An "input unit" is a device or interface used by users to input data related to discussions and decision-making, and it plays a role in communicating with the server.

[0589] An "information generation request" refers to the process of instructing an inference device to create information based on input provided by the user.

[0590] "Information integration" is the process of combining output information generated by multiple inference devices and consolidating it into a unified and consistent format.

[0591] "Visual visualization" refers to the use of visual representations such as diagrams and tables to display integrated information in a way that is easy for users to understand.

[0592] This invention provides an information processing system whose main components are a server, a terminal, and a user. The server has multiple inference devices (generative AI models), which generate information from different perspectives. Specifically, it is capable of generating optimistic information, critical information, and risk assessment information.

[0593] Users input discussion and decision-making topics in text format via their terminals. For example, a prompt might read, "Please tell me the success factors for a new product launch strategy."

[0594] The terminal parses the input topic, converts it to the required format, and sends it to the server. Based on the received input, the server processes the information generation request using each inference unit. Each inference unit generates information based on a predetermined perspective and returns its output to the server.

[0595] The server integrates information obtained from multiple inference devices, ensures consistency, and then sends it back to the terminal. The terminal visualizes this integrated information for the user. The information is displayed using visual means such as graphs and charts, making it easy for the user to understand and gain new insights.

[0596] In this way, a system is realized that allows users to acquire information based on diverse perspectives and opinions, and effectively support their decision-making.

[0597] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0598] Step 1:

[0599] Users input topics related to discussion and decision-making into the terminal in text format. Prompts can be used for this input. For example, "Please tell me the success factors for a new product launch strategy." The input topic is received by the terminal.

[0600] Step 2:

[0601] The terminal analyzes the topic entered by the user. Using natural language processing technology, it analyzes the intent and category of the entered text and converts it into data in a format that can be easily processed by the server. This analysis result is then sent to the server.

[0602] Step 3:

[0603] The server receives the analyzed data sent from the terminal. Next, based on the analysis results, it determines and selects which generative AI model is most suitable for generating information. Based on this selection, an information generation request is sent to each AI model.

[0604] Step 4:

[0605] Each selected generative AI model processes data based on requests from the server. Each model utilizes specific algorithms and databases to generate new information from a predetermined perspective (e.g., success factors, potential risks, customer reactions, etc.). This generated information is then returned to the server.

[0606] Step 5:

[0607] The server integrates information provided by multiple generative AI models. It performs data calculations to remove duplicate information and organize it into consistent and non-contradictory data. This integrated information is converted into a consistent report format and sent to the terminal.

[0608] Step 6:

[0609] The terminal receives integrated information sent from the server and visualizes it for the user. At this stage, the information is often displayed using graphs and charts to make it easy to understand. The user gains insights from the visualized information and uses it to support their decision-making.

[0610] (Application Example 1)

[0611] 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".

[0612] In existing consumer goods sales and marketing environments, there is a challenge in analyzing diverse customer behaviors and reactions in real time and visually presenting immediate marketing strategies based on that analysis on-site. To address this problem, there is a need to provide an effective system that enables quicker and more comprehensive situational assessment and appropriate action at the sales site.

[0613] 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.

[0614] In this invention, the server includes means for having multiple different intelligent models, each of which generates data based on a different perspective; means for receiving information from a user and transmitting data generation requests to the multiple intelligent models; means for integrating the data generated from the multiple intelligent models and presenting it to the user; and means for providing strategic suggestions in real time through a visual device based on behavioral data acquired from an image acquisition device. This makes it possible to immediately grasp customer behavior at the sales site and determine and execute the optimal marketing strategy in real time accordingly.

[0615] An "intelligent model" is a form of artificial intelligence that possesses different perspectives and viewpoints and generates information based on those characteristics.

[0616] A "user" is an entity that inputs information, requests data generation from an intelligent model, and receives the resulting output.

[0617] A "data generation request" is the process by which a user requests an intelligent model to generate specific data or information.

[0618] "Integration" is the process of combining data generated from multiple intelligence models and presenting it in a consistent manner.

[0619] "Image acquisition device" is a general term for cameras and sensors used to capture customer movements and reactions within a store or sales environment.

[0620] A "visual device" is a device that presents integrated information and suggestions to users in real time, and includes smart glasses, among others.

[0621] "Real-time strategic proposals" refer to suggestions for appropriate marketing and sales tactics that are implemented immediately in response to customer behavior and circumstances.

[0622] The system implementing this invention consists of a server, a terminal, and a user. The server provides multiple intelligent models, each designed to generate data from a different perspective. This allows the server to analyze user input using generative AI models and provide diverse opinions and data. Examples of intelligent models include a model for calculating the probability of success, a model for evaluating potential risks, and a model for predicting customer reactions.

[0623] The terminal functions as hardware that allows users to input behavioral data and transmit it to a server. Typical devices include smart glasses and smartphones, which process information obtained from in-store image acquisition devices in real time.

[0624] The user is the entity that requests information generation from the system based on the input information. For example, when deciding on specific strategies regarding store operations, the user can refer to data presented from the server through a visual device.

[0625] In this system, the image acquisition device collects data such as customer movement patterns and product attention levels. The collected data is analyzed by various intelligent models on the server, and the results are integrated and presented on the visual device. This real-time information provision allows users to dynamically determine appropriate marketing strategies.

[0626] A concrete example is when introducing a new product to the market. The system analyzes customer reactions in real time and, based on that information, suggests an "effective display method for the new product" to the user. An example of a prompt to the generating AI model in this case would be, "Based on customer in-store behavior data, please suggest an effective marketing strategy for the new product." This allows the user to immediately formulate and implement a concrete market strategy.

[0627] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0628] Step 1:

[0629] Users input tasks and information requiring analysis using a terminal. This information may include data obtained from image acquisition devices and sensors. The terminal receives this input information, formats it into a data format, and sends it to the server. The input may include customer movement data and information on products of interest.

[0630] Step 2:

[0631] The server requests data generation from multiple intelligent models based on the received information. Each intelligent model generates information utilizing its own characteristics (success probability, risk assessment, customer response, etc.). Specifically, it generates information on the optimal placement of products based on the received movement data. This process uses a generative AI model, which operates based on specific prompt statements.

[0632] Step 3:

[0633] The server integrates multiple pieces of information generated from the intelligent model. This integration process compiles and outputs the data in a consistent and actionable format. This output may include, for example, suggestions for effective display methods for new products. The server then sends this back to the terminal.

[0634] Step 4:

[0635] The terminal presents the user with integrated data received from the server. Information is provided visually to the user via smart glasses or a display device. This information includes real-time updated marketing strategies and customer response analyses, allowing the user to make quick decisions based on it.

[0636] Through these steps, users can make data-driven strategic decisions in real time.

[0637] 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.

[0638] The present invention is implemented by a system whose main components are a server, a terminal, a user, and an emotion engine. This system enables multifaceted information generation that takes into account the user's emotional state. The server is equipped with multiple different artificial intelligence models, each trained to generate information from a different perspective. These models include optimistic, critical, and risk assessment models.

[0639] The terminal receives input from the user and sends it to the server. During this process, the emotion engine analyzes the user's input and recognizes their emotional state. The emotion engine can evaluate the user's emotions (e.g., joy, sadness, surprise) using text analysis technology.

[0640] The emotion engine optimizes the instructions for information generation to the appropriate artificial intelligence model on the server based on the evaluation of that emotion. As a result, the generated information takes the user's emotional state into account and becomes more personalized.

[0641] The server integrates the information generated by the artificial intelligence model and sends it to the terminal. The terminal visually presents the received information to the user. Here, the information is presented in a way that is tailored to the user's emotional state, allowing the user to easily understand the generated opinions and gain insights.

[0642] For example, when a user provides input regarding the market evaluation of a new product, the emotion engine senses the user's level of interest and concern, and adjusts the information generation process accordingly. If the user has an optimistic sentiment, optimistic opinions will be emphasized and presented more prominently. In this way, a form of improving the user experience and providing richer, more meaningful information is revealed.

[0643] The following describes the processing flow.

[0644] Step 1:

[0645] The user enters a specific topic they want to discuss or analyze into the terminal. This input data is captured by the terminal in text format.

[0646] Step 2:

[0647] The device sends user input to the emotion engine. The emotion engine analyzes the input text and identifies the user's emotional state. This analysis uses natural language processing techniques.

[0648] Step 3:

[0649] The emotion engine identifies the user's emotions (e.g., joy, sadness, surprise) based on the analysis results. This emotion data is crucial to the information generation process.

[0650] Step 4:

[0651] The device sends user sentiment data and topic information to the server.

[0652] Step 5:

[0653] The server transmits the received sentiment data to each artificial intelligence model and instructs each model to generate topic-based information. Based on the sentiment data, the style and perspective of the information generated by the models are adjusted.

[0654] Step 6:

[0655] An optimistic AI model takes advantage of the user's positive emotions and generates information from a positive perspective. For example, it might generate information such as, "The new product will be well-received by many consumers."

[0656] Step 7:

[0657] Critical AI models, when negative sentiment data is present, perform careful analysis while taking it into account, and generate information that points out potential challenges. For example, they might generate information such as, "Due to intense competition, careful consideration is needed regarding your market position."

[0658] Step 8:

[0659] The server integrates the output from all models, taking sentiment data into consideration and constructing aggregated data while ensuring the consistency of the information.

[0660] Step 9:

[0661] The device receives integrated information and presents it in a format appropriate to the user's emotional state. The information is visually distinguishable and displayed in a way that allows the user to quickly gain insights.

[0662] Step 10:

[0663] Users form new ideas and judgments based on the information presented. Through this process, users acquire rich information and can analyze it from multiple perspectives.

[0664] (Example 2)

[0665] 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".

[0666] Conventional information generation systems present information uniformly without considering the user's emotional state, making it difficult for users to obtain personalized information they need. Furthermore, existing systems fail to adequately cover different perspectives, resulting in a lack of multifaceted information. This has led to a problem where users struggle to gain meaningful insights.

[0667] 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.

[0668] In this invention, the server includes a terminal for receiving input from the user, means for analyzing the user's emotional state from the input text, and means for sending information generation requests to multiple different artificial intelligence models based on the emotional analysis. This enables the provision of multifaceted and personalized information tailored to the user's emotional state.

[0669] "User input" refers to text and data provided by the user through their device.

[0670] A "terminal" refers to a device or interface used by a user to provide input data and display the received information.

[0671] "Emotional state" refers to the mental and emotional characteristics analyzed from user input, including joy, sadness, surprise, and so on.

[0672] "Means of analysis" refers to technologies and devices used to process input data and extract useful information.

[0673] An "artificial intelligence model" refers to an algorithm or program trained to perform a specific task and possesses the ability to generate information from different perspectives.

[0674] "Means for sending information generation requests" refers to a function that instructs an artificial intelligence model based on the results of analysis to generate information.

[0675] "Means of integrating information" refers to technologies that combine information generated by multiple artificial intelligence models to form a final output.

[0676] "Means of presenting information" refers to interfaces and display methods used to show integrated results to users.

[0677] To implement this invention, a configuration including a server, a terminal, a user, and an emotion engine is required. The server has multiple artificial intelligence models that generate information based on various different perspectives. These models are trained to take into account specific viewpoints such as optimism, critical thinking, and risk assessment. The server has the ability to receive information generation requests for these models and perform the necessary processing. The emotion engine utilizes natural language processing (NLP) to analyze the user's input as text and evaluate their emotional state.

[0678] The terminal receives text input from the user and sends it to the server. The input is analyzed by an emotion engine to identify the user's emotional state. The emotion engine's analysis results optimize instructions to the artificial intelligence model on the server, generating information appropriate to the user's emotions. At this time, the terminal plays the role of visually presenting the received information to the user.

[0679] As a concrete example, a user inputs "How will this new product perform in the market?" into the device. The device passes this input to the emotion engine, which then transmits the analysis result—that the user is optimistic—to the server. The server selects an optimistic model and sends back the positive market assessment information generated by the model to the device. The device then analyzes this information and presents it to the user using bright colors and appropriate visuals, thereby deepening the user's insights.

[0680] As an example of a prompt, consider the sentence, "Please give me your optimistic view on the likelihood of this new product succeeding in the market." This prompt is used as an instruction to the model, and appropriate information is generated based on the user's input.

[0681] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0682] Step 1:

[0683] The terminal receives text input from the user. The user enters questions or opinions on topics of interest. This input serves as the starting point for the next process.

[0684] Step 2:

[0685] The device passes the user's input to the emotion engine. The emotion engine uses natural language processing techniques to analyze the emotional state from the input text. For example, it analyzes expressions like "I'm really looking forward to it" and identifies an optimistic emotional state. This analysis result is then used to optimize the subsequent information generation process.

[0686] Step 3:

[0687] The terminal sends instructions to the server to select an appropriate artificial intelligence model based on the analysis results of the emotion engine. Here, an optimistic, critical, or risk-assessing model is selected depending on the emotional state. The selection process then begins generating information from each respective perspective.

[0688] Step 4:

[0689] The server generates information using multiple generative AI models in response to instructions from the terminal. Each model provides information based on its own perspective (optimistic, critical, risk assessment). This information generation process aims to form a multifaceted collection of data based on user input.

[0690] Step 5:

[0691] The server integrates the information provided by the generative AI model. During the integration process, information is weighted and emphasized according to the user's emotional state. This results in a collection of personalized information that is most relevant to the user.

[0692] Step 6:

[0693] The server sends the integrated information to the terminal. The terminal then visually organizes and presents this information to the user. For example, optimistic opinions might be highlighted with bright colors and positive icons. This visualization allows the user to easily understand the information and gain insights.

[0694] (Application Example 2)

[0695] 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".

[0696] Traditional information delivery systems have a problem in that they provide uniform information without considering the user's emotional state, resulting in a lack of improvement in the user experience. In particular, in shopping scenarios at physical stores, it was difficult for users to select appropriate products according to their emotions at the time. Therefore, there is a need to provide more personalized and emotionally relevant information.

[0697] 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.

[0698] In this invention, the server includes means for providing a plurality of different data processing models, each of which generates information based on different evaluation criteria; means for receiving input from a user and sending information generation requests to the plurality of data processing models; and means for integrating the information generated from the plurality of data processing models and presenting it to the user. This makes it possible to provide personalized information in accordance with the user's emotional state.

[0699] A "data processing model" is a computer program that analyzes data based on various perspectives and criteria to generate information.

[0700] An "information generation request" is a process that instructs a data processing model to generate information based on user input.

[0701] "User emotional state" refers to the emotions and psychological reactions a user experiences in a particular situation, including joy, sadness, surprise, and so on.

[0702] "Emotionally relevant information" refers to information that is highly relevant to the user, generated while taking into account the user's current emotional state.

[0703] "Means of selecting and presenting products" refers to a function that selects appropriate products based on the user's emotional state and presents them to the user visually or otherwise.

[0704] The system of this invention, centered around a server, combines multifaceted data processing and sentiment analysis to provide personalized information to users. The server uses multiple data processing models to evaluate user input from various perspectives and generates information based on these evaluations. This enables the presentation of optimal information that takes into account the user's emotional state. The hardware requires the server itself and a data input terminal, while the software implements a sentiment analysis engine and multiple AI models.

[0705] When users select products in a physical store using smart glasses or a head-mounted display, they provide information about accessories via voice or eye-tracking input. The device collects this information, and an emotion analysis engine performs the analysis. After the analysis, the server selects an appropriate data processing model and adjusts the information generation process. Finally, information tailored to the user's emotional state is visually presented, making product selection easier.

[0706] For example, if a user says, "I'm feeling cheerful today," the sentiment analysis engine will capture that positive emotion, and the server will select a highly novel fashion item. An example of a prompt to the generating AI model would be, "Please recommend new products that are suitable for when the user is feeling positive."

[0707] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0708] Step 1:

[0709] The device accepts voice or eye-tracking input from the user. The input data is used as foundational data to be sent to a server for analysis of the user's current emotional state.

[0710] Step 2:

[0711] The server receives the transmitted user input and analyzes its content using an emotion analysis engine. The emotion analysis engine uses text analysis techniques to determine the user's emotional state (i.e., positive, negative, neutral, etc.) from the input data. The output of this step is data related to the user's emotional state.

[0712] Step 3:

[0713] The server selects the appropriate model from several data processing models based on the output of the sentiment analysis engine. Here, it determines which model to use to generate information that best reflects the user's emotions. The selected model may be optimistic, critical, or risk-assessing. The output of this step is the selected data processing model.

[0714] Step 4:

[0715] The server uses a selected data processing model to generate information tailored to the user's emotional state. Here, the generating AI model generates product information based on the prompt text and processes it into information highly relevant to the user. The output of this step is information to be suggested to the user.

[0716] Step 5:

[0717] The terminal receives information transmitted from the server and presents it visually to the user. The user can then select specific products based on the presented information. The final output is product information displayed on a visual display.

[0718] 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.

[0719] 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.

[0720] In the above embodiment, an example was given in which the 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.

[0721] 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.

[0722] 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.

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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."

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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 to be incorporated by reference.

[0739] The following is further disclosed regarding the embodiments described above.

[0740] (Claim 1)

[0741] A means comprising multiple different artificial intelligence models, each of which generates information based on a different perspective,

[0742] A means for receiving input from a user and sending information generation requests to the aforementioned multiple artificial intelligence models,

[0743] A means for integrating the information generated from the aforementioned multiple artificial intelligence models and presenting it to the user,

[0744] A system that includes this.

[0745] (Claim 2)

[0746] The system according to claim 1, characterized in that the plurality of artificial intelligence models include models that generate optimistic, critical, and risk-assessing opinions.

[0747] (Claim 3)

[0748] The system according to claim 1, characterized in that it generates information related to a specific topic based on the input from the user.

[0749] "Example 1"

[0750] (Claim 1)

[0751] A means comprising multiple different inference devices, each of which generates information based on a different perspective,

[0752] A means for receiving data from an input unit and transmitting information generation requests to the plurality of inference devices,

[0753] A means for integrating the information generated from the aforementioned multiple inference devices and presenting it as an input unit,

[0754] A means for performing topic analysis based on input units and converting the analysis results into a format that can be sent to a server,

[0755] A means of visually representing the received integrated information and presenting it in an easy-to-understand format,

[0756] A system that includes this.

[0757] (Claim 2)

[0758] The system according to claim 1, characterized in that the plurality of reasoning devices include devices that generate optimistic, critical, and risk-assessing opinions.

[0759] (Claim 3)

[0760] The system according to claim 1, characterized in that it generates information related to a specific subject based on input from the aforementioned input units.

[0761] "Application Example 1"

[0762] (Claim 1)

[0763] A means by which multiple different intelligence models are provided, each of which generates data based on a different perspective,

[0764] A means for receiving information from a user and sending data generation requests to the aforementioned multiple intelligent models,

[0765] A means for integrating the data generated from the aforementioned multiple intelligent models and presenting it to the user,

[0766] A means of providing strategic suggestions in real time through a visual device based on behavioral data acquired from an image acquisition device,

[0767] A system that includes this.

[0768] (Claim 2)

[0769] The system according to claim 1, characterized in that the plurality of intelligent models include a model that generates opinions evaluating the probability of success, potential risks, and customer reactions.

[0770] (Claim 3)

[0771] The system according to claim 1, characterized in that it generates data related to a specific subject based on information from the user.

[0772] "Example 2 of combining an emotion engine"

[0773] (Claim 1)

[0774] A terminal that receives input from the user,

[0775] A means of analyzing the user's emotional state from input text,

[0776] A means for sending information generation requests to multiple different artificial intelligence models based on emotion analysis,

[0777] A server equipped with multiple artificial intelligence models that generate information based on different perspectives,

[0778] A means for integrating the information generated from the aforementioned artificial intelligence model and adjusting it according to the user's emotional state,

[0779] A means of presenting integrated information to the user,

[0780] A system that includes this.

[0781] (Claim 2)

[0782] The system according to claim 1, characterized in that it optimizes the method of presenting information based on the emotional state of the user.

[0783] (Claim 3)

[0784] The system according to claim 1, characterized in that it emphasizes optimistic, critical, and risk-assessing perspectives based on the user's emotional state.

[0785] "Application example 2 when combining with an emotional engine"

[0786] (Claim 1)

[0787] A means comprising multiple different data processing models, each of which generates information based on different evaluation criteria,

[0788] A means for receiving input from a user and sending information generation requests to the aforementioned multiple data processing models,

[0789] A means for integrating the information generated from the aforementioned multiple data processing models and presenting it to the user,

[0790] A means for analyzing the user's emotional state and optimizing the data processing model to generate information that is in line with those emotions,

[0791] A means of selecting and presenting products based on information that resonates with emotions,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, characterized in that the plurality of data processing models include models that generate positive, negative, and risk assessment opinions.

[0795] (Claim 3)

[0796] The system according to claim 1, characterized in that it generates information related to a specific product based on the emotional state of the user. [Explanation of Symbols]

[0797] 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 comprising multiple different artificial intelligence models, each of which generates information based on a different perspective, A means for receiving input from a user and sending information generation requests to the aforementioned multiple artificial intelligence models, A means for integrating the information generated from the aforementioned multiple artificial intelligence models and presenting it to the user, A system that includes this.

2. The system according to claim 1, characterized in that the plurality of artificial intelligence models include models that generate optimistic, critical, and risk-assessing opinions.

3. The system according to claim 1, characterized in that it generates information related to a specific topic based on the input from the user.

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