system

Abstract

The system according to this embodiment aims to enable users to find reliable options even without specialized knowledge. [Solution] The system according to the embodiment comprises a reception unit, a review unit, a comparison unit, and a proposal unit. The reception unit receives information about options from the user. The review unit reviews the information received by the reception unit and presents highly reliable options. The comparison unit compares the options presented by the review unit and displays their advantages and disadvantages. The proposal unit proposes the option best suited to the user's situation and needs based on the information displayed by the comparison unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 chatbot character, 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]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for a user without expertise to find an optimal option and there is a risk of relying on the opinions of experts.

[0005] The system according to the embodiment aims to enable a user without expertise to find a highly reliable option.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a review unit, a comparison unit, and a proposal unit. The reception unit receives information about options from the user. The review unit reviews the information received by the reception unit and presents highly reliable options. The comparison unit compares the options presented by the review unit and displays their advantages and disadvantages. The proposal unit proposes the option best suited to the user's situation and needs based on the information displayed by the comparison unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to find reliable options even without specialized knowledge. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The decision support system according to an embodiment of the present invention is a system that utilizes AI to support expert choices. In this decision support system, the user inputs information about options, the AI ​​scrutinizes the provided information, and presents the user with reliable options. Furthermore, it compares multiple options, clearly displays their advantages and disadvantages, and proposes the optimal option based on the user's situation and needs. For example, if a car battery dies, the AI ​​compares the cost of battery replacement and other repair options and proposes the best option to the user. In addition, the AI ​​proposes other possible options in response to the opinions and suggestions of salespeople or experts, helping the user make the best decision. This system can provide appropriate information and support reliable decision-making to users who feel anxious because they cannot fully understand expert information, or to users whose options are limited because not all options are presented. As a result, the decision support system can help users obtain reliable options even without expert knowledge, and support optimal decision-making.

[0029] The decision support system according to the embodiment comprises a reception unit, a scrutiny unit, a comparison unit, and a proposal unit. The reception unit receives information about options from the user. The reception unit provides, for example, an interface for the user to input information about options. The reception unit can receive information by methods such as text input, voice input, and image input. For example, if a user's car battery dies, the reception unit can receive information about the cost of battery replacement and other repair options. The scrutiny unit scrutinizes the information received by the reception unit and presents reliable options. The scrutiny unit analyzes the provided information and identifies reliable options. The scrutiny unit evaluates reliability based on, for example, user reviews, expert evaluations, and data accuracy. The scrutiny unit evaluates the reliability of, for example, the cost of battery replacement and other repair options. The comparison unit compares the options presented by the scrutiny unit and displays their advantages and disadvantages. The comparison unit analyzes the advantages and disadvantages of each option and displays them in an easy-to-understand manner for the user. The comparison unit compares the advantages and disadvantages of, for example, the cost of battery replacement and other repair options. The proposal unit suggests the best option based on the user's situation and needs, based on the information displayed by the comparison unit. The proposal unit identifies the best option based, for example, the user's budget and time constraints. The proposal unit suggests the best battery replacement option based, for example, the user's budget and time constraints. As a result, the decision support system according to the embodiment can provide users with reliable options even without specialized knowledge, and can support them in making optimal decisions.

[0030] The reception desk receives information about user choices. For example, it provides an interface for users to input information about their choices. Specifically, it designs an interface that users can easily access through a web browser or mobile application. For text input, users can enter information using a keyboard; for voice input, speech recognition technology is used to convert the user's speech into text; and for image input, users upload relevant images using a camera, and the system analyzes the content using image recognition technology. For example, if a user's car battery dies, they can input information about the cost of battery replacement or other repair options. The reception desk centrally manages this information and stores it in a database. Furthermore, the reception desk has a feedback function to verify the accuracy of the information entered by the user, automatically detecting input errors or incomplete information and prompting the user to correct them. This allows the reception desk to efficiently and accurately collect information from users and smoothly pass it on to the next processing step.

[0031] The review department scrutinizes the information received by the reception department and presents highly reliable options. Specifically, it uses natural language processing techniques and machine learning algorithms to analyze the provided information and identify reliable options. For example, when analyzing user reviews and expert evaluations, it uses text mining techniques to classify the content of the reviews and quantify the reliability of the evaluations. To assess the accuracy of the data, it compares it with existing data in the database to check for matches. Furthermore, the review department further enhances reliability by obtaining information from external, reliable data sources and comparing it with the information provided by the user. For example, when evaluating the cost of battery replacement or the reliability of other repair options, it refers to pricing information and past repair history from multiple repair companies to identify the most reliable option. This allows the review department to provide users with reliable information and present the optimal options.

[0032] The comparison unit compares the options presented by the scrutiny unit and displays their advantages and disadvantages. Specifically, it analyzes the advantages and disadvantages of each option and provides a visual interface to display them clearly to the user. For example, graphs and charts can be used to visually compare elements such as cost, time, and reliability of each option. When comparing the cost of battery replacement or the advantages and disadvantages of other repair options, detailed information for each option is displayed in a list format, making it easy for the user to compare them. Furthermore, the comparison unit has a function to filter options based on the user's individual needs and conditions. For example, if the user enters budget or time constraints, the optimal options can be narrowed down based on those constraints. In this way, the comparison unit can help the user efficiently compare multiple options and make the best decision.

[0033] The suggestion unit proposes the best option for the user's situation and needs based on the information displayed by the comparison unit. Specifically, it uses AI algorithms to identify the optimal option based on the user's budget and time constraints. For example, it considers the budget and time constraints entered by the user and proposes the most cost-effective battery replacement option. The suggestion unit can learn the user's past selection history and preferences to make suggestions tailored to individual needs. Furthermore, the suggestion unit provides an interface to explain the suggestions to the user in an easy-to-understand manner. For example, it includes a function to provide detailed explanations of the proposed options and to simulate the expected results if an option is selected. This allows the user to fully understand the suggestions and make decisions with confidence. The suggestion unit can also collect feedback from users and continuously improve the accuracy of its suggestion algorithm. This enables the suggestion unit to provide users with the best options and support their decision-making.

[0034] The scrutiny unit can analyze the provided information and identify reliable options. For example, the scrutiny unit can evaluate the source of the provided information and identify reliable information. For example, the scrutiny unit can analyze the content of the information and identify reliable information. For example, the scrutiny unit can evaluate the past history of the information provider and identify reliable information. This allows the reliability of the provided information to be evaluated and reliable options to be provided to the user. Some or all of the above processing in the scrutiny unit may be performed using AI, for example, or without AI. For example, the scrutiny unit can input the provided information into a generating AI and have the generating AI perform the task of identifying reliable options.

[0035] The comparison unit can analyze the advantages and disadvantages of each option and display them to the user in an easy-to-understand manner. For example, the comparison unit can analyze the advantages and disadvantages of each option and display them to the user in an easy-to-understand manner. For example, the comparison unit can compare the advantages and disadvantages of battery replacement costs and other repair options. For example, the comparison unit can evaluate advantages and disadvantages based on criteria such as performance, cost, and ease of use. This makes it easier for the user to understand the advantages and disadvantages of the options. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the advantages and disadvantages of each option into a generating AI and have the generating AI perform the analysis of the advantages and disadvantages.

[0036] The suggestion unit can propose the optimal option based on the user's budget and time constraints. For example, the suggestion unit identifies the optimal option based on the user's budget and time constraints. For example, the suggestion unit proposes the optimal battery replacement option based on the user's budget and time constraints. For example, the suggestion unit proposes the optimal option considering the user's needs, budget, usage environment, etc. This allows the suggestion unit to propose the optimal option according to the user's situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information about the user's budget and time constraints into a generating AI and have the generating AI propose the optimal option.

[0037] The reception desk can collect information about the user's situation and needs. For example, the reception desk collects information about the user's situation and needs. For example, the reception desk collects information through methods such as questionnaires, interviews, and behavioral data. For example, if a user's car battery dies, the reception desk collects information about the cost of battery replacement and other repair options. This allows for the collection of information based on the user's situation and needs. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input information about the user's situation and needs into a generating AI and have the generating AI perform the information collection.

[0038] The reception unit can analyze the user's past selection history and select the optimal information reception method. For example, the reception unit may prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception unit may predict and suggest input methods to be used during specific time periods based on the user's past selection history. For example, the reception unit may suggest the optimal input method based on the type of information the user has previously selected. This allows the reception unit to provide the optimal information reception method based on the user's past selection history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may input the user's past selection history into a generating AI and have the generating AI select the optimal information reception method.

[0039] The reception unit can filter information based on the user's current situation and areas of interest when receiving it. For example, when a user enters their current situation, the reception unit will only accept information related to that situation. For example, the reception unit will prioritize accepting highly relevant information based on the user's areas of interest. For example, the reception unit will filter out unnecessary information based on the user's current situation and areas of interest. This allows the reception unit to provide information based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input information about the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0040] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, when a user enters their current location, the reception unit prioritizes receiving information related to that region. For example, the reception unit filters highly relevant information based on the user's geographical location. For example, the reception unit excludes unnecessary information by considering the user's geographical location. This allows the reception unit to provide information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform the filtering of highly relevant information.

[0041] The reception unit can analyze a user's social media activity and receive relevant information upon receiving information. For example, the reception unit can identify topics of interest from the user's social media activity and receive relevant information. For example, the reception unit can analyze the content of the user's social media posts and prioritize receiving relevant information. For example, the reception unit can filter highly relevant information based on the user's social media activity history. This allows the reception unit to provide information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the filtering of relevant information.

[0042] The scrutiny unit can apply its own algorithms to evaluate the reliability of information during the scrutiny process. For example, the scrutiny unit can evaluate the source of the provided information and identify reliable information. For example, the scrutiny unit can analyze the content of the information and identify reliable information. For example, the scrutiny unit can evaluate the past history of the information provider and identify reliable information. This allows the scrutiny unit to provide its own algorithms for evaluating the reliability of information. Some or all of the above processes in the scrutiny unit may be performed using AI, for example, or without AI. For example, the scrutiny unit can input the provided information into a generating AI and have the generating AI perform the reliability evaluation.

[0043] The scrutiny unit can perform scrutiny while considering the reliability of the information source and provider. For example, the scrutiny unit can evaluate the information source and identify reliable information. For example, the scrutiny unit can evaluate the information provider's past history and identify reliable information. For example, the scrutiny unit can comprehensively evaluate the reliability of the information source and provider and identify reliable information. This makes it possible to provide scrutiny that takes into account the reliability of the information source and provider. Some or all of the above processing in the scrutiny unit may be performed using AI, for example, or without AI. For example, the scrutiny unit can input data on the reliability of the information source and provider into a generating AI and have the generating AI perform a reliability evaluation.

[0044] The scrutiny unit can perform scrutiny while considering the geographical distribution of information. For example, the scrutiny unit can evaluate the geographical distribution of information and identify highly reliable information. For example, the scrutiny unit can scrutinize highly relevant information while considering the geographical distribution of information. For example, the scrutiny unit can identify highly reliable information based on the geographical distribution of information. This makes it possible to provide scrutiny that takes the geographical distribution of information into consideration. Some or all of the above-described processes in the scrutiny unit may be performed using AI, for example, or without AI. For example, the scrutiny unit can input data on the geographical distribution of information into a generating AI and have the generating AI perform the scrutiny.

[0045] The scrutiny unit can improve the accuracy of its scrutiny by referring to relevant literature during the scrutiny process. For example, the scrutiny unit can refer to relevant literature to identify reliable information. For example, the scrutiny unit can improve the accuracy of its scrutiny based on the relevant literature. For example, the scrutiny unit can comprehensively evaluate the relevant literature to identify reliable information. This makes it possible to provide a scrutiny that refers to relevant literature. Some or all of the above processes in the scrutiny unit may be performed using AI, for example, or without AI. For example, the scrutiny unit can input relevant literature into a generating AI and have the generating AI perform the task of improving the accuracy of the scrutiny.

[0046] The comparison unit can improve the accuracy of the comparison by considering the interrelationships of the options during the comparison process. For example, the comparison unit evaluates the interrelationships of the options to improve the accuracy of the comparison. For example, the comparison unit compares highly relevant options by considering the interrelationships of the options. For example, the comparison unit improves the accuracy of the comparison based on the interrelationships of the options. This makes it possible to provide a comparison that takes into account the interrelationships of the options. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input data on the interrelationships of the options into a generating AI and have the generating AI perform the improvement of the comparison accuracy.

[0047] The comparison unit can perform comparisons while considering the attribute information of the option providers. For example, the comparison unit can evaluate the attribute information of the option providers and identify highly reliable options. For example, the comparison unit can evaluate the past history of the option providers and identify highly reliable options. For example, the comparison unit can comprehensively evaluate the attribute information of the option providers and identify highly reliable options. This makes it possible to provide a comparison that takes into account the attribute information of the option providers. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the attribute information of the option providers into a generating AI and have the generating AI perform the comparison.

[0048] The comparison unit can perform comparisons while considering the geographical distribution of the options. For example, the comparison unit can evaluate the geographical distribution of the options and identify the most reliable options. For example, the comparison unit can compare highly relevant options while considering the geographical distribution of the options. For example, the comparison unit can improve the accuracy of the comparison based on the geographical distribution of the options. This makes it possible to provide a comparison that takes the geographical distribution of the options into account. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input data on the geographical distribution of the options into a generating AI and have the generating AI perform the comparison.

[0049] The comparison unit can improve the accuracy of the comparison by referring to relevant literature for the options during the comparison process. For example, the comparison unit can refer to relevant literature for the options and identify the most reliable option. For example, the comparison unit can improve the accuracy of the comparison based on the relevant literature for the options. For example, the comparison unit can comprehensively evaluate the relevant literature for the options and identify the most reliable option. This makes it possible to provide a comparison that refers to the relevant literature for the options. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the relevant literature for the options into a generating AI and have the generating AI perform the improvement of the comparison accuracy.

[0050] The suggestion unit can analyze the user's past selection history to select the optimal suggestion method when making a suggestion. For example, the suggestion unit can provide the optimal suggestion method based on the options the user has previously selected. For example, the suggestion unit can find specific patterns from the user's past selection history and make suggestions based on them. For example, the suggestion unit can analyze the success rate of the options the user has previously selected and select the optimal suggestion method. This allows the suggestion unit to provide the optimal suggestion method based on the user's past selection history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past selection history into a generating AI and have the generating AI select the optimal suggestion method.

[0051] The suggestion unit can customize the suggestion method based on the user's current situation when making a suggestion. For example, when the user inputs their current situation, the suggestion unit provides the most suitable suggestion method for that situation. For example, the suggestion unit makes highly relevant suggestions based on the user's current situation. For example, the suggestion unit customizes the suggestion method considering the user's current situation. This allows the suggestion unit to provide a suggestion method based on the user's current situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information about the user's current situation into a generating AI and have the generating AI perform the customization of the suggestion method.

[0052] The suggestion unit can select the optimal suggestion method when making suggestions, taking into account the user's geographical location information. For example, when the user enters their current location, the suggestion unit makes suggestions relevant to that region. For example, the suggestion unit makes highly relevant suggestions based on the user's geographical location information. For example, the suggestion unit selects the optimal suggestion method, taking into account the user's geographical location information. This makes it possible to provide the optimal suggestion method based on the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal suggestion method.

[0053] The proposal unit can analyze the user's social media activity and propose methods for making suggestions. For example, the proposal unit can identify topics of interest from the user's social media activity and make suggestions based on those topics. For example, the proposal unit can analyze the content of the user's social media posts and make relevant suggestions. For example, the proposal unit can select the optimal suggestion method based on the user's social media activity history. This makes it possible to provide methods for making suggestions based on the user's social media activity. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's social media activity data into a generating AI and have the generating AI select methods for making suggestions.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] A decision support system may include a prediction unit that analyzes the user's past selection history and predicts future options. The prediction unit may, for example, analyze patterns in options the user has selected in the past and predict future options. The prediction unit may, for example, analyze the success rate of options the user has selected in the past and predict the optimal option. The prediction unit may, for example, find trends in choices made during specific time periods from the user's past selection history and predict options based on that. This makes it possible to provide future options based on the user's past selection history. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit may input the user's past selection history into a generating AI and have the generating AI perform predictions of future options.

[0056] A decision support system may include a geographic unit that presents options while considering the user's geographic location. For example, when the user inputs their current location, the geographic unit prioritizes presenting options relevant to that region. For example, the geographic unit filters out highly relevant options based on the user's geographic location. For example, the geographic unit excludes unnecessary options while considering the user's geographic location. This allows the system to provide options based on the user's geographic location. Some or all of the above processing in the geographic unit may be performed using AI, for example, or without AI. For example, the geographic unit can input the user's geographic location into a generating AI and have the generating AI perform the filtering of highly relevant options.

[0057] The decision support system may include a social media section that analyzes the user's social media activity and presents relevant options. For example, the social media section may identify topics of interest from the user's social media activity and present relevant options. Alternatively, it may analyze the user's social media posts and prioritize the presentation of relevant options. Finally, it may filter highly relevant options based on the user's social media activity history. This allows the system to provide options based on the user's social media activity. Some or all of the above processing in the social media section may be performed using AI, or without AI. For example, the social media section may input the user's social media activity data into a generating AI and have the generating AI perform the filtering of relevant options.

[0058] A decision support system may include a filtering unit that filters options based on the user's current situation and areas of interest. For example, when the user inputs their current situation, the filtering unit may present only options relevant to that situation. For example, the filtering unit may prioritize presenting highly relevant options based on the user's areas of interest. For example, the filtering unit may exclude unnecessary options based on the user's current situation and areas of interest. This allows the system to provide options based on the user's current situation and areas of interest. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit may input information about the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0059] A decision support system may include a confidence unit that analyzes the user's past selection history and evaluates the reliability of options. The confidence unit may, for example, analyze the success rate of options previously selected by the user and identify reliable options. The confidence unit may, for example, find specific patterns in the user's past selection history and evaluate reliability based on them. The confidence unit may, for example, identify reliable options based on evaluations of options previously selected by the user. This makes it possible to provide reliable options based on the user's past selection history. Some or all of the above processing in the confidence unit may be performed using AI, for example, or without AI. For example, the confidence unit may input the user's past selection history into a generating AI and have the generating AI perform the reliability evaluation.

[0060] A decision support system may include a customization unit that customizes options based on the user's current situation. For example, the customization unit may, when the user inputs their current situation, provide the most suitable options for that situation. For example, the customization unit may, based on the user's current situation, present highly relevant options. For example, the customization unit may, by taking the user's current situation into consideration, customize the options. This allows the system to provide options based on the user's current situation. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit may input information about the user's current situation into a generating AI and have the generating AI perform the customization of the options.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives information about the user's choices. The reception desk provides, for example, an interface for the user to input information about their choices. The reception desk can accept information in various ways, such as text input, voice input, or image input. For example, if a user's car battery dies, the reception desk can accept information about the cost of replacing the battery or other repair options. Step 2: The review department reviews the information received by the reception department and presents reliable options. The review department, for example, analyzes the provided information to identify reliable options. The review department evaluates reliability based on factors such as user reviews, expert ratings, and data accuracy. The review department evaluates reliability based on factors such as the cost of battery replacement and other repair options. Step 3: The comparison unit compares the options presented by the review unit and displays their advantages and disadvantages. For example, the comparison unit analyzes the advantages and disadvantages of each option and displays them clearly to the user. For example, the comparison unit compares the costs of battery replacement and the advantages and disadvantages of other repair options. Step 4: The suggestion unit proposes the best option for the user's situation and needs based on the information displayed by the comparison unit. For example, the suggestion unit identifies the best option based on the user's budget and time constraints. For example, the suggestion unit proposes the best battery replacement option based on the user's budget and time constraints.

[0063] (Example of form 2) The decision support system according to an embodiment of the present invention is a system that utilizes AI to support expert choices. In this decision support system, the user inputs information about options, the AI ​​scrutinizes the provided information, and presents the user with reliable options. Furthermore, it compares multiple options, clearly displays their advantages and disadvantages, and proposes the optimal option based on the user's situation and needs. For example, if a car battery dies, the AI ​​compares the cost of battery replacement and other repair options and proposes the best option to the user. In addition, the AI ​​proposes other possible options in response to the opinions and suggestions of salespeople or experts, helping the user make the best decision. This system can provide appropriate information and support reliable decision-making to users who feel anxious because they cannot fully understand expert information, or to users whose options are limited because not all options are presented. As a result, the decision support system can help users obtain reliable options even without expert knowledge, and support optimal decision-making.

[0064] The decision support system according to the embodiment comprises a reception unit, a scrutiny unit, a comparison unit, and a proposal unit. The reception unit receives information about options from the user. The reception unit provides, for example, an interface for the user to input information about options. The reception unit can receive information by methods such as text input, voice input, and image input. For example, if a user's car battery dies, the reception unit can receive information about the cost of battery replacement and other repair options. The scrutiny unit scrutinizes the information received by the reception unit and presents reliable options. The scrutiny unit analyzes the provided information and identifies reliable options. The scrutiny unit evaluates reliability based on, for example, user reviews, expert evaluations, and data accuracy. The scrutiny unit evaluates the reliability of, for example, the cost of battery replacement and other repair options. The comparison unit compares the options presented by the scrutiny unit and displays their advantages and disadvantages. The comparison unit analyzes the advantages and disadvantages of each option and displays them in an easy-to-understand manner for the user. The comparison unit compares the advantages and disadvantages of, for example, the cost of battery replacement and other repair options. The proposal unit suggests the best option based on the user's situation and needs, based on the information displayed by the comparison unit. The proposal unit identifies the best option based, for example, the user's budget and time constraints. The proposal unit suggests the best battery replacement option based, for example, the user's budget and time constraints. As a result, the decision support system according to the embodiment can provide users with reliable options even without specialized knowledge, and can support them in making optimal decisions.

[0065] The reception desk receives information about user choices. For example, it provides an interface for users to input information about their choices. Specifically, it designs an interface that users can easily access through a web browser or mobile application. For text input, users can enter information using a keyboard; for voice input, speech recognition technology is used to convert the user's speech into text; and for image input, users upload relevant images using a camera, and the system analyzes the content using image recognition technology. For example, if a user's car battery dies, they can input information about the cost of battery replacement or other repair options. The reception desk centrally manages this information and stores it in a database. Furthermore, the reception desk has a feedback function to verify the accuracy of the information entered by the user, automatically detecting input errors or incomplete information and prompting the user to correct them. This allows the reception desk to efficiently and accurately collect information from users and smoothly pass it on to the next processing step.

[0066] The review department scrutinizes the information received by the reception department and presents highly reliable options. Specifically, it uses natural language processing techniques and machine learning algorithms to analyze the provided information and identify reliable options. For example, when analyzing user reviews and expert evaluations, it uses text mining techniques to classify the content of the reviews and quantify the reliability of the evaluations. To assess the accuracy of the data, it compares it with existing data in the database to check for matches. Furthermore, the review department further enhances reliability by obtaining information from external, reliable data sources and comparing it with the information provided by the user. For example, when evaluating the cost of battery replacement or the reliability of other repair options, it refers to pricing information and past repair history from multiple repair companies to identify the most reliable option. This allows the review department to provide users with reliable information and present the optimal options.

[0067] The comparison unit compares the options presented by the scrutiny unit and displays their advantages and disadvantages. Specifically, it analyzes the advantages and disadvantages of each option and provides a visual interface to display them clearly to the user. For example, graphs and charts can be used to visually compare elements such as cost, time, and reliability of each option. When comparing the cost of battery replacement or the advantages and disadvantages of other repair options, detailed information for each option is displayed in a list format, making it easy for the user to compare them. Furthermore, the comparison unit has a function to filter options based on the user's individual needs and conditions. For example, if the user enters budget or time constraints, the optimal options can be narrowed down based on those constraints. In this way, the comparison unit can help the user efficiently compare multiple options and make the best decision.

[0068] The suggestion unit proposes the best option for the user's situation and needs based on the information displayed by the comparison unit. Specifically, it uses AI algorithms to identify the optimal option based on the user's budget and time constraints. For example, it considers the budget and time constraints entered by the user and proposes the most cost-effective battery replacement option. The suggestion unit can learn the user's past selection history and preferences to make suggestions tailored to individual needs. Furthermore, the suggestion unit provides an interface to explain the suggestions to the user in an easy-to-understand manner. For example, it includes a function to provide detailed explanations of the proposed options and to simulate the expected results if an option is selected. This allows the user to fully understand the suggestions and make decisions with confidence. The suggestion unit can also collect feedback from users and continuously improve the accuracy of its suggestion algorithm. This enables the suggestion unit to provide users with the best options and support their decision-making.

[0069] The scrutiny unit can analyze the provided information and identify reliable options. For example, the scrutiny unit can evaluate the source of the provided information and identify reliable information. For example, the scrutiny unit can analyze the content of the information and identify reliable information. For example, the scrutiny unit can evaluate the past history of the information provider and identify reliable information. This allows the reliability of the provided information to be evaluated and reliable options to be provided to the user. Some or all of the above processing in the scrutiny unit may be performed using AI, for example, or without AI. For example, the scrutiny unit can input the provided information into a generating AI and have the generating AI perform the task of identifying reliable options.

[0070] The comparison unit can analyze the advantages and disadvantages of each option and display them to the user in an easy-to-understand manner. For example, the comparison unit can analyze the advantages and disadvantages of each option and display them to the user in an easy-to-understand manner. For example, the comparison unit can compare the advantages and disadvantages of battery replacement costs and other repair options. For example, the comparison unit can evaluate advantages and disadvantages based on criteria such as performance, cost, and ease of use. This makes it easier for the user to understand the advantages and disadvantages of the options. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the advantages and disadvantages of each option into a generating AI and have the generating AI perform the analysis of the advantages and disadvantages.

[0071] The suggestion unit can propose the optimal option based on the user's budget and time constraints. For example, the suggestion unit identifies the optimal option based on the user's budget and time constraints. For example, the suggestion unit proposes the optimal battery replacement option based on the user's budget and time constraints. For example, the suggestion unit proposes the optimal option considering the user's needs, budget, usage environment, etc. This allows the suggestion unit to propose the optimal option according to the user's situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information about the user's budget and time constraints into a generating AI and have the generating AI propose the optimal option.

[0072] The reception desk can collect information about the user's situation and needs. For example, the reception desk collects information about the user's situation and needs. For example, the reception desk collects information through methods such as questionnaires, interviews, and behavioral data. For example, if a user's car battery dies, the reception desk collects information about the cost of battery replacement and other repair options. This allows for the collection of information based on the user's situation and needs. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input information about the user's situation and needs into a generating AI and have the generating AI perform the information collection.

[0073] The reception unit can estimate the user's emotions and adjust how information is received based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize the input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception unit can prioritize voice input to allow for quick information entry. This allows for information reception methods tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The reception unit can analyze the user's past selection history and select the optimal information reception method. For example, the reception unit may prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception unit may predict and suggest input methods to be used during specific time periods based on the user's past selection history. For example, the reception unit may suggest the optimal input method based on the type of information the user has previously selected. This allows the reception unit to provide the optimal information reception method based on the user's past selection history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may input the user's past selection history into a generating AI and have the generating AI select the optimal information reception method.

[0075] The reception unit can filter information based on the user's current situation and areas of interest when receiving it. For example, when a user enters their current situation, the reception unit will only accept information related to that situation. For example, the reception unit will prioritize accepting highly relevant information based on the user's areas of interest. For example, the reception unit will filter out unnecessary information based on the user's current situation and areas of interest. This allows the reception unit to provide information based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input information about the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0076] The reception desk can estimate the user's emotions and determine the priority of information to receive based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize receiving important information. For example, if the user is relaxed, the reception desk will prioritize receiving detailed information. For example, if the user is in a hurry, the reception desk will prioritize receiving information that needs to be processed quickly. This allows for the provision of information prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, when a user enters their current location, the reception unit prioritizes receiving information related to that region. For example, the reception unit filters highly relevant information based on the user's geographical location. For example, the reception unit excludes unnecessary information by considering the user's geographical location. This allows the reception unit to provide information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform the filtering of highly relevant information.

[0078] The reception unit can analyze a user's social media activity and receive relevant information upon receiving information. For example, the reception unit can identify topics of interest from the user's social media activity and receive relevant information. For example, the reception unit can analyze the content of the user's social media posts and prioritize receiving relevant information. For example, the reception unit can filter highly relevant information based on the user's social media activity history. This allows the reception unit to provide information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the filtering of relevant information.

[0079] The scrutiny unit can estimate the user's emotions and adjust the method of scrutinizing information based on the estimated user emotions. For example, if the user is stressed, the scrutiny unit will prioritize scrutinizing important information. For example, if the user is relaxed, the scrutiny unit will prioritize scrutinizing detailed information. For example, if the user is in a hurry, the scrutiny unit will prioritize scrutinizing information that needs to be quickly reviewed. This provides a method of scrutinizing information that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the scrutiny unit may be performed using AI, for example, or not using AI. For example, the scrutiny unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The scrutiny unit can apply its own algorithms to evaluate the reliability of information during the scrutiny process. For example, the scrutiny unit can evaluate the source of the provided information and identify reliable information. For example, the scrutiny unit can analyze the content of the information and identify reliable information. For example, the scrutiny unit can evaluate the past history of the information provider and identify reliable information. This allows the scrutiny unit to provide its own algorithms for evaluating the reliability of information. Some or all of the above processes in the scrutiny unit may be performed using AI, for example, or without AI. For example, the scrutiny unit can input the provided information into a generating AI and have the generating AI perform the reliability evaluation.

[0081] The scrutiny unit can perform scrutiny while considering the reliability of the information source and provider. For example, the scrutiny unit can evaluate the information source and identify reliable information. For example, the scrutiny unit can evaluate the information provider's past history and identify reliable information. For example, the scrutiny unit can comprehensively evaluate the reliability of the information source and provider and identify reliable information. This makes it possible to provide scrutiny that takes into account the reliability of the information source and provider. Some or all of the above processing in the scrutiny unit may be performed using AI, for example, or without AI. For example, the scrutiny unit can input data on the reliability of the information source and provider into a generating AI and have the generating AI perform a reliability evaluation.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. This makes it possible to provide a display method of the analysis results that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The scrutiny unit can perform scrutiny while considering the geographical distribution of information. For example, the scrutiny unit can evaluate the geographical distribution of information and identify highly reliable information. For example, the scrutiny unit can scrutinize highly relevant information while considering the geographical distribution of information. For example, the scrutiny unit can identify highly reliable information based on the geographical distribution of information. This makes it possible to provide scrutiny that takes the geographical distribution of information into consideration. Some or all of the above-described processes in the scrutiny unit may be performed using AI, for example, or without AI. For example, the scrutiny unit can input data on the geographical distribution of information into a generating AI and have the generating AI perform the scrutiny.

[0084] The scrutiny unit can improve the accuracy of its scrutiny by referring to relevant literature during the scrutiny process. For example, the scrutiny unit can refer to relevant literature to identify reliable information. For example, the scrutiny unit can improve the accuracy of its scrutiny based on the relevant literature. For example, the scrutiny unit can comprehensively evaluate the relevant literature to identify reliable information. This makes it possible to provide a scrutiny that refers to relevant literature. Some or all of the above processes in the scrutiny unit may be performed using AI, for example, or without AI. For example, the scrutiny unit can input relevant literature into a generating AI and have the generating AI perform the task of improving the accuracy of the scrutiny.

[0085] The comparison unit can estimate the user's emotions and adjust the comparison criteria based on the estimated user emotions. For example, if the user is stressed, the comparison unit will prioritize comparing important criteria. For example, if the user is relaxed, the comparison unit will compare detailed criteria. For example, if the user is in a hurry, the comparison unit will prioritize comparing criteria that require quick comparison. This allows the comparison criteria to be provided in accordance with the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the comparison unit may be performed using AI, for example, or not using AI. For example, the comparison unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The comparison unit can improve the accuracy of the comparison by considering the interrelationships of the options during the comparison process. For example, the comparison unit evaluates the interrelationships of the options to improve the accuracy of the comparison. For example, the comparison unit compares highly relevant options by considering the interrelationships of the options. For example, the comparison unit improves the accuracy of the comparison based on the interrelationships of the options. This makes it possible to provide a comparison that takes into account the interrelationships of the options. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input data on the interrelationships of the options into a generating AI and have the generating AI perform the improvement of the comparison accuracy.

[0087] The comparison unit can perform comparisons while considering the attribute information of the option providers. For example, the comparison unit can evaluate the attribute information of the option providers and identify highly reliable options. For example, the comparison unit can evaluate the past history of the option providers and identify highly reliable options. For example, the comparison unit can comprehensively evaluate the attribute information of the option providers and identify highly reliable options. This makes it possible to provide a comparison that takes into account the attribute information of the option providers. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the attribute information of the option providers into a generating AI and have the generating AI perform the comparison.

[0088] The comparison unit can estimate the user's emotions and adjust the display order of the comparison results based on the estimated user emotions. For example, if the user is tense, the comparison unit will prioritize displaying important information. For example, if the user is relaxed, the comparison unit will prioritize displaying detailed information. For example, if the user is in a hurry, the comparison unit will prioritize displaying information that needs to be processed quickly. This allows for a display order of comparison results that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the comparison unit may be performed using AI, for example, or not using AI. For example, the comparison unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The comparison unit can perform comparisons while considering the geographical distribution of the options. For example, the comparison unit can evaluate the geographical distribution of the options and identify the most reliable options. For example, the comparison unit can compare highly relevant options while considering the geographical distribution of the options. For example, the comparison unit can improve the accuracy of the comparison based on the geographical distribution of the options. This makes it possible to provide a comparison that takes the geographical distribution of the options into account. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input data on the geographical distribution of the options into a generating AI and have the generating AI perform the comparison.

[0090] The comparison unit can improve the accuracy of the comparison by referring to relevant literature for the options during the comparison process. For example, the comparison unit can refer to relevant literature for the options and identify the most reliable option. For example, the comparison unit can improve the accuracy of the comparison based on the relevant literature for the options. For example, the comparison unit can comprehensively evaluate the relevant literature for the options and identify the most reliable option. This makes it possible to provide a comparison that refers to the relevant literature for the options. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the relevant literature for the options into a generating AI and have the generating AI perform the improvement of the comparison accuracy.

[0091] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide a simple and easily understandable suggestion. If the user is relaxed, the suggestion unit can provide a suggestion that includes detailed information. If the user is in a hurry, the suggestion unit can provide a concise suggestion. This allows the suggestion unit to provide suggestions that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The suggestion unit can analyze the user's past selection history to select the optimal suggestion method when making a suggestion. For example, the suggestion unit can provide the optimal suggestion method based on the options the user has previously selected. For example, the suggestion unit can find specific patterns from the user's past selection history and make suggestions based on them. For example, the suggestion unit can analyze the success rate of the options the user has previously selected and select the optimal suggestion method. This allows the suggestion unit to provide the optimal suggestion method based on the user's past selection history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past selection history into a generating AI and have the generating AI select the optimal suggestion method.

[0093] The suggestion unit can customize the suggestion method based on the user's current situation when making a suggestion. For example, when the user inputs their current situation, the suggestion unit provides the most suitable suggestion method for that situation. For example, the suggestion unit makes highly relevant suggestions based on the user's current situation. For example, the suggestion unit customizes the suggestion method considering the user's current situation. This allows the suggestion unit to provide a suggestion method based on the user's current situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information about the user's current situation into a generating AI and have the generating AI perform the customization of the suggestion method.

[0094] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will prioritize important suggestions. For example, if the user is relaxed, the suggestion unit will prioritize detailed suggestions. For example, if the user is in a hurry, the suggestion unit will prioritize suggestions that require quick processing. This allows for the provision of suggestion priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The suggestion unit can select the optimal suggestion method when making suggestions, taking into account the user's geographical location information. For example, when the user enters their current location, the suggestion unit makes suggestions relevant to that region. For example, the suggestion unit makes highly relevant suggestions based on the user's geographical location information. For example, the suggestion unit selects the optimal suggestion method, taking into account the user's geographical location information. This makes it possible to provide the optimal suggestion method based on the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal suggestion method.

[0096] The proposal unit can analyze the user's social media activity and propose methods for making suggestions. For example, the proposal unit can identify topics of interest from the user's social media activity and make suggestions based on those topics. For example, the proposal unit can analyze the content of the user's social media posts and make relevant suggestions. For example, the proposal unit can select the optimal suggestion method based on the user's social media activity history. This makes it possible to provide methods for making suggestions based on the user's social media activity. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's social media activity data into a generating AI and have the generating AI select methods for making suggestions.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] A decision support system may include a prediction unit that analyzes the user's past selection history and predicts future options. The prediction unit may, for example, analyze patterns in options the user has selected in the past and predict future options. The prediction unit may, for example, analyze the success rate of options the user has selected in the past and predict the optimal option. The prediction unit may, for example, find trends in choices made during specific time periods from the user's past selection history and predict options based on that. This makes it possible to provide future options based on the user's past selection history. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit may input the user's past selection history into a generating AI and have the generating AI perform predictions of future options.

[0099] A decision support system may include a presentation unit that estimates the user's emotions and adjusts the order in which options are presented based on the estimated emotions. For example, if the user is feeling stressed, the presentation unit may prioritize presenting important options. For example, if the user is relaxed, the presentation unit may prioritize presenting detailed options. For example, if the user is in a hurry, the presentation unit may prioritize presenting options that require quick processing. This provides an order in which options are presented that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] A decision support system may include a geographic unit that presents options while considering the user's geographic location. For example, when the user inputs their current location, the geographic unit prioritizes presenting options relevant to that region. For example, the geographic unit filters out highly relevant options based on the user's geographic location. For example, the geographic unit excludes unnecessary options while considering the user's geographic location. This allows the system to provide options based on the user's geographic location. Some or all of the above processing in the geographic unit may be performed using AI, for example, or without AI. For example, the geographic unit can input the user's geographic location into a generating AI and have the generating AI perform the filtering of highly relevant options.

[0101] The decision support system may include a social media section that analyzes the user's social media activity and presents relevant options. For example, the social media section may identify topics of interest from the user's social media activity and present relevant options. Alternatively, it may analyze the user's social media posts and prioritize the presentation of relevant options. Finally, it may filter highly relevant options based on the user's social media activity history. This allows the system to provide options based on the user's social media activity. Some or all of the above processing in the social media section may be performed using AI, or without AI. For example, the social media section may input the user's social media activity data into a generating AI and have the generating AI perform the filtering of relevant options.

[0102] A decision support system may include an evaluation unit that estimates the user's emotions and adjusts the evaluation criteria for choices based on the estimated user emotions. For example, if the user is stressed, the evaluation unit prioritizes evaluating important criteria. For example, if the user is relaxed, the evaluation unit evaluates detailed criteria. For example, if the user is in a hurry, the evaluation unit prioritizes evaluating criteria that need to be evaluated quickly. This allows the system to provide evaluation criteria for choices that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] A decision support system may include a filtering unit that filters options based on the user's current situation and areas of interest. For example, when the user inputs their current situation, the filtering unit may present only options relevant to that situation. For example, the filtering unit may prioritize presenting highly relevant options based on the user's areas of interest. For example, the filtering unit may exclude unnecessary options based on the user's current situation and areas of interest. This allows the system to provide options based on the user's current situation and areas of interest. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit may input information about the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0104] The decision support system may include a display unit that estimates the user's emotions and adjusts the way options are displayed based on the estimated emotions. For example, if the user is nervous, the display unit provides a simple and highly visible display method. If the user is relaxed, the display unit provides a display method that includes detailed information. If the user is in a hurry, the display unit provides a display method that gets straight to the point. This allows the system to provide a way of displaying options that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0105] A decision support system may include a confidence unit that analyzes the user's past selection history and evaluates the reliability of options. The confidence unit may, for example, analyze the success rate of options previously selected by the user and identify reliable options. The confidence unit may, for example, find specific patterns in the user's past selection history and evaluate reliability based on them. The confidence unit may, for example, identify reliable options based on evaluations of options previously selected by the user. This makes it possible to provide reliable options based on the user's past selection history. Some or all of the above processing in the confidence unit may be performed using AI, for example, or without AI. For example, the confidence unit may input the user's past selection history into a generating AI and have the generating AI perform the reliability evaluation.

[0106] A decision support system may include a suggestion unit that estimates the user's emotions and adjusts the method of suggesting options based on the estimated user emotions. For example, if the user is nervous, the suggestion unit may provide a simple and highly visible suggestion method. If the user is relaxed, the suggestion unit may provide a suggestion method that includes detailed information. If the user is in a hurry, the suggestion unit may provide a suggestion method that gets straight to the point. This allows the system to provide a suggestion method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0107] A decision support system may include a customization unit that customizes options based on the user's current situation. For example, the customization unit may, when the user inputs their current situation, provide the most suitable options for that situation. For example, the customization unit may, based on the user's current situation, present highly relevant options. For example, the customization unit may, by taking the user's current situation into consideration, customize the options. This allows the system to provide options based on the user's current situation. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit may input information about the user's current situation into a generating AI and have the generating AI perform the customization of the options.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk receives information about the user's choices. The reception desk provides, for example, an interface for the user to input information about their choices. The reception desk can accept information in various ways, such as text input, voice input, or image input. For example, if a user's car battery dies, the reception desk can accept information about the cost of replacing the battery or other repair options. Step 2: The review department reviews the information received by the reception department and presents reliable options. The review department, for example, analyzes the provided information to identify reliable options. The review department evaluates reliability based on factors such as user reviews, expert ratings, and data accuracy. The review department evaluates reliability based on factors such as the cost of battery replacement and other repair options. Step 3: The comparison unit compares the options presented by the review unit and displays their advantages and disadvantages. For example, the comparison unit analyzes the advantages and disadvantages of each option and displays them clearly to the user. For example, the comparison unit compares the costs of battery replacement and the advantages and disadvantages of other repair options. Step 4: The suggestion unit proposes the best option for the user's situation and needs based on the information displayed by the comparison unit. For example, the suggestion unit identifies the best option based on the user's budget and time constraints. For example, the suggestion unit proposes the best battery replacement option based on the user's budget and time constraints.

[0110] 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.

[0111] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the reception unit, scrutiny unit, comparison unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and provides an interface for the user to input information about the options. The scrutiny unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the provided information to identify the most reliable option. The comparison unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the advantages and disadvantages of each option and displays them to the user in an easy-to-understand manner. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal option based on the user's situation and needs. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0117] 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.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0119] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] 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.

[0121] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the reception unit, scrutiny unit, comparison unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and provides an interface for the user to input information about the options by voice. The scrutiny unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the provided information to identify the most reliable option. The comparison unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the advantages and disadvantages of each option and displays them to the user in an easy-to-understand manner. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and proposes the optimal option based on the user's situation and needs. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0133] 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.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0135] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] 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.

[0137] 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.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, scrutiny unit, comparison unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and provides an interface for the user to input information about the options by voice. The scrutiny unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the provided information to identify the most reliable option. The comparison unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the advantages and disadvantages of each option and displays them to the user in an easy-to-understand manner. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal option based on the user's situation and needs. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0149] 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.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0151] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] 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.

[0153] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] 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.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the reception unit, scrutiny unit, comparison unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and provides an interface for the user to input information about the options by voice. The scrutiny unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the provided information to identify the most reliable option. The comparison unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the advantages and disadvantages of each option and displays them to the user in an easy-to-understand manner. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and proposes the optimal option based on the user's situation and needs. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0163] 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.

[0164] Figure 9 shows the 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.

[0165] 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.

[0166] 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.

[0167] 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, and motorcycles, 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 based, for example, 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.

[0168] 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."

[0169] 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.

[0170] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] 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 other things 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.

[0180] 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.

[0181] (Note 1) A reception desk that receives information about user choices, The review unit examines the information received by the reception unit and presents highly reliable options. A comparison unit compares the options presented by the aforementioned examination unit and displays their advantages and disadvantages, The system includes a suggestion unit that proposes the most suitable option for the user's situation and needs based on the information displayed by the comparison unit. A system characterized by the following features. (Note 2) The aforementioned inspection unit, Analyze the provided information and identify the most reliable options. The system described in Appendix 1, characterized by the features described herein. (Note 3) The comparison unit is, The advantages and disadvantages of each option are analyzed and displayed to the user in an easy-to-understand manner. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose the best option based on the user's budget and time constraints. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Collect information about the user's situation and needs. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts how information is received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past selection history to select the optimal method for receiving information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving information, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of information to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving information, the system prioritizes receiving highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving information, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned inspection unit, We estimate the user's emotions and adjust the information scrutiny method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned inspection unit, During the scrutiny process, we apply a proprietary algorithm to evaluate the reliability of the information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned inspection unit, During the review process, the reliability of the information source and provider will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned inspection unit, It estimates the user's emotions and adjusts how the review results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned inspection unit, During the review process, the geographical distribution of the information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned inspection unit, During the review process, refer to relevant literature to improve the accuracy of the review. The system described in Appendix 1, characterized by the features described herein. (Note 18) The comparison unit is, It estimates the user's emotions and adjusts the comparison criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The comparison unit is, When making comparisons, consider the interrelationships between options to improve the accuracy of the comparison. The system described in Appendix 1, characterized by the features described herein. (Note 20) The comparison unit is, When making comparisons, the attribute information of the providers of the options should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The comparison unit is, It estimates the user's emotions and adjusts the display order of comparison results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The comparison unit is, When making comparisons, the geographical distribution of the options should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The comparison unit is, When making comparisons, refer to relevant literature for each option to improve the accuracy of the comparison. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, the system analyzes the user's past selection history to select the most suitable proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, customize the proposal method based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making a proposal, the optimal proposal method will be selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and suggest methods for making the proposal. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives information about user choices, The review unit examines the information received by the reception unit and presents highly reliable options. A comparison unit compares the options presented by the aforementioned examination unit and displays their advantages and disadvantages, The system includes a suggestion unit that proposes the most suitable option for the user's situation and needs based on the information displayed by the comparison unit. A system characterized by the following features.

2. The aforementioned inspection unit, Analyze the provided information and identify the most reliable options. The system according to feature 1.

3. The comparison unit is, The advantages and disadvantages of each option are analyzed and displayed to the user in an easy-to-understand manner. The system according to feature 1.

4. The aforementioned proposal section is, We propose the best option based on the user's budget and time constraints. The system according to feature 1.

5. The aforementioned reception unit is Collect information about the user's situation and needs. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts how information is received based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past selection history to select the optimal method for receiving information. The system according to feature 1.

8. The aforementioned reception unit is When receiving information, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

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