System

Abstract

An object of a system according to an embodiment is to efficiently propose an optimal option based on a user's preference or needs.SOLUTION: A system includes an information collection part, an option extraction part, and an option narrowing-down part. The information gathering component uses the generated AI to automatically gather relevant information based on the user's preferences and needs. The option extracting unit extracts options that match the preferences and needs of the user based on the information collected by the information collecting unit. The option narrowing unit narrows the options sorted out by the option sorting unit to seven or less.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that it takes a lot of time and effort to find the best option from a huge number of options.

[0005] The system according to the embodiment aims to efficiently suggest optimal options based on the preferences and needs of a user. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an option identification unit, and an option narrowing unit. The information collection unit uses a generation AI to automatically collect relevant information based on a user's preferences and needs. The option identification unit identifies options that match the user's preferences and needs based on the information collected by the information collection unit. The option narrowing unit narrows down the options identified by the option identification unit to seven or less. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently suggest optimal options based on the preferences and needs of the user. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The option suggestion system according to an embodiment of the present invention is a system that proposes seven or fewer carefully selected options based on the user's preferences and needs. This allows the option suggestion system to significantly reduce the time and effort required for the user to make a selection or decision, and improve the satisfaction of the user with the selection.

[0029] An option suggestion system according to an embodiment includes an information gathering unit, an option selection unit, and an option narrowing unit. The information gathering unit automatically collects relevant information based on a user's preferences and needs. For example, if a user wants to purchase a new smartphone, the generation AI collects reviews, specifications, and price information for the latest smartphones. The generation AI receives input from a prompt containing instructions from the user, and the generation AI collects information based on the prompt. The option selection unit selects options that match the user's preferences and needs based on the information collected by the information gathering unit. For example, the generation AI lists smartphone models that match the specifications and price range desired by the user. The generation AI selects options based on prompts containing the user's instructions. The option narrowing unit narrows the options selected by the option selection unit to seven or fewer. For example, the option suggestion system considers the user's most important features, design, price, and other criteria to suggest the seven most suitable smartphones. The generation AI narrows the options based on prompts containing the user's instructions. This allows the option suggestion system according to an embodiment to carefully select options based on the user's preferences and needs, thereby improving selection satisfaction. For example, if a user is considering purchasing a new smartphone, generative AI can help them find the best model in a short amount of time, and because the options are carefully curated, they can reduce regret and anxiety about their choice.

[0030] The information collection unit can analyze a user's past selection history and automatically generate an information collection pattern that is optimal for each individual user. For example, the information collection unit uses a generation AI to analyze a user's past purchase history and search history to identify the user's preferences and needs. For example, it predicts the model the user is likely to purchase next based on the specifications and brands of smartphones previously purchased. The information collection unit also analyzes product reviews and ratings viewed by the user in the past to extract preference trends. For example, if a particular brand or feature is highly rated, it will prioritize collecting information on products with that brand or feature. The information collection unit also automatically generates information collection patterns based on the user's past selection history. For example, it takes into account the specifications and price range that the user has prioritized in the past and collects product information that meets similar conditions. This makes it possible to provide an optimal information collection pattern based on the user's past selection history.

[0031] The information collection unit collects real-time behavioral data of the user and can dynamically adjust the scope of information collection. For example, the information collection unit uses a generation AI to analyze the user's web browsing history in real time and collect information about products and services in which the user is interested. For example, if the user frequently visits a page about a specific smartphone, the information collection unit collects the latest information about that model. The information collection unit also analyzes the user's social media posts and comments to identify topics and products in which the user is interested. For example, if the user posts frequently about smartphones, the information collection unit prioritizes collecting information in that field. The information collection unit also dynamically adjusts the scope of information collection based on the user's real-time behavioral data. For example, if the user shows interest in a specific brand or feature, the information collection unit focuses on collecting information about that brand or feature. This allows the scope of information collection to be dynamically adjusted based on real-time behavioral data.

[0032] The information collection unit can integrate information from different devices to provide the user with a consistent information collection experience. For example, the generation AI can integrate information from different devices, such as smartphones, tablets, and PCs, to provide the user with a consistent information collection experience. For example, it can ensure that the same information is displayed regardless of which device the user accesses it from. The information collection unit also synchronizes information between different devices to allow the user to access the latest information from any device. For example, it can ensure that information viewed on a smartphone is also displayed on a PC. The information collection unit also integrates information between devices to allow the user to collect information seamlessly. For example, it can allow information collection started on a smartphone to be continued on a tablet. This allows the information from different devices to be integrated to provide a consistent information collection experience.

[0033] The information collection unit can analyze the user's voice input and collect information based on the voice command. For example, the information collection unit uses a generation AI to analyze the user's voice input and collect information based on the voice command. For example, it collects information in response to a voice command such as "Tell me reviews of the latest smartphones." The information collection unit also uses voice recognition technology to convert the user's voice input into text and collects information based on that text. For example, it analyzes the voice command and searches for related information. The information collection unit also dynamically adjusts the scope of information collection based on the user's voice command. For example, it narrows down the information in response to a voice command such as "narrow the price range and search." This makes it possible to collect information based on voice commands.

[0034] The option identification unit can analyze user preferences and needs in detail and automatically generate filtering criteria that are optimal for each individual user when identifying options. For example, the option identification unit uses a generation AI to analyze a user's past selection history and search history and perform a detailed analysis of the user's preferences and needs. For example, it identifies products that are likely to be purchased next based on the characteristics of products purchased in the past. The option identification unit also automatically generates filtering criteria when identifying options based on the user's preferences and needs. For example, for a user who places importance on specific brands or features, it prioritizes a list of products that meet those conditions. The option identification unit also uses a generation AI to analyze user preferences and needs in detail and automatically generate filtering criteria that are optimal for each individual user. For example, it identifies options taking into account the specifications and price range that the user particularly values. This makes it possible to provide optimal filtering criteria based on the user's preferences and needs.

[0035] The option identification unit takes into account the user's past selection results and their satisfaction levels, allowing it to provide more accurate options. For example, the option identification unit uses a generation AI to analyze the user's past selection results and their satisfaction levels, and reflects this in the identification of options. For example, it prioritizes listing products similar to products that have previously shown high satisfaction levels. The option identification unit also improves the accuracy of options based on the user's past selection results and their satisfaction levels. For example, it excludes products that have the same characteristics as products that have previously caused dissatisfaction levels. The option identification unit also takes into account the user's past selection results and their satisfaction levels, allowing it to provide more accurate options. For example, it prioritizes listing options with high satisfaction levels. This allows it to provide more accurate options by taking into account the user's past selection results and their satisfaction levels.

[0036] The option identification unit incorporates information from different languages ​​and cultural spheres, enabling it to identify options from a global perspective. For example, the generation AI automatically collects information from different languages ​​and cultural spheres and reflects this in identifying options. For example, it incorporates reviews and ratings in multiple languages, such as English, French, and Chinese. The option identification unit also incorporates information from different cultural spheres to identify options from a global perspective. For example, it lists products and services that are popular in a particular region. The generation AI also incorporates information from different languages ​​and cultural spheres in the option identification unit, ensuring a diversity of options. For example, it provides the most suitable options to users with different cultural backgrounds. This makes it possible to incorporate information from different languages ​​and cultural spheres and identify options from a global perspective.

[0037] The option identification unit can analyze the user's visual preferences and identify options based on images and visual information. For example, the option identification unit uses a generation AI to analyze the user's visual preferences and identify options based on images and visual information. For example, it lists products based on the user's preferred designs and colors. The option identification unit also uses image recognition technology to analyze images and visual information that the user has viewed in the past and extract preference trends. For example, it prioritizes listing products with specific designs and styles. The option identification unit also automatically generates filtering criteria for identifying options based on the user's visual preferences. For example, it lists products that emphasize the user's particularly preferred designs and colors. This makes it possible to identify options based on the user's visual preferences.

[0038] The option narrowing unit can collect real-time user feedback and dynamically adjust the option narrowing criteria based on that feedback. For example, the option narrowing unit uses a generation AI to collect real-time user feedback and dynamically adjust the option narrowing criteria based on that feedback. For example, if a user provides positive feedback for a particular option, other options related to that option are preferentially presented. The option narrowing unit also analyzes user feedback in real time and adjusts the option narrowing criteria. For example, if a user places importance on a particular feature, options that have that feature are preferentially listed. The option narrowing unit also uses a generation AI to dynamically adjust the option narrowing criteria based on the user's real-time feedback. For example, if a user desires a particular price range, options that match that price range are preferentially presented. This allows the option narrowing criteria to be dynamically adjusted based on real-time feedback.

[0039] The option narrowing unit can predict a user's long-term satisfaction and suggest optimal options. For example, the option narrowing unit uses a generation AI to analyze a user's past selection history and satisfaction data and develop an algorithm to predict long-term satisfaction. For example, it prioritizes suggesting options similar to options that have shown high satisfaction in the past. In addition, to predict a user's long-term satisfaction, the option narrowing unit uses the generation AI to consider the user's lifestyle and usage conditions. For example, it prioritizes listing products that are expected to be used for a long time. In addition, the option narrowing unit uses the generation AI to predict a user's long-term satisfaction and suggest optimal options. For example, it lists products with functions and designs that will satisfy the user over the long term. This makes it possible to predict a user's long-term satisfaction and suggest optimal options.

[0040] The option narrowing unit can simulate different scenarios and narrow down the options based on them. For example, the option narrowing unit simulates different scenarios using the generation AI and narrows down the options based on them. For example, it simulates scenarios in which the budget increases or new technology emerges and proposes the optimal option. The option narrowing unit also uses scenario simulations to narrow down the options that best suit the user's needs. For example, it simulates a scenario that takes into account functions that the user will need in the future. The option narrowing unit also simulates different scenarios using the generation AI and reflects these in narrowing down the options. For example, it simulates a scenario that takes into account changes in the user's lifestyle and proposes the optimal option. This makes it possible to simulate different scenarios and narrow down the options based on them.

[0041] The option narrowing unit can incorporate opinions from the user's social network and narrow down the options based on them. For example, the option narrowing unit uses a generation AI to collect opinions from the user's social network (e.g., friends and family) and narrow down the options based on them. For example, it prioritizes listing products that the user's friends have given high ratings to. The option narrowing unit also analyzes the opinions of the user's social network and reflects them in narrowing down the options. For example, it prioritizes listing products recommended by the user's family. The option narrowing unit also uses a generation AI to incorporate opinions from the user's social network and adjust the option narrowing criteria. For example, it prioritizes listing products that the user's friends use. This makes it possible to incorporate opinions from the social network and narrow down the options based on them.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The option suggestion system can also collect the user's health data and suggest options based on their health condition. For example, it can analyze data from the user's fitness tracker or smartwatch to suggest products and services suited to their health condition. Specifically, it can suggest products that help reduce stress and services that have a relaxing effect based on the user's heart rate and sleep data. It can also prioritize fitness-related products and services by taking into account the user's exercise habits. This allows it to provide the best options based on the user's health condition.

[0044] The option suggestion system can also collect data on the user's social network and suggest options that reflect the opinions of friends and family. For example, it can analyze the user's social media posts and comments to list products and services recommended by friends and family. Specifically, it can prioritize suggested products that the user's friends have given high ratings to or that their family members use. It can also adjust the criteria for narrowing down options based on the opinions of the user's social network. This allows it to provide options that reflect the opinions of the user's social network.

[0045] The option suggestion system can also provide more accurate options by taking into account the user's past selections and their satisfaction levels. For example, it can analyze the user's past selections and their satisfaction levels, and prioritize listing products and services that provide high satisfaction. Specifically, it can suggest products similar to products that have previously generated high satisfaction levels, and conversely, it can exclude products that have the same characteristics as products that generated dissatisfaction levels. It can also develop algorithms to improve the accuracy of options based on the user's past selections. This makes it possible to provide more accurate options by taking into account the user's past selections and their satisfaction levels.

[0046] The option suggestion system can also analyze the user's visual preferences and suggest options based on images and visual information. For example, it can analyze images and visual information viewed by the user in the past to extract preference trends. Specifically, it can list products based on specific designs or colors and present information using visually appealing images and videos. It can also automatically generate filtering criteria for filtering options based on the user's visual preferences. This allows it to provide options based on the user's visual preferences.

[0047] The option suggestion system can also incorporate information from different languages ​​and cultural spheres to propose options from a global perspective. For example, the generation AI can automatically collect information from different languages ​​and cultural spheres and reflect it in identifying options. Specifically, it can incorporate reviews and ratings in multiple languages, such as English, French, and Chinese, to list products and services that are popular in a specific region. It can also automatically generate filtering criteria to provide optimal options to users with different cultural backgrounds. This allows it to incorporate information from different languages ​​and cultural spheres and provide options from a global perspective.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The information gathering unit automatically collects relevant information based on the user's preferences and needs. For example, if a user wants to buy a new smartphone, the generation AI collects reviews, specifications, and pricing information for the latest smartphones. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI collects information based on the prompt. Step 2: The option identification unit uses the information collected by the information collection unit to identify options that match the user's preferences and needs. For example, the generation AI lists models that match the specifications and price range of the smartphone the user is looking for. The generation AI identifies options based on prompts that include the user's instructions. Step 3: The option narrowing unit narrows down the options identified by the option identification unit to seven or fewer. For example, it proposes the seven most suitable smartphones, taking into account the features, design, price, and other criteria that the user particularly values. The generation AI narrows down the options based on prompts that include the user's instructions.

[0050] (Example 2) The option suggestion system according to an embodiment of the present invention is a system that proposes seven or fewer carefully selected options based on the user's preferences and needs. This allows the option suggestion system to significantly reduce the time and effort required for the user to make a selection or decision, and improve the satisfaction of the user with the selection.

[0051] An option suggestion system according to an embodiment includes an information gathering unit, an option selection unit, and an option narrowing unit. The information gathering unit automatically collects relevant information based on a user's preferences and needs. For example, if a user wants to purchase a new smartphone, the generation AI collects reviews, specifications, and price information for the latest smartphones. The generation AI receives input from a prompt containing instructions from the user, and the generation AI collects information based on the prompt. The option selection unit selects options that match the user's preferences and needs based on the information collected by the information gathering unit. For example, the generation AI lists smartphone models that match the specifications and price range desired by the user. The generation AI selects options based on prompts containing the user's instructions. The option narrowing unit narrows the options selected by the option selection unit to seven or fewer. For example, the option suggestion system considers the user's most important features, design, price, and other criteria to suggest the seven most suitable smartphones. The generation AI narrows the options based on prompts containing the user's instructions. This allows the option suggestion system according to an embodiment to carefully select options based on the user's preferences and needs, thereby improving selection satisfaction. For example, if a user is considering purchasing a new smartphone, generative AI can help them find the best model in a short amount of time, and because the options are carefully curated, they can reduce regret and anxiety about their choice.

[0052] The information collection unit can analyze a user's past selection history and automatically generate an information collection pattern that is optimal for each individual user. For example, the information collection unit uses a generation AI to analyze a user's past purchase history and search history to identify the user's preferences and needs. For example, it predicts the model the user is likely to purchase next based on the specifications and brands of smartphones previously purchased. The information collection unit also analyzes product reviews and ratings viewed by the user in the past to extract preference trends. For example, if a particular brand or feature is highly rated, it will prioritize collecting information on products with that brand or feature. The information collection unit also automatically generates information collection patterns based on the user's past selection history. For example, it takes into account the specifications and price range that the user has prioritized in the past and collects product information that meets similar conditions. This makes it possible to provide an optimal information collection pattern based on the user's past selection history.

[0053] The information collection unit collects real-time behavioral data of the user and can dynamically adjust the scope of information collection. For example, the information collection unit uses a generation AI to analyze the user's web browsing history in real time and collect information about products and services in which the user is interested. For example, if the user frequently visits a page about a specific smartphone, the information collection unit collects the latest information about that model. The information collection unit also analyzes the user's social media posts and comments to identify topics and products in which the user is interested. For example, if the user posts frequently about smartphones, the information collection unit prioritizes collecting information in that field. The information collection unit also dynamically adjusts the scope of information collection based on the user's real-time behavioral data. For example, if the user shows interest in a specific brand or feature, the information collection unit focuses on collecting information about that brand or feature. This allows the scope of information collection to be dynamically adjusted based on real-time behavioral data.

[0054] The information collection unit can use the emotion estimation function to detect stress or anxiety felt by the user while collecting information, and adjust the information presentation method accordingly. The information collection unit, for example, uses the emotion estimation function to detect stress or anxiety felt by the user while collecting information in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. Furthermore, if the user is feeling stressed or anxious, the information collection unit adjusts the information presentation method. For example, it reduces the amount of information or presents it in a visually easy-to-understand format. Furthermore, the information collection unit provides an information presentation method that allows the user to relax, based on the emotion estimation data. For example, it plays positive messages or music that has a relaxing effect. In this way, it is possible to detect the user's stress or anxiety, and adjust the information presentation method accordingly.

[0055] The information collection unit can integrate information from different devices to provide the user with a consistent information collection experience. For example, the generation AI can integrate information from different devices, such as smartphones, tablets, and PCs, to provide the user with a consistent information collection experience. For example, it can ensure that the same information is displayed regardless of which device the user accesses it from. The information collection unit also synchronizes information between different devices to allow the user to access the latest information from any device. For example, it can ensure that information viewed on a smartphone is also displayed on a PC. The information collection unit also integrates information between devices to allow the user to collect information seamlessly. For example, it can allow information collection started on a smartphone to be continued on a tablet. This allows the information from different devices to be integrated to provide a consistent information collection experience.

[0056] The information collection unit can analyze the user's voice input and collect information based on the voice command. For example, the information collection unit uses a generation AI to analyze the user's voice input and collect information based on the voice command. For example, it collects information in response to a voice command such as "Tell me reviews of the latest smartphones." The information collection unit also uses voice recognition technology to convert the user's voice input into text and collects information based on that text. For example, it analyzes the voice command and searches for related information. The information collection unit also dynamically adjusts the scope of information collection based on the user's voice command. For example, it narrows down the information in response to a voice command such as "narrow the price range and search." This makes it possible to collect information based on voice commands.

[0057] The information collection unit can use the emotion estimation function to preferentially present information that reinforces the positive emotions felt by the user while collecting information. The information collection unit, for example, uses the emotion estimation function to detect the positive emotions felt by the user while collecting information in real time. For example, the information collection unit analyzes the user's facial expressions and voice and calculates an emotion score. Furthermore, if the user is feeling positive emotions, the information collection unit preferentially presents information that reinforces those emotions. For example, the information collection unit provides information related to topics that interest the user with a focus on the emotion estimation data. Furthermore, the information collection unit provides an information presentation method that makes it easy for the user to feel positive emotions, based on the emotion estimation data. For example, the information is presented using visually appealing images and videos. This allows the information to be preferentially presented that reinforces positive emotions.

[0058] The option identification unit can analyze user preferences and needs in detail and automatically generate filtering criteria that are optimal for each individual user when identifying options. For example, the option identification unit uses a generation AI to analyze a user's past selection history and search history and perform a detailed analysis of the user's preferences and needs. For example, it identifies products that are likely to be purchased next based on the characteristics of products purchased in the past. The option identification unit also automatically generates filtering criteria when identifying options based on the user's preferences and needs. For example, for a user who places importance on specific brands or features, it prioritizes a list of products that meet those conditions. The option identification unit also uses a generation AI to analyze user preferences and needs in detail and automatically generate filtering criteria that are optimal for each individual user. For example, it identifies options taking into account the specifications and price range that the user particularly values. This makes it possible to provide optimal filtering criteria based on the user's preferences and needs.

[0059] The option identification unit takes into account the user's past selection results and their satisfaction levels, allowing it to provide more accurate options. For example, the option identification unit uses a generation AI to analyze the user's past selection results and their satisfaction levels, and reflects this in the identification of options. For example, it prioritizes listing products similar to products that have previously shown high satisfaction levels. The option identification unit also improves the accuracy of options based on the user's past selection results and their satisfaction levels. For example, it excludes products that have the same characteristics as products that have previously caused dissatisfaction levels. The option identification unit also takes into account the user's past selection results and their satisfaction levels, allowing it to provide more accurate options. For example, it prioritizes listing options with high satisfaction levels. This allows it to provide more accurate options by taking into account the user's past selection results and their satisfaction levels.

[0060] The option identification unit can use the emotion estimation function to detect anxiety or hesitation felt by the user when selecting options, and adjust the way options are presented accordingly. The option identification unit, for example, uses the emotion estimation function to detect in real time the anxiety or hesitation felt by the user when selecting options. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. Furthermore, if the user is feeling anxious or hesitant, the option identification unit adjusts the way options are presented. For example, it reduces the amount of information or presents the options in a visually easy-to-understand format. Furthermore, the option identification unit provides an option presentation method that allows the user to relax, based on the emotion estimation data. For example, it plays positive messages or music that has a relaxing effect. In this way, it is possible to detect the user's anxiety or hesitation, and adjust the way options are presented accordingly.

[0061] The option identification unit incorporates information from different languages ​​and cultural spheres, enabling it to identify options from a global perspective. For example, the generation AI automatically collects information from different languages ​​and cultural spheres and reflects this in identifying options. For example, it incorporates reviews and ratings in multiple languages, such as English, French, and Chinese. The option identification unit also incorporates information from different cultural spheres to identify options from a global perspective. For example, it lists products and services that are popular in a particular region. The generation AI also incorporates information from different languages ​​and cultural spheres in the option identification unit, ensuring a diversity of options. For example, it provides the most suitable options to users with different cultural backgrounds. This makes it possible to incorporate information from different languages ​​and cultural spheres and identify options from a global perspective.

[0062] The option identification unit can analyze the user's visual preferences and identify options based on images and visual information. For example, the option identification unit uses a generation AI to analyze the user's visual preferences and identify options based on images and visual information. For example, it lists products based on the user's preferred designs and colors. The option identification unit also uses image recognition technology to analyze images and visual information that the user has viewed in the past and extract preference trends. For example, it prioritizes listing products with specific designs and styles. The option identification unit also automatically generates filtering criteria for identifying options based on the user's visual preferences. For example, it lists products that emphasize the user's particularly preferred designs and colors. This makes it possible to identify options based on the user's visual preferences.

[0063] The option identification unit can use the emotion estimation function to preferentially present options that reinforce positive emotions felt by the user when selecting options. The option identification unit, for example, uses the emotion estimation function to detect positive emotions felt by the user in real time when selecting options. For example, the option identification unit analyzes the user's facial expressions and voice to calculate an emotion score. Furthermore, if the user is feeling positive emotions, the option identification unit preferentially presents options that reinforce those emotions. For example, the option identification unit provides options related to topics that interest the user. Furthermore, the option identification unit provides a method of presenting options that makes it easier for the user to feel positive emotions based on the emotion estimation data. For example, the option identification unit presents options using visually appealing images or videos. This allows options that reinforce positive emotions to be preferentially presented.

[0064] The option narrowing unit can collect real-time user feedback and dynamically adjust the option narrowing criteria based on that feedback. For example, the option narrowing unit uses a generation AI to collect real-time user feedback and dynamically adjust the option narrowing criteria based on that feedback. For example, if a user provides positive feedback for a particular option, other options related to that option are preferentially presented. The option narrowing unit also analyzes user feedback in real time and adjusts the option narrowing criteria. For example, if a user places importance on a particular feature, options that have that feature are preferentially listed. The option narrowing unit also uses a generation AI to dynamically adjust the option narrowing criteria based on the user's real-time feedback. For example, if a user desires a particular price range, options that match that price range are preferentially presented. This allows the option narrowing criteria to be dynamically adjusted based on real-time feedback.

[0065] The option narrowing unit can predict a user's long-term satisfaction and suggest optimal options. For example, the option narrowing unit uses a generation AI to analyze a user's past selection history and satisfaction data and develop an algorithm to predict long-term satisfaction. For example, it prioritizes suggesting options similar to options that have shown high satisfaction in the past. In addition, to predict a user's long-term satisfaction, the option narrowing unit uses the generation AI to consider the user's lifestyle and usage conditions. For example, it prioritizes listing products that are expected to be used for a long time. In addition, the option narrowing unit uses the generation AI to predict a user's long-term satisfaction and suggest optimal options. For example, it lists products with functions and designs that will satisfy the user over the long term. This makes it possible to predict a user's long-term satisfaction and suggest optimal options.

[0066] The option narrowing unit can use the emotion estimation function to detect the stress or anxiety the user feels when narrowing down the options, and adjust the way the options are presented accordingly. The option narrowing unit, for example, uses the emotion estimation function to detect in real time the stress or anxiety the user feels when narrowing down the options. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. Furthermore, if the user is feeling stressed or anxious, the option narrowing unit adjusts the way the options are presented. For example, it reduces the amount of information or presents it in a visually easy-to-understand format. Furthermore, the option narrowing unit provides an option presentation method that allows the user to relax, based on the emotion estimation data. For example, it plays positive messages or music that has a relaxing effect. In this way, the user's stress or anxiety can be detected, and the way the options are presented can be adjusted accordingly.

[0067] The option narrowing unit can simulate different scenarios and narrow down the options based on them. For example, the option narrowing unit simulates different scenarios using the generation AI and narrows down the options based on them. For example, it simulates scenarios in which the budget increases or new technology emerges and proposes the optimal option. The option narrowing unit also uses scenario simulations to narrow down the options that best suit the user's needs. For example, it simulates a scenario that takes into account functions that the user will need in the future. The option narrowing unit also simulates different scenarios using the generation AI and reflects these in narrowing down the options. For example, it simulates a scenario that takes into account changes in the user's lifestyle and proposes the optimal option. This makes it possible to simulate different scenarios and narrow down the options based on them.

[0068] The option narrowing unit can incorporate opinions from the user's social network and narrow down the options based on them. For example, the option narrowing unit uses a generation AI to collect opinions from the user's social network (e.g., friends and family) and narrow down the options based on them. For example, it prioritizes listing products that the user's friends have given high ratings to. The option narrowing unit also analyzes the opinions of the user's social network and reflects them in narrowing down the options. For example, it prioritizes listing products recommended by the user's family. The option narrowing unit also uses a generation AI to incorporate opinions from the user's social network and adjust the option narrowing criteria. For example, it prioritizes listing products that the user's friends use. This makes it possible to incorporate opinions from the social network and narrow down the options based on them.

[0069] The option narrowing unit can use the emotion estimation function to preferentially present options that reinforce positive emotions felt by the user when narrowing down options. The option narrowing unit, for example, uses the emotion estimation function to detect positive emotions felt by the user in real time when narrowing down options. For example, the option narrowing unit analyzes the user's facial expressions and voice to calculate an emotion score. Furthermore, if the user is feeling positive emotions, the option narrowing unit preferentially presents options that reinforce those emotions. For example, the option narrowing unit provides options related to topics that interest the user. Furthermore, the option narrowing unit provides an option presentation method that makes it easier for the user to feel positive emotions based on the emotion estimation data. For example, the option narrowing unit presents options using visually appealing images or videos. This allows options that reinforce positive emotions to be preferentially presented.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The option suggestion system can also collect the user's health data and suggest options based on their health condition. For example, it can analyze data from the user's fitness tracker or smartwatch to suggest products and services suited to their health condition. Specifically, it can suggest products that help reduce stress and services that have a relaxing effect based on the user's heart rate and sleep data. It can also prioritize fitness-related products and services by taking into account the user's exercise habits. This allows it to provide the best options based on the user's health condition.

[0072] The option suggestion system can also collect data on the user's social network and suggest options that reflect the opinions of friends and family. For example, it can analyze the user's social media posts and comments to list products and services recommended by friends and family. Specifically, it can prioritize suggested products that the user's friends have given high ratings to or that their family members use. It can also adjust the criteria for narrowing down options based on the opinions of the user's social network. This allows it to provide options that reflect the opinions of the user's social network.

[0073] The option suggestion system can also provide more accurate options by taking into account the user's past selections and their satisfaction levels. For example, it can analyze the user's past selections and their satisfaction levels, and prioritize listing products and services that provide high satisfaction. Specifically, it can suggest products similar to products that have previously generated high satisfaction levels, and conversely, it can exclude products that have the same characteristics as products that generated dissatisfaction levels. It can also develop algorithms to improve the accuracy of options based on the user's past selections. This makes it possible to provide more accurate options by taking into account the user's past selections and their satisfaction levels.

[0074] The option suggestion system can also analyze the user's visual preferences and suggest options based on images and visual information. For example, it can analyze images and visual information viewed by the user in the past to extract preference trends. Specifically, it can list products based on specific designs or colors and present information using visually appealing images and videos. It can also automatically generate filtering criteria for filtering options based on the user's visual preferences. This allows it to provide options based on the user's visual preferences.

[0075] The option suggestion system can also incorporate information from different languages ​​and cultural spheres to propose options from a global perspective. For example, the generation AI can automatically collect information from different languages ​​and cultural spheres and reflect it in identifying options. Specifically, it can incorporate reviews and ratings in multiple languages, such as English, French, and Chinese, to list products and services that are popular in a specific region. It can also automatically generate filtering criteria to provide optimal options to users with different cultural backgrounds. This allows it to incorporate information from different languages ​​and cultural spheres and provide options from a global perspective.

[0076] The option suggestion system can also use emotion estimation to detect anxiety or hesitation felt by the user as they sort through options, and adjust the way options are presented accordingly. For example, it can analyze the user's facial expressions and voice to calculate an emotion score. Specifically, if the user is feeling anxious or hesitant, it can reduce the amount of information or present it in a visually easy-to-understand format. It can also provide an option presentation method that helps the user relax, based on the emotion estimation data. This allows the system to detect the user's anxiety or hesitation and adjust the way options are presented accordingly.

[0077] The option suggestion system can also use emotion estimation to detect the stress or anxiety the user feels when narrowing down the options and adjust the way options are presented accordingly. For example, it can analyze the user's facial expressions and voice to calculate an emotion score. Specifically, if the user is feeling stressed or anxious, it can reduce the amount of information or present it in a visually easy-to-understand format. It can also provide an option presentation method that helps the user relax based on the emotion estimation data. This allows the system to detect the user's stress or anxiety and adjust the way options are presented accordingly.

[0078] The option suggestion system can also use an emotion estimation function to preferentially present options that reinforce positive emotions felt by the user when sorting through options. For example, the system can analyze the user's facial expressions and voice to calculate an emotion score. Specifically, if the user is feeling positive emotions, it can preferentially present options that reinforce those emotions. Furthermore, it can provide an option presentation method that makes it easier for the user to feel positive emotions based on the emotion estimation data. This allows options that reinforce positive emotions to be preferentially presented.

[0079] The option suggestion system can also use emotion estimation to detect the stress or anxiety a user feels while gathering information and adjust the way information is presented accordingly. For example, it can analyze the user's facial expressions and voice to calculate an emotion score. Specifically, if the user feels stressed or anxious, it can reduce the amount of information or present it in a visually easy-to-understand format. It can also provide an information presentation method that helps the user relax based on the emotion estimation data. This allows the system to detect the user's stress or anxiety and adjust the way information is presented accordingly.

[0080] The option suggestion system can also use an emotion estimation function to preferentially present information that reinforces the positive emotions felt by the user while gathering information. For example, the system can analyze the user's facial expressions and voice to calculate an emotion score. Specifically, if the user is feeling positive emotions, the system can preferentially present information that reinforces those emotions. Furthermore, based on the emotion estimation data, the system can provide a method of presenting information that makes it easier for the user to feel positive emotions. This allows the system to preferentially present information that reinforces positive emotions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The information gathering unit automatically collects relevant information based on the user's preferences and needs. For example, if a user wants to buy a new smartphone, the generation AI collects reviews, specifications, and pricing information for the latest smartphones. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI collects information based on the prompt. Step 2: The option identification unit uses the information collected by the information collection unit to identify options that match the user's preferences and needs. For example, the generation AI lists models that match the specifications and price range of the smartphone the user is looking for. The generation AI identifies options based on prompts that include the user's instructions. Step 3: The option narrowing unit narrows down the options identified by the option identification unit to seven or fewer. For example, it proposes the seven most suitable smartphones, taking into account the features, design, price, and other criteria that the user particularly values. The generation AI narrows down the options based on prompts that include the user's instructions.

[0083] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a 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.

[0118] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] 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.

[0142] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Using generative AI, an information collection unit that automatically collects relevant information based on the user's preferences and needs; an option identification unit that identifies options that match the preferences and needs of a user based on the information collected by the information collection unit; an option narrowing down unit that narrows down the options identified by the option identifying unit to seven or less options; A system characterized by:

2. The information collecting unit Analyzing the user's past selection history and automatically generating the optimal information collection pattern for each individual user 2. The system of claim 1.

3. The information collecting unit Collect real-time behavioral data of the user and dynamically adjust the scope of information collection.

2. The system of claim 1.

4. The information collecting unit Detecting stress or anxiety felt by the user while gathering information and adjusting the way information is presented accordingly 2. The system of claim 1.

5. The information collecting unit Integrate information from different devices to provide the user with a consistent information gathering experience 2. The system of claim 1.

6. The information collecting unit Analyzing the user's voice input and collecting information based on the voice command.

2. The system of claim 1.

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