System

Abstract

An object of a system according to an embodiment is to create individuality of an inner surface of an agent.SOLUTION: A system includes a collection unit, an analysis unit, and a generation unit. The collection unit collects information on a seller or a purchaser. The analysis unit analyzes characteristics and culture of the region based on the information collected by the collection unit. The generation unit generates an inner surface of the agent on the basis of the analysis result obtained by the analysis unit.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] In conventional technology, the technology for creating an agent's inner personality is still in its infancy and there is room for improvement.

[0005] The system according to the embodiment aims to create an inner personality for the agent. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects information on sellers or purchasers. The analysis unit analyzes regional characteristics and culture based on the information collected by the collection unit. The generation unit generates the inner world of an agent based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can create an inner personality for the agent. [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) A system according to an embodiment of the present invention creates an internal personality for an agent. This system collects information about sellers and buyers, analyzes the characteristics and culture of the region, and generates the internal personality of the agent. For example, the system collects information such as the location of the seller and the residence, age, gender, and occupation of the buyer. Next, the system analyzes the characteristics and culture of the region based on the collected information. Furthermore, the system generates the internal personality of the agent using a large-scale language model (LLM). This allows the agent to have an internal personality adapted to each region and culture. This allows the system to adapt the internal personality of the agent to the region and culture. For example, an agent in a rural area can have knowledge about nature and agriculture, while an agent in an urban area can have knowledge about the latest technology and trends. This allows the agent to discuss nature and agriculture with local residents, and the agent in an urban area can discuss the latest technology and trends. This allows the agent to function not just as a tool, but as a member of society.

[0029] An agent internal world generation system according to an embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects information about sellers or purchasers. The information about sellers or purchasers includes, but is not limited to, names, addresses, contact information, ages, genders, and occupations. The collection unit can collect, for example, the location of sellers and the residences of purchasers. The collection unit can also collect information about purchasers, such as their ages, genders, and occupations. For example, the collection unit can obtain the location of sellers from a database and the residences of purchasers from GPS data. The analysis unit analyzes regional characteristics and culture based on the information collected by the collection unit. Regional characteristics and culture include, but are not limited to, language, customs, and history. For example, the analysis unit can analyze regional language and customs based on the collected information. The analysis unit can also analyze regional history and culture. For example, the analysis unit can analyze the collected information using natural language processing technology to extract regional characteristics. The generation unit generates an internal world of an agent based on the analysis results obtained by the analysis unit. The generation unit generates the agent's personality, values, interests, etc., using, for example, a large-scale language model (LLM). The generation unit can, for example, give a rural agent knowledge about nature or agriculture. The generation unit can also give an urban agent knowledge about the latest technology and trends. For example, the generation unit can use the LLM to input prompts that give a rural agent knowledge about nature or agriculture, and set the generated text as the agent's internal world. In this way, the agent internal world generation system according to the embodiment can adapt the agent's internal world to the region and culture.

[0030] The collection unit can collect information on the location of the seller or the residence, age, gender, and occupation of the purchaser. Examples of locations include, but are not limited to, countries, states, cities, towns, and villages. For example, the collection unit can acquire the location of the seller from a database. The collection unit can also acquire the residence of the purchaser from GPS data. Examples of ages include, but are not limited to, teens, twenties, and thirties. For example, the collection unit can acquire the age of the purchaser from survey data. Examples of genders include, but are not limited to, male, female, and other. For example, the collection unit can acquire the gender of the purchaser from registration data. Examples of occupations include, but are not limited to, doctor, engineer, teacher, and the like. For example, the collection unit can acquire the occupation of the purchaser from an occupation database. This allows the collection unit to collect detailed information for generating the inner world of the agent.

[0031] The analysis unit can analyze regional characteristics and culture based on the collected information. Regional characteristics and culture include, but are not limited to, language, customs, and history. For example, the analysis unit can analyze regional language based on the collected information. The analysis unit can also analyze regional customs. For example, the analysis unit can analyze the collected information using natural language processing technology to extract regional characteristics. The analysis unit can also analyze regional history. For example, the analysis unit can analyze regional history based on the collected information and reflect it in the agent's inner world. This enables the analysis unit to take regional characteristics and culture into account.

[0032] The generation unit can generate the agent's personality, values, and interests based on the analysis results. Examples of personalities include, but are not limited to, extroversion and introversion. The generation unit can generate the agent's personality using, for example, an LLM. Examples of values ​​include, but are not limited to, ethical values ​​and religious beliefs. The generation unit can generate the agent's values ​​using, for example, an LLM. Examples of interests include, but are not limited to, hobbies and interests. The generation unit can generate the agent's interests using, for example, an LLM. For example, the generation unit can use an LLM to input a prompt that will inspire an agent living in a rural area to develop an interest in nature or agriculture, and set the generated text as the agent's inner world. This allows the generation unit to generate the agent's inner world in detail.

[0033] The generation unit can provide the rural agent with knowledge about nature or agriculture. Knowledge about nature or agriculture includes, but is not limited to, examples such as how to grow crops and how to protect the natural environment. The generation unit can provide the rural agent with knowledge about nature or agriculture using, for example, an LLM. For example, the generation unit can use the LLM to input a prompt that will provide the rural agent with knowledge about how to grow crops and set the generated text as the agent's internal state. The generation unit can also use the LLM to provide the rural agent with knowledge about how to protect the natural environment. For example, the generation unit can use the LLM to input a prompt that will provide the rural agent with knowledge about how to protect the natural environment and set the generated text as the agent's internal state. This allows the rural agent to discuss nature and agriculture with local residents.

[0034] The generation unit can provide the urban agent with knowledge of the latest technologies or trends. Examples of knowledge of the latest technologies or trends include, but are not limited to, AI technologies and fashion trends. The generation unit can provide the urban agent with knowledge of the latest technologies and trends using, for example, an LLM. For example, the generation unit can use the LLM to input a prompt that will provide the urban agent with knowledge of AI technologies and set the generated text as the agent's internal state. The generation unit can also provide the urban agent with knowledge of fashion trends using the LLM. For example, the generation unit can use the LLM to input a prompt that will provide the urban agent with knowledge of fashion trends and set the generated text as the agent's internal state. This allows the urban agent to discuss the latest technologies and trends.

[0035] The collection unit can analyze the past behavioral history of the seller or the purchaser and select an appropriate information collection method. Examples of past behavioral history include, but are not limited to, purchase history and browsing history. For example, the collection unit can analyze the type and frequency of information provided by the seller in the past and collect information in a similar pattern. The collection unit can also analyze what information the purchaser has previously sought and prioritize collecting related information. Furthermore, the collection unit can select the optimal information collection method (email, telephone, chat, etc.) based on the history of past communication between the seller and the purchaser. For example, the collection unit can retrieve information provided by the seller in the past from a database and analyze the purchaser's past search history. This allows the collection unit to select the optimal information collection method based on the past behavioral history.

[0036] When collecting information, the collection unit can filter the information based on the user's current living situation or areas of interest. Examples of the current living situation include, but are not limited to, family structure, income status, etc. Examples of areas of interest include, but are not limited to, hobbies, topics of interest, etc. The collection unit can, for example, preferentially collect information related to topics in which the user is currently interested. The collection unit can also filter and provide appropriate information depending on the user's living situation (e.g., childcare, work, etc.). Furthermore, the collection unit can collect information related to areas of interest based on the user's past search history and browsing history. For example, the collection unit can analyze the user's past search history and preferentially collect information related to the user's current areas of interest. This allows the collection unit to provide information tailored to the user's living situation and areas of interest.

[0037] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, if the user uses text input, the collection unit can collect information using text analysis technology. Furthermore, if the user uses image input, the collection unit can collect information using image recognition technology. For example, the collection unit can convert the user's voice data into text data using voice recognition technology and collect information. Furthermore, the collection unit can analyze the user's text data using natural language processing technology and collect information. Furthermore, the collection unit can analyze the user's image data using image recognition technology and collect information. This enables the collection unit to collect information optimally depending on the user's input method.

[0038] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the collection unit can prioritize collecting news and event information related to the user's current location. The collection unit can also collect information about nearby stores and services based on the user's geographical location. Furthermore, the collection unit can collect information about local culture and customs based on the user's location information. For example, the collection unit can analyze the user's GPS data and prioritize collecting information related to the user's current location. This allows the collection unit to collect highly relevant information based on the user's geographical location information.

[0039] When collecting information, the collection unit can collect related information based on the user's social media activity. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. For example, the collection unit can analyze the content of posts from accounts the user follows on social media and collect related information. The collection unit can also collect information that the user may be interested in based on the content of the user's social media posts. Furthermore, the collection unit can also collect related information by taking into account the activities of the user's friends on social media. For example, the collection unit can analyze the content of the user's social media posts and collect related information. This allows the collection unit to collect related information based on the user's social media activity.

[0040] When collecting information, the collection unit can customize the collection method based on the user's past feedback. Past feedback includes, but is not limited to, user ratings and comments, for example. The collection unit can improve the information collection method, for example, based on feedback provided by the user in the past. The collection unit can also adjust the type and amount of information to be collected by referring to the user's past feedback. Furthermore, the collection unit can optimize the timing and frequency of information collection by reflecting the user's feedback. For example, the collection unit can obtain the user's past feedback from a database and customize the information collection method. This allows the collection unit to customize the information collection method based on the user's past feedback.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the collected information. Examples of interrelationships between information include, but are not limited to, combinations of highly related data. For example, the analysis unit can correlate and analyze information about sellers and buyers to provide more accurate results. The analysis unit can also analyze the interrelationships between information taking into account regional characteristics and culture. Furthermore, the analysis unit can analyze the past behavioral history of a buyer and their current situation to improve accuracy. For example, the analysis unit can obtain information about sellers and buyers from a database and analyze the information by correlating them with each other. This enables the analysis unit to perform analysis taking into account the interrelationships between the collected information.

[0042] During analysis, the analysis unit can perform the analysis taking into account attribute information of the person submitting the information. Attribute information includes, for example, age, gender, occupation, etc., but is not limited to these examples. The analysis unit can evaluate the reliability of the information taking into account, for example, the location and industry of the seller. The analysis unit can also evaluate the relevance of the information taking into account the age and gender of the purchaser. Furthermore, the analysis unit can evaluate the importance of the information taking into account the occupation and position of the submitter. For example, the analysis unit can obtain the location of the seller from a database and evaluate the reliability of the information taking into account the industry. This enables the analysis unit to perform analysis taking into account attribute information of the person submitting the information.

[0043] During analysis, the analysis unit can weight the analysis based on the frequency of information submission. Examples of the frequency of submission include, but are not limited to, the number of submissions per month and the number of submissions per year. For example, the analysis unit can weight information that is submitted frequently more heavily and reflect this in the analysis results. The analysis unit can also weight information that is submitted less frequently and reflect this in the analysis results. Furthermore, the analysis unit can dynamically adjust the weighting in consideration of fluctuations in the submission frequency. For example, the analysis unit can obtain the number of submissions per month from a database and weight information that is submitted frequently more heavily. This allows the analysis unit to weight the analysis based on the frequency of information submission.

[0044] During analysis, the analysis unit can perform analysis based on the geographical distribution of information. Geographical distribution includes, but is not limited to, data distribution by region. For example, the analysis unit can aggregate information by region and analyze it taking geographical characteristics into consideration. The analysis unit can also associate and analyze information that is geographically close to each other to reveal trends specific to each region. Furthermore, the analysis unit can evaluate the importance and relevance of information based on the geographical distribution. For example, the analysis unit can obtain data distribution by region from a database and analyze it taking geographical characteristics into consideration. This enables the analysis unit to perform analysis taking the geographical distribution of information into consideration.

[0045] The analysis unit can improve the accuracy of the analysis by referring to related literature during the analysis. Related literature includes, but is not limited to, academic papers and technical reports, for example. The analysis unit can, for example, perform the analysis by referring to academic papers and research reports related to the collected information. The analysis unit can also perform the analysis taking into account the technical background by referring to related patent documents. Furthermore, the analysis unit can perform the analysis taking into account market trends by referring to related industry reports. For example, the analysis unit can retrieve academic papers from a database and perform the analysis by referring to content related to the collected information. This allows the analysis unit to improve the accuracy of the analysis by referring to related literature.

[0046] During analysis, the analysis unit can perform analysis based on the market value of the information. Market value includes, but is not limited to, sales data, market share, and the like. For example, the analysis unit can evaluate the market value of the collected information and focus on high-value information for analysis. The analysis unit can also dynamically adjust the importance of information in consideration of fluctuations in market value. Furthermore, the analysis unit can prioritize analysis of information with high market value and reflect this in the results. For example, the analysis unit can obtain sales data from a database and focus analysis on information with high market value. This enables the analysis unit to take the market value of the information into consideration.

[0047] During generation, the generation unit can adjust the level of detail of the generation based on the importance of the agent's personality or values. The importance of personality or values ​​can be determined, for example, from survey results, behavioral analysis, etc., but is not limited to these examples. For example, if the agent's personality is friendly, the generation unit can generate a detailed inner profile. Furthermore, if the agent's values ​​are clear, the generation unit can generate an inner profile based on those values. Furthermore, if the agent's personality or values ​​are unclear, the generation unit can generate a concise inner profile. For example, the generation unit can retrieve survey results from a database and generate an inner profile based on the importance of the agent's personality or values. This allows the generation unit to generate a detailed inner profile based on the agent's personality and values.

[0048] The generation unit can apply different generation algorithms based on the category of the agent during generation. Agent categories include, but are not limited to, business agents, educational agents, etc. For example, the generation unit can apply a generation algorithm specialized for education to educational agents. The generation unit can also apply a generation algorithm specialized for business to business agents. Furthermore, the generation unit can apply a generation algorithm specialized for entertainment to entertainment agents. For example, the generation unit can obtain the category of the agent from a database and apply a generation algorithm according to the category. This allows the generation unit to apply the optimal generation algorithm according to the category of the agent.

[0049] During generation, the generation unit can improve the accuracy of generation based on the user's past generation results. Past generation results include, but are not limited to, past dialogue logs, generated profiles, etc. For example, the generation unit can generate a new inner self by referring to the inner self of an agent previously generated by the user. The generation unit can also extract preferred inner self features from the user's past generation results and reflect them in the generation. Furthermore, the generation unit can analyze the user's past generation results and optimize the generation algorithm. For example, the generation unit can retrieve past dialogue logs from a database and refer to them to improve the accuracy of generation. In this way, the generation unit can improve the accuracy of generation by referring to the user's past generation results.

[0050] At the time of generation, the generation unit can determine the generation priority based on the submission time of the agent. The submission time includes, but is not limited to, for example, the submission date and the submission time. For example, the generation unit can prioritize the generation of an agent that is submitted earlier. The generation unit can also postpone the generation of an agent that is submitted later. Furthermore, the generation unit can dynamically adjust the generation schedule according to the submission time. For example, the generation unit can obtain the submission date from a database and determine the generation priority based on the submission time. This allows the generation unit to determine the generation priority based on the submission time of the agent.

[0051] The generation unit can adjust the order of generation based on the relevance of the agents at the time of generation. The relevance of the agents includes, but is not limited to, for example, related tasks, common goals, etc. The generation unit can, for example, generate highly related agents with priority. The generation unit can also generate less related agents at a later date. Furthermore, the generation unit can dynamically adjust the order of generation according to the relevance. For example, the generation unit can retrieve related tasks from a database and adjust the order of generation based on the relevance. This allows the generation unit to adjust the order of generation based on the relevance of the agents.

[0052] During generation, the generation unit can adjust the use of technical terms in the generated content based on the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has specialized knowledge, the generation unit can generate a content that uses a lot of technical terms. Furthermore, if the user does not have specialized knowledge, the generation unit can generate a content that explains the content in simple terms. Furthermore, the generation unit can dynamically adjust the frequency of use of technical terms according to the user's level of expertise. For example, the generation unit can obtain the user's level of expertise from a survey result and adjust the use of technical terms. This allows the generation unit to adjust the use of technical terms according to the user's level of expertise.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The analysis unit can also take into account the local climate and seasonal variations when analyzing the characteristics and culture of a region based on the collected information. For example, the analysis unit can analyze local climate data based on the collected information and reflect this in the agent's inner world. The analysis unit can also analyze information on seasonal events and festivals and reflect this in the agent's inner world. Furthermore, the analysis unit can analyze information on local climate changes and reflect this in the agent's inner world. This enables the analysis unit to take into account the local climate and seasonal variations in analysis.

[0055] When collecting information about the seller or purchaser, the collection unit may also collect information about the user's health condition. For example, the collection unit may collect questionnaire data about the user's health condition and reflect it in the agent's inner world. The collection unit may also collect medical data about the user's health condition and reflect it in the agent's inner world. Furthermore, the collection unit may collect fitness data about the user's health condition and reflect it in the agent's inner world. This allows the collection unit to collect information based on the user's health condition.

[0056] The generation unit can also take into account the user's past dialogue history when generating the agent's personality, values, and interests based on the analysis results. For example, the generation unit can analyze the user's past dialogue history and reflect it in the agent's inner world. The generation unit can also generate the agent's interests based on keywords extracted from the user's past dialogue history. Furthermore, the generation unit can adjust the agent's personality and values ​​based on the user's past dialogue history. In this way, the generation unit can generate the agent's inner world based on the user's past dialogue history.

[0057] The analysis unit can also take into account the economic situation of the region when analyzing the characteristics and culture of the region based on the collected information. For example, the analysis unit can analyze regional economic data based on the collected information and reflect the data in the agent's internal state. The analysis unit can also analyze information on the regional economic situation and reflect the data in the agent's internal state. Furthermore, the analysis unit can analyze information on regional economic trends and reflect the data in the agent's internal state. This enables the analysis unit to take the regional economic situation into account in its analysis.

[0058] When collecting information about sellers or purchasers, the collection unit can also collect information about the user's hobbies and preferences. For example, the collection unit can collect survey data about the user's hobbies and reflect it in the agent's inner world. The collection unit can also collect data about the user's preferences and reflect it in the agent's inner world. Furthermore, the collection unit can collect social media data about the user's hobbies and preferences and reflect it in the agent's inner world. This allows the collection unit to collect information based on the user's hobbies and preferences.

[0059] The analysis unit can also take into account the educational level of the region when analyzing the characteristics and culture of the region based on the collected information. For example, the analysis unit can analyze regional educational data based on the collected information and reflect the data in the agent's inner world. The analysis unit can also analyze information on the educational level of the region and reflect the data in the agent's inner world. Furthermore, the analysis unit can analyze information on educational institutions in the region and reflect the data in the agent's inner world. This enables the analysis unit to take the educational level of the region into account in its analysis.

[0060] When collecting information about sellers or purchasers, the collection unit can also collect information about the user's lifestyle. For example, the collection unit can collect survey data about the user's lifestyle and reflect it in the agent's inner world. The collection unit can also collect social media data about the user's lifestyle and reflect it in the agent's inner world. Furthermore, the collection unit can collect purchasing data about the user's lifestyle and reflect it in the agent's inner world. This allows the collection unit to collect information based on the user's lifestyle.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The collection unit collects information about the seller or buyer. Collected information includes name, address, contact information, age, gender, occupation, etc. The collection unit can obtain the location of the seller or the buyer's residence from a database or GPS data. Step 2: The analysis unit analyzes the characteristics and culture of the region based on the information collected by the collection unit. The analyzed characteristics and culture include language, customs, history, etc. The analysis unit analyzes the collected information using natural language processing technology and extracts the characteristics of the region. Step 3: The generation unit generates the agent's inner world based on the analysis results obtained by the analysis unit. The generation unit uses a large-scale language model (LLM) to generate the agent's personality, values, interests, etc. For example, an agent in a rural area might be given knowledge about nature and agriculture, while an agent in an urban area might be given knowledge about the latest technology and trends.

[0063] (Example 2) A system according to an embodiment of the present invention creates an internal personality for an agent. This system collects information about sellers and buyers, analyzes the characteristics and culture of the region, and generates the internal personality of the agent. For example, the system collects information such as the location of the seller and the residence, age, gender, and occupation of the buyer. Next, the system analyzes the characteristics and culture of the region based on the collected information. Furthermore, the system generates the internal personality of the agent using a large-scale language model (LLM). This allows the agent to have an internal personality adapted to each region and culture. This allows the system to adapt the internal personality of the agent to the region and culture. For example, an agent in a rural area can have knowledge about nature and agriculture, while an agent in an urban area can have knowledge about the latest technology and trends. This allows the agent to discuss nature and agriculture with local residents, and the agent in an urban area can discuss the latest technology and trends. This allows the agent to function not just as a tool, but as a member of society.

[0064] An agent internal world generation system according to an embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects information about sellers or purchasers. The information about sellers or purchasers includes, but is not limited to, names, addresses, contact information, ages, genders, and occupations. The collection unit can collect, for example, the location of sellers and the residences of purchasers. The collection unit can also collect information about purchasers, such as their ages, genders, and occupations. For example, the collection unit can obtain the location of sellers from a database and the residences of purchasers from GPS data. The analysis unit analyzes regional characteristics and culture based on the information collected by the collection unit. Regional characteristics and culture include, but are not limited to, language, customs, and history. For example, the analysis unit can analyze regional language and customs based on the collected information. The analysis unit can also analyze regional history and culture. For example, the analysis unit can analyze the collected information using natural language processing technology to extract regional characteristics. The generation unit generates an internal world of an agent based on the analysis results obtained by the analysis unit. The generation unit generates the agent's personality, values, interests, etc., using, for example, a large-scale language model (LLM). The generation unit can, for example, give a rural agent knowledge about nature or agriculture. The generation unit can also give an urban agent knowledge about the latest technology and trends. For example, the generation unit can use the LLM to input prompts that give a rural agent knowledge about nature or agriculture, and set the generated text as the agent's internal world. In this way, the agent internal world generation system according to the embodiment can adapt the agent's internal world to the region and culture.

[0065] The collection unit can collect information on the location of the seller or the residence, age, gender, and occupation of the purchaser. Examples of locations include, but are not limited to, countries, states, cities, towns, and villages. For example, the collection unit can acquire the location of the seller from a database. The collection unit can also acquire the residence of the purchaser from GPS data. Examples of ages include, but are not limited to, teens, twenties, and thirties. For example, the collection unit can acquire the age of the purchaser from survey data. Examples of genders include, but are not limited to, male, female, and other. For example, the collection unit can acquire the gender of the purchaser from registration data. Examples of occupations include, but are not limited to, doctor, engineer, teacher, and the like. For example, the collection unit can acquire the occupation of the purchaser from an occupation database. This allows the collection unit to collect detailed information for generating the inner world of the agent.

[0066] The analysis unit can analyze regional characteristics and culture based on the collected information. Regional characteristics and culture include, but are not limited to, language, customs, and history. For example, the analysis unit can analyze regional language based on the collected information. The analysis unit can also analyze regional customs. For example, the analysis unit can analyze the collected information using natural language processing technology to extract regional characteristics. The analysis unit can also analyze regional history. For example, the analysis unit can analyze regional history based on the collected information and reflect it in the agent's inner world. This enables the analysis unit to take regional characteristics and culture into account.

[0067] The generation unit can generate the agent's personality, values, and interests based on the analysis results. Examples of personalities include, but are not limited to, extroversion and introversion. The generation unit can generate the agent's personality using, for example, an LLM. Examples of values ​​include, but are not limited to, ethical values ​​and religious beliefs. The generation unit can generate the agent's values ​​using, for example, an LLM. Examples of interests include, but are not limited to, hobbies and interests. The generation unit can generate the agent's interests using, for example, an LLM. For example, the generation unit can use an LLM to input a prompt that will inspire an agent living in a rural area to develop an interest in nature or agriculture, and set the generated text as the agent's inner world. This allows the generation unit to generate the agent's inner world in detail.

[0068] The generation unit can provide the rural agent with knowledge about nature or agriculture. Knowledge about nature or agriculture includes, but is not limited to, examples such as how to grow crops and how to protect the natural environment. The generation unit can provide the rural agent with knowledge about nature or agriculture using, for example, an LLM. For example, the generation unit can use the LLM to input a prompt that will provide the rural agent with knowledge about how to grow crops and set the generated text as the agent's internal state. The generation unit can also use the LLM to provide the rural agent with knowledge about how to protect the natural environment. For example, the generation unit can use the LLM to input a prompt that will provide the rural agent with knowledge about how to protect the natural environment and set the generated text as the agent's internal state. This allows the rural agent to discuss nature and agriculture with local residents.

[0069] The generation unit can provide the urban agent with knowledge of the latest technologies or trends. Examples of knowledge of the latest technologies or trends include, but are not limited to, AI technologies and fashion trends. The generation unit can provide the urban agent with knowledge of the latest technologies and trends using, for example, an LLM. For example, the generation unit can use the LLM to input a prompt that will provide the urban agent with knowledge of AI technologies and set the generated text as the agent's internal state. The generation unit can also provide the urban agent with knowledge of fashion trends using the LLM. For example, the generation unit can use the LLM to input a prompt that will provide the urban agent with knowledge of fashion trends and set the generated text as the agent's internal state. This allows the urban agent to discuss the latest technologies and trends.

[0070] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is relaxed, the collection unit can immediately collect information and select a timing when the user is most likely to receive information. Furthermore, if the user is feeling stressed, the collection unit can temporarily delay information collection and resume collection after the user has calmed down. Furthermore, if the user is busy, the collection unit can collect information in the background to reduce the user's burden. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the collection unit to optimize the timing of information collection according to the user's emotions.

[0071] The collection unit can analyze the past behavioral history of the seller or the purchaser and select an appropriate information collection method. Examples of past behavioral history include, but are not limited to, purchase history and browsing history. For example, the collection unit can analyze the type and frequency of information provided by the seller in the past and collect information in a similar pattern. The collection unit can also analyze what information the purchaser has previously sought and prioritize collecting related information. Furthermore, the collection unit can select the optimal information collection method (email, telephone, chat, etc.) based on the history of past communication between the seller and the purchaser. For example, the collection unit can retrieve information provided by the seller in the past from a database and analyze the purchaser's past search history. This allows the collection unit to select the optimal information collection method based on the past behavioral history.

[0072] When collecting information, the collection unit can filter the information based on the user's current living situation or areas of interest. Examples of the current living situation include, but are not limited to, family structure, income status, etc. Examples of areas of interest include, but are not limited to, hobbies, topics of interest, etc. The collection unit can, for example, preferentially collect information related to topics in which the user is currently interested. The collection unit can also filter and provide appropriate information depending on the user's living situation (e.g., childcare, work, etc.). Furthermore, the collection unit can collect information related to areas of interest based on the user's past search history and browsing history. For example, the collection unit can analyze the user's past search history and preferentially collect information related to the user's current areas of interest. This allows the collection unit to provide information tailored to the user's living situation and areas of interest.

[0073] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, if the user uses text input, the collection unit can collect information using text analysis technology. Furthermore, if the user uses image input, the collection unit can collect information using image recognition technology. For example, the collection unit can convert the user's voice data into text data using voice recognition technology and collect information. Furthermore, the collection unit can analyze the user's text data using natural language processing technology and collect information. Furthermore, the collection unit can analyze the user's image data using image recognition technology and collect information. This enables the collection unit to collect information optimally depending on the user's input method.

[0074] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is excited, the collection unit prioritizes collecting interesting information. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, when the user is stressed, the collection unit can prioritize collecting concise and to-the-point information. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the collection unit to determine the priority of information according to the user's emotions.

[0075] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the collection unit can prioritize collecting news and event information related to the user's current location. The collection unit can also collect information about nearby stores and services based on the user's geographical location. Furthermore, the collection unit can collect information about local culture and customs based on the user's location information. For example, the collection unit can analyze the user's GPS data and prioritize collecting information related to the user's current location. This allows the collection unit to collect highly relevant information based on the user's geographical location information.

[0076] When collecting information, the collection unit can collect related information based on the user's social media activity. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. For example, the collection unit can analyze the content of posts from accounts the user follows on social media and collect related information. The collection unit can also collect information that the user may be interested in based on the content of the user's social media posts. Furthermore, the collection unit can also collect related information by taking into account the activities of the user's friends on social media. For example, the collection unit can analyze the content of the user's social media posts and collect related information. This allows the collection unit to collect related information based on the user's social media activity.

[0077] When collecting information, the collection unit can customize the collection method based on the user's past feedback. Past feedback includes, but is not limited to, user ratings and comments, for example. The collection unit can improve the information collection method, for example, based on feedback provided by the user in the past. The collection unit can also adjust the type and amount of information to be collected by referring to the user's past feedback. Furthermore, the collection unit can optimize the timing and frequency of information collection by reflecting the user's feedback. For example, the collection unit can obtain the user's past feedback from a database and customize the information collection method. This allows the collection unit to customize the information collection method based on the user's past feedback.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide deep insights. If the user is in a hurry, the analysis unit can also perform a concise and to-the-point analysis. Furthermore, if the user is feeling stressed, the analysis unit can provide a visually easy-to-understand analysis result. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. This allows the analysis unit to adjust the analysis criteria according to the user's emotions.

[0079] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the collected information. Examples of interrelationships between information include, but are not limited to, combinations of highly related data. For example, the analysis unit can correlate and analyze information about sellers and buyers to provide more accurate results. The analysis unit can also analyze the interrelationships between information taking into account regional characteristics and culture. Furthermore, the analysis unit can analyze the past behavioral history of a buyer and their current situation to improve accuracy. For example, the analysis unit can obtain information about sellers and buyers from a database and analyze the information by correlating them with each other. This enables the analysis unit to perform analysis taking into account the interrelationships between the collected information.

[0080] During analysis, the analysis unit can perform the analysis taking into account attribute information of the person submitting the information. Attribute information includes, for example, age, gender, occupation, etc., but is not limited to these examples. The analysis unit can evaluate the reliability of the information taking into account, for example, the location and industry of the seller. The analysis unit can also evaluate the relevance of the information taking into account the age and gender of the purchaser. Furthermore, the analysis unit can evaluate the importance of the information taking into account the occupation and position of the submitter. For example, the analysis unit can obtain the location of the seller from a database and evaluate the reliability of the information taking into account the industry. This enables the analysis unit to perform analysis taking into account attribute information of the person submitting the information.

[0081] During analysis, the analysis unit can weight the analysis based on the frequency of information submission. Examples of the frequency of submission include, but are not limited to, the number of submissions per month and the number of submissions per year. For example, the analysis unit can weight information that is submitted frequently more heavily and reflect this in the analysis results. The analysis unit can also weight information that is submitted less frequently and reflect this in the analysis results. Furthermore, the analysis unit can dynamically adjust the weighting in consideration of fluctuations in the submission frequency. For example, the analysis unit can obtain the number of submissions per month from a database and weight information that is submitted frequently more heavily. This allows the analysis unit to weight the analysis based on the frequency of information submission.

[0082] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated user emotions. For example, when the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can prioritize displaying analysis results that focus on the main points. Furthermore, when the user is stressed, the analysis unit can prioritize displaying analysis results that are easy to understand visually. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the analysis unit to adjust the display order of the analysis results according to the user's emotions.

[0083] During analysis, the analysis unit can perform analysis based on the geographical distribution of information. Geographical distribution includes, but is not limited to, data distribution by region. For example, the analysis unit can aggregate information by region and analyze it taking geographical characteristics into consideration. The analysis unit can also associate and analyze information that is geographically close to each other to reveal trends specific to each region. Furthermore, the analysis unit can evaluate the importance and relevance of information based on the geographical distribution. For example, the analysis unit can obtain data distribution by region from a database and analyze it taking geographical characteristics into consideration. This enables the analysis unit to perform analysis taking the geographical distribution of information into consideration.

[0084] The analysis unit can improve the accuracy of the analysis by referring to related literature during the analysis. Related literature includes, but is not limited to, academic papers and technical reports, for example. The analysis unit can, for example, perform the analysis by referring to academic papers and research reports related to the collected information. The analysis unit can also perform the analysis taking into account the technical background by referring to related patent documents. Furthermore, the analysis unit can perform the analysis taking into account market trends by referring to related industry reports. For example, the analysis unit can retrieve academic papers from a database and perform the analysis by referring to content related to the collected information. This allows the analysis unit to improve the accuracy of the analysis by referring to related literature.

[0085] During analysis, the analysis unit can perform analysis based on the market value of the information. Market value includes, but is not limited to, sales data, market share, and the like. For example, the analysis unit can evaluate the market value of the collected information and focus on high-value information for analysis. The analysis unit can also dynamically adjust the importance of information in consideration of fluctuations in market value. Furthermore, the analysis unit can prioritize analysis of information with high market value and reflect this in the results. For example, the analysis unit can obtain sales data from a database and focus analysis on information with high market value. This enables the analysis unit to take the market value of the information into consideration.

[0086] The generation unit can estimate the user's emotions and adjust the way in which the generated internal feelings are expressed based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a calm and friendly internal feeling. Furthermore, if the user is excited, the generation unit can generate a lively and energetic internal feeling. Furthermore, if the user is stressed, the generation unit can generate a calm internal feeling. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the generation unit to adjust the way in which the internal feelings are expressed according to the user's emotions.

[0087] During generation, the generation unit can adjust the level of detail of the generation based on the importance of the agent's personality or values. The importance of personality or values ​​can be determined, for example, from survey results, behavioral analysis, etc., but is not limited to these examples. For example, if the agent's personality is friendly, the generation unit can generate a detailed inner profile. Furthermore, if the agent's values ​​are clear, the generation unit can generate an inner profile based on those values. Furthermore, if the agent's personality or values ​​are unclear, the generation unit can generate a concise inner profile. For example, the generation unit can retrieve survey results from a database and generate an inner profile based on the importance of the agent's personality or values. This allows the generation unit to generate a detailed inner profile based on the agent's personality and values.

[0088] The generation unit can apply different generation algorithms based on the category of the agent during generation. Agent categories include, but are not limited to, business agents, educational agents, etc. For example, the generation unit can apply a generation algorithm specialized for education to educational agents. The generation unit can also apply a generation algorithm specialized for business to business agents. Furthermore, the generation unit can apply a generation algorithm specialized for entertainment to entertainment agents. For example, the generation unit can obtain the category of the agent from a database and apply a generation algorithm according to the category. This allows the generation unit to apply the optimal generation algorithm according to the category of the agent.

[0089] During generation, the generation unit can improve the accuracy of generation based on the user's past generation results. Past generation results include, but are not limited to, past dialogue logs, generated profiles, etc. For example, the generation unit can generate a new inner self by referring to the inner self of an agent previously generated by the user. The generation unit can also extract preferred inner self features from the user's past generation results and reflect them in the generation. Furthermore, the generation unit can analyze the user's past generation results and optimize the generation algorithm. For example, the generation unit can retrieve past dialogue logs from a database and refer to them to improve the accuracy of generation. In this way, the generation unit can improve the accuracy of generation by referring to the user's past generation results.

[0090] The generation unit can estimate the user's emotions and adjust the length of the generated internal message based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a detailed and long internal message. If the user is in a hurry, the generation unit can also generate a concise and short internal message. Furthermore, if the user is stressed, the generation unit can generate an internal message that focuses on the main points. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. This allows the generation unit to adjust the length of the internal message according to the user's emotions.

[0091] At the time of generation, the generation unit can determine the generation priority based on the submission time of the agent. The submission time includes, but is not limited to, for example, the submission date and the submission time. For example, the generation unit can prioritize the generation of an agent that is submitted earlier. The generation unit can also postpone the generation of an agent that is submitted later. Furthermore, the generation unit can dynamically adjust the generation schedule according to the submission time. For example, the generation unit can obtain the submission date from a database and determine the generation priority based on the submission time. This allows the generation unit to determine the generation priority based on the submission time of the agent.

[0092] The generation unit can adjust the order of generation based on the relevance of the agents at the time of generation. The relevance of the agents includes, but is not limited to, for example, related tasks, common goals, etc. The generation unit can, for example, generate highly related agents with priority. The generation unit can also generate less related agents at a later date. Furthermore, the generation unit can dynamically adjust the order of generation according to the relevance. For example, the generation unit can retrieve related tasks from a database and adjust the order of generation based on the relevance. This allows the generation unit to adjust the order of generation based on the relevance of the agents.

[0093] During generation, the generation unit can adjust the use of technical terms in the generated content based on the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has specialized knowledge, the generation unit can generate a content that uses a lot of technical terms. Furthermore, if the user does not have specialized knowledge, the generation unit can generate a content that explains the content in simple terms. Furthermore, the generation unit can dynamically adjust the frequency of use of technical terms according to the user's level of expertise. For example, the generation unit can obtain the user's level of expertise from a survey result and adjust the use of technical terms. This allows the generation unit to adjust the use of technical terms according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and generation unit described above may be realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit may collect information about sellers and purchasers using the camera 42 and microphone 38B of the smart device 14. For example, the analysis unit may analyze regional characteristics and culture based on information collected by the specific processing unit 290 of the data processing device 12. For example, the generation unit may generate the inner world of an agent using an LLM by the specific processing unit 290 of the data processing device 12. The collection unit, analysis unit, and generation unit may also be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and generation unit described above may be realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit may collect information about sellers and purchasers using the camera 42 and microphone 238 of the smart glasses 214. For example, the analysis unit may analyze regional characteristics and culture based on information collected by the specific processing unit 290 of the data processing device 12. For example, the generation unit may generate an agent's inner world using an LLM by the specific processing unit 290 of the data processing device 12. The collection unit, analysis unit, and generation unit may also be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect information about sellers and purchasers using the camera 42 and microphone 238 of the headset type terminal 314. For example, the analysis unit can analyze regional characteristics and culture based on information collected by the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate the inner world of an agent using an LLM by the specific processing unit 290 of the data processing device 12. The collection unit, analysis unit, and generation unit may also be realized, for example, by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect information about sellers and purchasers using the camera 42 and microphone 238 of the robot 414. For example, the analysis unit can analyze the characteristics and culture of a region based on information collected by the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate the inner world of an agent using an LLM by the specific processing unit 290 of the data processing device 12. The collection unit, analysis unit, and generation unit may also be realized, for example, by the control unit 46A of the robot 414.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The analysis unit can also take into account the local climate and seasonal variations when analyzing the characteristics and culture of a region based on the collected information. For example, the analysis unit can analyze local climate data based on the collected information and reflect this in the agent's inner world. The analysis unit can also analyze information on seasonal events and festivals and reflect this in the agent's inner world. Furthermore, the analysis unit can analyze information on local climate changes and reflect this in the agent's inner world. This enables the analysis unit to take into account the local climate and seasonal variations in analysis.

[0096] When collecting information about the seller or purchaser, the collection unit may also collect information about the user's health condition. For example, the collection unit may collect questionnaire data about the user's health condition and reflect it in the agent's inner world. The collection unit may also collect medical data about the user's health condition and reflect it in the agent's inner world. Furthermore, the collection unit may collect fitness data about the user's health condition and reflect it in the agent's inner world. This allows the collection unit to collect information based on the user's health condition.

[0097] The generation unit can also take into account the user's past dialogue history when generating the agent's personality, values, and interests based on the analysis results. For example, the generation unit can analyze the user's past dialogue history and reflect it in the agent's inner world. The generation unit can also generate the agent's interests based on keywords extracted from the user's past dialogue history. Furthermore, the generation unit can adjust the agent's personality and values ​​based on the user's past dialogue history. In this way, the generation unit can generate the agent's inner world based on the user's past dialogue history.

[0098] The collection unit can estimate the user's emotions and adjust the frequency of information collection based on the estimated user emotions. For example, the collection unit can increase the frequency of information collection when the user is relaxed. Also, the collection unit can decrease the frequency of information collection when the user is stressed. Furthermore, the collection unit can adjust the frequency of information collection when the user is busy to reduce the burden on the user. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. This allows the collection unit to optimize the frequency of information collection according to the user's emotions.

[0099] The analysis unit can also take into account the economic situation of the region when analyzing the characteristics and culture of the region based on the collected information. For example, the analysis unit can analyze regional economic data based on the collected information and reflect the data in the agent's internal state. The analysis unit can also analyze information on the regional economic situation and reflect the data in the agent's internal state. Furthermore, the analysis unit can analyze information on regional economic trends and reflect the data in the agent's internal state. This enables the analysis unit to take the regional economic situation into account in its analysis.

[0100] When generating the agent's personality, values, and interests based on the analysis results, the generation unit can estimate the user's emotions and adjust the content to be generated based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a calm and friendly personality. If the user is excited, the generation unit can also generate a lively and energetic personality. Furthermore, if the user is stressed, the generation unit can generate a calm personality. In this way, the generation unit can generate the agent's inner self according to the user's emotions.

[0101] When collecting information about sellers or purchasers, the collection unit can also collect information about the user's hobbies and preferences. For example, the collection unit can collect survey data about the user's hobbies and reflect it in the agent's inner world. The collection unit can also collect data about the user's preferences and reflect it in the agent's inner world. Furthermore, the collection unit can collect social media data about the user's hobbies and preferences and reflect it in the agent's inner world. This allows the collection unit to collect information based on the user's hobbies and preferences.

[0102] The analysis unit can also take into account the educational level of the region when analyzing the characteristics and culture of the region based on the collected information. For example, the analysis unit can analyze regional educational data based on the collected information and reflect the data in the agent's inner world. The analysis unit can also analyze information on the educational level of the region and reflect the data in the agent's inner world. Furthermore, the analysis unit can analyze information on educational institutions in the region and reflect the data in the agent's inner world. This enables the analysis unit to take the educational level of the region into account in its analysis.

[0103] When generating the agent's personality, values, and interests based on the analysis results, the generation unit can estimate the user's emotions and adjust the level of detail of the generated internal state based on the estimated user's emotions. For example, the generation unit can generate a detailed internal state when the user is relaxed. The generation unit can also generate a concise internal state when the user is in a hurry. Furthermore, the generation unit can generate an internal state that is concise to the main points when the user is stressed. In this way, the generation unit can generate an internal state of the agent according to the user's emotions.

[0104] When collecting information about sellers or purchasers, the collection unit can also collect information about the user's lifestyle. For example, the collection unit can collect survey data about the user's lifestyle and reflect it in the agent's inner world. The collection unit can also collect social media data about the user's lifestyle and reflect it in the agent's inner world. Furthermore, the collection unit can collect purchasing data about the user's lifestyle and reflect it in the agent's inner world. This allows the collection unit to collect information based on the user's lifestyle.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The collection unit collects information about the seller or buyer. Collected information includes name, address, contact information, age, gender, occupation, etc. The collection unit can obtain the location of the seller or the buyer's residence from a database or GPS data. Step 2: The analysis unit analyzes the characteristics and culture of the region based on the information collected by the collection unit. The analyzed characteristics and culture include language, customs, history, etc. The analysis unit analyzes the collected information using natural language processing technology and extracts the characteristics of the region. Step 3: The generation unit generates the agent's inner world based on the analysis results obtained by the analysis unit. The generation unit uses a large-scale language model (LLM) to generate the agent's personality, values, interests, etc. For example, an agent in a rural area might be given knowledge about nature and agriculture, while an agent in an urban area might be given knowledge about the latest technology and trends.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0135] 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.

[0136] 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.

[0137] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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, in order to avoid confusion and to 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.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 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. a collection department that collects information on sellers or buyers; an analysis unit that analyzes regional characteristics and culture based on the information collected by the collection unit; a generation unit that generates an inner surface of an agent based on the analysis result obtained by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Collect information about the seller's location or the buyer's location, age, gender, and occupation 2. The system of claim 1.

3. The analysis unit Analyzing regional characteristics and culture based on collected information 2. The system of claim 1.

4. The generation unit Generate an agent's personality, values, and interests based on the analysis results 2. The system of claim 1.

5. The generation unit Equip rural agents with natural or agricultural knowledge 2. The system of claim 1.

6. The generation unit Equip urban agents with the latest technology or trends 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the past behavioral history of sellers or buyers and select the appropriate information collection method 2. The system of claim 1.

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