system

Abstract

The system according to the embodiment aims to provide information based on diversity and provide useful marketing data to companies. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, a storage unit, and a company provision unit. The collection unit collects input information from users. The analysis unit analyzes the information collected by the collection unit and provides diversity information. The provision unit provides information to users based on the analysis results obtained by the analysis unit. The storage unit stores the information provided by the provision unit or the reactions of users as data. The company provision unit analyzes the data stored by the storage unit and provides useful marketing data to companies.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately provide information based on diversity or collect marketing data, and there is room for improvement.

[0005] The system according to the embodiment aims to provide information based on diversity and provide useful marketing data to companies. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a storage unit, and a company provision unit. The collection unit collects input information from users. The analysis unit analyzes the information collected by the collection unit and provides diversity information. The provision unit provides information to users based on the analysis results obtained by the analysis unit. The storage unit stores the information provided by the provision unit or the reactions of users as data. The company provision unit analyzes the data stored by the storage unit and provides useful marketing data to companies. [Effects of the Invention]

[0007] The system according to the embodiment can provide information based on diversity and provide useful marketing data to companies. [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 DE&I promotion system according to an embodiment of the present invention utilizes AI to promote and encourage diversity, equity, and inclusion in society. This system collects and analyzes user input information, provides optimal diversity information, and accumulates the provided information and user responses as data to provide useful marketing data to companies. This allows the DE&I promotion system to collect, analyze, provide, and accumulate user input information to provide useful marketing data to companies. For example, the DE&I promotion system collects information to help users respond to various situations they face in business, daily life, and interpersonal communication. The DE&I promotion system then analyzes the collected information and provides diversity information such as methods for communicating with people of different cultural backgrounds and approaches to bridging generational gaps. Furthermore, the DE&I promotion system provides optimal diversity information to users based on the analysis results. The DE&I promotion system then accumulates the information provided by the provider and the user responses as data. Finally, the DE&I promotion system analyzes the accumulated big data and provides useful marketing data to companies. In this way, the DE&I promotion system can create an environment in which diversity is respected throughout society.

[0029] The DE&I promotion system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a storage unit, and a company provision unit. The collection unit collects input information from users. The input information from users includes, but is not limited to, text, audio, and images. The collection unit collects information to help users respond to various situations they face in business, daily life, and interpersonal communication. The analysis unit analyzes the information collected by the collection unit and provides diversity information. The diversity information includes, but is not limited to, methods for communicating with people of different cultural backgrounds and approaches to bridging generational gaps. The analysis unit analyzes the information using, for example, statistical analysis or a machine learning algorithm. The provision unit provides information to users based on the analysis results obtained by the analysis unit. The provision unit provides the information via, for example, email, dashboard, or notification. The storage unit stores the information provided by the provision unit and user responses as data. The storage unit collects and stores responses such as clicks, feedback, and survey responses as data. The company providing unit analyzes the data accumulated by the accumulation unit and provides useful marketing data to companies. The company providing unit provides marketing data such as consumer behavior data and purchase history to companies. As a result, the DE&I promotion system according to the embodiment can collect, analyze, provide, and accumulate input information from users and provide useful marketing data to companies.

[0030] The collection unit can collect information to respond to various situations that the user faces in business, daily life, and interpersonal communication. The various situations include, but are not limited to, business situations, daily life, interpersonal relationships, etc. The collection unit, for example, collects information regarding problems and challenges that the user faces in business situations. The collection unit can also collect information regarding various situations that the user faces in daily life. Furthermore, the collection unit can also collect information regarding problems and challenges that the user faces in interpersonal relationships. This makes it possible to collect information to respond to various situations that the user faces.

[0031] The analysis unit can analyze the collected information and provide diversity information such as communication methods with people of different cultural backgrounds or approaches to bridging the gap between generations. Different cultural backgrounds include, but are not limited to, nationalities, religions, regions, etc. The analysis unit can, for example, analyze and provide communication methods with people of different cultural backgrounds. The analysis unit can also analyze and provide approaches to bridging the gap between generations. For example, the analysis unit can analyze differences in communication styles and values ​​and provide specific approaches to bridging the gap between generations. This makes it possible to provide diversity information such as communication methods with people of different cultural backgrounds and approaches to bridging the gap between generations.

[0032] The providing unit can provide diversity information to the user based on the analysis results. Optimal diversity information includes, but is not limited to, customization and personalization based on the user's needs, for example. The providing unit can provide optimal diversity information to the user based on the analysis results, for example. For example, the providing unit can provide diversity information customized based on the user's needs. The providing unit can also provide personalized diversity information according to the user's specific situation. This makes it possible to provide optimal diversity information to the user.

[0033] The storage unit can store the information provided by the providing unit or the user's reactions as data. Examples of reactions include, but are not limited to, clicks, feedback, and questionnaire responses. The storage unit, for example, stores the information provided by the providing unit as data. The storage unit can also store the user's reactions as data. For example, the storage unit collects and stores the clicks and feedback made by the user in response to the provided information as data. The storage unit can also store the content of the user's responses to questionnaires as data. This allows the information provided by the providing unit and the user's reactions to be stored as data.

[0034] The company providing unit can analyze the large amount of accumulated data and provide useful marketing data to companies. Large amounts of data include, but are not limited to, big data and real-time data. The company providing unit can analyze the accumulated big data and provide useful marketing data to companies. For example, the company providing unit can provide marketing data such as consumer behavior data and purchase history to companies. The company providing unit can also analyze real-time data and provide the latest marketing data to companies. This makes it possible to provide useful marketing data to companies.

[0035] The collection unit can analyze the user's past behavioral history and select an information collection method. The behavioral history includes, but is not limited to, for example, website browsing history and purchase history. For example, the collection unit preferentially selects an information collection method that the user has used favorably in the past. The collection unit can also select the most effective information collection method from the user's past behavioral history. Furthermore, the collection unit can analyze the user's behavioral patterns and suggest the optimal information collection method. This makes it possible to select the optimal information collection method based on the user's past behavioral history.

[0036] When collecting information, the collection unit can filter the information based on the user's current living situation and areas of interest. Examples of living situation include, but are not limited to, place of residence, occupation, and family structure. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. For example, the collection unit preferentially collects information related to areas in which the user is currently interested. The collection unit can also filter appropriate information according to the user's living situation. Furthermore, the collection unit can also collect highly relevant information based on the user's areas of interest. This makes it possible to filter information based on the user's current living situation and areas of interest.

[0037] When collecting information, the collection unit can select a collection means according to the user's input method. Examples of 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. Also, 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. This makes it possible to select the optimal collection means according to the user's input method.

[0038] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, the collection unit prioritizes collecting information related to the user's current location. The collection unit can also collect the most relevant information based on the user's geographical location information. Furthermore, the collection unit can also collect information specific to a region by taking into account the user's location information. This makes it possible to prioritize collecting highly relevant information by taking into account the user's geographical location information.

[0039] When collecting information, the collection unit can analyze the user's social media activity and collect related information. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The collection unit can collect, for example, information related to topics in which the user has shown interest on social media. The collection unit can also analyze the user's social media activity and collect highly relevant information. Furthermore, the collection unit can also refer to the activities of the user's friends on social media to collect related information. In this way, the user's social media activity can be analyzed and related information can be collected.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. Feedback includes, but is not limited to, for example, survey results, reviews, and comments. The collection unit can optimize the collection method based on, for example, feedback provided by the user in the past. The collection unit can also improve the accuracy of information collection by reflecting the user's past feedback. Furthermore, the collection unit can customize the collection method by referring to the user's feedback. This allows the collection method to be customized by reflecting the user's past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The importance includes, for example, the user's level of interest, the level of urgency, etc., but is not limited to these examples. For example, the analysis unit performs a detailed analysis on information with high importance. The analysis unit can also perform a simplified analysis on information with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the importance of the information. This makes it possible to adjust the level of detail of the analysis based on the importance of the information.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. Examples of categories include, but are not limited to, news, entertainment, and education. For example, the analysis unit can apply a culture-specific analysis algorithm to information related to culture. The analysis unit can also apply a generation-specific analysis algorithm to information related to the gap between generations. Furthermore, the analysis unit can apply a region-specific analysis algorithm to information related to a region. This makes it possible to apply different analysis algorithms depending on the category of information.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past reports, analysis data, etc. For example, the analysis unit optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and propose an optimal analysis method. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The submission time includes, for example, the submission date and time, a timestamp, etc., but is not limited to these examples. The analysis unit, for example, prioritizes analysis of the most recent information. The analysis unit can also postpone analysis of information that was submitted earlier. Furthermore, the analysis unit can also determine the priority of analysis based on the time of submission of information. This makes it possible to determine the priority of analysis based on the time of submission of information.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of information. Relevance includes, but is not limited to, common keywords, matching topics, and the like. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of information. This makes it possible to adjust the order of analysis based on the relevance of information.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. This allows the use of technical terms in the analysis to be adjusted according to the user's level of expertise.

[0047] When providing information, the providing unit can select the optimal information providing method by analyzing the user's past behavioral history. The behavioral history includes, but is not limited to, for example, website browsing history and purchase history. For example, the providing unit preferentially selects the information providing method that the user has used favorably in the past. The providing unit can also select the most effective information providing method from the user's past behavioral history. Furthermore, the providing unit can analyze the user's behavioral patterns and suggest the optimal information providing method. This makes it possible to select the optimal information providing method based on the user's past behavioral history.

[0048] When providing information, the providing unit can customize the provided content based on the user's current living situation. Examples of living situations include, but are not limited to, place of residence, occupation, and family structure. For example, the providing unit can provide information related to areas in which the user is currently interested, with priority. The providing unit can also provide appropriate information according to the user's living situation. Furthermore, the providing unit can also provide highly relevant information based on the user's areas of interest. This makes it possible to customize the provided content based on the user's current living situation.

[0049] The providing unit can improve the information providing method by reflecting user feedback when providing information. Feedback includes, but is not limited to, for example, survey results, reviews, and comments. The providing unit can optimize the information providing method based on, for example, feedback provided by the user in the past. The providing unit can also improve the accuracy of information provision by reflecting the user's past feedback. Furthermore, the providing unit can also customize the information providing method by referring to the user's feedback. This makes it possible to improve the information providing method by reflecting the user's feedback.

[0050] When providing information, the providing unit can select the optimal providing method by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, etc. The providing unit, for example, can provide information related to the user's current location with priority. The providing unit can also provide the most relevant information based on the user's geographical location information. Furthermore, the providing unit can provide information specialized for a particular region by taking into account the user's location information. This allows the optimal providing method to be selected by taking into account the user's geographical location information.

[0051] When providing information, the providing unit can analyze the user's social media activity and suggest content to be provided. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The providing unit can provide, for example, information related to topics in which the user has shown interest on social media. The providing unit can also analyze the user's social media activity and provide highly relevant information. Furthermore, the providing unit can provide related information by referring to the activity of the user's friends on social media. In this way, content to be provided can be suggested by analyzing the user's social media activity.

[0052] The providing unit can customize the information providing method by reflecting the user's past feedback when providing information. The feedback includes, but is not limited to, for example, survey results, reviews, comments, etc. The providing unit can optimize the information providing method based on, for example, feedback provided by the user in the past. The providing unit can also improve the accuracy of information provision by reflecting the user's past feedback. Furthermore, the providing unit can also customize the information providing method by referring to the user's feedback. This makes it possible to customize the information providing method by reflecting the user's past feedback.

[0053] The storage unit can optimize the storage algorithm by referring to past data when storing data. Past data includes, but is not limited to, past reports and analysis data. For example, the storage unit selects an optimal storage algorithm based on the past data. The storage unit can also optimize the storage algorithm by referring to the past data. Furthermore, the storage unit can analyze the past data and propose an optimal storage method. This makes it possible to optimize the storage algorithm by referring to the past data.

[0054] The storage unit can update the stored data by reflecting user feedback when storing data. Feedback includes, but is not limited to, for example, survey results, reviews, and comments. The storage unit updates the stored data based on, for example, feedback provided by the user. The storage unit can also improve the accuracy of the stored data by reflecting user feedback. Furthermore, the storage unit can customize the stored data by referring to user feedback. This allows the stored data to be updated by reflecting user feedback.

[0055] When storing data, the storage unit can weight the stored data based on the time of submission of the information. The time of submission includes, for example, the date and time of submission, a timestamp, etc., but is not limited to these examples. For example, the storage unit stores the most recent information preferentially. Furthermore, the storage unit can store information that was submitted earlier with a lower weight. Furthermore, the storage unit can weight the stored data based on the time of submission of the information. This allows the stored data to be weighted based on the time of submission of the information.

[0056] When storing data, the storage unit can integrate information from different data sources to enrich the stored data. Examples of different data sources include, but are not limited to, websites, social media, and sensor data. For example, the storage unit can integrate information from different data sources to enrich the stored data. The storage unit can also analyze information from different data sources and propose an optimal storage method. Furthermore, the storage unit can refer to information from different data sources to improve the accuracy of the stored data. This allows the information from different data sources to be integrated and enriched.

[0057] When providing marketing data, the company providing department can optimize the providing algorithm by referring to past data. Past data includes, for example, past reports, analysis data, etc., but is not limited to these examples. For example, the company providing department selects an optimal providing algorithm based on the past data. The company providing department can also optimize the providing algorithm by referring to the past data. Furthermore, the company providing department can analyze the past data and propose an optimal providing method. In this way, the providing algorithm can be optimized by referring to the past data.

[0058] When providing marketing data, the company providing unit can update the provided data by reflecting feedback from the company. Feedback includes, but is not limited to, for example, survey results, reviews, and comments. The company providing unit updates the provided data, for example, based on feedback provided by the company. The company providing unit can also improve the accuracy of the provided data by reflecting the company's feedback. Furthermore, the company providing unit can customize the provided data by referring to the company's feedback. This allows the provided data to be updated by reflecting the company's feedback.

[0059] When providing marketing data, the company providing unit can weight the provided data based on the time of submission of the information. The time of submission includes, for example, the date and time of submission, a timestamp, etc., but is not limited to these examples. For example, the company providing unit can provide the most recent information preferentially. Furthermore, the company providing unit can provide information that was submitted earlier with a lower weight. Furthermore, the company providing unit can weight the provided data based on the time of submission of the information. This makes it possible to weight the provided data based on the time of submission of the information.

[0060] When providing marketing data, the company providing unit can integrate information from different data sources to enrich the provided data. Examples of different data sources include, but are not limited to, websites, social media, and sensor data. For example, the company providing unit can integrate information from different data sources to enrich the provided data. The company providing unit can also analyze information from different data sources and propose an optimal method of providing the data. Furthermore, the company providing unit can refer to information from different data sources to improve the accuracy of the provided data. This allows the provided data to be enriched by integrating information from different data sources.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The collection unit can analyze the user's past behavioral history and select an information collection method. The behavioral history includes, but is not limited to, for example, website browsing history and purchase history. For example, the collection unit preferentially selects an information collection method that the user has used favorably in the past. The collection unit can also select the most effective information collection method from the user's past behavioral history. Furthermore, the collection unit can analyze the user's behavioral patterns and suggest the optimal information collection method. This makes it possible to select the optimal information collection method based on the user's past behavioral history.

[0063] When collecting information, the collection unit can filter the information based on the user's current living situation and areas of interest. Examples of living situation include, but are not limited to, place of residence, occupation, and family structure. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. For example, the collection unit preferentially collects information related to areas in which the user is currently interested. The collection unit can also filter appropriate information according to the user's living situation. Furthermore, the collection unit can also collect highly relevant information based on the user's areas of interest. This makes it possible to filter information based on the user's current living situation and areas of interest.

[0064] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, the collection unit prioritizes collecting information related to the user's current location. The collection unit can also collect the most relevant information based on the user's geographical location information. Furthermore, the collection unit can also collect information specific to a region by taking into account the user's location information. This makes it possible to prioritize collecting highly relevant information by taking into account the user's geographical location information.

[0065] When collecting information, the collection unit can analyze the user's social media activity and collect related information. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The collection unit can collect, for example, information related to topics in which the user has shown interest on social media. The collection unit can also analyze the user's social media activity and collect highly relevant information. Furthermore, the collection unit can also refer to the activities of the user's friends on social media to collect related information. In this way, the user's social media activity can be analyzed and related information can be collected.

[0066] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. Feedback includes, but is not limited to, for example, survey results, reviews, and comments. The collection unit can optimize the collection method based on, for example, feedback provided by the user in the past. The collection unit can also improve the accuracy of information collection by reflecting the user's past feedback. Furthermore, the collection unit can customize the collection method by referring to the user's feedback. This allows the collection method to be customized by reflecting the user's past feedback.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection unit collects user input information. User input information includes text, audio, and images. The collection unit collects information to respond to the various situations users face in business, daily life, and interpersonal communication. Step 2: The analysis unit analyzes the information collected by the collection unit and provides diversity information. Diversity information includes methods for communicating with people of different cultural backgrounds and approaches to bridging the gap between generations. The analysis unit analyzes the information using statistical analysis and machine learning algorithms. Step 3: The provider provides information to the user based on the analysis results obtained by the analyzer. The provider provides the information by email, dashboard, notification, or other means. Step 4: The storage unit stores the information provided by the provider and the user's reactions as data. The storage unit collects and stores reactions such as clicks, feedback, and survey responses as data. Step 5: The business data provider analyzes the data accumulated by the data storage unit and provides useful marketing data to businesses. The business data provider provides businesses with marketing data such as consumer behavior data and purchase history.

[0069] (Example 2) A DE&I promotion system according to an embodiment of the present invention utilizes AI to promote and encourage diversity, equity, and inclusion in society. This system collects and analyzes user input information, provides optimal diversity information, and accumulates the provided information and user responses as data to provide useful marketing data to companies. This allows the DE&I promotion system to collect, analyze, provide, and accumulate user input information to provide useful marketing data to companies. For example, the DE&I promotion system collects information to help users respond to various situations they face in business, daily life, and interpersonal communication. The DE&I promotion system then analyzes the collected information and provides diversity information such as methods for communicating with people of different cultural backgrounds and approaches to bridging generational gaps. Furthermore, the DE&I promotion system provides optimal diversity information to users based on the analysis results. The DE&I promotion system then accumulates the information provided by the provider and the user responses as data. Finally, the DE&I promotion system analyzes the accumulated big data and provides useful marketing data to companies. In this way, the DE&I promotion system can create an environment in which diversity is respected throughout society.

[0070] The DE&I promotion system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a storage unit, and a company provision unit. The collection unit collects input information from users. The input information from users includes, but is not limited to, text, audio, and images. The collection unit collects information to help users respond to various situations they face in business, daily life, and interpersonal communication. The analysis unit analyzes the information collected by the collection unit and provides diversity information. The diversity information includes, but is not limited to, methods for communicating with people of different cultural backgrounds and approaches to bridging generational gaps. The analysis unit analyzes the information using, for example, statistical analysis or a machine learning algorithm. The provision unit provides information to users based on the analysis results obtained by the analysis unit. The provision unit provides the information via, for example, email, dashboard, or notification. The storage unit stores the information provided by the provision unit and user responses as data. The storage unit collects and stores responses such as clicks, feedback, and survey responses as data. The company providing unit analyzes the data accumulated by the accumulation unit and provides useful marketing data to companies. The company providing unit provides marketing data such as consumer behavior data and purchase history to companies. As a result, the DE&I promotion system according to the embodiment can collect, analyze, provide, and accumulate input information from users and provide useful marketing data to companies.

[0071] The collection unit can collect information to respond to various situations that the user faces in business, daily life, and interpersonal communication. The various situations include, but are not limited to, business situations, daily life, interpersonal relationships, etc. The collection unit, for example, collects information regarding problems and challenges that the user faces in business situations. The collection unit can also collect information regarding various situations that the user faces in daily life. Furthermore, the collection unit can also collect information regarding problems and challenges that the user faces in interpersonal relationships. This makes it possible to collect information to respond to various situations that the user faces.

[0072] The analysis unit can analyze the collected information and provide diversity information such as communication methods with people of different cultural backgrounds or approaches to bridging the gap between generations. Different cultural backgrounds include, but are not limited to, nationalities, religions, regions, etc. The analysis unit can, for example, analyze and provide communication methods with people of different cultural backgrounds. The analysis unit can also analyze and provide approaches to bridging the gap between generations. For example, the analysis unit can analyze differences in communication styles and values ​​and provide specific approaches to bridging the gap between generations. This makes it possible to provide diversity information such as communication methods with people of different cultural backgrounds and approaches to bridging the gap between generations.

[0073] The providing unit can provide diversity information to the user based on the analysis results. Optimal diversity information includes, but is not limited to, customization and personalization based on the user's needs, for example. The providing unit can provide optimal diversity information to the user based on the analysis results, for example. For example, the providing unit can provide diversity information customized based on the user's needs. The providing unit can also provide personalized diversity information according to the user's specific situation. This makes it possible to provide optimal diversity information to the user.

[0074] The storage unit can store the information provided by the providing unit or the user's reactions as data. Examples of reactions include, but are not limited to, clicks, feedback, and questionnaire responses. The storage unit, for example, stores the information provided by the providing unit as data. The storage unit can also store the user's reactions as data. For example, the storage unit collects and stores the clicks and feedback made by the user in response to the provided information as data. The storage unit can also store the content of the user's responses to questionnaires as data. This allows the information provided by the providing unit and the user's reactions to be stored as data.

[0075] The company providing unit can analyze the large amount of accumulated data and provide useful marketing data to companies. Large amounts of data include, but are not limited to, big data and real-time data. The company providing unit can analyze the accumulated big data and provide useful marketing data to companies. For example, the company providing unit can provide marketing data such as consumer behavior data and purchase history to companies. The company providing unit can also analyze real-time data and provide the latest marketing data to companies. This makes it possible to provide useful marketing data to companies.

[0076] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user's emotions. Examples of emotion estimation include, but are not limited to, facial expression analysis, voice analysis, and text analysis. For example, if the user is feeling stressed, the collection unit delays information collection until the user is relaxed. Also, if the user is relaxed, the collection unit can immediately start information collection. Furthermore, if the user is in a hurry, the collection unit can quickly collect information. This makes it possible to adjust the timing of information collection based on the user's emotions.

[0077] The collection unit can analyze the user's past behavioral history and select an information collection method. The behavioral history includes, but is not limited to, for example, website browsing history and purchase history. For example, the collection unit preferentially selects an information collection method that the user has used favorably in the past. The collection unit can also select the most effective information collection method from the user's past behavioral history. Furthermore, the collection unit can analyze the user's behavioral patterns and suggest the optimal information collection method. This makes it possible to select the optimal information collection method based on the user's past behavioral history.

[0078] When collecting information, the collection unit can filter the information based on the user's current living situation and areas of interest. Examples of living situation include, but are not limited to, place of residence, occupation, and family structure. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. For example, the collection unit preferentially collects information related to areas in which the user is currently interested. The collection unit can also filter appropriate information according to the user's living situation. Furthermore, the collection unit can also collect highly relevant information based on the user's areas of interest. This makes it possible to filter information based on the user's current living situation and areas of interest.

[0079] When collecting information, the collection unit can select a collection means according to the user's input method. Examples of 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. Also, 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. This makes it possible to select the optimal collection means according to the user's input method.

[0080] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. Examples of emotion estimation include, but are not limited to, facial expression analysis, voice analysis, and text analysis. For example, when the user is feeling stressed, the collection unit prioritizes collecting information that helps the user relax. Furthermore, when the user is relaxed, the collection unit can also prioritize collecting information that is likely to interest the user. Furthermore, when the user is in a hurry, the collection unit can also prioritize collecting information that is quickly useful. In this way, the priority of information to be collected can be determined based on the user's emotions.

[0081] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, the collection unit prioritizes collecting information related to the user's current location. The collection unit can also collect the most relevant information based on the user's geographical location information. Furthermore, the collection unit can also collect information specific to a region by taking into account the user's location information. This makes it possible to prioritize collecting highly relevant information by taking into account the user's geographical location information.

[0082] When collecting information, the collection unit can analyze the user's social media activity and collect related information. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The collection unit can collect, for example, information related to topics in which the user has shown interest on social media. The collection unit can also analyze the user's social media activity and collect highly relevant information. Furthermore, the collection unit can also refer to the activities of the user's friends on social media to collect related information. In this way, the user's social media activity can be analyzed and related information can be collected.

[0083] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. Feedback includes, but is not limited to, for example, survey results, reviews, and comments. The collection unit can optimize the collection method based on, for example, feedback provided by the user in the past. The collection unit can also improve the accuracy of information collection by reflecting the user's past feedback. Furthermore, the collection unit can customize the collection method by referring to the user's feedback. This allows the collection method to be customized by reflecting the user's past feedback.

[0084] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. Examples of emotion estimation include, but are not limited to, facial expression analysis, voice analysis, and text analysis. For example, if the user is nervous, the analysis unit can provide a simple and highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. This makes it possible to adjust the way the analysis is presented based on the user's emotions.

[0085] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The importance includes, for example, the user's level of interest, the level of urgency, etc., but is not limited to these examples. For example, the analysis unit performs a detailed analysis on information with high importance. The analysis unit can also perform a simplified analysis on information with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the importance of the information. This makes it possible to adjust the level of detail of the analysis based on the importance of the information.

[0086] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. Examples of categories include, but are not limited to, news, entertainment, and education. For example, the analysis unit can apply a culture-specific analysis algorithm to information related to culture. The analysis unit can also apply a generation-specific analysis algorithm to information related to the gap between generations. Furthermore, the analysis unit can apply a region-specific analysis algorithm to information related to a region. This makes it possible to apply different analysis algorithms depending on the category of information.

[0087] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past reports, analysis data, etc. For example, the analysis unit optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and propose an optimal analysis method. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results.

[0088] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can also provide a visually stimulating analysis result. This allows the length of the analysis to be adjusted based on the user's emotions.

[0089] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The submission time includes, for example, the submission date and time, a timestamp, etc., but is not limited to these examples. The analysis unit, for example, prioritizes analysis of the most recent information. The analysis unit can also postpone analysis of information that was submitted earlier. Furthermore, the analysis unit can also determine the priority of analysis based on the time of submission of information. This makes it possible to determine the priority of analysis based on the time of submission of information.

[0090] During analysis, the analysis unit can adjust the order of analysis based on the relevance of information. Relevance includes, but is not limited to, common keywords, matching topics, and the like. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of information. This makes it possible to adjust the order of analysis based on the relevance of information.

[0091] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. This allows the use of technical terms in the analysis to be adjusted according to the user's level of expertise.

[0092] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. Examples of emotion estimation include, but are not limited to, facial expression analysis, voice analysis, and text analysis. For example, if the user is nervous, the providing unit can provide information in a simple and highly visible manner. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is in a hurry, the providing unit can provide information that focuses on the main points. This makes it possible to adjust the method of providing information based on the user's emotions.

[0093] When providing information, the providing unit can select the optimal information providing method by analyzing the user's past behavioral history. The behavioral history includes, but is not limited to, for example, website browsing history and purchase history. For example, the providing unit preferentially selects the information providing method that the user has used favorably in the past. The providing unit can also select the most effective information providing method from the user's past behavioral history. Furthermore, the providing unit can analyze the user's behavioral patterns and suggest the optimal information providing method. This makes it possible to select the optimal information providing method based on the user's past behavioral history.

[0094] When providing information, the providing unit can customize the provided content based on the user's current living situation. Examples of living situations include, but are not limited to, place of residence, occupation, and family structure. For example, the providing unit can provide information related to areas in which the user is currently interested, with priority. The providing unit can also provide appropriate information according to the user's living situation. Furthermore, the providing unit can also provide highly relevant information based on the user's areas of interest. This makes it possible to customize the provided content based on the user's current living situation.

[0095] The providing unit can improve the information providing method by reflecting user feedback when providing information. Feedback includes, but is not limited to, for example, survey results, reviews, and comments. The providing unit can optimize the information providing method based on, for example, feedback provided by the user in the past. The providing unit can also improve the accuracy of information provision by reflecting the user's past feedback. Furthermore, the providing unit can also customize the information providing method by referring to the user's feedback. This makes it possible to improve the information providing method by reflecting the user's feedback.

[0096] The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user's emotions. Examples of emotion estimation include, but are not limited to, facial expression analysis, voice analysis, and text analysis. For example, when the user is feeling stressed, the providing unit can prioritize providing information that helps the user relax. Furthermore, when the user is relaxed, the providing unit can also prioritize providing information that is likely to be of interest to the user. Furthermore, when the user is in a hurry, the providing unit can prioritize providing information that is quickly useful. In this way, the priority of information provision can be determined based on the user's emotions.

[0097] When providing information, the providing unit can select the optimal providing method by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, etc. The providing unit, for example, can provide information related to the user's current location with priority. The providing unit can also provide the most relevant information based on the user's geographical location information. Furthermore, the providing unit can provide information specialized for a particular region by taking into account the user's location information. This allows the optimal providing method to be selected by taking into account the user's geographical location information.

[0098] When providing information, the providing unit can analyze the user's social media activity and suggest content to be provided. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The providing unit can provide, for example, information related to topics in which the user has shown interest on social media. The providing unit can also analyze the user's social media activity and provide highly relevant information. Furthermore, the providing unit can provide related information by referring to the activity of the user's friends on social media. In this way, content to be provided can be suggested by analyzing the user's social media activity.

[0099] The providing unit can customize the information providing method by reflecting the user's past feedback when providing information. The feedback includes, but is not limited to, for example, survey results, reviews, comments, etc. The providing unit can optimize the information providing method based on, for example, feedback provided by the user in the past. The providing unit can also improve the accuracy of information provision by reflecting the user's past feedback. Furthermore, the providing unit can also customize the information providing method by referring to the user's feedback. This makes it possible to customize the information providing method by reflecting the user's past feedback.

[0100] The storage unit can estimate the user's emotions and adjust the data storage method based on the estimated user emotions. Examples of emotion estimation include, but are not limited to, facial expression analysis, voice analysis, and text analysis. For example, the storage unit can reduce the frequency of data storage when the user is stressed. The storage unit can also increase the frequency of data storage when the user is relaxed. Furthermore, the storage unit can prioritize storage of only important data when the user is in a hurry. This makes it possible to adjust the data storage method based on the user's emotions.

[0101] The storage unit can optimize the storage algorithm by referring to past data when storing data. Past data includes, but is not limited to, past reports and analysis data. For example, the storage unit selects an optimal storage algorithm based on the past data. The storage unit can also optimize the storage algorithm by referring to the past data. Furthermore, the storage unit can analyze the past data and propose an optimal storage method. This makes it possible to optimize the storage algorithm by referring to the past data.

[0102] The storage unit can update the stored data by reflecting user feedback when storing data. Feedback includes, but is not limited to, for example, survey results, reviews, and comments. The storage unit updates the stored data based on, for example, feedback provided by the user. The storage unit can also improve the accuracy of the stored data by reflecting user feedback. Furthermore, the storage unit can customize the stored data by referring to user feedback. This allows the stored data to be updated by reflecting user feedback.

[0103] The storage unit can estimate the user's emotions and adjust the frequency of data storage based on the estimated user emotions. Examples of emotion estimation include, but are not limited to, facial expression analysis, voice analysis, and text analysis. For example, the storage unit can reduce the frequency of data storage when the user is feeling stressed. The storage unit can also increase the frequency of data storage when the user is relaxed. Furthermore, the storage unit can prioritize storing only important data when the user is in a hurry. This makes it possible to adjust the frequency of data storage based on the user's emotions.

[0104] When storing data, the storage unit can weight the stored data based on the time of submission of the information. The time of submission includes, for example, the date and time of submission, a timestamp, etc., but is not limited to these examples. For example, the storage unit stores the most recent information preferentially. Furthermore, the storage unit can store information that was submitted earlier with a lower weight. Furthermore, the storage unit can weight the stored data based on the time of submission of the information. This allows the stored data to be weighted based on the time of submission of the information.

[0105] When storing data, the storage unit can integrate information from different data sources to enrich the stored data. Examples of different data sources include, but are not limited to, websites, social media, and sensor data. For example, the storage unit can integrate information from different data sources to enrich the stored data. The storage unit can also analyze information from different data sources and propose an optimal storage method. Furthermore, the storage unit can refer to information from different data sources to improve the accuracy of the stored data. This allows the information from different data sources to be integrated and enriched.

[0106] The company providing unit can estimate the user's emotions and adjust the method of providing marketing data based on the estimated user emotions. Examples of emotion estimation include, but are not limited to, facial expression analysis, voice analysis, and text analysis. For example, if the user is feeling stressed, the company providing unit can provide marketing data in a simple, highly visible manner. Furthermore, if the user is relaxed, the company providing unit can provide detailed marketing data. Furthermore, if the user is in a hurry, the company providing unit can provide marketing data that focuses on the main points. This makes it possible to adjust the method of providing marketing data based on the user's emotions.

[0107] When providing marketing data, the company providing department can optimize the providing algorithm by referring to past data. Past data includes, for example, past reports, analysis data, etc., but is not limited to these examples. For example, the company providing department selects an optimal providing algorithm based on the past data. The company providing department can also optimize the providing algorithm by referring to the past data. Furthermore, the company providing department can analyze the past data and propose an optimal providing method. In this way, the providing algorithm can be optimized by referring to the past data.

[0108] When providing marketing data, the company providing unit can update the provided data by reflecting feedback from the company. Feedback includes, but is not limited to, for example, survey results, reviews, and comments. The company providing unit updates the provided data, for example, based on feedback provided by the company. The company providing unit can also improve the accuracy of the provided data by reflecting the company's feedback. Furthermore, the company providing unit can customize the provided data by referring to the company's feedback. This allows the provided data to be updated by reflecting the company's feedback.

[0109] The company providing unit can estimate the user's emotions and adjust the frequency of providing marketing data based on the estimated user emotions. Examples of emotion estimation include, but are not limited to, facial expression analysis, voice analysis, and text analysis. For example, the company providing unit can reduce the frequency of providing marketing data when the user is feeling stressed. The company providing unit can also increase the frequency of providing marketing data when the user is relaxed. Furthermore, the company providing unit can prioritize providing only important data when the user is in a hurry. This makes it possible to adjust the frequency of providing marketing data based on the user's emotions.

[0110] When providing marketing data, the company providing unit can weight the provided data based on the time of submission of the information. The time of submission includes, for example, the date and time of submission, a timestamp, etc., but is not limited to these examples. For example, the company providing unit can provide the most recent information preferentially. Furthermore, the company providing unit can provide information that was submitted earlier with a lower weight. Furthermore, the company providing unit can weight the provided data based on the time of submission of the information. This makes it possible to weight the provided data based on the time of submission of the information.

[0111] When providing marketing data, the company providing unit can integrate information from different data sources to enrich the provided data. Examples of different data sources include, but are not limited to, websites, social media, and sensor data. For example, the company providing unit can integrate information from different data sources to enrich the provided data. The company providing unit can also analyze information from different data sources and propose an optimal method of providing the data. Furthermore, the company providing unit can refer to information from different data sources to improve the accuracy of the provided data. This allows the provided data to be enriched by integrating information from different data sources. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, storage unit, and company provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects input information from the user using the camera 42 and microphone 38B of the smart device 14. For example, the analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and provides diversity information. For example, the provision unit provides information based on the analysis results by the control unit 46A of the smart device 14. For example, the storage unit stores the provided information and the user's reactions in the database 24 of the data processing device 12. For example, the company provision unit analyzes the data stored by the specific processing unit 290 of the data processing device 12 and provides useful marketing data to companies. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, storage unit, and company provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects input information from the user using the camera 42 and microphone 238 of the smart glasses 214. For example, the analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and provides diversity information. For example, the provision unit provides information based on the analysis results by the control unit 46A of the smart glasses 214. For example, the storage unit stores the provided information and the user's reactions in the database 24 of the data processing device 12. For example, the company provision unit analyzes the data stored by the specific processing unit 290 of the data processing device 12 and provides useful marketing data to companies. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, provision unit, storage unit, and company provision 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 collects input information from the user using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit analyzes the information collected by, for example, the specific processing unit 290 of the data processing device 12 and provides diversity information. The provision unit provides information based on the analysis results by, for example, the control unit 46A of the headset type terminal 314. The storage unit stores, for example, the provided information and the user's reactions in the database 24 of the data processing device 12. The company provision unit analyzes, for example, the data stored by the specific processing unit 290 of the data processing device 12 and provides useful marketing data to companies. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, provision unit, storage unit, and company provision 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 collects input information from the user using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the information collected, for example, by the specific processing unit 290 of the data processing device 12, and provides diversity information. The provision unit provides information based on the analysis results, for example, by the control unit 46A of the robot 414. The storage unit stores the provided information and the user's reactions in the database 24 of the data processing device 12, for example. The company provision unit analyzes the data stored, for example, by the specific processing unit 290 of the data processing device 12, and provides useful marketing data to companies.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user's emotions. Examples of emotion estimation include, but are not limited to, facial expression analysis, voice analysis, and text analysis. For example, if the user is feeling stressed, the collection unit delays information collection until the user is relaxed. Also, if the user is relaxed, the collection unit can immediately start information collection. Furthermore, if the user is in a hurry, the collection unit can quickly collect information. This makes it possible to adjust the timing of information collection based on the user's emotions.

[0114] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. Examples of emotion estimation include, but are not limited to, facial expression analysis, voice analysis, and text analysis. For example, if the user is nervous, the analysis unit can provide a simple and highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. This makes it possible to adjust the way the analysis is presented based on the user's emotions.

[0115] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. Examples of emotion estimation include, but are not limited to, facial expression analysis, voice analysis, and text analysis. For example, if the user is nervous, the providing unit can provide information in a simple and highly visible manner. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is in a hurry, the providing unit can provide information that focuses on the main points. This makes it possible to adjust the method of providing information based on the user's emotions.

[0116] The storage unit can estimate the user's emotions and adjust the data storage method based on the estimated user emotions. Examples of emotion estimation include, but are not limited to, facial expression analysis, voice analysis, and text analysis. For example, the storage unit can reduce the frequency of data storage when the user is stressed. The storage unit can also increase the frequency of data storage when the user is relaxed. Furthermore, the storage unit can prioritize storage of only important data when the user is in a hurry. This makes it possible to adjust the data storage method based on the user's emotions.

[0117] The company providing unit can estimate the user's emotions and adjust the method of providing marketing data based on the estimated user emotions. Examples of emotion estimation include, but are not limited to, facial expression analysis, voice analysis, and text analysis. For example, if the user is feeling stressed, the company providing unit can provide marketing data in a simple, highly visible manner. Furthermore, if the user is relaxed, the company providing unit can provide detailed marketing data. Furthermore, if the user is in a hurry, the company providing unit can provide marketing data that focuses on the main points. This makes it possible to adjust the method of providing marketing data based on the user's emotions.

[0118] The collection unit can analyze the user's past behavioral history and select an information collection method. The behavioral history includes, but is not limited to, for example, website browsing history and purchase history. For example, the collection unit preferentially selects an information collection method that the user has used favorably in the past. The collection unit can also select the most effective information collection method from the user's past behavioral history. Furthermore, the collection unit can analyze the user's behavioral patterns and suggest the optimal information collection method. This makes it possible to select the optimal information collection method based on the user's past behavioral history.

[0119] When collecting information, the collection unit can filter the information based on the user's current living situation and areas of interest. Examples of living situation include, but are not limited to, place of residence, occupation, and family structure. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. For example, the collection unit preferentially collects information related to areas in which the user is currently interested. The collection unit can also filter appropriate information according to the user's living situation. Furthermore, the collection unit can also collect highly relevant information based on the user's areas of interest. This makes it possible to filter information based on the user's current living situation and areas of interest.

[0120] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, the collection unit prioritizes collecting information related to the user's current location. The collection unit can also collect the most relevant information based on the user's geographical location information. Furthermore, the collection unit can also collect information specific to a region by taking into account the user's location information. This makes it possible to prioritize collecting highly relevant information by taking into account the user's geographical location information.

[0121] When collecting information, the collection unit can analyze the user's social media activity and collect related information. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The collection unit can collect, for example, information related to topics in which the user has shown interest on social media. The collection unit can also analyze the user's social media activity and collect highly relevant information. Furthermore, the collection unit can also refer to the activities of the user's friends on social media to collect related information. In this way, the user's social media activity can be analyzed and related information can be collected.

[0122] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. Feedback includes, but is not limited to, for example, survey results, reviews, and comments. The collection unit can optimize the collection method based on, for example, feedback provided by the user in the past. The collection unit can also improve the accuracy of information collection by reflecting the user's past feedback. Furthermore, the collection unit can customize the collection method by referring to the user's feedback. This allows the collection method to be customized by reflecting the user's past feedback.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The collection unit collects user input information. User input information includes text, audio, and images. The collection unit collects information to respond to the various situations users face in business, daily life, and interpersonal communication. Step 2: The analysis unit analyzes the information collected by the collection unit and provides diversity information. Diversity information includes methods for communicating with people of different cultural backgrounds and approaches to bridging the gap between generations. The analysis unit analyzes the information using statistical analysis and machine learning algorithms. Step 3: The provider provides information to the user based on the analysis results obtained by the analyzer. The provider provides the information by email, dashboard, notification, or other means. Step 4: The storage unit stores the information provided by the provider and the user's reactions as data. The storage unit collects and stores reactions such as clicks, feedback, and survey responses as data. Step 5: The business data provider analyzes the data accumulated by the data storage unit and provides useful marketing data to businesses. The business data provider provides businesses with marketing data such as consumer behavior data and purchase history.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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 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.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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 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.

[0176] 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.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0195] 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.

[0196] [Explanation of symbols]

[0197] 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 unit that collects input information from users; an analysis unit that analyzes the information collected by the collection unit and provides diversity information; a providing unit that provides information to a user based on the analysis result obtained by the analyzing unit; a storage unit that stores the information provided by the providing unit or the user's reaction as data; a company providing unit that analyzes the data accumulated by the accumulation unit and provides useful marketing data to companies. A system characterized by:

2. The collecting unit Collect information to help users respond to various situations they face in business, daily life, and interpersonal communication 2. The system of claim 1.

3. The analysis unit Analyze the collected information and provide diversity information such as methods for communicating with people of different cultural backgrounds or approaches to bridging generational gaps.

2. The system of claim 1.

4. The providing unit Based on the analysis results, diversity information is provided to users.

2. The system of claim 1.

5. The storage unit is The information provided by the providing unit or the user's reaction is stored as data.

2. The system of claim 1.

6. The corporate providing department: Analyzing large amounts of accumulated data and providing useful marketing data to companies 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 users' past behavioral history and select information collection methods 2. The system of claim 1.

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