system

Abstract

The system according to the embodiment aims to efficiently and accurately determine whether a business partner or individual is an anti-social force. [Solution] A system according to an embodiment includes a collection unit, a determination unit, and a proposal unit. The collection unit collects at least one of the following data: the name of a business partner or individual, criminal history, and information on anti-social forces. The determination unit analyzes the data collected by the collection unit and determines whether the business partner or individual is an anti-social force. The proposal unit proposes countermeasures to the company based on the determination result obtained by the determination unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making the process of determining whether a business partner or individual is a member of an anti-social force laborious and vague.

[0005] The system according to the embodiment aims to efficiently and accurately determine whether a business partner or individual is an anti-social force. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a determination unit, and a proposal unit. The collection unit collects at least one of the following data: the name of a business partner or individual, criminal history, and information on anti-social forces. The determination unit analyzes the data collected by the collection unit and determines whether the business partner or individual is an anti-social force. The proposal unit proposes countermeasures to the company based on the determination result obtained by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently and accurately determine whether a business partner or individual is an anti-social force. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) The anti-social forces assessment system according to an embodiment of the present invention provides a system and service that helps companies determine whether their business partners or individuals (such as executives of business partners) are anti-social forces. This system collects data such as the names of business partners and individuals, their criminal histories, and information about anti-social forces organizations. The AI ​​analyzes this data to determine whether the business partners or individuals are anti-social forces. Based on the results of the analysis, the AI ​​then recommends appropriate countermeasures to the company. For example, when a business partner's name is entered, the AI ​​searches for and analyzes the criminal history and anti-social forces organization information associated with that name. If the analysis results indicate a high probability that the business partner is an anti-social force, the AI ​​recommends that the company discontinue the transaction. Even if the probability of the business partner being an anti-social force is low, the AI ​​also issues a warning. This system allows companies to efficiently sever ties with anti-social forces. Furthermore, because the AI ​​constantly collects and analyzes the latest data, companies can always make decisions based on the most up-to-date information. This anti-social forces assessment system allows companies to efficiently sever ties with anti-social forces.

[0029] An anti-social forces determination system according to an embodiment includes a collection unit, a determination unit, and a proposal unit. The collection unit collects at least one of the following data: the name of a business partner or individual, criminal history, and anti-social forces organization information. The collection unit can collect data, for example, from public databases on the Internet or news articles. The collection unit can collect data, for example, from public government databases or public corporate databases. The collection unit can also collect data from online news, newspaper articles, magazine articles, etc. The determination unit analyzes the data collected by the collection unit and determines whether a business partner or individual is an anti-social force. The determination unit analyzes the collected data using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, semantic analysis, etc. The determination unit analyzes the data using, for example, techniques such as text mining, data mining, and statistical analysis. The proposal unit proposes countermeasures to the company based on the determination result obtained by the determination unit. The proposal unit proposes countermeasures, for example, if a business partner is determined to be an anti-social force. Countermeasures include suspension of transactions, warning notices, legal action, etc. The suggestion unit issues a warning even when the possibility of the person being an anti-social force is low, for example. Warnings include email notifications, alert displays, report creation, etc. As a result, the anti-social forces determination system according to the embodiment allows companies to efficiently cut off ties with anti-social forces.

[0030] The collection unit can collect data from public databases or news articles on the Internet. The collection unit collects data from, for example, public government databases or public corporate databases. For example, public government databases may include criminal records and information on anti-social forces. Public corporate databases may include names of business partners and related information. The collection unit can also collect data from online news, newspaper articles, magazine articles, etc. For example, online news may include the latest crime information or articles on anti-social forces. Newspaper articles and magazine articles may include past criminal records and information on anti-social forces. Thus, by collecting data from public databases and news articles on the Internet, the latest information can be obtained. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input public databases and news articles on the Internet into a generation AI, which then collects the data.

[0031] The determination unit analyzes the collected data using natural language processing technology and can determine whether a business partner or individual is an anti-social force. The determination unit analyzes the collected data using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, morphological analysis is a technology that divides a sentence into words and analyzes the part of speech of each word. Grammatical analysis is a technology that analyzes the grammatical structure of a sentence and clarifies relationships such as between subjects and predicates. Semantic analysis is a technology that analyzes the meaning of a sentence and understands the meaning based on the context. The determination unit analyzes the data using, for example, text mining, data mining, and statistical analysis. For example, text mining is a technology that extracts useful information from large amounts of text data. Data mining is a technology that discovers patterns and relationships from large amounts of data. Statistical analysis is a technology that analyzes the statistical characteristics of data and reveals trends and correlations. As a result, the accuracy of data analysis is improved by using natural language processing technology. Some or all of the above-described processing in the determination unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the determination unit may input collected data into the generation AI, which may analyze the data and make a determination.

[0032] The proposal unit can propose countermeasures to be taken if a business partner is determined to be an anti-social force. The proposal unit proposes, for example, countermeasures to be taken if a business partner is determined to be an anti-social force. Countermeasures include suspension of business, warning notice, legal action, etc. For example, suspension of business is a measure to stop business with the business partner. A warning notice is a measure to issue a warning to the business partner. Legal action is a measure to take legal action against the business partner. The proposal unit, for example, proposes specific procedures for suspension of business. For example, it proposes procedures for preparing a notice of suspension of business and sending it to the business partner. The proposal unit, for example, proposes specific content of a warning notice. For example, it proposes procedures for preparing the text of the warning notice and sending it to the business partner. The proposal unit, for example, proposes specific procedures for legal action. For example, it proposes procedures for consulting with a lawyer and proceeding with legal action. This allows a company to take appropriate action by proposing countermeasures when there is a high possibility that the party is an anti-social force. Some or all of the above-described processing by the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the judgment results into the generation AI, which can then propose countermeasures.

[0033] The suggestion unit can issue a warning even if the party is not determined to be an anti-social force. The suggestion unit issues a warning even if the party is not determined to be an anti-social force, for example. Warnings include email notification, alert display, report creation, etc. For example, email notification is a measure of sending a warning email to a business partner. Alert display is a measure of displaying a warning alert on the system. Report creation is a measure of compiling the content of the warning into a report. The suggestion unit, for example, proposes specific content of the email notification. For example, it proposes a procedure for creating the text of the warning email and sending it to the business partner. The suggestion unit, for example, proposes a specific method for displaying the alert. For example, it proposes a procedure for displaying a warning alert on the system. The suggestion unit, for example, proposes a specific procedure for report creation. For example, it proposes a procedure for compiling the content of the warning into a report and distributing it to relevant parties. This allows companies to avoid risks by issuing a warning even if the party is unlikely to be an anti-social force. Some or all of the above-mentioned processing by the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the judgment results into the generation AI, which can then suggest the content of the warning.

[0034] The collection unit can perform periodic data updates or real-time data collection. The collection unit, for example, performs periodic data updates. Periodic data updates include daily, weekly, and monthly updates. For example, the collection unit updates data daily to obtain the latest information. The collection unit can also update data weekly to obtain the latest information. The collection unit can also update data monthly to obtain the latest information. The collection unit, for example, performs real-time data collection. Real-time data collection includes collecting streaming data and using a real-time API. For example, the collection unit collects streaming data to obtain the latest information in real time. The collection unit can also collect data using a real-time API to obtain the latest information. In this way, by performing periodic data updates and real-time data collection, the latest information can always be obtained. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can cause the generation AI to perform periodic data updates and real-time data collection.

[0035] During data collection, the collection unit can analyze the past behavioral history of business partners and individuals and select the optimal collection method. The collection unit, for example, analyzes the past behavioral history of business partners and individuals and selects the optimal collection method. The past behavioral history includes past transaction history, visit history, purchase history, etc. For example, the collection unit analyzes the past transaction history of business partners and individuals and collects information from reliable data sources. For example, the collection unit analyzes the past criminal history of individuals and collects information from related databases. For example, the collection unit analyzes past news articles of business partners and individuals and collects reliable information. In this way, the optimal collection method can be selected by analyzing the past behavioral history. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit inputs the past behavioral history of business partners and individuals into a generation AI, which can select the optimal collection method.

[0036] The collection unit can filter data based on the current activities and areas of interest of business partners and individuals when collecting data. For example, the collection unit can filter data based on the current activities and areas of interest of business partners and individuals when collecting data. The current activities include current business activities, areas of activity, and areas of interest. For example, the collection unit analyzes the current business activities of business partners and prioritizes collecting relevant data. For example, the collection unit analyzes the current occupation and areas of interest of individuals and filters and collects relevant data. For example, the collection unit analyzes the current social media activities of business partners and individuals and collects relevant data. This allows highly relevant data to be collected by filtering based on the current activities and areas of interest. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the current activities and areas of interest of business partners and individuals into a generation AI, which can then perform filtering.

[0037] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of business partners and individuals when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking into account the geographical location information of business partners and individuals when collecting data. Geographical location information includes GPS data, address information, area codes, etc. For example, the collection unit prioritizes collecting data related to an area by taking into account the location of business partners. For example, the collection unit prioritizes collecting criminal records and news articles related to an area by taking into account the residence of an individual. For example, the collection unit analyzes the geographical movement patterns of business partners and individuals and collects highly relevant data. In this way, highly relevant data can be prioritized by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the geographical location information of business partners and individuals into a generation AI, which then prioritizes collecting highly relevant data.

[0038] The collection unit can analyze the social media activities of business partners and individuals during data collection and collect related data. For example, the collection unit can analyze the social media activities of business partners and individuals during data collection and collect related data. Social media activities include the content of posts, the number of followers, the number of likes, etc. For example, the collection unit can analyze the social media accounts of business partners and collect related posts and comments. For example, the collection unit can analyze the social media activities of individuals and collect related information. For example, the collection unit can analyze the relationships between business partners and individuals on social media and collect related data. In this way, related data can be collected by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, the generation AI. For example, the collection unit can input the social media activities of business partners and individuals into the generation AI, which can collect related data.

[0039] The determination unit can improve the accuracy of the determination by taking into account the interrelationships between business partners and individuals when making a determination. The determination unit, for example, improves the accuracy of the determination by taking into account the interrelationships between business partners and individuals when making a determination. Interrelationships include business relationships, family relationships, business partnerships, etc. For example, the determination unit analyzes the relationships between executives of business partners and determines whether the individual has any affiliation with anti-social forces. For example, the determination unit analyzes an individual's past relationships with business partners and determines whether the individual has any affiliation with anti-social forces. For example, the determination unit analyzes the social networks of business partners and individuals and determines whether the individual has any affiliation with anti-social forces. In this way, the accuracy of the determination is improved by taking into account the interrelationships. Some or all of the above-described processing in the determination unit may be performed using, or without, a generation AI. For example, the determination unit inputs interrelationship data between business partners and individuals into a generation AI, which can improve the accuracy of the determination.

[0040] The determination unit can make a determination by taking into account attribute information of business partners and individuals. The determination unit makes a determination by taking into account attribute information of business partners and individuals, for example. Attribute information includes age, gender, occupation, place of residence, etc. For example, the determination unit determines a relationship with anti-social forces by taking into account the business partner's industry and size. The determination unit determines a relationship with anti-social forces by taking into account, for example, the individual's occupation and career history. The determination unit determines a relationship with anti-social forces by taking into account, for example, the business partner's or individual's past criminal history and news articles. By taking attribute information into account, the accuracy of the determination is improved. Some or all of the above-mentioned processing in the determination unit may be performed using, or without, a generation AI, for example. For example, the determination unit can input attribute information of business partners and individuals into a generation AI, which then makes a determination.

[0041] The determination unit may make a determination taking into account the geographical distribution of business partners and individuals. For example, the determination unit may make a determination taking into account the geographical distribution of business partners and individuals. Geographical distribution may include distribution by region, by city, by country, etc. For example, the determination unit may make a determination taking into account the location of business partners and information on anti-social forces related to the region. For example, the determination unit may make a determination taking into account the individual's place of residence and based on criminal history and news articles related to the region. For example, the determination unit may analyze the geographical movement patterns of business partners and individuals to determine their association with anti-social forces. By taking geographical distribution into account, the accuracy of the determination is improved. Some or all of the above-described processing in the determination unit may be performed using, or without, a generation AI. For example, the determination unit may input geographical distribution data of business partners and individuals into a generation AI, which may then make a determination.

[0042] The judgment unit can improve the accuracy of the judgment by referring to related literature of the business partner or individual when making a judgment. The judgment unit, for example, improves the accuracy of the judgment by referring to related literature of the business partner or individual when making a judgment. Related literature includes academic papers, industry reports, news articles, etc. For example, the judgment unit refers to literature related to the business partner's past transactions to determine whether the person has ties to anti-social forces. For example, the judgment unit refers to literature related to the individual's past criminal history to determine whether the person has ties to anti-social forces. For example, the judgment unit refers to news articles and reports related to the business partner or individual to determine whether the person has ties to anti-social forces. By referring to related literature, the accuracy of the judgment is improved. Some or all of the above-mentioned processing in the judgment unit may be performed using, or without, a generation AI. For example, the judgment unit can input related literature data of the business partner or individual into the generation AI, which then makes the judgment.

[0043] The proposal unit can adjust the level of detail of the proposal based on the importance of the business partner or individual when making a proposal. The proposal unit, for example, adjusts the level of detail of the proposal based on the importance of the business partner or individual when making a proposal. Importance includes transaction amount, transaction frequency, business partner size, etc. For example, the proposal unit makes a detailed proposal for an important business partner. For example, the proposal unit makes a concise proposal for a less important business partner. The proposal unit, for example, gradually adjusts the level of detail of the proposal according to the importance of the business partner. This enables appropriate proposals to be made by adjusting the level of detail of the proposal based on the importance of the business partner or individual. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input importance data of the business partner or individual into the generation AI, which then adjusts the level of detail of the proposal.

[0044] The proposal unit can apply different proposal algorithms depending on the category of the business partner or individual when making a proposal. The proposal unit, for example, applies different proposal algorithms depending on the category of the business partner or individual when making a proposal. Categories include industry, size, region, etc. For example, if the business partner is in the financial industry, the proposal unit applies a proposal algorithm specialized for the financial industry. For example, if the business partner is in the manufacturing industry, the proposal unit applies a proposal algorithm specialized for the manufacturing industry. For example, if the business partner is in the service industry, the proposal unit applies a proposal algorithm specialized for the service industry. This enables more appropriate proposals by applying different proposal algorithms depending on the category of the business partner or individual. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI, for example. For example, the proposal unit can input category data of the business partner or individual into a generation AI, which then applies different proposal algorithms.

[0045] The proposal unit can determine the priority of proposals based on the submission times of business partners and individuals when making proposals. The proposal unit, for example, determines the priority of proposals based on the submission times of business partners and individuals when making proposals. The submission times include a submission deadline, a submission date, and the like. For example, the proposal unit prioritizes proposals to business partners whose submission deadlines are approaching. For example, the proposal unit postpones proposals to business partners whose submission deadlines are farther away. The proposal unit, for example, gradually adjusts the priority of proposals according to the submission times. This enables proposals to be made at an appropriate time by determining the priority of proposals based on the submission times. Some or all of the above-described processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input submission time data of business partners and individuals into the generation AI, which then determines the priority of proposals.

[0046] The proposal unit can adjust the order of proposals based on the relevance of business partners and individuals when making a proposal. The proposal unit, for example, adjusts the order of proposals based on the relevance of business partners and individuals when making a proposal. Relevance includes business relevance, business relevance, etc. For example, the proposal unit prioritizes proposals when the relevance of business partners is high. For example, the proposal unit postpones proposals when the relevance of business partners is low. The proposal unit, for example, gradually adjusts the order of proposals according to the relevance of business partners and individuals. This enables more appropriate proposals by adjusting the order of proposals based on relevance. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input relevance data of business partners and individuals into the generation AI, which can then adjust the order of proposals.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The collection unit can analyze the past behavioral history of business partners and individuals and select the optimal collection method. For example, it can analyze the past transaction history of business partners and collect information from highly reliable data sources. It can analyze the past criminal history of individuals and collect information from related databases. It can analyze past news articles of business partners and individuals and collect highly reliable information. In this way, it can select the optimal collection method by analyzing past behavioral history.

[0049] The judgment unit can improve the accuracy of judgment by taking into account the mutual relationships between business partners and individuals. For example, it analyzes the relationships between executives at business partners to determine whether they are related to anti-social forces. It analyzes an individual's past relationships with business partners to determine whether they are related to anti-social forces. It analyzes the social networks of business partners and individuals to determine whether they are related to anti-social forces. In this way, the accuracy of judgment is improved by taking into account mutual relationships.

[0050] The proposal unit can adjust the level of detail in the proposal based on the importance of the business partner or individual. For example, detailed proposals are made to important business partners. Brief proposals are made to less important business partners. The level of detail in the proposal is adjusted in stages depending on the importance of the business partner. This allows for appropriate proposals to be made by adjusting the level of detail in the proposal based on the importance of the business partner or individual.

[0051] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of business partners and individuals. For example, by taking into account the location of business partners, data related to the area can be prioritized. By taking into account the place of residence of individuals, criminal records and news articles related to the area can be prioritized. By analyzing the geographical movement patterns of business partners and individuals, highly relevant data can be collected. In this way, by taking into account geographical location information, highly relevant data can be prioritized.

[0052] The judgment unit can make judgments by taking into account the geographical distribution of business partners and individuals. For example, it can make judgments based on information about anti-social forces related to the area, taking into account the location of business partners. It can also make judgments based on criminal records and news articles related to the area, taking into account the individual's place of residence. It can analyze the geographical movement patterns of business partners and individuals to determine their association with anti-social forces. By taking geographical distribution into account, the accuracy of judgments can be improved.

[0053] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the business partner or individual. For example, if the business partner is in the financial industry, a proposal algorithm specialized for the financial industry is applied. If the business partner is in the manufacturing industry, a proposal algorithm specialized for the manufacturing industry is applied. If the business partner is in the service industry, a proposal algorithm specialized for the service industry is applied. In this way, by applying different proposal algorithms depending on the category of the business partner or individual, more appropriate proposals can be made.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The collection unit collects at least one of the following data: names of business partners or individuals, criminal records, and information on anti-social forces organizations. The collection unit collects data from, for example, public databases and news articles on the Internet, public government databases, public corporate databases, online news, newspaper articles, magazine articles, etc. Step 2: The determination unit analyzes the data collected by the collection unit and determines whether the business partner or individual is an anti-social force. The determination unit analyzes the data using, for example, natural language processing technology (morphological analysis, grammatical analysis, semantic analysis, etc.), text mining, data mining, statistical analysis, etc. Step 3: The proposal department proposes countermeasures to the company based on the judgment results obtained by the judgment department. For example, the proposal department proposes countermeasures (suspension of transactions, warning notices, legal action, etc.) if a business partner is judged to be an anti-social force, or warnings (email notifications, alert displays, report creation, etc.) if the possibility of the business partner being an anti-social force is low.

[0056] (Example 2) The anti-social forces assessment system according to an embodiment of the present invention provides a system and service that helps companies determine whether their business partners or individuals (such as executives of business partners) are anti-social forces. This system collects data such as the names of business partners and individuals, their criminal histories, and information about anti-social forces organizations. The AI ​​analyzes this data to determine whether the business partners or individuals are anti-social forces. Based on the results of the analysis, the AI ​​then recommends appropriate countermeasures to the company. For example, when a business partner's name is entered, the AI ​​searches for and analyzes the criminal history and anti-social forces organization information associated with that name. If the analysis results indicate a high probability that the business partner is an anti-social force, the AI ​​recommends that the company discontinue the transaction. Even if the probability of the business partner being an anti-social force is low, the AI ​​also issues a warning. This system allows companies to efficiently sever ties with anti-social forces. Furthermore, because the AI ​​constantly collects and analyzes the latest data, companies can always make decisions based on the most up-to-date information. This anti-social forces assessment system allows companies to efficiently sever ties with anti-social forces.

[0057] An anti-social forces determination system according to an embodiment includes a collection unit, a determination unit, and a proposal unit. The collection unit collects at least one of the following data: the name of a business partner or individual, criminal history, and anti-social forces organization information. The collection unit can collect data, for example, from public databases on the Internet or news articles. The collection unit can collect data, for example, from public government databases or public corporate databases. The collection unit can also collect data from online news, newspaper articles, magazine articles, etc. The determination unit analyzes the data collected by the collection unit and determines whether a business partner or individual is an anti-social force. The determination unit analyzes the collected data using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, semantic analysis, etc. The determination unit analyzes the data using, for example, techniques such as text mining, data mining, and statistical analysis. The proposal unit proposes countermeasures to the company based on the determination result obtained by the determination unit. The proposal unit proposes countermeasures, for example, if a business partner is determined to be an anti-social force. Countermeasures include suspension of transactions, warning notices, legal action, etc. The suggestion unit issues a warning even when the possibility of the person being an anti-social force is low, for example. Warnings include email notifications, alert displays, report creation, etc. As a result, the anti-social forces determination system according to the embodiment allows companies to efficiently cut off ties with anti-social forces.

[0058] The collection unit can collect data from public databases or news articles on the Internet. The collection unit collects data from, for example, public government databases or public corporate databases. For example, public government databases may include criminal records and information on anti-social forces. Public corporate databases may include names of business partners and related information. The collection unit can also collect data from online news, newspaper articles, magazine articles, etc. For example, online news may include the latest crime information or articles on anti-social forces. Newspaper articles and magazine articles may include past criminal records and information on anti-social forces. Thus, by collecting data from public databases and news articles on the Internet, the latest information can be obtained. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input public databases and news articles on the Internet into a generation AI, which then collects the data.

[0059] The determination unit analyzes the collected data using natural language processing technology and can determine whether a business partner or individual is an anti-social force. The determination unit analyzes the collected data using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, morphological analysis is a technology that divides a sentence into words and analyzes the part of speech of each word. Grammatical analysis is a technology that analyzes the grammatical structure of a sentence and clarifies relationships such as between subjects and predicates. Semantic analysis is a technology that analyzes the meaning of a sentence and understands the meaning based on the context. The determination unit analyzes the data using, for example, text mining, data mining, and statistical analysis. For example, text mining is a technology that extracts useful information from large amounts of text data. Data mining is a technology that discovers patterns and relationships from large amounts of data. Statistical analysis is a technology that analyzes the statistical characteristics of data and reveals trends and correlations. As a result, the accuracy of data analysis is improved by using natural language processing technology. Some or all of the above-described processing in the determination unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the determination unit may input collected data into the generation AI, which may analyze the data and make a determination.

[0060] The proposal unit can propose countermeasures to be taken if a business partner is determined to be an anti-social force. The proposal unit proposes, for example, countermeasures to be taken if a business partner is determined to be an anti-social force. Countermeasures include suspension of business, warning notice, legal action, etc. For example, suspension of business is a measure to stop business with the business partner. A warning notice is a measure to issue a warning to the business partner. Legal action is a measure to take legal action against the business partner. The proposal unit, for example, proposes specific procedures for suspension of business. For example, it proposes procedures for preparing a notice of suspension of business and sending it to the business partner. The proposal unit, for example, proposes specific content of a warning notice. For example, it proposes procedures for preparing the text of the warning notice and sending it to the business partner. The proposal unit, for example, proposes specific procedures for legal action. For example, it proposes procedures for consulting with a lawyer and proceeding with legal action. This allows a company to take appropriate action by proposing countermeasures when there is a high possibility that the party is an anti-social force. Some or all of the above-described processing by the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the judgment results into the generation AI, which can then propose countermeasures.

[0061] The suggestion unit can issue a warning even if the party is not determined to be an anti-social force. The suggestion unit issues a warning even if the party is not determined to be an anti-social force, for example. Warnings include email notification, alert display, report creation, etc. For example, email notification is a measure of sending a warning email to a business partner. Alert display is a measure of displaying a warning alert on the system. Report creation is a measure of compiling the content of the warning into a report. The suggestion unit, for example, proposes specific content of the email notification. For example, it proposes a procedure for creating the text of the warning email and sending it to the business partner. The suggestion unit, for example, proposes a specific method for displaying the alert. For example, it proposes a procedure for displaying a warning alert on the system. The suggestion unit, for example, proposes a specific procedure for report creation. For example, it proposes a procedure for compiling the content of the warning into a report and distributing it to relevant parties. This allows companies to avoid risks by issuing a warning even if the party is unlikely to be an anti-social force. Some or all of the above-mentioned processing by the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the judgment results into the generation AI, which can then suggest the content of the warning.

[0062] The collection unit can perform periodic data updates or real-time data collection. The collection unit, for example, performs periodic data updates. Periodic data updates include daily, weekly, and monthly updates. For example, the collection unit updates data daily to obtain the latest information. The collection unit can also update data weekly to obtain the latest information. The collection unit can also update data monthly to obtain the latest information. The collection unit, for example, performs real-time data collection. Real-time data collection includes collecting streaming data and using a real-time API. For example, the collection unit collects streaming data to obtain the latest information in real time. The collection unit can also collect data using a real-time API to obtain the latest information. In this way, by performing periodic data updates and real-time data collection, the latest information can always be obtained. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can cause the generation AI to perform periodic data updates and real-time data collection.

[0063] The collection unit can estimate a user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit estimates a user's emotions and adjusts the timing of data collection based on the estimated user emotions. To estimate a user's emotions, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, facial expression recognition is a technology that captures a user's facial expressions with a camera and estimates their emotions. Voice analysis is a technology that analyzes the tone and speed of a user's voice and estimates their emotions. Text analysis is a technology that analyzes a user's text messages and estimates their emotions. For example, if a user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the user's burden. For example, if a user is relaxed, the collection unit increases the frequency of data collection and collects detailed information. For example, if a user is in a hurry, the collection unit prioritizes collecting only important data and processes it quickly. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit may input user emotion data into the generation AI, and the generation AI may adjust the timing of data collection.

[0064] During data collection, the collection unit can analyze the past behavioral history of business partners and individuals and select the optimal collection method. The collection unit, for example, analyzes the past behavioral history of business partners and individuals and selects the optimal collection method. The past behavioral history includes past transaction history, visit history, purchase history, etc. For example, the collection unit analyzes the past transaction history of business partners and individuals and collects information from reliable data sources. For example, the collection unit analyzes the past criminal history of individuals and collects information from related databases. For example, the collection unit analyzes past news articles of business partners and individuals and collects reliable information. In this way, the optimal collection method can be selected by analyzing the past behavioral history. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit inputs the past behavioral history of business partners and individuals into a generation AI, which can select the optimal collection method.

[0065] The collection unit can filter data based on the current activities and areas of interest of business partners and individuals when collecting data. For example, the collection unit can filter data based on the current activities and areas of interest of business partners and individuals when collecting data. The current activities include current business activities, areas of activity, and areas of interest. For example, the collection unit analyzes the current business activities of business partners and prioritizes collecting relevant data. For example, the collection unit analyzes the current occupation and areas of interest of individuals and filters and collects relevant data. For example, the collection unit analyzes the current social media activities of business partners and individuals and collects relevant data. This allows highly relevant data to be collected by filtering based on the current activities and areas of interest. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the current activities and areas of interest of business partners and individuals into a generation AI, which can then perform filtering.

[0066] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, the collection unit estimates the user's emotions and prioritizes the data to be collected based on the estimated user emotions. To estimate the user's emotions, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, facial expression recognition is a technology that captures the user's facial expressions with a camera and estimates their emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice and estimates their emotions. Text analysis is a technology that analyzes the user's text messages and estimates their emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting only important data. For example, if the user is relaxed, the collection unit prioritizes collecting detailed data. For example, if the user is in a hurry, the collection unit prioritizes collecting data that can be collected quickly. This allows important data to be collected preferentially by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit may input user emotion data into the generation AI, and the generation AI may determine the priority of the data.

[0067] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of business partners and individuals when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking into account the geographical location information of business partners and individuals when collecting data. Geographical location information includes GPS data, address information, area codes, etc. For example, the collection unit prioritizes collecting data related to an area by taking into account the location of business partners. For example, the collection unit prioritizes collecting criminal records and news articles related to an area by taking into account the residence of an individual. For example, the collection unit analyzes the geographical movement patterns of business partners and individuals and collects highly relevant data. In this way, highly relevant data can be prioritized by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the geographical location information of business partners and individuals into a generation AI, which then prioritizes collecting highly relevant data.

[0068] The collection unit can analyze the social media activities of business partners and individuals during data collection and collect related data. For example, the collection unit can analyze the social media activities of business partners and individuals during data collection and collect related data. Social media activities include the content of posts, the number of followers, the number of likes, etc. For example, the collection unit can analyze the social media accounts of business partners and collect related posts and comments. For example, the collection unit can analyze the social media activities of individuals and collect related information. For example, the collection unit can analyze the relationships between business partners and individuals on social media and collect related data. In this way, related data can be collected by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, the generation AI. For example, the collection unit can input the social media activities of business partners and individuals into the generation AI, which can collect related data.

[0069] The determination unit can estimate the user's emotions and adjust the determination criteria based on the estimated user emotions. For example, the determination unit estimates the user's emotions and adjusts the determination criteria based on the estimated user emotions. To estimate the user's emotions, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, facial expression recognition is a technology that captures the user's facial expressions with a camera and estimates their emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice and estimates their emotions. Text analysis is a technology that analyzes the user's text messages and estimates their emotions. For example, if the user is feeling stressed, the determination unit relaxes the determination criteria and provides a quick result. For example, if the user is relaxed, the determination unit tightens the determination criteria and performs a detailed analysis. For example, if the user is in a hurry, the determination unit focuses on important factors when making a determination. This allows for quick and appropriate determination by adjusting the determination criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the determination unit may input user emotion data into the generation AI, and the generation AI may adjust the criteria for determination.

[0070] The determination unit can improve the accuracy of the determination by taking into account the interrelationships between business partners and individuals when making a determination. The determination unit, for example, improves the accuracy of the determination by taking into account the interrelationships between business partners and individuals when making a determination. Interrelationships include business relationships, family relationships, business partnerships, etc. For example, the determination unit analyzes the relationships between executives of business partners and determines whether the individual has any affiliation with anti-social forces. For example, the determination unit analyzes an individual's past relationships with business partners and determines whether the individual has any affiliation with anti-social forces. For example, the determination unit analyzes the social networks of business partners and individuals and determines whether the individual has any affiliation with anti-social forces. In this way, the accuracy of the determination is improved by taking into account the interrelationships. Some or all of the above-described processing in the determination unit may be performed using, or without, a generation AI. For example, the determination unit inputs interrelationship data between business partners and individuals into a generation AI, which can improve the accuracy of the determination.

[0071] The determination unit can make a determination by taking into account attribute information of business partners and individuals. The determination unit makes a determination by taking into account attribute information of business partners and individuals, for example. Attribute information includes age, gender, occupation, place of residence, etc. For example, the determination unit determines a relationship with anti-social forces by taking into account the business partner's industry and size. The determination unit determines a relationship with anti-social forces by taking into account, for example, the individual's occupation and career history. The determination unit determines a relationship with anti-social forces by taking into account, for example, the business partner's or individual's past criminal history and news articles. By taking attribute information into account, the accuracy of the determination is improved. Some or all of the above-mentioned processing in the determination unit may be performed using, or without, a generation AI, for example. For example, the determination unit can input attribute information of business partners and individuals into a generation AI, which then makes a determination.

[0072] The determination unit can estimate the user's emotions and adjust the display order of the determination results based on the estimated user emotions. The determination unit, for example, estimates the user's emotions and adjusts the display order of the determination results based on the estimated user emotions. Technologies such as facial expression recognition, voice analysis, and text analysis are used to estimate the user's emotions. For example, facial expression recognition is a technology that captures the user's facial expressions with a camera and estimates their emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice and estimates their emotions. Text analysis is a technology that analyzes the user's text messages and estimates their emotions. For example, if the user is feeling stressed, the determination unit prioritizes displaying important determination results. For example, if the user is relaxed, the determination unit sequentially displays detailed determination results. For example, if the user is in a hurry, the determination unit displays key results so that the user can quickly check them. This allows the user to quickly check important information by adjusting the display order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the determination unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the determination unit may input user emotion data to the generation AI, and the generation AI may adjust the display order of the determination results.

[0073] The determination unit may make a determination taking into account the geographical distribution of business partners and individuals. For example, the determination unit may make a determination taking into account the geographical distribution of business partners and individuals. Geographical distribution may include distribution by region, by city, by country, etc. For example, the determination unit may make a determination taking into account the location of business partners and information on anti-social forces related to the region. For example, the determination unit may make a determination taking into account the individual's place of residence and based on criminal history and news articles related to the region. For example, the determination unit may analyze the geographical movement patterns of business partners and individuals to determine their association with anti-social forces. By taking geographical distribution into account, the accuracy of the determination is improved. Some or all of the above-described processing in the determination unit may be performed using, or without, a generation AI. For example, the determination unit may input geographical distribution data of business partners and individuals into a generation AI, which may then make a determination.

[0074] The judgment unit can improve the accuracy of the judgment by referring to related literature of the business partner or individual when making a judgment. The judgment unit, for example, improves the accuracy of the judgment by referring to related literature of the business partner or individual when making a judgment. Related literature includes academic papers, industry reports, news articles, etc. For example, the judgment unit refers to literature related to the business partner's past transactions to determine whether the person has ties to anti-social forces. For example, the judgment unit refers to literature related to the individual's past criminal history to determine whether the person has ties to anti-social forces. For example, the judgment unit refers to news articles and reports related to the business partner or individual to determine whether the person has ties to anti-social forces. By referring to related literature, the accuracy of the judgment is improved. Some or all of the above-mentioned processing in the judgment unit may be performed using, or without, a generation AI. For example, the judgment unit can input related literature data of the business partner or individual into the generation AI, which then makes the judgment.

[0075] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, the suggestion unit estimates the user's emotions and adjusts the way suggestions are presented based on the estimated user emotions. To estimate the user's emotions, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, facial expression recognition is a technology that captures the user's facial expressions with a camera and estimates their emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice and estimates their emotions. Text analysis is a technology that analyzes the user's text messages and estimates their emotions. For example, if the user is feeling stressed, the suggestion unit makes concise and clear suggestions. For example, if the user is relaxed, the suggestion unit makes suggestions that include detailed explanations. For example, if the user is in a hurry, the suggestion unit makes suggestions that focus on the main points so that they can be quickly confirmed. This enables more appropriate suggestions to be made by adjusting the way suggestions are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit may input user emotion data into the generation AI, which may then adjust the way the suggestion is presented.

[0076] The proposal unit can adjust the level of detail of the proposal based on the importance of the business partner or individual when making a proposal. The proposal unit, for example, adjusts the level of detail of the proposal based on the importance of the business partner or individual when making a proposal. Importance includes transaction amount, transaction frequency, business partner size, etc. For example, the proposal unit makes a detailed proposal for an important business partner. For example, the proposal unit makes a concise proposal for a less important business partner. The proposal unit, for example, gradually adjusts the level of detail of the proposal according to the importance of the business partner. This enables appropriate proposals to be made by adjusting the level of detail of the proposal based on the importance of the business partner or individual. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input importance data of the business partner or individual into the generation AI, which then adjusts the level of detail of the proposal.

[0077] The proposal unit can apply different proposal algorithms depending on the category of the business partner or individual when making a proposal. The proposal unit, for example, applies different proposal algorithms depending on the category of the business partner or individual when making a proposal. Categories include industry, size, region, etc. For example, if the business partner is in the financial industry, the proposal unit applies a proposal algorithm specialized for the financial industry. For example, if the business partner is in the manufacturing industry, the proposal unit applies a proposal algorithm specialized for the manufacturing industry. For example, if the business partner is in the service industry, the proposal unit applies a proposal algorithm specialized for the service industry. This enables more appropriate proposals by applying different proposal algorithms depending on the category of the business partner or individual. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI, for example. For example, the proposal unit can input category data of the business partner or individual into a generation AI, which then applies different proposal algorithms.

[0078] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, the suggestion unit estimates the user's emotions and adjusts the length of the suggestions based on the estimated user emotions. To estimate the user's emotions, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, facial expression recognition is a technology that captures the user's facial expressions with a camera and estimates their emotions. Voice analysis is a technology that analyzes the tone and speed of the user's voice and estimates their emotions. Text analysis is a technology that analyzes the user's text messages and estimates their emotions. For example, if the user is feeling stressed, the suggestion unit makes short, concise suggestions. For example, if the user is relaxed, the suggestion unit makes longer suggestions with detailed explanations. For example, if the user is in a hurry, the suggestion unit makes short suggestions that can be quickly confirmed. This allows for more appropriate suggestions by adjusting the length of the suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit may input user emotion data into the generation AI, which may then adjust the length of the suggestion.

[0079] The proposal unit can determine the priority of proposals based on the submission times of business partners and individuals when making proposals. The proposal unit, for example, determines the priority of proposals based on the submission times of business partners and individuals when making proposals. The submission times include a submission deadline, a submission date, and the like. For example, the proposal unit prioritizes proposals to business partners whose submission deadlines are approaching. For example, the proposal unit postpones proposals to business partners whose submission deadlines are farther away. The proposal unit, for example, gradually adjusts the priority of proposals according to the submission times. This enables proposals to be made at an appropriate time by determining the priority of proposals based on the submission times. Some or all of the above-described processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input submission time data of business partners and individuals into the generation AI, which then determines the priority of proposals.

[0080] The proposal unit can adjust the order of proposals based on the relevance of business partners and individuals when making a proposal. The proposal unit, for example, adjusts the order of proposals based on the relevance of business partners and individuals when making a proposal. Relevance includes business relevance, business relevance, etc. For example, the proposal unit prioritizes proposals when the relevance of business partners is high. For example, the proposal unit postpones proposals when the relevance of business partners is low. The proposal unit, for example, gradually adjusts the order of proposals according to the relevance of business partners and individuals. This enables more appropriate proposals by adjusting the order of proposals based on relevance. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input relevance data of business partners and individuals into the generation AI, which can then adjust the order of proposals. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, determination unit, and suggestion 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 is realized by the control unit 46A of the smart device 14 and collects data from public databases and news articles on the Internet. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to determine whether or not the person is an anti-social force. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes appropriate countermeasures to the company based on the determination result. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, determination unit, and suggestion 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 is realized by the control unit 46A of the smart glasses 214 and collects data from public databases and news articles on the Internet. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to determine whether or not the person is an anti-social force. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes appropriate countermeasures to the company based on the determination result. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, determination unit, and suggestion 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 is realized by the control unit 46A of the headset type terminal 314 and collects data from public databases and news articles on the Internet. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to determine whether or not the person is an anti-social force. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes appropriate countermeasures to the company based on the determination result. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, determination unit, and suggestion 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 is realized by the control unit 46A of the robot 414 and collects data from public databases and news articles on the Internet. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to determine whether or not the person is an anti-social force. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes appropriate countermeasures to the company based on the determination result.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The collection unit can analyze the past behavioral history of business partners and individuals and select the optimal collection method. For example, it can analyze the past transaction history of business partners and collect information from highly reliable data sources. It can analyze the past criminal history of individuals and collect information from related databases. It can analyze past news articles of business partners and individuals and collect highly reliable information. In this way, it can select the optimal collection method by analyzing past behavioral history.

[0083] The judgment unit can improve the accuracy of judgment by taking into account the mutual relationships between business partners and individuals. For example, it analyzes the relationships between executives at business partners to determine whether they are related to anti-social forces. It analyzes an individual's past relationships with business partners to determine whether they are related to anti-social forces. It analyzes the social networks of business partners and individuals to determine whether they are related to anti-social forces. In this way, the accuracy of judgment is improved by taking into account mutual relationships.

[0084] The proposal unit can adjust the level of detail in the proposal based on the importance of the business partner or individual. For example, detailed proposals are made to important business partners. Brief proposals are made to less important business partners. The level of detail in the proposal is adjusted in stages depending on the importance of the business partner. This allows for appropriate proposals to be made by adjusting the level of detail in the proposal based on the importance of the business partner or individual.

[0085] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data collection is reduced to reduce the burden on the user. If the user is relaxed, the frequency of data collection is increased to collect more detailed information. If the user is in a hurry, only important data is collected as a priority and processed quickly. In this way, the burden on the user can be reduced by adjusting the timing of data collection according to the user's emotions.

[0086] The determination unit can estimate the user's emotions and adjust the criteria for determination based on the estimated user emotions. For example, if the user is feeling stressed, the determination criteria can be relaxed to provide a quick result. If the user is relaxed, the determination criteria can be tightened to perform a detailed analysis. If the user is in a hurry, the determination can be made by focusing on important factors. In this way, by adjusting the criteria for determination according to the user's emotions, quick and appropriate determination can be made.

[0087] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is feeling stressed, a concise and clear suggestion is made. If the user is relaxed, a suggestion including detailed explanations is made. If the user is in a hurry, a suggestion that focuses on the main points so that the user can confirm it quickly is made. In this way, by adjusting the way suggestions are expressed according to the user's emotions, more appropriate suggestions can be made.

[0088] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of business partners and individuals. For example, by taking into account the location of business partners, data related to the area can be prioritized. By taking into account the place of residence of individuals, criminal records and news articles related to the area can be prioritized. By analyzing the geographical movement patterns of business partners and individuals, highly relevant data can be collected. In this way, by taking into account geographical location information, highly relevant data can be prioritized.

[0089] The judgment unit can make judgments by taking into account the geographical distribution of business partners and individuals. For example, it can make judgments based on information about anti-social forces related to the area, taking into account the location of business partners. It can also make judgments based on criminal records and news articles related to the area, taking into account the individual's place of residence. It can analyze the geographical movement patterns of business partners and individuals to determine their association with anti-social forces. By taking geographical distribution into account, the accuracy of judgments can be improved.

[0090] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is feeling stressed, a short and to-the-point suggestion is made. If the user is relaxed, a longer suggestion with detailed explanations is made. If the user is in a hurry, a short suggestion is made so that the user can quickly confirm it. In this way, by adjusting the length of the suggestion according to the user's emotions, more appropriate suggestions can be made.

[0091] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the business partner or individual. For example, if the business partner is in the financial industry, a proposal algorithm specialized for the financial industry is applied. If the business partner is in the manufacturing industry, a proposal algorithm specialized for the manufacturing industry is applied. If the business partner is in the service industry, a proposal algorithm specialized for the service industry is applied. In this way, by applying different proposal algorithms depending on the category of the business partner or individual, more appropriate proposals can be made.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The collection unit collects at least one of the following data: names of business partners or individuals, criminal records, and information on anti-social forces organizations. The collection unit collects data from, for example, public databases and news articles on the Internet, public government databases, public corporate databases, online news, newspaper articles, magazine articles, etc. Step 2: The determination unit analyzes the data collected by the collection unit and determines whether the business partner or individual is an anti-social force. The determination unit analyzes the data using, for example, natural language processing technology (morphological analysis, grammatical analysis, semantic analysis, etc.), text mining, data mining, statistical analysis, etc. Step 3: The proposal department proposes countermeasures to the company based on the judgment results obtained by the judgment department. For example, the proposal department proposes countermeasures (suspension of transactions, warning notices, legal action, etc.) if a business partner is judged to be an anti-social force, or warnings (email notifications, alert displays, report creation, etc.) if the possibility of the business partner being an anti-social force is low.

[0094] 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.

[0095] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 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 at least one of the following data: names of business partners or individuals, criminal records, and information on anti-social forces; a determination unit that analyzes the data collected by the collection unit and determines whether a business partner or individual is an anti-social force; a proposal unit that proposes a countermeasure to the company based on the determination result obtained by the determination unit. A system characterized by:

2. The collecting unit Collect data from public databases or news articles on the internet 2. The system of claim 1.

3. The determination unit Analyze collected data using natural language processing technology to determine whether business partners or individuals are anti-social forces 2. The system of claim 1.

4. The proposal unit Proposing countermeasures when a business partner is determined to be an anti-social force 2. The system of claim 1.

5. The proposal unit Issue warnings even if they are not determined to be anti-social forces 2. The system of claim 1.

6. The collecting unit Regularly update data or collect data in real time 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit When collecting data, analyze the past behavioral history of business partners and individuals to select the most appropriate collection method.

2. The system of claim 1.

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