system

Abstract

To provide a system capable of efficiently detecting slander messages on SNS and taking appropriate measures.SOLUTION: A system in an embodiment includes a filtering unit, a collection unit, a generation unit, and a distribution unit. The filtering unit analyzes messages on SNS and determines the degree of slander. The collection unit collects slander messages detected by the filtering unit. The generation unit creates documents of damage reports and evidence based on the messages collected by the collection unit. The distribution unit distributes a mental care message based on the documents created by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to efficiently detect abusive messages on social media and take appropriate measures.

[0005] The system according to the embodiment aims to efficiently detect abusive messages on SNS and take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a filtering unit, a collection unit, a generation unit, and a distribution unit. The filtering unit analyzes messages on the SNS and determines the degree of slander. The collection unit collects the slanderous messages detected by the filtering unit. The generation unit creates a damage report and documentary evidence based on the messages collected by the collection unit. The distribution unit distributes mental care messages based on the documents created by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can efficiently detect abusive messages on SNS and take appropriate measures. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The slander prevention system according to an embodiment of the present invention is a system that prevents slander on SNS and supports victims. This slander prevention system uses a generation AI to analyze messages on SNS and determine the degree of slander. Next, excessive slanderous messages are collected, and documents such as a damage report are automatically created. Furthermore, a function of delivering a mental care message is provided. For example, the slander prevention system filters excessive slander against a specified person. The generation AI analyzes messages on SNS and determines the degree of slander. Next, the slander prevention system collects excessive slanderous messages, and automatically creates documents such as a damage report. Based on the slanderous messages collected by the generation AI, a damage report and evidential documents are automatically created. Furthermore, the slander prevention system delivers a mental care message. The generative AI analyzes the victim's mental state and automatically creates and delivers appropriate mental care messages. This service is expected to reduce the number of suicides caused by slander on social media. In this way, the slander prevention system can prevent slander on social media and support victims.

[0029] The defamation prevention system according to the embodiment includes a filtering unit, a collecting unit, a generating unit, and a distributing unit. The filtering unit analyzes messages on SNS and determines the degree of defamation. The filtering unit detects, for example, specific keywords or phrases and determines the degree of defamation. The filtering unit uses a generating AI to analyze messages on SNS and determines the degree of defamation. For example, the generating AI analyzes messages using a text generating AI (for example, LLM) and determines the degree of defamation. The collecting unit collects the defamatory messages detected by the filtering unit. For example, the collecting unit stores the detected defamatory messages in a database. The collecting unit collects the detected defamatory messages using the generating AI. For example, the generating AI automatically classifies the detected messages and stores them in a database. The generating unit creates a damage report and evidential documents based on the messages collected by the collecting unit. The generation unit automatically creates a damage report and evidential documents in a legally valid format based on the collected messages, for example. The generation unit creates a damage report and evidential documents based on the collected messages using a generation AI. For example, the generation AI analyzes the collected messages, extracts necessary information, and creates a damage report and evidential documents. The distribution unit distributes a mental care message based on the documents created by the generation unit. The distribution unit analyzes the mental state of the victim, for example, and automatically creates and distributes an appropriate mental care message. The distribution unit analyzes the mental state of the victim using a generation AI, and automatically creates and distributes a mental care message. For example, the generation AI analyzes the mental state of the victim and creates a mental care message including words of encouragement and a method of providing psychological support. In this way, the slander prevention system according to the embodiment can prevent slander on SNS and support the victim.

[0030] The filtering unit analyzes messages on SNS and judges the degree of slander. For example, the filtering unit detects specific keywords or phrases and judges the degree of slander. Specifically, the filtering unit uses natural language processing technology to analyze the contents of messages and detects keywords or phrases that may be slanderous. This includes word frequency analysis and context analysis. Furthermore, the generation AI is used to analyze messages on SNS and judge the degree of slander. The generation AI uses text generation AI (e.g., LLM) to analyze messages and judge the degree of slander. The generation AI has the ability to learn past data and identify slander patterns. For example, the generation AI performs sentiment analysis of messages and judges messages with strong negative sentiments as having a high degree of slander. The generation AI also understands the context and evaluates the degree of slander by considering the meaning of the entire sentence, not just the matching of keywords. This allows the filtering unit to perform more advanced analysis without relying on simple keyword matching. Furthermore, the filtering unit can continuously learn and respond to new defamatory patterns and trends. For example, even if new slang or jargon emerges, the generation AI can quickly learn about it and respond appropriately. This allows the filtering unit to always perform highly accurate defamatory judgments based on the latest information, effectively preventing defamatory comments on social media.

[0031] The collection unit collects the defamatory messages detected by the filtering unit. The collection unit, for example, stores the detected defamatory messages in a database. Specifically, the collection unit receives the defamatory messages sent from the filtering unit and stores them in a database in an appropriate format. The collection unit uses the generation AI to collect the detected defamatory messages. The generation AI automatically classifies the detected messages and stores them in a database. For example, the generation AI analyzes metadata such as the content, sender, and sending date and time of the message, and classifies the messages based on this information. In addition, the collection unit can evaluate the importance and urgency of the messages and store them in a prioritized manner. This allows the collection unit to efficiently manage a huge amount of defamatory messages and quickly search and extract necessary information. In addition, the collection unit takes measures to ensure the security of the database and prevent unauthorized access to the stored messages. For example, the access rights to the database are strictly controlled and encryption technology is used to protect the data. This enables the collection unit to safely collect and store abusive messages, improving the reliability of the entire system.

[0032] The generation unit creates a damage report and evidential documents based on the messages collected by the collection unit. For example, the generation unit automatically creates a damage report and evidential documents in a legally valid format based on the collected messages. Specifically, the generation unit analyzes the contents of the collected messages, extracts necessary information, and creates a damage report and evidential documents. The generation unit uses a generation AI to create a damage report and evidential documents based on the collected messages. The generation AI analyzes the collected messages, extracts necessary information, and creates a damage report and evidential documents. For example, the generation AI automatically extracts information such as the sender, sending date and time, and content of the message, and creates a damage report and evidential documents based on this information. Furthermore, the generation unit can automatically apply formats and wording to meet legal requirements and create legally valid documents. This allows the generation unit to provide support for victims to quickly take legal procedures. In addition, the generation unit authenticates the created documents with digital signatures and time stamps to ensure the reliability and evidential admissibility of the documents. This allows the generation unit to reliably provide the evidence necessary for victims to take legal procedures and support legal responses to slander.

[0033] The distribution unit distributes the mental care message based on the document created by the generation unit. The distribution unit, for example, analyzes the mental state of the victim and automatically creates and distributes an appropriate mental care message. Specifically, the distribution unit monitors the victim's activities on SNS and the contents of messages in order to analyze the victim's mental state. The generation AI analyzes the victim's mental state and creates a mental care message including words of encouragement and a method of providing psychological support. For example, the generation AI performs an emotion analysis of the victim's message, and if the negative emotion is strong, creates a mental care message including words of encouragement and a method of providing psychological support. Furthermore, the distribution unit can adjust the content and distribution timing of the mental care message according to the victim's mental state. This allows the distribution unit to provide the victim with an appropriate mental care message at an appropriate time and support the victim's mental health. In addition, the distribution unit can collect feedback from the victim and continuously improve the content and distribution method of the mental care message. This allows the distribution unit to provide effective mental care to the victim and reduce the mental damage caused by slander.

[0034] The filtering unit can detect a specific keyword or phrase and determine the degree of slander. The filtering unit can, for example, detect a specific keyword or phrase and determine the degree of slander. For example, the filtering unit can detect insulting words or offensive expressions and determine the degree of slander. The filtering unit can also determine the degree of slander using context analysis. For example, the filtering unit can analyze the context before and after a message and determine the degree of slander. This makes it possible to accurately determine the degree of slander by detecting a specific keyword or phrase. Some or all of the above-mentioned processing in the filtering unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the filtering unit can input a specific keyword or phrase into the generation AI and cause the generation AI to execute a determination of the degree of slander.

[0035] The collection unit can save the detected defamatory message. The collection unit, for example, saves the detected defamatory message in a database. For example, the collection unit can automatically classify the detected message and save it in a database. The collection unit can also save the message in a file format. For example, the collection unit can save the detected message as a text file or a CSV file. In this way, by saving the defamatory message, it can be used as evidence later. Some or all of the above-mentioned 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 can input the detected defamatory message into the generation AI and have the generation AI classify and save the message.

[0036] The generation unit can create a damage report and evidential documents based on the collected messages. The generation unit, for example, automatically creates a damage report and evidential documents in a legally valid format based on the collected messages. For example, the generation unit can analyze the collected messages and extract necessary information to create a damage report and evidential documents. The generation unit can also create documents in a format that can be submitted as evidence based on the collected messages. For example, the generation unit can create documents in a format that can be submitted as evidence based on the collected messages. For example, the generation unit can create documents in a format that can be printed from the collected messages. This can reduce the burden on the victim by automatically creating a damage report and evidential documents. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the collected messages into the generation AI and have the generation AI execute the creation of a damage report and evidential documents.

[0037] The distribution unit can analyze the mental state of the victim and automatically create and distribute a mental care message. The distribution unit, for example, analyzes the mental state of the victim and automatically creates and distributes an appropriate mental care message. For example, the distribution unit can analyze the mental state of the victim and create a mental care message including words of encouragement and a method of providing psychological support. The distribution unit can also adjust the content of the mental care message according to the mental state of the victim. For example, if the victim is feeling stressed, the distribution unit can distribute a mental care message that has a relaxing effect. This allows mental care of the victim to be provided, thereby providing mental support. A part or all of the above-mentioned processing in the distribution unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the distribution unit can input the mental state of the victim into the generation AI and cause the generation AI to create and distribute the mental care message.

[0038] The filtering unit can optimize the filtering algorithm by referring to past defamatory data. For example, the filtering unit analyzes past defamatory data, identifies frequently occurring keywords and phrases, and reflects them in the filtering algorithm. The filtering unit can also learn aggressive expressions used in specific contexts from past defamatory data and incorporate them into the filtering algorithm. Furthermore, the filtering unit can also quantify the degree of defamation based on past defamatory data and adjust the threshold of the filtering algorithm. This improves the accuracy of the filtering algorithm by referring to past data. Some or all of the above-mentioned processing in the filtering unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the filtering unit can input past defamatory data into the generation AI and cause the generation AI to execute optimization of the filtering algorithm.

[0039] The filtering unit can perform filtering by taking into account attribute information of the sender of the message. For example, if the sender of the message has a history of making slanderous remarks in the past, the filtering unit can tighten the filtering criteria. In addition, if the sender of the message is anonymous, the filtering unit can strengthen the filtering criteria and increase the detection sensitivity of slanderous remarks. Furthermore, if the sender of the message has a specific attribute (e.g., age, gender), the filtering unit can adjust the filtering criteria based on the attribute. In this way, the accuracy of filtering is improved by taking into account the attribute information of the sender. A part or all of the above-mentioned processing in the filtering unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the filtering unit can input attribute information of the sender of the message to the generation AI and cause the generation AI to adjust the filtering criteria.

[0040] The filtering unit can perform filtering taking into consideration the geographical distribution of messages. For example, when the sender of a message is from a specific region, the filtering unit performs filtering taking into consideration expressions specific to that region. In addition, when the sender of a message is from a different region, the filtering unit can analyze the tendency of slander for each region and adjust the filtering criteria. Furthermore, when the senders of messages are concentrated in a specific region, the filtering unit can perform filtering based on the culture and language of that region. In this way, by taking into consideration the geographical distribution, slander specific to the region can be accurately detected. Part or all of the above-mentioned processing in the filtering unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the filtering unit can input the geographical distribution data of messages to the generation AI and cause the generation AI to adjust the filtering criteria.

[0041] The filtering unit can improve the accuracy of filtering by referring to literature related to the message. For example, when the content of the message is related to a specific literature, the filtering unit performs filtering by referring to the content of the literature. In addition, when the content of the message is related to a specific topic, the filtering unit can also perform filtering by referring to literature related to the topic. Furthermore, when the content of the message is related to a specific incident or event, the filtering unit can also perform filtering by referring to literature related to the incident or event. As a result, the accuracy of filtering is improved by referring to related literature. A part or all of the above-mentioned processing in the filtering unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the filtering unit can input literature related to the message to the generation AI and cause the generation AI to perform improvement of the accuracy of filtering.

[0042] The collection unit can improve the accuracy of collection by taking into account the interrelationships between messages. For example, if the sender of the message is the same person, the collection unit also collects other messages from that person. In addition, if the message is related to a specific topic, the collection unit can also collect other messages related to that topic. Furthermore, if the message is related to a specific incident or event, the collection unit can also collect other messages related to the incident or event. This improves the accuracy of collection by taking into account the interrelationships between messages. Some or all of the above-mentioned 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 can input message interrelation data into the generation AI and cause the generation AI to perform improvement of the accuracy of collection.

[0043] The collection unit can perform collection while taking into consideration attribute information of the message sender. For example, if the message sender has a history of slander in the past, the collection unit preferentially collects messages from that person. In addition, if the message sender is anonymous, the collection unit can also preferentially collect messages from that person. Furthermore, if the message sender has a specific attribute (e.g., age, gender), the collection unit can also collect messages based on the attribute. This improves the accuracy of collection by taking into consideration the attribute information of the sender. Part or all of the above-mentioned 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 can input attribute information of the message sender to the generation AI and have the generation AI perform collection.

[0044] The collection unit can collect messages taking into account the geographical distribution of the messages. For example, when the sender of the message is from a specific region, the collection unit collects messages taking into account expressions specific to the region. In addition, when the sender of the message is from a different region, the collection unit can analyze the tendency of slander in each region and adjust the collection criteria. Furthermore, when the senders of the message are concentrated in a specific region, the collection unit can collect messages based on the culture and language of the region. In this way, by taking into account the geographical distribution, slander specific to the region can be accurately collected. Part or all of the above-mentioned 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 can input the geographical distribution data of the messages to the generation AI and cause the generation AI to adjust the collection criteria.

[0045] The collection unit can improve the accuracy of collection by referring to literature related to the message. For example, when the content of the message is related to a specific literature, the collection unit performs collection by referring to the content of the literature. In addition, when the content of the message is related to a specific topic, the collection unit can also perform collection by referring to literature related to the topic. Furthermore, when the content of the message is related to a specific incident or event, the collection unit can also perform collection by referring to literature related to the incident or event. As a result, the accuracy of collection is improved by referring to related literature. A part or all of the above-mentioned 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 can input the literature related to the message to the generation AI and cause the generation AI to perform the improvement of the accuracy of collection.

[0046] The generation unit can improve the accuracy of generation by taking into account the interrelationships between messages. For example, if the sender of the messages is the same person, the generation unit generates a document by taking into account other messages from that person. In addition, if the message is related to a specific topic, the generation unit can also generate a document by taking into account other messages related to that topic. Furthermore, if the message is related to a specific incident or event, the generation unit can also generate a document by taking into account other messages related to the incident or event. This improves the accuracy of generation by taking into account the interrelationships between messages. Part or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input message interrelation data into the generation AI and cause the generation AI to perform improvement of the accuracy of generation.

[0047] The generation unit can perform generation while taking into consideration attribute information of the message sender. For example, if the message sender has a history of slander, the generation unit generates a document based on the message of that person. In addition, if the message sender is anonymous, the generation unit can also generate a document based on the message of that person. Furthermore, if the message sender has a specific attribute (e.g., age, gender), the generation unit can also generate a document based on the attribute. In this way, by taking into consideration the attribute information of the sender, the accuracy of generation is improved. A part or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input attribute information of the message sender to the generation AI and have the generation AI execute the generation.

[0048] The generation unit can perform generation taking into consideration the geographical distribution of messages. For example, when the sender of the message is from a specific region, the generation unit generates the document taking into consideration expressions specific to that region. In addition, when the sender of the message is from a different region, the generation unit can analyze the tendency of slander in each region and adjust the content of the document. Furthermore, when the senders of the message are concentrated in a specific region, the generation unit can generate the document based on the culture and language of that region. In this way, by taking into consideration the geographical distribution, a document that accurately reflects slander specific to the region can be generated. Part or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the geographical distribution data of the message to the generation AI and cause the generation AI to execute the generation.

[0049] The generation unit can improve the accuracy of generation by referring to literature related to the message. For example, when the content of the message is related to a specific literature, the generation unit generates a document by referring to the content of the literature. In addition, when the content of the message is related to a specific topic, the generation unit can also generate a document by referring to literature related to the topic. Furthermore, when the content of the message is related to a specific incident or event, the generation unit can also generate a document by referring to literature related to the incident or event. As a result, the accuracy of generation is improved by referring to related literature. A part or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input literature related to the message to the generation AI and cause the generation AI to execute the improvement of the accuracy of generation.

[0050] The delivery unit can create an optimal mental care message by referring to the victim's past mental state. The delivery unit, for example, refers to a mental care message from a time when the victim felt stress in the past and delivers a similar message. The delivery unit can also refer to a mental care message from a time when the victim felt relaxed in the past and deliver a similar message. Furthermore, the delivery unit can also refer to a mental care message from a time when the victim felt angry in the past and deliver a similar message. In this way, a more appropriate mental care message can be created by referring to the past mental state. A part or all of the above-mentioned processing in the delivery unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the delivery unit can input the victim's past mental state data into the generation AI and cause the generation AI to create an optimal mental care message.

[0051] The delivery unit can customize the mental care message based on the victim's current living situation. For example, if the victim is currently feeling stressed at work, the delivery unit delivers a mental care message that reduces work stress. In addition, if the victim is currently relaxing at home, the delivery unit can also deliver a mental care message that promotes relaxation at home. Furthermore, if the victim is currently feeling angry at school, the delivery unit can also deliver a mental care message that reduces anger at school. This enables more appropriate mental care by customizing the mental care message based on the current living situation. Part or all of the above-mentioned processing in the delivery unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the delivery unit can input the victim's current living situation data into the generation AI and cause the generation AI to customize the mental care message.

[0052] The distribution unit can create an optimal mental care message by taking into account the geographical location information of the victim. For example, if the victim lives in a specific region, the distribution unit creates a mental care message based on the culture and language of the region. In addition, if the victim lives in a different region, the distribution unit can also create a mental care message by taking into account the culture and language of each region. Furthermore, if the victim moves to a specific region, the distribution unit can also create a mental care message based on the culture and language of the region. In this way, a region-specific mental care message can be created by taking into account the geographical location information. Part or all of the above-mentioned processing in the distribution unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the distribution unit can input the geographical location information of the victim to the generation AI and cause the generation AI to create an optimal mental care message.

[0053] The distribution unit can create a mental care message by analyzing the victim's social media activity. For example, if the victim feels stressed on social media, the distribution unit can create a mental care message that reduces the stress. In addition, if the victim feels relaxed on social media, the distribution unit can create a mental care message that promotes relaxation. Furthermore, if the victim feels angry on social media, the distribution unit can create a mental care message that reduces the anger. In this way, a more appropriate mental care message can be created by analyzing social media activity. Some or all of the above-mentioned processing in the distribution unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the distribution unit can input the victim's social media activity data into the generation AI and cause the generation AI to create a mental care message.

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

[0055] The filtering unit can perform filtering by taking into account the sender's past behavioral history. For example, if the sender has a history of slanderous comments in the past, the messages of that sender can be filtered strictly. Also, if the sender has sent many positive messages in the past, the messages of that sender can be filtered loosely. Furthermore, if the sender has caused problems in the past regarding a specific topic, messages related to that topic can be filtered with a focus. In this way, the accuracy of filtering can be improved by taking into account the sender's past behavioral history.

[0056] The collection unit can evaluate the trustworthiness of the sender of a message and collect the message based on the trustworthiness. For example, if the trustworthiness of a sender is low, the messages of that sender are collected preferentially. Also, if the trustworthiness of a sender is high, the messages of that sender can be put on hold. Furthermore, if the trustworthiness of a sender is medium, the messages of that sender can be collected with normal priority. In this way, the accuracy of collection is improved by taking the trustworthiness of the sender into consideration.

[0057] The generation unit can analyze the intention of the sender of the message and generate a document based on the intention. For example, if the intention of the sender is aggressive, a document based on the message is generated with priority. Also, if the intention of the sender is neutral, a document based on the message can be generated with normal priority. Furthermore, if the intention of the sender is positive, a document based on the message can be postponed. In this way, the accuracy of generation is improved by taking the intention of the sender into consideration.

[0058] The distribution unit can evaluate the effectiveness of past mental care messages for the victim and create a new mental care message based on the effectiveness. For example, a mental care message that was effective in the past can be distributed again. Also, a mental care message that was ineffective in the past can be improved and distributed. Furthermore, a mental care message whose effectiveness was unknown in the past can be reevaluated and distributed as necessary. This allows for more appropriate mental care by taking into account the effectiveness of past mental care messages.

[0059] The filtering unit can perform filtering by taking into account the social influence of the sender of the message. For example, if the sender has high social influence, the messages of the sender are filtered strictly. Also, if the sender has low social influence, the messages of the sender can be filtered loosely. Furthermore, if the sender has medium social influence, the messages of the sender can be filtered according to normal standards. In this way, the accuracy of filtering is improved by taking into account the social influence of the sender.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The filtering unit analyzes messages on social media and determines the degree of slander. For example, it detects specific keywords or phrases and uses a generation AI to analyze the message to determine the degree of slander. Step 2: The collection unit collects the defamatory messages detected by the filtering unit. For example, the detected defamatory messages are stored in a database and automatically classified using a generation AI. Step 3: The generation unit creates a damage report and evidential documents based on the messages collected by the collection unit. For example, a damage report and evidential documents in a legally valid format can be automatically created based on the collected messages, and the necessary information can be extracted using a generation AI. Step 4: The distribution unit distributes mental care messages based on the documents created by the generation unit. For example, the mental state of the victim is analyzed, and an appropriate mental care message is automatically created and distributed using a generation AI.

[0062] (Example 2) The slander prevention system according to an embodiment of the present invention is a system that prevents slander on SNS and supports victims. This slander prevention system uses a generation AI to analyze messages on SNS and determine the degree of slander. Next, excessive slanderous messages are collected, and documents such as a damage report are automatically created. Furthermore, a function of delivering a mental care message is provided. For example, the slander prevention system filters excessive slander against a specified person. The generation AI analyzes messages on SNS and determines the degree of slander. Next, the slander prevention system collects excessive slanderous messages, and automatically creates documents such as a damage report. Based on the slanderous messages collected by the generation AI, a damage report and evidential documents are automatically created. Furthermore, the slander prevention system delivers a mental care message. The generative AI analyzes the victim's mental state and automatically creates and delivers appropriate mental care messages. This service is expected to reduce the number of suicides caused by slander on social media. In this way, the slander prevention system can prevent slander on social media and support victims.

[0063] The defamation prevention system according to the embodiment includes a filtering unit, a collecting unit, a generating unit, and a distributing unit. The filtering unit analyzes messages on SNS and determines the degree of defamation. The filtering unit detects, for example, specific keywords or phrases and determines the degree of defamation. The filtering unit uses a generating AI to analyze messages on SNS and determines the degree of defamation. For example, the generating AI analyzes messages using a text generating AI (for example, LLM) and determines the degree of defamation. The collecting unit collects the defamatory messages detected by the filtering unit. For example, the collecting unit stores the detected defamatory messages in a database. The collecting unit collects the detected defamatory messages using the generating AI. For example, the generating AI automatically classifies the detected messages and stores them in a database. The generating unit creates a damage report and evidential documents based on the messages collected by the collecting unit. The generation unit automatically creates a damage report and evidential documents in a legally valid format based on the collected messages, for example. The generation unit creates a damage report and evidential documents based on the collected messages using a generation AI. For example, the generation AI analyzes the collected messages, extracts necessary information, and creates a damage report and evidential documents. The distribution unit distributes a mental care message based on the documents created by the generation unit. The distribution unit analyzes the mental state of the victim, for example, and automatically creates and distributes an appropriate mental care message. The distribution unit analyzes the mental state of the victim using a generation AI, and automatically creates and distributes a mental care message. For example, the generation AI analyzes the mental state of the victim and creates a mental care message including words of encouragement and a method of providing psychological support. In this way, the slander prevention system according to the embodiment can prevent slander on SNS and support the victim.

[0064] The filtering unit analyzes messages on SNS and judges the degree of slander. For example, the filtering unit detects specific keywords or phrases and judges the degree of slander. Specifically, the filtering unit uses natural language processing technology to analyze the contents of messages and detects keywords or phrases that may be slanderous. This includes word frequency analysis and context analysis. Furthermore, the generation AI is used to analyze messages on SNS and judge the degree of slander. The generation AI uses text generation AI (e.g., LLM) to analyze messages and judge the degree of slander. The generation AI has the ability to learn past data and identify slander patterns. For example, the generation AI performs sentiment analysis of messages and judges messages with strong negative sentiments as having a high degree of slander. The generation AI also understands the context and evaluates the degree of slander by considering the meaning of the entire sentence, not just the matching of keywords. This allows the filtering unit to perform more advanced analysis without relying on simple keyword matching. Furthermore, the filtering unit can continuously learn and respond to new defamatory patterns and trends. For example, even if new slang or jargon emerges, the generation AI can quickly learn about it and respond appropriately. This allows the filtering unit to always perform highly accurate defamatory judgments based on the latest information, effectively preventing defamatory comments on social media.

[0065] The collection unit collects the defamatory messages detected by the filtering unit. The collection unit, for example, stores the detected defamatory messages in a database. Specifically, the collection unit receives the defamatory messages sent from the filtering unit and stores them in a database in an appropriate format. The collection unit uses the generation AI to collect the detected defamatory messages. The generation AI automatically classifies the detected messages and stores them in a database. For example, the generation AI analyzes metadata such as the content, sender, and sending date and time of the message, and classifies the messages based on this information. In addition, the collection unit can evaluate the importance and urgency of the messages and store them in a prioritized manner. This allows the collection unit to efficiently manage a huge amount of defamatory messages and quickly search and extract necessary information. In addition, the collection unit takes measures to ensure the security of the database and prevent unauthorized access to the stored messages. For example, the access rights to the database are strictly controlled and encryption technology is used to protect the data. This enables the collection unit to safely collect and store abusive messages, improving the reliability of the entire system.

[0066] The generation unit creates a damage report and evidential documents based on the messages collected by the collection unit. For example, the generation unit automatically creates a damage report and evidential documents in a legally valid format based on the collected messages. Specifically, the generation unit analyzes the contents of the collected messages, extracts necessary information, and creates a damage report and evidential documents. The generation unit uses a generation AI to create a damage report and evidential documents based on the collected messages. The generation AI analyzes the collected messages, extracts necessary information, and creates a damage report and evidential documents. For example, the generation AI automatically extracts information such as the sender, sending date and time, and content of the message, and creates a damage report and evidential documents based on this information. Furthermore, the generation unit can automatically apply formats and wording to meet legal requirements and create legally valid documents. This allows the generation unit to provide support for victims to quickly take legal procedures. In addition, the generation unit authenticates the created documents with digital signatures and time stamps to ensure the reliability and evidential admissibility of the documents. This allows the generation unit to reliably provide the evidence necessary for victims to take legal procedures and support legal responses to slander.

[0067] The distribution unit distributes the mental care message based on the document created by the generation unit. The distribution unit, for example, analyzes the mental state of the victim and automatically creates and distributes an appropriate mental care message. Specifically, the distribution unit monitors the victim's activities on SNS and the contents of messages in order to analyze the victim's mental state. The generation AI analyzes the victim's mental state and creates a mental care message including words of encouragement and a method of providing psychological support. For example, the generation AI performs an emotion analysis of the victim's message, and if the negative emotion is strong, creates a mental care message including words of encouragement and a method of providing psychological support. Furthermore, the distribution unit can adjust the content and distribution timing of the mental care message according to the victim's mental state. This allows the distribution unit to provide the victim with an appropriate mental care message at an appropriate time and support the victim's mental health. In addition, the distribution unit can collect feedback from the victim and continuously improve the content and distribution method of the mental care message. This allows the distribution unit to provide effective mental care to the victim and reduce the mental damage caused by slander.

[0068] The filtering unit can detect a specific keyword or phrase and determine the degree of slander. The filtering unit can, for example, detect a specific keyword or phrase and determine the degree of slander. For example, the filtering unit can detect insulting words or offensive expressions and determine the degree of slander. The filtering unit can also determine the degree of slander using context analysis. For example, the filtering unit can analyze the context before and after a message and determine the degree of slander. This makes it possible to accurately determine the degree of slander by detecting a specific keyword or phrase. Some or all of the above-mentioned processing in the filtering unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the filtering unit can input a specific keyword or phrase into the generation AI and cause the generation AI to execute a determination of the degree of slander.

[0069] The collection unit can save the detected defamatory message. The collection unit, for example, saves the detected defamatory message in a database. For example, the collection unit can automatically classify the detected message and save it in a database. The collection unit can also save the message in a file format. For example, the collection unit can save the detected message as a text file or a CSV file. In this way, by saving the defamatory message, it can be used as evidence later. Some or all of the above-mentioned 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 can input the detected defamatory message into the generation AI and have the generation AI classify and save the message.

[0070] The generation unit can create a damage report and evidential documents based on the collected messages. The generation unit, for example, automatically creates a damage report and evidential documents in a legally valid format based on the collected messages. For example, the generation unit can analyze the collected messages and extract necessary information to create a damage report and evidential documents. The generation unit can also create documents in a format that can be submitted as evidence based on the collected messages. For example, the generation unit can create documents in a format that can be submitted as evidence based on the collected messages. For example, the generation unit can create documents in a format that can be printed from the collected messages. This can reduce the burden on the victim by automatically creating a damage report and evidential documents. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the collected messages into the generation AI and have the generation AI execute the creation of a damage report and evidential documents.

[0071] The distribution unit can analyze the mental state of the victim and automatically create and distribute a mental care message. The distribution unit, for example, analyzes the mental state of the victim and automatically creates and distributes an appropriate mental care message. For example, the distribution unit can analyze the mental state of the victim and create a mental care message including words of encouragement and a method of providing psychological support. The distribution unit can also adjust the content of the mental care message according to the mental state of the victim. For example, if the victim is feeling stressed, the distribution unit can distribute a mental care message that has a relaxing effect. This allows mental care of the victim to be provided, thereby providing mental support. A part or all of the above-mentioned processing in the distribution unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the distribution unit can input the mental state of the victim into the generation AI and cause the generation AI to create and distribute the mental care message.

[0072] The filtering unit can estimate the user's emotions and adjust the filtering criteria based on the estimated user's emotions. For example, when the user is stressed, the filtering unit tightens the filtering criteria and increases the detection sensitivity of slander. Also, when the user is relaxed, the filtering unit can loosen the filtering criteria and lower the detection sensitivity of slander. Furthermore, when the user is angry, the filtering unit can strengthen the filtering criteria for a specific offensive keyword. This allows more appropriate filtering by adjusting the filtering criteria according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the filtering unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the filtering unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the filtering criteria.

[0073] The filtering unit can optimize the filtering algorithm by referring to past defamatory data. For example, the filtering unit analyzes past defamatory data, identifies frequently occurring keywords and phrases, and reflects them in the filtering algorithm. The filtering unit can also learn aggressive expressions used in specific contexts from past defamatory data and incorporate them into the filtering algorithm. Furthermore, the filtering unit can also quantify the degree of defamation based on past defamatory data and adjust the threshold of the filtering algorithm. This improves the accuracy of the filtering algorithm by referring to past data. Some or all of the above-mentioned processing in the filtering unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the filtering unit can input past defamatory data into the generation AI and cause the generation AI to execute optimization of the filtering algorithm.

[0074] The filtering unit can perform filtering by taking into account attribute information of the sender of the message. For example, if the sender of the message has a history of making slanderous remarks in the past, the filtering unit can tighten the filtering criteria. In addition, if the sender of the message is anonymous, the filtering unit can strengthen the filtering criteria and increase the detection sensitivity of slanderous remarks. Furthermore, if the sender of the message has a specific attribute (e.g., age, gender), the filtering unit can adjust the filtering criteria based on the attribute. In this way, the accuracy of filtering is improved by taking into account the attribute information of the sender. A part or all of the above-mentioned processing in the filtering unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the filtering unit can input attribute information of the sender of the message to the generation AI and cause the generation AI to adjust the filtering criteria.

[0075] The filtering unit can estimate the user's emotion and adjust the order of displaying the filtering results based on the estimated user's emotion. For example, when the user is stressed, the filtering unit preferentially displays messages with a high degree of slander. In addition, when the user is relaxed, the filtering unit can also preferentially display messages with a low degree of slander. Furthermore, when the user is angry, the filtering unit can also preferentially display messages containing specific offensive keywords. This makes it possible to provide more appropriate information by adjusting the display order according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the filtering unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the filtering unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display order.

[0076] The filtering unit can perform filtering taking into consideration the geographical distribution of messages. For example, when the sender of a message is from a specific region, the filtering unit performs filtering taking into consideration expressions specific to that region. In addition, when the sender of a message is from a different region, the filtering unit can analyze the tendency of slander for each region and adjust the filtering criteria. Furthermore, when the senders of messages are concentrated in a specific region, the filtering unit can perform filtering based on the culture and language of that region. In this way, by taking into consideration the geographical distribution, slander specific to the region can be accurately detected. Part or all of the above-mentioned processing in the filtering unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the filtering unit can input the geographical distribution data of messages to the generation AI and cause the generation AI to adjust the filtering criteria.

[0077] The filtering unit can improve the accuracy of filtering by referring to literature related to the message. For example, when the content of the message is related to a specific literature, the filtering unit performs filtering by referring to the content of the literature. In addition, when the content of the message is related to a specific topic, the filtering unit can also perform filtering by referring to literature related to the topic. Furthermore, when the content of the message is related to a specific incident or event, the filtering unit can also perform filtering by referring to literature related to the incident or event. As a result, the accuracy of filtering is improved by referring to related literature. A part or all of the above-mentioned processing in the filtering unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the filtering unit can input literature related to the message to the generation AI and cause the generation AI to perform improvement of the accuracy of filtering.

[0078] The collection unit can estimate the user's emotion and determine the priority of messages to be collected based on the estimated user's emotion. For example, when the user is stressed, the collection unit preferentially collects messages with a high degree of slander. In addition, when the user is relaxed, the collection unit can also preferentially collect messages with a low degree of slander. Furthermore, when the user is angry, the collection unit can also preferentially collect messages containing specific offensive keywords. In this way, important messages can be preferentially collected by determining the priority of messages to be collected according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of messages to be collected.

[0079] The collection unit can improve the accuracy of collection by taking into account the interrelationships between messages. For example, if the sender of the message is the same person, the collection unit also collects other messages from that person. In addition, if the message is related to a specific topic, the collection unit can also collect other messages related to that topic. Furthermore, if the message is related to a specific incident or event, the collection unit can also collect other messages related to the incident or event. This improves the accuracy of collection by taking into account the interrelationships between messages. Some or all of the above-mentioned 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 can input message interrelation data into the generation AI and cause the generation AI to perform improvement of the accuracy of collection.

[0080] The collection unit can perform collection while taking into consideration attribute information of the message sender. For example, if the message sender has a history of slander in the past, the collection unit preferentially collects messages from that person. In addition, if the message sender is anonymous, the collection unit can also preferentially collect messages from that person. Furthermore, if the message sender has a specific attribute (e.g., age, gender), the collection unit can also collect messages based on the attribute. This improves the accuracy of collection by taking into consideration the attribute information of the sender. Part or all of the above-mentioned 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 can input attribute information of the message sender to the generation AI and have the generation AI perform collection.

[0081] The collection unit can estimate the user's emotion and adjust the display method of the collected message based on the estimated user's emotion. For example, when the user is stressed, the collection unit prominently displays a message with a high degree of slander. In addition, when the user is relaxed, the collection unit can also prominently display a message with a low degree of slander. Furthermore, when the user is angry, the collection unit can also prominently display a message containing a specific offensive keyword. This makes it possible to provide more appropriate information by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0082] The collection unit can collect messages taking into account the geographical distribution of the messages. For example, when the sender of the message is from a specific region, the collection unit collects messages taking into account expressions specific to the region. In addition, when the sender of the message is from a different region, the collection unit can analyze the tendency of slander in each region and adjust the collection criteria. Furthermore, when the senders of the message are concentrated in a specific region, the collection unit can collect messages based on the culture and language of the region. In this way, by taking into account the geographical distribution, slander specific to the region can be accurately collected. Part or all of the above-mentioned 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 can input the geographical distribution data of the messages to the generation AI and cause the generation AI to adjust the collection criteria.

[0083] The collection unit can improve the accuracy of collection by referring to literature related to the message. For example, when the content of the message is related to a specific literature, the collection unit performs collection by referring to the content of the literature. In addition, when the content of the message is related to a specific topic, the collection unit can also perform collection by referring to literature related to the topic. Furthermore, when the content of the message is related to a specific incident or event, the collection unit can also perform collection by referring to literature related to the incident or event. As a result, the accuracy of collection is improved by referring to related literature. A part or all of the above-mentioned 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 can input the literature related to the message to the generation AI and cause the generation AI to perform the improvement of the accuracy of collection.

[0084] The generation unit can estimate the user's emotion and determine the priority of documents to be generated based on the estimated user's emotion. For example, when the user is stressed, the generation unit preferentially generates documents based on messages with a high degree of slander. In addition, when the user is relaxed, the generation unit can also preferentially generate documents based on messages with a low degree of slander. Furthermore, when the user is angry, the generation unit can also preferentially generate documents based on messages containing specific offensive keywords. In this way, important documents can be preferentially generated by determining the priority of documents according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of documents.

[0085] The generation unit can improve the accuracy of generation by taking into account the interrelationships between messages. For example, if the sender of the messages is the same person, the generation unit generates a document by taking into account other messages from that person. In addition, if the message is related to a specific topic, the generation unit can also generate a document by taking into account other messages related to that topic. Furthermore, if the message is related to a specific incident or event, the generation unit can also generate a document by taking into account other messages related to the incident or event. This improves the accuracy of generation by taking into account the interrelationships between messages. Part or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input message interrelation data into the generation AI and cause the generation AI to perform improvement of the accuracy of generation.

[0086] The generation unit can perform generation while taking into consideration attribute information of the message sender. For example, if the message sender has a history of slander, the generation unit generates a document based on the message of that person. In addition, if the message sender is anonymous, the generation unit can also generate a document based on the message of that person. Furthermore, if the message sender has a specific attribute (e.g., age, gender), the generation unit can also generate a document based on the attribute. In this way, by taking into consideration the attribute information of the sender, the accuracy of generation is improved. A part or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input attribute information of the message sender to the generation AI and have the generation AI execute the generation.

[0087] The generation unit can estimate the user's emotion and adjust the display method of the document to be generated based on the estimated user's emotion. For example, when the user is stressed, the generation unit prominently displays a document based on a message with a high degree of slander. In addition, when the user is relaxed, the generation unit can also prominently display a document based on a message with a low degree of slander. Furthermore, when the user is angry, the generation unit can also prominently display a document based on a message including a specific offensive keyword. This makes it possible to provide more appropriate information by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0088] The generation unit can perform generation taking into consideration the geographical distribution of messages. For example, when the sender of the message is from a specific region, the generation unit generates the document taking into consideration expressions specific to that region. In addition, when the sender of the message is from a different region, the generation unit can analyze the tendency of slander in each region and adjust the content of the document. Furthermore, when the senders of the message are concentrated in a specific region, the generation unit can generate the document based on the culture and language of that region. In this way, by taking into consideration the geographical distribution, a document that accurately reflects slander specific to the region can be generated. Part or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the geographical distribution data of the message to the generation AI and cause the generation AI to execute the generation.

[0089] The generation unit can improve the accuracy of generation by referring to literature related to the message. For example, when the content of the message is related to a specific literature, the generation unit generates a document by referring to the content of the literature. In addition, when the content of the message is related to a specific topic, the generation unit can also generate a document by referring to literature related to the topic. Furthermore, when the content of the message is related to a specific incident or event, the generation unit can also generate a document by referring to literature related to the incident or event. As a result, the accuracy of generation is improved by referring to related literature. A part or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input literature related to the message to the generation AI and cause the generation AI to execute the improvement of the accuracy of generation.

[0090] The delivery unit can estimate the user's emotion and adjust the content of the mental care message to be delivered based on the estimated user's emotion. For example, when the user is feeling stressed, the delivery unit delivers a mental care message with a relaxing effect. Furthermore, when the user is relaxed, the delivery unit can also deliver a positive mental care message. Furthermore, when the user is feeling angry, the delivery unit can also deliver a mental care message to calm the user. This allows for more appropriate mental care by adjusting the content of the mental care message according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the delivery unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the delivery unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the content of the mental care message.

[0091] The delivery unit can create an optimal mental care message by referring to the victim's past mental state. The delivery unit, for example, refers to a mental care message from a time when the victim felt stress in the past and delivers a similar message. The delivery unit can also refer to a mental care message from a time when the victim felt relaxed in the past and deliver a similar message. Furthermore, the delivery unit can also refer to a mental care message from a time when the victim felt angry in the past and deliver a similar message. In this way, a more appropriate mental care message can be created by referring to the past mental state. A part or all of the above-mentioned processing in the delivery unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the delivery unit can input the victim's past mental state data into the generation AI and cause the generation AI to create an optimal mental care message.

[0092] The delivery unit can customize the mental care message based on the victim's current living situation. For example, if the victim is currently feeling stressed at work, the delivery unit delivers a mental care message that reduces work stress. In addition, if the victim is currently relaxing at home, the delivery unit can also deliver a mental care message that promotes relaxation at home. Furthermore, if the victim is currently feeling angry at school, the delivery unit can also deliver a mental care message that reduces anger at school. This enables more appropriate mental care by customizing the mental care message based on the current living situation. Part or all of the above-mentioned processing in the delivery unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the delivery unit can input the victim's current living situation data into the generation AI and cause the generation AI to customize the mental care message.

[0093] The delivery unit can estimate the user's emotion and determine the priority of mental care messages to be delivered based on the estimated user's emotion. For example, when the user is feeling stressed, the delivery unit preferentially delivers mental care messages with a relaxing effect. In addition, when the user is relaxed, the delivery unit can also preferentially deliver positive mental care messages. Furthermore, when the user is feeling angry, the delivery unit can also preferentially deliver mental care messages to calm the user. This enables more appropriate mental care by determining the priority of mental care messages according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the delivery unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the delivery unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of mental care messages.

[0094] The distribution unit can create an optimal mental care message by taking into account the geographical location information of the victim. For example, if the victim lives in a specific region, the distribution unit creates a mental care message based on the culture and language of the region. In addition, if the victim lives in a different region, the distribution unit can also create a mental care message by taking into account the culture and language of each region. Furthermore, if the victim moves to a specific region, the distribution unit can also create a mental care message based on the culture and language of the region. In this way, a region-specific mental care message can be created by taking into account the geographical location information. Part or all of the above-mentioned processing in the distribution unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the distribution unit can input the geographical location information of the victim to the generation AI and cause the generation AI to create an optimal mental care message.

[0095] The distribution unit can create a mental care message by analyzing the victim's social media activity. For example, if the victim feels stressed on social media, the distribution unit can create a mental care message that reduces the stress. In addition, if the victim feels relaxed on social media, the distribution unit can create a mental care message that promotes relaxation. Furthermore, if the victim feels angry on social media, the distribution unit can create a mental care message that reduces the anger. In this way, a more appropriate mental care message can be created by analyzing social media activity. Some or all of the above-mentioned processing in the distribution unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the distribution unit can input the victim's social media activity data into the generation AI and cause the generation AI to create a mental care message.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The filtering unit can perform filtering by taking into account the sender's past behavioral history. For example, if the sender has a history of slanderous comments in the past, the messages of that sender can be filtered strictly. Also, if the sender has sent many positive messages in the past, the messages of that sender can be filtered loosely. Furthermore, if the sender has caused problems in the past regarding a specific topic, messages related to that topic can be filtered with a focus. In this way, the accuracy of filtering can be improved by taking into account the sender's past behavioral history.

[0098] The collection unit can evaluate the trustworthiness of the sender of a message and collect the message based on the trustworthiness. For example, if the trustworthiness of a sender is low, the messages of that sender are collected preferentially. Also, if the trustworthiness of a sender is high, the messages of that sender can be put on hold. Furthermore, if the trustworthiness of a sender is medium, the messages of that sender can be collected with normal priority. In this way, the accuracy of collection is improved by taking the trustworthiness of the sender into consideration.

[0099] The generation unit can analyze the intention of the sender of the message and generate a document based on the intention. For example, if the intention of the sender is aggressive, a document based on the message is generated with priority. Also, if the intention of the sender is neutral, a document based on the message can be generated with normal priority. Furthermore, if the intention of the sender is positive, a document based on the message can be postponed. In this way, the accuracy of generation is improved by taking the intention of the sender into consideration.

[0100] The distribution unit can evaluate the effectiveness of past mental care messages for the victim and create a new mental care message based on the effectiveness. For example, a mental care message that was effective in the past can be distributed again. Also, a mental care message that was ineffective in the past can be improved and distributed. Furthermore, a mental care message whose effectiveness was unknown in the past can be reevaluated and distributed as necessary. This allows for more appropriate mental care by taking into account the effectiveness of past mental care messages.

[0101] The filtering unit can perform filtering by taking into account the social influence of the sender of the message. For example, if the sender has high social influence, the messages of the sender are filtered strictly. Also, if the sender has low social influence, the messages of the sender can be filtered loosely. Furthermore, if the sender has medium social influence, the messages of the sender can be filtered according to normal standards. In this way, the accuracy of filtering is improved by taking into account the social influence of the sender.

[0102] The filtering unit can estimate the user's emotions and adjust the filtering criteria based on the estimated user's emotions. For example, when the user is stressed, the filtering criteria can be tightened to increase the detection sensitivity of slander. Also, when the user is relaxed, the filtering criteria can be loosened to decrease the detection sensitivity of slander. Furthermore, when the user is angry, the filtering criteria for specific offensive keywords can be strengthened. This allows for more appropriate filtering by adjusting the filtering criteria according to the user's emotions.

[0103] The collection unit can estimate the user's emotions and determine the priority order of messages to be collected based on the estimated user's emotions. For example, when the user is stressed, messages with a high degree of slander are preferentially collected. Also, when the user is relaxed, messages with a low degree of slander can be preferentially collected. Furthermore, when the user is angry, messages containing specific offensive keywords can be preferentially collected. In this way, important messages can be preferentially collected by determining the priority order of messages to be collected according to the user's emotions.

[0104] The generation unit can estimate the user's emotions and determine the priority order of documents to be generated based on the estimated user's emotions. For example, when the user is stressed, documents based on messages that are highly slanderous can be generated with priority. Also, when the user is relaxed, documents based on messages that are less slanderous can be generated with priority. Furthermore, when the user is angry, documents based on messages that include specific offensive keywords can be generated with priority. In this way, important documents can be generated with priority by determining the priority order of documents according to the user's emotions.

[0105] The delivery unit can estimate the user's emotions and adjust the content of the mental care message to be delivered based on the estimated user's emotions. For example, if the user is feeling stressed, a mental care message with a relaxing effect is delivered. Also, if the user is relaxed, a positive mental care message can be delivered. Furthermore, if the user is feeling angry, a mental care message to calm the user can be delivered. In this way, more appropriate mental care can be provided by adjusting the content of the mental care message according to the user's emotions.

[0106] The delivery unit can estimate the user's emotions and determine the priority of mental care messages to be delivered based on the estimated user's emotions. For example, if the user is feeling stressed, mental care messages with a relaxing effect can be delivered preferentially. Also, if the user is feeling relaxed, positive mental care messages can be delivered preferentially. Furthermore, if the user is feeling angry, mental care messages to help the user calm down can be delivered preferentially. In this way, more appropriate mental care can be provided by determining the priority of mental care messages according to the user's emotions.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The filtering unit analyzes messages on social media and determines the degree of slander. For example, it detects specific keywords or phrases and uses a generation AI to analyze the message to determine the degree of slander. Step 2: The collection unit collects the defamatory messages detected by the filtering unit. For example, the detected defamatory messages are stored in a database and automatically classified using a generation AI. Step 3: The generation unit creates a damage report and evidential documents based on the messages collected by the collection unit. For example, a damage report and evidential documents in a legally valid format can be automatically created based on the collected messages, and the necessary information can be extracted using a generation AI. Step 4: The distribution unit distributes mental care messages based on the documents created by the generation unit. For example, the mental state of the victim is analyzed, and an appropriate mental care message is automatically created and distributed using a generation AI.

[0109] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0110] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0111] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements including the filtering unit, the collecting unit, the generating unit, and the distributing 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 filtering unit is realized by the control unit 46A of the smart device 14, and analyzes messages on SNS to determine the degree of slander. The collecting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects the detected slanderous messages. The generating unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a damage report and evidential documents based on the collected messages. The distributing unit is realized, for example, by the control unit 46A of the smart device 14, and analyzes the mental state of the victim, and automatically creates and distributes a mental care message. The correspondence between each unit and the device or the control unit is not limited to the above example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0116] 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.

[0117] 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 the voice according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] 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 performed in a secure state.

[0120] Fig. 4 shows an example of 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.

[0121] 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.

[0122] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0125] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0126] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0127] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements including the filtering unit, the collecting unit, the generating unit, and the distributing 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 filtering unit is realized by the control unit 46A of the smart glasses 214, and analyzes messages on SNS to determine the degree of slander. The collecting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects the detected slanderous messages. The generating unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a damage report and evidential documents based on the collected messages. The distributing unit is realized, for example, by the control unit 46A of the smart glasses 214, and analyzes the mental state of the victim, and automatically creates and distributes a mental care message. The correspondence between each unit and the device or the control unit is not limited to the above example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The 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.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] 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 voice indicating a user input for 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements including the filtering unit, the collecting unit, the generating unit, and the distributing 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 filtering unit is realized by the control unit 46A of the headset type terminal 314, and analyzes messages on SNS to determine the degree of slander. The collecting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects the detected slanderous messages. The generating unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a damage report and evidential documents based on the collected messages. The distributing unit is realized, for example, by the control unit 46A of the headset type terminal 314, and analyzes the mental state of the victim, and automatically creates and distributes a mental care message. The correspondence between each unit and the device or the control unit is not limited to the above example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The 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.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0153] 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.

[0154] 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.

[0155] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0157] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0158] 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 a voice indicating a user input for 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.

[0159] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0160] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0161] Each of the multiple elements including the filtering unit, the collecting unit, the generating unit, and the distributing unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the filtering unit is realized by the control unit 46A of the robot 414, and analyzes messages on SNS to determine the degree of slander. The collecting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects the detected slanderous messages. The generating unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a damage report and evidential documents based on the collected messages. The distributing unit is realized, for example, by the control unit 46A of the robot 414, and analyzes the mental state of the victim, and automatically creates and distributes a mental care message. The correspondence between each unit and the device or the control unit is not limited to the above example, and various changes are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0163] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0165] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0167] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0169] 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.

[0170] 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 Universal Serial Bus (USB) 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.

[0171] In addition, 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 upon request from the data processing device 12.

[0172] 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.

[0173] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0174] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0175] As an example of a configuration using one 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 configuration in which a processor is used that realizes the functions of the 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.

[0176] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0178] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0180] (Appendix 1) A filtering section that analyzes messages on SNS and determines the degree of slander; a collection unit that collects the defamatory messages detected by the filtering unit; a generation unit that generates a damage report and / or documentary evidence based on the messages collected by the collection unit; A delivery unit that delivers a mental care message based on the document created by the creation unit. system. (Appendix 2) The filtering unit is Detecting specific keywords or phrases and determining their abusiveness 2. The system of claim 1, (Appendix 3) The collecting unit includes: Save detected abusive messages 2. The system of claim 1, (Appendix 4) The generation unit is Create a damage report and document evidence based on collected messages 2. The system of claim 1, (Appendix 5) The distribution unit is Analyze the mental state of the victim and automatically create and distribute mental care messages 2. The system of claim 1, (Appendix 6) The filtering unit is Inferring a user's emotions and adjusting filtering criteria based on the estimated user's emotions 2. The system of claim 1, (Appendix 7) The filtering unit is Optimize filtering algorithms by referencing historical defamation data 2. The system of claim 1, (Appendix 8) The filtering unit is Filtering is performed taking into account the attribute information of the message sender 2. The system of claim 1, (Appendix 9) The filtering unit is Infer user emotion and adjust the order in which filtering results are displayed based on the estimated user emotion 2. The system of claim 1, (Appendix 10) The filtering unit is Filtering messages based on their geographic distribution 2. The system of claim 1, (Appendix 11) The filtering unit is Improve filtering by looking up related literature for messages 2. The system of claim 1, (Appendix 12) The collecting unit includes: To estimate a user's emotion and determine a priority of messages to be collected based on the estimated user's emotion. 2. The system of claim 1, (Appendix 13) The collecting unit includes: Improve collection accuracy by considering message correlation 2. The system of claim 1, (Appendix 14) The collecting unit includes: Collect information by considering the attributes of the message sender 2. The system of claim 1, (Appendix 15) The collecting unit includes: To estimate a user's emotion and adjust a display method of a message to be collected based on the estimated user's emotion. 2. The system of claim 1, (Appendix 16) The collecting unit includes: Consider the geographic distribution of messages when collecting them 2. The system of claim 1, (Appendix 17) The collecting unit includes: Improve collection accuracy by referencing related literature for messages 2. The system of claim 1, (Appendix 18) The generation unit is Inferring user emotions and prioritizing documents to be generated based on the estimated user emotions 2. The system of claim 1, (Appendix 19) The generation unit is Improve generation accuracy by considering message correlation 2. The system of claim 1, (Appendix 20) The generation unit is Generate messages taking into account the attributes of the message sender 2. The system of claim 1, (Appendix 21) The generation unit is Inferring a user's emotions and adjusting the display method of a generated document based on the estimated user's emotions 2. The system of claim 1, (Appendix 22) The generation unit is Generate messages taking into account geographic distribution 2. The system of claim 1, (Appendix 23) The generation unit is Improve message generation accuracy by referencing related literature 2. The system of claim 1, (Appendix 24) The distribution unit is The system estimates the user's emotions and adjusts the content of mental care messages to be delivered based on the estimated user's emotions. 2. The system of claim 1, (Appendix 25) The distribution unit is Create the best message by referring to the victim's past mental state 2. The system of claim 1, (Appendix 26) The distribution unit is Customize mental health messages based on the victim's current life situation 2. The system of claim 1, (Appendix 27) The distribution unit is Estimate the user's emotions and determine the priority of mental care messages to be delivered based on the estimated user's emotions. 2. The system of claim 1, (Appendix 28) The distribution unit is Create optimal mental health messages based on the victim's geographic location 2. The system of claim 1, (Appendix 29) The distribution unit is Analyzing victims' social media activity to create mental health messages 2. The system of claim 1, [Explanation of symbols]

[0181] 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 filtering section that analyzes messages on SNS and determines the degree of slander; a collection unit that collects the defamatory messages detected by the filtering unit; a generation unit that generates a damage report and / or documentary evidence based on the messages collected by the collection unit; A delivery unit that delivers a mental care message based on the document created by the creation unit. system.

2. The filtering unit is Detecting specific keywords or phrases and determining their abusiveness 2. The system of claim 1 .

3. The collecting unit includes: Save detected abusive messages 2. The system of claim 1 .

4. The generation unit is Create a damage report and document evidence based on collected messages 2. The system of claim 1 .

5. The distribution unit is Analyze the victim's mental state and automatically create and distribute mental care messages 2. The system of claim 1 .

6. The filtering unit is Inferring a user's emotions and adjusting filtering criteria based on the estimated user's emotions 2. The system of claim 1 .

7. The filtering unit is Optimize filtering algorithms by referencing historical defamation data 2. The system of claim 1 .

8. The filtering unit is Filtering is performed taking into account the attribute information of the message sender 2. The system of claim 1 .

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