System

Abstract

To provide a system according to an embodiment, the system intending to efficiently determine consistency and authenticity of a content of statements.SOLUTION: A system according to an embodiment includes: a reception part; an analysis part; and a determination part. The reception part inputs a content of statements. The analysis part analyzes the content of the statements input by the reception part. The determination part determines at least one of the consistency or the truth or falsity of the content of the statements analyzed by the analysis part.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] Conventional technology had the problem that the process of determining the consistency and veracity of statements was complicated and difficult to carry out efficiently.

[0005] The system according to the embodiment aims to efficiently determine the consistency and authenticity of statements. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, and a determining unit. The receiving unit inputs a statement. The analyzing unit analyzes the statement input by the receiving unit. The determining unit determines at least one of the consistency or truth of the statement analyzed by the analyzing unit. Effect of the Invention

[0007] The system according to the embodiment can efficiently determine the consistency and authenticity of the statements. [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 the 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 interrogation support system according to the embodiment of the present invention is a system that inputs the contents of a statement, analyzes the contents, and judges the consistency and authenticity of the statement. The interrogation support system includes a reception unit that inputs the contents of a statement, an analysis unit that analyzes the contents of the statement, a judgment unit that judges the consistency and authenticity of the contents of the statement, and a posting unit that posts the provisions of the law if the contents of the statement violate the law. For example, the reception unit that inputs the contents of the statement can input the contents of the statement as text data. It can also accept voice input and handwritten input. Next, the analysis unit that analyzes the contents of the statement input by the reception unit analyzes the contents of the statement and judges whether they are inconsistent with other evidence. Natural language processing technology, similarity calculation, and pattern matching are used for the analysis. Furthermore, the judgment unit that judges the consistency and authenticity of the contents of the statement analyzed by the analysis unit judges whether the contents of the statement are consistent with past precedents and cases. In addition, the interrogation support system includes a posting unit that posts the provisions of the law if the contents of the statement violate the law. This system provides support for the interrogator to quickly judge the consistency and authenticity of the contents of the statement and take appropriate measures. Furthermore, it can also be deployed to legal professionals such as prosecutors, judges, and lawyers. This will enable the interrogation support system to quickly determine the consistency and veracity of statements and take appropriate action.

[0029] The interrogation support system according to the embodiment includes a reception unit, an analysis unit, and a judgment unit. The reception unit inputs the statement content. The statement content includes, for example, an oral statement, a written statement, electronic data, and the like, but is not limited to such examples. The reception unit can input the statement content as, for example, text data. The reception unit can also accept voice input and handwritten input. For example, the reception unit can convert voice input into text data using a voice recognition technology. The reception unit can convert handwritten input into text data using a handwriting recognition technology. The analysis unit analyzes the statement content. For example, natural language processing technology, similarity calculation, pattern matching, and the like are used for the analysis. For example, the analysis unit analyzes the statement content using a natural language processing technology to understand the meaning of the statement content. The analysis unit can calculate the similarity between the statement content and other evidence using similarity calculation. Furthermore, the analysis unit can evaluate the degree of agreement between the statement content and other evidence using pattern matching. The judgment unit judges the consistency and authenticity of the statement content analyzed by the analysis unit. For example, the judgment unit judges whether the statement is consistent with past precedents or cases. If the statement violates a law, the judgment unit can display the provisions of the law. This allows the interrogation support system according to the embodiment to quickly judge the consistency and authenticity of the statement and take appropriate measures.

[0030] The reception unit inputs the statement contents. The statement contents include, for example, oral statements, written statements, electronic data, etc., but are not limited to such examples. The reception unit can input the statement contents as, for example, text data. The reception unit can also accept voice input and handwritten input. For example, the reception unit can convert the voice input into text data using voice recognition technology. The reception unit can convert the handwritten input into text data using handwriting recognition technology. Specifically, the voice recognition technology uses a voice model using deep learning, and can convert the utterance of the declarant into text with high accuracy. The voice recognition system has functions such as removing background noise, identifying the speaker, and understanding the context of the utterance, and can accurately record the statement contents. The handwriting recognition technology uses optical character recognition (OCR) technology, and scans the handwritten statement contents and converts them into digital text. The OCR technology can recognize handwritten characters and symbols with high accuracy and store the statement contents as digital data. Furthermore, the reception unit can add metadata when inputting the statement contents. For example, information such as the name of the person making the statement, the date and time of the statement, and the location of the statement can be added to the input data to facilitate subsequent analysis and search. This allows the reception department to efficiently input statements in a variety of formats and centrally manage them as digital data.

[0031] The analysis unit analyzes the statement. For example, natural language processing technology, similarity calculation, pattern matching, etc. are used for the analysis. For example, the analysis unit uses natural language processing technology to analyze the statement and understand the meaning of the statement. Natural language processing technology includes morphological analysis, syntactic analysis, semantic analysis, etc., and can analyze the grammatical structure and meaning of the statement in detail. In morphological analysis, the statement is divided into words and the part of speech and meaning of each word are identified. In syntactic analysis, the grammatical structure of the statement is analyzed and the relationship between the subject, predicate, object, etc. is clarified. In semantic analysis, the context and meaning of the statement are understood and the intention and emotion of the statementr are analyzed. Furthermore, the analysis unit can calculate the similarity between the statement and other evidence using similarity calculation. For similarity calculation, a method such as cosine similarity or Jaccard coefficient is used, and the similarity is calculated by comparing the text data of the statement and other evidence. This makes it possible to evaluate the degree to which the statement matches other evidence. In addition, the analysis unit can use pattern matching to evaluate the consistency of the statement with other evidence. Pattern matching uses techniques such as regular expressions and template matching to evaluate whether specific patterns or phrases in the statement match other evidence, allowing for detailed analysis of the veracity and consistency of the statement.

[0032] The judgment unit judges the consistency and authenticity of the statement contents analyzed by the analysis unit. For example, the judgment unit judges whether the statement contents are consistent with past precedents or cases. Specifically, the statement contents are compared with a database of past precedents to confirm whether there are similar cases or precedents. As a result, if the statement contents are consistent with past cases, their credibility is increased. In addition, the judgment unit can display the provisions of the law if the statement contents violate the law. For example, if the statement contents conflict with a specific law, the provisions of the law are automatically displayed to encourage the statement holder and the interrogator to take appropriate action. Furthermore, the judgment unit can evaluate the consistency and inconsistencies of the statement contents. If the statement contents are consistent, their credibility is increased, but if there are many inconsistencies, their credibility is decreased. In order to evaluate the consistency of the statement contents, the judgment unit analyzes the chronological order and causal relationships of the statement contents and identifies inconsistencies. Furthermore, the judgment unit can perform emotion analysis of the statement contents to evaluate the emotional state of the statement holder. For emotion analysis, natural language processing technology is used to analyze the emotions in the statement and evaluate the emotional state of the statementr. This allows the credibility of the statement and the psychological state of the statementr to be evaluated in detail. As a result, the interrogation support system according to the embodiment can quickly determine the consistency and veracity of the statement and take appropriate measures.

[0033] The reception unit can input the statement as text data. The text data includes, for example, character codes, input interfaces, and the like, but is not limited to such examples. The reception unit can input the text data using, for example, a keyboard. The reception unit can also input the text data using a touch screen. Furthermore, the reception unit can convert the voice input into text data using voice recognition technology. For example, the reception unit analyzes the voice using voice recognition software and saves it as text data. Thereby, by inputting the statement as text data, the accuracy of the input is improved. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input voice recognition software to the generation AI and cause the generation AI to convert the voice data into text data.

[0034] The analysis unit can analyze the contents of the statement and determine whether it is inconsistent with other evidence. The other evidence includes, for example, physical evidence, testimony, digital data, etc., but is not limited to such examples. The analysis unit can, for example, analyze the contents of the statement and determine whether it is inconsistent with physical evidence. The analysis unit can also analyze the contents of the statement and determine whether it is inconsistent with testimony. Furthermore, the analysis unit can analyze the contents of the statement and determine whether it is inconsistent with digital data. For example, the analysis unit can analyze the contents of the statement using natural language processing technology to understand the meaning of the contents of the statement. Furthermore, the analysis unit can calculate the similarity between the contents of the statement and the physical evidence using similarity calculation. Furthermore, the analysis unit can evaluate the degree of agreement between the contents of the statement and the testimony using pattern matching. This can increase the reliability of the statement by determining the inconsistency between the contents of the statement and other evidence. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the contents of the statement to the generation AI and cause the generation AI to execute the analysis of the contents of the statement.

[0035] The judgment unit can determine whether the statement content is consistent with past precedents or cases. Past precedents include, but are not limited to, court judgment databases, legal literature, etc. Examples of cases include, but are not limited to, police records, past case data, etc. The judgment unit can, for example, determine whether the statement content is consistent with a court judgment database. The judgment unit can also determine whether the statement content is consistent with legal literature. Furthermore, the judgment unit can also determine whether the statement content is consistent with police records. For example, the judgment unit analyzes the statement content using natural language processing technology to understand the meaning of the statement content. Furthermore, the judgment unit can calculate the similarity between the statement content and the precedents using similarity calculation. Furthermore, the judgment unit can evaluate the degree of agreement between the statement content and the case using pattern matching. This can increase the reliability of the statement by determining whether it is consistent with past precedents or cases. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the content of a statement to a generation AI and have the generation AI analyze the content of the statement.

[0036] The display unit includes a display unit that displays the provision of the law when the statement content violates the law. The provision of the law includes, but is not limited to, a statute database, highlighting of the provision, and the like. For example, when the statement content violates the law, the display unit can acquire the provision of the law from the statute database and display it. Furthermore, when the statement content violates the law, the display unit can highlight the provision of the law. Furthermore, when the statement content violates the law, the display unit can send the provision of the law by email. For example, the display unit analyzes the statement content and determines whether the statement content violates the law. Furthermore, when the statement content violates the law, the display unit acquires the provision of the law from the statute database and displays it. As a result, when the law is violated, the provision of the law is displayed, so that legal action can be taken quickly. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can input the content of a statement into a generation AI and have the generation AI determine whether the content of the statement violates the law.

[0037] The reception unit can also accept voice input or handwritten input. Voice input includes, for example, voice recognition technology, microphone types, etc., but is not limited to such examples. Handwritten input includes, for example, handwriting recognition technology, tablet device, etc., but is not limited to such examples. The reception unit can convert voice input into text data, for example, using voice recognition technology. The reception unit can also convert handwritten input into text data, using handwriting recognition technology. For example, the reception unit analyzes voice using voice recognition software and saves it as text data. The reception unit can also accept handwritten input using a tablet device and convert it into text data using handwriting recognition software. Thereby, by accepting voice input and handwritten input, the diversity of input methods is improved. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input voice recognition software to the generation AI and cause the generation AI to convert voice data into text data.

[0038] The analysis unit can analyze the statement contents using natural language processing technology, similarity calculation, and pattern matching. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, etc., but is not limited to such examples. Similarity calculation includes, for example, cosine similarity, Jaccard coefficient, etc., but is not limited to such examples. Pattern matching includes, for example, regular expression, template matching, etc., but is not limited to such examples. The analysis unit analyzes the statement contents using, for example, natural language processing technology to understand the meaning of the statement contents. In addition, the analysis unit can calculate the similarity between the statement contents and other evidence using similarity calculation. Furthermore, the analysis unit can evaluate the degree of agreement between the statement contents and other evidence using pattern matching. Thereby, by using natural language processing technology, similarity calculation, and pattern matching, the analysis accuracy of the statement contents is improved. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the statement contents to the generation AI and cause the generation AI to execute the analysis of the statement contents.

[0039] The reception unit can analyze the user's past statement history when inputting the statement content and select the optimal input method. The past statement history includes, for example, a database search method, a history storage period, and the like, but is not limited to such examples. For example, if the user has used text input in the past, the reception unit preferentially suggests text input. In addition, if the user has used voice input frequently in the past, the reception unit can also preferentially suggest voice input. Furthermore, if the user has used handwriting input in the past, the reception unit can also preferentially suggest handwriting input. In this way, the optimal input method can be selected by analyzing the past statement history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input past statement history data to the generation AI and cause the generation AI to select the optimal input method.

[0040] When inputting the content of the statement, the reception unit can preferentially input a statement having a high relevance in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, location information services, and the like, but is not limited to such examples. For example, when the user is in a specific area, the reception unit preferentially inputs a statement related to the area. In addition, when the user is moving, the reception unit can also preferentially input a statement related to the current location. Furthermore, when the user is in a specific location, the reception unit can also preferentially input a statement related to the location. In this way, by considering the geographical location information, a statement having a high relevance can be preferentially input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select a statement having a high relevance.

[0041] The reception unit can analyze the social media activity of the user when the statement content is input, and input the related statement. The social media activity includes, for example, an analysis of the post content, an analysis of the followers, and the like, but is not limited to such examples. For example, when the user posts about a specific topic on social media, the reception unit preferentially inputs a statement related to the topic. In addition, when the user posts about a specific place on social media, the reception unit can also preferentially input a statement related to the place. Furthermore, when the user posts about a specific person on social media, the reception unit can also preferentially input a statement related to the person. In this way, the related statement can be input by analyzing the social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the social media activity data of the user to the generation AI and cause the generation AI to select the related statement.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the statement content during the analysis. The importance includes, for example, impact evaluation, risk evaluation, and the like, but is not limited to such examples. For example, the analysis unit performs a detailed analysis of important statement content. In addition, the analysis unit can perform a concise analysis of statement content with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis stepwise according to the importance of the statement content. Thereby, the efficiency of the analysis is improved by adjusting the level of detail of the analysis based on the importance of the statement content. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the importance data of the statement content to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the statement content during analysis. The categories include, for example, topic classification, theme classification, and the like, but are not limited to such examples. For example, when the statement content is related to a crime, the analysis unit applies a crime analysis algorithm. Furthermore, when the statement content is related to a traffic violation, the analysis unit can apply a traffic violation analysis algorithm. Furthermore, when the statement content is related to a civil case, the analysis unit can apply a civil case analysis algorithm. Thereby, by applying the analysis algorithm according to the category of the statement content, the accuracy of the analysis is improved. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input category data of the statement content to the generation AI and cause the generation AI to apply the analysis algorithm.

[0044] The analysis unit can determine the priority of the analysis based on the submission time of the statement contents at the time of analysis. The submission time includes, but is not limited to, for example, the submission date and the submission time. For example, the analysis unit performs the analysis preferentially when the statement contents are submitted recently. In addition, the analysis unit can lower the priority of the analysis when the statement contents are old. Furthermore, the analysis unit can gradually adjust the priority of the analysis according to the submission time. This improves the efficiency of the analysis by determining the priority of the analysis based on the submission time of the statement contents. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the submission time data of the statement contents to the generation AI and cause the generation AI to execute the determination of the priority of the analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the statement contents during analysis. The relevance includes, for example, the degree of agreement of the contents, the relevance score, and the like, but is not limited to such examples. For example, when the statement contents are highly relevant to other evidence, the analysis unit performs the analysis preferentially. In addition, when the statement contents are less relevant to other evidence, the analysis unit can lower the priority of the analysis. Furthermore, the analysis unit can gradually adjust the order of analysis according to the relevance of the statement contents. Thereby, the efficiency of the analysis is improved by adjusting the order of analysis based on the relevance of the statement contents. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance data of the statement contents to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The judgment unit can improve the accuracy of the judgment by considering the interrelationships of the statement contents at the time of judgment. The interrelationships include, for example, analysis of causal relationships, evaluation of correlations, and the like, but are not limited to such examples. For example, the judgment unit can increase the accuracy of the judgment when the statement contents are consistent with other evidence. In addition, the judgment unit can decrease the accuracy of the judgment when the statement contents are inconsistent with other evidence. Furthermore, the judgment unit can gradually adjust the accuracy of the judgment based on the interrelationships of the statement contents. As a result, the accuracy of the judgment is improved by considering the interrelationships of the statement contents. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the interrelationship data of the statement contents to the generation AI and cause the generation AI to adjust the accuracy of the judgment.

[0047] The judgment unit can make a judgment by taking into consideration the attribute information of the person who submitted the statement. The attribute information includes, for example, age, sex, occupation, etc., but is not limited to such examples. For example, the judgment unit makes a strict judgment when the person who submitted the statement has a criminal record. Also, the judgment unit can make a flexible judgment when the person who submitted the statement is a highly reliable person. Furthermore, the judgment unit can gradually adjust the judgment criteria based on the attribute information of the person who submitted the statement. As a result, the accuracy of the judgment is improved by taking into consideration the attribute information of the person who submitted the statement. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the attribute information data of the person who submitted the statement to the generation AI and cause the generation AI to adjust the judgment criteria.

[0048] The judgment unit can make a judgment taking into consideration the geographical distribution of the statement contents at the time of judgment. The geographical distribution includes, for example, distribution by region, map display, and the like, but is not limited to such examples. For example, when the statement contents are concentrated in a specific region, the judgment unit prioritizes a judgment related to that region. In addition, when the statement contents are dispersed over a wide area, the judgment unit can make an overall judgment. Furthermore, the judgment unit can adjust the priority of the judgment based on the geographical distribution of the statement contents. As a result, the accuracy of the judgment is improved by taking the geographical distribution into consideration. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input geographical distribution data of the statement contents to the generation AI and cause the generation AI to adjust the priority of the judgment.

[0049] The judgment unit can improve the accuracy of the judgment by referring to related literature of the statement content at the time of judgment. The related literature includes, but is not limited to, academic papers, specialized books, and the like. For example, when the statement content matches past precedents, the judgment unit can increase the accuracy of the judgment. Also, when the statement content contradicts past precedents, the judgment unit can decrease the accuracy of the judgment. Furthermore, the judgment unit can gradually adjust the accuracy of the judgment based on the related literature of the statement content. As a result, the accuracy of the judgment is improved by referring to the related literature. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input related literature data of the statement content to the generation AI and cause the generation AI to adjust the accuracy of the judgment.

[0050] The posting unit can improve the accuracy of posting by considering the interrelationships of the statement contents at the time of posting. Posting includes, for example, posting to a bulletin board, sending of e-mail, and the like, but is not limited to such examples. For example, when the statement contents are consistent with other evidence, the posting unit posts the related legal provisions preferentially. In addition, when the statement contents are inconsistent with other evidence, the posting unit can refrain from posting the related legal provisions. Furthermore, the posting unit can adjust the priority of the legal provisions to be posted based on the interrelationships of the statement contents. As a result, the accuracy of posting is improved by considering the interrelationships of the statement contents. A part or all of the above-mentioned processing in the posting unit may be performed, for example, using AI, or may be performed without using AI. For example, the posting unit can input interrelationship data of the statement contents to the generation AI and cause the generation AI to adjust the accuracy of posting.

[0051] The posting unit can post the statement while taking into consideration the attribute information of the person who submitted the statement. The attribute information of the person who submitted the statement includes, but is not limited to, the age, sex, and occupation. For example, if the person who submitted the statement has a criminal record, the posting unit strictly posts the relevant legal provisions. Also, if the person who submitted the statement is a highly reliable person, the posting unit can post the legal provisions flexibly. Furthermore, the posting unit can adjust the content of the legal provisions to be posted based on the attribute information of the person who submitted the statement. This improves the accuracy of posting by taking into consideration the attribute information of the person who submitted the statement. A part or all of the above-mentioned processing in the posting unit may be performed, for example, using AI or may be performed without using AI. For example, the posting unit can input the attribute information data of the person who submitted the statement to the generation AI and cause the generation AI to adjust the content of the posting.

[0052] The posting unit can post the statement taking into consideration the geographical distribution of the statement contents. The geographical distribution includes, but is not limited to, distribution by region and map display. For example, when the statement contents are related to a specific region, the posting unit preferentially posts legal provisions related to the region. In addition, when the statement contents are widely distributed, the posting unit can post the overall legal provisions. Furthermore, the posting unit can adjust the priority of the legal provisions to be posted based on the geographical distribution of the statement contents. This improves the accuracy of posting by taking the geographical distribution into consideration. A part or all of the above-mentioned processing in the posting unit may be performed, for example, using AI or may be performed without using AI. For example, the posting unit can input geographical distribution data of the statement contents to the generation AI and cause the generation AI to adjust the priority of posting.

[0053] The posting unit can improve the accuracy of posting by referring to related literature of the statement content at the time of posting. The related literature includes, but is not limited to, academic papers, specialized books, and the like. For example, when the statement content is consistent with past precedents, the posting unit posts the related legal provisions preferentially. In addition, when the statement content is inconsistent with past precedents, the posting unit can refrain from posting the related legal provisions. Furthermore, the posting unit can adjust the contents of the legal provisions to be posted based on the related literature of the statement content. As a result, the accuracy of posting is improved by referring to the related literature. A part or all of the above-mentioned processing in the posting unit may be performed, for example, using AI, or may be performed without using AI. For example, the posting unit can input related literature data of the statement content to the generation AI and cause the generation AI to adjust the content of the posting.

[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 reception unit can analyze the user's past statement history and select the optimal input method. For example, if the user has used text input in the past, the reception unit can preferentially suggest text input. Also, if the user has used voice input a lot in the past, the reception unit can preferentially suggest voice input. Furthermore, if the user has used handwriting input in the past, the reception unit can preferentially suggest handwriting input. In this way, the optimal input method can be selected by analyzing the past statement history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input past statement history data to the generation AI and cause the generation AI to select the optimal input method.

[0056] The analysis unit can determine the priority of the analysis based on the submission time of the statement. For example, if the statement has been submitted recently, the analysis can be prioritized. Also, if the statement is old, the priority of the analysis can be lowered. Furthermore, the priority of the analysis can be adjusted stepwise according to the submission time. This improves the efficiency of the analysis by determining the priority of the analysis based on the submission time of the statement. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the statement to the generation AI and have the generation AI execute the determination of the priority of the analysis.

[0057] The judgment unit can improve the accuracy of the judgment by taking into account the interrelationships of the statements. For example, when the statements match other evidence, the accuracy of the judgment can be increased. Also, when the statements contradict other evidence, the accuracy of the judgment can be decreased. Furthermore, the accuracy of the judgment can be adjusted stepwise based on the interrelationships of the statements. This improves the accuracy of the judgment by taking into account the interrelationships of the statements. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI or may be performed without using AI. For example, the judgment unit can input interrelationship data of the statements to the generation AI and cause the generation AI to adjust the accuracy of the judgment.

[0058] The posting unit can post the statements taking into consideration the geographic distribution of the statements. For example, if the statements relate to a specific region, legal provisions related to that region are posted with priority. Also, if the statements are widely distributed, the overall legal provisions can be posted. Furthermore, the priority of the legal provisions to be posted can be adjusted based on the geographic distribution of the statements. This improves the accuracy of posting by taking the geographic distribution into consideration. Some or all of the above-mentioned processing in the posting unit can be performed, for example, using AI or without AI. For example, the posting unit can input geographic distribution data of the statements to the generation AI and cause the generation AI to adjust the priority of posting.

[0059] The analysis unit can apply different analysis algorithms depending on the category of the statement content. For example, if the statement content is related to a crime, a crime analysis algorithm can be applied. Also, if the statement content is related to a traffic violation, a traffic violation analysis algorithm can be applied. Furthermore, if the statement content is related to a civil case, a civil case analysis algorithm can be applied. Thereby, by applying an analysis algorithm according to the category of the statement content, the accuracy of the analysis is improved. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input category data of the statement content to the generation AI and cause the generation AI to apply the analysis algorithm.

[0060] The judgment unit can make a judgment taking into account the attribute information of the person who submitted the statement. For example, if the person who submitted the statement has a criminal record, a strict judgment can be made. Also, if the person who submitted the statement is a highly reliable person, a flexible judgment can be made. Furthermore, the judgment criteria can be gradually adjusted based on the attribute information of the person who submitted the statement. As a result, the accuracy of the judgment can be improved by taking into account the attribute information of the person who submitted the statement. Some or all of the above-mentioned processing in the judgment unit can be performed, for example, using AI or without using AI. For example, the judgment unit can input the attribute information data of the person who submitted the statement to the generation AI and cause the generation AI to adjust the judgment criteria.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The reception unit inputs the statement content. The statement content includes oral statements, written statements, electronic data, and the like. The reception unit can input the statement content as text data, and can also accept voice input and handwritten input. For example, voice input can be converted into text data using voice recognition technology, and handwritten input can be converted into text data using handwriting recognition technology. Step 2: The analysis unit analyzes the statement. Natural language processing technology, similarity calculation, pattern matching, etc. are used for the analysis. For example, natural language processing technology is used to understand the meaning of the statement, similarity calculation is used to calculate the similarity between the statement and other evidence, and pattern matching is used to evaluate the degree of agreement between the statement and other evidence. Step 3: The judgment unit judges the consistency and veracity of the statement analyzed by the analysis unit. For example, it can judge whether the statement matches past precedents or cases, and if the statement violates the law, it can display the provisions of the law.

[0063] (Example 2) The interrogation support system according to the embodiment of the present invention is a system that inputs the contents of a statement, analyzes the contents, and judges the consistency and authenticity of the statement. The interrogation support system includes a reception unit that inputs the contents of a statement, an analysis unit that analyzes the contents of the statement, a judgment unit that judges the consistency and authenticity of the contents of the statement, and a posting unit that posts the provisions of the law if the contents of the statement violate the law. For example, the reception unit that inputs the contents of the statement can input the contents of the statement as text data. It can also accept voice input and handwritten input. Next, the analysis unit that analyzes the contents of the statement input by the reception unit analyzes the contents of the statement and judges whether they are inconsistent with other evidence. Natural language processing technology, similarity calculation, and pattern matching are used for the analysis. Furthermore, the judgment unit that judges the consistency and authenticity of the contents of the statement analyzed by the analysis unit judges whether the contents of the statement are consistent with past precedents and cases. In addition, the interrogation support system includes a posting unit that posts the provisions of the law if the contents of the statement violate the law. This system provides support for the interrogator to quickly judge the consistency and authenticity of the contents of the statement and take appropriate measures. Furthermore, it can also be deployed to legal professionals such as prosecutors, judges, and lawyers. This will enable the interrogation support system to quickly determine the consistency and veracity of statements and take appropriate action.

[0064] The interrogation support system according to the embodiment includes a reception unit, an analysis unit, and a judgment unit. The reception unit inputs the statement content. The statement content includes, for example, an oral statement, a written statement, electronic data, and the like, but is not limited to such examples. The reception unit can input the statement content as, for example, text data. The reception unit can also accept voice input and handwritten input. For example, the reception unit can convert voice input into text data using a voice recognition technology. The reception unit can convert handwritten input into text data using a handwriting recognition technology. The analysis unit analyzes the statement content. For example, natural language processing technology, similarity calculation, pattern matching, and the like are used for the analysis. For example, the analysis unit analyzes the statement content using a natural language processing technology to understand the meaning of the statement content. The analysis unit can calculate the similarity between the statement content and other evidence using similarity calculation. Furthermore, the analysis unit can evaluate the degree of agreement between the statement content and other evidence using pattern matching. The judgment unit judges the consistency and authenticity of the statement content analyzed by the analysis unit. For example, the judgment unit judges whether the statement is consistent with past precedents or cases. If the statement violates a law, the judgment unit can display the provisions of the law. This allows the interrogation support system according to the embodiment to quickly judge the consistency and authenticity of the statement and take appropriate measures.

[0065] The reception unit inputs the statement contents. The statement contents include, for example, oral statements, written statements, electronic data, etc., but are not limited to such examples. The reception unit can input the statement contents as, for example, text data. The reception unit can also accept voice input and handwritten input. For example, the reception unit can convert the voice input into text data using voice recognition technology. The reception unit can convert the handwritten input into text data using handwriting recognition technology. Specifically, the voice recognition technology uses a voice model using deep learning, and can convert the utterance of the declarant into text with high accuracy. The voice recognition system has functions such as removing background noise, identifying the speaker, and understanding the context of the utterance, and can accurately record the statement contents. The handwriting recognition technology uses optical character recognition (OCR) technology, and scans the handwritten statement contents and converts them into digital text. The OCR technology can recognize handwritten characters and symbols with high accuracy and store the statement contents as digital data. Furthermore, the reception unit can add metadata when inputting the statement contents. For example, information such as the name of the person making the statement, the date and time of the statement, and the location of the statement can be added to the input data to facilitate subsequent analysis and search. This allows the reception department to efficiently input statements in a variety of formats and centrally manage them as digital data.

[0066] The analysis unit analyzes the statement. For example, natural language processing technology, similarity calculation, pattern matching, etc. are used for the analysis. For example, the analysis unit uses natural language processing technology to analyze the statement and understand the meaning of the statement. Natural language processing technology includes morphological analysis, syntactic analysis, semantic analysis, etc., and can analyze the grammatical structure and meaning of the statement in detail. In morphological analysis, the statement is divided into words and the part of speech and meaning of each word are identified. In syntactic analysis, the grammatical structure of the statement is analyzed and the relationship between the subject, predicate, object, etc. is clarified. In semantic analysis, the context and meaning of the statement are understood and the intention and emotion of the statementr are analyzed. Furthermore, the analysis unit can calculate the similarity between the statement and other evidence using similarity calculation. For similarity calculation, a method such as cosine similarity or Jaccard coefficient is used, and the similarity is calculated by comparing the text data of the statement and other evidence. This makes it possible to evaluate the degree to which the statement matches other evidence. In addition, the analysis unit can use pattern matching to evaluate the consistency of the statement with other evidence. Pattern matching uses techniques such as regular expressions and template matching to evaluate whether specific patterns or phrases in the statement match other evidence, allowing for detailed analysis of the veracity and consistency of the statement.

[0067] The judgment unit judges the consistency and authenticity of the statement contents analyzed by the analysis unit. For example, the judgment unit judges whether the statement contents are consistent with past precedents or cases. Specifically, the statement contents are compared with a database of past precedents to confirm whether there are similar cases or precedents. As a result, if the statement contents are consistent with past cases, their credibility is increased. In addition, the judgment unit can display the provisions of the law if the statement contents violate the law. For example, if the statement contents conflict with a specific law, the provisions of the law are automatically displayed to encourage the statement holder and the interrogator to take appropriate action. Furthermore, the judgment unit can evaluate the consistency and inconsistencies of the statement contents. If the statement contents are consistent, their credibility is increased, but if there are many inconsistencies, their credibility is decreased. In order to evaluate the consistency of the statement contents, the judgment unit analyzes the chronological order and causal relationships of the statement contents and identifies inconsistencies. Furthermore, the judgment unit can perform emotion analysis of the statement contents to evaluate the emotional state of the statement holder. For emotion analysis, natural language processing technology is used to analyze the emotions in the statement and evaluate the emotional state of the statementr. This allows the credibility of the statement and the psychological state of the statementr to be evaluated in detail. As a result, the interrogation support system according to the embodiment can quickly determine the consistency and veracity of the statement and take appropriate measures.

[0068] The reception unit can input the statement as text data. The text data includes, for example, character codes, input interfaces, and the like, but is not limited to such examples. The reception unit can input the text data using, for example, a keyboard. The reception unit can also input the text data using a touch screen. Furthermore, the reception unit can convert the voice input into text data using voice recognition technology. For example, the reception unit analyzes the voice using voice recognition software and saves it as text data. Thereby, by inputting the statement as text data, the accuracy of the input is improved. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input voice recognition software to the generation AI and cause the generation AI to convert the voice data into text data.

[0069] The analysis unit can analyze the contents of the statement and determine whether it is inconsistent with other evidence. The other evidence includes, for example, physical evidence, testimony, digital data, etc., but is not limited to such examples. The analysis unit can, for example, analyze the contents of the statement and determine whether it is inconsistent with physical evidence. The analysis unit can also analyze the contents of the statement and determine whether it is inconsistent with testimony. Furthermore, the analysis unit can analyze the contents of the statement and determine whether it is inconsistent with digital data. For example, the analysis unit can analyze the contents of the statement using natural language processing technology to understand the meaning of the contents of the statement. Furthermore, the analysis unit can calculate the similarity between the contents of the statement and the physical evidence using similarity calculation. Furthermore, the analysis unit can evaluate the degree of agreement between the contents of the statement and the testimony using pattern matching. This can increase the reliability of the statement by determining the inconsistency between the contents of the statement and other evidence. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the contents of the statement to the generation AI and cause the generation AI to execute the analysis of the contents of the statement.

[0070] The judgment unit can determine whether the statement content is consistent with past precedents or cases. Past precedents include, but are not limited to, court judgment databases, legal literature, etc. Examples of cases include, but are not limited to, police records, past case data, etc. The judgment unit can, for example, determine whether the statement content is consistent with a court judgment database. The judgment unit can also determine whether the statement content is consistent with legal literature. Furthermore, the judgment unit can also determine whether the statement content is consistent with police records. For example, the judgment unit analyzes the statement content using natural language processing technology to understand the meaning of the statement content. Furthermore, the judgment unit can calculate the similarity between the statement content and the precedents using similarity calculation. Furthermore, the judgment unit can evaluate the degree of agreement between the statement content and the case using pattern matching. This can increase the reliability of the statement by determining whether it is consistent with past precedents or cases. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the content of a statement to a generation AI and have the generation AI analyze the content of the statement.

[0071] The display unit includes a display unit that displays the provision of the law when the statement content violates the law. The provision of the law includes, but is not limited to, a statute database, highlighting of the provision, and the like. For example, when the statement content violates the law, the display unit can acquire the provision of the law from the statute database and display it. Furthermore, when the statement content violates the law, the display unit can highlight the provision of the law. Furthermore, when the statement content violates the law, the display unit can send the provision of the law by email. For example, the display unit analyzes the statement content and determines whether the statement content violates the law. Furthermore, when the statement content violates the law, the display unit acquires the provision of the law from the statute database and displays it. As a result, when the law is violated, the provision of the law is displayed, so that legal action can be taken quickly. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can input the content of a statement into a generation AI and have the generation AI determine whether the content of the statement violates the law.

[0072] The reception unit can also accept voice input or handwritten input. Voice input includes, for example, voice recognition technology, microphone types, etc., but is not limited to such examples. Handwritten input includes, for example, handwriting recognition technology, tablet device, etc., but is not limited to such examples. The reception unit can convert voice input into text data, for example, using voice recognition technology. The reception unit can also convert handwritten input into text data, using handwriting recognition technology. For example, the reception unit analyzes voice using voice recognition software and saves it as text data. The reception unit can also accept handwritten input using a tablet device and convert it into text data using handwriting recognition software. Thereby, by accepting voice input and handwritten input, the diversity of input methods is improved. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input voice recognition software to the generation AI and cause the generation AI to convert voice data into text data.

[0073] The analysis unit can analyze the statement contents using natural language processing technology, similarity calculation, and pattern matching. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, etc., but is not limited to such examples. Similarity calculation includes, for example, cosine similarity, Jaccard coefficient, etc., but is not limited to such examples. Pattern matching includes, for example, regular expression, template matching, etc., but is not limited to such examples. The analysis unit analyzes the statement contents using, for example, natural language processing technology to understand the meaning of the statement contents. In addition, the analysis unit can calculate the similarity between the statement contents and other evidence using similarity calculation. Furthermore, the analysis unit can evaluate the degree of agreement between the statement contents and other evidence using pattern matching. Thereby, by using natural language processing technology, similarity calculation, and pattern matching, the analysis accuracy of the statement contents is improved. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the statement contents to the generation AI and cause the generation AI to execute the analysis of the statement contents.

[0074] The reception unit can estimate the user's emotion and adjust the input timing of the statement content based on the estimated user's emotion. For example, when the user is nervous, the reception unit prompts the user to input the statement content after allowing time for the user to relax. In addition, when the user is impatient, the reception unit can wait until the user calms down before starting to input the statement content. Furthermore, when the user is relaxed, the reception unit can immediately start to input the statement content. Thereby, the accuracy of the statement is improved by adjusting the input timing based on 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0075] The reception unit can analyze the user's past statement history when inputting the statement content and select the optimal input method. The past statement history includes, for example, a database search method, a history storage period, and the like, but is not limited to such examples. For example, if the user has used text input in the past, the reception unit preferentially suggests text input. In addition, if the user has used voice input frequently in the past, the reception unit can also preferentially suggest voice input. Furthermore, if the user has used handwriting input in the past, the reception unit can also preferentially suggest handwriting input. In this way, the optimal input method can be selected by analyzing the past statement history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input past statement history data to the generation AI and cause the generation AI to select the optimal input method.

[0076] The reception unit can perform filtering based on the user's current psychological state when inputting the statement content. The psychological state includes, for example, a psychological test, a behavioral analysis, and the like, but is not limited to such examples. For example, when the user is nervous, the reception unit starts with simple questions and gradually requests detailed statements. Also, when the user is relaxed, the reception unit can immediately request detailed statements. Furthermore, when the user is impatient, the reception unit can also request statements by prioritizing important questions. As a result, filtering based on the user's psychological state improves the accuracy of the statement. 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's psychological state data to the generation AI and cause the generation AI to execute filtering.

[0077] The reception unit can estimate the user's emotion and determine the priority of the statement contents to be input based on the estimated user's emotion. The priority includes, for example, importance evaluation, urgency evaluation, and the like, but is not limited to such examples. For example, when the user is nervous, the reception unit starts with simple questions and gradually requests detailed statements. Also, when the user is relaxed, the reception unit can immediately request detailed statements. Furthermore, when the user is impatient, the reception unit can also request statements by prioritizing important questions. This improves the accuracy of the statements by determining the priority of the statement contents based on the user's emotion. The estimation of the emotion 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority.

[0078] When inputting the content of the statement, the reception unit can preferentially input a statement having a high relevance in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, location information services, and the like, but is not limited to such examples. For example, when the user is in a specific area, the reception unit preferentially inputs a statement related to the area. In addition, when the user is moving, the reception unit can also preferentially input a statement related to the current location. Furthermore, when the user is in a specific location, the reception unit can also preferentially input a statement related to the location. In this way, by considering the geographical location information, a statement having a high relevance can be preferentially input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select a statement having a high relevance.

[0079] The reception unit can analyze the social media activity of the user when the statement content is input, and input the related statement. The social media activity includes, for example, an analysis of the post content, an analysis of the followers, and the like, but is not limited to such examples. For example, when the user posts about a specific topic on social media, the reception unit preferentially inputs a statement related to the topic. In addition, when the user posts about a specific place on social media, the reception unit can also preferentially input a statement related to the place. Furthermore, when the user posts about a specific person on social media, the reception unit can also preferentially input a statement related to the person. In this way, the related statement can be input by analyzing the social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the social media activity data of the user to the generation AI and cause the generation AI to select the related statement.

[0080] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The expression method includes, for example, but is not limited to, changing the wording, using a chart, and the like. For example, when the user is nervous, the analysis unit uses concise and easy-to-understand expressions. In addition, when the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, when the user is impatient, the analysis unit can also provide analysis results with important points emphasized. This improves understanding of the analysis results by adjusting the expression method of the analysis based on 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 analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the statement content during the analysis. The importance includes, for example, impact evaluation, risk evaluation, and the like, but is not limited to such examples. For example, the analysis unit performs a detailed analysis of important statement content. In addition, the analysis unit can perform a concise analysis of statement content with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis stepwise according to the importance of the statement content. Thereby, the efficiency of the analysis is improved by adjusting the level of detail of the analysis based on the importance of the statement content. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the importance data of the statement content to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the statement content during analysis. The categories include, for example, topic classification, theme classification, and the like, but are not limited to such examples. For example, when the statement content is related to a crime, the analysis unit applies a crime analysis algorithm. Furthermore, when the statement content is related to a traffic violation, the analysis unit can apply a traffic violation analysis algorithm. Furthermore, when the statement content is related to a civil case, the analysis unit can apply a civil case analysis algorithm. Thereby, by applying the analysis algorithm according to the category of the statement content, the accuracy of the analysis is improved. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input category data of the statement content to the generation AI and cause the generation AI to apply the analysis algorithm.

[0083] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The length includes, for example, a character limit, a time limit, and the like, but is not limited to such examples. For example, when the user is nervous, the analysis unit performs a short and to-the-point analysis. Furthermore, when the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, when the user is impatient, the analysis unit can perform a short analysis with emphasis on important points. This improves the understanding of the analysis result by adjusting the length of the analysis based on 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 analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the analysis.

[0084] The analysis unit can determine the priority of the analysis based on the submission time of the statement contents at the time of analysis. The submission time includes, but is not limited to, for example, the submission date and the submission time. For example, the analysis unit performs the analysis preferentially when the statement contents are submitted recently. In addition, the analysis unit can lower the priority of the analysis when the statement contents are old. Furthermore, the analysis unit can gradually adjust the priority of the analysis according to the submission time. This improves the efficiency of the analysis by determining the priority of the analysis based on the submission time of the statement contents. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the submission time data of the statement contents to the generation AI and cause the generation AI to execute the determination of the priority of the analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the statement contents during analysis. The relevance includes, for example, the degree of agreement of the contents, the relevance score, and the like, but is not limited to such examples. For example, when the statement contents are highly relevant to other evidence, the analysis unit performs the analysis preferentially. In addition, when the statement contents are less relevant to other evidence, the analysis unit can lower the priority of the analysis. Furthermore, the analysis unit can gradually adjust the order of analysis according to the relevance of the statement contents. Thereby, the efficiency of the analysis is improved by adjusting the order of analysis based on the relevance of the statement contents. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance data of the statement contents to the generation AI and cause the generation AI to adjust the order of analysis.

[0086] The judgment unit can estimate the user's emotion and adjust the judgment criteria based on the estimated user's emotion. The judgment criteria include, for example, but are not limited to, a change in evaluation criteria and an adjustment of weighting. For example, the judgment unit applies strict criteria when the user is nervous. Furthermore, the judgment unit can apply flexible criteria when the user is relaxed. Furthermore, the judgment unit can apply criteria for making a quick judgment when the user is impatient. Thereby, by adjusting the judgment criteria based on the user's emotion, the accuracy of the judgment is improved. The estimation of the emotion 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 judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the judgment criteria.

[0087] The judgment unit can improve the accuracy of the judgment by considering the interrelationships of the statement contents at the time of judgment. The interrelationships include, for example, analysis of causal relationships, evaluation of correlations, and the like, but are not limited to such examples. For example, the judgment unit can increase the accuracy of the judgment when the statement contents are consistent with other evidence. In addition, the judgment unit can decrease the accuracy of the judgment when the statement contents are inconsistent with other evidence. Furthermore, the judgment unit can gradually adjust the accuracy of the judgment based on the interrelationships of the statement contents. As a result, the accuracy of the judgment is improved by considering the interrelationships of the statement contents. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the interrelationship data of the statement contents to the generation AI and cause the generation AI to adjust the accuracy of the judgment.

[0088] The judgment unit can make a judgment by taking into consideration the attribute information of the person who submitted the statement. The attribute information includes, for example, age, sex, occupation, etc., but is not limited to such examples. For example, the judgment unit makes a strict judgment when the person who submitted the statement has a criminal record. Also, the judgment unit can make a flexible judgment when the person who submitted the statement is a highly reliable person. Furthermore, the judgment unit can gradually adjust the judgment criteria based on the attribute information of the person who submitted the statement. As a result, the accuracy of the judgment is improved by taking into consideration the attribute information of the person who submitted the statement. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the attribute information data of the person who submitted the statement to the generation AI and cause the generation AI to adjust the judgment criteria.

[0089] The judgment unit can estimate the user's emotion and adjust the order of displaying the judgment results based on the estimated user's emotion. The display order includes, for example, an order of importance, a chronological order, and the like, but is not limited to such examples. For example, when the user is nervous, the judgment unit displays important results first. Furthermore, when the user is relaxed, the judgment unit can display detailed results sequentially. Furthermore, when the user is impatient, the judgment unit can quickly display the results. Thereby, by adjusting the display order of the results based on the user's emotion, the understanding of the results is improved. The estimation of the emotion 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 judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display order.

[0090] The judgment unit can make a judgment taking into consideration the geographical distribution of the statement contents at the time of judgment. The geographical distribution includes, for example, distribution by region, map display, and the like, but is not limited to such examples. For example, when the statement contents are concentrated in a specific region, the judgment unit prioritizes a judgment related to that region. In addition, when the statement contents are dispersed over a wide area, the judgment unit can make an overall judgment. Furthermore, the judgment unit can adjust the priority of the judgment based on the geographical distribution of the statement contents. As a result, the accuracy of the judgment is improved by taking the geographical distribution into consideration. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input geographical distribution data of the statement contents to the generation AI and cause the generation AI to adjust the priority of the judgment.

[0091] The judgment unit can improve the accuracy of the judgment by referring to related literature of the statement content at the time of judgment. The related literature includes, but is not limited to, academic papers, specialized books, and the like. For example, when the statement content matches past precedents, the judgment unit can increase the accuracy of the judgment. Also, when the statement content contradicts past precedents, the judgment unit can decrease the accuracy of the judgment. Furthermore, the judgment unit can gradually adjust the accuracy of the judgment based on the related literature of the statement content. As a result, the accuracy of the judgment is improved by referring to the related literature. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input related literature data of the statement content to the generation AI and cause the generation AI to adjust the accuracy of the judgment.

[0092] The posting unit can estimate the user's emotion and determine the priority of legal provisions to be posted based on the estimated user's emotion. The display method includes, for example, but is not limited to, font size, color change, and the like. For example, when the user is nervous, the posting unit posts important legal provisions first. Also, when the user is relaxed, the posting unit can sequentially post detailed legal provisions. Furthermore, when the user is impatient, the posting unit can quickly post important legal provisions. This improves the understanding of legal provisions by determining the priority of legal provisions based on 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 posting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the posting 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 legal provisions.

[0093] The posting unit can improve the accuracy of posting by considering the interrelationships of the statement contents at the time of posting. Posting includes, for example, posting to a bulletin board, sending of e-mail, and the like, but is not limited to such examples. For example, when the statement contents are consistent with other evidence, the posting unit posts the related legal provisions preferentially. In addition, when the statement contents are inconsistent with other evidence, the posting unit can refrain from posting the related legal provisions. Furthermore, the posting unit can adjust the priority of the legal provisions to be posted based on the interrelationships of the statement contents. As a result, the accuracy of posting is improved by considering the interrelationships of the statement contents. A part or all of the above-mentioned processing in the posting unit may be performed, for example, using AI, or may be performed without using AI. For example, the posting unit can input interrelationship data of the statement contents to the generation AI and cause the generation AI to adjust the accuracy of posting.

[0094] The posting unit can post the statement while taking into consideration the attribute information of the person who submitted the statement. The attribute information of the person who submitted the statement includes, but is not limited to, the age, sex, and occupation. For example, if the person who submitted the statement has a criminal record, the posting unit strictly posts the relevant legal provisions. Also, if the person who submitted the statement is a highly reliable person, the posting unit can post the legal provisions flexibly. Furthermore, the posting unit can adjust the content of the legal provisions to be posted based on the attribute information of the person who submitted the statement. This improves the accuracy of posting by taking into consideration the attribute information of the person who submitted the statement. A part or all of the above-mentioned processing in the posting unit may be performed, for example, using AI or may be performed without using AI. For example, the posting unit can input the attribute information data of the person who submitted the statement to the generation AI and cause the generation AI to adjust the content of the posting.

[0095] The posting unit can estimate the user's emotion and adjust the display method of the legal provisions to be posted based on the estimated user's emotion. The display method includes, for example, but is not limited to, font size, color change, and the like. For example, when the user is nervous, the posting unit provides a simple and easy-to-understand display method. Furthermore, when the user is relaxed, the posting unit can display detailed legal provisions. Furthermore, when the user is impatient, the posting unit can highlight and display important points. This improves the understanding of the legal provisions by adjusting the display method based on 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 posting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the posting unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0096] The posting unit can post the statement taking into consideration the geographical distribution of the statement contents. The geographical distribution includes, but is not limited to, distribution by region and map display. For example, when the statement contents are related to a specific region, the posting unit preferentially posts legal provisions related to the region. In addition, when the statement contents are widely distributed, the posting unit can post the overall legal provisions. Furthermore, the posting unit can adjust the priority of the legal provisions to be posted based on the geographical distribution of the statement contents. This improves the accuracy of posting by taking the geographical distribution into consideration. A part or all of the above-mentioned processing in the posting unit may be performed, for example, using AI or may be performed without using AI. For example, the posting unit can input geographical distribution data of the statement contents to the generation AI and cause the generation AI to adjust the priority of posting.

[0097] The posting unit can improve the accuracy of posting by referring to related literature of the statement content at the time of posting. The related literature includes, but is not limited to, academic papers, specialized books, and the like. For example, when the statement content is consistent with past precedents, the posting unit posts the related legal provisions preferentially. In addition, when the statement content is inconsistent with past precedents, the posting unit can refrain from posting the related legal provisions. Furthermore, the posting unit can adjust the contents of the legal provisions to be posted based on the related literature of the statement content. As a result, the accuracy of posting is improved by referring to the related literature. A part or all of the above-mentioned processing in the posting unit may be performed, for example, using AI, or may be performed without using AI. For example, the posting unit can input related literature data of the statement content to the generation AI and cause the generation AI to adjust the content of the posting.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The reception unit can analyze the user's past statement history and select the optimal input method. For example, if the user has used text input in the past, the reception unit can preferentially suggest text input. Also, if the user has used voice input a lot in the past, the reception unit can preferentially suggest voice input. Furthermore, if the user has used handwriting input in the past, the reception unit can preferentially suggest handwriting input. In this way, the optimal input method can be selected by analyzing the past statement history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input past statement history data to the generation AI and cause the generation AI to select the optimal input method.

[0100] The analysis unit can determine the priority of the analysis based on the submission time of the statement. For example, if the statement has been submitted recently, the analysis can be prioritized. Also, if the statement is old, the priority of the analysis can be lowered. Furthermore, the priority of the analysis can be adjusted stepwise according to the submission time. This improves the efficiency of the analysis by determining the priority of the analysis based on the submission time of the statement. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the statement to the generation AI and have the generation AI execute the determination of the priority of the analysis.

[0101] The judgment unit can improve the accuracy of the judgment by taking into account the interrelationships of the statements. For example, when the statements match other evidence, the accuracy of the judgment can be increased. Also, when the statements contradict other evidence, the accuracy of the judgment can be decreased. Furthermore, the accuracy of the judgment can be adjusted stepwise based on the interrelationships of the statements. This improves the accuracy of the judgment by taking into account the interrelationships of the statements. Some or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI or may be performed without using AI. For example, the judgment unit can input interrelationship data of the statements to the generation AI and cause the generation AI to adjust the accuracy of the judgment.

[0102] The posting unit can post the statements taking into consideration the geographic distribution of the statements. For example, if the statements relate to a specific region, legal provisions related to that region are posted with priority. Also, if the statements are widely distributed, the overall legal provisions can be posted. Furthermore, the priority of the legal provisions to be posted can be adjusted based on the geographic distribution of the statements. This improves the accuracy of posting by taking the geographic distribution into consideration. Some or all of the above-mentioned processing in the posting unit can be performed, for example, using AI or without AI. For example, the posting unit can input geographic distribution data of the statements to the generation AI and cause the generation AI to adjust the priority of posting.

[0103] The reception unit can estimate the user's emotion and adjust the input timing of the statement content based on the estimated user's emotion. For example, if the user is nervous, the reception unit prompts the user to input the statement content after allowing time for the user to relax. In addition, if the user is impatient, the reception unit can wait until the user calms down before starting to input the statement content. In addition, if the user is relaxed, the reception unit can immediately start to input the statement content. In this way, the accuracy of the statement is improved by adjusting the input timing based on 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0104] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, if the user is nervous, a concise and easy-to-understand expression is used. Also, if the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is impatient, the analysis result can be provided with important points emphasized. This improves the understanding of the analysis result by adjusting the expression method of the analysis based on 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 analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method.

[0105] The judgment unit can estimate the user's emotion and adjust the judgment criteria based on the estimated user's emotion. For example, if the user is nervous, strict criteria can be applied. Also, if the user is relaxed, flexible criteria can be applied. Furthermore, if the user is impatient, criteria for making a quick judgment can be applied. In this way, by adjusting the judgment criteria based on the user's emotion, the accuracy of the judgment is improved. The estimation of the emotion 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 judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the judgment criteria.

[0106] The posting unit can estimate the user's emotions and determine the priority of legal provisions to be posted based on the estimated user's emotions. For example, if the user is nervous, important legal provisions are posted first. Also, if the user is relaxed, detailed legal provisions can be posted sequentially. Furthermore, if the user is impatient, important legal provisions can be posted quickly. This improves the understanding of legal provisions by determining the priority of legal provisions based on 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 posting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the posting 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 legal provisions.

[0107] The analysis unit can apply different analysis algorithms depending on the category of the statement content. For example, if the statement content is related to a crime, a crime analysis algorithm can be applied. Also, if the statement content is related to a traffic violation, a traffic violation analysis algorithm can be applied. Furthermore, if the statement content is related to a civil case, a civil case analysis algorithm can be applied. Thereby, by applying an analysis algorithm according to the category of the statement content, the accuracy of the analysis is improved. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input category data of the statement content to the generation AI and cause the generation AI to apply the analysis algorithm.

[0108] The judgment unit can make a judgment taking into account the attribute information of the person who submitted the statement. For example, if the person who submitted the statement has a criminal record, a strict judgment can be made. Also, if the person who submitted the statement is a highly reliable person, a flexible judgment can be made. Furthermore, the judgment criteria can be gradually adjusted based on the attribute information of the person who submitted the statement. As a result, the accuracy of the judgment can be improved by taking into account the attribute information of the person who submitted the statement. Some or all of the above-mentioned processing in the judgment unit can be performed, for example, using AI or without using AI. For example, the judgment unit can input the attribute information data of the person who submitted the statement to the generation AI and cause the generation AI to adjust the judgment criteria.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The reception unit inputs the statement content. The statement content includes oral statements, written statements, electronic data, and the like. The reception unit can input the statement content as text data, and can also accept voice input and handwritten input. For example, voice input can be converted into text data using voice recognition technology, and handwritten input can be converted into text data using handwriting recognition technology. Step 2: The analysis unit analyzes the statement. Natural language processing technology, similarity calculation, pattern matching, etc. are used for the analysis. For example, natural language processing technology is used to understand the meaning of the statement, similarity calculation is used to calculate the similarity between the statement and other evidence, and pattern matching is used to evaluate the degree of agreement between the statement and other evidence. Step 3: The judgment unit judges the consistency and veracity of the statement analyzed by the analysis unit. For example, it can judge whether the statement matches past precedents or cases, and if the statement violates the law, it can display the provisions of the law.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] For example, the reception unit is realized by the reception device 38 of the smart device 14, and can input the statement as text data. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the statement and judges whether it is inconsistent with other evidence. The judgment unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and judges the consistency and authenticity of the statement. The posting unit is realized by, for example, the output device 40 of the smart device 14, and posts the provisions of the law if the statement violates the law. The reception unit can estimate the user's emotions by, for example, the reception device 38 of the smart device 14, and adjust the input timing of the statement. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and can input the statement as voice data. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the statement and judges whether it is inconsistent with other evidence. The judgment unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and judges the consistency and authenticity of the statement. The posting unit is realized by, for example, the speaker 240 of the smart glasses 214, and posts the provisions of the law if the statement violates the law. The reception unit can estimate the user's emotions by, for example, the microphone 238 of the smart glasses 214, and adjust the input timing of the statement. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0141] 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.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0145] 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.

[0146] For example, the reception unit is realized by the microphone 238 of the headset terminal 314, and can input the statement contents as voice data. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the statement contents and judges whether they are inconsistent with other evidence. The judgment unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and judges the consistency and authenticity of the statement contents. The posting unit is realized by, for example, the display 343 of the headset terminal 314, and posts the provisions of the law if the statement contents violate the law. The reception unit can estimate the user's emotions by, for example, the microphone 238 of the headset terminal 314, and adjust the input timing of the statement contents. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] For example, the reception unit is realized by the microphone 238 of the robot 414, and can input the statement as voice data. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the statement and judges whether it is inconsistent with other evidence. The judgment unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and judges the consistency and authenticity of the statement. The posting unit is realized by, for example, the speaker 240 of the robot 414, and posts the provisions of the law if the statement violates the law. The reception unit can estimate the user's emotions by, for example, the microphone 238 of the robot 414, and adjust the input timing of the statement. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0164] 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.

[0165] 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.

[0166] These emotions are distributed in the three 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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 using a processor 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Appendix 1) A reception unit for inputting the contents of a statement; an analysis unit that analyzes the statement content input by the reception unit; A determination unit that determines at least one of the consistency or authenticity of the statement contents analyzed by the analysis unit; Equipped A system characterized by: (Appendix 2) The reception unit is Enter the statement as text data 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze statements to determine whether they are contradicted by other evidence (e.g., physical evidence or testimony) 2. The system of claim 1, (Appendix 4) The determination unit is Determine whether the statements are consistent with previous precedent (e.g., court decisions) or cases (e.g., police records) 2. The system of claim 1, (Appendix 5) If the testimony violates the law, a notice board will be provided to display the relevant legal provisions. 2. The system of claim 1, (Appendix 6) The reception unit is Accepts at least one of the following voice inputs or handwritten inputs 2. The system of claim 1, (Appendix 7) The analysis unit is Analyze statements using natural language processing techniques, similarity calculations, and pattern matching 2. The system of claim 1, (Appendix 8) The reception unit is To estimate a user's emotion and adjust the timing of inputting the content of a statement based on the estimated user's emotion. 2. The system of claim 1, (Appendix 9) The reception unit is When entering a statement, the system analyzes the user's past statement history and selects the optimal input method. 2. The system of claim 1, (Appendix 10) The reception unit is Filter statements as they are entered based on the user's current state of mind 2. The system of claim 1, (Appendix 11) The reception unit is The user's emotions are estimated, and the priority of the statements to be input is determined based on the estimated user's emotions. 2. The system of claim 1, (Appendix 12) The reception unit is When inputting statements, the system takes into account the user's geographic location information and prioritizes inputting statements that are highly relevant. 2. The system of claim 1, (Appendix 13) The reception unit is When entering a statement, analyze the user's social media activity and enter relevant statements 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, adjust the level of detail based on the importance of the statements 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of the statement. 2. The system of claim 1, (Appendix 17) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 18) The analysis unit is During the analysis, the analysis will be prioritized based on the timing of the submission of the statements. 2. The system of claim 1, (Appendix 19) The analysis unit is During analysis, adjust the order of analysis based on the relevance of statements 2. The system of claim 1, (Appendix 20) The determination unit is Infer the user's emotions and adjust the decision criteria based on the estimated user emotions. 2. The system of claim 1, (Appendix 21) The determination unit is When making judgments, consider the interrelationships between statements to improve the accuracy of judgments. 2. The system of claim 1, (Appendix 22) The determination unit is When making a decision, the attribute information of the person submitting the statement will be taken into consideration. 2. The system of claim 1, (Appendix 23) The determination unit is Inferring user emotions and adjusting the order in which judgment results are displayed based on the estimated user emotions 2. The system of claim 1, (Appendix 24) The determination unit is When making a decision, the geographical distribution of the statements will be taken into consideration. 2. The system of claim 1, (Appendix 25) The determination unit is When making a judgment, refer to related literature on the testimony to improve the accuracy of the judgment. 2. The system of claim 1, (Appendix 26) The display unit includes: Inferring user sentiment and determining the priority of legal provisions to be displayed based on the estimated user sentiment 2. The system of claim 1, (Appendix 27) The display unit includes: When posting, improve the accuracy of posting by taking into account the correlation of statements 2. The system of claim 1, (Appendix 28) The display unit includes: When posting, the attribute information of the person submitting the statement will be taken into consideration. 2. The system of claim 1, (Appendix 29) The display unit includes: Inferring a user's emotions and adjusting the display method of legal provisions based on the estimated user's emotions 2. The system of claim 1, (Appendix 30) The display unit includes: When posting, the geographical distribution of the statements will be taken into consideration. 2. The system of claim 1, (Appendix 31) The display unit includes: When posting, refer to related literature to improve the accuracy of the posting. 2. The system of claim 1, [Explanation of symbols]

[0183] 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 reception unit for inputting the contents of a statement; an analysis unit that analyzes the statement content input by the reception unit; A determination unit that determines at least one of the consistency or authenticity of the statement contents analyzed by the analysis unit; Equipped A system characterized by:

2. The reception unit is Enter the statement as text data 2. The system of claim 1 .

3. The analysis unit is Analyze the statements to determine whether they are contradicted by other evidence 2. The system of claim 1 .

4. The determination unit is Determine whether the statements are consistent with previous legal precedents or cases 2. The system of claim 1 .

5. If the testimony violates the law, a notice board will be provided to display the relevant legal provisions.

2. The system of claim 1 .

6. The reception unit is Accepts at least one of the following voice inputs or handwritten inputs 2. The system of claim 1 .

7. The analysis unit is Analyze statements using natural language processing techniques, similarity calculations, and pattern matching 2. The system of claim 1 .

8. The reception unit is To estimate a user's emotion and adjust the timing of inputting the content of a statement based on the estimated user's emotion.

2. The system of claim 1 .

9. The reception unit is When entering a statement, the system analyzes the user's past statement history and selects the optimal input method.

2. The system of claim 1 .

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