system

Abstract

The system according to this embodiment aims to reduce the time spent gathering information for self-evaluation and reflection. [Solution] The system according to the embodiment comprises a collection unit, an evaluation unit, and a summarization unit. The collection unit collects information. The evaluation unit evaluates the information collected by the collection unit. The summarization unit summarizes the evaluation results obtained by the evaluation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it takes time to collect information for self - evaluation and reflection.

[0005] The system according to the embodiment aims to reduce the information collection time for self - evaluation and reflection.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an evaluation unit, and a summarization unit. The collection unit collects information. The evaluation unit evaluates the information collected by the collection unit. The summarization unit summarizes the evaluation results obtained by the evaluation unit.

Effects of the Invention

[0007] The system according to this embodiment can reduce the time required for information gathering for self-evaluation and reflection. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The information gathering system according to an embodiment of the present invention is a system that reduces the time spent gathering information for self-evaluation and reflection by utilizing a generating AI. This information gathering system is a mechanism that reduces the time spent gathering information for self-evaluation and reflection. The information gathering system allows the user to specify an account and a target period. The generating AI extracts activities within the company environment. The generating AI extracts content aligned with keywords from the information within the company environment and creates a summary and compilation text. Furthermore, the information gathering system generates an evaluation text based on a specified persona for the created text. This mechanism significantly reduces the time spent gathering information for self-evaluation and reflection, and increases the time available for action after reflection. For example, it solves problems such as the need for a considerable amount of time to reflect on one's own actions during self-evaluation at the end of the period, and the slowdown in efforts to improve actions due to only receiving peer evaluation of one's actions once at the end of the period. The information gathering system targets all users and estimates the market size based on the estimated time spent on reflection during the period (3 hours) x the average hourly wage of users. As a result, we believe that now is the time to enter the market because we can see a way to reduce the time spent gathering material for reflection and increase the time available for action after reflection. Ultimately, I want to enable individuals to gather highly relevant information that can serve as a means of self-improvement. This would allow the information gathering system to significantly reduce the time spent on self-assessment and reflection, and increase the time available for action following that reflection.

[0029] The information gathering system according to the embodiment comprises a collection unit, an evaluation unit, and a summarization unit. The collection unit collects information. For example, the collection unit collects information about the company environment. The collection unit states that information about the company environment includes, but is not limited to, text information, numerical data, image data, etc. The collection unit can efficiently collect information about the company environment using a generation AI. The evaluation unit evaluates the information collected by the collection unit. For example, the evaluation unit performs an evaluation based on the collected information according to an evaluation scale and an algorithm to be used. The evaluation unit can appropriately evaluate the collected information using a generation AI. The summarization unit summarizes the evaluation results obtained by the evaluation unit. For example, the summarization unit summarizes the evaluation results and compiles them according to an information organization method. The summarization unit can efficiently compile the evaluation results using a generation AI. As a result, the information gathering system according to the embodiment can efficiently perform information gathering, evaluation, and summarization.

[0030] The data collection unit collects information. For example, the data collection unit collects information about the internal environment. Specifically, it collects text information, numerical data, and image data from various departments and divisions within the company. Text information includes reports, emails, and chat logs, while numerical data includes performance data, sales data, and employee performance data. Image data includes product photos, work site photos, and meeting screenshots. The data collection unit centrally manages this information and utilizes generative AI to efficiently collect it. The generative AI analyzes text information using natural language processing technology and extracts important keywords and phrases. It also analyzes image data using image recognition technology to identify specific objects and scenes. Furthermore, for the analysis of numerical data, it uses statistical methods and machine learning algorithms to detect data trends and outliers. As a result, the data collection unit can efficiently and accurately collect information about the internal environment and provide it to the evaluation unit.

[0031] The evaluation unit evaluates the information collected by the collection unit. For example, the evaluation unit performs an evaluation based on the collected information, according to the evaluation scale and algorithm used. Specifically, it uses generative AI to appropriately evaluate the collected information. The generative AI analyzes text information using natural language processing technology and evaluates the importance and relevance of the information. It also analyzes image data using image recognition technology and evaluates the quality and content of the images. Furthermore, for the evaluation of numerical data, it uses statistical methods and machine learning algorithms to evaluate the reliability and usefulness of the data. Based on these evaluation results, the evaluation unit determines the value and reliability of the information and provides it to the subsequent summarization unit. For example, the evaluation unit extracts important information from the collected information and evaluates the reliability of the information. It also evaluates the relevance of the information and groups related information together. In this way, the evaluation unit can appropriately evaluate the collected information and provide it to the subsequent summarization unit.

[0032] The summarization unit compiles the evaluation results obtained by the evaluation unit. For example, the summarization unit summarizes the evaluation results and compiles them according to the information organization method. Specifically, it uses a generative AI to efficiently compile the evaluation results. The generative AI uses natural language generation technology to summarize the evaluation results and extract important points. It also organizes the evaluation results by category according to the information organization method and provides them in a visually easy-to-understand format. For example, it converts the evaluation results into graphs and charts to visually show trends and patterns in the information. It also compiles the evaluation results in a report format and provides it to stakeholders. In this way, the summarization unit can efficiently compile the evaluation results and provide them to stakeholders. Furthermore, the summarization unit can collect feedback on the evaluation results and use it to improve the evaluation process and summarization method. As a result, the information gathering system according to the embodiment can efficiently perform information gathering, evaluation, and summarization, and can provide useful information to stakeholders.

[0033] The data collection unit can collect information about the internal environment. For example, the data collection unit collects information about the internal environment. This information includes, but is not limited to, the physical environment, business processes, and employee behavior. The data collection unit can efficiently collect information about the internal environment using generative AI. This enables efficient collection of information about the internal environment.

[0034] The evaluation unit can evaluate the collected information. For example, the evaluation unit performs an evaluation based on the collected information according to the evaluation scale and the algorithm used. The collected information may include, but is not limited to, text data, numerical data, and image data. The evaluation unit can appropriately evaluate the collected information using generative AI. This allows for an appropriate evaluation of the collected information.

[0035] The summarization section can compile the evaluation results. For example, the summarization section summarizes the evaluation results and organizes them according to a method for organizing information. The evaluation results may include, but are not limited to, scores, ranks, and comments. The summarization section can efficiently compile the evaluation results using generative AI. This allows for efficient compilation of the evaluation results.

[0036] The data collection unit includes an extraction unit that extracts content aligned with keywords. The extraction unit extracts content aligned with keywords from, for example, information about the company's internal environment. Keywords in the extraction unit include, but are not limited to, frequently occurring words and important phrases. The extraction unit can efficiently extract content aligned with keywords using generation AI. This allows for efficient extraction of content aligned with keywords.

[0037] The evaluation unit includes a generation unit that generates evaluation text based on a specified persona. The generation unit generates evaluation text based on a specified persona, for example. The generation unit defines a persona as including, but is not limited to, the attributes and behavioral patterns of a target user. The generation unit can efficiently generate evaluation text based on a specified persona using a generation AI. This enables the efficient generation of evaluation text based on a specified persona.

[0038] The summarization unit includes a creation unit that generates summaries and summary texts. The creation unit, for example, summarizes evaluation results and creates summary texts according to a method of organizing information. The creation unit defines summaries as including, but not limited to, the length of the text and the importance of the information being summarized. The creation unit can efficiently generate summaries and summary texts using generative AI. This enables the efficient creation of summaries and summary texts.

[0039] The data collection unit can analyze the user's past activity history and select the optimal information collection method. For example, the data collection unit prioritizes collecting information sources that the user has frequently used in the past. For example, the data collection unit selects the types of information to collect during specific time periods based on the user's past activity history. For example, the data collection unit analyzes the user's past activity history and proposes the most efficient information collection method. This allows the system to select the optimal information collection method based on the user's past activity history. Past activity history includes, but is not limited to, log data and activity history.

[0040] The data collection unit can filter information based on the user's current projects and areas of interest during data collection. For example, the data collection unit prioritizes collecting information related to the user's current projects. For example, the data collection unit filters highly relevant information based on the user's areas of interest. For example, the data collection unit appropriately filters necessary information according to the progress of the user's projects. This allows information to be filtered based on the user's current projects and areas of interest. Current projects include, but are not limited to, project objectives and progress. Areas of interest include, but are not limited to, the user's interests and areas of expertise.

[0041] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information related to that region. For example, the data collection unit will filter highly relevant information based on the user's geographical location information. For example, if the user is on the move, the data collection unit will collect necessary information based on their current location. This allows the collection of highly relevant information based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location-based services.

[0042] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect relevant information based on information shared by the user on social media. For example, the data collection unit can analyze a user's social media activity and collect information related to topics of interest. For example, the data collection unit can collect relevant information by referring to the activities of the user's social media followers and friends. This allows the data collection unit to collect relevant information based on the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers.

[0043] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the information during the evaluation process. For example, the evaluation unit's generating AI performs a detailed evaluation of highly important information. For example, the evaluation unit's generating AI performs a concise evaluation of less important information. The evaluation unit dynamically adjusts the level of detail of the evaluation according to the importance of the information. This allows the level of detail of the evaluation to be adjusted according to the importance of the information. The importance of information includes, but is not limited to, factors such as impact and relevance.

[0044] The evaluation unit can apply different evaluation algorithms depending on the category of information during evaluation. For example, for technical information, the evaluation unit's generative AI applies a specialized evaluation algorithm. For example, for business-related information, the evaluation unit's generative AI applies a business evaluation algorithm. The evaluation unit's generative AI selects the optimal evaluation algorithm depending on the category of information. This allows the optimal evaluation algorithm to be applied according to the category of information. Evaluation algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms.

[0045] The evaluation unit can determine the priority of evaluation based on the timing of information submission during the evaluation process. For example, the evaluation unit's generating AI will prioritize evaluation of recently submitted information. For example, the evaluation unit's generating AI will prioritize evaluation of older information. The evaluation unit's generating AI will dynamically adjust the evaluation priority according to the timing of information submission. This allows the evaluation priority to be determined according to the timing of information submission. The timing of information submission includes, but is not limited to, the submission date and time.

[0046] The evaluation unit can adjust the order of evaluation based on the relevance of the information during the evaluation process. For example, the evaluation unit prioritizes evaluating highly relevant information using the generating AI. For example, the evaluation unit postpones evaluating less relevant information using the generating AI. The evaluation unit dynamically adjusts the order of evaluation using the generating AI according to the relevance of the information. This allows the order of evaluation to be adjusted according to the relevance of the information. The relevance of the information includes, but is not limited to, the degree of content agreement and relevant keywords.

[0047] The summarization section can adjust the level of detail in the summary based on the importance of the evaluation results when creating the summary. For example, the summarization section will generate a detailed summary for evaluation results with high importance, and a concise summary for evaluation results with low importance. The summarization section can dynamically adjust the level of detail in the summary according to the importance of the evaluation results. This allows the level of detail in the summary to be adjusted according to the importance of the evaluation results. The level of detail in the summary includes, but is not limited to, comprehensiveness of information and detailed explanations.

[0048] The summarization unit can apply different summarization algorithms depending on the category of the evaluation results when creating summaries. For example, for technical evaluation results, the summarization unit's generative AI applies a specialized summarization algorithm. For example, for business-related evaluation results, the generative AI applies a business-oriented summarization algorithm. The summarization unit selects the optimal summarization algorithm depending on the category of the evaluation results. This ensures that the optimal summarization algorithm is applied according to the category of the evaluation results. Summarization algorithms include, but are not limited to, natural language processing algorithms and rule-based algorithms.

[0049] The summarization unit can determine the priority of summaries based on the submission date of the evaluation results when creating them. For example, the summarization unit's generating AI will prioritize summarizing recently submitted evaluation results. For example, the summarization unit's generating AI will postpone summarizing older evaluation results. The summarization unit's generating AI will dynamically adjust the priority of summaries according to the submission date of the evaluation results. This allows the priority of summaries to be determined according to the submission date of the evaluation results. The priority of summaries may include, but is not limited to, importance and urgency.

[0050] The summarization unit can adjust the order of summaries based on the relevance of the evaluation results when creating them. For example, the summarization unit will prioritize summarizing highly relevant evaluation results using the generating AI. For example, the summarization unit will postpone summarizing less relevant evaluation results using the generating AI. The summarization unit can dynamically adjust the order of summaries using the generating AI according to the relevance of the evaluation results. This allows the order of summaries to be adjusted according to the relevance of the evaluation results. The order of summaries may include, but is not limited to, relevance and importance.

[0051] The excerpting function can adjust the level of detail of the excerpt based on the importance of the information during the extraction process. For example, the generating AI will create a detailed excerpt for highly important information. For example, the generating AI will create a concise excerpt for less important information. The generating AI dynamically adjusts the level of detail of the excerpt according to the importance of the information. This allows the level of detail of the excerpt to be adjusted according to the importance of the information. The level of detail of the excerpt includes, but is not limited to, comprehensiveness of information and detailed explanations.

[0052] The extraction function can adjust the order of extractions based on when the information was submitted. For example, the generation AI will prioritize extracting recently submitted information. For example, the generation AI will postpone extracting older information. The extraction function dynamically adjusts the order of extractions according to when the information was submitted. This allows the order of extractions to be adjusted according to when the information was submitted. The order of extractions may include, but is not limited to, relevance and importance.

[0053] The generation unit can adjust the level of detail in the generated evaluation text based on the importance of the information during generation. For example, the generation unit generates detailed evaluation text for highly important information. For example, the generation unit generates concise evaluation text for less important information. The generation unit dynamically adjusts the level of detail in the evaluation text according to the importance of the information. This allows the level of detail in the evaluation text to be adjusted according to the importance of the information. The level of detail in the evaluation text includes, but is not limited to, comprehensiveness of information and detailed explanations.

[0054] The generation unit can determine the priority of evaluation texts to be generated based on the submission date of the information. For example, the generation unit will prioritize generating evaluation texts for recently submitted information. For example, the generation unit will postpone generating evaluation texts for older information. The generation unit will dynamically adjust the priority of evaluation texts according to the submission date of the information. This allows the priority of evaluation texts to be determined according to the submission date of the information. The priority of evaluation texts may include, but is not limited to, importance and urgency.

[0055] The creation unit can adjust the level of detail of the summaries and summary texts created during the creation process based on the importance of the evaluation results. For example, for highly important evaluation results, the generation AI will create detailed summaries and summary texts. For example, for less important evaluation results, the generation AI will create concise summaries and summary texts. The creation unit can dynamically adjust the level of detail of the summaries and summary texts according to the importance of the evaluation results. This allows the level of detail of the summaries and summary texts to be adjusted according to the importance of the evaluation results. The level of detail of the summaries and summary texts includes, but is not limited to, comprehensiveness of information and detailed explanations.

[0056] The creation unit can determine the priority of the summaries and summary texts to be created based on the submission timing of the evaluation results. For example, the generation AI will prioritize creating summaries and summary texts for recently submitted evaluation results. For example, the generation AI will postpone creating summaries and summary texts for older evaluation results. The creation unit can dynamically adjust the priority of the summaries and summary texts according to the submission timing of the evaluation results. This allows the priority of summaries and summary texts to be determined according to the submission timing of the evaluation results. The priority of summaries and summary texts may include, but is not limited to, importance and urgency.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The information gathering system can analyze a user's past activity history and select the most optimal information gathering method. For example, it can prioritize collecting information from sources frequently used by the user in the past. It can also select the types of information to collect at specific time periods based on the user's past activity history. Furthermore, it can analyze the user's past activity history and suggest the most efficient information gathering method. This allows for the selection of the optimal information gathering method based on the user's past activity history.

[0059] The information gathering system can filter information based on the user's current projects and areas of interest. For example, it can prioritize the collection of information related to the user's current project. It can also filter highly relevant information based on the user's areas of interest. Furthermore, it can appropriately filter necessary information according to the progress of the user's project. This allows for information filtering based on the user's current projects and areas of interest.

[0060] The information gathering system can prioritize the collection of highly relevant information by considering the user's geographical location. For example, if a user is in a specific region, it can prioritize the collection of information related to that region. It can also filter highly relevant information based on the user's geographical location. Furthermore, if a user is on the move, it can collect necessary information based on their current location. This allows for the collection of highly relevant information based on the user's geographical location.

[0061] The information gathering system can analyze a user's social media activity and collect relevant information. For example, it can collect relevant information based on information a user shares on social media. It can also analyze a user's social media activity and collect information related to topics of interest. Furthermore, it can collect relevant information by referring to the activities of the user's social media followers and friends. In this way, relevant information can be collected based on the user's social media activity.

[0062] The information gathering system can adjust the level of detail of its evaluation based on the importance of the information. For example, the evaluation unit can perform a detailed evaluation of highly important information, while performing a concise evaluation of less important information. Furthermore, the evaluation unit can dynamically adjust the level of detail of its evaluation according to the importance of the information. This allows the level of detail of the evaluation to be adjusted according to the importance of the information.

[0063] The information gathering system can apply different evaluation algorithms depending on the category of information during the evaluation process. For example, for technical information, the evaluation unit can apply a specialized evaluation algorithm. For business-related information, the evaluation unit can apply a business evaluation algorithm. Furthermore, the evaluation unit can select the most appropriate evaluation algorithm depending on the category of information. This ensures that the most suitable evaluation algorithm is applied according to the information category.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The collection unit collects information. For example, it collects information about the internal environment, including text information, numerical data, and image data. The collection unit can efficiently collect information about the internal environment using generative AI. Step 2: The evaluation unit evaluates the information collected by the collection unit. For example, it performs an evaluation based on the collected information according to the evaluation scale and the algorithm used. The evaluation unit can appropriately evaluate the collected information using generative AI. Step 3: The summarization unit compiles the evaluation results obtained by the evaluation unit. For example, it summarizes the evaluation results and compiles them according to the information organization method. The summarization unit can efficiently compile the evaluation results using generation AI.

[0066] (Example of form 2) The information gathering system according to an embodiment of the present invention is a system that reduces the time spent gathering information for self-evaluation and reflection by utilizing a generating AI. This information gathering system is a mechanism that reduces the time spent gathering information for self-evaluation and reflection. The information gathering system allows the user to specify an account and a target period. The generating AI extracts activities within the company environment. The generating AI extracts content aligned with keywords from the information within the company environment and creates a summary and compilation text. Furthermore, the information gathering system generates an evaluation text based on a specified persona for the created text. This mechanism significantly reduces the time spent gathering information for self-evaluation and reflection, and increases the time available for action after reflection. For example, it solves problems such as the need for a considerable amount of time to reflect on one's own actions during self-evaluation at the end of the period, and the slowdown in efforts to improve actions due to only receiving peer evaluation of one's actions once at the end of the period. The information gathering system targets all users and estimates the market size based on the estimated time spent on reflection during the period (3 hours) x the average hourly wage of users. As a result, we believe that now is the time to enter the market because we can see a way to reduce the time spent gathering material for reflection and increase the time available for action after reflection. Ultimately, I want to enable individuals to gather highly relevant information that can serve as a means of self-improvement. This would allow the information gathering system to significantly reduce the time spent on self-assessment and reflection, and increase the time available for action following that reflection.

[0067] The information gathering system according to the embodiment comprises a collection unit, an evaluation unit, and a summarization unit. The collection unit collects information. For example, the collection unit collects information about the company environment. The collection unit states that information about the company environment includes, but is not limited to, text information, numerical data, image data, etc. The collection unit can efficiently collect information about the company environment using a generation AI. The evaluation unit evaluates the information collected by the collection unit. For example, the evaluation unit performs an evaluation based on the collected information according to an evaluation scale and an algorithm to be used. The evaluation unit can appropriately evaluate the collected information using a generation AI. The summarization unit summarizes the evaluation results obtained by the evaluation unit. For example, the summarization unit summarizes the evaluation results and compiles them according to an information organization method. The summarization unit can efficiently compile the evaluation results using a generation AI. As a result, the information gathering system according to the embodiment can efficiently perform information gathering, evaluation, and summarization.

[0068] The data collection unit collects information. For example, the data collection unit collects information about the internal environment. Specifically, it collects text information, numerical data, and image data from various departments and divisions within the company. Text information includes reports, emails, and chat logs, while numerical data includes performance data, sales data, and employee performance data. Image data includes product photos, work site photos, and meeting screenshots. The data collection unit centrally manages this information and utilizes generative AI to efficiently collect it. The generative AI analyzes text information using natural language processing technology and extracts important keywords and phrases. It also analyzes image data using image recognition technology to identify specific objects and scenes. Furthermore, for the analysis of numerical data, it uses statistical methods and machine learning algorithms to detect data trends and outliers. As a result, the data collection unit can efficiently and accurately collect information about the internal environment and provide it to the evaluation unit.

[0069] The evaluation unit evaluates the information collected by the collection unit. For example, the evaluation unit performs an evaluation based on the collected information, according to the evaluation scale and algorithm used. Specifically, it uses generative AI to appropriately evaluate the collected information. The generative AI analyzes text information using natural language processing technology and evaluates the importance and relevance of the information. It also analyzes image data using image recognition technology and evaluates the quality and content of the images. Furthermore, for the evaluation of numerical data, it uses statistical methods and machine learning algorithms to evaluate the reliability and usefulness of the data. Based on these evaluation results, the evaluation unit determines the value and reliability of the information and provides it to the subsequent summarization unit. For example, the evaluation unit extracts important information from the collected information and evaluates the reliability of the information. It also evaluates the relevance of the information and groups related information together. In this way, the evaluation unit can appropriately evaluate the collected information and provide it to the subsequent summarization unit.

[0070] The summarization unit compiles the evaluation results obtained by the evaluation unit. For example, the summarization unit summarizes the evaluation results and compiles them according to the information organization method. Specifically, it uses a generative AI to efficiently compile the evaluation results. The generative AI uses natural language generation technology to summarize the evaluation results and extract important points. It also organizes the evaluation results by category according to the information organization method and provides them in a visually easy-to-understand format. For example, it converts the evaluation results into graphs and charts to visually show trends and patterns in the information. It also compiles the evaluation results in a report format and provides it to stakeholders. In this way, the summarization unit can efficiently compile the evaluation results and provide them to stakeholders. Furthermore, the summarization unit can collect feedback on the evaluation results and use it to improve the evaluation process and summarization method. As a result, the information gathering system according to the embodiment can efficiently perform information gathering, evaluation, and summarization, and can provide useful information to stakeholders.

[0071] The data collection unit can collect information about the internal environment. For example, the data collection unit collects information about the internal environment. This information includes, but is not limited to, the physical environment, business processes, and employee behavior. The data collection unit can efficiently collect information about the internal environment using generative AI. This enables efficient collection of information about the internal environment.

[0072] The evaluation unit can evaluate the collected information. For example, the evaluation unit performs an evaluation based on the collected information according to the evaluation scale and the algorithm used. The collected information may include, but is not limited to, text data, numerical data, and image data. The evaluation unit can appropriately evaluate the collected information using generative AI. This allows for an appropriate evaluation of the collected information.

[0073] The summarization section can compile the evaluation results. For example, the summarization section summarizes the evaluation results and organizes them according to a method for organizing information. The evaluation results may include, but are not limited to, scores, ranks, and comments. The summarization section can efficiently compile the evaluation results using generative AI. This allows for efficient compilation of the evaluation results.

[0074] The data collection unit includes an extraction unit that extracts content aligned with keywords. The extraction unit extracts content aligned with keywords from, for example, information about the company's internal environment. Keywords in the extraction unit include, but are not limited to, frequently occurring words and important phrases. The extraction unit can efficiently extract content aligned with keywords using generation AI. This allows for efficient extraction of content aligned with keywords.

[0075] The evaluation unit includes a generation unit that generates evaluation text based on a specified persona. The generation unit generates evaluation text based on a specified persona, for example. The generation unit defines a persona as including, but is not limited to, the attributes and behavioral patterns of a target user. The generation unit can efficiently generate evaluation text based on a specified persona using a generation AI. This enables the efficient generation of evaluation text based on a specified persona.

[0076] The summarization unit includes a creation unit that generates summaries and summary texts. The creation unit, for example, summarizes evaluation results and creates summary texts according to a method of organizing information. The creation unit defines summaries as including, but not limited to, the length of the text and the importance of the information being summarized. The creation unit can efficiently generate summaries and summary texts using generative AI. This enables the efficient creation of summaries and summary texts.

[0077] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit's generating AI will reduce the frequency of information collection, alleviating the user's burden. For example, if the user is relaxed, the data collection unit's generating AI will increase the frequency of information collection, collecting more detailed data. For example, if the user is busy, the data collection unit's generating AI will adjust the timing of information collection to match the user's schedule. This allows the timing of information collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The data collection unit can analyze the user's past activity history and select the optimal information collection method. For example, the data collection unit prioritizes collecting information sources that the user has frequently used in the past. For example, the data collection unit selects the types of information to collect during specific time periods based on the user's past activity history. For example, the data collection unit analyzes the user's past activity history and proposes the most efficient information collection method. This allows the system to select the optimal information collection method based on the user's past activity history. Past activity history includes, but is not limited to, log data and activity history.

[0079] The data collection unit can filter information based on the user's current projects and areas of interest during data collection. For example, the data collection unit prioritizes collecting information related to the user's current projects. For example, the data collection unit filters highly relevant information based on the user's areas of interest. For example, the data collection unit appropriately filters necessary information according to the progress of the user's projects. This allows information to be filtered based on the user's current projects and areas of interest. Current projects include, but are not limited to, project objectives and progress. Areas of interest include, but are not limited to, the user's interests and areas of expertise.

[0080] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority information using the generative AI. If the user is relaxed, the data collection unit will prioritize collecting detailed information using the generative AI. If the user is busy, the data collection unit will prioritize collecting information that can be collected quickly using the generative AI. This allows the data collection unit to determine the priority of information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information related to that region. For example, the data collection unit will filter highly relevant information based on the user's geographical location information. For example, if the user is on the move, the data collection unit will collect necessary information based on their current location. This allows the collection of highly relevant information based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location-based services.

[0082] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect relevant information based on information shared by the user on social media. For example, the data collection unit can analyze a user's social media activity and collect information related to topics of interest. For example, the data collection unit can collect relevant information by referring to the activities of the user's social media followers and friends. This allows the data collection unit to collect relevant information based on the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers.

[0083] The evaluation unit can estimate the user's emotions and adjust the way the evaluation is expressed based on the estimated emotions. For example, if the user is stressed, the evaluation unit's generative AI will summarize the evaluation concisely. If the user is relaxed, the evaluation unit's generative AI will provide a detailed evaluation. If the user is in a hurry, the evaluation unit's generative AI will provide a rapid evaluation. This allows the evaluation to be expressed in accordance with the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the information during the evaluation process. For example, the evaluation unit's generating AI performs a detailed evaluation of highly important information. For example, the evaluation unit's generating AI performs a concise evaluation of less important information. The evaluation unit dynamically adjusts the level of detail of the evaluation according to the importance of the information. This allows the level of detail of the evaluation to be adjusted according to the importance of the information. The importance of information includes, but is not limited to, factors such as impact and relevance.

[0085] The evaluation unit can apply different evaluation algorithms depending on the category of information during evaluation. For example, for technical information, the evaluation unit's generative AI applies a specialized evaluation algorithm. For example, for business-related information, the evaluation unit's generative AI applies a business evaluation algorithm. The evaluation unit's generative AI selects the optimal evaluation algorithm depending on the category of information. This allows the optimal evaluation algorithm to be applied according to the category of information. Evaluation algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms.

[0086] The evaluation unit can estimate the user's emotions and adjust the length of the evaluation based on the estimated emotions. For example, if the user is stressed, the evaluation unit's generating AI will make the evaluation shorter. If the user is relaxed, the evaluation unit's generating AI will provide a more detailed evaluation. If the user is in a hurry, the evaluation unit's generating AI will provide a quick evaluation. This allows the length of the evaluation to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The evaluation unit can determine the priority of evaluation based on the timing of information submission during the evaluation process. For example, the evaluation unit's generating AI will prioritize evaluation of recently submitted information. For example, the evaluation unit's generating AI will prioritize evaluation of older information. The evaluation unit's generating AI will dynamically adjust the evaluation priority according to the timing of information submission. This allows the evaluation priority to be determined according to the timing of information submission. The timing of information submission includes, but is not limited to, the submission date and time.

[0088] The evaluation unit can adjust the order of evaluation based on the relevance of the information during the evaluation process. For example, the evaluation unit prioritizes evaluating highly relevant information using the generating AI. For example, the evaluation unit postpones evaluating less relevant information using the generating AI. The evaluation unit dynamically adjusts the order of evaluation using the generating AI according to the relevance of the information. This allows the order of evaluation to be adjusted according to the relevance of the information. The relevance of the information includes, but is not limited to, the degree of content agreement and relevant keywords.

[0089] The summarization section can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is stressed, the summarization section will provide a concise summary using the generating AI. If the user is relaxed, the summarization section will provide a detailed summary using the generating AI. If the user is in a hurry, the summarization section will provide a quick summary using the generating AI. This allows the way the summary is presented to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The summarization section can adjust the level of detail in the summary based on the importance of the evaluation results when creating the summary. For example, the summarization section will generate a detailed summary for evaluation results with high importance, and a concise summary for evaluation results with low importance. The summarization section can dynamically adjust the level of detail in the summary according to the importance of the evaluation results. This allows the level of detail in the summary to be adjusted according to the importance of the evaluation results. The level of detail in the summary includes, but is not limited to, comprehensiveness of information and detailed explanations.

[0091] The summarization unit can apply different summarization algorithms depending on the category of the evaluation results when creating summaries. For example, for technical evaluation results, the summarization unit's generative AI applies a specialized summarization algorithm. For example, for business-related evaluation results, the generative AI applies a business-oriented summarization algorithm. The summarization unit selects the optimal summarization algorithm depending on the category of the evaluation results. This ensures that the optimal summarization algorithm is applied according to the category of the evaluation results. Summarization algorithms include, but are not limited to, natural language processing algorithms and rule-based algorithms.

[0092] The summarization section can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, if the user is stressed, the summarization section will shorten the summary using the generating AI. If the user is relaxed, the summarization section will provide a more detailed summary using the generating AI. If the user is in a hurry, the summarization section will provide a quick summary using the generating AI. This allows the length of the summary to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The summarization unit can determine the priority of summaries based on the submission date of the evaluation results when creating them. For example, the summarization unit's generating AI will prioritize summarizing recently submitted evaluation results. For example, the summarization unit's generating AI will postpone summarizing older evaluation results. The summarization unit's generating AI will dynamically adjust the priority of summaries according to the submission date of the evaluation results. This allows the priority of summaries to be determined according to the submission date of the evaluation results. The priority of summaries may include, but is not limited to, importance and urgency.

[0094] The summarization unit can adjust the order of summaries based on the relevance of the evaluation results when creating them. For example, the summarization unit will prioritize summarizing highly relevant evaluation results using the generating AI. For example, the summarization unit will postpone summarizing less relevant evaluation results using the generating AI. The summarization unit can dynamically adjust the order of summaries using the generating AI according to the relevance of the evaluation results. This allows the order of summaries to be adjusted according to the relevance of the evaluation results. The order of summaries may include, but is not limited to, relevance and importance.

[0095] The excerpting function can estimate the user's emotions and adjust the excerpting criteria based on those emotions. For example, if the user is stressed, the generative AI will prioritize extracting information of high importance. If the user is relaxed, the generative AI will prioritize extracting detailed information. If the user is busy, the generative AI will prioritize extracting information that can be quickly extracted. This allows the excerpting criteria to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The excerpting function can adjust the level of detail of the excerpt based on the importance of the information during the extraction process. For example, the generating AI will create a detailed excerpt for highly important information. For example, the generating AI will create a concise excerpt for less important information. The generating AI dynamically adjusts the level of detail of the excerpt according to the importance of the information. This allows the level of detail of the excerpt to be adjusted according to the importance of the information. The level of detail of the excerpt includes, but is not limited to, comprehensiveness of information and detailed explanations.

[0097] The excerpting function can estimate the user's emotions and determine the priority of excerpts based on those emotions. For example, if the user is stressed, the generative AI will prioritize extracting information of high importance. If the user is relaxed, the generative AI will prioritize extracting detailed information. If the user is busy, the generative AI will prioritize extracting information that can be quickly retrieved. This allows the priority of excerpts to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The extraction function can adjust the order of extractions based on when the information was submitted. For example, the generation AI will prioritize extracting recently submitted information. For example, the generation AI will postpone extracting older information. The extraction function dynamically adjusts the order of extractions according to when the information was submitted. This allows the order of extractions to be adjusted according to when the information was submitted. The order of extractions may include, but is not limited to, relevance and importance.

[0099] The generation unit can estimate the user's emotions and adjust the expression of the generated evaluation text based on the estimated user emotions. For example, if the user is stressed, the generation AI will generate a concise evaluation text. For example, if the user is relaxed, the generation AI will generate a detailed evaluation text. For example, if the user is in a hurry, the generation AI will quickly generate an evaluation text. This allows the expression of the evaluation text to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0100] The generation unit can adjust the level of detail in the generated evaluation text based on the importance of the information during generation. For example, the generation unit generates detailed evaluation text for highly important information. For example, the generation unit generates concise evaluation text for less important information. The generation unit dynamically adjusts the level of detail in the evaluation text according to the importance of the information. This allows the level of detail in the evaluation text to be adjusted according to the importance of the information. The level of detail in the evaluation text includes, but is not limited to, comprehensiveness of information and detailed explanations.

[0101] The generation unit can estimate the user's emotions and adjust the length of the generated evaluation text based on the estimated emotions. For example, if the user is stressed, the generation AI will shorten the evaluation text. If the user is relaxed, the generation AI will provide a more detailed evaluation text. If the user is in a hurry, the generation AI will quickly generate the evaluation text. This allows the length of the evaluation text to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] The generation unit can determine the priority of evaluation texts to be generated based on the submission date of the information. For example, the generation unit will prioritize generating evaluation texts for recently submitted information. For example, the generation unit will postpone generating evaluation texts for older information. The generation unit will dynamically adjust the priority of evaluation texts according to the submission date of the information. This allows the priority of evaluation texts to be determined according to the submission date of the information. The priority of evaluation texts may include, but is not limited to, importance and urgency.

[0103] The generation unit can estimate the user's emotions and adjust the expression of the summary and concise text based on the estimated emotions. For example, if the user is stressed, the generation AI will create a concise summary and concise text. If the user is relaxed, the generation AI will create a detailed summary and concise text. If the user is in a hurry, the generation AI will create a quick summary and concise text. This allows the expression of the summary and concise text to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0104] The creation unit can adjust the level of detail of the summaries and summary texts created during the creation process based on the importance of the evaluation results. For example, for highly important evaluation results, the generation AI will create detailed summaries and summary texts. For example, for less important evaluation results, the generation AI will create concise summaries and summary texts. The creation unit can dynamically adjust the level of detail of the summaries and summary texts according to the importance of the evaluation results. This allows the level of detail of the summaries and summary texts to be adjusted according to the importance of the evaluation results. The level of detail of the summaries and summary texts includes, but is not limited to, comprehensiveness of information and detailed explanations.

[0105] The generation unit can estimate the user's emotions and adjust the length of the summary and concise text it creates based on the estimated emotions. For example, if the user is stressed, the generation AI will create a shorter summary and concise text. For example, if the user is relaxed, the generation AI will provide a more detailed summary and concise text. For example, if the user is in a hurry, the generation AI will quickly create the summary and concise text. This allows the length of the summary and concise text to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The creation unit can determine the priority of the summaries and summary texts to be created based on the submission timing of the evaluation results. For example, the generation AI will prioritize creating summaries and summary texts for recently submitted evaluation results. For example, the generation AI will postpone creating summaries and summary texts for older evaluation results. The creation unit can dynamically adjust the priority of the summaries and summary texts according to the submission timing of the evaluation results. This allows the priority of summaries and summary texts to be determined according to the submission timing of the evaluation results. The priority of summaries and summary texts may include, but is not limited to, importance and urgency.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The information gathering system can estimate the user's emotions and adjust the information gathering method based on those emotions. For example, if the user is stressed, the gathering unit can reduce the frequency of information gathering to lessen the user's burden. Conversely, if the user is relaxed, the gathering unit can increase the frequency of information gathering to collect more detailed data. Furthermore, if the user is busy, the gathering unit can adjust the timing of information gathering to match the user's schedule. This allows for flexible adjustment of the information gathering method according to the user's emotions.

[0109] The information gathering system can analyze a user's past activity history and select the most optimal information gathering method. For example, it can prioritize collecting information from sources frequently used by the user in the past. It can also select the types of information to collect at specific time periods based on the user's past activity history. Furthermore, it can analyze the user's past activity history and suggest the most efficient information gathering method. This allows for the selection of the optimal information gathering method based on the user's past activity history.

[0110] The information gathering system can filter information based on the user's current projects and areas of interest. For example, it can prioritize the collection of information related to the user's current project. It can also filter highly relevant information based on the user's areas of interest. Furthermore, it can appropriately filter necessary information according to the progress of the user's project. This allows for information filtering based on the user's current projects and areas of interest.

[0111] The information gathering system can estimate the user's emotions and prioritize the information to collect based on those emotions. For example, if the user is stressed, the gathering unit can prioritize collecting high-priority information. If the user is relaxed, the gathering unit can prioritize collecting detailed information. Furthermore, if the user is busy, the gathering unit can prioritize collecting information that can be retrieved quickly. In this way, the system can prioritize the information to be collected according to the user's emotions.

[0112] The information gathering system can prioritize the collection of highly relevant information by considering the user's geographical location. For example, if a user is in a specific region, it can prioritize the collection of information related to that region. It can also filter highly relevant information based on the user's geographical location. Furthermore, if a user is on the move, it can collect necessary information based on their current location. This allows for the collection of highly relevant information based on the user's geographical location.

[0113] The information gathering system can analyze a user's social media activity and collect relevant information. For example, it can collect relevant information based on information a user shares on social media. It can also analyze a user's social media activity and collect information related to topics of interest. Furthermore, it can collect relevant information by referring to the activities of the user's social media followers and friends. In this way, relevant information can be collected based on the user's social media activity.

[0114] The information gathering system can estimate the user's emotions and adjust the way the evaluation is presented based on those estimated emotions. For example, if the user is stressed, the evaluation unit can summarize the evaluation concisely. If the user is relaxed, the evaluation unit can provide a more detailed evaluation. Furthermore, if the user is in a hurry, the evaluation unit can provide a quick evaluation. This allows the evaluation to be expressed in accordance with the user's emotions.

[0115] The information gathering system can adjust the level of detail of its evaluation based on the importance of the information. For example, the evaluation unit can perform a detailed evaluation of highly important information, while performing a concise evaluation of less important information. Furthermore, the evaluation unit can dynamically adjust the level of detail of its evaluation according to the importance of the information. This allows the level of detail of the evaluation to be adjusted according to the importance of the information.

[0116] The information gathering system can apply different evaluation algorithms depending on the category of information during the evaluation process. For example, for technical information, the evaluation unit can apply a specialized evaluation algorithm. For business-related information, the evaluation unit can apply a business evaluation algorithm. Furthermore, the evaluation unit can select the most appropriate evaluation algorithm depending on the category of information. This ensures that the most suitable evaluation algorithm is applied according to the information category.

[0117] The information gathering system can estimate the user's emotions and adjust the way the summary is presented based on those emotions. For example, if the user is stressed, the summary can provide a concise summary. If the user is relaxed, the summary can provide a detailed summary. Furthermore, if the user is in a hurry, the summary can provide a quick summary. This allows the system to adjust the way the summary is presented according to the user's emotions.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The collection unit collects information. For example, it collects information about the internal environment, including text information, numerical data, and image data. The collection unit can efficiently collect information about the internal environment using generative AI. Step 2: The evaluation unit evaluates the information collected by the collection unit. For example, it performs an evaluation based on the collected information according to the evaluation scale and the algorithm used. The evaluation unit can appropriately evaluate the collected information using generative AI. Step 3: The summarization unit compiles the evaluation results obtained by the evaluation unit. For example, it summarizes the evaluation results and compiles them according to the information organization method. The summarization unit can efficiently compile the evaluation results using generation AI.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0122] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] Each of the multiple elements described above, including the data collection unit, evaluation unit, summarization unit, and extraction unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects information about the company environment using the camera 42 and microphone 38B of the smart device 14 and processes the information with the control unit 46A. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the collected information. The summarization unit summarizes the evaluation results and creates a summary text using the control unit 46A of the smart device 14. The extraction unit extracts content according to keywords using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0131] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0132] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0133] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0134] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements described above, including the data collection unit, evaluation unit, summarization unit, and extraction unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects information about the company environment using the camera 42 and microphone 238 of the smart glasses 214 and processes the information with the control unit 46A. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and evaluates the collected information. The summarization unit summarizes the evaluation results and creates a summary text, for example, by the control unit 46A of the smart glasses 214. The extraction unit extracts content according to keywords, for example, by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0143] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0144] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the data collection unit, evaluation unit, summarization unit, and extraction unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects information about the company environment using the camera 42 and microphone 238 of the headset terminal 314 and processes the information with the control unit 46A. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and evaluates the collected information. The summarization unit summarizes the evaluation results and creates a summary text using, for example, the control unit 46A of the headset terminal 314. The extraction unit extracts content according to keywords using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0160] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0162] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0163] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0164] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0165] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the data collection unit, evaluation unit, summarization unit, and extraction unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects information about the company environment using the camera 42 and microphone 238 of the robot 414 and processes the information with the control unit 46A. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and evaluates the collected information. The summarization unit summarizes the evaluation results and creates a summary text by, for example, the control unit 46A of the robot 414. The extraction unit extracts content according to keywords by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0173] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0175] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0176] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0177] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0181] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0184] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0185] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0186] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0188] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0189] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0190] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0191] (Note 1) The information collection unit, An evaluation unit that evaluates the information collected by the collection unit, A summarizing unit that compiles the evaluation results obtained by the evaluation unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Gather information about the internal environment. The system described in Appendix 1, characterized by the features described herein. (Note 3) The evaluation unit described above, Evaluate the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned summary section is, Summarize the evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It includes an excerpt section that extracts content based on keywords. The system described in Appendix 1, characterized by the features described herein. (Note 6) The evaluation unit described above, It includes a generation unit that generates evaluation text based on a specified persona. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned summary section is, It includes a section for creating summaries and summary texts. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past activity history and select the optimal method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit described above, It estimates the user's emotions and adjusts the way evaluations are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit described above, During the evaluation process, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit described above, During evaluation, different evaluation algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The evaluation unit described above, It estimates the user's emotions and adjusts the length of the rating based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit described above, During the evaluation process, the priority of evaluations will be determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit described above, During the evaluation process, the order of evaluation will be adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned summary section is, It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned summary section is, When creating the summary, adjust the level of detail based on the importance of the evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned summary section is, When creating summaries, different summarization algorithms are applied depending on the category of the evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned summary section is, It estimates the user's emotions and adjusts the length of the summary based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned summary section is, When creating the summaries, prioritize the summaries based on the submission dates of the evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned summary section is, When creating the summary, adjust the order of the summaries based on the relevance of the evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned excerpt is, We estimate the user's sentiment and adjust the excerpt selection criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned excerpt is, When extracting information, adjust the level of detail in the excerpt based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned excerpt is, The system estimates the user's emotions and determines the priority of the excerpts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned excerpt is, When extracting information, adjust the order of the excerpts based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is It estimates the user's emotions and adjusts how the evaluation text generated based on those estimated emotions is expressed. The system described in Appendix 1, characterized by the features described herein. (Note 31) The generating unit is During generation, adjust the level of detail in the generated evaluation text based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The generating unit is It estimates the user's sentiment and adjusts the length of the evaluation text generated based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The generating unit is During generation, the priority of the evaluation text to be generated is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned creation unit, It estimates the user's emotions and adjusts the way the summary and concise text are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned creation unit, During creation, adjust the level of detail in the summary and concise text based on the importance of the evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned creation unit, It estimates the user's emotions and adjusts the length of the summary and concise text created based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned creation unit, When creating the summary, prioritize the summaries and summary texts based on the submission deadline for the evaluation results. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The information collection unit, An evaluation unit that evaluates the information collected by the collection unit, A summarizing unit that compiles the evaluation results obtained by the evaluation unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Gather information about the internal environment. The system according to feature 1.

3. The evaluation unit described above, Evaluate the collected information The system according to feature 1.

4. The aforementioned summary section is, Summarize the evaluation results. The system according to feature 1.

5. The aforementioned collection unit is It includes an excerpt section that extracts content based on keywords. The system according to feature 1.

6. The evaluation unit described above, It includes a generation unit that generates evaluation text based on a specified persona. The system according to feature 1.

7. The aforementioned summary section is, It includes a section for creating summaries and summary texts. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past activity history and select the optimal method for collecting information. The system according to feature 1.

10. The aforementioned collection unit is When gathering information, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

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