system

Abstract

The system according to this embodiment aims to enable even personnel with low literacy levels to provide appropriate service introductions and proposals. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The proposal unit provides appropriate service introductions and proposals based on the information analyzed by the analysis 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, the method 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 in 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 was a problem that it was difficult to provide appropriate service introductions and proposals to personnel with low literacy.

[0005] The system according to the embodiment aims to provide appropriate service introductions and proposals even to personnel with low literacy.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The proposal unit makes appropriate service introductions and proposals based on the information analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment allows even personnel with low literacy levels to provide appropriate service introductions and proposals. [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 line 34. Also, the database 24 and the communication I / F 26 are connected to the bus line 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 line 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus line 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 IT tool sales support system for small and medium-sized enterprises according to an embodiment of the present invention is a system that provides friendly and flexible support even to staff with low IT literacy by incorporating a generating AI. This system reduces the number of users who leave the site and also guides them to key products. Specifically, the generating AI learns service information, materials, and contact information, and provides appropriate service introductions and suggestions like a sales representative, even for vague searches. For example, a user enters vague search terms on the site, such as "how to install IT tools" or "recommended IT services." These search terms are entered into the generating AI. Next, the generating AI analyzes the entered search terms and identifies relevant service information, materials, and contact information. Based on the information it has learned in advance, the generating AI understands the user's search intent and provides optimal service introductions and suggestions. For example, in response to the search term "how to install IT tools," the generating AI presents specific installation procedures and proposal materials for related services. Furthermore, the generating AI analyzes the user's search history and behavioral data to guide them to key products. For example, it suggests relevant key products based on what the user has searched for in the past and the pages they have viewed. This allows users to easily find the service that is best suited to them. This system allows even staff with low digital literacy to easily obtain necessary information, reducing the number of users who leave the site. Furthermore, the generating AI understands the user's search intent and provides appropriate service introductions and suggestions, guiding them towards key products. For example, if a user searches for "how to implement IT tools," the generating AI will present specific implementation procedures and related service proposals, guiding the user towards key products. In this way, using generating AI, the IT tool sales support system for small and medium-sized enterprises (SMEs) can provide user-friendly and flexible support even to staff with low digital literacy, reducing site abandonment and guiding users towards key products.

[0029] The IT tool sales support system for small and medium-sized enterprises according to this embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects information. For example, the collection unit can collect service information, documents, and contact information. For example, the collection unit collects publicly available information on the internet using web scraping technology. The collection unit can also obtain information from specific databases through API integration. Furthermore, the collection unit can also collect information directly from users by conducting user surveys. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can extract useful patterns and trends from the collected information using data mining technology. For example, the analysis unit can also reveal the statistical characteristics of the collected information using statistical analysis technology. For example, the analysis unit can build a predictive model based on the collected information using machine learning algorithms. The proposal unit provides appropriate service introductions and proposals based on the information analyzed by the analysis unit. For example, the proposal unit can analyze the user's search history and behavioral data and recommend services that meet the user's needs. For example, the proposal unit can also propose the optimal service to the user based on real-time needs. The proposal section can, for example, provide personalized service suggestions based on the user's past behavior history. This enables the IT tool sales support system for small and medium-sized enterprises according to the embodiment to collect and analyze information and provide appropriate service introductions and suggestions.

[0030] The data collection unit collects information. For example, the data collection unit can collect service information, documents, and contact information. Specifically, the data collection unit automatically collects publicly available information on the internet using web scraping technology. Web scraping technology involves the process of extracting necessary information from specific websites and storing it in a database. For example, it can collect information such as product features, pricing, and user reviews from the official website or review site of a specific IT tool. The data collection unit can also obtain information from specific databases through API integration. API integration allows for real-time acquisition of the latest industry trends and market research data, for example, and reflection of this data in the system. Furthermore, the data collection unit can also collect information directly from users by conducting user surveys. User surveys are conducted using methods such as online forms, email, and telephone interviews, allowing for a detailed understanding of user needs and challenges. This enables the data collection unit to collect a wide range of data from diverse sources and strengthen the information infrastructure of the entire system. The collected data is stored in a central database and managed so that the analysis and proposal units can access it. This allows the data collection unit to collect information efficiently and effectively and improve the overall performance of the system.

[0031] The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit can extract useful patterns and trends from the collected information using data mining techniques. Data mining techniques are methods for finding hidden patterns and relationships in large amounts of data, and can reveal, for example, which industries use a particular IT tool most, or which features are most highly valued. The analysis unit can also reveal the statistical characteristics of the collected information using statistical analysis techniques. Statistical analysis techniques analyze the distribution and correlation of data, and can evaluate, for example, the market share and growth rate of a particular IT tool. Furthermore, the analysis unit can build predictive models based on the collected information using machine learning algorithms. Machine learning algorithms learn from past data and generate models to predict future trends and demand. For example, they can predict how the demand for a particular IT tool will change in the future and what new features will be required. This allows the analysis unit to analyze the collected data from multiple perspectives and maximize the informational value of the entire system. Additionally, the analysis unit can visualize the analysis results and generate dashboards and reports that users can intuitively understand. This allows the analysis unit to quickly and accurately analyze the collected data and support decision-making for the entire system.

[0032] The Proposal Department provides appropriate service introductions and suggestions based on information analyzed by the Analysis Department. For example, the Proposal Department can analyze a user's search history and behavioral data to recommend services that meet the user's needs. Specifically, it can suggest relevant IT tools and services based on keywords the user has previously searched for and pages they have viewed. For example, if a user has searched for a project management tool, the Proposal Department can recommend other popular project management tools or tools with related functions to that user. The Proposal Department can also suggest the most suitable services to a user based on their real-time needs. Since real-time needs change based on the user's current situation and environment, the Proposal Department always makes suggestions based on the latest information. For example, if a user is participating in a specific event or campaign, the Proposal Department can suggest services and tools related to that event. Furthermore, the Proposal Department can provide personalized service suggestions based on the user's past behavioral history. Personalized suggestions are a method of providing the most suitable services to individual users based on their past purchase and usage history. For example, if a user has previously purchased a specific IT tool, the Proposal Department can suggest an upgraded version of that tool or related add-ons. This allows the Proposal Department to provide the most suitable services to meet the user's needs and improve user satisfaction. Furthermore, the proposal department can continuously monitor the effectiveness of its proposals and improve its proposal algorithms based on the feedback. This allows the proposal department to consistently make optimal proposals and improve the overall system performance.

[0033] The data collection unit can collect service information, documents, and contact information. For example, the data collection unit can collect product catalogs. For example, the data collection unit can collect user manuals. For example, the data collection unit can collect FAQs. For example, the data collection unit can collect phone numbers and email addresses. For example, the data collection unit can collect chat support links. By collecting service information, documents, and contact information, appropriate information can be provided to users. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use web scraping technology to input publicly available information from the internet into the AI, and the AI ​​can collect the information.

[0034] The analysis unit can analyze the collected information and understand the user's search intent. For example, the analysis unit can analyze search keywords. For example, the analysis unit can analyze past search history. For example, the analysis unit can analyze user behavior data and identify search intent. By understanding the user's search intent, it becomes possible to provide more appropriate service introductions and suggestions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into AI, and the AI ​​can analyze the search intent.

[0035] The suggestion unit can provide users with appropriate service introductions and suggestions based on the analysis results. For example, the suggestion unit can recommend services based on the user's past behavior history. For example, the suggestion unit can also suggest services based on real-time needs. For example, the suggestion unit can provide personalized service suggestions based on the user's search history. This makes it possible to provide information that meets the user's needs by providing appropriate service introductions and suggestions based on the analysis results. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the analysis results into AI, which can then provide appropriate service introductions and suggestions.

[0036] The suggestion department can analyze users' search history and behavioral data and guide them to key products. For example, the suggestion department can suggest relevant key products based on a user's past search history. For example, the suggestion department can also analyze users' behavioral data and guide them to key products. For example, the suggestion department can recommend key products based on a user's browsing history. This makes it possible to guide users to key products by analyzing their search history and behavioral data. Some or all of the above processing in the suggestion department may be performed using AI, for example, or without AI. For example, the suggestion department can input users' search history and behavioral data into AI, which can then guide them to key products.

[0037] The data collection unit can analyze the user's past search history and select the optimal information collection method. For example, the data collection unit can prioritize collecting relevant information based on keywords the user has frequently searched for in the past. The data collection unit can also collect information from the user's past search history at specific time periods. The data collection unit can also select the optimal information collection method based on search methods (voice, text, etc.) the user has used in the past. This allows the optimal information collection method to be selected by analyzing the user's past search history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past search history into AI, which can then select the optimal information collection method.

[0038] The data collection unit can filter information based on the user's current work situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to projects the user is currently working on. The data collection unit can also filter and collect relevant information based on the user's areas of interest. For example, the data collection unit can collect only the necessary information and exclude unnecessary information depending on the user's work situation. This allows the collection of only the necessary information by filtering it based on the user's work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's work situation and areas of interest into the AI, which can then filter the information.

[0039] The data collection unit can prioritize collecting highly relevant information based on the user's geographical location information during data collection. For example, the data collection unit can prioritize collecting information related to the user's current location. The data collection unit can also collect region-specific information based on the user's geographical location information. For example, if the user is on the move, the data collection unit can collect the most relevant information based on their current location. This enables the provision of more appropriate information by collecting highly relevant information based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, and the AI ​​can collect highly relevant information.

[0040] The data collection unit can analyze the 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. The data collection unit can also analyze the user's social media activity history and collect information of interest. The data collection unit can also collect relevant information based on the content of posts from accounts the user follows. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI, and the AI ​​can collect relevant information.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the information. By adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical information. For example, the analysis unit can also apply a simpler analysis algorithm to general information. The analysis unit can also select the optimal analysis algorithm depending on the category of information. By applying the optimal analysis algorithm according to the category of information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into the AI, and the AI ​​can apply the optimal analysis algorithm.

[0043] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent information. For example, the analysis unit can also lower the priority of analysis for older information. The analysis unit can also adjust the analysis schedule according to the timing of information collection. By determining the priority of analysis based on the timing of information collection, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information collection timing data into the AI, and the AI ​​can determine the priority of analysis.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. For example, the analysis unit can postpone the analysis of less relevant information. The analysis unit can also adjust the analysis schedule according to the relevance of the information. By adjusting the order of analysis based on the relevance of the information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the information into the AI, and the AI ​​can adjust the order of analysis.

[0045] The proposal department can adjust the level of detail of a proposal based on the importance of the service. For example, the proposal department will provide a detailed proposal for high-importance services. For example, it can provide a simplified proposal for low-importance services. The proposal department can also determine the priority of proposals based on the importance of the service. This allows for more appropriate proposals by adjusting the level of detail based on the importance of the service. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input service importance data into the AI, which can then adjust the level of detail of the proposal.

[0046] The proposal unit can apply different proposal algorithms depending on the service category when making a proposal. For example, the proposal unit can apply a specialized proposal algorithm to technical services. For example, the proposal unit can also apply a simpler proposal algorithm to general services. The proposal unit can also select the optimal proposal algorithm depending on the service category. By applying the optimal proposal algorithm according to the service category, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input service category data into AI, and the AI ​​can apply the optimal proposal algorithm.

[0047] The proposal department can determine the priority of proposals based on the service delivery time. For example, the proposal department will prioritize proposals for services that will be delivered soon. The proposal department can also lower the priority of proposals for services that will be delivered in the distant future. The proposal department can also adjust the proposal schedule according to the service delivery time. This allows for more appropriate proposals by determining the priority of proposals based on the service delivery time. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input service delivery time data into AI, and the AI ​​can determine the priority of proposals.

[0048] The proposal department can adjust the order of proposals based on the relevance of the services. For example, the proposal department can prioritize proposals for highly relevant services. For example, the proposal department can postpone proposals for less relevant services. The proposal department can also adjust the proposal schedule according to the relevance of the services. This allows for more appropriate proposals by adjusting the order of proposals based on the relevance of the services. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input service relevance data into the AI, and the AI ​​can adjust the order of proposals.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The data collection unit can prioritize the collection of region-specific service information based on the user's geographical location. For example, if the user is in a specific region, it can collect information on services available in that region. If the user is on the move, it can also collect the most relevant service information based on their current location. Furthermore, if the user is interested in a particular region, it can prioritize the collection of information related to that region. This allows for the provision of more appropriate information by collecting highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's geographical location information into an AI, which can then collect highly relevant information.

[0051] The suggestion unit can analyze a user's past purchase history and suggest relevant services. For example, it can suggest new services based on services the user has purchased in the past. It can also analyze the usage of services the user has purchased in the past and suggest additional services. Furthermore, it can suggest services that other users have given high ratings to, based on the user's ratings of services they have purchased in the past. This allows for more appropriate service suggestions by analyzing the user's past purchase history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's purchase history data into an AI, which can then suggest relevant services.

[0052] The analysis unit can evaluate the reliability of the analysis based on the source of the information and reflect this in the analysis results. For example, it can assign high reliability to the analysis results for information collected from highly reliable sources, and low reliability to the analysis results for information collected from unreliable sources. Furthermore, it can determine the priority of the analysis according to the reliability of the information source. This allows for the provision of more appropriate analysis results by evaluating the reliability of the analysis based on the source of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input reliability data of the information source into the AI, and the AI ​​can evaluate the reliability of the analysis.

[0053] The data collection unit can select the optimal timing for information collection based on the user's past behavioral data. For example, it can prioritize collecting relevant information based on the time periods when the user frequently collected information in the past. It can also collect information at specific time periods based on the user's past behavioral data. Furthermore, it can select the optimal information collection method based on the information collection methods (voice, text, etc.) the user has used in the past. This allows the optimal timing for information collection to be selected by analyzing the user's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into AI, which can then select the optimal timing for information collection.

[0054] The analysis unit can apply different analysis methods depending on the category of information. For example, a specialized analysis method can be applied to technical information, while a simpler analysis method can be applied to general information. Furthermore, the optimal analysis method can be selected depending on the category of information. By applying the most appropriate analysis method according to the category of information, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into the AI, and the AI ​​can apply the most appropriate analysis method.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The collection unit collects information. The collection unit can collect information such as service information, documents, and contact information. The collection unit collects publicly available information from the internet using web scraping technology. It can also obtain information from specific databases through API integration. Furthermore, the collection unit can also collect information directly from users by conducting user surveys. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can extract useful patterns and trends from the collected information using data mining techniques. Furthermore, it can also reveal the statistical characteristics of the collected information using statistical analysis techniques. The analysis unit can also build predictive models based on the collected information using machine learning algorithms. Step 3: The Proposal Department provides appropriate service introductions and suggestions based on the information analyzed by the Analysis Department. The Proposal Department can analyze the user's search history and behavioral data to recommend services that meet the user's needs. Furthermore, it can propose the most suitable service to the user based on real-time needs. The Proposal Department can also provide personalized service suggestions based on the user's past behavioral history.

[0057] (Example of form 2) The IT tool sales support system for small and medium-sized enterprises according to an embodiment of the present invention is a system that provides friendly and flexible support even to staff with low IT literacy by incorporating a generating AI. This system reduces the number of users who leave the site and also guides them to key products. Specifically, the generating AI learns service information, materials, and contact information, and provides appropriate service introductions and suggestions like a sales representative, even for vague searches. For example, a user enters vague search terms on the site, such as "how to install IT tools" or "recommended IT services." These search terms are entered into the generating AI. Next, the generating AI analyzes the entered search terms and identifies relevant service information, materials, and contact information. Based on the information it has learned in advance, the generating AI understands the user's search intent and provides optimal service introductions and suggestions. For example, in response to the search term "how to install IT tools," the generating AI presents specific installation procedures and proposal materials for related services. Furthermore, the generating AI analyzes the user's search history and behavioral data to guide them to key products. For example, it suggests relevant key products based on what the user has searched for in the past and the pages they have viewed. This allows users to easily find the service that is best suited to them. This system allows even staff with low digital literacy to easily obtain necessary information, reducing the number of users who leave the site. Furthermore, the generating AI understands the user's search intent and provides appropriate service introductions and suggestions, guiding them towards key products. For example, if a user searches for "how to implement IT tools," the generating AI will present specific implementation procedures and related service proposals, guiding the user towards key products. In this way, using generating AI, the IT tool sales support system for small and medium-sized enterprises (SMEs) can provide user-friendly and flexible support even to staff with low digital literacy, reducing site abandonment and guiding users towards key products.

[0058] The IT tool sales support system for small and medium-sized enterprises according to this embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects information. For example, the collection unit can collect service information, documents, and contact information. For example, the collection unit collects publicly available information on the internet using web scraping technology. The collection unit can also obtain information from specific databases through API integration. Furthermore, the collection unit can also collect information directly from users by conducting user surveys. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can extract useful patterns and trends from the collected information using data mining technology. For example, the analysis unit can also reveal the statistical characteristics of the collected information using statistical analysis technology. For example, the analysis unit can build a predictive model based on the collected information using machine learning algorithms. The proposal unit provides appropriate service introductions and proposals based on the information analyzed by the analysis unit. For example, the proposal unit can analyze the user's search history and behavioral data and recommend services that meet the user's needs. For example, the proposal unit can also propose the optimal service to the user based on real-time needs. The proposal section can, for example, provide personalized service suggestions based on the user's past behavior history. This enables the IT tool sales support system for small and medium-sized enterprises according to the embodiment to collect and analyze information and provide appropriate service introductions and suggestions.

[0059] The data collection unit collects information. For example, the data collection unit can collect service information, documents, and contact information. Specifically, the data collection unit automatically collects publicly available information on the internet using web scraping technology. Web scraping technology involves the process of extracting necessary information from specific websites and storing it in a database. For example, it can collect information such as product features, pricing, and user reviews from the official website or review site of a specific IT tool. The data collection unit can also obtain information from specific databases through API integration. API integration allows for real-time acquisition of the latest industry trends and market research data, for example, and reflection of this data in the system. Furthermore, the data collection unit can also collect information directly from users by conducting user surveys. User surveys are conducted using methods such as online forms, email, and telephone interviews, allowing for a detailed understanding of user needs and challenges. This enables the data collection unit to collect a wide range of data from diverse sources and strengthen the information infrastructure of the entire system. The collected data is stored in a central database and managed so that the analysis and proposal units can access it. This allows the data collection unit to collect information efficiently and effectively and improve the overall performance of the system.

[0060] The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit can extract useful patterns and trends from the collected information using data mining techniques. Data mining techniques are methods for finding hidden patterns and relationships in large amounts of data, and can reveal, for example, which industries use a particular IT tool most, or which features are most highly valued. The analysis unit can also reveal the statistical characteristics of the collected information using statistical analysis techniques. Statistical analysis techniques analyze the distribution and correlation of data, and can evaluate, for example, the market share and growth rate of a particular IT tool. Furthermore, the analysis unit can build predictive models based on the collected information using machine learning algorithms. Machine learning algorithms learn from past data and generate models to predict future trends and demand. For example, they can predict how the demand for a particular IT tool will change in the future and what new features will be required. This allows the analysis unit to analyze the collected data from multiple perspectives and maximize the informational value of the entire system. Additionally, the analysis unit can visualize the analysis results and generate dashboards and reports that users can intuitively understand. This allows the analysis unit to quickly and accurately analyze the collected data and support decision-making for the entire system.

[0061] The Proposal Department provides appropriate service introductions and suggestions based on information analyzed by the Analysis Department. For example, the Proposal Department can analyze a user's search history and behavioral data to recommend services that meet the user's needs. Specifically, it can suggest relevant IT tools and services based on keywords the user has previously searched for and pages they have viewed. For example, if a user has searched for a project management tool, the Proposal Department can recommend other popular project management tools or tools with related functions to that user. The Proposal Department can also suggest the most suitable services to a user based on their real-time needs. Since real-time needs change based on the user's current situation and environment, the Proposal Department always makes suggestions based on the latest information. For example, if a user is participating in a specific event or campaign, the Proposal Department can suggest services and tools related to that event. Furthermore, the Proposal Department can provide personalized service suggestions based on the user's past behavioral history. Personalized suggestions are a method of providing the most suitable services to individual users based on their past purchase and usage history. For example, if a user has previously purchased a specific IT tool, the Proposal Department can suggest an upgraded version of that tool or related add-ons. This allows the Proposal Department to provide the most suitable services to meet the user's needs and improve user satisfaction. Furthermore, the proposal department can continuously monitor the effectiveness of its proposals and improve its proposal algorithms based on the feedback. This allows the proposal department to consistently make optimal proposals and improve the overall system performance.

[0062] The data collection unit can collect service information, documents, and contact information. For example, the data collection unit can collect product catalogs. For example, the data collection unit can collect user manuals. For example, the data collection unit can collect FAQs. For example, the data collection unit can collect phone numbers and email addresses. For example, the data collection unit can collect chat support links. By collecting service information, documents, and contact information, appropriate information can be provided to users. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use web scraping technology to input publicly available information from the internet into the AI, and the AI ​​can collect the information.

[0063] The analysis unit can analyze the collected information and understand the user's search intent. For example, the analysis unit can analyze search keywords. For example, the analysis unit can analyze past search history. For example, the analysis unit can analyze user behavior data and identify search intent. By understanding the user's search intent, it becomes possible to provide more appropriate service introductions and suggestions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into AI, and the AI ​​can analyze the search intent.

[0064] The suggestion unit can provide users with appropriate service introductions and suggestions based on the analysis results. For example, the suggestion unit can recommend services based on the user's past behavior history. For example, the suggestion unit can also suggest services based on real-time needs. For example, the suggestion unit can provide personalized service suggestions based on the user's search history. This makes it possible to provide information that meets the user's needs by providing appropriate service introductions and suggestions based on the analysis results. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the analysis results into AI, which can then provide appropriate service introductions and suggestions.

[0065] The suggestion department can analyze users' search history and behavioral data and guide them to key products. For example, the suggestion department can suggest relevant key products based on a user's past search history. For example, the suggestion department can also analyze users' behavioral data and guide them to key products. For example, the suggestion department can recommend key products based on a user's browsing history. This makes it possible to guide users to key products by analyzing their search history and behavioral data. Some or all of the above processing in the suggestion department may be performed using AI, for example, or without AI. For example, the suggestion department can input users' search history and behavioral data into AI, which can then guide them to key products.

[0066] 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 can reduce the frequency of information collection and collect information when the user is relaxed. For example, if the user is excited, the data collection unit can collect information immediately and provide it quickly. For example, if the user is concentrating, the data collection unit can adjust the timing of information collection to avoid interrupting the user's work. This allows for more appropriate information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into an AI, which can then adjust the timing of information collection.

[0067] The data collection unit can analyze the user's past search history and select the optimal information collection method. For example, the data collection unit can prioritize collecting relevant information based on keywords the user has frequently searched for in the past. The data collection unit can also collect information from the user's past search history at specific time periods. The data collection unit can also select the optimal information collection method based on search methods (voice, text, etc.) the user has used in the past. This allows the optimal information collection method to be selected by analyzing the user's past search history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past search history into AI, which can then select the optimal information collection method.

[0068] The data collection unit can filter information based on the user's current work situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to projects the user is currently working on. The data collection unit can also filter and collect relevant information based on the user's areas of interest. For example, the data collection unit can collect only the necessary information and exclude unnecessary information depending on the user's work situation. This allows the collection of only the necessary information by filtering it based on the user's work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's work situation and areas of interest into the AI, which can then filter the information.

[0069] 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 may prioritize collecting information of high importance. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed information. For example, if the user is in a hurry, the data collection unit may prioritize collecting information that can be provided quickly. This allows for more appropriate information to be provided by prioritizing information 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. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then determine the priority of information.

[0070] The data collection unit can prioritize collecting highly relevant information based on the user's geographical location information during data collection. For example, the data collection unit can prioritize collecting information related to the user's current location. The data collection unit can also collect region-specific information based on the user's geographical location information. For example, if the user is on the move, the data collection unit can collect the most relevant information based on their current location. This enables the provision of more appropriate information by collecting highly relevant information based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, and the AI ​​can collect highly relevant information.

[0071] The data collection unit can analyze the 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. The data collection unit can also analyze the user's social media activity history and collect information of interest. The data collection unit can also collect relevant information based on the content of posts from accounts the user follows. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI, and the AI ​​can collect relevant information.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can also provide a concise analysis result. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI, and the AI ​​can adjust the presentation of the analysis.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the information. By adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical information. For example, the analysis unit can also apply a simpler analysis algorithm to general information. The analysis unit can also select the optimal analysis algorithm depending on the category of information. By applying the optimal analysis algorithm according to the category of information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into the AI, and the AI ​​can apply the optimal analysis algorithm.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI, and the AI ​​can adjust the length of the analysis.

[0076] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent information. For example, the analysis unit can also lower the priority of analysis for older information. The analysis unit can also adjust the analysis schedule according to the timing of information collection. By determining the priority of analysis based on the timing of information collection, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information collection timing data into the AI, and the AI ​​can determine the priority of analysis.

[0077] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. For example, the analysis unit can postpone the analysis of less relevant information. The analysis unit can also adjust the analysis schedule according to the relevance of the information. By adjusting the order of analysis based on the relevance of the information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the information into the AI, and the AI ​​can adjust the order of analysis.

[0078] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can then adjust the way suggestions are presented.

[0079] The proposal department can adjust the level of detail of a proposal based on the importance of the service. For example, the proposal department will provide a detailed proposal for high-importance services. For example, it can provide a simplified proposal for low-importance services. The proposal department can also determine the priority of proposals based on the importance of the service. This allows for more appropriate proposals by adjusting the level of detail based on the importance of the service. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input service importance data into the AI, which can then adjust the level of detail of the proposal.

[0080] The proposal unit can apply different proposal algorithms depending on the service category when making a proposal. For example, the proposal unit can apply a specialized proposal algorithm to technical services. For example, the proposal unit can also apply a simpler proposal algorithm to general services. The proposal unit can also select the optimal proposal algorithm depending on the service category. By applying the optimal proposal algorithm according to the service category, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input service category data into AI, and the AI ​​can apply the optimal proposal algorithm.

[0081] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is excited, the suggestion unit can also provide visually stimulating suggestions. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can then adjust the length of the suggestions.

[0082] The proposal department can determine the priority of proposals based on the service delivery time. For example, the proposal department will prioritize proposals for services that will be delivered soon. The proposal department can also lower the priority of proposals for services that will be delivered in the distant future. The proposal department can also adjust the proposal schedule according to the service delivery time. This allows for more appropriate proposals by determining the priority of proposals based on the service delivery time. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input service delivery time data into AI, and the AI ​​can determine the priority of proposals.

[0083] The proposal department can adjust the order of proposals based on the relevance of the services. For example, the proposal department can prioritize proposals for highly relevant services. For example, the proposal department can postpone proposals for less relevant services. The proposal department can also adjust the proposal schedule according to the relevance of the services. This allows for more appropriate proposals by adjusting the order of proposals based on the relevance of the services. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input service relevance data into the AI, and the AI ​​can adjust the order of proposals.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can reduce the frequency of suggestions and make suggestions when the user is relaxed. If the user is excited, the suggestion unit can make suggestions immediately and respond quickly. Furthermore, if the user is concentrating, the suggestion unit can adjust the timing of suggestions to avoid interrupting the user's work. By adjusting the timing of suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into an AI, which can then adjust the timing of suggestions.

[0086] The data collection unit can prioritize the collection of region-specific service information based on the user's geographical location. For example, if the user is in a specific region, it can collect information on services available in that region. If the user is on the move, it can also collect the most relevant service information based on their current location. Furthermore, if the user is interested in a particular region, it can prioritize the collection of information related to that region. This allows for the provision of more appropriate information by collecting highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's geographical location information into an AI, which can then collect highly relevant information.

[0087] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, it can prioritize the analysis of high-priority information. If the user is relaxed, it can also prioritize the analysis of detailed information. Furthermore, if the user is in a hurry, it can prioritize the analysis of information that can be provided quickly. By determining the priority of analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into an AI, and the AI ​​can determine the priority of analysis.

[0088] The suggestion unit can analyze a user's past purchase history and suggest relevant services. For example, it can suggest new services based on services the user has purchased in the past. It can also analyze the usage of services the user has purchased in the past and suggest additional services. Furthermore, it can suggest services that other users have given high ratings to, based on the user's ratings of services they have purchased in the past. This allows for more appropriate service suggestions by analyzing the user's past purchase history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's purchase history data into an AI, which can then suggest relevant services.

[0089] The data collection unit can estimate the user's emotions and adjust its information collection methods based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection and collect information when the user is relaxed. If the user is excited, the data collection unit can collect information immediately and provide it quickly. Furthermore, if the user is focused, the data collection unit can adjust its information collection methods to avoid interrupting the user's work. This allows for more appropriate information collection by adjusting the information collection methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then adjust its information collection methods.

[0090] The analysis unit can evaluate the reliability of the analysis based on the source of the information and reflect this in the analysis results. For example, it can assign high reliability to the analysis results for information collected from highly reliable sources, and low reliability to the analysis results for information collected from unreliable sources. Furthermore, it can determine the priority of the analysis according to the reliability of the information source. This allows for the provision of more appropriate analysis results by evaluating the reliability of the analysis based on the source of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input reliability data of the information source into the AI, and the AI ​​can evaluate the reliability of the analysis.

[0091] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will provide simple and easy-to-understand suggestions. If the user is relaxed, it can provide more detailed suggestions. Furthermore, if the user is in a hurry, it can provide concise suggestions. By adjusting the content of suggestions according to the user's emotions, more appropriate suggestions can be made. 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. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can then adjust the content of its suggestions.

[0092] The data collection unit can select the optimal timing for information collection based on the user's past behavioral data. For example, it can prioritize collecting relevant information based on the time periods when the user frequently collected information in the past. It can also collect information at specific time periods based on the user's past behavioral data. Furthermore, it can select the optimal information collection method based on the information collection methods (voice, text, etc.) the user has used in the past. This allows the optimal timing for information collection to be selected by analyzing the user's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into AI, which can then select the optimal timing for information collection.

[0093] The analysis unit can apply different analysis methods depending on the category of information. For example, a specialized analysis method can be applied to technical information, while a simpler analysis method can be applied to general information. Furthermore, the optimal analysis method can be selected depending on the category of information. By applying the most appropriate analysis method according to the category of information, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into the AI, and the AI ​​can apply the most appropriate analysis method.

[0094] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, it can prioritize high-priority suggestions. If the user is relaxed, it can prioritize detailed suggestions. Furthermore, if the user is in a hurry, it can prioritize suggestions that can be delivered quickly. By prioritizing suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can then determine the priority of suggestions.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The collection unit collects information. The collection unit can collect information such as service information, documents, and contact information. The collection unit collects publicly available information from the internet using web scraping technology. It can also obtain information from specific databases through API integration. Furthermore, the collection unit can also collect information directly from users by conducting user surveys. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can extract useful patterns and trends from the collected information using data mining techniques. Furthermore, it can also reveal the statistical characteristics of the collected information using statistical analysis techniques. The analysis unit can also build predictive models based on the collected information using machine learning algorithms. Step 3: The Proposal Department provides appropriate service introductions and suggestions based on the information analyzed by the Analysis Department. The Proposal Department can analyze the user's search history and behavioral data to recommend services that meet the user's needs. Furthermore, it can propose the most suitable service to the user based on real-time needs. The Proposal Department can also provide personalized service suggestions based on the user's past behavioral history.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information through web scraping technology and API integration. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information using data mining technology and machine learning algorithms. The proposal unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12 and proposes services based on the user's search history and behavioral data. 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.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information through web scraping technology and API integration. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information using data mining technology and machine learning algorithms. The proposal unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12 and proposes services based on the user's search history and behavioral data. 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.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements, including the collection unit, analysis unit, and proposal unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information through web scraping technology and API integration. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information using data mining technology and machine learning algorithms. The proposal unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12 and proposes services based on the user's search history and behavioral data. 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.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects information through web scraping technology and API integration. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information using data mining technology and machine learning algorithms. The proposal unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12 and proposes services based on the user's search history and behavioral data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the information analyzed by the aforementioned analysis unit, the proposal unit provides appropriate service introductions and proposals. Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect service information, documents, and contact details. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the collected information to understand the user's search intent. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analysis results, we will provide users with appropriate service introductions and suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Analyze user search history and behavioral data to guide users to products that are a focus of attention. The system described in Appendix 1, characterized by the features described herein. (Note 6) 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 7) The aforementioned collection unit is Analyze the user's past search history to select the most suitable information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting information, filtering is performed based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) The aforementioned collection unit is When gathering information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, 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 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, reduce the level of detail in the proposal based on the importance of the service. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the service category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, prioritize the proposals based on the service delivery timeline. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the services. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 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 analysis unit analyzes the information collected by the aforementioned collection unit, Based on the information analyzed by the aforementioned analysis unit, the proposal unit provides appropriate service introductions and proposals. Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Collect service information, documents, and contact details. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the collected information to understand the user's search intent. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results, we will provide users with appropriate service introductions and suggestions. The system according to feature 1.

5. The aforementioned proposal section is, Analyze user search history and behavioral data to guide users to products that are a focus of attention. The system according to feature 1.

6. 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.

7. The aforementioned collection unit is Analyze the user's past search history to select the most suitable information gathering method. The system according to feature 1.

8. The aforementioned collection unit is When collecting information, filtering is performed based on the user's current work situation and areas of interest. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is When gathering information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system according to feature 1.

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