system

Abstract

The system according to this embodiment aims to efficiently analyze customer information and create appropriate proposals. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a checking unit. The reception unit receives information from customers. The analysis unit analyzes the information received by the reception unit. The generation unit creates a proposal based on the analysis results obtained by the analysis unit. The checking unit checks the contents of the proposal created by the generation 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the process of efficiently analyzing information from customers and creating appropriate proposal documents is complicated and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently analyze information from customers and create appropriate proposal documents.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a checking unit. The reception unit receives information from customers. The analysis unit analyzes the information received by the reception unit. The generation unit creates a proposal based on the analysis results obtained by the analysis unit. The checking unit checks the contents of the proposal created by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently analyze customer information and create appropriate 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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F 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 3, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The proposal creation system according to an embodiment of the present invention is a system that uses generative AI to analyze customer needs from chat conversations and create an optimal proposal. When a customer clicks "Request Information Here," a chat conversation begins. The generative AI analyzes the content of the conversation with the customer and identifies what the customer truly wants. Then, the generative AI creates a proposal based on the identified needs. The system checks the content of the proposal created by the generative AI to ensure there are no errors. This system can provide proposals that are effectively used by customers in their purchasing decisions and internal reviews. Furthermore, it is expected to provide a new user experience unlike those offered by other companies, reducing the time spent on document creation and review, while contributing to increased sales profits. For example, by enabling customers to quickly make purchasing decisions and reach contracts based on the proposal, the burden on sales representatives can be reduced, and efficient sales activities can be achieved. Thus, the proposal creation system can quickly and accurately create and provide proposals based on customer needs.

[0029] The proposal creation system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a checking unit. The reception unit receives information from customers. Information from customers includes, but is not limited to, text information, audio information, and image information. The reception unit can receive information, for example, through chat conversations with customers. The analysis unit analyzes the information received by the reception unit. The analysis is performed by, for example, text mining, data mining, and statistical analysis, but is not limited to, such methods. The analysis unit can analyze the content of the conversation and identify the customer's needs. The generation unit creates a proposal based on the analysis results obtained by the analysis unit. The proposal is created in, for example, a business proposal, a technical proposal, or a marketing proposal, but is not limited to, such forms. The generation unit can create a proposal based on the identified needs. The checking unit checks the content of the proposal created by the generation unit to confirm that there are no errors. The checking is performed by, for example, checking for typographical errors, confirming the accuracy of the content, and checking the format, but is not limited to, such methods. The checking unit can check the contents of the proposal and verify that there are no errors. As a result, the proposal creation system according to this embodiment can efficiently receive and analyze customer information, create proposals, and check them.

[0030] The reception department receives information from customers. This information includes, but is not limited to, text, audio, and image information. The reception department can receive information, for example, through chat interactions with customers. Specifically, the reception department can chat with customers in real time via websites and mobile applications to receive their requests and questions. The chat interactions support not only text-based messages but also voice input and image transmission, and are designed to allow customers to provide information intuitively. For example, if a customer requests the creation of a business proposal, the reception department will collect details such as the customer's business overview, goals, target market, and competitor information through chat. By using voice input, the content explained verbally by the customer can be automatically converted into text, allowing for efficient information collection. Furthermore, image information can also be received, such as photos and diagrams of products provided by the customer, which can be used in proposal creation. The reception department can centrally manage this information and smoothly hand it over to the subsequent analysis and generation departments. This allows the reception department to efficiently receive diverse information from customers and smoothly proceed with the first stage of the proposal creation process.

[0031] The Analysis Department analyzes the information received by the Reception Department. This analysis is performed using methods such as text mining, data mining, and statistical analysis, but is not limited to these examples. Specifically, the Analysis Department analyzes the received text information using natural language processing techniques to extract customer needs and requests. For example, using text mining techniques, it extracts keywords and important phrases from customer messages to identify the content of the proposal the customer desires. Audio information is converted to text using speech recognition technology and similarly analyzed. Image information is analyzed using image recognition technology to extract the information necessary for the proposal. Furthermore, the Analysis Department can use data mining techniques to analyze past proposal creation data and customer history data to derive the optimal proposal structure and content to meet customer needs. Statistical analysis can also be used to understand industry and market trends and reflect them in the proposal. This allows the Analysis Department to analyze the received information from multiple perspectives and accurately identify customer needs. The analysis results are then handed over to the Generation Department and used in the creation of proposals.

[0032] The generation unit creates proposals based on the analysis results obtained by the analysis unit. These proposals may take the form of business proposals, technical proposals, or marketing proposals, but are not limited to these examples. Specifically, the generation unit determines the optimal proposal structure and generates the content based on the customer's needs and requests provided by the analysis unit. Using natural language generation technology, the generation unit can automatically create text tailored to customer needs. For example, in the case of a business proposal, it creates sections including an overview of the customer's business, goals, target market, and competitor information, and fills each section with appropriate content. In the case of a technical proposal, it creates sections including technical details, implementation methods, and expected outcomes, providing technical explanations tailored to customer requests. In the case of a marketing proposal, it creates sections including marketing strategies, campaign details, and expected effects, proposing a marketing plan tailored to customer needs. The generation unit quickly and accurately creates these proposals and hands them over to the review unit. This allows the generation unit to efficiently create high-quality proposals tailored to customer needs.

[0033] The checking unit reviews the content of the proposal created by the generation unit to ensure there are no errors. Checking is performed by methods such as verifying typographical errors, confirming the accuracy of the content, and checking the formatting, but is not limited to these examples. Specifically, the checking unit uses automated proofreading tools to detect and correct typographical errors in the generated proposal text. Furthermore, to confirm the accuracy of the content, the checking unit cross-checks the data and facts contained in the proposal to ensure there are no errors. For example, it verifies the accuracy of market data and technical details contained in the proposal and makes corrections as necessary. Regarding formatting, the checking unit verifies that the proposal is created according to the specified format, maintaining consistency in layout and style. In addition, the checking unit reviews the overall flow and logical structure of the proposal and evaluates whether the content is easy for the customer to understand. This allows the checking unit to guarantee the quality of the proposal created by the generation unit and correct errors and deficiencies before providing it to the customer. The checked proposal is then finally provided to the customer. This ensures the quality of the proposal and allows the checking unit to provide a reliable proposal to the customer.

[0034] The reception desk can receive information through chat interactions with customers. For example, the reception desk can interact with customers using text chat and receive information. It can also interact with customers using voice chat and receive information. Furthermore, it can interact with customers using video chat and receive information. This allows the reception desk to receive customer information through chat interactions. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the content of the chat interaction with the customer into a generating AI, which can then analyze the interaction content and receive the information.

[0035] The analysis unit can analyze the content of conversations and identify customer needs. For example, the analysis unit can analyze the content of conversations using text mining techniques to identify customer needs. The analysis unit can also analyze the content of conversations using data mining techniques to identify customer needs. Furthermore, the analysis unit can analyze the content of conversations using statistical analysis techniques to identify customer needs. In this way, customer needs can be identified by analyzing the content of conversations. 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 content of conversations into a generating AI, and the generating AI can analyze the content of conversations to identify customer needs.

[0036] The generation unit can create proposals based on identified needs. For example, the generation unit can create business proposals. It can also create technical proposals. Furthermore, it can create marketing proposals. This allows proposals to be created based on identified needs. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs the identified needs into the generation AI, and the generation AI can create a proposal.

[0037] The checking unit can check the contents of the proposal and verify that there are no errors. For example, the checking unit can check for typographical errors. The checking unit can also verify the accuracy of the content. Furthermore, the checking unit can also verify the format. This allows the contents of the proposal to be checked and verified for errors. Some or all of the above processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the contents of the proposal into a generating AI, which can then check the contents for errors.

[0038] The generation unit can create proposals that conform to the company's internal formats and document creation trends. For example, the generation unit can create proposals using the company's internal proposal template. It can also create proposals that conform to the document layout. Furthermore, it can create proposals that conform to the fonts used. This allows the generation unit to create proposals that conform to the company's internal formats and document creation trends. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can input the company's internal formats and document creation trends into the generation AI, which can then create a proposal based on that information.

[0039] The checking unit can refer to pre-inputted internal knowledge to avoid hallucinations and verify the validity of the content. For example, the checking unit can verify the validity of the content by referring to past proposals. The checking unit can also avoid hallucinations by referring to internal guidelines. Furthermore, the checking unit can verify the validity of the content by referring to a database of expert knowledge. This allows the checking unit to refer to pre-inputted internal knowledge to avoid hallucinations and verify the validity of the content. Some or all of the above processes in the checking unit may be performed using AI, for example, or not using AI. For example, the checking unit can input internal knowledge into a generating AI, which can then verify the avoidance of hallucinations and the validity of the content.

[0040] The reception desk can analyze a customer's past conversation history and select the optimal conversation scenario. For example, the reception desk can select the optimal conversation scenario based on questions the customer has frequently asked in the past. It can also select a scenario that addresses a specific need from the customer's past conversation history. Furthermore, the reception desk can prioritize selecting conversation scenarios that the customer has previously found satisfactory. This allows for the selection of the optimal conversation scenario by analyzing the customer's past conversation history. Some or all of the above processes in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the customer's past conversation history into a generating AI, which can then analyze the conversation history and select the optimal conversation scenario.

[0041] The reception desk can customize the conversation content at the start of the interaction based on the customer's current interests and circumstances. For example, the reception desk can customize the conversation content based on keywords the customer has recently searched for. It can also customize the conversation content based on the customer's current situation (e.g., purchasing a new smartphone). Furthermore, the reception desk can customize the conversation content based on the customer's interests (e.g., battery life). By customizing the conversation content based on the customer's current interests and circumstances, a more appropriate conversation becomes possible. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the customer's current interests and circumstances into a generating AI, which can then customize the conversation content.

[0042] The reception desk can prioritize providing highly relevant information based on the customer's geographical location during interactions. For example, the reception desk can provide information on nearby stores based on the customer's current location. It can also provide information on region-specific campaigns based on the customer's geographical location. Furthermore, the reception desk can provide information on the nearest service center, taking the customer's location into consideration. This enables more appropriate interactions by providing highly relevant information based on the customer's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the customer's geographical location into a generating AI, which can then provide highly relevant information.

[0043] The reception desk can analyze the customer's social media activity during the interaction and provide relevant information. For example, the reception desk can provide relevant product information based on the customer's social media posts. It can also analyze the customer's interests on social media and provide relevant campaign information. Furthermore, the reception desk can select the optimal conversation scenario based on the customer's social media activity history. This allows the reception desk to provide relevant information by analyzing the customer's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the customer's social media activity data into a generating AI, which can then analyze the social media activity and provide relevant information.

[0044] The analysis department can identify needs by referring to the customer's past purchase history and behavioral patterns during analysis. For example, the analysis department can identify current needs based on the customer's past purchase history. The analysis department can also identify potential needs by analyzing the customer's behavioral patterns. Furthermore, the analysis department can combine the customer's past purchase history and behavioral patterns to make optimal recommendations. This allows for the identification of needs by referring to the customer's past purchase history and behavioral patterns. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input customer's past purchase history and behavioral pattern data into a generating AI, which can then analyze it to identify needs.

[0045] The analysis department can apply different analysis algorithms depending on the customer's industry and job type during the analysis process. For example, if the customer is in the IT industry, the analysis department can apply an analysis algorithm that addresses their technical needs. Similarly, if the customer is in the healthcare industry, the analysis department can apply an analysis algorithm that addresses their healthcare-related needs. Furthermore, if the customer is in the education industry, the analysis department can apply an analysis algorithm that addresses their education-related needs. This allows for more appropriate analysis by applying different analysis algorithms depending on the customer's industry and job type. Some or all of the above-described processes in the analysis department may be performed using AI, for example, or without AI. For instance, the analysis department can input customer industry and job type data into a generating AI, which can then apply different analysis algorithms based on that data.

[0046] The analysis department can prioritize analyses based on customer submission deadlines. For example, if a customer is in a hurry, the analysis department will prioritize the analysis based on the deadline. Conversely, if a customer is relaxed, the analysis department can prioritize the analysis based on the deadline. Furthermore, the analysis department can set an optimal analysis schedule based on customer submission deadlines. This allows for more appropriate analysis by prioritizing analyses based on customer submission deadlines. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input customer submission deadline data into a generating AI, which can then determine the analysis priority based on that data.

[0047] The analysis department can improve the accuracy of its analysis by referring to relevant customer literature and market data during the analysis process. For example, the analysis department can improve the accuracy of its analysis by referring to relevant customer literature. Furthermore, the analysis department can also improve the accuracy of its analysis by identifying customer needs based on market data. In addition, the analysis department can combine relevant customer literature and market data to perform an optimal analysis. This allows for improved analysis accuracy by referring to relevant customer literature and market data. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input relevant customer literature and market data into a generating AI, which can then refer to them to improve the accuracy of its analysis.

[0048] The generation unit can adjust the level of detail in a proposal based on customer needs when creating it. For example, if a customer requests detailed information, the generation unit will create a proposal that includes detailed data. Alternatively, if a customer requests concise information, the generation unit can create a proposal that focuses on the key points. Furthermore, the generation unit can adjust the level of detail in the proposal based on customer needs to provide the most suitable proposal. This allows for the creation of more appropriate proposals by adjusting the level of detail based on customer needs. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can input customer needs data into the generation AI, which can then adjust the level of detail in the proposal based on that data.

[0049] The generation unit can apply different formats to proposals depending on the client's industry and job type. For example, if the client is in the IT industry, the generation unit will apply a format that emphasizes technical information. If the client is in the medical industry, the generation unit can also apply a format that emphasizes medical-related information. Furthermore, if the client is in the education industry, the generation unit can also apply a format that emphasizes education-related information. This allows for the creation of more appropriate proposals by applying different formats according to the client's industry and job type. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the client's industry and job type data into the generation AI, which can then apply different formats based on that data.

[0050] The generation unit can determine the priority of proposals based on the client's submission deadlines when creating proposals. For example, if the client is in a hurry, the generation unit will set a higher priority for the proposal, taking the submission deadline into consideration. Conversely, if the client is relaxed, the generation unit can also set a lower priority for the proposal, taking the submission deadline into consideration. Furthermore, the generation unit can set an optimal proposal creation schedule based on the client's submission deadlines. This allows for the creation of more appropriate proposals by determining the priority of proposals based on the client's submission deadlines. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can input client submission deadline data into the generation AI, which can then determine the priority of proposals based on that data.

[0051] The generation unit can adjust the order of proposals based on customer relevance when creating them. For example, the generation unit places the information most relevant to the customer's needs at the beginning of the proposal. It can also adjust the order of proposals based on customer interests. Furthermore, the generation unit can optimize the order of proposals according to the customer's industry and job type. This allows for the creation of more appropriate proposals by adjusting the order of proposals based on customer relevance. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs customer relevance data into the generation AI, which can then adjust the order of proposals based on that data.

[0052] The checking unit can optimize its checking algorithm by referring to past checking history during the checking process. For example, the checking unit can select the optimal checking algorithm based on past checking history. Furthermore, the checking unit can analyze specific error trends from the customer's past checking history and optimize the checking algorithm accordingly. In addition, the checking unit can improve the accuracy of the checks by referring to past checking history. This allows for the optimization of the checking algorithm by referring to past checking history. Some or all of the above processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input past checking history data into a generating AI, which can then optimize the checking algorithm based on that data.

[0053] The checking unit can apply different checking methods to each category of proposal during the checking process. For example, if a proposal is technical, the checking unit can apply a checking method specialized for technical content. Similarly, if a proposal is medical, the checking unit can apply a checking method specialized for medical content. Furthermore, if a proposal is educational, the checking unit can apply a checking method specialized for educational content. This allows for more appropriate checking by applying different checking methods to each category of proposal. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input proposal category data into a generating AI, which can then apply different checking methods based on that data.

[0054] The checking unit can weight the checks based on the submission date of the proposals. For example, if the proposal submission deadline is approaching, the checking unit can set a higher weight for the check and respond quickly. Conversely, if the proposal submission deadline is far away, the checking unit can set a lower weight for the check and prioritize other important checks. Furthermore, the checking unit can set an optimal checking schedule based on the proposal submission date. This allows for more appropriate checks by weighting the checks based on the proposal submission date. Some or all of the above processing in the checking unit may be performed using AI, for example, or not. For example, the checking unit can input proposal submission date data into a generating AI, which can then weight the checks based on that data.

[0055] The checking unit can improve the accuracy of its checks by referring to relevant literature in the proposal during the checking process. For example, the checking unit can improve the accuracy of its checks by referring to relevant literature in the proposal. The checking unit can also improve the accuracy of its checks based on market data related to the content of the proposal. Furthermore, the checking unit can combine relevant literature in the proposal with market data to perform optimal checks. This allows the accuracy of the checks to be improved by referring to relevant literature in the proposal. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input data on relevant literature in the proposal into a generating AI, which can then improve the accuracy of its checks based on that data.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The proposal creation system can also include a feedback collection unit. This unit collects customer reactions and opinions after they receive the proposal. For example, it can collect feedback on whether the customer is satisfied with the proposal's content, which parts were particularly helpful, and what areas for improvement are needed. The feedback collection unit can also track what actions the customer takes based on the proposal (e.g., purchasing decision, additional questions, requests for other proposals). Furthermore, the feedback collection unit can analyze the collected feedback to provide insights for improving the quality of the proposal. This allows the proposal creation system to continuously improve the quality of its proposals by leveraging customer feedback.

[0058] The proposal creation system can also include a customization feature. This customization feature allows for the design and content of proposals to be tailored to the client's corporate culture and brand guidelines. For example, the client's corporate logo and color scheme can be incorporated into the proposal. Furthermore, the customization feature can incorporate industry-specific terminology and expressions. Additionally, the customization feature can create proposals by referencing the style and format of the client's past proposals. This allows the proposal creation system to deliver proposals that align with the client's corporate culture and brand.

[0059] The proposal creation system can also include a competitive analysis department. This department analyzes proposals and marketing materials from the client's competitors, providing insights to create competitive proposals for the client. For example, it can analyze what kinds of proposals competitors are making and what strengths they are highlighting. The competitive analysis department can also grasp the content and design trends of competitor proposals and reflect them in the client's proposals. Furthermore, the competitive analysis department can provide data and evidence demonstrating how the client's proposals are superior to those of competitors. This allows the proposal creation system to deliver competitive proposals to the client.

[0060] The proposal creation system can also be equipped with a predictive analytics unit. This unit predicts the success rate of a proposal based on past proposal results and customer response data. For example, it can analyze past proposal success rates and customer feedback to predict the probability of a current proposal being awarded. The predictive analytics unit can also simulate the impact of changes to the proposal's content and design on the success rate. Furthermore, it can suggest the optimal proposal strategy based on the timing of submission and the customer's situation. This allows the proposal creation system to provide customers with proposals that have a higher probability of success.

[0061] The proposal creation system can also be equipped with a multilingual support function. This function translates proposals into multiple languages, providing appropriate proposals to clients who speak different languages. For example, proposals can be created in languages ​​such as English, Japanese, Chinese, and Spanish, depending on the client's needs. Furthermore, the multilingual support function can consider specialized terminology and industry-specific expressions to improve translation accuracy. Additionally, the multilingual support function can culturally adapt proposals, providing effective proposals to clients with diverse cultural backgrounds. This allows the proposal creation system to provide proposals that are suitable for global clients.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives information from the customer. This information may include, but is not limited to, text, audio, and image information. The reception desk can, for example, receive information through chat interactions with the customer. Step 2: The analysis department analyzes the information received by the reception department. The analysis may be performed using methods such as text mining, data mining, and statistical analysis, but is not limited to these examples. The analysis department can analyze the content of the conversation and identify customer needs. Step 3: The generation unit creates a proposal based on the analysis results obtained by the analysis unit. The proposal may be created in the form of a business proposal, technical proposal, marketing proposal, etc., but is not limited to these examples. The generation unit can create a proposal based on identified needs. Step 4: The checking unit checks the content of the proposal created by the generation unit to ensure there are no errors. The checking may include, but is not limited to, methods such as checking for typographical errors, verifying the accuracy of the content, and checking the format.

[0064] (Example of form 2) The proposal creation system according to an embodiment of the present invention is a system that uses generative AI to analyze customer needs from chat conversations and create an optimal proposal. When a customer clicks "Request Information Here," a chat conversation begins. The generative AI analyzes the content of the conversation with the customer and identifies what the customer truly wants. Then, the generative AI creates a proposal based on the identified needs. The system checks the content of the proposal created by the generative AI to ensure there are no errors. This system can provide proposals that are effectively used by customers in their purchasing decisions and internal reviews. Furthermore, it is expected to provide a new user experience unlike those offered by other companies, reducing the time spent on document creation and review, while contributing to increased sales profits. For example, by enabling customers to quickly make purchasing decisions and reach contracts based on the proposal, the burden on sales representatives can be reduced, and efficient sales activities can be achieved. Thus, the proposal creation system can quickly and accurately create and provide proposals based on customer needs.

[0065] The proposal creation system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a checking unit. The reception unit receives information from customers. Information from customers includes, but is not limited to, text information, audio information, and image information. The reception unit can receive information, for example, through chat conversations with customers. The analysis unit analyzes the information received by the reception unit. The analysis is performed by, for example, text mining, data mining, and statistical analysis, but is not limited to, such methods. The analysis unit can analyze the content of the conversation and identify the customer's needs. The generation unit creates a proposal based on the analysis results obtained by the analysis unit. The proposal is created in, for example, a business proposal, a technical proposal, or a marketing proposal, but is not limited to, such forms. The generation unit can create a proposal based on the identified needs. The checking unit checks the content of the proposal created by the generation unit to confirm that there are no errors. The checking is performed by, for example, checking for typographical errors, confirming the accuracy of the content, and checking the format, but is not limited to, such methods. The checking unit can check the contents of the proposal and verify that there are no errors. As a result, the proposal creation system according to this embodiment can efficiently receive and analyze customer information, create proposals, and check them.

[0066] The reception department receives information from customers. This information includes, but is not limited to, text, audio, and image information. The reception department can receive information, for example, through chat interactions with customers. Specifically, the reception department can chat with customers in real time via websites and mobile applications to receive their requests and questions. The chat interactions support not only text-based messages but also voice input and image transmission, and are designed to allow customers to provide information intuitively. For example, if a customer requests the creation of a business proposal, the reception department will collect details such as the customer's business overview, goals, target market, and competitor information through chat. By using voice input, the content explained verbally by the customer can be automatically converted into text, allowing for efficient information collection. Furthermore, image information can also be received, such as photos and diagrams of products provided by the customer, which can be used in proposal creation. The reception department can centrally manage this information and smoothly hand it over to the subsequent analysis and generation departments. This allows the reception department to efficiently receive diverse information from customers and smoothly proceed with the first stage of the proposal creation process.

[0067] The Analysis Department analyzes the information received by the Reception Department. This analysis is performed using methods such as text mining, data mining, and statistical analysis, but is not limited to these examples. Specifically, the Analysis Department analyzes the received text information using natural language processing techniques to extract customer needs and requests. For example, using text mining techniques, it extracts keywords and important phrases from customer messages to identify the content of the proposal the customer desires. Audio information is converted to text using speech recognition technology and similarly analyzed. Image information is analyzed using image recognition technology to extract the information necessary for the proposal. Furthermore, the Analysis Department can use data mining techniques to analyze past proposal creation data and customer history data to derive the optimal proposal structure and content to meet customer needs. Statistical analysis can also be used to understand industry and market trends and reflect them in the proposal. This allows the Analysis Department to analyze the received information from multiple perspectives and accurately identify customer needs. The analysis results are then handed over to the Generation Department and used in the creation of proposals.

[0068] The generation unit creates proposals based on the analysis results obtained by the analysis unit. These proposals may take the form of business proposals, technical proposals, or marketing proposals, but are not limited to these examples. Specifically, the generation unit determines the optimal proposal structure and generates the content based on the customer's needs and requests provided by the analysis unit. Using natural language generation technology, the generation unit can automatically create text tailored to customer needs. For example, in the case of a business proposal, it creates sections including an overview of the customer's business, goals, target market, and competitor information, and fills each section with appropriate content. In the case of a technical proposal, it creates sections including technical details, implementation methods, and expected outcomes, providing technical explanations tailored to customer requests. In the case of a marketing proposal, it creates sections including marketing strategies, campaign details, and expected effects, proposing a marketing plan tailored to customer needs. The generation unit quickly and accurately creates these proposals and hands them over to the review unit. This allows the generation unit to efficiently create high-quality proposals tailored to customer needs.

[0069] The checking unit reviews the content of the proposal created by the generation unit to ensure there are no errors. Checking is performed by methods such as verifying typographical errors, confirming the accuracy of the content, and checking the formatting, but is not limited to these examples. Specifically, the checking unit uses automated proofreading tools to detect and correct typographical errors in the generated proposal text. Furthermore, to confirm the accuracy of the content, the checking unit cross-checks the data and facts contained in the proposal to ensure there are no errors. For example, it verifies the accuracy of market data and technical details contained in the proposal and makes corrections as necessary. Regarding formatting, the checking unit verifies that the proposal is created according to the specified format, maintaining consistency in layout and style. In addition, the checking unit reviews the overall flow and logical structure of the proposal and evaluates whether the content is easy for the customer to understand. This allows the checking unit to guarantee the quality of the proposal created by the generation unit and correct errors and deficiencies before providing it to the customer. The checked proposal is then finally provided to the customer. This ensures the quality of the proposal and allows the checking unit to provide a reliable proposal to the customer.

[0070] The reception desk can receive information through chat interactions with customers. For example, the reception desk can interact with customers using text chat and receive information. It can also interact with customers using voice chat and receive information. Furthermore, it can interact with customers using video chat and receive information. This allows the reception desk to receive customer information through chat interactions. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the content of the chat interaction with the customer into a generating AI, which can then analyze the interaction content and receive the information.

[0071] The analysis unit can analyze the content of conversations and identify customer needs. For example, the analysis unit can analyze the content of conversations using text mining techniques to identify customer needs. The analysis unit can also analyze the content of conversations using data mining techniques to identify customer needs. Furthermore, the analysis unit can analyze the content of conversations using statistical analysis techniques to identify customer needs. In this way, customer needs can be identified by analyzing the content of conversations. 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 content of conversations into a generating AI, and the generating AI can analyze the content of conversations to identify customer needs.

[0072] The generation unit can create proposals based on identified needs. For example, the generation unit can create business proposals. It can also create technical proposals. Furthermore, it can create marketing proposals. This allows proposals to be created based on identified needs. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs the identified needs into the generation AI, and the generation AI can create a proposal.

[0073] The checking unit can check the contents of the proposal and verify that there are no errors. For example, the checking unit can check for typographical errors. The checking unit can also verify the accuracy of the content. Furthermore, the checking unit can also verify the format. This allows the contents of the proposal to be checked and verified for errors. Some or all of the above processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the contents of the proposal into a generating AI, which can then check the contents for errors.

[0074] The generation unit can create proposals that conform to the company's internal formats and document creation trends. For example, the generation unit can create proposals using the company's internal proposal template. It can also create proposals that conform to the document layout. Furthermore, it can create proposals that conform to the fonts used. This allows the generation unit to create proposals that conform to the company's internal formats and document creation trends. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can input the company's internal formats and document creation trends into the generation AI, which can then create a proposal based on that information.

[0075] The checking unit can refer to pre-inputted internal knowledge to avoid hallucinations and verify the validity of the content. For example, the checking unit can verify the validity of the content by referring to past proposals. The checking unit can also avoid hallucinations by referring to internal guidelines. Furthermore, the checking unit can verify the validity of the content by referring to a database of expert knowledge. This allows the checking unit to refer to pre-inputted internal knowledge to avoid hallucinations and verify the validity of the content. Some or all of the above processes in the checking unit may be performed using AI, for example, or not using AI. For example, the checking unit can input internal knowledge into a generating AI, which can then verify the avoidance of hallucinations and the validity of the content.

[0076] The reception desk can estimate the customer's emotions and adjust the pace of the conversation based on the estimated emotions. For example, if the customer is anxious, the reception desk can speed up the conversation and provide information quickly. Conversely, if the customer is relaxed, the reception desk can slow down the conversation and provide more detailed information. Furthermore, if the customer is feeling anxious, the reception desk can adjust the conversation pace to provide reassurance. By adjusting the conversation pace according to the customer's emotions, a more appropriate conversation becomes possible. 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 reception desk may be performed using AI or not using AI. For example, the reception desk can input customer emotion data into a generative AI, which can estimate the emotions and adjust the conversation pace.

[0077] The reception desk can analyze a customer's past conversation history and select the optimal conversation scenario. For example, the reception desk can select the optimal conversation scenario based on questions the customer has frequently asked in the past. It can also select a scenario that addresses a specific need from the customer's past conversation history. Furthermore, the reception desk can prioritize selecting conversation scenarios that the customer has previously found satisfactory. This allows for the selection of the optimal conversation scenario by analyzing the customer's past conversation history. Some or all of the above processes in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the customer's past conversation history into a generating AI, which can then analyze the conversation history and select the optimal conversation scenario.

[0078] The reception desk can customize the conversation content at the start of the interaction based on the customer's current interests and circumstances. For example, the reception desk can customize the conversation content based on keywords the customer has recently searched for. It can also customize the conversation content based on the customer's current situation (e.g., purchasing a new smartphone). Furthermore, the reception desk can customize the conversation content based on the customer's interests (e.g., battery life). By customizing the conversation content based on the customer's current interests and circumstances, a more appropriate conversation becomes possible. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the customer's current interests and circumstances into a generating AI, which can then customize the conversation content.

[0079] The reception desk can estimate the customer's emotions and determine the priority of the conversation based on the estimated emotions. For example, if the customer is in a hurry, the reception desk can set a high priority for the conversation and respond quickly. Conversely, if the customer is relaxed, the reception desk can set a low priority for the conversation and prioritize other important conversations. Furthermore, if the customer is feeling anxious, the reception desk can adjust the priority of the conversation to provide reassurance. This allows for more appropriate conversations by determining the priority of conversations according to the customer's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input customer emotion data into a generative AI, which can estimate the emotions and determine the priority of the conversation.

[0080] The reception desk can prioritize providing highly relevant information based on the customer's geographical location during interactions. For example, the reception desk can provide information on nearby stores based on the customer's current location. It can also provide information on region-specific campaigns based on the customer's geographical location. Furthermore, the reception desk can provide information on the nearest service center, taking the customer's location into consideration. This enables more appropriate interactions by providing highly relevant information based on the customer's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the customer's geographical location into a generating AI, which can then provide highly relevant information.

[0081] The reception desk can analyze the customer's social media activity during the interaction and provide relevant information. For example, the reception desk can provide relevant product information based on the customer's social media posts. It can also analyze the customer's interests on social media and provide relevant campaign information. Furthermore, the reception desk can select the optimal conversation scenario based on the customer's social media activity history. This allows the reception desk to provide relevant information by analyzing the customer's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the customer's social media activity data into a generating AI, which can then analyze the social media activity and provide relevant information.

[0082] The analysis unit can estimate the customer's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the customer is relaxed, the analysis unit can perform a detailed analysis to improve accuracy. It can also perform a rapid analysis and adjust accuracy if the customer is in a hurry. Furthermore, if the customer is feeling anxious, the analysis unit can adjust the accuracy of the analysis to provide reassurance. This allows for more appropriate analysis by adjusting the accuracy of the analysis according to the customer'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 analysis unit may be performed using AI or not. For example, the analysis unit can input customer emotion data into a generative AI, which can estimate emotions and adjust the accuracy of the analysis.

[0083] The analysis department can identify needs by referring to the customer's past purchase history and behavioral patterns during analysis. For example, the analysis department can identify current needs based on the customer's past purchase history. The analysis department can also identify potential needs by analyzing the customer's behavioral patterns. Furthermore, the analysis department can combine the customer's past purchase history and behavioral patterns to make optimal recommendations. This allows for the identification of needs by referring to the customer's past purchase history and behavioral patterns. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input customer's past purchase history and behavioral pattern data into a generating AI, which can then analyze it to identify needs.

[0084] The analysis department can apply different analysis algorithms depending on the customer's industry and job type during the analysis process. For example, if the customer is in the IT industry, the analysis department can apply an analysis algorithm that addresses their technical needs. Similarly, if the customer is in the healthcare industry, the analysis department can apply an analysis algorithm that addresses their healthcare-related needs. Furthermore, if the customer is in the education industry, the analysis department can apply an analysis algorithm that addresses their education-related needs. This allows for more appropriate analysis by applying different analysis algorithms depending on the customer's industry and job type. Some or all of the above-described processes in the analysis department may be performed using AI, for example, or without AI. For instance, the analysis department can input customer industry and job type data into a generating AI, which can then apply different analysis algorithms based on that data.

[0085] The analysis unit can estimate the customer's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the customer is nervous, the analysis unit can provide a simple and highly visible display method. If the customer is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the customer is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results according to the customer's emotions, a more appropriate display becomes possible. 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 customer emotion data into a generative AI, which can estimate emotions and adjust the display method of the analysis results.

[0086] The analysis department can prioritize analyses based on customer submission deadlines. For example, if a customer is in a hurry, the analysis department will prioritize the analysis based on the deadline. Conversely, if a customer is relaxed, the analysis department can prioritize the analysis based on the deadline. Furthermore, the analysis department can set an optimal analysis schedule based on customer submission deadlines. This allows for more appropriate analysis by prioritizing analyses based on customer submission deadlines. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input customer submission deadline data into a generating AI, which can then determine the analysis priority based on that data.

[0087] The analysis department can improve the accuracy of its analysis by referring to relevant customer literature and market data during the analysis process. For example, the analysis department can improve the accuracy of its analysis by referring to relevant customer literature. Furthermore, the analysis department can also improve the accuracy of its analysis by identifying customer needs based on market data. In addition, the analysis department can combine relevant customer literature and market data to perform an optimal analysis. This allows for improved analysis accuracy by referring to relevant customer literature and market data. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input relevant customer literature and market data into a generating AI, which can then refer to them to improve the accuracy of its analysis.

[0088] The generation unit can estimate the customer's emotions and adjust the presentation of the proposal based on those emotions. For example, if the customer is relaxed, the generation unit can create a proposal that includes detailed information. If the customer is in a hurry, the generation unit can create a concise proposal that gets straight to the point. Furthermore, if the customer is feeling anxious, the generation unit can create a proposal that uses reassuring language. This allows for the creation of more appropriate proposals by adjusting the presentation according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The 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 generation unit is performed using the generative AI. For example, the generation unit can input customer emotion data into the generative AI, which can estimate the emotions and adjust the presentation of the proposal.

[0089] The generation unit can adjust the level of detail in a proposal based on customer needs when creating it. For example, if a customer requests detailed information, the generation unit will create a proposal that includes detailed data. Alternatively, if a customer requests concise information, the generation unit can create a proposal that focuses on the key points. Furthermore, the generation unit can adjust the level of detail in the proposal based on customer needs to provide the most suitable proposal. This allows for the creation of more appropriate proposals by adjusting the level of detail based on customer needs. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can input customer needs data into the generation AI, which can then adjust the level of detail in the proposal based on that data.

[0090] The generation unit can apply different formats to proposals depending on the client's industry and job type. For example, if the client is in the IT industry, the generation unit will apply a format that emphasizes technical information. If the client is in the medical industry, the generation unit can also apply a format that emphasizes medical-related information. Furthermore, if the client is in the education industry, the generation unit can also apply a format that emphasizes education-related information. This allows for the creation of more appropriate proposals by applying different formats according to the client's industry and job type. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the client's industry and job type data into the generation AI, which can then apply different formats based on that data.

[0091] The generation unit can estimate the customer's emotions and adjust the length of the proposal based on those emotions. For example, if the customer is in a hurry, the generation unit can create a short, concise proposal. If the customer is relaxed, the generation unit can create a longer proposal with more detailed explanations. Furthermore, if the customer is feeling anxious, the generation unit can create a proposal of an appropriate length to provide reassurance. By adjusting the length of the proposal according to the customer's emotions, a more appropriate proposal can be created. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generative AI. For example, the generation unit can input customer emotion data into the generative AI, which can estimate the emotions and adjust the length of the proposal.

[0092] The generation unit can determine the priority of proposals based on the client's submission deadlines when creating proposals. For example, if the client is in a hurry, the generation unit will set a higher priority for the proposal, taking the submission deadline into consideration. Conversely, if the client is relaxed, the generation unit can also set a lower priority for the proposal, taking the submission deadline into consideration. Furthermore, the generation unit can set an optimal proposal creation schedule based on the client's submission deadlines. This allows for the creation of more appropriate proposals by determining the priority of proposals based on the client's submission deadlines. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can input client submission deadline data into the generation AI, which can then determine the priority of proposals based on that data.

[0093] The generation unit can adjust the order of proposals based on customer relevance when creating them. For example, the generation unit places the information most relevant to the customer's needs at the beginning of the proposal. It can also adjust the order of proposals based on customer interests. Furthermore, the generation unit can optimize the order of proposals according to the customer's industry and job type. This allows for the creation of more appropriate proposals by adjusting the order of proposals based on customer relevance. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs customer relevance data into the generation AI, which can then adjust the order of proposals based on that data.

[0094] The checking unit can estimate the customer's emotions and adjust the accuracy of the check based on the estimated emotions. For example, if the customer is relaxed, the checking unit can perform a detailed check to improve accuracy. It can also perform a quick check and adjust accuracy if the customer is in a hurry. Furthermore, if the customer is feeling anxious, the checking unit can adjust the accuracy of the check to provide reassurance. This allows for more appropriate checks by adjusting the accuracy according to the customer'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 checking unit may be performed using AI, or not. For example, the checking unit can input customer emotion data into a generative AI, which can estimate emotions and adjust the accuracy of the check.

[0095] The checking unit can optimize its checking algorithm by referring to past checking history during the checking process. For example, the checking unit can select the optimal checking algorithm based on past checking history. Furthermore, the checking unit can analyze specific error trends from the customer's past checking history and optimize the checking algorithm accordingly. In addition, the checking unit can improve the accuracy of the checks by referring to past checking history. This allows for the optimization of the checking algorithm by referring to past checking history. Some or all of the above processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input past checking history data into a generating AI, which can then optimize the checking algorithm based on that data.

[0096] The checking unit can apply different checking methods to each category of proposal during the checking process. For example, if a proposal is technical, the checking unit can apply a checking method specialized for technical content. Similarly, if a proposal is medical, the checking unit can apply a checking method specialized for medical content. Furthermore, if a proposal is educational, the checking unit can apply a checking method specialized for educational content. This allows for more appropriate checking by applying different checking methods to each category of proposal. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input proposal category data into a generating AI, which can then apply different checking methods based on that data.

[0097] The checking unit can estimate the customer's emotions and determine the priority of checks based on the estimated emotions. For example, if the customer is in a hurry, the checking unit can set a high priority on the check and respond quickly. Conversely, if the customer is relaxed, the checking unit can set a low priority on the check and prioritize other important checks. Furthermore, if the customer is feeling anxious, the checking unit can adjust the priority of checks to provide reassurance. This allows for more appropriate checks by determining the priority of checks according to the customer'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 checking unit may be performed using AI or not using AI. For example, the checking unit can input customer emotion data into a generative AI, which can estimate emotions and determine the priority of checks.

[0098] The checking unit can weight the checks based on the submission date of the proposals. For example, if the proposal submission deadline is approaching, the checking unit can set a higher weight for the check and respond quickly. Conversely, if the proposal submission deadline is far away, the checking unit can set a lower weight for the check and prioritize other important checks. Furthermore, the checking unit can set an optimal checking schedule based on the proposal submission date. This allows for more appropriate checks by weighting the checks based on the proposal submission date. Some or all of the above processing in the checking unit may be performed using AI, for example, or not. For example, the checking unit can input proposal submission date data into a generating AI, which can then weight the checks based on that data.

[0099] The checking unit can improve the accuracy of its checks by referring to relevant literature in the proposal during the checking process. For example, the checking unit can improve the accuracy of its checks by referring to relevant literature in the proposal. The checking unit can also improve the accuracy of its checks based on market data related to the content of the proposal. Furthermore, the checking unit can combine relevant literature in the proposal with market data to perform optimal checks. This allows the accuracy of the checks to be improved by referring to relevant literature in the proposal. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input data on relevant literature in the proposal into a generating AI, which can then improve the accuracy of its checks based on that data.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The proposal creation system can also include a feedback collection unit. This unit collects customer reactions and opinions after they receive the proposal. For example, it can collect feedback on whether the customer is satisfied with the proposal's content, which parts were particularly helpful, and what areas for improvement are needed. The feedback collection unit can also track what actions the customer takes based on the proposal (e.g., purchasing decision, additional questions, requests for other proposals). Furthermore, the feedback collection unit can analyze the collected feedback to provide insights for improving the quality of the proposal. This allows the proposal creation system to continuously improve the quality of its proposals by leveraging customer feedback.

[0102] The proposal creation system can also include a customization feature. This customization feature allows for the design and content of proposals to be tailored to the client's corporate culture and brand guidelines. For example, the client's corporate logo and color scheme can be incorporated into the proposal. Furthermore, the customization feature can incorporate industry-specific terminology and expressions. Additionally, the customization feature can create proposals by referencing the style and format of the client's past proposals. This allows the proposal creation system to deliver proposals that align with the client's corporate culture and brand.

[0103] The proposal creation system can also include a competitive analysis department. This department analyzes proposals and marketing materials from the client's competitors, providing insights to create competitive proposals for the client. For example, it can analyze what kinds of proposals competitors are making and what strengths they are highlighting. The competitive analysis department can also grasp the content and design trends of competitor proposals and reflect them in the client's proposals. Furthermore, the competitive analysis department can provide data and evidence demonstrating how the client's proposals are superior to those of competitors. This allows the proposal creation system to deliver competitive proposals to the client.

[0104] The proposal creation system can also be equipped with a predictive analytics unit. This unit predicts the success rate of a proposal based on past proposal results and customer response data. For example, it can analyze past proposal success rates and customer feedback to predict the probability of a current proposal being awarded. The predictive analytics unit can also simulate the impact of changes to the proposal's content and design on the success rate. Furthermore, it can suggest the optimal proposal strategy based on the timing of submission and the customer's situation. This allows the proposal creation system to provide customers with proposals that have a higher probability of success.

[0105] The proposal creation system can also be equipped with a multilingual support function. This function translates proposals into multiple languages, providing appropriate proposals to clients who speak different languages. For example, proposals can be created in languages ​​such as English, Japanese, Chinese, and Spanish, depending on the client's needs. Furthermore, the multilingual support function can consider specialized terminology and industry-specific expressions to improve translation accuracy. Additionally, the multilingual support function can culturally adapt proposals, providing effective proposals to clients with diverse cultural backgrounds. This allows the proposal creation system to provide proposals that are suitable for global clients.

[0106] The proposal creation system can also include an emotion analysis unit. This unit estimates the customer's emotions and adjusts the content and tone of the proposal based on those estimates. For example, if the customer is excited, the proposal's tone can be made positive and energetic. If the customer is anxious, the proposal's tone can be calmed and reassuring. Furthermore, if the customer is relaxed, the proposal's content can be made more detailed and information-rich. This allows the proposal creation system to provide an optimal proposal tailored to the customer's emotions.

[0107] The proposal creation system can also be equipped with an emotional feedback unit. This unit collects and analyzes the customer's emotions after receiving the proposal. For example, it can collect data on satisfaction, excitement, and anxiety levels the customer felt after reading the proposal. Furthermore, the emotional feedback unit can identify areas for improvement in the proposal based on the collected emotional data. Additionally, the emotional feedback unit can analyze the customer's emotional data and incorporate it into future proposal creation. This allows the proposal creation system to improve the quality of proposals by utilizing customer emotion-based feedback.

[0108] The proposal creation system can also be equipped with an emotion prediction unit. This unit predicts the emotions a customer will feel upon receiving a proposal, based on their past emotional data. For example, it can analyze customer response data to past proposals to predict what emotions a current proposal will evoke. Furthermore, the emotion prediction unit can simulate the impact of changes to the proposal's content or design on the customer's emotions. In addition, the emotion prediction unit can suggest the optimal proposal strategy based on the timing of submission and the customer's situation. This allows the proposal creation system to provide proposals that are sensitive to the customer's emotions.

[0109] The proposal creation system can also be equipped with an emotion tracking unit. This unit tracks the customer's emotions in real time throughout the entire proposal creation process. For example, it can continuously monitor the emotions the customer expresses through chat interactions. The emotion tracking unit can also record the customer's emotional responses to each section of the proposal, helping to improve it. Furthermore, the emotion tracking unit can analyze the customer's emotional data and suggest the optimal structure and content of the proposal. This allows the proposal creation system to provide proposals based on the customer's emotions.

[0110] The proposal creation system can also include an emotional reporting section. This section provides emotional data regarding the proposal creation process and customer reactions in the form of reports. For example, it can summarize the customer's emotions at each stage of the proposal creation process. Furthermore, the emotional reporting section can meticulously record customer emotional responses to each section of the proposal, identifying areas for improvement. Additionally, the emotional reporting section can analyze customer emotional data and provide insights to be incorporated into future proposal creation. This allows the proposal creation system to leverage customer emotion-based feedback to improve the quality of proposals.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk receives information from the customer. This information may include, but is not limited to, text, audio, and image information. The reception desk can, for example, receive information through chat interactions with the customer. Step 2: The analysis department analyzes the information received by the reception department. The analysis may be performed using methods such as text mining, data mining, and statistical analysis, but is not limited to these examples. The analysis department can analyze the content of the conversation and identify customer needs. Step 3: The generation unit creates a proposal based on the analysis results obtained by the analysis unit. The proposal may be created in the form of a business proposal, technical proposal, marketing proposal, etc., but is not limited to these examples. The generation unit can create a proposal based on identified needs. Step 4: The checking unit checks the content of the proposal created by the generation unit to ensure there are no errors. The checking may include, but is not limited to, methods such as checking for typographical errors, verifying the accuracy of the content, and checking the format.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and checking unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives information from the customer. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a proposal based on the analysis results. The checking unit is implemented by the specific processing unit 290 of the data processing unit 12 and checks the contents of the generated proposal. 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.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0132] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and checking unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives information from the customer. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and creates a proposal based on the analysis results. The checking unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and checks the contents of the generated proposal. 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.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[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 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.

[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 (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).

[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] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and checking unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives information from the customer. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a proposal based on the analysis results. The checking unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and checks the contents of the generated proposal. 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.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and checking unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives information from the customer. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a proposal based on the analysis results. The checking unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and checks the contents of the generated proposal. 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) The reception area receives information from customers, An analysis unit analyzes the information received by the aforementioned reception unit, A generation unit that creates a proposal based on the analysis results obtained by the aforementioned analysis unit, The system includes a checking unit that checks the contents of the proposal created by the generation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is We receive information through chat conversations with customers. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyze the content of the conversation and identify customer needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Create a proposal based on the identified needs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned checking unit is Check the contents of the proposal and make sure there are no errors. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Create proposals that align with the company's internal formats and document creation practices. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned checking unit is Refer to the internal knowledge you have previously inputted to avoid hallucination and to verify the validity of the content. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the customer's emotions and adjusts the pace of the conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the customer's past interaction history and select the optimal interaction scenario. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is At the start of the conversation, customize the content based on the customer's current interests and situation. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the customer's emotions and determines the priority of conversations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is During conversations, we prioritize providing highly relevant information based on the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is During the conversation, we analyze the customer's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is We estimate customer emotions and adjust the accuracy of the analysis based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, we identify needs by referring to the customer's past purchase history and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the customer's industry and job type. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates customer emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, we prioritize the analysis based on when the customer submitted the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, we refer to relevant customer literature and market data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is We estimate the customer's emotions and adjust the wording of the proposal based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When creating a proposal, adjust the level of detail in the proposal based on the customer's needs. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When creating a proposal, apply different formats depending on the client's industry and job type. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is Estimate the customer's emotions and adjust the length of the proposal based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When creating a proposal, prioritize the proposals based on the client's submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When creating a proposal, adjust the order of the proposal based on the relevance to the client. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned checking unit is The system estimates customer emotions and adjusts the accuracy of the checks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned checking unit is During the check, the check algorithm is optimized by referring to past check history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned checking unit is During the review process, different review methods will be applied to each category of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned checking unit is Estimate the customer's emotions and prioritize checks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned checking unit is During the review process, the weighting of the review will be based on when the proposal was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned checking unit is During the review process, we refer to relevant literature in the proposal to improve the accuracy of the review. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 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 reception area receives information from customers, An analysis unit analyzes the information received by the aforementioned reception unit, A generation unit that creates a proposal based on the analysis results obtained by the aforementioned analysis unit, The system includes a checking unit that checks the contents of the proposal created by the generation unit. A system characterized by the following features.

2. The aforementioned reception unit is We receive information through chat conversations with customers. The system according to feature 1.

3. The aforementioned analysis unit is Analyze the content of the conversation and identify customer needs. The system according to feature 1.

4. The generating unit is Create a proposal based on the identified needs. The system according to feature 1.

5. The aforementioned checking unit is Check the contents of the proposal and make sure there are no errors. The system according to feature 1.

6. The generating unit is Create proposals that align with the company's internal formats and document creation practices. The system according to feature 1.

7. The aforementioned checking unit is Refer to the internal knowledge you have previously inputted to avoid hallucination and to verify the validity of the content. The system according to feature 1.

8. The aforementioned reception unit is It estimates the customer's emotions and adjusts the pace of the conversation based on those estimated emotions. The system according to feature 1.

Images