System

Abstract

To provide a system that efficiently utilizes success instances or data of proposals, and rapidly generates proposals that meets user's requirements.SOLUTION: A system according to the embodiment comprises a storage unit, a search unit, and a generation unit. The storage unit stores success instances or proposals in a database. The search unit analyzes user requirements based on data stored by the storage unit, and searches for related success instances. The generation unit generates a proposal based on the success instances searched for by the search unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem that it is difficult to efficiently utilize data on success cases and proposals and quickly generate proposals that meet user requirements.

[0005] The system according to the embodiment aims to efficiently utilize data on success cases and proposals, and to quickly generate proposals that meet the needs of users. [Means for solving the problem]

[0006] The system according to the embodiment includes a storage unit, a search unit, and a generation unit. The storage unit stores success cases or proposals in a database. The search unit analyzes a user's request based on the data stored by the storage unit and searches for related success cases. The generation unit generates a proposal based on the success cases searched for by the search unit. Effect of the Invention

[0007] The system according to the embodiment can efficiently utilize data on success stories and proposals, and can quickly generate proposals that meet the needs of users. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

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

[0028] (Example 1) The automatic proposal generation system according to the embodiment of the present invention is a system that automatically generates proposals based on each successful case or proposal. This automatic proposal generation system is specialized in making proposals based on successful cases in the same industry. In order to solve the problem of not knowing where something is in a normal storage location, AI is used to instantly generate the necessary information. First, the automatic proposal generation system stores past successful cases and proposals in a database. This includes successful cases and proposals for each industry. Next, when a user needs a proposal for a specific industry or purpose, the user inputs the request into the automatic proposal generation system. For example, a request such as "I want a proposal based on a successful case of cost reduction in the manufacturing industry." The automatic proposal generation system analyzes the input request and searches the database for related successful cases and proposals. Based on this information, AI automatically generates a proposal that is optimal for the user's request. The generated proposal is provided in a format that the user can use immediately. This automatic proposal generation system allows the user to quickly and efficiently create high-quality proposals. Furthermore, by automatically generating proposals based on success cases in the same industry, horizontal expansion becomes easier, and similar success can be expected in other industries. For example, when generating a proposal based on a success case of cost reduction in the manufacturing industry, the automatic proposal generation system searches a database for past success cases and extracts related information to create the proposal. This allows the user to quickly create a high-quality proposal. This allows the automatic proposal generation system to search for success cases based on the user's request and automatically generate a proposal.

[0029] The automatic proposal generation system according to the embodiment includes a storage unit, a search unit, and a generation unit. The storage unit stores success cases and proposals in a database. The success cases and proposals include, for example, cases that meet a specific performance indicator and cases that have good customer feedback, but are not limited to such examples. The storage unit includes, for example, a classification unit that classifies and stores success cases for each industry. The classification unit classifies the success cases based on specific classification criteria and ranges for each industry. The search unit analyzes a user's request based on the data stored by the storage unit and searches for related success cases. The search unit includes, for example, an analysis unit that analyzes the user's request. The analysis unit clarifies how to collect and analyze the user's request. The generation unit generates a proposal based on the success cases searched for by the search unit. The generation unit includes, for example, a provision unit that provides the generated proposal to the user. The provision unit clarifies a specific method and criteria for providing the generated proposal to the user. As a result, the automatic proposal generation system according to the embodiment can search for success cases based on the user's request and automatically generate a proposal.

[0030] The storage unit stores the success cases and proposals in a database. The success cases and proposals include, but are not limited to, cases that meet specific performance indicators and cases that have good customer feedback. The storage unit includes, for example, a classification unit that classifies and stores the success cases for each industry. The classification unit classifies the success cases based on the specific classification criteria and range of each industry. Specifically, the storage unit uses an algorithm to extract the characteristics of the success cases that differ for each industry and classify them into appropriate categories. For example, for different industries such as manufacturing, service, and IT, the respective performance indicators and customer satisfaction standards are set, and the success cases are classified based on the standards. Furthermore, the storage unit records metadata of the success cases (for example, the date and time of the occurrence of the case, the project members involved, the technologies and tools used, etc.) in detail, and uses them for later search and analysis. This allows the storage unit to efficiently manage a huge amount of success cases and proposals and quickly retrieve required information. In addition, the storage unit regularly updates the database and adds new success cases and proposals, thereby always maintaining the latest information. This allows users to always generate proposals based on the most recent best practices.

[0031] The search unit analyzes the user's request based on the data stored by the storage unit, and searches for related success cases. The search unit includes, for example, an analysis unit that analyzes the user's request. The analysis unit clarifies how to collect and analyze the user's request. Specifically, the search unit uses natural language processing technology to analyze keywords and conditions entered by the user and search for optimal success cases based on the keywords and conditions. For example, if the user wants to search for "cases where new products have been successfully launched into the market," the analysis unit analyzes the request and extracts relevant keywords and phrases. Furthermore, the analysis unit uses a machine learning algorithm to provide more accurate search results by taking into account the user's past search history and behavioral patterns. This allows the search unit to quickly and accurately search for the success case that best suits the user's request. In addition, the search unit provides an interface for displaying search results in an easy-to-understand manner for the user, allowing the user to easily access the information he or she needs. For example, the search unit includes a function for displaying search results by category and sorting them in order of relevance. This allows the search unit to efficiently search for optimal success cases that meet the user's request, improving user convenience.

[0032] The generation unit generates a proposal based on the success cases searched by the search unit. The generation unit includes, for example, a provision unit that provides the generated proposal to the user. The provision unit clarifies the specific method and criteria for providing the generated proposal to the user. Specifically, the generation unit uses templates and formats for automatically generating a proposal that best suits the user's requirements based on the contents of the searched success cases. For example, the generation unit uses an algorithm that defines the components of the proposal (purpose, background, proposal content, expected effect, etc.) in advance and appropriately places the data of the success cases in these components. Furthermore, the generation unit includes a function for automatically inserting visual elements such as graphs, charts, and images to make the contents of the proposal more attractive. As a result, the generated proposal has specific and persuasive content in response to the user's requirements. In addition, the provision unit provides multiple methods for providing the generated proposal to the user. For example, the provision unit includes a function for making the proposal downloadable in PDF format or sending it by email. In addition, the provision unit provides an interface that allows the contents of the proposal to be edited and modified in real time, allowing the user to customize the contents of the proposal as needed. This enables the generation unit to quickly generate and provide a high-quality proposal based on the user's requirements.

[0033] The storage unit may include a classification unit that classifies and stores success cases for each industry. The classification unit classifies the success cases based on, for example, specific classification criteria and ranges for each industry. For example, the classification unit classifies the success cases based on industries such as manufacturing, service, and medical. The classification unit also includes an algorithm for efficiently classifying and storing the success cases for each industry. For example, the classification unit may automatically classify the success cases using a machine learning algorithm. This allows the success cases for each industry to be efficiently classified and stored.

[0034] The search unit may include an analysis unit that analyzes the user's request. The analysis unit, for example, clarifies how to collect and analyze the user's request. For example, the analysis unit may analyze the user's request using a natural language processing technique. The analysis unit may also include an algorithm for analyzing the user's request. For example, the analysis unit may analyze the user's request using a machine learning algorithm. This makes it possible to analyze the user's request and search for appropriate success cases.

[0035] The generation unit may include a provision unit that provides the generated proposal to the user. The provision unit, for example, clarifies a specific method or criteria for providing the generated proposal to the user. For example, the provision unit provides the generated proposal to the user through a web application or a mobile application. The provision unit may also transmit the generated proposal to the user by email. Furthermore, the provision unit may also provide the generated proposal on a paper medium. In this way, the generated proposal can be provided to the user.

[0036] The storage unit can evaluate the reliability of the data at the time of storage, and store highly reliable data with priority. The storage unit, for example, evaluates the reliability of the data and stores highly reliable data with priority. For example, the storage unit checks the source of the data and stores highly reliable information with priority. The storage unit can also evaluate the update frequency of the data, and store the latest information with priority. Furthermore, the storage unit can also store highly reliable information with priority based on the evaluation score of the data. This makes it possible to improve the quality of the data by storing highly reliable data with priority.

[0037] The storage unit can adjust the storage method based on the update frequency of the data when storing. The storage unit adjusts the storage method based on, for example, the update frequency of the data. For example, the storage unit preferentially stores data that is updated frequently. The storage unit can also store data that is updated less frequently as an archive. Furthermore, the storage unit can adjust the storage location of the data depending on the update frequency. This allows for efficient data management by adjusting the storage method based on the update frequency of the data.

[0038] The storage unit can select a storage method taking into consideration the geographical distribution of data when storing data. The storage unit selects a storage method taking into consideration, for example, the geographical distribution of data. For example, the storage unit stores geographically related data together. The storage unit can also select a storage location for data based on the geographical distribution. Furthermore, the storage unit can adjust the data storage method taking into consideration geographical factors. As a result, efficient data management is possible by selecting a storage method taking into consideration the geographical distribution of data.

[0039] The storage unit can improve the accuracy of storage by referring to literature related to the data when storing the data. The storage unit, for example, improves the accuracy of storage by referring to literature related to the data. For example, the storage unit checks the reliability of the data by referring to the related literature. The storage unit can also adjust the data storage method based on the related literature. Furthermore, the storage unit can also improve the accuracy of data storage by referring to the related literature. Thereby, by improving the accuracy of storage by referring to literature related to the data, the quality of the data can be improved.

[0040] The search unit can improve the accuracy of a search by taking into account the interrelationships of data during a search. The search unit improves the accuracy of a search by taking into account, for example, the interrelationships of data. For example, the search unit analyzes the interrelationships of data and preferentially displays highly relevant data. The search unit can also filter search results based on the interrelationships of data. Furthermore, the search unit can adjust a search algorithm by taking into account the interrelationships of data. In this way, by improving the accuracy of a search by taking into account the interrelationships of data, it is possible to provide more relevant search results.

[0041] When performing a search, the search unit can perform a search taking into consideration the attribute information of the data submitter. The search unit performs a search taking into consideration, for example, the attribute information of the data submitter. For example, the search unit filters search results taking into consideration the specialty field of the data submitter. The search unit can also evaluate the reliability of the data submitter and preferentially display highly reliable data. Furthermore, the search unit can customize search results based on the attribute information of the data submitter. As a result, by performing a search taking into consideration the attribute information of the data submitter, more reliable search results can be provided.

[0042] The search unit can perform a search taking into consideration the geographic distribution of data. The search unit performs a search taking into consideration, for example, the geographic distribution of data. For example, the search unit preferentially searches for geographically related data. The search unit can also filter search results based on the geographic distribution. Furthermore, the search unit can adjust a search algorithm taking into consideration geographic factors. In this way, by performing a search taking into consideration the geographic distribution of data, it is possible to provide more relevant search results.

[0043] The search unit can improve the accuracy of the search by referring to literature related to the data during the search. The search unit can improve the accuracy of the search by, for example, referring to literature related to the data. For example, the search unit can check the reliability of the search results by referring to the related literature. The search unit can also filter the search results based on the related literature. Furthermore, the search unit can adjust the search algorithm by referring to the related literature. In this way, by improving the accuracy of the search by referring to literature related to the data, it is possible to provide more reliable search results.

[0044] The generation unit can adjust the level of detail of the proposal based on the importance of the data at the time of generation. The generation unit adjusts the level of detail of the proposal based on, for example, the importance of the data. For example, the generation unit generates a detailed proposal based on important data. The generation unit can also generate a concise proposal based on data with low importance. Furthermore, the generation unit can adjust the level of detail of the proposal according to the importance of the data. In this way, a more appropriate proposal can be generated by adjusting the level of detail of the proposal based on the importance of the data.

[0045] The generation unit can apply different generation algorithms depending on the category of data at the time of generation. The generation unit applies different generation algorithms depending on, for example, the category of data. For example, the generation unit applies an algorithm that generates a proposal for the manufacturing industry based on data for the manufacturing industry. The generation unit can also apply an algorithm that generates a proposal for the service industry based on data for the service industry. Furthermore, the generation unit can also apply an optimal generation algorithm depending on the data for each industry. In this way, a more appropriate proposal can be generated by applying different generation algorithms depending on the category of data.

[0046] The generation unit can determine the priority of the proposals based on the submission time of the data at the time of generation. The generation unit determines the priority of the proposals based on, for example, the submission time of the data. For example, the generation unit generates a proposal preferentially based on the latest data. The generation unit can also generate a supplementary proposal based on data that was submitted earlier. Furthermore, the generation unit can adjust the priority of the proposals depending on the submission time. In this way, by determining the priority of the proposals based on the submission time of the data, a more appropriate proposal can be generated.

[0047] The generation unit can adjust the order of the proposals based on the relevance of the data at the time of generation. The generation unit adjusts the order of the proposals based on, for example, the relevance of the data. For example, the generation unit adjusts the order of the proposals based on highly relevant data. The generation unit can also generate a supplementary proposal based on less relevant data. Furthermore, the generation unit can also adjust the order of the proposals according to the relevance of the data. In this way, a more appropriate proposal can be generated by adjusting the order of the proposals based on the relevance of the data.

[0048] The classification unit can improve the accuracy of classification by taking into account the interrelationships of data during classification. The classification unit improves the accuracy of classification by taking into account, for example, the interrelationships of data. For example, the classification unit analyzes the interrelationships of data and classifies highly related data into the same category. The classification unit can also adjust the classification criteria based on the interrelationships of data. Furthermore, the classification unit can adjust the classification algorithm by taking into account the interrelationships of data. In this way, more appropriate data classification is possible by improving the accuracy of classification by taking into account the interrelationships of data.

[0049] The classification unit can perform classification taking into consideration the geographical distribution of data during classification. The classification unit performs classification taking into consideration, for example, the geographical distribution of data. For example, the classification unit classifies geographically related data into the same category. The classification unit can also adjust the classification criteria for data based on the geographical distribution. Furthermore, the classification unit can also adjust the classification algorithm taking into consideration geographical factors. As a result, more appropriate data classification is possible by performing classification taking into consideration the geographical distribution of data.

[0050] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the data during the analysis. The analysis unit, for example, improves the accuracy of the analysis by taking into account the interrelationships of the data. For example, the analysis unit analyzes the interrelationships of the data and preferentially analyzes data that is highly related. The analysis unit can also adjust the analysis algorithm based on the interrelationships of the data. Furthermore, the analysis unit can also improve the accuracy of the analysis results by taking into account the interrelationships of the data. As a result, more appropriate data analysis is possible by improving the accuracy of the analysis by taking into account the interrelationships of the data.

[0051] The analysis unit can perform the analysis while taking into consideration the geographical distribution of the data. For example, the analysis unit performs the analysis while taking into consideration the geographical distribution of the data. For example, the analysis unit preferentially analyzes geographically related data. The analysis unit can also adjust the analysis algorithm based on the geographical distribution. Furthermore, the analysis unit can display the analysis results while taking into consideration geographical factors. This allows for more appropriate data analysis by performing the analysis while taking into consideration the geographical distribution of the data.

[0052] The providing unit can select the optimal providing method by referring to the user's past usage history at the time of providing. The providing unit, for example, selects the optimal providing method by referring to the user's past usage history. For example, the providing unit provides the proposal in a format that the user has used in the past. The providing unit can also select the optimal providing method based on the user's past usage history. Furthermore, the providing unit can also adjust the method of providing the proposal by referring to the user's past usage history. In this way, a more appropriate proposal can be provided by selecting the optimal providing method by referring to the user's past usage history.

[0053] The providing unit can select the optimal providing method in consideration of the device information of the user at the time of providing. The providing unit selects the optimal providing method in consideration of, for example, the device information of the user. For example, when the user is using a smartphone, the providing unit provides the proposal in a format that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide the proposal in a format optimized for a large screen. Furthermore, when the user is using a desktop, the providing unit can also provide the proposal in a format that includes detailed information. In this way, a more appropriate proposal can be provided by selecting the optimal providing method in consideration of the device information of the user.

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

[0055] The automatic proposal generation system may further include a history analysis unit that analyzes the user's past proposal usage history. The history analysis unit records what types of proposals the user has used in the past and analyzes the trends. For example, the history analysis unit may reflect the trends when generating the next proposal based on the formats and contents of proposals that the user has frequently used in the past. The history analysis unit may also extract features of proposals that the user has given high ratings in the past and use them in generating future proposals. Furthermore, the history analysis unit may identify improvements to the proposal based on the user's past feedback and reflect them in generating the next proposal. This makes it possible to generate a more personalized proposal by utilizing the user's past usage history.

[0056] The automatic proposal generation system may further include a situation grasping unit that grasps the user's current situation in real time. The situation grasping unit acquires the user's current work situation and schedule in real time, and optimizes the generation of the proposal based on the information. For example, when the user is in a meeting, the situation grasping unit can generate a proposal related to the contents of the meeting with priority. Also, when the user is on a business trip, the situation grasping unit can generate a proposal that reflects information about the business trip destination. Furthermore, the situation grasping unit can generate a proposal that takes into account the submission deadline based on the user's schedule. This makes it possible to provide an optimal proposal according to the user's current situation.

[0057] The automatic proposal generation system may further include a performance evaluation unit that evaluates the user's business performance. The performance evaluation unit evaluates the user's business results and the success rate of the proposal, and optimizes the generation of the proposal based on the results. For example, the performance evaluation unit may analyze the characteristics of proposals that have been successful in the past by the user, and generate a proposal with similar characteristics. The performance evaluation unit may also adjust the content of the proposal based on the user's business results. Furthermore, the performance evaluation unit may also generate a proposal to improve the user's business performance. This makes it possible to provide an optimal proposal based on the user's business performance.

[0058] The automatic proposal generation system may further include an environment adaptation unit that takes into account the user's work environment. The environment adaptation unit considers the user's work environment and the device used, and optimizes the generation of the proposal based on the information. For example, when the user is working remotely, the environment adaptation unit can generate a proposal suitable for remote work. The environment adaptation unit can also adjust the layout of the proposal according to the screen size and resolution of the device used by the user. Furthermore, the environment adaptation unit can customize the contents of the proposal based on the user's work environment. This makes it possible to provide an optimal proposal adapted to the user's work environment.

[0059] The process flow of the first embodiment will be briefly described below.

[0060] Step 1: The storage unit stores success stories and proposals in a database. The success stories and proposals include cases that meet specific performance indicators and cases that have received good customer feedback. The storage unit includes a classification unit that classifies and stores success stories for each industry. The classification unit classifies the success stories based on the specific classification criteria and scope of each industry. Step 2: The search unit analyzes the user's request based on the data stored by the storage unit, and searches for related success cases. The search unit includes an analysis unit that analyzes the user's request. The analysis unit collects and analyzes the user's request. Step 3: The generation unit generates a proposal based on the success cases searched by the search unit. The generation unit includes a provision unit that provides the generated proposal to a user. The provision unit clarifies a specific method and criteria for providing the generated proposal to a user.

[0061] (Example 2) The automatic proposal generation system according to the embodiment of the present invention is a system that automatically generates proposals based on each successful case or proposal. This automatic proposal generation system is specialized in making proposals based on successful cases in the same industry. In order to solve the problem of not knowing where something is in a normal storage location, AI is used to instantly generate the necessary information. First, the automatic proposal generation system stores past successful cases and proposals in a database. This includes successful cases and proposals for each industry. Next, when a user needs a proposal for a specific industry or purpose, the user inputs the request into the automatic proposal generation system. For example, a request such as "I want a proposal based on a successful case of cost reduction in the manufacturing industry." The automatic proposal generation system analyzes the input request and searches the database for related successful cases and proposals. Based on this information, AI automatically generates a proposal that is optimal for the user's request. The generated proposal is provided in a format that the user can use immediately. This automatic proposal generation system allows the user to quickly and efficiently create high-quality proposals. Furthermore, by automatically generating proposals based on success cases in the same industry, horizontal expansion becomes easier, and similar success can be expected in other industries. For example, when generating a proposal based on a success case of cost reduction in the manufacturing industry, the automatic proposal generation system searches a database for past success cases and extracts related information to create the proposal. This allows the user to quickly create a high-quality proposal. This allows the automatic proposal generation system to search for success cases based on the user's request and automatically generate a proposal.

[0062] The automatic proposal generation system according to the embodiment includes a storage unit, a search unit, and a generation unit. The storage unit stores success cases and proposals in a database. The success cases and proposals include, for example, cases that meet a specific performance indicator and cases that have good customer feedback, but are not limited to such examples. The storage unit includes, for example, a classification unit that classifies and stores success cases for each industry. The classification unit classifies the success cases based on specific classification criteria and ranges for each industry. The search unit analyzes a user's request based on the data stored by the storage unit and searches for related success cases. The search unit includes, for example, an analysis unit that analyzes the user's request. The analysis unit clarifies how to collect and analyze the user's request. The generation unit generates a proposal based on the success cases searched for by the search unit. The generation unit includes, for example, a provision unit that provides the generated proposal to the user. The provision unit clarifies a specific method and criteria for providing the generated proposal to the user. As a result, the automatic proposal generation system according to the embodiment can search for success cases based on the user's request and automatically generate a proposal.

[0063] The storage unit stores the success cases and proposals in a database. The success cases and proposals include, but are not limited to, cases that meet specific performance indicators and cases that have good customer feedback. The storage unit includes, for example, a classification unit that classifies and stores the success cases for each industry. The classification unit classifies the success cases based on the specific classification criteria and range of each industry. Specifically, the storage unit uses an algorithm to extract the characteristics of the success cases that differ for each industry and classify them into appropriate categories. For example, for different industries such as manufacturing, service, and IT, the respective performance indicators and customer satisfaction standards are set, and the success cases are classified based on the standards. Furthermore, the storage unit records metadata of the success cases (for example, the date and time of the occurrence of the case, the project members involved, the technologies and tools used, etc.) in detail, and uses them for later search and analysis. This allows the storage unit to efficiently manage a huge amount of success cases and proposals and quickly retrieve required information. In addition, the storage unit regularly updates the database and adds new success cases and proposals, thereby always maintaining the latest information. This allows users to always generate proposals based on the most recent best practices.

[0064] The search unit analyzes the user's request based on the data stored by the storage unit, and searches for related success cases. The search unit includes, for example, an analysis unit that analyzes the user's request. The analysis unit clarifies how to collect and analyze the user's request. Specifically, the search unit uses natural language processing technology to analyze keywords and conditions entered by the user and search for optimal success cases based on the keywords and conditions. For example, if the user wants to search for "cases where new products have been successfully launched into the market," the analysis unit analyzes the request and extracts relevant keywords and phrases. Furthermore, the analysis unit uses a machine learning algorithm to provide more accurate search results by taking into account the user's past search history and behavioral patterns. This allows the search unit to quickly and accurately search for the success case that best suits the user's request. In addition, the search unit provides an interface for displaying search results in an easy-to-understand manner for the user, allowing the user to easily access the information he or she needs. For example, the search unit includes a function for displaying search results by category and sorting them in order of relevance. This allows the search unit to efficiently search for optimal success cases that meet the user's request, improving user convenience.

[0065] The generation unit generates a proposal based on the success cases searched by the search unit. The generation unit includes, for example, a provision unit that provides the generated proposal to the user. The provision unit clarifies the specific method and criteria for providing the generated proposal to the user. Specifically, the generation unit uses templates and formats for automatically generating a proposal that best suits the user's requirements based on the contents of the searched success cases. For example, the generation unit uses an algorithm that defines the components of the proposal (purpose, background, proposal content, expected effect, etc.) in advance and appropriately places the data of the success cases in these components. Furthermore, the generation unit includes a function for automatically inserting visual elements such as graphs, charts, and images to make the contents of the proposal more attractive. As a result, the generated proposal has specific and persuasive content in response to the user's requirements. In addition, the provision unit provides multiple methods for providing the generated proposal to the user. For example, the provision unit includes a function for making the proposal downloadable in PDF format or sending it by email. In addition, the provision unit provides an interface that allows the contents of the proposal to be edited and modified in real time, allowing the user to customize the contents of the proposal as needed. This enables the generation unit to quickly generate and provide a high-quality proposal based on the user's requirements.

[0066] The storage unit may include a classification unit that classifies and stores success cases for each industry. The classification unit classifies the success cases based on, for example, specific classification criteria and ranges for each industry. For example, the classification unit classifies the success cases based on industries such as manufacturing, service, and medical. The classification unit also includes an algorithm for efficiently classifying and storing the success cases for each industry. For example, the classification unit may automatically classify the success cases using a machine learning algorithm. This allows the success cases for each industry to be efficiently classified and stored.

[0067] The search unit may include an analysis unit that analyzes the user's request. The analysis unit, for example, clarifies how to collect and analyze the user's request. For example, the analysis unit may analyze the user's request using a natural language processing technique. The analysis unit may also include an algorithm for analyzing the user's request. For example, the analysis unit may analyze the user's request using a machine learning algorithm. This makes it possible to analyze the user's request and search for appropriate success cases.

[0068] The generation unit may include a provision unit that provides the generated proposal to the user. The provision unit, for example, clarifies a specific method or criteria for providing the generated proposal to the user. For example, the provision unit provides the generated proposal to the user through a web application or a mobile application. The provision unit may also transmit the generated proposal to the user by email. Furthermore, the provision unit may also provide the generated proposal on a paper medium. In this way, the generated proposal can be provided to the user.

[0069] The storage unit can estimate the user's emotion and determine the priority of data to be stored based on the estimated user's emotion. The storage unit, for example, estimates the user's emotion and determines the priority of data to be stored based on the estimated user's emotion. For example, when the user is stressed, the storage unit preferentially stores important success stories. In addition, when the user is relaxed, the storage unit can also preferentially store detailed proposals. Furthermore, when the user is in a hurry, the storage unit can also preferentially store concise success stories. In this way, by determining the priority of data according to the user's emotion, more appropriate data can be stored. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the data.

[0070] The storage unit can evaluate the reliability of the data at the time of storage, and store highly reliable data with priority. The storage unit, for example, evaluates the reliability of the data and stores highly reliable data with priority. For example, the storage unit checks the source of the data and stores highly reliable information with priority. The storage unit can also evaluate the update frequency of the data, and store the latest information with priority. Furthermore, the storage unit can also store highly reliable information with priority based on the evaluation score of the data. This makes it possible to improve the quality of the data by storing highly reliable data with priority.

[0071] The storage unit can adjust the storage method based on the update frequency of the data when storing. The storage unit adjusts the storage method based on, for example, the update frequency of the data. For example, the storage unit preferentially stores data that is updated frequently. The storage unit can also store data that is updated less frequently as an archive. Furthermore, the storage unit can adjust the storage location of the data depending on the update frequency. This allows for efficient data management by adjusting the storage method based on the update frequency of the data.

[0072] The storage unit can estimate the user's emotion and adjust the format of the data to be stored based on the estimated user's emotion. The storage unit, for example, estimates the user's emotion and adjusts the format of the data to be stored based on the estimated user's emotion. For example, the storage unit stores data in a concise format when the user is stressed. The storage unit can also store data in a detailed format when the user is relaxed. Furthermore, the storage unit can also store data in a format that focuses on the main points when the user is in a hurry. This allows more appropriate data to be stored by adjusting the format of the data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the format of the data.

[0073] The storage unit can select a storage method taking into consideration the geographical distribution of data when storing data. The storage unit selects a storage method taking into consideration, for example, the geographical distribution of data. For example, the storage unit stores geographically related data together. The storage unit can also select a storage location for data based on the geographical distribution. Furthermore, the storage unit can adjust the data storage method taking into consideration geographical factors. As a result, efficient data management is possible by selecting a storage method taking into consideration the geographical distribution of data.

[0074] The storage unit can improve the accuracy of storage by referring to literature related to the data when storing the data. The storage unit, for example, improves the accuracy of storage by referring to literature related to the data. For example, the storage unit checks the reliability of the data by referring to the related literature. The storage unit can also adjust the data storage method based on the related literature. Furthermore, the storage unit can also improve the accuracy of data storage by referring to the related literature. Thereby, by improving the accuracy of storage by referring to literature related to the data, the quality of the data can be improved.

[0075] The search unit can estimate the user's emotion and adjust the display method of the search results based on the estimated user's emotion. The search unit, for example, estimates the user's emotion and adjusts the display method of the search results based on the estimated user's emotion. For example, the search unit provides a simple display method when the user is stressed. The search unit can also provide a detailed display method when the user is relaxed. Furthermore, the search unit can also provide a display method that focuses on the main points when the user is in a hurry. This makes it possible to provide more appropriate search results by adjusting the display method of the search results according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the search results.

[0076] The search unit can improve the accuracy of a search by taking into account the interrelationships of data during a search. The search unit improves the accuracy of a search by taking into account, for example, the interrelationships of data. For example, the search unit analyzes the interrelationships of data and preferentially displays highly relevant data. The search unit can also filter search results based on the interrelationships of data. Furthermore, the search unit can adjust a search algorithm by taking into account the interrelationships of data. In this way, by improving the accuracy of a search by taking into account the interrelationships of data, it is possible to provide more relevant search results.

[0077] When performing a search, the search unit can perform a search taking into consideration the attribute information of the data submitter. The search unit performs a search taking into consideration, for example, the attribute information of the data submitter. For example, the search unit filters search results taking into consideration the specialty field of the data submitter. The search unit can also evaluate the reliability of the data submitter and preferentially display highly reliable data. Furthermore, the search unit can customize search results based on the attribute information of the data submitter. As a result, by performing a search taking into consideration the attribute information of the data submitter, more reliable search results can be provided.

[0078] The search unit can estimate the user's emotion and determine the priority of the search results based on the estimated user's emotion. The search unit, for example, estimates the user's emotion and determines the priority of the search results based on the estimated user's emotion. For example, when the user is stressed, the search unit preferentially displays important search results. In addition, when the user is relaxed, the search unit can also preferentially display detailed search results. Furthermore, when the user is in a hurry, the search unit can also preferentially display concise search results. In this way, by determining the priority of the search results according to the user's emotion, more appropriate search results can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the search results.

[0079] The search unit can perform a search taking into consideration the geographic distribution of data. The search unit performs a search taking into consideration, for example, the geographic distribution of data. For example, the search unit preferentially searches for geographically related data. The search unit can also filter search results based on the geographic distribution. Furthermore, the search unit can adjust a search algorithm taking into consideration geographic factors. In this way, by performing a search taking into consideration the geographic distribution of data, it is possible to provide more relevant search results.

[0080] The search unit can improve the accuracy of the search by referring to literature related to the data during the search. The search unit can improve the accuracy of the search by, for example, referring to literature related to the data. For example, the search unit can check the reliability of the search results by referring to the related literature. The search unit can also filter the search results based on the related literature. Furthermore, the search unit can adjust the search algorithm by referring to the related literature. In this way, by improving the accuracy of the search by referring to literature related to the data, it is possible to provide more reliable search results.

[0081] The generation unit can estimate the user's emotion and adjust the expression method of the proposal to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the expression method of the proposal to be generated based on the estimated user's emotion. For example, when the user is stressed, the generation unit generates a concise proposal that is concise and to the point. In addition, when the user is relaxed, the generation unit can also generate a proposal that includes detailed information. Furthermore, when the user is in a hurry, the generation unit can also generate a proposal in a format that can be quickly understood. In this way, by adjusting the expression method of the proposal according to the user's emotion, a more appropriate proposal can be generated. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the proposal.

[0082] The generation unit can adjust the level of detail of the proposal based on the importance of the data at the time of generation. The generation unit adjusts the level of detail of the proposal based on, for example, the importance of the data. For example, the generation unit generates a detailed proposal based on important data. The generation unit can also generate a concise proposal based on data with low importance. Furthermore, the generation unit can adjust the level of detail of the proposal according to the importance of the data. In this way, a more appropriate proposal can be generated by adjusting the level of detail of the proposal based on the importance of the data.

[0083] The generation unit can apply different generation algorithms depending on the category of data at the time of generation. The generation unit applies different generation algorithms depending on, for example, the category of data. For example, the generation unit applies an algorithm that generates a proposal for the manufacturing industry based on data for the manufacturing industry. The generation unit can also apply an algorithm that generates a proposal for the service industry based on data for the service industry. Furthermore, the generation unit can also apply an optimal generation algorithm depending on the data for each industry. In this way, a more appropriate proposal can be generated by applying different generation algorithms depending on the category of data.

[0084] The generation unit can estimate the user's emotion and adjust the length of the proposal to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the proposal to be generated based on the estimated user's emotion. For example, when the user is stressed, the generation unit generates a short proposal that is concise. Also, when the user is relaxed, the generation unit can generate a longer proposal that includes detailed information. Furthermore, when the user is in a hurry, the generation unit can generate a short proposal that can be quickly understood. In this way, by adjusting the length of the proposal according to the user's emotion, a more appropriate proposal can be generated. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the proposal.

[0085] The generation unit can determine the priority of the proposals based on the submission time of the data at the time of generation. The generation unit determines the priority of the proposals based on, for example, the submission time of the data. For example, the generation unit generates a proposal preferentially based on the latest data. The generation unit can also generate a supplementary proposal based on data that was submitted earlier. Furthermore, the generation unit can adjust the priority of the proposals depending on the submission time. In this way, by determining the priority of the proposals based on the submission time of the data, a more appropriate proposal can be generated.

[0086] The generation unit can adjust the order of the proposals based on the relevance of the data at the time of generation. The generation unit adjusts the order of the proposals based on, for example, the relevance of the data. For example, the generation unit adjusts the order of the proposals based on highly relevant data. The generation unit can also generate a supplementary proposal based on less relevant data. Furthermore, the generation unit can also adjust the order of the proposals according to the relevance of the data. In this way, a more appropriate proposal can be generated by adjusting the order of the proposals based on the relevance of the data.

[0087] The classification unit can estimate the user's emotion and adjust the classification criteria of the data based on the estimated user's emotion. The classification unit, for example, estimates the user's emotion and adjusts the classification criteria of the data based on the estimated user's emotion. For example, the classification unit applies a concise classification criterion when the user is stressed. The classification unit can also apply a detailed classification criterion when the user is relaxed. Furthermore, the classification unit can also apply a criterion that allows quick classification when the user is in a hurry. This enables more appropriate data classification by adjusting the classification criteria of the data according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the classification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the classification unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the classification criteria.

[0088] The classification unit can improve the accuracy of classification by taking into account the interrelationships of data during classification. The classification unit improves the accuracy of classification by taking into account, for example, the interrelationships of data. For example, the classification unit analyzes the interrelationships of data and classifies highly related data into the same category. The classification unit can also adjust the classification criteria based on the interrelationships of data. Furthermore, the classification unit can adjust the classification algorithm by taking into account the interrelationships of data. In this way, more appropriate data classification is possible by improving the accuracy of classification by taking into account the interrelationships of data.

[0089] The classification unit can estimate the user's emotion and adjust the display method of the classification result based on the estimated user's emotion. The classification unit, for example, estimates the user's emotion and adjusts the display method of the classification result based on the estimated user's emotion. For example, the classification unit provides a simple display method when the user is stressed. The classification unit can also provide a detailed display method when the user is relaxed. Furthermore, the classification unit can also provide a display method that focuses on the main points when the user is in a hurry. This enables more appropriate data display by adjusting the display method of the classification result according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the classification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the classification unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the classification result.

[0090] The classification unit can perform classification taking into consideration the geographical distribution of data during classification. The classification unit performs classification taking into consideration, for example, the geographical distribution of data. For example, the classification unit classifies geographically related data into the same category. The classification unit can also adjust the classification criteria for data based on the geographical distribution. Furthermore, the classification unit can also adjust the classification algorithm taking into consideration geographical factors. As a result, more appropriate data classification is possible by performing classification taking into consideration the geographical distribution of data.

[0091] The analysis unit can estimate the user's emotion and adjust the analysis method based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the analysis method based on the estimated user's emotion. For example, the analysis unit provides a simple analysis method when the user is stressed. The analysis unit can also provide a detailed analysis method when the user is relaxed. Furthermore, the analysis unit can also provide a method that can analyze quickly when the user is in a hurry. This enables more appropriate data analysis by adjusting the analysis method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the analysis method.

[0092] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the data during the analysis. The analysis unit, for example, improves the accuracy of the analysis by taking into account the interrelationships of the data. For example, the analysis unit analyzes the interrelationships of the data and preferentially analyzes data that is highly related. The analysis unit can also adjust the analysis algorithm based on the interrelationships of the data. Furthermore, the analysis unit can also improve the accuracy of the analysis results by taking into account the interrelationships of the data. As a result, more appropriate data analysis is possible by improving the accuracy of the analysis by taking into account the interrelationships of the data.

[0093] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. For example, the analysis unit provides a simple display method when the user is stressed. The analysis unit can also provide a detailed display method when the user is relaxed. Furthermore, the analysis unit can also provide a display method that focuses on the main points when the user is in a hurry. This enables more appropriate data display by adjusting the display method of the analysis result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the analysis result.

[0094] The analysis unit can perform the analysis while taking into consideration the geographical distribution of the data. For example, the analysis unit performs the analysis while taking into consideration the geographical distribution of the data. For example, the analysis unit preferentially analyzes geographically related data. The analysis unit can also adjust the analysis algorithm based on the geographical distribution. Furthermore, the analysis unit can display the analysis results while taking into consideration geographical factors. This allows for more appropriate data analysis by performing the analysis while taking into consideration the geographical distribution of the data.

[0095] The providing unit can estimate the user's emotion and adjust the format of the proposal to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the format of the proposal to be provided based on the estimated user's emotion. For example, the providing unit provides the proposal in a concise format when the user is stressed. The providing unit can also provide the proposal in a detailed format when the user is relaxed. Furthermore, the providing unit can also provide the proposal in a format that can be quickly understood when the user is in a hurry. This allows a more appropriate proposal to be provided by adjusting the format of the proposal according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the format of the proposal.

[0096] The providing unit can select the optimal providing method by referring to the user's past usage history at the time of providing. The providing unit, for example, selects the optimal providing method by referring to the user's past usage history. For example, the providing unit provides the proposal in a format that the user has used in the past. The providing unit can also select the optimal providing method based on the user's past usage history. Furthermore, the providing unit can also adjust the method of providing the proposal by referring to the user's past usage history. In this way, a more appropriate proposal can be provided by selecting the optimal providing method by referring to the user's past usage history.

[0097] The providing unit can estimate the user's emotion and determine the priority order of the proposals to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and determines the priority order of the proposals to be provided based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit can provide important proposals with priority. In addition, when the user is relaxed, the providing unit can also provide detailed proposals with priority. Furthermore, when the user is in a hurry, the providing unit can also provide concise proposals with priority. In this way, by determining the priority order of the proposals according to the user's emotion, a more appropriate proposal can be provided. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority order of the proposals.

[0098] The providing unit can select the optimal providing method in consideration of the device information of the user at the time of providing. The providing unit selects the optimal providing method in consideration of, for example, the device information of the user. For example, when the user is using a smartphone, the providing unit provides the proposal in a format that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide the proposal in a format optimized for a large screen. Furthermore, when the user is using a desktop, the providing unit can also provide the proposal in a format that includes detailed information. In this way, a more appropriate proposal can be provided by selecting the optimal providing method in consideration of the device information of the user.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The automatic proposal generation system may further include a history analysis unit that analyzes the user's past proposal usage history. The history analysis unit records what types of proposals the user has used in the past and analyzes the trends. For example, the history analysis unit may reflect the trends when generating the next proposal based on the formats and contents of proposals that the user has frequently used in the past. The history analysis unit may also extract features of proposals that the user has given high ratings in the past and use them in generating future proposals. Furthermore, the history analysis unit may identify improvements to the proposal based on the user's past feedback and reflect them in generating the next proposal. This makes it possible to generate a more personalized proposal by utilizing the user's past usage history.

[0101] The automatic proposal generation system may further include a situation grasping unit that grasps the user's current situation in real time. The situation grasping unit acquires the user's current work situation and schedule in real time, and optimizes the generation of the proposal based on the information. For example, when the user is in a meeting, the situation grasping unit can generate a proposal related to the contents of the meeting with priority. Also, when the user is on a business trip, the situation grasping unit can generate a proposal that reflects information about the business trip destination. Furthermore, the situation grasping unit can generate a proposal that takes into account the submission deadline based on the user's schedule. This makes it possible to provide an optimal proposal according to the user's current situation.

[0102] The automatic proposal generation system may further include a performance evaluation unit that evaluates the user's business performance. The performance evaluation unit evaluates the user's business results and the success rate of the proposal, and optimizes the generation of the proposal based on the results. For example, the performance evaluation unit may analyze the characteristics of proposals that have been successful in the past by the user, and generate a proposal with similar characteristics. The performance evaluation unit may also adjust the content of the proposal based on the user's business results. Furthermore, the performance evaluation unit may also generate a proposal to improve the user's business performance. This makes it possible to provide an optimal proposal based on the user's business performance.

[0103] The automatic proposal generation system may further include an environment adaptation unit that takes into account the user's work environment. The environment adaptation unit considers the user's work environment and the device used, and optimizes the generation of the proposal based on the information. For example, when the user is working remotely, the environment adaptation unit can generate a proposal suitable for remote work. The environment adaptation unit can also adjust the layout of the proposal according to the screen size and resolution of the device used by the user. Furthermore, the environment adaptation unit can customize the contents of the proposal based on the user's work environment. This makes it possible to provide an optimal proposal adapted to the user's work environment.

[0104] The proposal automatic generation system can further estimate the user's emotions and adjust the tone of the proposal based on the estimated user's emotions. For example, if the user is stressed, the tone of the proposal can be made calm. If the user is relaxed, the tone of the proposal can be made brighter. Furthermore, if the user is in a hurry, the tone of the proposal can be made concise and direct. In this way, a proposal with an optimal tone according to the user's emotions can be provided.

[0105] The automatic proposal generation system can further estimate the user's emotions and adjust the design of the proposal based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and visually soothing design can be adopted. If the user is relaxed, a colorful and visually pleasing design can be adopted. Furthermore, if the user is in a hurry, a design that allows information to be understood at a glance can be adopted. In this way, a proposal with an optimal design according to the user's emotions can be provided.

[0106] The automatic proposal generation system can further estimate the user's emotions and adjust the contents of the proposal based on the estimated user's emotions. For example, if the user is stressed, the proposal can be concise with important points emphasized. If the user is relaxed, the proposal can include detailed information. If the user is in a hurry, the proposal can be short and to the point. This makes it possible to provide a proposal with optimal content according to the user's emotions.

[0107] The automatic proposal generation system can further estimate the user's emotions and adjust the format of the proposal based on the estimated user's emotions. For example, if the user is stressed, a simple format can be adopted. If the user is relaxed, a detailed format can be adopted. If the user is in a hurry, a format that focuses on the main points can be adopted. In this way, a proposal in an optimal format according to the user's emotions can be provided.

[0108] The automatic proposal generation system can further estimate the user's emotions and adjust the color scheme of the proposal based on the estimated user's emotions. For example, if the user is stressed, calm colors can be used. If the user is relaxed, bright colors can be used. If the user is in a hurry, colors with high visibility can be used. This makes it possible to provide a proposal with an optimal color scheme according to the user's emotions.

[0109] The automatic proposal generation system can further estimate the user's emotions and adjust the layout of the proposal based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and intuitive layout can be adopted. If the user is relaxed, a layout including detailed information can be adopted. If the user is in a hurry, a layout focusing on the main points can be adopted. In this way, a proposal with an optimal layout according to the user's emotions can be provided.

[0110] The process flow of the second embodiment will be briefly described below.

[0111] Step 1: The storage unit stores success stories and proposals in a database. The success stories and proposals include cases that meet specific performance indicators and cases that have received good customer feedback. The storage unit includes a classification unit that classifies and stores success stories for each industry. The classification unit classifies the success stories based on the specific classification criteria and scope of each industry. Step 2: The search unit analyzes the user's request based on the data stored by the storage unit, and searches for related success cases. The search unit includes an analysis unit that analyzes the user's request. The analysis unit collects and analyzes the user's request. Step 3: The generation unit generates a proposal based on the success cases searched by the search unit. The generation unit includes a provision unit that provides the generated proposal to a user. The provision unit clarifies a specific method and criteria for providing the generated proposal to a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0113] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0114] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements including the above-mentioned storage unit, search unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the storage unit is realized by the specific processing unit 290 of the data processing device 12, and stores success cases and proposals in the database 24. The search unit is realized, for example, by the control unit 46A of the smart device 14, and analyzes a user's request and searches for related success cases. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a proposal based on the searched success cases. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the generated proposal to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0123] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements including the above-mentioned storage unit, search unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the storage unit is realized by the specific processing unit 290 of the data processing device 12, and stores success cases and proposals in the database 24. The search unit is realized, for example, by the control unit 46A of the smart glasses 214, and analyzes a user's request and searches for related success cases. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a proposal based on the searched success cases. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the generated proposal to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0139] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0142] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements including the above-mentioned storage unit, search unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the storage unit is realized by the specific processing unit 290 of the data processing device 12, and stores success cases and proposals in the database 24. The search unit is realized, for example, by the control unit 46A of the headset type terminal 314, and analyzes a user's request and searches for related success cases. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a proposal based on the searched success cases. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the generated proposal to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0155] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0156] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0159] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] Each of the multiple elements including the above-mentioned storage unit, search unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the storage unit is realized by the specific processing unit 290 of the data processing device 12, and stores success cases and proposals in the database 24. The search unit is realized, for example, by the control unit 46A of the robot 414, and analyzes a user's request and searches for related success cases. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a proposal based on the searched success cases. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the generated proposal to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0165] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0166] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0167] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0168] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0169] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0170] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0173] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0174] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0175] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0176] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0177] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0178] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0179] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0180] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0181] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0182] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0183] (Appendix 1) a storage unit for storing the success cases or proposals in a database; a search unit that analyzes a user's request based on the data stored by the storage unit and searches for related success cases; A generation unit that generates a proposal based on the success cases searched for by the search unit. A system characterized by: (Appendix 2) Equipped with a classification section that classifies and stores success stories for each industry 2. The system of claim 1, (Appendix 3) Equipped with an analysis section that analyzes user requests 2. The system of claim 1, (Appendix 4) A providing unit that provides the generated proposal to the user 2. The system of claim 1, (Appendix 5) The storage unit includes: Inferring user emotions and prioritizing data to be stored based on the estimated user emotions 2. The system of claim 1, (Appendix 6) The storage unit includes: When storing data, the reliability of the data is evaluated and the most reliable data is stored preferentially. 2. The system of claim 1, (Appendix 7) The storage unit includes: At storage time, adjust the storage method based on how frequently the data is updated 2. The system of claim 1, (Appendix 8) The storage unit includes: Inferring a user's emotion and adjusting the format of data to be stored based on the estimated user's emotion 2. The system of claim 1, (Appendix 9) The storage unit includes: When storing data, choose a storage method that takes into account the geographical distribution of the data. 2. The system of claim 1, (Appendix 10) The storage unit includes: When storing, refer to the relevant literature of the data to improve the accuracy of storage. 2. The system of claim 1, (Appendix 11) The search unit is Infer user sentiment and adjust how search results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 12) The search unit is When searching, the correlation of data is taken into account to improve search accuracy 2. The system of claim 1, (Appendix 13) The search unit is When searching, the attribute information of the data submitter is taken into consideration. 2. The system of claim 1, (Appendix 14) The search unit is Infer user sentiment and prioritize search results based on the inferred user sentiment 2. The system of claim 1, (Appendix 15) The search unit is When searching, the geographical distribution of data is taken into account. 2. The system of claim 1, (Appendix 16) The search unit is When searching, refer to relevant literature on the data to improve search accuracy 2. The system of claim 1, (Appendix 17) The generation unit is Inferring user emotions and adjusting the presentation of generated proposals based on the estimated user emotions 2. The system of claim 1, (Appendix 18) The generation unit is At generation time, adjust the level of detail in the proposal based on the importance of the data 2. The system of claim 1, (Appendix 19) The generation unit is During generation, different generation algorithms are applied depending on the category of data 2. The system of claim 1, (Appendix 20) The generation unit is Infer user sentiment and adjust the length of the generated proposal based on the estimated user sentiment 2. The system of claim 1, (Appendix 21) The generation unit is At generation time, prioritize proposals based on when data was submitted 2. The system of claim 1, (Appendix 22) The generation unit is Reorder proposals based on data relevance at generation time 2. The system of claim 1, (Appendix 23) The classification unit includes: Infer user sentiment and adjust classification criteria for data based on the inferred user sentiment 3. The system of claim 2, (Appendix 24) The classification unit includes: During classification, data correlations are taken into account to improve classification accuracy 3. The system of claim 2, (Appendix 25) The classification unit includes: Inferring user emotion and adjusting the way classification results are displayed based on the inferred user emotion 3. The system of claim 2, (Appendix 26) The classification unit includes: When classifying, the geographic distribution of data is taken into account. 3. The system of claim 2, (Appendix 27) The analysis unit is Infer user emotions and adjust analysis methods based on the inferred user emotions 4. The system of claim 3. (Appendix 28) The analysis unit is During analysis, the accuracy of the analysis is improved by considering the correlation of data. 4. The system of claim 3. (Appendix 29) The analysis unit is Infer user sentiment and adjust how analysis results are displayed based on the inferred user sentiment 4. The system of claim 3. (Appendix 30) The analysis unit is During analysis, the geographic distribution of data is taken into account. 4. The system of claim 3. (Appendix 31) The providing unit is Inferring user sentiment and adjusting the format of the proposal provided based on the estimated user sentiment 5. The system of claim 4. (Appendix 32) The providing unit is When providing the service, the optimal method of providing the service is selected by referring to the user's past usage history. 5. The system of claim 4. (Appendix 33) The providing unit is Infer user sentiment and prioritize proposals based on the inferred user sentiment 5. The system of claim 4. (Appendix 34) The providing unit is When providing information, the optimal method of providing the information is selected taking into account the user's device information. 5. The system of claim 4. [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a storage unit for storing the success cases or proposals in a database; a search unit that analyzes a user's request based on the data stored by the storage unit and searches for related success cases; A generation unit that generates a proposal based on the success cases searched for by the search unit. A system characterized by:

2. Equipped with a classification section that classifies and stores success stories for each industry 2. The system of claim 1 .

3. Equipped with an analysis section that analyzes user requests 2. The system of claim 1 .

4. A providing unit that provides the generated proposal to the user 2. The system of claim 1 .

5. The storage unit includes: Inferring user emotions and prioritizing data to be stored based on the estimated user emotions 2. The system of claim 1 .

6. The storage unit includes: When storing data, the reliability of the data is evaluated and the most reliable data is stored preferentially.

2. The system of claim 1 .

7. The storage unit includes: At storage time, adjust the storage method based on how frequently the data is updated 2. The system of claim 1 .

8. The storage unit includes: Inferring a user's emotion and adjusting the format of data to be stored based on the estimated user's emotion 2. The system of claim 1 .

9. The storage unit includes: When storing data, choose a storage method that takes into account the geographical distribution of the data.

2. The system of claim 1 .

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