system

Abstract

The system according to this embodiment aims to predict the extent to which a company's IT investments contribute to performance, business growth, and profitability improvement, and to propose an optimal investment strategy. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data on a company's IT investments. The analysis unit analyzes the data collected by the data collection unit and performs predictive analysis on how much it will contribute to the company's performance, business growth, and profitability improvement. Based on the results of the predictive analysis obtained by the analysis unit, the proposal unit evaluates the timing and scale of the investment, the balance of risk and return, and proposes the optimal IT investment strategy.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to accurately predict how much a company's IT investment contributes to performance, business growth, and profitability improvement, and to propose an optimal investment strategy.

[0005] The system according to the embodiment aims to predict how much a company's IT investment contributes to performance, business growth, and profitability improvement, and to propose an optimal investment strategy.

Means for Solving the Problems

[0007] The system according to this embodiment can predict the extent to which a company's IT investments contribute to performance, business growth, and profitability improvement, and can propose an optimal investment strategy. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI investment advisor system according to an embodiment of the present invention is a system that proposes an optimal IT investment strategy by collecting data on a company's IT investments and performing predictive analysis using AI. The AI ​​investment advisor system collects data on a company's IT investments and proposes an optimal IT investment strategy by performing predictive analysis using AI. For example, the AI ​​investment advisor system collects data on a company's IT infrastructure, investments in software and hardware, and the status of resource purchases and adoptions. Next, the AI ​​investment advisor system uses AI to analyze the collected data. The AI ​​performs predictive analysis to determine the extent to which a company's IT investments will contribute to the company's performance, business growth, and profitability improvement. Furthermore, based on the results of the predictive analysis, the AI ​​investment advisor system evaluates the timing and scale of investments, the balance between risk and return, etc., and proposes an optimal IT investment strategy. This enables companies to make data-driven, rational IT investments, thereby improving business growth and profitability.

[0029] The AI ​​investment advisor system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data related to a company's IT investments. The data collection unit can collect data such as the purchase and adoption status of a company's IT infrastructure, software, hardware, and resources. For example, the data collection unit can collect data related to a company's IT infrastructure. The data collection unit can also collect data related to a company's software investments. The data collection unit can also collect data related to a company's hardware investments. The analysis unit analyzes the data collected by the data collection unit and performs predictive analysis on how much it contributes to the company's performance, business growth, and profitability improvement. For example, the analysis unit performs regression analysis based on historical data. For example, the analysis unit can perform predictive analysis using machine learning algorithms. For example, the analysis unit can perform predictive analysis using neural networks based on historical data. Based on the results of the predictive analysis obtained by the analysis unit, the proposal unit evaluates the timing and scale of investments, the balance of risk and return, and proposes the optimal IT investment strategy. For example, the proposal unit proposes an investment strategy that yields the maximum return by investing a specific amount at a specific time. The proposal function can, for example, evaluate the timing of an investment and propose the optimal investment period. The proposal function can also, for example, evaluate the scale of an investment and propose the optimal investment amount. As a result, the AI ​​investment advisor system according to this embodiment can collect and analyze data on a company's IT investments and propose the optimal IT investment strategy, thereby improving the company's performance, business growth, and profitability.

[0030] The data collection unit collects data related to a company's IT investments. Specifically, it can collect data on the purchase and adoption status of a company's IT infrastructure, software, hardware, and resources. For example, data related to a company's IT infrastructure includes server operating status, network bandwidth, and data center utilization rates. This data can be obtained directly from the company's IT department, as well as from cloud service providers and network management tools. Data related to software investments includes license purchase history, software usage, and upgrade frequency. This data can be obtained from software vendors and internal asset management systems. Data related to hardware investments includes server and storage device purchase history, maintenance contract status, and equipment service life. This data can be obtained from hardware vendors and internal asset management systems. Furthermore, data related to resource purchase and adoption includes IT personnel hiring history, training participation status, and utilization of external consultants. This data can be obtained from human resources and training departments. By centrally managing and updating this diverse data in real time, the data collection unit can accurately grasp the company's IT investment status.

[0031] The analysis unit analyzes the data collected by the data collection unit and performs predictive analysis to determine how much it contributes to a company's performance, business growth, and profitability improvement. Specifically, it performs regression analysis based on historical data to quantitatively evaluate the impact of IT investment on a company's performance. For example, it analyzes the relationship between past IT investment amounts and a company's sales and profit margins to predict how much performance improvement can be expected from an increase in investment. It can also perform more advanced predictive analysis using machine learning algorithms. For example, it uses algorithms such as random forests and support vector machines to analyze the correlation between multiple variables and derive the optimal investment strategy. Furthermore, it can use neural networks to capture nonlinear relationships. For example, it uses deep learning to train a model on past investment data and company performance data to predict new investment strategies. By combining these methods, the analysis unit can perform more accurate predictive analysis and evaluate how much return a company's IT investment will bring. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early risk detection. This allows the analysis unit to comprehensively analyze data on companies' IT investments and derive optimal investment strategies that contribute to improving company performance, business growth, and profitability.

[0032] The Proposal Department, based on the predictive analysis results obtained by the Analysis Department, evaluates the timing and scale of investments, as well as the balance between risk and return, and proposes the optimal IT investment strategy. Specifically, it proposes an investment strategy that yields the greatest return by investing a specific amount at a specific time. For example, if the Analysis Department's forecast results indicate that upgrading the IT infrastructure in the next quarter will contribute to improving the company's performance, the Proposal Department will propose investing at that time. It can also evaluate the timing of investments and propose the optimal investment period. For example, if historical data indicates that investing at a specific time is most effective, the Proposal Department will propose investing at that time. Furthermore, it can evaluate the scale of investments and propose the optimal investment amount. For example, if the Analysis Department's forecast results indicate that investing a specific amount will yield the greatest return, the Proposal Department will propose investing at that amount. The Proposal Department provides these proposals to the company's management team to support their decision-making. The Proposal Department can also evaluate the balance between risk and return and propose an investment strategy that minimizes risk while maximizing returns. For example, it can simulate multiple investment scenarios and select the scenario with the lowest risk and highest return. In this way, the Proposal Department can propose the optimal strategy for a company's IT investments, thereby improving the company's performance, business growth, and profitability.

[0033] The data collection unit can collect data such as the status of IT infrastructure, software, hardware, and resource purchases and adoptions. For example, the data collection unit can collect data on a company's IT infrastructure. For example, the data collection unit can also collect data on a company's software investments. For example, the data collection unit can also collect data on a company's hardware investments. This allows for more accurate analysis by enabling the data collection unit to collect diverse data on a company's IT investments. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on a company's IT infrastructure into an AI, which can then collect the data.

[0034] The analysis unit can perform predictive analysis using regression analysis or machine learning algorithms based on historical data. For example, the analysis unit can perform regression analysis based on historical data. The analysis unit can also perform predictive analysis using machine learning algorithms. The analysis unit can also perform predictive analysis using neural networks based on historical data. This allows the analysis unit to perform more accurate predictions by performing predictive analysis based on historical data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input historical data into AI, and the AI ​​can perform predictive analysis.

[0035] The proposal department can propose an investment strategy that yields the maximum return by investing a specific amount at a specific time. For example, the proposal department can propose an investment strategy that yields the maximum return by investing a specific amount at a specific time. The proposal department can also evaluate the timing of investments and propose the optimal investment timing. The proposal department can also evaluate the scale of investments and propose the optimal investment amount. This improves the investment efficiency of companies by having the proposal department propose an investment strategy that yields the maximum return. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the results of predictive analysis into AI, and the AI ​​can propose the optimal investment strategy.

[0036] The data collection unit may include a filtering unit for filtering the collected data. For example, the data collection unit may include a filtering unit for filtering the collected data. For example, the data collection unit may filter the collected data based on importance. For example, the data collection unit may also filter the collected data based on relevance. By including a filtering unit in the data collection unit, only the necessary data can be efficiently collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input the collected data into an AI, which can then filter the data.

[0037] The analysis unit may include a visualization unit for visualizing the results of predictive analysis. For example, the analysis unit may include a visualization unit for visualizing the results of predictive analysis. For example, the analysis unit may visualize the results of predictive analysis in a graph. For example, the analysis unit may visualize the results of predictive analysis in a chart. This makes it easier to visually understand the results of predictive analysis by including a visualization unit in the analysis unit. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit may input the results of predictive analysis into AI, and the AI ​​may visualize the results.

[0038] The data collection unit can analyze a company's past investment history and select the optimal data collection method. For example, the data collection unit can select a similar data collection method based on a company's past successful investment patterns. The data collection unit can also select a different data collection method to avoid a company's past unsuccessful investment patterns. For example, the data collection unit can analyze a company's past investment history and select the most effective data collection method. This allows for the selection of the optimal data collection method by analyzing a company's past investment history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a company's past investment history into AI, which can then select the optimal data collection method.

[0039] The data collection unit can filter data based on a company's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to projects the company is currently working on. The data collection unit can also filter and collect highly relevant data based on a company's areas of interest. For example, the data collection unit can collect necessary data according to the progress of a company's current projects. This allows for the collection of highly relevant data by filtering data based on a company's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about a company's current projects and areas of interest into an AI, which can then filter the data.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of companies during data collection. For example, the data collection unit can prioritize the collection of region-specific data based on the location of companies. The data collection unit can also filter and collect highly relevant data by considering the geographical location information of companies. The data collection unit can also select the optimal data collection method based on the geographical location information of companies. This allows for the efficient collection of region-specific data by considering the geographical location information of companies during data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of companies into AI, which can then prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze a company's social media activities and collect relevant data during data collection. For example, the data collection unit can analyze a company's social media activities and prioritize the collection of relevant data. The data collection unit can also filter and collect highly relevant data based on a company's social media activities. The data collection unit can also select the optimal data collection method, taking into account a company's social media activities. This allows for the efficient collection of highly relevant data by analyzing a company's social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input company social media activity data into AI, which can then collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit can perform a simplified analysis on less important data. The analysis unit can also adjust the level of detail of the analysis according to the importance of the data. This allows for a detailed analysis of important data by adjusting the level of detail according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a specific analysis algorithm to data related to IT infrastructure. The analysis unit can also apply a different analysis algorithm to data related to software investment. The analysis unit can also apply yet another analysis algorithm to data related to hardware investment. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can apply different analysis algorithms.

[0044] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may postpone the analysis of older data. The analysis unit can also adjust the priority of analysis based on the data submission date. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into the AI, and the AI ​​can determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of analysis.

[0046] The proposal unit can adjust the level of detail of a proposal based on the importance of the investment. For example, the proposal unit will provide a detailed proposal for important investments. For example, the proposal unit can provide a simplified proposal for less important investments. The proposal unit can also adjust the level of detail of a proposal according to the importance of the investment. This allows for detailed proposals for important investments by adjusting the level of detail according to the importance of the investment. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the investment into the AI, and the AI ​​can adjust the level of detail of the proposal.

[0047] The proposal unit can apply different proposal algorithms depending on the investment category when making a proposal. For example, the proposal unit applies a specific proposal algorithm to proposals related to IT infrastructure investments. The proposal unit can also apply a different proposal algorithm to proposals related to software investments. The proposal unit can also apply yet another proposal algorithm to proposals related to hardware investments. By applying different proposal algorithms depending on the investment category, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the investment category into the AI, and the AI ​​can apply a different proposal algorithm.

[0048] The proposal department can determine the priority of proposals based on the timing of investment submissions when submitting proposals. For example, the proposal department will prioritize the most recent investment projects. The proposal department can also postpone proposals for older projects. The proposal department can also adjust the priority of proposals based on the timing of investment submissions. This allows for prioritizing the most recent investment projects by determining the priority of proposals based on the timing of investment submissions. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the timing of investment submissions into the AI, and the AI ​​can determine the priority of proposals.

[0049] The proposal unit can adjust the order of proposals based on the relevance of the investments when making a proposal. For example, the proposal unit will prioritize proposing highly relevant investment projects. For example, the proposal unit may postpone proposing less relevant investment projects. The proposal unit can also adjust the order of proposals based on the relevance of the investments. This allows for prioritizing the proposal of highly relevant investment projects by adjusting the order of proposals based on the relevance of the investments. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of the investments into the AI, and the AI ​​can adjust the order of proposals.

[0050] The filtering unit can improve the accuracy of filtering by considering the interrelationships of the data during the filtering process. For example, the filtering unit analyzes the interrelationships of the data and prioritizes filtering highly relevant data. The filtering unit can also improve the accuracy of filtering by considering the interrelationships of the data. For example, the filtering unit can set optimal filtering criteria based on the interrelationships of the data. This allows for efficient filtering of highly relevant data by improving the accuracy of filtering by considering the interrelationships of the data. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input the interrelationships of the data into the AI, which can then improve the accuracy of the filtering.

[0051] The filtering unit can perform filtering while considering the geographical distribution of the data. For example, the filtering unit can prioritize filtering region-specific data based on the location of companies. The filtering unit can also filter highly relevant data by considering the geographical distribution of the data. The filtering unit can also set optimal filtering criteria based on geographical distribution. This allows for efficient filtering of region-specific data by considering the geographical distribution of the data. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input the geographical distribution of the data into AI, which can then perform the filtering.

[0052] The visualization unit can predict current data by referencing past data during visualization. For example, the visualization unit can predict current investment performance based on past investment data. The visualization unit can also predict current growth trends based on past business growth data. The visualization unit can also predict current profitability based on past revenue data. This makes it easier to grasp future trends by predicting current data by referencing past data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input past data into AI, and the AI ​​can predict current data.

[0053] The visualization unit can analyze changes in visualization based on the data submission date during visualization. For example, the visualization unit can analyze changes in visualization based on the latest data. The visualization unit can also analyze changes in visualization based on older data. For example, the visualization unit can analyze changes in visualization based on the data submission date. This makes it easier to understand changes in data by analyzing changes in visualization based on the data submission date. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the data submission date into the AI, and the AI ​​can analyze changes in visualization.

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

[0055] The AI ​​investment advisor system can further analyze a company's past investment performance and identify successful investment patterns. For example, the data collection unit can propose similar investment strategies based on the company's past successful investment patterns. The data collection unit can also propose different investment strategies to avoid the company's past unsuccessful investment patterns. The data collection unit can also analyze a company's past investment history and select the most effective investment strategy. This allows the system to propose the optimal investment strategy by analyzing a company's past investment history. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input a company's past investment history into an AI, which can then select the optimal investment strategy.

[0056] The AI ​​investment advisor system can further propose investment strategies by considering the geographical location of companies. For example, the data collection unit can propose region-specific investment strategies based on the company's location. The data collection unit can also propose highly relevant investment strategies by considering the company's geographical location. The data collection unit can also select the optimal investment strategy based on the company's geographical location. This allows for the efficient proposal of region-specific investment strategies by considering the company's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the company's geographical location into the AI, which can then propose highly relevant investment strategies.

[0057] The AI ​​investment advisor system can further analyze a company's social media activities and collect relevant data. For example, the data collection unit can analyze a company's social media activities and prioritize the collection of relevant data. The data collection unit can also filter and collect highly relevant data based on a company's social media activities. The data collection unit can also select the optimal data collection method, taking into account a company's social media activities. This allows for the efficient collection of highly relevant data by analyzing a company's social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input company social media activity data into an AI, which can then collect relevant data.

[0058] The AI ​​investment advisor system can further filter data based on a company's current projects and areas of interest. For example, the data collection unit can prioritize collecting data related to projects the company is currently working on. The data collection unit can also filter and collect highly relevant data based on the company's areas of interest. The data collection unit can also collect necessary data according to the progress of the company's current projects. This allows for the collection of highly relevant data by filtering it based on the company's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the company's current projects and areas of interest into the AI, which can then filter the data.

[0059] The AI ​​investment advisor system can further prioritize the collection of highly relevant data by considering the geographical location of companies during data collection. For example, the collection unit can prioritize the collection of region-specific data based on the company's location. The collection unit can also filter and collect highly relevant data by considering the geographical location of companies. The collection unit can also select the optimal data collection method based on the geographical location of companies. This allows for the efficient collection of region-specific data by considering the geographical location of companies during data collection. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the geographical location of companies into the AI, which can then prioritize the collection of highly relevant data.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects data on companies' IT investments. Specifically, it can collect data on the purchase and adoption status of companies' IT infrastructure, software, hardware, and resources. Step 2: The analysis unit analyzes the data collected by the collection unit and performs predictive analysis to determine how much it contributes to the company's performance, business growth, and profitability improvement. Specifically, it can perform regression analysis based on historical data, or use machine learning algorithms and neural networks for predictive analysis. Step 3: Based on the predictive analysis results obtained by the analysis department, the proposal department evaluates the timing and scale of investments, as well as the balance of risk and return, and proposes the optimal IT investment strategy. Specifically, it can propose investment strategies that yield the greatest return by investing a specific amount at a specific time, or the optimal timing and amount of investment.

[0062] (Example of form 2) An AI investment advisor system according to an embodiment of the present invention is a system that proposes an optimal IT investment strategy by collecting data on a company's IT investments and performing predictive analysis using AI. The AI ​​investment advisor system collects data on a company's IT investments and proposes an optimal IT investment strategy by performing predictive analysis using AI. For example, the AI ​​investment advisor system collects data on a company's IT infrastructure, investments in software and hardware, and the status of resource purchases and adoptions. Next, the AI ​​investment advisor system uses AI to analyze the collected data. The AI ​​performs predictive analysis to determine the extent to which a company's IT investments will contribute to the company's performance, business growth, and profitability improvement. Furthermore, based on the results of the predictive analysis, the AI ​​investment advisor system evaluates the timing and scale of investments, the balance between risk and return, etc., and proposes an optimal IT investment strategy. This enables companies to make data-driven, rational IT investments, thereby improving business growth and profitability.

[0063] The AI ​​investment advisor system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data related to a company's IT investments. The data collection unit can collect data such as the purchase and adoption status of a company's IT infrastructure, software, hardware, and resources. For example, the data collection unit can collect data related to a company's IT infrastructure. The data collection unit can also collect data related to a company's software investments. The data collection unit can also collect data related to a company's hardware investments. The analysis unit analyzes the data collected by the data collection unit and performs predictive analysis on how much it contributes to the company's performance, business growth, and profitability improvement. For example, the analysis unit performs regression analysis based on historical data. For example, the analysis unit can perform predictive analysis using machine learning algorithms. For example, the analysis unit can perform predictive analysis using neural networks based on historical data. Based on the results of the predictive analysis obtained by the analysis unit, the proposal unit evaluates the timing and scale of investments, the balance of risk and return, and proposes the optimal IT investment strategy. For example, the proposal unit proposes an investment strategy that yields the maximum return by investing a specific amount at a specific time. The proposal function can, for example, evaluate the timing of an investment and propose the optimal investment period. The proposal function can also, for example, evaluate the scale of an investment and propose the optimal investment amount. As a result, the AI ​​investment advisor system according to this embodiment can collect and analyze data on a company's IT investments and propose the optimal IT investment strategy, thereby improving the company's performance, business growth, and profitability.

[0064] The data collection unit collects data related to a company's IT investments. Specifically, it can collect data on the purchase and adoption status of a company's IT infrastructure, software, hardware, and resources. For example, data related to a company's IT infrastructure includes server operating status, network bandwidth, and data center utilization rates. This data can be obtained directly from the company's IT department, as well as from cloud service providers and network management tools. Data related to software investments includes license purchase history, software usage, and upgrade frequency. This data can be obtained from software vendors and internal asset management systems. Data related to hardware investments includes server and storage device purchase history, maintenance contract status, and equipment service life. This data can be obtained from hardware vendors and internal asset management systems. Furthermore, data related to resource purchase and adoption includes IT personnel hiring history, training participation status, and utilization of external consultants. This data can be obtained from human resources and training departments. By centrally managing and updating this diverse data in real time, the data collection unit can accurately grasp the company's IT investment status.

[0065] The analysis unit analyzes the data collected by the data collection unit and performs predictive analysis to determine how much it contributes to a company's performance, business growth, and profitability improvement. Specifically, it performs regression analysis based on historical data to quantitatively evaluate the impact of IT investment on a company's performance. For example, it analyzes the relationship between past IT investment amounts and a company's sales and profit margins to predict how much performance improvement can be expected from an increase in investment. It can also perform more advanced predictive analysis using machine learning algorithms. For example, it uses algorithms such as random forests and support vector machines to analyze the correlation between multiple variables and derive the optimal investment strategy. Furthermore, it can use neural networks to capture nonlinear relationships. For example, it uses deep learning to train a model on past investment data and company performance data to predict new investment strategies. By combining these methods, the analysis unit can perform more accurate predictive analysis and evaluate how much return a company's IT investment will bring. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling early risk detection. This allows the analysis unit to comprehensively analyze data on companies' IT investments and derive optimal investment strategies that contribute to improving company performance, business growth, and profitability.

[0066] The Proposal Department, based on the predictive analysis results obtained by the Analysis Department, evaluates the timing and scale of investments, as well as the balance between risk and return, and proposes the optimal IT investment strategy. Specifically, it proposes an investment strategy that yields the greatest return by investing a specific amount at a specific time. For example, if the Analysis Department's forecast results indicate that upgrading the IT infrastructure in the next quarter will contribute to improving the company's performance, the Proposal Department will propose investing at that time. It can also evaluate the timing of investments and propose the optimal investment period. For example, if historical data indicates that investing at a specific time is most effective, the Proposal Department will propose investing at that time. Furthermore, it can evaluate the scale of investments and propose the optimal investment amount. For example, if the Analysis Department's forecast results indicate that investing a specific amount will yield the greatest return, the Proposal Department will propose investing at that amount. The Proposal Department provides these proposals to the company's management team to support their decision-making. The Proposal Department can also evaluate the balance between risk and return and propose an investment strategy that minimizes risk while maximizing returns. For example, it can simulate multiple investment scenarios and select the scenario with the lowest risk and highest return. In this way, the Proposal Department can propose the optimal strategy for a company's IT investments, thereby improving the company's performance, business growth, and profitability.

[0067] The data collection unit can collect data such as the status of IT infrastructure, software, hardware, and resource purchases and adoptions. For example, the data collection unit can collect data on a company's IT infrastructure. For example, the data collection unit can also collect data on a company's software investments. For example, the data collection unit can also collect data on a company's hardware investments. This allows for more accurate analysis by enabling the data collection unit to collect diverse data on a company's IT investments. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on a company's IT infrastructure into an AI, which can then collect the data.

[0068] The analysis unit can perform predictive analysis using regression analysis or machine learning algorithms based on historical data. For example, the analysis unit can perform regression analysis based on historical data. The analysis unit can also perform predictive analysis using machine learning algorithms. The analysis unit can also perform predictive analysis using neural networks based on historical data. This allows the analysis unit to perform more accurate predictions by performing predictive analysis based on historical data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input historical data into AI, and the AI ​​can perform predictive analysis.

[0069] The proposal department can propose an investment strategy that yields the maximum return by investing a specific amount at a specific time. For example, the proposal department can propose an investment strategy that yields the maximum return by investing a specific amount at a specific time. The proposal department can also evaluate the timing of investments and propose the optimal investment timing. The proposal department can also evaluate the scale of investments and propose the optimal investment amount. This improves the investment efficiency of companies by having the proposal department propose an investment strategy that yields the maximum return. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the results of predictive analysis into AI, and the AI ​​can propose the optimal investment strategy.

[0070] The data collection unit may include a filtering unit for filtering the collected data. For example, the data collection unit may include a filtering unit for filtering the collected data. For example, the data collection unit may filter the collected data based on importance. For example, the data collection unit may also filter the collected data based on relevance. By including a filtering unit in the data collection unit, only the necessary data can be efficiently collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input the collected data into an AI, which can then filter the data.

[0071] The analysis unit may include a visualization unit for visualizing the results of predictive analysis. For example, the analysis unit may include a visualization unit for visualizing the results of predictive analysis. For example, the analysis unit may visualize the results of predictive analysis in a graph. For example, the analysis unit may visualize the results of predictive analysis in a chart. This makes it easier to visually understand the results of predictive analysis by including a visualization unit in the analysis unit. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit may input the results of predictive analysis into AI, and the AI ​​may visualize the results.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection and collect only important data. For example, if the user is relaxed, the data collection unit can collect detailed data and provide more information. For example, if the user is in a hurry, the data collection unit can quickly collect the necessary data and immediately proceed to analysis. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can then adjust the timing of data collection.

[0073] The data collection unit can analyze a company's past investment history and select the optimal data collection method. For example, the data collection unit can select a similar data collection method based on a company's past successful investment patterns. The data collection unit can also select a different data collection method to avoid a company's past unsuccessful investment patterns. For example, the data collection unit can analyze a company's past investment history and select the most effective data collection method. This allows for the selection of the optimal data collection method by analyzing a company's past investment history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a company's past investment history into AI, which can then select the optimal data collection method.

[0074] The data collection unit can filter data based on a company's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to projects the company is currently working on. The data collection unit can also filter and collect highly relevant data based on a company's areas of interest. For example, the data collection unit can collect necessary data according to the progress of a company's current projects. This allows for the collection of highly relevant data by filtering data based on a company's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about a company's current projects and areas of interest into an AI, which can then filter the data.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting only important data. If the user is relaxed, the data collection unit may also prioritize collecting detailed data. If the user is in a hurry, the data collection unit may also prioritize collecting data that can be collected quickly. This allows for the priority collection of important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then determine the priority of data to collect.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of companies during data collection. For example, the data collection unit can prioritize the collection of region-specific data based on the location of companies. The data collection unit can also filter and collect highly relevant data by considering the geographical location information of companies. The data collection unit can also select the optimal data collection method based on the geographical location information of companies. This allows for the efficient collection of region-specific data by considering the geographical location information of companies during data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of companies into AI, which can then prioritize the collection of highly relevant data.

[0077] The data collection unit can analyze a company's social media activities and collect relevant data during data collection. For example, the data collection unit can analyze a company's social media activities and prioritize the collection of relevant data. The data collection unit can also filter and collect highly relevant data based on a company's social media activities. The data collection unit can also select the optimal data collection method, taking into account a company's social media activities. This allows for the efficient collection of highly relevant data by analyzing a company's social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input company social media activity data into AI, which can then collect relevant data.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can also provide a concise analysis result. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI, and the AI ​​can adjust the presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit can perform a simplified analysis on less important data. The analysis unit can also adjust the level of detail of the analysis according to the importance of the data. This allows for a detailed analysis of important data by adjusting the level of detail according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a specific analysis algorithm to data related to IT infrastructure. The analysis unit can also apply a different analysis algorithm to data related to software investment. The analysis unit can also apply yet another analysis algorithm to data related to hardware investment. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can apply different analysis algorithms.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI, and the AI ​​can adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may postpone the analysis of older data. The analysis unit can also adjust the priority of analysis based on the data submission date. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into the AI, and the AI ​​can determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of analysis.

[0084] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can then adjust the way suggestions are presented.

[0085] The proposal unit can adjust the level of detail of a proposal based on the importance of the investment. For example, the proposal unit will provide a detailed proposal for important investments. For example, the proposal unit can provide a simplified proposal for less important investments. The proposal unit can also adjust the level of detail of a proposal according to the importance of the investment. This allows for detailed proposals for important investments by adjusting the level of detail according to the importance of the investment. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the investment into the AI, and the AI ​​can adjust the level of detail of the proposal.

[0086] The proposal unit can apply different proposal algorithms depending on the investment category when making a proposal. For example, the proposal unit applies a specific proposal algorithm to proposals related to IT infrastructure investments. The proposal unit can also apply a different proposal algorithm to proposals related to software investments. The proposal unit can also apply yet another proposal algorithm to proposals related to hardware investments. By applying different proposal algorithms depending on the investment category, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the investment category into the AI, and the AI ​​can apply a different proposal algorithm.

[0087] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is excited, the suggestion unit can also provide visually stimulating suggestions. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can then adjust the length of suggestions.

[0088] The proposal department can determine the priority of proposals based on the timing of investment submissions when submitting proposals. For example, the proposal department will prioritize the most recent investment projects. The proposal department can also postpone proposals for older projects. The proposal department can also adjust the priority of proposals based on the timing of investment submissions. This allows for prioritizing the most recent investment projects by determining the priority of proposals based on the timing of investment submissions. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the timing of investment submissions into the AI, and the AI ​​can determine the priority of proposals.

[0089] The proposal unit can adjust the order of proposals based on the relevance of the investments when making a proposal. For example, the proposal unit will prioritize proposing highly relevant investment projects. For example, the proposal unit may postpone proposing less relevant investment projects. The proposal unit can also adjust the order of proposals based on the relevance of the investments. This allows for prioritizing the proposal of highly relevant investment projects by adjusting the order of proposals based on the relevance of the investments. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of the investments into the AI, and the AI ​​can adjust the order of proposals.

[0090] The filtering unit can estimate the user's emotions and adjust the filtering criteria based on the estimated emotions. For example, if the user is stressed, the filtering unit may filter only important data. If the user is relaxed, the filtering unit may also filter detailed data. If the user is in a hurry, the filtering unit may also set criteria that allow for quick filtering. This allows for filtering of more appropriate data by adjusting the filtering criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the filtering unit may be performed using AI or not using AI. For example, the filtering unit can input user emotion data into an AI, which can then adjust the filtering criteria.

[0091] The filtering unit can improve the accuracy of filtering by considering the interrelationships of the data during the filtering process. For example, the filtering unit analyzes the interrelationships of the data and prioritizes filtering highly relevant data. The filtering unit can also improve the accuracy of filtering by considering the interrelationships of the data. For example, the filtering unit can set optimal filtering criteria based on the interrelationships of the data. This allows for efficient filtering of highly relevant data by improving the accuracy of filtering by considering the interrelationships of the data. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input the interrelationships of the data into the AI, which can then improve the accuracy of the filtering.

[0092] The filtering unit can estimate the user's emotions and adjust the order in which the filtering results are displayed based on the estimated emotions. For example, if the user is stressed, the filtering unit may display important data first. If the user is relaxed, the filtering unit may also display detailed data first. If the user is in a hurry, the filtering unit may also display data that can be quickly reviewed first. This allows for the display of more appropriate data by adjusting the order in which the filtering results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the filtering unit may be performed using AI or not using AI. For example, the filtering unit can input user emotion data into the AI, which can then adjust the order in which the filtering results are displayed.

[0093] The filtering unit can perform filtering while considering the geographical distribution of the data. For example, the filtering unit can prioritize filtering region-specific data based on the location of companies. The filtering unit can also filter highly relevant data by considering the geographical distribution of the data. The filtering unit can also set optimal filtering criteria based on geographical distribution. This allows for efficient filtering of region-specific data by considering the geographical distribution of the data. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input the geographical distribution of the data into AI, which can then perform the filtering.

[0094] The visualization unit can estimate the user's emotions and adjust the display method of the visualization based on the estimated user emotions. For example, if the user is tense, the visualization unit can provide a simple and highly visible display method. For example, if the user is relaxed, the visualization unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the visualization unit can also provide a display method that gets straight to the point. In this way, by adjusting the display method of the visualization according to the user's emotions, a more appropriate display can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input user emotion data into the AI, and the AI ​​can adjust the display method of the visualization.

[0095] The visualization unit can predict current data by referencing past data during visualization. For example, the visualization unit can predict current investment performance based on past investment data. The visualization unit can also predict current growth trends based on past business growth data. The visualization unit can also predict current profitability based on past revenue data. This makes it easier to grasp future trends by predicting current data by referencing past data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input past data into AI, and the AI ​​can predict current data.

[0096] The visualization unit can estimate the user's emotions and adjust the importance of the visualization based on the estimated user emotions. For example, if the user is tense, the visualization unit can highlight important data. For example, if the user is relaxed, the visualization unit can also highlight detailed data. For example, if the user is in a hurry, the visualization unit can also highlight concise data. In this way, important data can be highlighted by adjusting the importance of the visualization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input user emotion data into the AI, and the AI ​​can adjust the importance of the visualization.

[0097] The visualization unit can analyze changes in visualization based on the data submission date during visualization. For example, the visualization unit can analyze changes in visualization based on the latest data. The visualization unit can also analyze changes in visualization based on older data. For example, the visualization unit can analyze changes in visualization based on the data submission date. This makes it easier to understand changes in data by analyzing changes in visualization based on the data submission date. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the data submission date into the AI, and the AI ​​can analyze changes in visualization.

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

[0099] The AI ​​investment advisor system can further estimate the user's emotions and customize investment strategy suggestions based on those emotions. For example, if the user is afraid of risk, the recommendation unit can prioritize suggesting low-risk investment strategies. If the user is willing to take risks, the recommendation unit can suggest high-risk but also high-return investment strategies. Furthermore, if the user is stressed, the recommendation unit can provide simple and easy-to-understand suggestions. This allows the system to provide the optimal investment strategy tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input user emotion data into an AI, which can then customize the suggestions.

[0100] The AI ​​investment advisor system can further analyze a company's past investment performance and identify successful investment patterns. For example, the data collection unit can propose similar investment strategies based on the company's past successful investment patterns. The data collection unit can also propose different investment strategies to avoid the company's past unsuccessful investment patterns. The data collection unit can also analyze a company's past investment history and select the most effective investment strategy. This allows the system to propose the optimal investment strategy by analyzing a company's past investment history. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input a company's past investment history into an AI, which can then select the optimal investment strategy.

[0101] The AI ​​investment advisor system can further estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection and collect only the important data. If the user is relaxed, the data collection unit can collect detailed data and provide more information. If the user is in a hurry, the data collection unit can quickly collect the necessary data and immediately proceed to analysis. This allows for more appropriate data collection by adjusting the data collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the user's emotion data into the AI, which can then adjust the data collection method.

[0102] The AI ​​investment advisor system can further propose investment strategies by considering the geographical location of companies. For example, the data collection unit can propose region-specific investment strategies based on the company's location. The data collection unit can also propose highly relevant investment strategies by considering the company's geographical location. The data collection unit can also select the optimal investment strategy based on the company's geographical location. This allows for the efficient proposal of region-specific investment strategies by considering the company's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the company's geographical location into the AI, which can then propose highly relevant investment strategies.

[0103] The AI ​​investment advisor system can further estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting how the analysis results are displayed according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into the AI, and the AI ​​can adjust how the analysis results are displayed.

[0104] The AI ​​investment advisor system can further analyze a company's social media activities and collect relevant data. For example, the data collection unit can analyze a company's social media activities and prioritize the collection of relevant data. The data collection unit can also filter and collect highly relevant data based on a company's social media activities. The data collection unit can also select the optimal data collection method, taking into account a company's social media activities. This allows for the efficient collection of highly relevant data by analyzing a company's social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input company social media activity data into an AI, which can then collect relevant data.

[0105] The AI ​​investment advisor system can further estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and easy-to-understand suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, the system can provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the AI, which can then adjust the way it presents its suggestions.

[0106] The AI ​​investment advisor system can further filter data based on a company's current projects and areas of interest. For example, the data collection unit can prioritize collecting data related to projects the company is currently working on. The data collection unit can also filter and collect highly relevant data based on the company's areas of interest. The data collection unit can also collect necessary data according to the progress of the company's current projects. This allows for the collection of highly relevant data by filtering it based on the company's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the company's current projects and areas of interest into the AI, which can then filter the data.

[0107] The AI ​​investment advisor system can further estimate the user's emotions and determine the priority of data to collect based on those emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only important data. If the user is relaxed, the data collection unit can prioritize collecting detailed data. If the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. This allows for the priority collection of important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then determine the priority of data to collect.

[0108] The AI ​​investment advisor system can further prioritize the collection of highly relevant data by considering the geographical location of companies during data collection. For example, the collection unit can prioritize the collection of region-specific data based on the company's location. The collection unit can also filter and collect highly relevant data by considering the geographical location of companies. The collection unit can also select the optimal data collection method based on the geographical location of companies. This allows for the efficient collection of region-specific data by considering the geographical location of companies during data collection. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the geographical location of companies into the AI, which can then prioritize the collection of highly relevant data.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects data on companies' IT investments. Specifically, it can collect data on the purchase and adoption status of companies' IT infrastructure, software, hardware, and resources. Step 2: The analysis unit analyzes the data collected by the collection unit and performs predictive analysis to determine how much it contributes to the company's performance, business growth, and profitability improvement. Specifically, it can perform regression analysis based on historical data, or use machine learning algorithms and neural networks for predictive analysis. Step 3: Based on the predictive analysis results obtained by the analysis department, the proposal department evaluates the timing and scale of investments, as well as the balance of risk and return, and proposes the optimal IT investment strategy. Specifically, it can propose investment strategies that yield the greatest return by investing a specific amount at a specific time, or the optimal timing and amount of investment.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data on the company's IT infrastructure, software, and hardware using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs predictive analysis based on the collected data to determine how much it contributes to the company's performance, business growth, and profitability improvement. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes an optimal IT investment strategy based on the results of the predictive analysis. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data on the company's IT infrastructure, software, and hardware using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs predictive analysis based on the collected data to determine how much it contributes to the company's performance, business growth, and profitability improvement. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes an optimal IT investment strategy based on the results of the predictive analysis. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

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

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data on the company's IT infrastructure, software, and hardware using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs predictive analysis based on the collected data to determine how much it contributes to the company's performance, business growth, and profitability improvement. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes an optimal IT investment strategy based on the results of the predictive analysis. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

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

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit collects data on a company's IT infrastructure, software, and hardware using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs predictive analysis based on the collected data to determine how much it contributes to the company's performance, business growth, and profitability improvement. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes an optimal IT investment strategy based on the results of the predictive analysis. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) The data collection department collects data on companies' IT investments, The analysis unit analyzes the data collected by the aforementioned collection unit and predicts how much it contributes to the company's performance, business growth, and profitability improvement. Based on the results of predictive analysis obtained by the aforementioned analysis unit, the system includes a proposal unit that evaluates the timing and scale of investments, the balance of risk and return, and proposes the optimal IT investment strategy. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data on the purchasing and adoption status of IT infrastructure, software, hardware, and resources. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Predictive analytics are performed using regression analysis and machine learning algorithms based on historical data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, This proposal suggests an investment strategy that maximizes returns by investing a specific amount of money at a specific time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It includes a filtering unit for filtering the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, It includes a visualization unit that visualizes the results of predictive analysis. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze a company's past investment history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the company's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the geographical location of companies. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, we analyze the company's social media activities and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the investment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the investment category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting a proposal, the priority of proposals will be determined based on the timing of the investment submission. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the investments. The system described in Appendix 1, characterized by the features described herein. (Note 25) The filtering unit is It estimates the user's sentiment and adjusts the filtering criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The filtering unit is When filtering, consider the interrelationships between data to improve the accuracy of the filtering process. The system described in Appendix 1, characterized by the features described herein. (Note 27) The filtering unit is It estimates the user's sentiment and adjusts the order in which filtering results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The filtering unit is When filtering, consider the geographical distribution of the data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned visualization unit, It estimates the user's emotions and adjusts the display method of the visualization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned visualization unit, When visualizing data, we refer to past data to predict current data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned visualization unit, It estimates the user's emotions and adjusts the importance of visualizations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned visualization unit, When visualizing data, analyze changes in visualization based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects data on companies' IT investments, The analysis unit analyzes the data collected by the aforementioned collection unit and predicts how much it contributes to the company's performance, business growth, and profitability improvement. Based on the results of predictive analysis obtained by the aforementioned analysis unit, the system includes a proposal unit that evaluates the timing and scale of investments, the balance of risk and return, and proposes the optimal IT investment strategy. A system characterized by the following features.

2. The aforementioned collection unit is We collect data on the purchasing and adoption status of IT infrastructure, software, hardware, and resources. The system according to feature 1.

3. The aforementioned analysis unit, Predictive analytics are performed using regression analysis and machine learning algorithms based on historical data. The system according to feature 1.

4. The aforementioned proposal section is, This proposal suggests an investment strategy that maximizes returns by investing a specific amount of money at a specific time. The system according to feature 1.

5. The aforementioned collection unit is It includes a filtering unit for filtering the collected data. The system according to feature 1.

6. The aforementioned analysis unit, It includes a visualization unit that visualizes the results of predictive analysis. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze a company's past investment history and select the optimal data collection method. The system according to feature 1.

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