Resource input destination selection support device, social impact bond support device, resource input destination selection support method, social impact bond support method, resource input destination selection support program, social impact bond support program, and recording medium

Abstract

This disclosure aims to provide a resource input destination selection support device that can assist in selecting the destination for resource input. [Solution] The resource input destination selection support device of the present disclosure includes an information acquisition unit, a classification unit, and a selection unit, wherein the information acquisition unit acquires future prediction information for each component constituting a group, and the future prediction information includes event occurrence probability information, which is the probability that a specific event will occur in the component in the future, the classification unit classifies the components in the group into a plurality of groups based on the event occurrence probability information, and the selection unit selects an input destination group from the plurality of groups to be used as the resource input destination according to predetermined criteria.

Description

Technical Field

[0004] , , , , , , ,

[0001] The present disclosure relates to a resource investment destination selection support device, a social impact bond support device, a resource investment destination selection support method, a social impact bond support method, a resource investment destination selection support program, a social impact bond support program, and a recording medium.

Background Art

[0002] How to allocate limited resources to achieve results is an issue that is constantly debated among individuals, companies, administrative agencies, etc. Patent Document 1 discloses an investment support system that realizes flexible investment according to the monthly income and expenditure amount of a user. The investment support system includes a household ledger information acquisition unit that acquires household ledger information indicating the situation of the user's household economy, including monthly income and expenditure information regarding monthly income and expenditure for at least one month, from an external first information processing system used by the user, a setting information acquisition unit that acquires setting information regarding the user's asset management policy from the first information processing system, and a monthly investment amount generation unit that generates monthly recommended investment amount information indicating the investment amount for a predetermined month to be proposed to the user based on the household ledger information and the setting information. <0000​​​​​​​​​​​​​​​​​​​​​The effective use of limited resources is particularly important in the use of administrative resources by national and local governments. For example, Japan has a more advanced aging population than other countries, and the resulting soaring medical and nursing care costs are a problem, making the search for solutions an urgent issue. On the other hand, Social Impact Bonds (SIBs) are a method for reducing administrative resources. SIBs are a mechanism in which the costs of addressing administrative problems are covered by private investment, and profits (returns) are returned to the investors in proportion to the administrative resources (costs) that are reduced. SIBs can reduce administrative resources, and in the UK, for example, administrative costs have been reduced by lowering the recidivism rate of prisoners. However, there is a problem in that it is unclear which areas of administration would benefit most from applying SIBs. This problem is not limited to administrative resources, but is also a problem for private organizations such as companies, and international organizations such as the United Nations.

[0005] Therefore, this disclosure aims to provide a resource input destination selection support device, a resource input destination selection support method, a resource input destination selection support program, and a recording medium that can assist in selecting a destination for resource input. [Means for solving the problem]

[0006] To achieve the aforementioned objective, the resource input destination selection support device of this disclosure is: It includes an information acquisition unit, a classification unit, and a selection unit, The information acquisition unit acquires future prediction information for each component that makes up the group. The aforementioned future prediction information includes, in the aforementioned components, event occurrence probability information, which is the probability that a specific event will occur in the future. The classification unit classifies the constituent elements in the group into multiple groups based on the event occurrence probability information. The selection unit selects an input group from among the plurality of groups to be used as the input group for resources, according to predetermined criteria. It is a device.

[0007] The resource allocation selection support method described in this disclosure is: This includes an information acquisition process, a classification process, and a selection process. The aforementioned information acquisition process acquires future prediction information for each component that makes up the group. The aforementioned future prediction information includes, in the aforementioned components, event occurrence probability information, which is the probability that a specific event will occur in the future. The classification step involves classifying the constituent elements of the population into a plurality of groups based on the event occurrence probability information. The selection step involves selecting an input group from among the multiple groups to be used as the input destination for resources, according to predetermined criteria. This method involves each of the aforementioned steps being performed by a computer.

[0008] The resource input selection support program described in this disclosure is This includes information acquisition procedures, classification procedures, and selection procedures. The aforementioned information acquisition procedure acquires future prediction information for each component that makes up the group, The aforementioned future prediction information includes, in the aforementioned components, event occurrence probability information, which is the probability that a specific event will occur in the future. The classification procedure involves classifying the constituent elements into multiple groups based on the event occurrence probability information within the population. The selection procedure involves selecting a destination group from among the multiple groups to which resources will be invested, according to predetermined criteria. This is a program that causes a computer to execute each of the aforementioned steps.

[0009] The recording medium disclosed herein is This includes information acquisition procedures, classification procedures, and selection procedures. The aforementioned information acquisition procedure acquires future prediction information for each component that makes up the group, The aforementioned future prediction information includes, in the aforementioned components, event occurrence probability information, which is the probability that a specific event will occur in the future. The classification procedure involves classifying the constituent elements into multiple groups based on the event occurrence probability information within the population. The selection procedure selects a destination group for resource input from among the plurality of groups according to a pre-specified criterion. A computer-readable recording medium recording a program for causing a computer to execute each of the procedures.

Advantages of the Invention

[0010] According to the present disclosure, it is possible to assist in the selection of a resource input destination. For example, if the present disclosure is applied to SIB, it is possible to select a destination for administrative resources where reduction of administrative resources can be achieved by applying SIB.

Brief Description of the Drawings

[0011] [Figure 1] FIG. 1 is a block diagram showing an example of the configuration of a resource input destination selection support device of the present disclosure. [Figure 2] FIG. 2 is a block diagram showing an example of the hardware configuration of a resource input destination selection support device of the present disclosure. [Figure 3] FIG. 3 is a flowchart showing an example of processing in a resource input destination selection support device of the present disclosure. [Figure 4] FIG. 4 is a block diagram showing another example of the configuration of a resource input destination selection support device of the present disclosure. [Figure 5] FIG. 5 is a block diagram showing another example of the hardware configuration of a resource input destination selection support device of the present disclosure. [Figure 6] FIG. 6 is a flowchart showing another example of processing in a resource input destination selection support device of the present disclosure. [Figure 7] FIG. 7 is a block diagram showing another example of the configuration of a resource input destination selection support device of the present disclosure. [Figure 8] FIG. 8 is a block diagram showing another example of the hardware configuration of a resource input destination selection support device of the present disclosure. [Figure 9] FIG. 9 is a flowchart showing another example of processing in a resource input destination selection support device of the present disclosure [Figure 10]FIG. 10 is an explanatory diagram showing an example of the mechanism of the SIB. [Figure 11] FIG. 11 is an explanatory diagram showing an example of reduction of administrative costs (resources) by the SIB. [Figure 12] FIG. 12 is an explanatory diagram showing the relationship between the prediction of disease onset and preventive measures. [Figure 13] FIG. 13 is a graph showing the relationship between the prediction of disease onset and the actual onset of the disease.

MODE FOR CARRYING OUT THE INVENTION

[0012] In the present disclosure, "resources" are not particularly limited, and for example, it means material resources, financial resources, human resources, etc. required for a certain activity. Further, in the present disclosure, "resources" include, for example, "administrative resources" required for administrative activities, and "group activity resources" required for activities of groups such as companies. Examples of the group include companies, public institutions (schools, hospitals, facilities, etc.), international institutions (subordinate institutions such as the United Nations), religious groups, political groups, etc. In the present invention, "group" is not particularly limited, and for example, there are groups of organisms including humans, groups of things such as industrial products and agricultural products, prefectures composed of municipalities, etc.

[0013] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. The present disclosure is not limited to the following embodiments. In the following drawings, the same parts are denoted by the same reference numerals. Further, the descriptions of the respective embodiments can be mutually referred to unless otherwise specified, and the configurations of the respective embodiments can be combined unless otherwise specified.

[0014] [Embodiment 1] FIG. 1 is a block diagram showing a configuration example of a resource input destination selection support device 10 (hereinafter also referred to as "this device") according to the present embodiment. As shown in FIG. 1, the device 10 includes an information acquisition unit 11, a classification unit 12, and a selection unit 13. Further, although not shown in the figure, the device 10 may include, for example, an input unit, an output unit, a display unit, and / or a storage unit.

[0015] The device 10 may be, for example, a single device including the aforementioned parts, or it may be a device in which each of the aforementioned parts can be connected via a communication network. Furthermore, the device 10 can be connected to an external device described later via the communication network. The communication network is not particularly limited and can use a known network, for example, it may be wired or wireless. Examples of the communication network include the Internet, WWW (World Wide Web), telephone lines, LAN (Local Area Network), SAN (Storage Area Network), DTN (Delay Tolerant Networking), LPWA (Low Power Wide Area), L5G (Local 5G), etc. Examples of wireless communication include Wi-Fi (registered trademark), Bluetooth (registered trademark), Local 5G, LPWA, etc. The wireless communication may be in the form of direct communication between devices (Ad Hoc communication), infrastructure communication, indirect communication via an access point, etc. The device 10 may be, for example, incorporated into a server as a system. Furthermore, the device 10 may be, for example, a personal computer (PC, e.g., desktop or notebook), smartphone, tablet terminal, etc., on which the program of this disclosure is installed. The device 10 may also be in the form of cloud computing or edge computing, for example, in which at least one of the aforementioned parts is on a server and the other aforementioned parts are on a terminal.

[0016] Figure 2 illustrates a block diagram of the hardware configuration of the device 10. The device 10 includes, for example, a central processing unit (CPU, GPU, etc.) 101, memory 102, bus 103, storage device 104, input device 105, output device 106, communication device 107, etc. Each part of the device 10 is interconnected via the bus 103 through its respective interface (I / F).

[0017] The central processing unit 101 operates in coordination with other components via controllers (system controller, I / O controller, etc.) and is responsible for the overall control of the device 10. In the device 10, the central processing unit 101 executes, for example, the program disclosed herein and other programs, and also reads and writes various types of information. Specifically, for example, the central processing unit 101 functions as an information acquisition unit 11, a classification unit 12, and a selection unit 13. The device 10 may also include other computing devices such as a CPU, GPU (Graphics Processing Unit), APU (Accelerated Processing Unit), or a combination thereof as computing devices.

[0018] Bus 103 can also be connected to external devices, for example. Examples of such external devices include a user terminal, an external storage device (such as an external database), a printer, an external input device, an external display device, and an external imaging device. The device 10 can be connected to an external network (the aforementioned communication network) via a communication device 107 connected to bus 103, for example, and can also be connected to other devices via the external network.

[0019] Memory 102 may be, for example, main memory. When the central processing unit 101 performs processing, memory 102 reads various operational programs, such as the program of this disclosure, stored in the storage device 104 (described later), and the central processing unit 101 receives data from memory 102 and executes the program. The main memory may be, for example, RAM (random access memory). Alternatively, memory 102 may be, for example, ROM (read-only memory).

[0020] The storage device 104 is also called a so-called auxiliary storage device, for example, in relation to the main memory (primary memory). As described above, the storage device 104 stores an operating program including the program of this disclosure. The storage device 104 may be, for example, a combination of a recording medium and a drive for reading and writing to the recording medium. The recording medium is not particularly limited and may be internal or external, for example, an HD (hard disk), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, memory card, etc. The storage device 104 may be, for example, a hard disk drive (HDD) in which the recording medium and the drive are integrated, or a solid state drive (SSD).

[0021] In this device 10, the memory 102 and the storage device 104 can also store various types of information, such as log information, information obtained from an external database (not shown) or external devices, information generated by this device 10, and information used by this device 10 when executing processing. At least some of the information may be stored on an external server other than the memory 102 and the storage device 104, or it may be stored in a distributed manner across multiple terminals using blockchain technology or the like. Furthermore, if this device 10 includes the storage unit, for example, the memory 102 and the storage device 104 function as the storage unit in this device 10.

[0022] The device 10 further includes, for example, an input device 105 and an output device 106. The input device 105 may include, for example, a pointing device such as a touch panel, trackpad, or mouse; a keyboard; imaging means such as a camera or scanner; a card reader such as an IC card reader or magnetic card reader; an audio input means such as a microphone; and so on. The output device 106 may include, for example, a display device such as an LED display or liquid crystal display; an audio output device such as a speaker; a printer; and so on. In this embodiment 1, the input device 105 and the output device 106 are configured separately, but the input device 105 and the output device 106 may be configured as an integrated unit, such as a touch panel display.

[0023] Next, an example of the resource input destination selection support method of this embodiment will be described based on the flowchart in Figure 3. The resource input destination selection support method of this embodiment is implemented as follows, for example, using the device 10 in Figure 1 or Figure 2. Note that the resource input destination selection support method of this embodiment is not limited to the use of the device 10 in Figure 1 or Figure 2.

[0024] The information acquisition unit 11 acquires future prediction information for each component constituting the group (S1). The future prediction information includes event occurrence probability information, which is the probability that a specific event will occur in the component in the future. The classification unit 12 classifies the components in the group into a plurality of groups based on the event occurrence probability information (S2). The selection unit 13 selects an input group from the plurality of groups to which resources will be input, according to predetermined criteria (S3). Information regarding the input group may be output to a display, printer, etc.

[0025] In the information acquisition unit 11, the acquisition of the future prediction information may be done by input using an input device, or by acquiring it via a communication network such as the Internet. The event occurrence probability information is not particularly limited and may include, for example, the future recidivism probability of an inmate, the future onset probability of a disease, the future maturation probability of a plant, or the future deterioration probability of a product. The event occurrence probability may be predicted by a system other than this device, or it may be predicted by this device. The prediction of the event occurrence probability may be done, for example, by collecting data statistically, or by using a trained model generated by machine learning.

[0026] In the classification unit 12, the classification into multiple groups may, for example, divide the probability of occurrence from 0 to 100% into four groups of 25% increments: greater than 0% and less than or equal to 25%, greater than 25% and less than or equal to 50%, greater than 50% and less than or equal to 75%, and greater than 75% and less than or equal to 100%. The number of groups to be classified is not particularly limited, as long as there are multiple groups (two or more), for example, there may be 2 to 10 groups. In addition, some groups may have zero (0) constituent elements. For example, the group with a probability of greater than 0% and less than or equal to 25% may have zero (0) constituent elements.

[0027] In the selection unit 13, the "pre-defined criteria" include, for example, the group with the highest probability among multiple groups, the group with the lowest probability among multiple groups, the group with the first and second highest (or lowest) probabilities among multiple groups, or the group that excludes the group with the highest probability and the group with the lowest probability among multiple groups.

[0028] [Embodiment 2] Next, an example of the device 10A including a prediction unit will be described. Figures 4 to 6 are a block diagram, a diagram showing the hardware configuration, and a flowchart illustrating an example of this embodiment, and are the same as the device of Embodiment 1 except that they include the prediction unit 14.

[0029] In this embodiment, the criterion of the selection unit 13 is to select the group with the highest probability of the specific event occurring, and the input group is the group with the highest probability of the specific event occurring. Furthermore, in the information acquisition unit 11, the future prediction information includes resource information relating to the resources and information on the reduction of the probability of the specific event occurring, and the information on the reduction of the probability of the specific event occurring is information on how the probability of the specific event occurring decreases when specific measures are taken to prevent the occurrence of the specific event for the components of the group with the highest probability of the event occurring.The prediction unit 14 then predicts the resource reduction information that can be reduced in preventing the occurrence of the specific event, based on the difference between the resources required when the specific event occurs and the resources required when the specific event does not occur in the group with the highest probability of the event occurring (S4).With the device of this embodiment, since the resource reduction information can be predicted, the resource input destination can be selected objectively.

[0030] [Embodiment 3] Next, an example of the device 10B including a calculation unit will be described. Figures 7 to 9 are a block diagram, a diagram showing the hardware configuration, and a flowchart illustrating an example of this embodiment, and are the same as the device of Embodiment 2 except that they include the calculation unit 15.

[0031] In this embodiment of the device, the future prediction information includes preventive cost information required for specific actions to prevent the occurrence of the specific event in the prediction unit, and the calculation unit 15 calculates investment return information based on the preventive cost information and the resource reduction information in the prediction unit (S5). With this configuration, investment return information is obtained, allowing for a more objective selection of resource investment destinations.

[0032] [Embodiment 4] Next, the SIB support device of the present invention will be described. In the device of this embodiment, "resources" are "administrative resources," "input target group" are SIB target group, and "reduction resource information" are "reduction administrative resource information." Other than these, it is the same as in Embodiment 1, Embodiment 2, or Embodiment 3.

[0033] Figure 10 shows a general scheme for Social Impact Bonds (SIBs). The source of this figure is the Ministry of Economy, Trade and Industry's website. As shown in Figure 10, the main parties to an SIB are funders, SIB operating organizations, service providers, beneficiaries, administrative organizations such as local governments, and evaluation organizations. Funders include, for example, institutional investors, financial institutions, foundations, and individual investors (investments or donations). Funders provide funds to the SIB operating organization. The SIB operating organization provides operating funds to service providers in administrative areas where a reduction in administrative resources is expected. Service providers provide services to beneficiaries on behalf of administrative agencies. Evaluation organizations evaluate the causal relationship between service provision and the reduction of social costs (administrative resources) and provide feedback on the evaluation results to the administrative organizations. Based on the evaluation results, administrative organizations pay the SIB operating organization the reduction in social costs (administrative resources) as performance-based compensation. The SIB operating organization pays a portion of the performance-based compensation to fund investors as dividends (investment returns).

[0034] Figure 11 shows an image of how Social Impact Bonds (SIBs) can reduce administrative costs. The source of Figure 11 is the Ministry of Economy, Trade and Industry's website. As shown in Figure 11, current administrative costs (administrative resources) are set at 100. When implementing an SIB, the operating costs required by the SIB operating organization or the non-profit organization (NPO) providing the services are set at 30, and the investor return is set at 5, so the cost of implementing an SIB is 35. Furthermore, even if an SIB is implemented, administrative costs (administrative resources) are still set at 50. As a result, as shown in Figure 11, the reduction in administrative costs (administrative resources) is 15.

[0035] As explained with reference to Figures 10 and 11, while the introduction of Social Impact Bonds (SIBs) can be expected to reduce administrative resources, the SIB support device of the present invention is useful in increasing the certainty of such reductions.

[0036] Next, examples of applying the SIB support device of this embodiment to the medical and nursing care fields will be described based on Figures 12 and 13.

[0037] Figure 12 shows the relationship between the testing cycle and the lifestyle improvement cycle proposed and provided by FonesLife.

[0038] As shown in Figure 12, in the examination cycle, for example, examinations are conducted using Forness Visual (FV), provided by the company, in municipal health checkups. The subjects of the examination are, for example, "dementia" and "stroke," which consume administrative resources in the medical and nursing care fields. Next, medical institutions evaluate the examination results, and local governments determine support policies. The examination results and support policies are explained to the subjects, who are asked to review their lifestyle habits and undergo examinations again after a certain period of time.

[0039] On the other hand, during the explanation of the test results, for high-risk individuals (those belonging to the group with the highest probability of developing either dementia or stroke) who are at least one of the two, consent to participate in a lifestyle improvement program will be confirmed in order to implement external interventions for lifestyle improvement. If consent is confirmed, the lifestyle improvement cycle will be implemented. First, a concierge will plan and create the lifestyle improvement program. Participants will then implement the program with the support of local government facilities and cooperating businesses. In addition, support (continuous follow-up) will be provided using human resources or IT to prevent dropouts and ensure that the program is continued. In the lifestyle improvement program, the implementation status will be checked and the need for changes to better menus or examinations will be evaluated, and the menu will be improved as necessary. In the lifestyle improvement cycle, examinations will be conducted in the examination cycle for regular risk checks.

[0040] Next, Figure 13 shows an example of classifying the subject population into 10 groups based on the probability of disease onset. As shown in Figure 13, the risk of developing myocardial infarction and cerebral infarction was examined using FonesLifes' diagnostic technology (protein diagnostic technology), and subjects were classified into groups 1 to 10 according to their risk of onset. As a result, in group 10, which had the highest risk of onset, the recurrence risk after 2 years was 45%, and the recurrence risk after 4 years was 70%, meaning that 70% of subjects were predicted to develop the disease in the future. On the other hand, in group 1, which had the lowest risk of onset, the survival rates after 2 years and 4 years were 99%.

[0041] Next, we will show an example where, using FonesLifes' diagnostic technology to predict the risk of developing a disease, the group with the highest risk was informed that they were at high risk of developing the disease in the future, resulting in a reduction in their actual risk of developing the disease.

[0042] The aforementioned disease onset risk prediction is performed using a disease onset risk prediction model that can predict the disease onset risk based on the amounts of multiple proteins. Specifically, the amounts of the multiple proteins are measured using SomaScan®, a measurement technology developed by FonesLifes. SomaScan® is a measurement technology that can simultaneously measure the amount of proteins corresponding to approximately 7,000 types of SOMAmer®, which are slow-off-rate modified nucleic acid aptamers that have the property of making it difficult for the bound target molecule to dissociate. The measurement process of SomaScan® is described below. The disease onset risk prediction model is a model that outputs the onset risk for a specific disease when the amounts of multiple proteins measured by SomaScan® are input. Furthermore, the disease onset risk prediction model is a model generated by machine learning using the amounts of multiple proteins measured by SomaScan® and clinical data related to various diseases. The application of SomaScan®, when combined with a biobank that stores samples used in large-scale cohort studies, can generate disease-specific predictive models for the risk of developing various diseases, such as cerebrovascular disease (CVD), lung cancer, and dementia. The disease-onset risk predictive models generated as described above can predict the risk of developing various diseases, such as CVD, lung cancer, and dementia, based on the amounts of multiple proteins corresponding to the disease.

[0043] The measurement process of SomaScan® is as follows. Specifically, a solution containing each SOMAmer® is mixed with human plasma at a predetermined mixing ratio, and then a complex of aptamers and proteins is formed by the binding of each SOMAmer® to its corresponding protein. After mixing the complex with avidin-immobilized beads at a predetermined mixing ratio, the complex is captured on the avidin-immobilized beads by the binding of biotin molecules, which have been pre-labeled on the aptamers in the complex, to avidin molecules immobilized on the beads. After removing components other than the complex by a washing operation, a biotinylated complex is formed by labeling the proteins contained in the complex with biotin molecules. After mixing the biotinylated complex with other avidin-immobilized beads at a predetermined mixing ratio, the biotinylated complex is captured on the other avidin-immobilized beads by the binding of biotin molecules, which have been labeled on the proteins in the complex, to avidin molecules immobilized on the beads. After dissociating only the aptamer from the biotinylated complex using a protein denaturation buffer, the aptamer is hybridized onto a microarray on which a probe having a base sequence complementary to the aptamer is immobilized. The fluorescence intensity of a fluorescent molecule pre-labeled to the hybridized aptamer is measured using a fluorescence plate reader. The amount of protein corresponding to the hybridized aptamer is calculated based on a calibration curve showing the correspondence between the fluorescence intensity and the amounts of various proteins. Through the above measurement process, SomaScan® can simultaneously measure the amounts of multiple proteins contained in human plasma.

[0044] (CVD-1) Data analysis of 58 individuals aged 22 to 79 (age at the time of examination) who underwent examinations two or more times between December 2020 and March 2023, using a test item (CVD1) that indicates the risk of the first onset of cardiovascular disease within four years, such as myocardial infarction and stroke, showed a reduction in disease risk in half of the participants, as shown in Table 1 below.

[0045] [Table 1]

[0046] (CVD-2) Further investigations were conducted on patients different from those in the aforementioned (CVD-1) study. Data analysis was performed on 143 patients who had undergone two or more examinations for a test item indicating the risk of initial onset of cardiovascular disease within four years (CVD1 or CVD2), such as myocardial infarction and stroke. As shown in Table 2 below, a reduction in disease risk was confirmed in approximately 60% of these patients. In Table 2, the average age of those who had undergone two or more examinations was 52.0 years, while the average age of those who showed improvement was 51.9 years.

[0047] [Table 2]

[0048] (Lung cancer - 1) Data analysis of 34 individuals aged 22 to 79 (age at the time of examination) who underwent examinations two or more times between December 2020 and March 2023, focusing on examination items indicating the risk of developing lung cancer within five years, revealed a reduction in disease risk in approximately 47% of these individuals, as shown in Table 3 below.

[0049] [Table 3]

[0050] (Lung cancer - 2) Further investigations were conducted on patients different from those described above (Lung Cancer-1). Data analysis was performed on 143 individuals who underwent two or more examinations for tests indicating the risk of developing lung cancer within five years. As shown in Table 2 below, a reduction in disease risk was observed in approximately 44% of these individuals. In Table 4 below, the average age of those who underwent two or more examinations was 52.0 years, while the average age of those who showed improvement was 50.7 years.

[0051] [Table 4]

[0052] (Dementia) Data analysis of 143 individuals who underwent two or more examinations for a test item indicating the risk of developing dementia within 20 years revealed a reduction in disease risk in approximately 55% of participants, as shown in Table 3 below. In Table 5 below, the average age of those who underwent two or more examinations was 52.0 years, while the average age of those who showed improvement was 51.9 years.

[0053] [Table 5]

[0054] As the results for CVD, lung cancer, and dementia show, it was confirmed that informing the group with the highest risk (probability) of developing these diseases about their risk level reduced their future risk of developing the disease.

[0055] [Embodiment 5] The program of this embodiment is a program that causes a computer to execute each step of the support method described above.

[0056] The program of this embodiment can be based on the descriptions in the support devices and support methods of the present disclosure. Each of the above steps can be read as, for example, a "step" instead of a "process". The program of this embodiment may also be recorded on, for example, a computer-readable storage medium. The storage medium is not particularly limited and includes, for example, random access memory (RAM), dedicated memory (ROM), hard disk (HD), flash memory (e.g., SSD (Solid State Drive), USB flash memory, SD / SDHC card, etc.), optical disc (e.g., CD-R / CD-RW, DVD-R / DVD-RW, BD-R / BD-RE, etc.), magneto-optical disk (MO), floppy disk (FD), etc. Furthermore, the manufacturing work management support program of this embodiment (also known as a programming product or an organizational philosophy dissemination support program product) may be delivered, for example, from an external computer. The "delivery" may be, for example, delivered via a communication network or delivered via a wired device. The manufacturing work management support program of this embodiment may be installed and executed on the delivered device, or it may be executed without being installed.

[0057] Although the present disclosure has been described above with reference to embodiments, the present disclosure is not limited to the embodiments described above. Various modifications to the structure and details of the present disclosure are possible, as can be understood by those skilled in the art within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0058] <Note> Some or all of the above embodiments may be described as follows, but are not limited to the following: (Note 1) It includes an information acquisition unit, a classification unit, and a selection unit, The information acquisition unit acquires future prediction information for each component that makes up the group. The aforementioned future prediction information includes, in the aforementioned components, event occurrence probability information, which is the probability that a specific event will occur in the future. The classification unit classifies the constituent elements in the group into multiple groups based on the event occurrence probability information. The selection unit selects an input group from among the plurality of groups to be used as the input group for resources, according to predetermined criteria. Resource input destination selection support device. (Note 2) In the selection unit, The aforementioned criterion is to select the group with the highest probability of the occurrence of the specific event. The aforementioned input group is the group with the highest probability of the specific event occurring, In the aforementioned information acquisition unit, The aforementioned future prediction information includes resource information relating to the resource, and information on the decrease in the probability of a specific event occurring. The aforementioned information on the reduction in the probability of a specific event occurring is information that indicates the probability of a specific event occurring decreases when a specific action is taken to prevent the occurrence of the specific event with respect to the component of the group with the highest probability of event occurrence. Furthermore, including a prediction unit, The prediction unit predicts resource reduction information that can be reduced in preventing the occurrence of the specific event, based on the difference between the resources required when the specific event occurs and the resources required when the specific event does not occur, in the group with the highest probability of event occurrence. Resource input destination selection support device as described in Appendix 1. (Note 3) In the aforementioned information acquisition unit, The aforementioned future prediction information includes information on the preventive costs required for specific actions to prevent the occurrence of the specific event in the prediction unit. Furthermore, including the calculation unit, The calculation unit calculates investment return information based on the prevention cost information and the reduction resource information of the prediction unit. Resource input destination selection support device as described in Appendix 2. (Note 4) Includes a resource input destination selection support device as described in any of Appendix 1 to 3, The aforementioned resources are administrative resources, The aforementioned investment recipient group is a group eligible for Social Impact Bonds (SIBs). SIB support equipment. (Note 5) Includes the resource input destination selection support device described in Appendix 2, The aforementioned resources are administrative resources, The aforementioned investment recipient group is a Social Impact Bond (SIB) target group, The aforementioned resource reduction information is information on reduced administrative resources. SIB support equipment. (Note 6) Includes the resource input destination selection support device described in Appendix 3, The aforementioned resources are administrative resources, The aforementioned investment recipient group is a Social Impact Bond (SIB) target group, The aforementioned resource reduction information is information on reduced administrative resources. SIB support equipment. (Note 7) The aforementioned specific event is the onset event of a specific disease, The aforementioned administrative resources are administrative resources of at least one of medical care and long-term care. SIB support device as described in any of the appendices 4 to 6. (Note 8) This includes an information acquisition process, a classification process, and a selection process. The aforementioned information acquisition process acquires future prediction information for each component that makes up the group. The aforementioned future prediction information includes, in the aforementioned components, event occurrence probability information, which is the probability that a specific event will occur in the future. The classification step involves classifying the constituent elements of the population into a plurality of groups based on the event occurrence probability information. The selection step involves selecting an input group from among the multiple groups to be used as the input destination for resources, according to predetermined criteria. A method for supporting the selection of resource input destinations, in which each of the above steps is performed by a computer. (Note 9) In the selection process, The aforementioned criterion is to select the group with the highest probability of the occurrence of the specific event. The aforementioned input group is the group with the highest probability of the specific event occurring, In the aforementioned information acquisition process, The aforementioned future prediction information includes resource information relating to the resource, and information on the decrease in the probability of a specific event occurring. The aforementioned information on the reduction in the probability of a specific event occurring is information that indicates the probability of a specific event occurring decreases when a specific action is taken to prevent the occurrence of the specific event with respect to the component of the group with the highest probability of event occurrence. Furthermore, including the prediction process, The prediction step predicts, in the group with the highest probability of event occurrence, information on resources that can be reduced in preventing the occurrence of the specific event, based on the difference between the resources required when the specific event occurs and the resources required when the specific event does not occur. The method for supporting the selection of resource input destinations as described in Appendix 8. (Note 10) In the aforementioned information acquisition process, The aforementioned future prediction information includes information on the preventive costs required for specific actions to prevent the occurrence of the specific event in the prediction process. Furthermore, including the calculation process, The calculation step calculates investment return information based on the prevention cost information and the resource reduction information from the prediction step. The method for supporting the selection of resource input destinations as described in Appendix 9. (Note 11) Includes a resource input destination selection support device that performs the resource input destination selection support method described in any of the appendices 8 to 10, The aforementioned resources are administrative resources, The aforementioned investment recipient group is a group eligible for Social Impact Bonds (SIBs). SIB support method. (Note 12) Includes a resource input destination selection support device that performs the resource input destination selection support method described in Appendix 9, The aforementioned resources are administrative resources, The aforementioned investment recipient group is a Social Impact Bond (SIB) target group, The aforementioned resource reduction information is information on reduced administrative resources. SIB support method. (Note 13) Includes a resource input destination selection support device that performs the resource input destination selection support method described in Appendix 10, The aforementioned resources are administrative resources, The aforementioned investment recipient group is a Social Impact Bond (SIB) target group, The aforementioned resource reduction information is information on reduced administrative resources. SIB support method. (Note 14) The aforementioned specific event is the onset event of a specific disease, The aforementioned administrative resources are administrative resources of at least one of medical care and long-term care. The SIB support method described in any of the appendices 11 to 13. (Note 15) This includes information acquisition procedures, classification procedures, and selection procedures. The aforementioned information acquisition procedure acquires future prediction information for each component that makes up the group, The aforementioned future prediction information includes, in the aforementioned components, event occurrence probability information, which is the probability that a specific event will occur in the future. The classification procedure involves classifying the constituent elements into multiple groups based on the event occurrence probability information within the population. The selection procedure involves selecting a destination group from among the multiple groups to which resources will be invested, according to predetermined criteria. A resource allocation selection support program that causes a computer to execute each of the above steps. (Note 16) In the above selection procedure, The aforementioned criterion is to select the group with the highest probability of the occurrence of the specific event. The aforementioned input group is the group with the highest probability of the specific event occurring, In the aforementioned information acquisition procedure, The aforementioned future prediction information includes resource information relating to the resource, and information on the decrease in the probability of a specific event occurring. The aforementioned information on the reduction in the probability of a specific event occurring is information that indicates the probability of a specific event occurring decreases when a specific action is taken to prevent the occurrence of the specific event with respect to the component of the group with the highest probability of event occurrence. Furthermore, including the prediction procedure, The prediction procedure predicts resource reduction information that can be reduced in preventing the occurrence of the specific event, based on the difference between the resources required when the specific event occurs and the resources required when the specific event does not occur, in the group with the highest probability of event occurrence. Resource input destination selection support program as described in Appendix 15. (Note 17) In the aforementioned information acquisition procedure, The aforementioned future prediction information includes information on the preventive costs required for specific actions to prevent the occurrence of the specific event in the prediction procedure. Furthermore, including the calculation procedure, The calculation procedure calculates investment return information based on the prevention cost information and the resource reduction information from the prediction procedure. The resource input destination selection support program described in Appendix 16. (Note 18) Includes a resource input destination selection support device that executes a resource input destination selection support program described in any of the appendices 15 to 17, The aforementioned resources are administrative resources, The aforementioned investment recipient group is a group eligible for Social Impact Bonds (SIBs). SIB support program. (Note 19) Includes a resource input destination selection support device that executes the resource input destination selection support program described in Appendix 16, The aforementioned resources are administrative resources, The aforementioned investment recipient group is a Social Impact Bond (SIB) target group, The aforementioned resource reduction information is information on reduced administrative resources. SIB support program. (Note 20) Includes a resource input destination selection support device that executes the resource input destination selection support program described in Appendix 17, The aforementioned resources are administrative resources, The aforementioned investment recipient group is a Social Impact Bond (SIB) target group, The aforementioned resource reduction information is information on reduced administrative resources. SIB support program. (Note 21) The aforementioned specific event is the onset event of a specific disease, The aforementioned administrative resources are administrative resources of at least one of medical care and long-term care. An SIB support program as described in any of the appendices 18 to 20. (Note 22) This includes information acquisition procedures, classification procedures, and selection procedures. The aforementioned information acquisition procedure acquires future prediction information for each component that makes up the group, The aforementioned future prediction information includes, in the aforementioned components, event occurrence probability information, which is the probability that a specific event will occur in the future. The classification procedure involves classifying the constituent elements into multiple groups based on the event occurrence probability information within the population. The selection procedure involves selecting a destination group from among the multiple groups to which resources will be invested, according to predetermined criteria. A computer-readable recording medium containing a program that causes a computer to perform each of the aforementioned steps. (Note 23) In the above selection procedure, The aforementioned criterion is to select the group with the highest probability of the occurrence of the specific event. The aforementioned input group is the group with the highest probability of the specific event occurring, In the aforementioned information acquisition procedure, The aforementioned future prediction information includes resource information relating to the resource, and information on the decrease in the probability of a specific event occurring. The aforementioned information on the reduction in the probability of a specific event occurring is information that indicates the probability of a specific event occurring decreases when a specific action is taken to prevent the occurrence of the specific event with respect to the component of the group with the highest probability of event occurrence. Furthermore, including the prediction procedure, The prediction procedure predicts resource reduction information that can be reduced in preventing the occurrence of the specific event, based on the difference between the resources required when the specific event occurs and the resources required when the specific event does not occur, in the group with the highest probability of event occurrence. Recording medium as described in Appendix 22. (Note 24) In the aforementioned information acquisition procedure, The aforementioned future prediction information includes information on the preventive costs required for specific actions to prevent the occurrence of the specific event in the prediction procedure. Furthermore, including the calculation procedure, The calculation procedure calculates investment return information based on the prevention cost information and the resource reduction information from the prediction procedure. Recording medium as described in Appendix 23. (Note 25) Includes a resource input destination selection support device equipped with a recording medium as described in any of Appendix 22 to 24, The aforementioned resources are administrative resources, The aforementioned investment recipient group is a group eligible for Social Impact Bonds (SIBs). A computer-readable recording medium containing the SIB support program. (Note 26) Includes a resource input destination selection support device equipped with the recording medium described in Appendix 23, The aforementioned resources are administrative resources, The aforementioned investment recipient group is a Social Impact Bond (SIB) target group, The aforementioned resource reduction information is information on reduced administrative resources. A computer-readable recording medium containing the SIB support program. (Note 27) Includes a resource input destination selection support device equipped with the recording medium described in Appendix 24, The aforementioned resources are administrative resources, The aforementioned investment recipient group is a Social Impact Bond (SIB) target group, The aforementioned resource reduction information is information on reduced administrative resources. A computer-readable recording medium containing the SIB support program. (Note 28) The aforementioned specific event is the onset event of a specific disease, The aforementioned administrative resources are administrative resources of at least one of medical care and long-term care. A computer-readable recording medium containing the SIB support program described in any of Appendix 25 to 27. [Industrial applicability]

[0059] This disclosure is useful, for example, in selecting where to allocate administrative resources, and is useful not only for administrative resources but also for a wide range of other areas, including private organizations such as companies and international organizations such as the United Nations. [Explanation of symbols]

[0060] 10, 10A, 10B Resource Input Destination Selection Support Device 11 Information acquisition department 12 Classification section 13 Selection Section 14 Prediction Section 15 Calculation Section 101 Central Processing Unit 102 memory 103 Bus 104 Storage device 105 Input device 106 Output device 107 Communication devices

Claims

1. It includes an information acquisition unit, a classification unit, and a selection unit, The information acquisition unit acquires future prediction information for each component that makes up the group. The aforementioned future prediction information includes, in the aforementioned components, event occurrence probability information, which is the probability that a specific event will occur in the future. The classification unit classifies the constituent elements in the group into multiple groups based on the event occurrence probability information. The selection unit selects an input group from among the plurality of groups to be used as the input group for resources, according to predetermined criteria. Resource input destination selection support device.

2. In the selection unit, The aforementioned criterion is to select the group with the highest probability of the occurrence of the specific event. The aforementioned input group is the group with the highest probability of the specific event occurring, In the aforementioned information acquisition unit, The aforementioned future prediction information includes resource information relating to the resource, and information on the decrease in the probability of a specific event occurring. The aforementioned information on the reduction in the probability of a specific event occurring is information that indicates the probability of a specific event occurring decreases when a specific action is taken to prevent the occurrence of the specific event with respect to the component of the group with the highest probability of event occurrence. Furthermore, including a prediction unit, The prediction unit predicts resource reduction information that can be reduced in preventing the occurrence of the specific event, based on the difference between the resources required when the specific event occurs and the resources required when the specific event does not occur, in the group with the highest probability of event occurrence. The resource input destination selection support device according to claim 1.

3. In the aforementioned information acquisition unit, The aforementioned future prediction information includes information on the preventive costs required for specific actions to prevent the occurrence of the specific event in the prediction unit. Furthermore, including the calculation unit, The calculation unit calculates investment return information based on the prevention cost information and the reduction resource information of the prediction unit. The resource input destination selection support device according to claim 2.

4. Includes the resource input destination selection support device described in claim 1, The aforementioned resources are administrative resources, The aforementioned investment recipient group is a group eligible for Social Impact Bonds (SIBs). SIB support equipment.

5. Includes the resource input destination selection support device described in claim 2, The aforementioned resources are administrative resources, The aforementioned investment recipient group is a Social Impact Bond (SIB) target group, The aforementioned resource reduction information is information on reduced administrative resources. SIB support equipment.

6. Includes the resource input destination selection support device described in claim 3, The aforementioned resources are administrative resources, The aforementioned investment recipient group is a Social Impact Bond (SIB) target group, The aforementioned resource reduction information is information on reduced administrative resources. SIB support equipment.

7. The aforementioned specific event is the onset event of a specific disease, The aforementioned administrative resources are administrative resources of at least one of medical care and long-term care. The SIB support device according to any one of claims 4 to 6.

8. This includes an information acquisition process, a classification process, and a selection process. The aforementioned information acquisition process acquires future prediction information for each component that makes up the group. The aforementioned future prediction information includes, in the aforementioned components, event occurrence probability information, which is the probability that a specific event will occur in the future. The classification step involves classifying the constituent elements of the population into a plurality of groups based on the event occurrence probability information. The selection step involves selecting an input group from among the multiple groups to be used as the input destination for resources, according to predetermined criteria. A method for supporting the selection of resource input destinations, in which each of the above steps is performed by a computer.

9. This includes information acquisition procedures, classification procedures, and selection procedures. The aforementioned information acquisition procedure acquires future prediction information for each component that makes up the group, The aforementioned future prediction information includes, in the aforementioned components, event occurrence probability information, which is the probability that a specific event will occur in the future. The classification procedure involves classifying the constituent elements into multiple groups based on the event occurrence probability information within the population. The selection procedure involves selecting a destination group from among the multiple groups to which resources will be invested, according to predetermined criteria. A resource allocation selection support program that causes a computer to execute each of the above steps.

10. This includes information acquisition procedures, classification procedures, and selection procedures. The aforementioned information acquisition procedure acquires future prediction information for each component that makes up the group, The aforementioned future prediction information includes, in the aforementioned components, event occurrence probability information, which is the probability that a specific event will occur in the future. The classification procedure involves classifying the constituent elements into multiple groups based on the event occurrence probability information within the population. The selection procedure involves selecting a destination group from among the multiple groups to which resources will be invested, according to predetermined criteria. A computer-readable recording medium containing a program that causes a computer to perform each of the aforementioned steps.

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