Information processing device and method

Abstract

To support regions such that both efficient solution of regional issues and improvement of effectiveness in solving regional issues are achieved.SOLUTION: A computer receives input of data representing interest pertaining to a region, performs search evaluation processing, and outputs a result of the processing. The search evaluation processing includes: a first search (identifying one or more regions that satisfy a prescribed condition regarding the interest represented by the input data from either attribute data or case data); a second search (identifying a dataset that satisfies a condition from another of the attribute data or the case data based on a result of the first search or the interest); and search evaluation (evaluating the result of the first search and the result of the second search). The attribute data represents an attribute of the region regarding each of a plurality of regions. The case data represents a case of the region for each of the plurality of regions.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention generally relates to information processing technology, and for example, to information processing technology for supporting a local area. [Background technology]

[0002] One example of a body that supports a region is a local government (a local public organization). A local government may collect information on administrative services provided by other local governments in order to improve administrative services in the region. For example, the technology disclosed in Patent Document 1 is an example of a technology that supports a local government to improve administrative services. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2018 / 225227 Summary of the Invention [Problem to be solved by the invention]

[0004] One of the main tasks of local governments is to solve local issues. Improving administrative services is part of solving local issues. When solving local issues, there are, for example, the following two demands. (Requirement A) To efficiently resolve regional issues. (Requirement B) Improving the effectiveness of resolving regional issues (for example, creating added value for the region, thereby, for example, gaining a competitive advantage over other municipalities).

[0005] Regarding both (Requirement A) and (Requirement B), it is known that local government officials attempt to investigate and utilize the examples of other local governments. The "examples" of a local government typically include at least one of the following sources of law, such as the local government's regulations, policies, programs, projects, and affairs, and national laws and regulations that affect these, or similar sources.

[0006] In order to satisfy (Requirement A), efficient case research is necessary. However, efficient case research is not necessarily easy even with the use of computers. The reason for this is that the number of case research subjects can be large. When performing a cross-sectional search of similar ordinances in many local governments, the number of local government ordinances or their provisions that contain the search term and are returned as search results can be large.

[0007] In order to satisfy (Requirement B), it is possible to consider appropriate use of the candidate cases, and in order to do so, it is useful to closely observe and understand the background of the candidate cases. In the case of a so-called side-by-side comparison of policies between municipalities that ignores influencing factors, even if a case is a success story of another municipality, differences in influencing factors may come into play and it may not necessarily work as well. However, since the background of the case is based on influencing factors specific to the municipality in which the case is located (for example, geopolitical factors), it is not necessarily possible to accurately understand it without consulting a large amount of materials, etc.

[0008] Information on influencing factors contributes to narrowing and prioritizing the scope of case studies, improving the efficiency of formulating guidelines for solving local issues. It also leads to the derivation of accurate problem-solving policies by selecting effective information. On the other hand, referring to the results of case studies also deepens understanding of influencing factors. In other words, information on either the case studies or the influencing factors tends to encourage appropriate selection of information on the other. Therefore, understanding the relationship between case studies and influencing factors leads to the compatibility of requirement A and requirement B.

[0009] The above issues may also exist for entities other than local governments (entities that support local communities). [Means for solving the problem]

[0010] The information processing device receives data representing an interest in a region as input data, performs a search evaluation process, and outputs processing result data representing a result of the search evaluation process. The search evaluation process includes a first search, a second search, and a search evaluation.

[0011] The first search is to identify, from one of the attribute data and the case data, a dataset for each of the one or more regions that satisfies a predetermined condition regarding the interest represented by the input data. The second search is to identify, from the other of the attribute data and the case data, a dataset corresponding to each of the one or more regions represented by the identified one or more datasets.

[0012] The search evaluation is to evaluate one or more datasets identified in the second search for at least a portion of the one or more regions identified in the first search. The attribute data is data including a dataset for each of the multiple regions that represents attributes of the region. The case data is data including a dataset for each of the multiple regions that represents cases of the region. Effect of the Invention

[0013] According to the present invention, it is possible to support a region so as to achieve both efficient resolution of local issues and improved effectiveness of the resolution of the local issues. [Brief description of the drawings]

[0014] [Figure 1] FIG. 1 is a diagram showing an example of the hardware configuration of an information processing apparatus according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a diagram showing an example of the functional configuration of an information processing device. [Diagram 3] FIG. 1 is a diagram showing an example of an overview of processing performed in an information processing device. [Figure 4] 13 is a flowchart of a first search evaluation process. [Diagram 5] FIG. 11 is an explanatory diagram of an example of the concept of attribute search and example search in the first search evaluation process. [Figure 6A] FIG. 11 is a diagram showing an example of a result of an attribute search in the first search evaluation process. [Figure 6B] FIG. 11 is a diagram showing an example of a result of a case example search in the first search evaluation process. [Figure 7] FIG. 13 is a diagram showing an example of a display of results represented by processing result data of the first search evaluation process. [Figure 8] 13 is a flowchart of a second search evaluation process. [Figure 9] FIG. 11 is a diagram showing an example of a result of a case example search in the second search evaluation process. [Figure 10] FIG. 11 is a diagram showing an example of a result of an attribute search in the second search evaluation process. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] In the following description, an "interface unit" may refer to one or more interface devices. The one or more interface devices may be at least one of the following: One or more I / O (Input / Output) interface devices. The I / O (Input / Output) interface devices are interface devices to at least one of the I / O devices and a remote display computer. The I / O interface device to the display computer may be a communications interface device. The at least one I / O device may be a user interface device, e.g., either an input device such as a keyboard and a pointing device, or an output device such as a display device. One or more communication interface devices. The one or more communication interface devices may be one or more homogeneous communication interface devices (e.g., one or more NICs (Network Interface Cards)) or two or more heterogeneous communication interface devices (e.g., a NIC and an HBA (Host Bus Adapter)).

[0016] In the following description, a "memory" refers to one or more memory devices, which are an example of one or more storage devices, and may typically be a primary storage device. At least one memory device in the memory may be a volatile memory device or a non-volatile memory device.

[0017] In the following description, a "persistent storage device" may be one or more persistent storage devices, which are an example of one or more persistent storage devices. A persistent storage device may typically be a non-volatile storage device (e.g., persistent storage device), and more specifically, may be, for example, a hard disk drive (HDD), a solid state drive (SSD), a non-volatile memory express (NVMe) drive, or a storage class memory (SCM).

[0018] Also, in the following description, "storage device" may be at least memory, including memory and persistent storage device.

[0019] Furthermore, in the following description, a "processor" may be one or more processor devices. The at least one processor device may typically be a microprocessor device such as a CPU (Central Processing Unit), but may also be other types of processor devices such as a GPU (Graphics Processing Unit). The at least one processor device may be a single-core or multi-core. The at least one processor device may be a processor core. The at least one processor device may also be a broader processor device such as a circuit that is a collection of gate arrays written in a hardware description language that performs part or all of the processing (e.g., an FPGA (Field-Programmable Gate Array), a CPLD (Complex Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit)).

[0020] In the following description, data that produces an output for an input may be described using expressions such as "xxxDB" ("DB" is an abbreviation for database), but the data may be data of any structure (for example, structured data or unstructured data) or a learning model such as a neural network that generates an output for an input. Therefore, "xxxDB" can be referred to as "xxx data." In the following description, the configuration of each DB is an example, and one DB may be divided into two or more DBs, or all or part of two or more DBs may be one DB.

[0021] In the following description, functions may be described using the expression "yyy unit", but the functions may be realized by one or more computer programs being executed by a processor, or by one or more hardware circuits (e.g., FPGA or ASIC), or by a combination thereof. When a function is realized by a program being executed by a processor, the function may be at least a part of the processor, since the specified processing is performed using a storage device and / or an interface device, etc., as appropriate. Processing described with a function as the subject may be processing performed by a processor or a device having the processor. A program may be installed from a program source. The program source may be, for example, a program distribution computer or a computer-readable recording medium (e.g., a non-transitory recording medium). The description of each function is an example, and multiple functions may be combined into one function, or one function may be divided into multiple functions.

[0022] In the following description, a "dataset" is a logical block of electronic data as seen from a program such as an application program, and may be, for example, a record, a file, a key-value pair, a tuple, etc.

[0023] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. The following description and drawings are examples for explaining the present invention, and appropriate omissions and simplifications have been made for clarity of explanation. The present invention can be implemented in various other forms. Unless otherwise specified, each component may be singular or plural. In addition, in the following embodiment, a local government is used as an example of an entity that supports a region, but the present invention can also be applied to entities other than a local government (another entity that supports a region).

[0024] FIG. 1 is a diagram illustrating an example of a hardware configuration of an information processing device according to an embodiment of the present invention.

[0025] The information processing device 1 includes a communication interface device 29, an I / OI / F (input / output interface device) 25, a persistent storage device 28, a memory 22, and a processor 21 connected to them. The information processing device 1 may further include an input device 26 and an output device 27 connected to the I / OI / F 25. The communication interface device 29 and the I / OI / F 25 are examples of interface devices.

[0026] The information processing device 1 can communicate with one or more servers 30 via a network (e.g., the Internet) 35 through the communication interface device 29. At least one of the servers 30 may be a server that stores an attribute DB and a case DB described below, or a server that stores data that are elements of the attribute DB and the case DB. In the latter case, the attribute DB and the case DB described below may be constructed based on data collected from one or more servers.

[0027] The input device 26 is, for example, a keyboard and a mouse operated by a user who uses the information processing device 1. The output device 27 is, for example, a liquid crystal display configured with a liquid crystal display element or an organic EL (Electro Luminescence) display element so that the user can visually recognize the output data. The input device 26 and the output device 27 may be configured with a touch panel that combines the functions of both. The input device 26 and the output device 27 may also be a remote computer (for example, a console) connected to the information processing device 1 via a network 35.

[0028] The persistent storage device 28 stores an OS (Operating System) and various programs, which may be read into the memory 22 and executed by the processor 21.

[0029] FIG. 2 is a diagram illustrating an example of a functional configuration of the information processing device 1.

[0030] The information processing device 1 has an attribute DB 5, a case DB 9, an attribute search unit 3, a case search unit 7, an input unit 2, a search evaluation unit 6, and an output unit 4.

[0031] The attribute DB5 is a DB including a data set (e.g., records) representing attributes of each of a plurality of municipalities. The attribute DB5 represents attributes for each fiscal year for each municipality, and the attributes may be, for example, the population of residents, the number of elderly welfare facilities, the value of manufactured goods shipped, the number of deaths, the number of elementary school children, and the like. These are examples of "attributes," but examples of "attributes" are not limited to the elements listed in this paragraph. In the attribute DB5, the data set for each municipality may include attribute items (e.g., item names) and attribute values ​​(e.g., numerical values ​​or text) for each attribute. At least a portion of the attribute DB5 may represent statistics of the attributes. The attributes may be called indicators.

[0032] The case DB9 is a DB including a data set representing at least one example of laws, policies, measures, projects, and affairs of a local government for each of a number of local governments. These are examples of "cases," but the examples of "cases" are not limited to the elements listed in this paragraph. Laws and regulations typically include ordinances, and may further include regulations. Examples of "laws and regulations" are also not limited to the elements listed in this paragraph.

[0033] In this embodiment, for the sake of simplicity, the attributes of the local government and the cases are aggregated (i.e., structured) in the DB, but at least some of the attributes of the local government and at least some of the cases of the local government do not necessarily have to be aggregated in the DB. For example, at least some of the attribute data and at least some of the case data may exist in a remote server 30. In particular, for example, with regard to cases, the names of administrative services and ordinances are not consistent between local governments, and the labor required to reflect amendments (addition, deletion, modification) of ordinances, etc. may also affect the structure and maintenance of all of the case data, so at least some of the case data may not be structured. The "attribute data" is demographic variables, geographic variables, psychographic variables, or data indicating budgets, tax revenues, etc., data indicating industrial structure, etc. The "case data" is data including a data set representing administrative efforts and execution contents (rules, activities, etc.) for each of a plurality of local governments, and at least one individual case planned or executed based on them. "Rules and activities" includes at least one of the legal sources such as the local government's regulations, policies, measures, business and administrative regulations, etc., and national laws and regulations that affect them. Here, attributes that reflect the importance and urgency of the government's (local government's, etc.) efforts and execution contents (rules, activities, etc.) at the time of planning, and the execution results, for example, attributes related to the budget, tax revenue, personnel expenses, property expenses, etc. of the local government, etc., as attributes that can be directly controlled by the government, and attributes as activity indicators such as the number of events and lectures held per year, the number of days held, and the location of the events, etc., held within the scope of the expenses, or attributes related to the results of government services, such as the number of participants who participated in the events and lectures, the percentage of participants who became aware of the issues at the events and deepened their understanding and interest, and the percentage of citizens who deepened their understanding and interest in the issues as a result, as attributes that can be indirectly controlled by the government, are often assumed to be included in "attribute data", but may also be included in "case data".

[0034] The attribute search unit 3 is a function for performing an attribute search, which is to identify a data set from the attribute DB 5, but may also be an interface to the attribute DB 5. In the latter case, the search evaluation unit 6 may access the attribute DB 5 through the attribute search unit 3 in the attribute search.

[0035] The case search unit 7 is a function for performing a case search, which is to identify a data set from the case DB 9, but may also be an interface to the case DB 9. In the latter case, the search evaluation unit 6 may access the case DB 9 through the case search unit 7 in the case search.

[0036] The input unit 2 accepts data representing interests, etc. related to a local government as input data. The search evaluation unit 6 performs a search evaluation process. The output unit 4 outputs processing result data that is data representing the results of the search evaluation process (for example, displays it on the output device 27).

[0037] The search evaluation process includes a first search, a second search, and a search evaluation. The first search is to identify a dataset for each of one or more municipalities that satisfies a predetermined condition regarding the interest represented by the input data from one of the attribute DB5 and the case DB9. The second search is to identify a dataset corresponding to each of one or more municipalities represented by the identified one or more datasets from the other DB of the attribute DB5 and the case DB9. The search evaluation is to evaluate one or more datasets identified in the second search for at least a part of the one or more municipalities identified in the first search.

[0038] Specifically, for example, the search evaluation unit 6 causes one of the attribute search unit 3 and the case search unit 7 to execute a first search, and causes the other of the attribute search unit 3 and the case search unit 7 to execute a second search. In other words, the first search is one of the attribute search and the case search, and the second search is the other of the attribute search and the case search. The attribute search unit 3 and the case search unit 7 may be functions included in the search evaluation unit 6.

[0039] In this manner, in this embodiment, both attribute search, which is a search for attributes of a local government, and case search, which is a search for cases of a local government, are performed. The results of the attribute search (e.g., search results for local governments with similar attributes) and the results of the case search (e.g., search results for local governments with similar cases) are evaluated, and the evaluation results are output (e.g., evaluation results of how differences in the cases (e.g., differences in ordinances and administrative services) affect the finances of the local government (one example of an attribute) are output). This makes it possible to support local governments in achieving both efficient resolution of local issues and improved effectiveness in solving local issues. The following is one example.

[0040] The search evaluation unit 6 may refer to the attribute DB 5 and the case DB 9 for the interest (e.g., field) represented by the input data using the attribute search unit 3 and the case search unit 7. The field of interest may be, for example, education or social welfare. For example, data related to laws and regulations in the case DB 9 may include data of the difference between original data before the revision of an ordinance and the original data of the revised ordinance.

[0041] The search evaluation unit 6 may specify and output at least one first similar municipality that is a municipality with attributes similar to that of the municipality X (municipality of X city) and has the field of interest. The municipality with attributes similar to that of the municipality X may be specified from the attribute DB5. For example, a municipality with a population similar to that of X city may be specified, or a municipality with a ratio of primary industry, secondary industry, or tertiary industry similar to that of X city may be specified (therefore, based on government statistical data (similar organization classification, etc.) that judges similarity based on population, industrial structure, etc. (the similar organization classification may also be included in the attribute DB5), it may be specified from among the municipalities classified into the same classification name as the municipality X). For example, if statistical data or the like showing the classification of municipalities based on financial status is available, it may be determined whether or not the municipality belongs to the same classification as the municipality X by referring to the statistical data (the municipal classification may also be included in the attribute DB5).

[0042] Attributes that may be adopted include, for example, population, population composition (e.g., ratio by age group), area, climate, budget, tax revenue, etc. Attributes that can be directly controlled by the government may include attributes related to expenses such as personnel expenses and property expenses, and attributes serving as activity indicators such as the number of events and courses held per year, the number of days they are held, and the locations where they are held, which are held within the scope of those expenses. Attributes that can be indirectly controlled by the government may include attributes related to service outcomes such as the number of participants who participated in the events and courses, the percentage of those participants who became aware of the issues at the events and deepened their understanding or interest, and the percentage of citizens who deepened their understanding or interest in those issues as a result.

[0043] Issues for holding such an event course include setting a theme, the timing of the event, and the popularity of the lecturer, which may be influencing factors for considering holding the event. Furthermore, influencing factors may also include the ease of understanding of the content of the event course, the degree of social interest, the venue and its size, etc. In case studies of other municipalities, these influencing factors are examples of attributes, and such influencing factors may affect the differences in cases between municipalities. In this embodiment, it is possible to output (typically visualize) the evaluation results of the differences in cases and the differences in attributes. For example, in City A, City C, and City D, which have the same or similar influencing factors, City A and City C have regulations related to the ordinance and implement a certain policy, while City D does not have regulations in the ordinance and does not implement the policy. If the evaluation results are obtained as outcome indicators (examples of attributes) of a certain policy, such as 10 million yen for City D, 30 million yen for City A, and 30 million yen for City C, it can be inferred that the difference (30 million yen - 10 million yen) is the causal effect of the policy between municipalities. Here, if City A has an investment of 30 million yen and City C has an investment of 40 million yen, then we can use the average amount of 35 million yen to estimate the difference as the causal effect (35 million yen - 10 million yen).

[0044] This embodiment will be described in detail below.

[0045] FIG. 3 is a diagram showing an example of an outline of processing performed in the information processing device 1. As shown in FIG.

[0046] The input unit 2 receives input data 110 from an employee (an example of a user) of a local government X (a local government of X city) via an input device 26. The interests expressed by the input data 110 include one or both of interests related to attributes of the local government (hereinafter, attribute interests) and interests other than attributes of the local government (hereinafter, non-attribute interests). In the example shown in FIG. 3, the interests expressed by the input data 110 include both attribute interests and non-attribute interests.

[0047] The search evaluation unit 6 can perform one or both of a first search evaluation process 111 and a second search evaluation process 112 as a search evaluation process. In this embodiment, the search evaluation unit 6 may determine whether or not the interest represented by the input data 110 includes an attribute interest. For an attribute interest, the search evaluation unit 6 may perform the first search evaluation process 111. For a non-attribute interest, the search evaluation unit 6 may perform the second search evaluation process 112. In this way, an appropriate search evaluation process according to the interest can be selectively performed, and thus, efficient and appropriate information output for solving local issues is expected.

[0048] The first search evaluation process 111 is a process that includes an attribute search as a first-stage search (an example of a first search) and a case search as a second-stage search (an example of a second search). Specifically, in the first search evaluation process 111, an attribute search is performed based on the attribute interest. The dataset group 113 (one or more datasets) identified in the attribute search may include at least one of a dataset of a municipality having an attribute corresponding to the attribute interest and a dataset of a municipality having an attribute identical or similar to the attribute of the municipality. An example of the attribute interest is "municipal name 'X City'", and an example of "municipalities having attributes identical or similar to the attributes of a municipality having an attribute corresponding to the attribute interest" is municipalities A, D, and E having attributes identical or similar to the attributes (all or a part of the attributes) of the municipality X corresponding to the municipality name "X City". "An attribute identical to the attribute of the municipality X" may be an attribute that matches all attributes of the municipality X. The "attributes similar to the attributes of the local government X" may be attributes that match some of the attributes of the local government X, or attributes whose difference from the attributes of the local government X is within a predetermined range (for example, one or more attributes whose feature values ​​are within a predetermined range from the feature values ​​of all attributes of the local government X). In the second stage search, the search range in the case DB9 may be a range related to one or more local governments identified in the first stage search. That is, the range of the second stage search may be narrowed down to a range according to the result of the first stage search. This is expected to make the solution of the regional problem more efficient. In the second stage search, the search range in the case DB9 may be further narrowed down to a range related to one or more local governments identified in the first stage search, to a range corresponding to matters (for example, fields) identified from the non-attribute interests of the input interests. In the second stage search, the dataset group 115 (one or more datasets) is identified from the range narrowed down in this way. The dataset group 113 and the dataset group 115 are evaluated, and data representing the evaluation results is output as the processing result data 121. The result represented by this processing result data 121 may be visualized (output), or instead of or in addition to the processing result data 121, the result represented by processing result data 125 described below may be visualized.

[0049] The second search evaluation process 112 is a process that includes a case search as a first-stage search and an attribute search as a second-stage search. Specifically, in the second search evaluation process 112, a case search is performed based on a non-attribute interest. An example of a non-attribute interest may be the "field 'social security'" shown in FIG. 3, but instead of or in addition to that, an interest such as "similar cases" may also be used. The dataset group 117 (one or more datasets) identified in the case search may include at least one of a dataset of a case corresponding to the non-attribute interest and a dataset of a case similar to the corresponding case. Examples of municipalities having "cases corresponding to the non-attribute interest" or "cases similar to the corresponding case" are municipalities C and E. In the second stage search, the search range in the attribute DB5 may be a range related to one or more municipalities identified in the first stage search. This is expected to make the solution of regional issues even more efficient. In the second stage search, a dataset group 119 (one or more datasets) is identified from the range narrowed down in this way. The data set group 117 and the data set group 119 are evaluated, and data representing the evaluation results is output as processing result data 123. The results represented by this processing result data 123 may be visualized (output), or instead of or in addition to the processing result data 123, the results represented by processing result data 125 described below may be visualized.

[0050] The processing result data 125 may be data output when both the first search evaluation process 111 and the second search evaluation process 112 are performed on the same input data 110. The processing result data 125 may be data representing a common municipality between one or more municipalities identified from the processing result data 121 and one or more municipalities identified from the processing result data 123. According to the above example, municipalities A, D, and E are identified from the processing result data 121 of the first search evaluation process 111, and municipalities C and E are identified from the processing result data 123 of the second search evaluation process 112, so that the common municipality is municipality E. As described above, one interest represented by the input data 110 includes attribute interests and non-attribute interests, and the first search evaluation process 111 (attribute search → case search) is performed for the attribute interests, and the second search evaluation process 112 (case search → attribute search) is performed for the non-attribute interests in the order opposite to the first search evaluation process 111, and a municipality is identified as a common result of these processes 111 and 112. In other words, searches are performed from different perspectives (in different orders) according to different interest types in one interest, and the results of those searches are specified as the final search result. The expected value of the suitability of this final search result is higher than the search results of only one of the first search evaluation process 111 and the second search evaluation process 112, and therefore a more efficient and appropriate solution to the local problem is expected.

[0051] In the first search evaluation process 111, in the attribute search, a municipality having an attribute that is distant from the attribute corresponding to the attribute interest (or the attribute of the municipality having the attribute) by a predetermined distance or more may be specified. "A municipality having an attribute that is distant from the attribute of municipality X by a predetermined distance or more" may be, for example, a municipality having an attribute that is the basis of a value whose difference from a value based on one or more attributes of municipality X (for example, a feature amount) is a predetermined value or more. Such a municipality can be regarded as a municipality having an attribute that is symmetrical to that of municipality X, that is, a municipality having characteristics that are almost the opposite when characterized from the viewpoint of a certain attribute (an example of a municipality having an attribute whose dissimilarity to the attribute of municipality X is greater than a certain value). Since such a municipality is also specified, it becomes possible to compare a similar municipality with a municipality that is symmetrical thereto, and therefore it is expected that the background of the case can be accurately and quickly inferred, and as a result, it is expected to further contribute to both efficient solving of regional issues and improving the effectiveness of solving regional issues.

[0052] Similarly, in the second search evaluation process 112, in the case search, a municipality having a case that is separated from the case corresponding to the non-attribute interest by a predetermined distance or more may be specified. "A municipality having a case that is separated from the case corresponding to the non-attribute interest by a predetermined distance or more" may be a municipality having a case that is the basis of a value that is a difference of a value (e.g., a feature) based on the case corresponding to the non-attribute interest by a predetermined value or more. Such a municipality may be a municipality having a symmetrical case to the corresponding case (an example of a municipality having a case in which the dissimilarity to the case of municipality X is greater than a certain value) among the municipalities having a dissimilar case to the case corresponding to the non-attribute interest. Since such a municipality is also specified, it becomes possible to compare a municipality having a corresponding or similar case with a municipality having a symmetrical case thereto, and therefore it is expected that the background of the case can be accurately and quickly inferred, and as a result, it is expected that the system will further contribute to both efficient solving of regional issues and improving the effectiveness of solving regional issues.

[0053] The first search evaluation process 111 and the second search evaluation process 112 will be described in detail below.

[0054] <First search evaluation process 111>

[0055] FIG. 4 is a flowchart of the first search evaluation process 111.

[0056] In S1, the search evaluation unit 6 inputs the attribute interest to the attribute search unit 3, causing the attribute search unit 3 to execute an attribute search. As a result, a dataset group 113 (at least one of a dataset of a municipality having an attribute corresponding to the attribute interest and a dataset of a municipality having an attribute identical or similar to the attribute of the municipality) is acquired from the attribute DB 5.

[0057] In S2, the search evaluation unit 6 selects one local government from the one or more local governments identified in S1.

[0058] In S3, the search evaluation unit 6 inputs at least a part of the dataset of the selected municipality (e.g., the name of the municipality) to the case search unit 7, thereby causing the case search unit 7 to execute a case search. As a result, a dataset representing cases of the municipality is acquired from the case DB 9.

[0059] In S4, the search evaluation unit 6 judges whether or not S3 has been performed for all the municipalities identified in the attribute search in S1. If the judgment result in S4 is false (S4: No), S2 is executed.

[0060] If the judgment result of S4 is true (S4: Yes), the search evaluation unit 6 performs search evaluation including evaluation of the dataset group 113 and the dataset group 115 (one or more datasets acquired in S3), and outputs processing result data 121 representing the result.

[0061] A specific example of the first search evaluation process 111 will now be described.

[0062] FIG. 5 is an explanatory diagram of an example of the concept of attribute search and example search in the first search evaluation process 111. In FIG.

[0063] For example, a user determines an interest under the hypothesis that "the record of penalties applied by each local government correlates with the strictness and detail of the laws and regulations of each local government." In this case, for example, the interest includes the attribute interest, and the attribute interest includes "record of penalties applied." The number of cases of penalties applied being "0" means that there is no record of penalties applied, and the number of cases of penalties applied being "1" or more means that there is a record of penalties applied.

[0064] Set 11 represented by a solid line is a set of local governments that have an attribute item of a record of penalties being applied. Subset 12 of set 11 is a set of local governments (e.g., local governments A, D, and E) identified in the attribute search. Subset 12 may include local government G that has no record of penalties being applied (e.g., a local government selected on a rule-based or random basis from multiple local governments that have no record of penalties being applied). Municipality G may be an example of a local government that has an attribute that corresponds to the interest represented by input data 110 or an attribute that is separated by a predetermined distance or more from the attribute of a local government that has the attribute.

[0065] Set 10 represented by the dashed line is a set of municipalities that includes at least one of municipalities that have cases corresponding to non-attribute interests and municipalities that have cases that are identical or similar to the cases (for example, a set of municipalities that include the expression "abc" or a collection of expressions (concepts, etc.) in their ordinances).

[0066] 5, the range of case search within set 10 (i.e., within the case data sets of all municipalities belonging to set 10 in the case DB 9) is narrowed down to a range that overlaps with subset 12. In other words, a case data set is obtained for each municipality belonging to subset 12 (the intersection between set 10 and set 11).

[0067] For example, as shown in Fig. 6A, for the record of penalties being applied (occurrence of problems related to personal information protection), in an attribute search, for each of the municipalities A to J that have an attribute item of the record of penalties being applied, "record of penalties being applied" or "record of no record of penalties being applied" is specified. According to the example shown in Fig. 6A, the municipalities A, D, and E have a record of penalties being applied (this set "municipalities A, D, and E" corresponds to the subset 12 illustrated in Fig. 5).

[0068] Next, as shown in FIG. 6B, the search evaluation unit 6 accesses the case DB 9 through the case search unit 7 and compares the Administrative Organs Personal Information Protection Act (a common name for the "Act on the Protection of Personal Information Held by Administrative Organs") with the ordinances of the local governments A to J. This allows the number of relevant provisions (provisions that are also covered by ordinances) in the Administrative Organs Personal Information Protection Act to be known for each of the local governments A to J. Assume that the number of relevant provisions is 12 for the local government A, 10 for the local government D, and 8 for the local government E. In other words, it can be seen that the average number of relevant provisions for the local governments with a history of applying penalties is 10 (=(12+10+8)÷3). For the local governments without a history of applying penalties, it can be seen that the average number of relevant provisions is 3.85 by using the same identification method. As a result, the average number of relevant provisions is higher for the local governments with a history of applying penalties, which means that the local governments with a history of applying penalties have stricter or more detailed legal regulations. Therefore, this result supports the above-mentioned hypothesis that "the record of each local government applying penalties correlates with the strictness and detail of each local government's legal regulations."

[0069] FIG. 7 is a diagram showing an example of a display of the results represented by the processing result data 121. As shown in FIG.

[0070] From this display, the user can see that the concept of "retained personal information" exists in common in the local governments A, D, and E. In addition, it can be seen that there are more restrictions on "retained personal information" than on "personal information."

[0071] Furthermore, a comparison of municipalities A, D, and E with municipality G reveals that municipality G does not have the concept of "personal information held."

[0072] In this way, the user can grasp the policy (ordinance) trends of the local governments A, D, and E, and thereby verify the validity of the user's hypothesis above.

[0073] Alternatively or in addition to such examples, at least one of the following may be expected in the first search evaluation process 111: · Attribute DB5, which includes industry, civil engineering, health and hygiene, the environment, welfare, etc., is compared with other municipalities in the same prefecture to which municipality X belongs, and the extracted characteristics of the municipality are that the city tax collection rate is relatively high and the forest area is relatively large. As goals, it is clear that it is desirable to formulate policies to make tax collection number one in the prefecture and to promote forestry. It is often assumed that businesses with similar populations will be similar, but when compared, differences become apparent. By comparing the financial index of Municipality X with other municipalities of similar size (similar wealth to Municipality X), we can see that these municipalities are expected to suffer greater damage in the event of an earthquake. For this reason, it is desirable to formulate policies to cooperate with remote areas rather than neighboring cities that are expected to suffer the same damage.

[0074] <Second search evaluation process 112>

[0075] FIG. 8 is a flow chart of the second search evaluation process 112.

[0076] In S11, the search evaluation unit 6 inputs the non-attribute interest to the case search unit 7, causing the case search unit 7 to execute a case search. As a result, a dataset group 117 (at least one of a dataset of a municipality having a case corresponding to the non-attribute attribute interest and a dataset of a municipality having a case similar to the case) is acquired from the case DB 9.

[0077] In S12, the search evaluation unit 6 selects one local government from the one or more local governments identified in S11.

[0078] In S13, the search evaluation unit 6 inputs at least a part of the dataset of the selected municipality (for example, the name of the municipality) to the attribute search unit 3, thereby causing the attribute search unit 3 to execute an attribute search. As a result, a dataset representing the attributes of the municipality is acquired from the attribute DB 5.

[0079] In S14, the search evaluation unit 6 judges whether or not S13 has been performed for all the municipalities identified in the case search in S11. If the judgment result in S14 is false (S14: No), S12 is executed.

[0080] If the judgment result of S14 is true (S14: Yes), the search evaluation unit 6 performs search evaluation including evaluation of the dataset group 117 and the dataset group 119 (one or more datasets acquired in S13), and outputs processing result data 123 representing the result.

[0081] In addition, in the second search evaluation process 112, similar cases may be determined based on a comprehensive evaluation of multiple laws and regulations, such as whether the wording (content) of the ordinances is similar, or whether the period since the laws and regulations were enacted (amended) is the same.

[0082] A specific example of the second search evaluation process 112 will now be described.

[0083] For example, suppose that local governments C and E are identified in a case search. For example, as shown in Figure 8, local government E has an ordinance on "processing of personal information handling affairs" (entrustment regulation), but local government C does not have such an ordinance. Taking into consideration the results of the attribute search, a user can be helped to infer the purpose (purpose, background, etc.) of this ordinance for local government E.

[0084] For example, as shown in Figure 9, regarding the availability of organizations that handle complaints, while local government E has a call center or other outsourced service provider that provides the liaison function, local government C does not have such a liaison function, and basically each bureau or department serves as the liaison function. In this case, the user can infer that the ordinance shown in Figure 9 was enacted for the purpose of establishing a consultation liaison (operated by outsourcing) to an outsourced service provider such as a call center.

[0085] As described above, the present invention is not limited to the above-mentioned embodiment, and various modifications and other configurations can be combined without departing from the scope of the present invention. Furthermore, the present invention is not limited to those having all the configurations described in the above-mentioned embodiment, and includes those in which some of the configurations are omitted.

[0086] For example, the "local government" referred to in this embodiment is not limited to local governments in Japan (e.g., the country in which the local government to which the user belongs is located), but may also apply to local governments in foreign countries. In this case, at least in the case search, it may be possible to continue the case search by inputting at least a portion of the case data into an existing translation system and obtaining translated data as the output. Also, as described above, a "local government" is an example of an entity that supports a region, and the present invention can be applied to entities other than local governments (entities that support a region). [Explanation of symbols]

[0087] 1. Information processing device

Claims

1. An input unit that accepts data representing an interest in a region as input data; a search evaluation unit that performs a first search evaluation process and a second search evaluation process as a search evaluation process; an output unit that outputs processing result data that is data representing a result of the search evaluation processing; having the first search evaluation process includes an attribute search as a first stage search and an example search as a second stage search; In the first search evaluation process, The attribute search as a first stage search is to identify, from the attribute data and the case data, a data set for each of one or more regions that satisfies a predetermined condition with respect to the interest represented by the input data; The case search as the second stage search is at least one of: identifying a dataset corresponding to each of one or more regions represented by one or more datasets identified in the attribute search as the first stage search from the case data of the attribute data and the case data; and identifying a dataset for each of the one or more regions that satisfies a predetermined condition regarding the interest represented by the input data; The predetermined condition includes at least one of a region having an attribute corresponding to the interest represented by the input data and a region having an attribute identical or similar to the region; In the case search as the second stage search, a search range of the case data is a range related to one or more regions specified in the attribute search as the first stage search, the attribute data includes a data set representing attributes of each of a plurality of regions; The case data includes, for each of a plurality of regions, a data set representing cases in the region; the second search evaluation process includes a case search as a first stage search and an attribute search as a second stage search; In the second search evaluation process, The case search as a first stage search is to identify, from the attribute data and the case data, a data set of each of one or more regions that satisfies a predetermined condition with respect to the interest represented by the input data; The attribute search as the second stage search is at least one of: identifying a dataset corresponding to each of one or more regions represented by one or more datasets identified in the case search as the first stage search from the attribute data and the attribute data of the case data; and identifying a dataset for each of the one or more regions that satisfies a predetermined condition regarding the interest represented by the input data; The predetermined condition includes at least one of the following: the area is one in which there is a case corresponding to the interest represented by the input data; and the area is one in which there is a case similar to the case corresponding to the interest; An information processing device, wherein in the attribute search as the second-stage search, a search range of the attribute data is a range relating to one or more regions identified in the case search as the first-stage search.

2. The processing result data is data on a common area among one or more areas identified in the first search evaluation processing and one or more areas identified in the second search evaluation processing. The information processing device according to claim 1 .

3. In the first search evaluation process, the predetermined condition also includes a region having an attribute corresponding to the interest represented by the input data or an attribute of a region having the attribute that is separated by a predetermined distance or more from the attribute of the region having the attribute, In the second search evaluation process, the predetermined condition also includes a region having cases that are apart from the cases corresponding to the interest represented by the input data by a predetermined distance or more. The information processing device according to claim 1 .

4. A computer receives as input data data representing interests in a region; the computer performs a first search evaluation process and a second search evaluation process as a search evaluation process; The computer outputs processing result data which is data representing a result of the search evaluation processing; the first search evaluation process includes an attribute search as a first stage search and an example search as a second stage search; In the first search evaluation process, The attribute search as a first stage search is to identify, from the attribute data and the case data, a data set for each of one or more regions that satisfies a predetermined condition with respect to the interest represented by the input data; The case search as the second stage search is at least one of: identifying a dataset corresponding to each of one or more regions represented by one or more datasets identified in the attribute search as the first stage search from the case data of the attribute data and the case data; and identifying a dataset for each of the one or more regions that satisfies a predetermined condition regarding the interest represented by the input data; The predetermined condition includes at least one of a region having an attribute corresponding to the interest represented by the input data and a region having an attribute identical or similar to the region; In the case search as the second stage search, a search range of the case data is a range related to one or more regions specified in the attribute search as the first stage search, the attribute data includes a data set representing attributes of each of a plurality of regions; The case data includes, for each of a plurality of regions, a data set representing cases in the region; the second search evaluation process includes a case search as a first stage search and an attribute search as a second stage search; In the second search evaluation process, The case search as a first stage search is to identify, from the attribute data and the case data, a data set of each of one or more regions that satisfies a predetermined condition with respect to the interest represented by the input data; The attribute search as the second stage search is at least one of: identifying a dataset corresponding to each of one or more regions represented by one or more datasets identified in the case search as the first stage search from the attribute data and the attribute data of the case data; and identifying a dataset for each of the one or more regions that satisfies a predetermined condition regarding the interest represented by the input data; The predetermined condition includes at least one of the following: the area is one in which there is a case corresponding to the interest represented by the input data; and the area is one in which there is a case similar to the case corresponding to the interest; An information processing method, wherein in the attribute search as the second stage search, the search range of the attribute data is a range relating to one or more regions identified in the case search as the first stage search.

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