JP7638853B2 - Information processing device and method - Google Patents

Description

The present invention generally relates to information processing technology, for example, information processing technology for supporting local communities.

Municipalities (local public organizations) are known as an example of an entity that supports a region. In order to improve administrative services in the region, municipalities may collect information on administrative services provided by other municipalities. One example of technology that supports municipalities to improve administrative services is the technology disclosed in Patent Document 1.

International Publication No. 2018/225227

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 local issues (for example, creating added value for the region, thereby, for example, gaining a competitive advantage over other local governments).

In both (Requirement A) and (Requirement B), local government officials are known to investigate and attempt to utilize examples from other local governments. A local government's "examples" typically include at least one of the following sources of law, or similar: local regulations, policies, programs, projects, and affairs of each local government, and national laws and regulations that affect these.

To satisfy (requirement A), efficient case study is necessary. However, efficient case study is not necessarily easy even with the use of a computer. The reason for this is that the number of case study subjects can be large. When searching similar ordinances in many municipalities across the board, the number of municipal ordinances or their provisions that contain the search term and are returned as search results can be large.

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. If local government policies are aligned across the board, ignoring influencing factors, it is not guaranteed that a case will work as well as a successful case from another local government, due to differences in influencing factors. However, because the background of the case is based on influencing factors specific to the local government where 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.

Information on influencing factors contributes to narrowing and prioritizing the scope of case studies, improving the efficiency of formulating guidelines for resolving local issues. It also leads to the derivation of accurate problem-solving policies by selecting useful 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 requirements A and B.

The above issues may also be faced by entities other than local governments (entities that support local communities).

The information processing device accepts data representing regional interests as input data, performs a search evaluation process, and outputs processing result data that represents the results of the search evaluation process. The search evaluation process includes a first search, a second search, and a search evaluation.

The first search is to identify, from one of the attribute data and the case data, a dataset for each of 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 that corresponds to each of one or more regions represented by the identified one or more datasets.

The search evaluation is to evaluate one or more datasets identified in the second search for at least a portion of 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.

The present invention makes it possible to support local areas so that they can be solved efficiently while also improving the effectiveness of solving local issues.

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. FIG. 2 is a diagram showing an example of the functional configuration of an information processing device. FIG. 1 is a diagram showing an example of an overview of processing performed in an information processing device. 13 is a flowchart of a first search evaluation process. FIG. 11 is a diagram illustrating an example of the concept of attribute search and example search in the first search evaluation process. FIG. 11 is a diagram showing an example of a result of an attribute search in the first search evaluation process. FIG. 11 is a diagram showing an example of a result of a case example search in the first search evaluation process. FIG. 13 is a diagram showing an example of a display of results represented by processing result data of the first search evaluation process. 13 is a flowchart of a second search evaluation process. FIG. 11 is a diagram showing an example of a result of a case example search in the second search evaluation process. FIG. 11 is a diagram showing an example of a result of an attribute search in the second search evaluation process.

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, for example, 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)).

In the following description, "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.

In the following description, a "persistent storage device" may be one or more persistent storage devices, which are an example of one or more 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).

In the following description, "storage device" may refer to at least one memory, including memory and persistent storage device.

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)).

In the following explanation, data that produces an output for an input may be described using expressions such as "xxx DB" ("DB" is an abbreviation for database), but the data may be data of any structure (for example, it may be structured data or unstructured data), or it may be a learning model such as a neural network that produces an output for an input. Therefore, a "xxx DB" can be referred to as "xxx data." In the following explanation, 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.

In the following description, functions are sometimes 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., FPGAs or ASICs), 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.

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

Below, 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).

Figure 1 is a diagram showing an example of the hardware configuration of an information processing device according to one embodiment of the present invention.

The information processing device 1 comprises a communication interface device 29, an I/O-I/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 comprise an input device 26 and an output device 27 connected to the I/O-I/F 25. The communication interface device 29 and the I/O-I/F 25 are examples of interface devices.

The information processing device 1 can communicate with one or more servers 30 via a network (e.g., the Internet) 35 through a 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 is an element 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.

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 composed of 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 composed of 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.

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.

Figure 2 is a diagram showing an example of the functional configuration of the information processing device 1.

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.

Attribute DB5 is a DB that includes a data set (e.g., records) that represents the attributes of each of a plurality of municipalities. 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 students, etc. These are examples of "attributes," but examples of "attributes" are not limited to the elements listed in this paragraph. In 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 attribute DB5 may represent statistics for the attributes. Attributes may be called indicators.

Case DB9 is a DB that includes a data set that represents at least one example of a law, policy, measure, project, or affairs of a local government for each of multiple 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.

In this embodiment, for the sake of simplicity, both 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 (additions, deletions, changes) to 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 dataset 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 results of execution, for example, attributes that can be directly controlled by the government, such as attributes related to the budget, tax revenue, personnel expenses, property expenses, etc. of the local 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, which are held within the budget, or attributes that can be indirectly controlled by the government, 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 event and deepened their understanding and interest, and the percentage of citizens who deepened their understanding and interest in the issues as a result, are often assumed to be included in "attribute data", but may also be included in "case data".

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.

The case search unit 7 is a function that performs 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.

The input unit 2 accepts data representing interests, etc. related to local governments as input data. The search evaluation unit 6 performs search evaluation processing. The output unit 4 outputs processing result data, which is data representing the results of the search evaluation processing (for example, displaying it on the output device 27).

The search evaluation process includes a first search, a second search, and a search evaluation. The first search is to identify, from one of the attribute DB 5 and the case DB 9, a dataset for each of one or more municipalities 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 DB 5 and the case DB 9, a dataset that corresponds to each of the one or more municipalities represented by the identified one or more datasets. The search evaluation is to evaluate the one or more datasets identified in the second search for at least some of the one or more municipalities identified in the first search.

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.

In this way, 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 (an 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.

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

The search evaluation unit 6 may identify 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 identified from the attribute DB 5. For example, a municipality with a population similar to that of X city may be identified, or a municipality with a ratio of primary industry, secondary industry, or tertiary industry similar to that of X city may be identified (therefore, based on government statistical data (similar entity classification, etc.) that judges similarity based on population, industrial structure, etc. (the similar entity classification may also be included in the attribute DB 5), it may be identified from among the municipalities classified into the same classification name as the municipality X). For example, if statistical data or the like showing municipal classification 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 DB 5).

Attributes that may be adopted include, for example, population, population composition (for example, the ratio by age group), area, climate, budget, tax revenue, etc. Attributes that the government can directly control may include cost-related attributes such as personnel and property costs, and activity indicator attributes 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, all of which are held within the budget. Attributes that the government can indirectly control may include service outcome attributes such as the number of people who participated in an event or course, the percentage of those participants who became aware of the issues at the event and deepened their understanding or interest, and the percentage of citizens who deepened their understanding or interest in those issues as a result.

The challenges for holding such an event course include setting a theme, the timing of the event, the popularity of the lecturer, etc., 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, and 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. If City A's income is 30 million yen and City C's income is 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).

This embodiment is described in detail below.

Figure 3 is a diagram showing an example of an overview of the processing performed in the information processing device 1.

The input unit 2 accepts input data 110 from an employee (an example of a user) of the local government X (the local government of X City) via the input device 26. The interests represented 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 the attributes of the local government (hereinafter, non-attribute interests). In the example shown in FIG. 3, the interests represented by the input data 110 include both attribute interests and non-attribute interests.

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 attribute interests, the search evaluation unit 6 may perform the first search evaluation process 111. For non-attribute interests, 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.

The first search evaluation process 111 is a process that includes attribute search as a first-stage search (an example of a first search) and case search as a second-stage search (an example of a second search). Specifically, in the first search evaluation process 111, attribute search is performed based on 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 some attributes) of municipality X corresponding to the municipal name "X City". "Attributes identical to the attributes of municipality X" may be attributes that match all attributes of 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 DB 9 may be a range related to one or more local governments identified in the first stage search. In other words, the range of the second stage search may be narrowed down to a range that follows the results of the first stage search. This is expected to make the solution of regional issues more efficient. In the second stage search, the search range in the case DB 9 may be further narrowed down to a range that corresponds to matters (for example, fields) identified from the non-attribute interests of the input interests, from the range related to one or more local governments identified in the first stage search. In the second stage search, a 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 results represented by this processing result data 121 may be visualized (output), or instead of or in addition to the processing result data 121, the results represented by processing result data 125 described below may be visualized.

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 non-attribute interests" or "cases similar to the corresponding case" are municipalities C and E. In the second-stage search, the search range in the attribute DB 5 may be a range related to one or more municipalities identified in the first-stage search. This is expected to make it possible to solve local issues more efficiently. In the second-stage search, a dataset group 119 (one or more datasets) is identified from the range narrowed down in this way. Data set group 117 and 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 processing result data 123, the results represented by processing result data 125 described below may be visualized.

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 exact opposite order 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 within one interest, and the results of these searches are identified as the final search result. The expected value of the suitability of this final search result is higher than the search results from 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.

In the first search evaluation process 111, in the attribute search, a municipality having an attribute that is separated 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 separated 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 (e.g., 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 similar municipalities with municipalities that are 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.

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 greater than a predetermined value and that is the difference between the value (e.g., feature) based on the case corresponding to the non-attribute interest. Such a municipality may be a municipality having a symmetrical case to the case (an example of a municipality having a case in which the dissimilarity to the case for municipality X is greater than a certain value) among municipalities having cases that are dissimilar 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 to further contribute to both efficiently solving regional issues and improving the effectiveness of solving regional issues.

The first search evaluation process 111 and the second search evaluation process 112 are each described in detail below.

<First search evaluation process 111>

Figure 4 is a flowchart of the first search evaluation process 111.

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. This causes 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) to be acquired from the attribute DB 5.

In S2, the search evaluation unit 6 selects one municipality from the one or more municipalities identified in S1.

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

In S4, the search evaluation unit 6 determines whether S3 has been performed for all of the municipalities identified in the attribute search in S1. If the determination result in S4 is false (S4: No), S2 is executed.

If the judgment result of S4 is true (S4: Yes), the search evaluation unit 6 performs a 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.

Below, a specific example of the first search evaluation process 111 is described.

Figure 5 is an explanatory diagram of an example of the concept of attribute search and case search in the first search evaluation process 111.

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

Set 11, represented by a solid line, is a set of local governments that have an attribute item called a record of applying penalties. 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 applying penalties (e.g., a local government selected on a rule-based or random basis from multiple local governments that have no record of applying penalties). Local government 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 that attribute.

Set 10, represented by the dashed line, is a set of municipalities that includes at least one of a municipality that has a case that corresponds to a non-attribute interest and a municipality that has a case that is identical or similar to the corresponding case (for example, a set of municipalities that include the expression "abc" or a collection of expressions (concepts, etc.) in their ordinances).

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

For example, as shown in FIG. 6A, for the record of penalties being applied (occurrence of problems related to personal information protection), an attribute search identifies whether "there is a record" or "there is no record" for the record of penalties being applied for each of the municipalities A to J that have an attribute item for the record of penalties being applied. In 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 subset 12 illustrated in FIG. 5).

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 (the 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 makes it possible to determine the number of relevant provisions (provisions that are also covered by ordinances) in the Administrative Organs Personal Information Protection Act for each of the local governments A to J. Assume that the number of relevant provisions is 12 for local government A, 10 for local government D, and 8 for local government E. In other words, it can be seen that for local governments with a history of applying penalties, the average number of relevant provisions is 10 (= (12 + 10 + 8) ÷ 3). For local governments without a history of applying penalties, the same identification method can be used to find that the average number of relevant provisions is 3.85. As a result, the average number of relevant provisions is higher for local governments with a history of applying penalties, which can be said to mean that local governments with a history of applying penalties have stricter or more detailed legal regulations. Therefore, this result supports the above hypothesis that "the record of each local government applying penalties correlates with the strictness and detail of each local government's legal regulations."

Figure 7 shows an example of the display of the results represented by the processing result data 121.

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

Furthermore, a comparison of local governments A, D, and E with local government G reveals that the concept of "personal information in possession" does not exist in local government G.

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

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 it is found that the characteristics of the municipality that are extracted from this include a relatively high city tax collection rate and a relatively large forest area. As goals, it is found 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 also be similar, but when compared, it becomes clear that there are differences.
・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, we can see that it is desirable to formulate policies to cooperate with remote areas rather than neighboring cities that are expected to suffer the same damage.

<Second search evaluation process 112>

Figure 8 is a flowchart of the second search evaluation process 112.

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.

In S12, the search evaluation unit 6 selects one municipality from the one or more municipalities identified in S11.

In S13, 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 attribute search unit 3, causing the attribute search unit 3 to execute an attribute search. As a result, a dataset representing the attributes of the municipality is obtained from the attribute DB 5.

In S14, the search evaluation unit 6 determines whether S13 has been performed for all of the municipalities identified in the case search in S11. If the determination result of S14 is false (S14: No), S12 is executed.

If the judgment result of S14 is true (S14: Yes), the search evaluation unit 6 performs a 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.

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.

Below, a specific example of the second search evaluation process 112 is described.

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

For example, as shown in Figure 9, in terms of the availability of agencies that handle complaints, local government E has a call center or other outsourced service provider that provides the liaison function, but 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) through an outsourced service provider such as a call center.

As mentioned 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 spirit of the invention. Furthermore, the present invention is not limited to those having all of the configurations described in the above-mentioned embodiment, and also includes those in which some of the configurations have been omitted.

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 the 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).

1 Information processing device

Claims (4)

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;
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 case data among 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 . The processing result data is data on a common area among one or more areas identified in the first search evaluation process and one or more areas identified in the second search evaluation process.
The information processing device according to claim 1 . 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 separated by a predetermined distance or more from cases corresponding to the interest represented by the input data.
The information processing device according to claim 1 . The 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 case data among 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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