Provided are an information processing apparatus, an information processing method, and a program that enable effective use of a search result by obtaining a reliability degree for the search result. In an information processing apparatus (10) including a processor, the processor is configured to: acquire a search query; acquire a first search result of search performed based on the search query on a database; acquire meta-information of the search query; acquire a second search result of search performed based on the search query on the database which is filtered by the meta-information; and obtain a reliability degree of the first search result based on the second search result.

BACKGROUND OF THE INVENTION

1. Field of the Invention

2. Description of the Related Art

In the related art, a facility manager requests a construction consultant to perform tasks such as inspections, and an inspector with specialized knowledge conducts inspections of various structures (also referred to as architectural structures, constructions, constructed structures, or infrastructure) such as bridges, roads, tunnels, dams, and buildings.

In WO2017/056804A, a first inspection result is acquired, a second inspection result is searched for from a database based on an image feature included in the first inspection result, a specific inspection result corresponding to a construction condition is searched for from the searched second inspection result, and the specific inspection result is preferentially displayed on a display unit.

SUMMARY OF THE INVENTION

Meanwhile, in recent years, there has been an increasing demand for inspections by non-experts due to rising costs and a shortage of labor. However, there are cases where a search precision for a search result acquired by executing a search query is low, and the search result includes items with low relevance to a search target. Therefore, it is not easy for a non-expert to use the search result.

The present invention has been made in view of such circumstances, and an object of the present invention is to provide an information processing apparatus, an information processing method, and a program that enable effective use of a search result by obtaining a reliability degree for the search result.

According to a first aspect, there is provided an information processing apparatus comprising a processor, in which the processor is configured to: acquire a search query; acquire a first search result of search performed based on the search query on a database; acquire meta-information of the search query; acquire a second search result of search performed based on the search query on the database which is filtered by the meta-information; and obtain a reliability degree of the first search result based on the second search result.

In the information processing apparatus according to a second aspect, the processor is configured to acquire the second search result of search performed based on the search query on the database which is filtered stepwise by the meta-information.

In the information processing apparatus according to a third aspect, the processor is configured to calculate a similarity in a case of obtaining the reliability degree.

In the information processing apparatus according to a fourth aspect, the processor is configured to, in a case of obtaining the reliability degree, calculate the similarity based on a method selected from the group of cosine similarity, deviation pattern similarity, Jaccard coefficient, Dice coefficient, Simpson coefficient, Pearson correlation coefficient, Spearman correlation coefficient, Earth Mover's Distance, Euclidean distance, weighted Euclidean distance, Hamming distance, Mahalanobis distance, or Canberra distance.

In the information processing apparatus according to a fifth aspect, the processor is configured to, in a case of obtaining the reliability degree, obtain statistical information for the first search result or the second search result based on the first search result or the second search result, and determine whether the statistical information is within a range of a threshold value.

In the information processing apparatus according to a sixth aspect, the processor is configured to output the reliability degree.

In the information processing apparatus according to a seventh aspect, the processor is configured to feed back the reliability degree to the acquisition of the first search result.

In the information processing apparatus according to an eighth aspect, the meta-information includes information about a structure.

In the information processing apparatus according to a ninth aspect, the information about the structure in the meta-information includes at least one selected from the group of a damage image, specifications, damage information, repair information, peripheral information, weather information, and statistical information.

In the information processing apparatus according to a tenth aspect, the search query includes information about a structure.

In the information processing apparatus according to an eleventh aspect, the information about the structure in the search query includes at least one selected from the group of a damage image, specifications, damage information, repair information, peripheral information, weather information, and statistical information.

According to a twelfth aspect, there is provided an information processing method executed by a processor, the method comprising: acquiring a search query; acquiring a first search result of search performed based on the search query on a database; acquiring meta-information of the search query; acquiring a second search result of search performed based on the search query on the database which is filtered by the meta-information; and obtaining a reliability degree of the first search result based on the second search result.

According to a thirteenth aspect, there is provided a program for executing an information processing method executed by a processor, the program causing the processor to execute: acquiring a search query; acquiring a first search result of search performed based on the search query on a database; acquiring meta-information of the search query; acquiring a second search result of search performed based on the search query on the database which is filtered by the meta-information; and obtaining a reliability degree of the first search result based on the second search result.

According to the present invention, by obtaining the reliability degree of the search result, the search result can be effectively used.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Hereinafter, preferred embodiments of an information processing apparatus, an information processing method, and a program according to the present invention will be described with reference to the accompanying drawings. In the present specification, the term “structure” includes a construction, for example, a civil engineering structure such as a bridge, a tunnel, and a dam, and also includes an architectural structure such as a building, a house, or a wall, a pillar, or a beam of a building.

FIG.1is a schematic diagram of an information processing apparatus10. The information processing apparatus10is connected to an operation unit18and a display device30. As shown inFIG.1, a user obtains an inspection result of a structure1via a camera3or a mobile terminal5.

Various mobile terminals having imaging and information input functions, such as smartphones, tablet terminals, and portable personal computers, are suitably used as the mobile terminal5.

The user inputs a search query based on an inspection result into the information processing apparatus10via the operation unit18. The information processing apparatus10acquires the input search query. The information processing apparatus10acquires a first search result of search performed based on the search query on a database40. The database40includes, for example, past inspection results related to the structure.

The information processing apparatus10acquires meta-information of the search query and acquires a second search result of search performed based on the search query on the database40which is filtered by the meta-information.

Next, the information processing apparatus10obtains a reliability degree of the first search result based on the second search result. Further, the information processing apparatus10displays the first search result and the reliability degree on the display device30. Hereinafter, the present embodiment will be described in detail.

[Hardware Configuration of Information Processing Apparatus]

FIG.2is a block diagram showing an example of a hardware configuration of the information processing apparatus according to the embodiment.

As the information processing apparatus10shown inFIG.2, a computer or a workstation can be used. The information processing apparatus10mainly includes an input/output interface12, a storage unit16, the operation unit18, a central processing unit (CPU)20, a random access memory (RAM)22, a read only memory (ROM)24, and a display control unit26. The display device30that constitutes a display is connected to the information processing apparatus10. Under a command of the CPU20, the display device30displays various types of information through control of the display control unit26.

The input/output interface12can input various data (information) into the information processing apparatus10. For example, data stored in the storage unit16is input via the input/output interface12.

The CPU (processor)20executes various types of processing of the information processing apparatus10by reading out various programs stored in the storage unit16, the ROM24, or like, and loading these programs into the RAM22to perform calculations.

FIG.3is a block diagram showing processing functions realized by the CPU20.

The CPU20mainly comprises a search query acquisition unit51, a first search result acquisition unit52, a meta-information acquisition unit53, a filtering unit54, a second search result acquisition unit55, a reliability degree calculation unit56, and an output unit57, and executes processing of each unit. The processing functions of the respective units will be described below.

Returning toFIG.2, the storage unit (memory)16is composed of a non-transitory storage medium, such as a hard disk device and various semiconductor memories, and a control unit for the non-transitory storage medium. The storage unit16stores programs for operating the information processing apparatus10, such as an operating system and a program for executing an information processing method. Further, the storage unit16stores information and the like used in the embodiment described below.

The operation unit18includes a keyboard and a mouse, and the user can cause the information processing apparatus10to perform necessary processing via these devices. By using a touch panel type device, the display device30and the operation unit18can be combined.

The display device30is, for example, a device such as a liquid crystal display and displays various types of information from the information processing apparatus10.

FIG.4is a diagram for describing the database40stored in the storage unit16. In the embodiment, the database40includes inspection data41which is an inspection result of the structure obtained from past inspections.

As shown inFIG.4, the inspection data41includes information on the structure, for example, at least one selected from the group of “specifications”, “damage information”, “repair information”, “peripheral information”, “weather information”, “statistical information”, and “damage image”. The “specifications”, the “damage information”, the “repair information”, the “peripheral information”, the “weather information”, and the “statistical information” are text data42, and the “damage image” is image data43. In addition, the inspection data41may further include text data such as “inspection date and time”, “imaging date and time”, and “repair date and time”.

The “specifications” include at least one of elapsed years, a structural form, a member name, or a material.Examples of the elapsed years include the number of years elapsed from a completion date or an opening date.Examples of the structural form include a girder bridge, a rigid-frame bridge, a truss bridge, an arch bridge, a cable-stayed bridge, or a suspension bridge in a case of a bridge.Examples of the member name include a slab, a pier, an abutment, or a girder in a case of a bridge.Examples of the material include steel, reinforced concrete, or prestressed concrete (PC).

The “damage information” includes at least one of a damage type, a degree of damage, a soundness degree, or a countermeasure category.

Examples of the damage type include a type of damage that occurs in the structure, such as cracking (fissuring), water leakage, corrosion, breakage, or stripping.The degree of damage is information indicating an objective state of the damage for each type of damage, and is classified according to the size, depth, and type and displayed, for example, as a to d.Examples of an indicator of the soundness degree include an indicator showing four-level diagnosis results of I to IV, as described in inspection guidelines established by the Japanese Ministry of Land, Infrastructure, Transport and Tourism.

Examples of the countermeasure category include a countermeasure category described in inspection guidelines established by the Japanese Ministry of Land, Infrastructure, Transport and Tourism.

Examples of the “repair information” include, for example, past repair contents.

Examples of the “peripheral information” include a traffic volume (per day, per month, per year, cumulative, etc.) or a location (distance from the sea).

Examples of the “weather information” include an average temperature, an average humidity, a rainfall, and a snowfall.

Examples of the “statistical information” include a proportion by the type of damage or by the size of damage.

FIG.4illustrates a case where the database40is stored in the storage unit16. However, as long as the information processing apparatus10can access the database40via a wired or wireless network, the database40may be stored in an external storage device.

FIG.5is a flowchart showing an information processing method using the information processing apparatus10. As shown inFIG.5, the information processing method comprises, as an example, a step of acquiring a search query (step S1), a step of acquiring a first search result (step S2), a step of acquiring meta-information of the search query (step S3), a step of filtering the database based on the meta-information (step S4), a step of acquiring a second search result (step S5), a step of obtaining a reliability degree of the first search result based on the second search result (step S6), and a step of outputting the reliability degree (step S7).

<Step of Acquiring Search Query>

In the step of acquiring the search query (step S1), the search query acquisition unit51acquires the search query. The user inputs the search query into the information processing apparatus10via the operation unit18, for example. In this case, the search query is created by the user based on the inspection result of the structure1. Then, the search query acquisition unit51acquires the search query. In addition, as another method, the user may create the search query by using the mobile terminal5. Further, as still another method, the search query may be automatically created from the inspection result. Here, the search query includes information for specifying a search target and is a type of processing request to the information processing apparatus10.

The search query can include, for example, text data and/or image data. In a case where the structure1is the target, the search query includes at least one selected from the group of “specifications”, “damage information”, “repair information”, “peripheral information”, “weather information”, “statistical information”, and “damage image”. The “specifications”, the “damage information”, the “repair information”, the “peripheral information”, the “weather information”, and the “statistical information” are examples of the text data, and the “damage image” is an example of the image data. The search query is not limited to the above-described text data and/or image data.

It is preferable that the image data and the text data included in the search query are of the same type as the image data43and the text data42included in the inspection data41.

FIG.6shows an example of a display screen on which the search query is displayed. A display screen100shown inFIG.6is displayed on the display device30under the control of the display control unit26. The display screen100includes an input display screen102and a result display screen104.

The input display screen102displays a search query Qu acquired by the search query acquisition unit51and displays characters “search query” in a type area106. In a case where the search query Qu includes image data, image data ID is displayed on the input display screen102, and the user can confirm the image data ID. In a case where the search query Qu includes text data, text data TD is displayed on the input display screen102, and the user can confirm the text data TD. The user can input the search query Qu (image data ID and/or text data TD) from the input display screen102, and the input search query Qu is acquired by the search query acquisition unit51.

The input display screen102shown inFIG.6can display or input meta-information which will be described below. In an area where the meta-information is displayed or input, characters “meta-information” are displayed in a type area107.

The input display screen102includes an execution button108. In a case where the execution button108is operated, the process proceeds to a next step. InFIG.6, a processing flow proceeds to step S2of acquiring the first search result.

<Step of Acquiring First Search Result>

In the step of acquiring the first search result (step S2), the first search result acquisition unit52acquires the first search result of search performed based on the search query Qu on the database40. As shown inFIG.7, for example, the first search result acquisition unit52acquires the inspection data41of inspection performed based on the search query Qu on the database40. The acquired inspection data41is an example of the first search result. InFIG.7, the search query Qu including the text data TD and the image data ID is displayed. The search query Qu need only include at least one of the text data TD or the image data ID.

In a case where the search query Qu includes the image data ID, the first search result acquisition unit52acquires the image data43from the database40based on the image data ID. The first search result acquisition unit52calculates feature amounts of the image data ID and the image data43using an image recognition algorithm, a machine learning model, or the like. Next, the first search result acquisition unit52compares the feature amount of the image data ID with the feature amount of the image data43and acquires the image data43corresponding to the image data ID as the first search result of search performed based on the search query Qu.

In a case where the search query Qu includes the text data TD, the first search result acquisition unit52acquires the text data42from the database40based on the text data TD. The first search result acquisition unit52compares the text data TD with the text data42using a text search algorithm, a machine learning model, or the like and acquires the text data42corresponding to the text data TD of the search query Qu as the first search result of search performed based on the search query Qu.

In addition, processing performed based on the search query referred to in the present specification means that some processing is performed on the database40by using information on the search query, and includes, for example, a search. The search includes concepts such as “match”, “similarity”, “dissimilarity”, and “evaluation” (for example, “ascending order” or “descending order”).

As shown inFIG.7, the first search result obtained by the first search result acquisition unit52is displayed on the result display screen104under the control of the display control unit26. In a case where the search query Qu is the image data ID, the image data43corresponding to the image data ID is displayed on the result display screen104. In addition, the text data42associated with the image data43on the database40is also displayed.

In a case where the search query Qu is the text data TD, the text data42corresponding to the text data TD is displayed on the result display screen104. In addition, the image data43associated with the text data42on the database40is also displayed.

AlthoughFIG.7shows the image data ID and the text data TD as the search query Qu, as described above, the search query Qu need only include at least one of the image data ID or the text data TD. In addition, the first search result acquisition unit52may acquire the first search result of search performed based on the search query that combines the image data ID and the text data TD.

For example, a technique described in WO2020/071216A, WO2020/255227A, JP2018-165926A, or JP2017-167987A may be applied to the acquisition of the first search result.

Next, the processing flow proceeds to step S3of acquiring the meta-information of the search query Qu.

<Step of Acquiring Meta-Information of Search Query>

In the step of acquiring the meta-information of the search query Qu (step S3), the meta-information acquisition unit53acquires the meta-information of the search query Qu.FIG.8shows an example of the display screen100on which meta-information Me is displayed, and the acquired meta-information Me is displayed on the input display screen102. InFIG.8, the first search result acquired in step S2is displayed on the result display screen104.

Similarly to the search query Qu, the meta-information Me can include the text data TD and/or the image data ID and includes, for example, at least one selected from the group of “specifications”, “damage information”, “repair information”, “peripheral information”, “weather information”, “statistical information”, and “damage image”. The “specifications”, the “damage information”, the “repair information”, the “peripheral information”, the “weather information”, and the “statistical information” are examples of the text data TD, and the “damage image” is an example of the image data ID. The search query Qu and the meta-information Me can include the same type of the image data ID and the text data TD. Note that the information included in the meta-information Me is not limited to these. InFIG.8, the meta-information Me including the text data TD and the image data ID is displayed. Note that the meta-information Me need only include either the text data TD or the image data ID.

Here, the meta-information Me is not the search query Qu itself but information that is related to the search query Qu.

Next, combinations of the search query Qu and the meta-information Me will be shown. Table 1 shows an example of the combinations of the search query Qu and the meta-information Me. In a case where the target is a structure, the following combinations can be exemplified. No. 1 shows that the search query Qu is the image data (damage image) and the meta-information Me is the text data (specifications, damage information, repair information, statistical information, and the like). No. 2 shows that the search query Qu is the text data (specifications, damage information, repair information, statistical information, and the like) and the meta-information Me is the image data (damage image). No. 3 shows that the search query Qu is the image data (damage image) and the meta-information Me is the image data (damage image). In No. 3, in a case where the search query Qu and the meta-information Me each include a damage image as the image data, the damage image different from the search query Qu is applied as the damage image of the meta-information Me. As the damage image of the meta-information Me, an image completely different from the search query Qu, a past image, a slightly processed image, or an image captured from a remote place can be exemplified.

Table 1 is an example of the combinations of the search query Qu and the meta-information Me, but the combinations are not limited thereto.

The meta-information acquisition unit53can automatically acquire the meta-information Me of the search query Qu. For example, in a case where the search query Qu is the image data, an exchangeable image file format (Exif) is automatically acquired as the meta-information Me by the meta-information acquisition unit53. The Exif is information attached to the image data during imaging and includes information such as circumstances and settings during imaging. Further, in a case where the image data of the search query Qu is captured in a GPS reception environment, the meta-information Me may include positional information, latitude and longitude, or altitude.

In addition, in a case where the image data of the search query Qu is a “damage image”, the meta-information Me can be acquired by using a machine-learned learning model. For example, the “damage information” can be specified by the learning model based on the “damage image” of the search query Qu. The specified “damage information” is acquired as the meta-information Me by the meta-information acquisition unit53.

The meta-information Me can be manually input into the information processing apparatus10by the user.FIG.8is an example of a display screen showing an input example of the meta-information Me. The display screen100ofFIG.8can execute the confirmation of the acquired meta-information Me and the input of the meta-information Me. That is, the user can manually input the image data ID and/or the text data TD as the meta-information Me onto the input display screen102of the display screen100. The input meta-information Me is acquired by the meta-information acquisition unit53.FIG.8shows a case where the image data ID and the text data TD are input as the meta-information Me, and the image data ID and the text data TD are displayed. The user can input either the text data TD or the image data ID as the meta-information Mc.

In a case where the user manually inputs the meta-information Me, the user associates the search query Qu and the meta-information Me with each other. For example, in a case where the meta-information Me is input, the user displays the search query Qu on the input display screen102and performs processing to associate the meta-information Me with the search query Qu, so that the search query Qu and the meta-information Me are associated. The user can optionally associate the search query Qu with the meta-information Me.

Even in a case where the meta-information Me is automatically acquired, the input display screen102can display the meta-information Me under the control of the display control unit26, and the user can confirm the meta-information Me.

In a case where the execution button108of the input display screen102is operated, the processing flow proceeds to step S4of filtering the database40. Further, the processing flow can proceed to step S5of acquiring the second search result after step S4.

In the step of filtering the database (step S4), the filtering unit54executes filtering on the database40based on the meta-information Me.

The filtering unit54filters the database40based on the image data ID or the text data TD included in the meta-information Me. The filtering unit54compares the text data TD or the image data ID of the meta-information Me with the text data42or the image data43of the database40.

In a case where the meta-information Me is the text data TD, the filtering unit54can filter the database40by, for example, extracting only the text data42that is the same as the text data TD of the meta-information Me or extracting other data.

In addition, the filtering unit54can filter the database40according to an analysis result of the meta-information Me. For example, in a case where the meta-information Me is the damage type, the filtering unit54can calculate a proportion of the damage type as the analysis result and compare the proportion of the damage type with the statistical information of the proportion of the damage type in the database40to filter the database40accordingly. In addition, in a case where the meta-information is a size of the damage type, the filtering unit54can calculate a proportion of the size of the damage type as the analysis result and compare the proportion of the size of the damage type with the statistical information of the proportion of the size of the damage type from the database40to filter the database40. These filtering methods are examples of filtering.

In addition, the filtering unit54may filter the database40in a stepwise manner. For example, in a case where the meta-information Me includes a plurality of types of text data TD, the filtering unit54can perform filtering with the “elapsed years” and further perform filtering with the “member name”. The stepwise filtering is not particularly limited.

As a result of the filtering, there are a case where a population parameter of the database40is narrowed down and a case where the population parameter is not narrowed down.

The processing flow proceeds to step S5of acquiring the second search result.

<Step of Acquiring Second Search Result>

In the step of acquiring the second search result (step S5), the second search result acquisition unit55acquires the second search result of search performed based on the search query on the filtered database40. The second search result acquisition unit55acquires, for example, the inspection data41of inspection performed based on the search query on the filtered database40.

A different point between the acquisition of the second search result of search performed based on the search query by the second search result acquisition unit55and the acquisition of the first search result of search performed based on the search query by the first search result acquisition unit52is that the second search result acquisition unit55targets the filtered database40, whereas the first search result acquisition unit52targets the database40before filtering. Except for that point, the second search result acquisition unit55acquires the second search result using the same method as the first search result acquisition unit52.

FIG.9is an example of a case where the second search result is displayed on the result display screen104. As shown inFIG.9, the second search result obtained by the second search result acquisition unit55is displayed on the result display screen104under the control of the display control unit26.FIG.9shows an example of the second search result according to the present invention. For example, in a case of comparing the second search result shown inFIG.9with the first search result shown inFIG.7, the displayed results differ because the database40is filtered.

Next, in a case where the execution button108is operated, the processing flow proceeds to step S6of obtaining the reliability degree, and the reliability degree calculation unit56calculates the reliability degree of the first search result.

AlthoughFIG.9shows a case where the second search result is displayed on the display screen100, the second search result may not be displayed on the display screen100. That is, the processing flow may proceed to step S6of executing reliability degree processing to obtain the reliability degree of the first search result after step S5of acquiring the second search result.

<Step of Obtaining Reliability Degree of First Search Result>

In the step of obtaining the reliability degree of the first search result (step S6), the reliability degree calculation unit56obtains the reliability degree of the first search result based on the second search result. By obtaining the reliability degree of the first search result, it is possible to improve an accuracy of the first search result.

A preferred aspect of a method of obtaining the reliability degree will be described. The reliability degree calculation unit56decides an application condition of the first search result and an application condition of the second search result, respectively, in order to obtain the reliability degree. The reliability degree calculation unit56can obtain the reliability degree from each of the application conditions.

For example, the reliability degree calculation unit56decides an index (so-called similarity rank) of the database40for the first search result and the second search result as the application condition for obtaining the reliability degree.FIGS.10A and10Bare diagrams conceptually illustrating cases where the index is the target.FIG.10Ashows an example of the first search result, and four data A, B, C, and D are acquired from the database40as the first search result. The four data A to D indicate the similarity ranks for the search query in numerical values. The smaller the numerical value, the more similar it is to the search query. That is, the first search result indicates that the search result is similar to the search query in the order of D, A, B, and C.

FIG.10Bshows an example of the second search result. In the second search result, two data A and D are acquired from the filtered database40. The two data A and D indicate the similarity ranks for the search query in numerical values. That is, the second search result indicates that the search result is similar to the search query in the order of D and A.

The reliability degree calculation unit56obtains the reliability degree of the first search result (including the index) based on the second search result (including the index). For example, in the examples ofFIGS.10A and10B, the order of D and A in the second search result matches the order of D and A in the first search result, and the reliability degree calculation unit56calculates that the reliability degree of the first search result is 100%.

In addition, from another viewpoint, the reliability degree calculation unit56can use the top N (N is a natural number) results of the first search result and the top N (N is a natural number) results of the second search result as the application conditions. In this case, the meta-information accompanying the search result may also be included. The reliability degree calculation unit56can obtain the reliability degree of the first search result (including the top N results) based on the second search result (including the top N results).

The reliability degree calculation unit56may calculate a similarity or a distance in the process of obtaining the reliability degree. The reliability degree calculation unit56can obtain the similarity or the distance by applying a method selected from the group of cosine similarity, deviation pattern similarity, Jaccard coefficient, Dice coefficient, Simpson coefficient, Pearson correlation coefficient, Spearman correlation coefficient, Earth Mover's Distance, Euclidean distance, weighted Euclidean distance, Hamming distance, Mahalanobis distance, or Canberra distance. The method shown here is a known technique, so the description thereof will be omitted.

In addition, in a case where the first search result (including the top N results) is used as the application condition based on the second search result (including the top N results), the reliability degree calculation unit56can obtain the statistical information for the first search result or the second search result based on the first search result or the second search result and determine whether the statistical information is within a range of a threshold value to obtain the reliability degree.

As the statistical information, the reliability degree calculation unit56calculates a proportion in which specific information included in the top N of the second search result is included in the top N of the first search result. The reliability degree calculation unit56can determine whether the proportion is within a preset threshold value and finally obtain the reliability degree.

As the statistical information, the reliability degree calculation unit56calculates a proportion in which specific information included in the top N of the first search result is included in the top N of the second search result. The reliability degree calculation unit56can determine whether the proportion is within a preset threshold value and finally obtain the reliability degree.

In a case where the reliability degree is obtained by the reliability degree calculation unit56, the processing flow proceeds to step S7of outputting the reliability degree.

In the step of outputting the reliability degree (step S7), the output unit57outputs the reliability degree to the display device30via the display control unit26.FIG.11is an example of the display screen to which the reliability degree is output. As shown inFIG.11, the reliability degree is displayed in a field of the result display screen104. InFIG.11, the reliability degree and the first search result are displayed on the display device30at the same time. Since the reliability degree for the first search result is displayed, the user can determine whether the past inspection result can be used from the first search result with reference to the reliability degree.

Although a case has been illustrated where the output unit57displays the reliability degree on the display device30, the output unit57can also output the reliability degree to a printer and as electronic data in various data formats.

The processing flow ends in a case where the output unit57outputs the reliability degree.

PREFERRED EMBODIMENTS

Hereinafter, preferred embodiments will be described.

First Embodiment

In a first embodiment, the reliability degree is compared with a preset threshold value X, and the processing flow is executed while changing the search query until the reliability degree satisfies the threshold value X.

In the example ofFIG.11, the display screen100displays that the reliability degree is 80%. The user can determine that the reliability degree of the first search result is high. On the other hand, a case is also conceivable in which the reliability degree calculation unit56calculates the reliability degree of the first search result as 20%.

In this respect, the reliability degree calculation unit56can determine whether the reliability degree is equal to or higher than the preset threshold value X and execute the processing flow according to the reliability degree.

FIG.12is a flowchart of the first embodiment, and step S6A of determining the reliability degree is added to the flow ofFIG.5. Differences fromFIG.5will be mainly described.

As described above, the CPU20of the information processing apparatus10executes steps S1to S6. In a case where the reliability degree calculation unit56obtains the reliability degree, the processing flow proceeds to the determination step (step S6A). In step S6A, it is determined whether the reliability degree is equal to or higher than the preset threshold value X.

In a case where it is determined that the reliability degree is smaller than the threshold value X, that is, in a case of “No” in step S6A, the processing flow proceeds to the step of acquiring the search query (step S1). In the step of acquiring the search query (step S1), the search query for which it is determined that the reliability degree of the first search result does not satisfy the threshold value X is changed. The changed search query is acquired by the search query acquisition unit51, and steps S1to S6A are repeated until the reliability degree is determined to be equal to or higher than the threshold value X.

In a case where the search query is the image data, the change of the search query can be exemplified by, for example, changing the image data to one captured from a different angle. The change of the search query can be performed automatically or manually. Note that the change of the search query is not limited to this example.

In a case where it is determined that the reliability degree is equal to or higher than the threshold value X, that is, in a case of “Yes” in step S6A, the processing proceeds to the step of outputting the reliability degree (step S7), and the processing flow ends in a case where the output unit57outputs the reliability degree.

In the first embodiment, since the first search result of the reliability degree equal to or higher than the threshold value X is output, the user can use the past inspection result based on the first search result with reference to the reliability degree.

Second Embodiment

In a second embodiment, the accuracy of the first search result can be improved by feeding back the reliability degree result to the acquisition of the first search result.

FIG.13is a flowchart of the second embodiment and shows a flow in which step S6B of feeding back the result of the reliability degree to the acquisition of the first search result is added. Differences fromFIG.5will be mainly described.

As described above, the CPU20of the information processing apparatus10executes steps S1to S6. In a case where the reliability degree calculation unit56obtains the reliability degree, the processing flow proceeds to the step of feeding back the result of the reliability degree to the acquisition of the first search result (step S6B).

In a case where the first search result acquisition unit52uses a machine learning model, parameters of the machine learning model may be optimized through machine learning by using the first search result and the second search result as training data. The accuracy of the first search result acquired by the first search result acquisition unit52is improved by using the optimized machine learning model.

The method of feeding back the result of the reliability degree to the acquisition of the first search result is not limited to the above method.

In the embodiments, a hardware structure of a processing unit that executes various processing is the following various processors. The various processors include a central processing unit (CPU) that is a general-purpose processor functioning as various processing units by executing software (program), a programmable logic device (PLD) such as a field programmable gate array (FPGA) that is a processor having a circuit configuration changeable after manufacture, a dedicated electric circuit such as an application specific integrated circuit (ASIC) that is a processor having a circuit configuration dedicatedly designed to execute specific processing, and the like.

One processing unit may be configured by one of these various processors, or may be configured by two or more same type or different types of processors (for example, a plurality of FPGAs or a combination of the CPU and the FPGA). Moreover, a plurality of processing units can be configured by one processor. As an example of configuring the plurality of processing units by one processor, first, there is a form in which one processor is configured by a combination of one or more CPUs and software, as represented by a computer such as a client or a server, and the one processor functions as the plurality of processing units. Second, as represented by a system on chip (SoC) or the like, there is a form of using a processor that realizes, by one integrated circuit (IC) chip, functions of the entire system including the plurality of processing units. As described above, the various processing units are configured using one or more of the above various processors as a hardware structure.

Further, as the hardware structure of the various processors, more specifically, an electric circuit (circuitry) in which circuit elements such as semiconductor elements are combined may be used.

Each of the configurations and functions described above can be appropriately realized by using any hardware, software, or a combination of both. For example, the present invention can also be applied to a program for causing a computer to execute the above-described processing steps (processing procedures), a computer-readable storage medium (non-transitory storage medium) in which such a program is stored, or a computer on which such a program can be installed.

Although examples of the present invention have been described above, it goes without saying that the present invention is not limited to the above-described embodiment and various modifications can be made without departing from the scope of the present invention.

EXPLANATION OF REFERENCES