JP6733364B2 - Task estimation device, task estimation method, and task estimation program - Google Patents

Description

The present invention relates to a technique for estimating a problem that requires verification.

Explain how to launch a business related to product development and manufacturing. One method of business development is wasteful by rapidly repeating the processes of “building a hypothesis of the product potentially needed by the customer”, “implementing the product” and “correcting the trajectory of the product and business” in the development stage. In addition, we will continue to improve products quickly while minimizing non-value elements, and lead the business to success. In this business development method, first, a person involved in the business (hereinafter referred to as a business operator) extracts a hypothetical problem related to a user such as a customer, verifies the problem, and produces a prototype product that is practically practical ( Minimum Viable Product) to be manufactured and delivered to users as quickly as possible. After that, the business operator reconstructs the problem of the original hypothesis based on the result obtained by verifying the reaction of the user to the prototype product, and corrects the trajectory of the product or business. Business owners need to spend only the minimum amount of time and money on non-essential elements (for example, creating specifications, approval documents, etc.) that do not lead to the success of the business by repeating only the minimum processes necessary to capture the demand of users. Or save waste such as labor. This makes it possible to understand the core issues without overly narrowing the scope of product development.

In order to grasp the true needs of users in such a development method, it is important to generate hypothetical tasks quickly and appropriately and obtain feedback through communication with the users. If it is determined that the hypothetical task is not appropriate, it is necessary to immediately verify the next task.

However, a group (for example, an organization that develops businesses for the elderly, etc.) having a certain common problem, a predetermined business field, or a predetermined industry (hereinafter, these are referred to as “domains” in the present application) have problems. When making a hypothesis, the business operator must comprehensively understand the information of the domain and investigate the information. In addition, the business operator needs to understand future problems of the domain and problems of fields around the domain, and it is difficult to quickly and appropriately establish a hypothesis of the problem. In addition, in the present application, the domain is also described as “information indicating the target of the problem”.

As a technique related to the above, a technique for providing a task for implementing a predetermined life improvement measure and supporting a user's life improvement measure is disclosed (see Patent Document 1).

In addition, as related technologies, a process of temporarily setting the customer's interests, a process of verifying the temporary interests, and a process of introducing a solution provided based on the verified interests and performing after-sales follow-up. A management consulting method provided is disclosed (see Patent Document 2).

International Publication No. 2015/125454 JP-A-2003-242318

In the technology of Patent Document 1, the information processing device is a specific subject corresponding to a problem with respect to a predetermined life improvement measure and another problem for implementing the same life improvement measure and the reason why the predetermined life improvement measure fails. It was necessary to create in advance the data of the task correspondence table that corresponded to those with different actions.

However, the problem to be verified and its surrounding situation in a certain domain change every moment, and therefore it is not possible to sufficiently deal with the change only by creating a correspondence table in advance.

According to the technology of Patent Document 2, a high-quality solution suitable for each client is designed and introduced, and after-sales follow-up is performed.

However, if the solution is of high quality but does not match the client, it does not create new hypotheses (solutions) one after another. That is, it is not a method based on failure, in which only the minimum processes necessary for properly grasping the user's problem are rapidly repeated by trial and error.

The present invention has been made to solve the above problems. The main object of the present invention is to provide a problem estimation device or the like that covers a wide range and appropriately extracts a large number of problems to be verified.

In order to solve the above problems, the problem estimation device according to the first aspect of the present invention is
Output at least one second task that is an extension of the input first task,
Of the output second tasks, an estimation unit that extracts a task that co-occurs with the information indicating the target of the task to which the first task belongs is provided.

The problem estimation method according to the second aspect of the present invention is
Output at least one second task that is an extension of the input first task,
Among the output second tasks, extracting a task that co-occurs with information indicating the target of the task to which the first task belongs.

The problem estimation program according to the third aspect of the present invention is
Output at least one second task that is an extension of the input first task,
The computer is caused to execute the extraction of the co-occurrence problem from the output second problem and the information indicating the target of the problem to which the first problem belongs.

According to the present invention, it is possible to provide a problem estimation device or the like that covers a wide range and appropriately extracts a large number of problems to be verified.

It is a figure which shows the structural example of the subject estimation apparatus in the 1st Embodiment of this invention. It is a figure which shows the structural example of the data of the pattern used for analysis of a parallel relationship. It is a figure which shows the structural example of the data in a table used for analysis of a parallel relationship. It is a figure which shows the structural example of the internal data of a domain keyword storage part. It is a flowchart which shows the operation example of the task estimation apparatus in the 1st Embodiment of this invention. It is a figure which shows the structural example of the subject estimation apparatus in the 2nd Embodiment of this invention. It is a figure which shows the structural example of the internal data of a co-occurrence relation storage part. It is a flowchart which shows the operation example of the task estimation apparatus in the 2nd Embodiment of this invention. It is a figure which shows the structural example of the subject estimation apparatus in the 3rd Embodiment of this invention. It is a flowchart which shows the operation example of the task estimation apparatus in the 3rd Embodiment of this invention. It is a figure which shows the structural example of the subject estimation apparatus in the 4th Embodiment of this invention. It is a figure which shows the structural example of the information processing apparatus applicable in embodiment of this invention.

Next, an embodiment of the present invention will be described with reference to the drawings. In the following description of the drawings, the same or similar reference numerals are given to the same or similar parts. However, the drawings schematically show the configuration in the embodiment of the present invention. Furthermore, the embodiment of the present invention described below is an example, and can be appropriately modified within the scope of the same essence.

<First Embodiment>
(Problem estimation device)
As shown in FIG. 1, the task estimation device 100 according to the first embodiment of the present invention stores a task estimation unit 1, a priority determination unit 2, a parallel relationship storage unit 3, a general information storage unit 4, and a domain keyword storage. The unit 5 is provided. The task estimation unit 1 includes a task extension unit 1a and a selection unit 1b. Note that in the present application, task estimation refers to extraction of candidates that can be tasks.

The general information storage unit 4 stores general information including a sentence (a set of character strings) used for extracting a problem to be verified, terms and character strings described in the table. Specifically, general information is a term written in a text sentence of a magazine or a newspaper, a text sentence on a website, or a chart on a website necessary for investigating a trend of a certain domain. When a diagram is described as an image, a text sentence may be extracted using an OCR (Optical character recognition) technique.

As shown in FIG. 2, the parallel relationship storage unit 3 stores a pattern used for analyzing a parallel relationship included in a sentence of general information from which a task to be verified is extracted. The pattern and method used to analyze the relationship with the problem to be verified are not limited to this. The parallel relationship means that two or more similar tasks are arranged in the left or right direction or the up and down direction in a sentence or a table. For example, if two tasks are expressed as “A” and “B”, and the general information includes “A and B”, the task estimation unit 1 will It is determined that the task “A” and the task “B” have a parallel relationship. As another example of the terms used in the parallel relationship, the task estimation apparatus 100 stores similar words (for example, “result” and “effect”, “elimination” and “solved”) in an external or internal storage device. You may search using the stored dictionary etc. Then, the task estimation unit 1 may add the search result to the task to be verified.

The parallel relationship storage unit 3 may store a pattern used to analyze the parallel relationship included in the table of general information from which the problem to be verified is extracted. For example, assume that the table shown in FIG. 3 is handled as general information. In the table shown in Fig. 3, fill in the cells for "Issue of new customer acquisition" with "Capturing female customers", "Capturing younger people", "Capturing older people", "Capturing light users" Has been done. When such a table is handled, the task estimation unit 1 may determine that the statements in the table in the table have a parallel relationship based on the pattern in the fourth row in FIG.

The domain keyword storage unit 5 stores keywords related to a certain domain in association with each other. The domain keyword storage unit 5 includes a keyword table as shown in FIG. 4, for example. In the table, for example, "convenience store" and "newspaper" are stored as the domain names of the domains, and "young and old" and "marketing and articles" are stored as frequently-used words related to these domains.

The task estimation unit 1 outputs a hypothetical task to be verified. Specifically, the task estimation unit 1 outputs a task in parallel with a task (first task) input via an input/output interface described later. A task in parallel with the first task is an example of a second task that is an extension of the first task, and in this embodiment, the task is also called a second task. Next, the task estimation unit 1 extracts a task that co-occurs with the domain to which the first task belongs from the output second tasks. Here, co-occurrence means that a character string and another character string appear at the same time in a sentence or term of general information, or that they frequently appear.

The task extension unit 1a extends the input task (first task) to another task (second task) in parallel with the task. Specifically, the task expansion unit 1a searches for one or more other tasks having a parallel relationship with the input task from the general information storage unit 4 by using the pattern stored in the parallel relationship storage 3, and outputs them. To do.

Based on the keyword table in the domain keyword storage unit 5, the selection unit 1b analyzes whether or not the task output from the task expansion unit 1a has relevance or affinity with the domain to which the first task belongs. As a result, a problem (co-occurrence problem) having relevance is extracted. Specifically, the selection unit 1b stores a certain domain to which the first assignment belongs and the individual assignments of the second assignments output from the assignment extension unit 1a in association with each other in the keyword table. It is determined whether or not the assignment has been performed, and as a result of the determination, those associated with the keyword table (issues that co-occur with the domain) are extracted.

The priority determination unit 2 sets the priority for verifying the problem (the problem that co-occurs with the domain among the first problem and the second problem) extracted by the problem estimation unit 1, that is, the order in which the verification should be started. .. There are various methods for setting the priority, but it is preferable that the user can freely set the priority. As a method of setting the priority, a method of increasing the likelihood that the issue is newer, a result of searching a general search website for the issues, and ascending or descending the number of hits There are a method of acquiring and a method of arranging a plurality of extracted problems in correspondence with the ranking of the search ranking at the time of extraction.

(Operation of the task estimation device)
The operation of the task estimation device 100 will be described with reference to the flowchart shown in FIG.

In step S110, the assignment estimation unit 1 receives the “assignment” (first assignment) and the “domain” from another device via an input/output interface described below. Specifically, the accepted “issue” is text information indicating the issue, and the accepted “domain” is text information indicating the domain (domain name shown in FIG. 3) and the like. In the following, a case will be described in which “capturing female customers” is accepted as an issue and “convenience store” is accepted as a domain.

In step S120, the task extension unit 1a of the task estimation unit 1 extends the received task (first task) to another task (second task) having a parallel relationship with the task. Specifically, the task expansion unit 1a searches for one or more other tasks having a parallel relationship with the input task from the general information storage unit 4 using the pattern stored in the parallel relationship storage unit 3, Output. The task expansion unit 1a, for example, with respect to the task “capturing female customers”, matches the pattern “A, B, C and D” of the parallel relationship stored in the parallel relationship storage unit 3, and acquires the sentence “new customer acquisition”. Is a problem to be taken in by female customers, younger people, older people, and light user groups.” from the general information storage unit 4. It should be noted that it may be acquired from the description in the table of general information as shown in FIG. The task expansion unit 1a has three second tasks, “younger people's capture”, “elderly people's capture”, and “light user”, which have a parallel relationship with the first task “capturing female customers”. Ingest".

In step S130, the selection unit 1b selects, from among the problems extracted from the general information storage unit 4, a problem that co-occurs with a keyword related to the domain "convenience store". The selecting unit 1b may use, as the co-occurring task and keyword, a word stored in the keyword table in the domain keyword storage unit 5 as a domain name and a domain frequent word. For example, the input task is “capturing female customers”, and the tasks extracted by the task expansion unit 1a are “1. capturing younger people”, “2. capturing older people”, “3. light user layer” Is taken in”. In this case, the selecting unit 1b includes the domain frequent words "younger" and "elderly" that co-occur with the domain, that is, corresponding to the domain name "convenience store" in the domain keyword storage unit "2. “Capturing” and “3. Capturing the elderly” and output a new subject corresponding to the selected subject. The expressions of the selected task and the new task may be slightly different.

In step S150, the priority determination part 2 sets the priority which should be verified about the subject which the selection part 1b extracted. That is, the priority determination unit 2 determines the order in which to tackle each of the tasks selected by the selection unit 1b, and outputs it as list data of the tasks with priorities. There are various methods for determining the priority. For example, the priority determination unit 2 searches the general information storage unit 4 for individual problems and extracts the problems as problems to be verified in ascending order of the number of search hits. The priority determination unit 2 may extract the tasks to be verified in descending order of the number of search hits in order to obtain a new idea.

According to the problem estimation apparatus 100 of the present embodiment, a large number of problems to be verified can be appropriately generated over a wide range. This is because the task estimation unit 1
This is because among the second tasks output by expanding the input first task, the tasks that co-occur with the domain to which the first task belongs (information indicating the target of the task) are extracted.

<Second Embodiment>
In the first embodiment of the present invention, the task extension unit 1a of the task estimation apparatus 100 extracts a new task based on the parallel relationship with the input task. However, the task can also be extracted by using another relationship with respect to the input task, for example, a co-occurrence relationship. Hereinafter, in the second embodiment of the present invention, a task estimation device for extracting a task using a co-occurrence relationship will be described.

(Problem estimation device)
As shown in FIG. 6, the task estimation device 200 according to the second embodiment of the present invention stores a task estimation unit 10, a priority determination unit 2, a co-occurrence relation storage unit 3a, a general information storage unit 4, and a domain keyword storage. The unit 5 is provided. The task estimation unit 10 includes a task extension unit 1c and a selection unit 1b.

As shown in FIG. 7, the co-occurrence relationship storage unit 3a stores another problem (co-occurrence problem) having a co-occurrence relationship with a certain problem in association with each other. It is assumed that the data in the table in the co-occurrence relation storage unit 3a is created in advance based on the information in the general information storage unit 4. The co-occurrence relationship refers to a relationship in which another task co-occurs with a certain task and appears frequently or frequently. For terms used in the co-occurrence relationship, search for similar words (for example, “result” and “effect”, “dissolve” and “solve”) using an external or internal dictionary, etc. You may enable it.

The task expansion unit 1c expands the input task (first task) to another task having a co-occurrence relationship with the task. Specifically, the task expansion unit 1a acquires another task having a co-occurrence relationship with the input task from the co-occurrence relationship storage unit 3a and outputs it. Another task having a co-occurrence relationship is also called a co-occurrence task, which is an example of a second task that is an extension of the first task.

Others are the same as those in the first embodiment.

(Operation of the task estimation device)
The operation of the task estimation device 200 will be described with reference to the flowchart shown in FIG.

In step S110, the task estimation unit 10 receives text information indicating a task and a domain from another device via an input device described later. For example, a case will be described below in which “customer decrease” is accepted as an issue and “convenience store” is accepted as a domain.

In step S121, the task extension unit 1c extends the input task to another task that has a co-occurrence relationship with the task. Specifically, the task expansion unit 1a acquires from the co-occurrence relationship storage unit 3a another task that has a co-occurrence relationship with the input task “decrease customer”. For example, when the task is “decrease in customer”, the task extension unit 1c refers to the co-occurrence relationship storage unit 3a (FIG. 7), and refers to the co-occurrence tasks of the task, “capturing younger people” and “capturing elderly people”. , “Personnel cost reduction” and “Population reduction” are extracted.

In step S131, the selection unit 1b selects an issue that co-occurs with the domain "convenience store" from the issues and co-occurrence tasks acquired from the co-occurrence relationship storage unit 6. Words stored as domain names and domain frequent words in the keyword table in the domain keyword storage unit 5 may be used as the tasks and keywords that co-occur with the domain. For example, the input task is “decrease in customer”, and the task extracted by the task expansion unit 1c is “1. capturing younger people”, “2. capturing older people”, “3. reducing labor costs”, “ 4. Decrease in population”. In this case, the selecting unit 1b includes the domain frequent words "younger" and "elderly" that co-occur with the domain, that is, corresponding to the domain name "convenience store" in the domain keyword storage unit "2. “Capturing” and “3. Capturing the elderly” are selected as new challenges, and new challenges corresponding to the selected challenges are output. The expressions of the selected task and the new task may be slightly different.

Steps S140 and S150 are the same as those in the first embodiment (FIG. 5).

According to the problem estimation device 200 of the present embodiment, a large number of problems to be verified can be appropriately extracted over a wide range. This is because the task estimation unit 10 extends the base task to the co-occurrence task, and extracts a task suitable for the domain from the expanded task and the co-occurrence task. In the present embodiment, since the co-occurrence task for the task is created in advance, the task extraction process can be executed at a higher speed.

<Third Embodiment>
A task estimation device 300 according to a third embodiment, which is a superordinate concept of the first and second embodiments of the present invention, will be described.

(Problem estimation device)
The task estimation device 300 according to the third embodiment of the present invention includes a task estimation unit 11 and a priority determination unit 20, as shown in FIG. 9.

The task estimation unit 11 extends the task and estimates the task to be verified. Specifically, the task estimation unit 11 expands a task (first task) input from another device and outputs at least one expanded task (second task). Further, the task estimation unit 11 extracts, from the output second tasks, a task that co-occurs with the domain (information indicating the target of the task) to which the first task belongs.

The priority determination unit 20 sets the priority for verifying the tasks extracted by the task estimation unit 11, that is, the order in which the verification should be started. There are various methods for setting the priority, but it is preferable that the user can freely set the priority.

(Operation of the task estimation device)
The operation of the task estimation device 300 will be described with reference to the flowchart shown in FIG.

In step S110, the task estimation unit 11 of the task estimation apparatus 300 receives the task and the domain, for example, as text information.

In step S122, the task estimation unit 11 expands the accepted task. As an extension method, there are a method of extending an issue having a parallel relationship with an issue, a method of extending an issue co-occurring with an issue, and the like.

In step S132, the task estimation unit 11 analyzes the affinity between the extended task and the accepted domain and selects a task having a higher affinity with the domain. That is, the task estimation unit 11 narrows down the expanded tasks based on the presence/absence of affinity with the domain, and selects only the tasks that match the accepted domain and task.

Steps S140 and S150 are the same as those in the first embodiment (FIG. 5).

According to the problem estimation device 300 of the present embodiment, a large number of problems to be verified can be appropriately extracted over a wide range. This is because the task estimation unit 11 outputs at least one second task that is an extension of the input first task,
This is because, out of the output second problems, a problem that co-occurs with the domain to which the first problem belongs (information indicating the target of the problem) is extracted. This makes it possible to cover a wide range and extract other problems to be verified.

<Fourth Embodiment>
FIG. 11 shows a task estimation device 400 according to the fourth embodiment of the present invention. The task estimation device 400 is the minimum configuration of the first to third embodiments. The task estimation device 400 includes an estimation unit 12. The estimation unit 12 outputs at least one or more second assignments obtained by expanding the input assignment (first assignment), and of the output second assignments, the domain (assignment) to which the first assignment belongs. The problem that co-occurs with the information indicating the target) is extracted.

According to the problem estimation device 400 of the present embodiment, a large number of problems to be verified can be appropriately extracted over a wide range. This is because the estimation unit 12 extracts only the problems that co-occur with the domain (information indicating the target of the problem) to which the first problem belongs, out of the second problems obtained by expanding the input first problem. Is.
(Configuration of information processing device)
In each of the above-described embodiments of the present invention, each component of the task estimation device represents a block of a functional unit. Some or all of the constituent elements of the task estimation device are realized by an arbitrary combination of an information processing device 500 and a program as shown in FIG. 12, for example. The information processing device 500 includes, for example, the following configuration.

-CPU (Central Processing Unit) 501
-ROM (Read Only Memory) 502
RAM (Random Access Memory) 503
-Program 504 loaded in RAM 503
-Storage device 505 that stores the program 504
-Drive device 507 for reading and writing the recording medium 506
-Communication interface 508 connected to the communication network 509
.Input/output interface 510 for inputting/outputting data
.Bus 511 for connecting each component
Each component of the task estimation device in each embodiment is realized by the CPU 501 acquiring and executing the program 504 that realizes these functions. The program 504 that realizes the function of each component of the task estimation device is stored in the storage device 505 or the RAM 503 in advance, for example, and is read by the CPU 501 as necessary. The program 504 may be supplied to the CPU 501 via the communication network 509, or may be stored in the recording medium 506 in advance and the drive device 507 may read the program and supply it to the CPU 501.

There are various modifications to the method of realizing each device. For example, the task estimation device may be realized by an arbitrary combination of the information processing device 500 and the program, which are different for each component. Further, a plurality of constituent elements included in the task estimation device may be realized by an arbitrary combination of one information processing device 500 and a program.

Further, some or all of the constituent elements of the task estimation device are realized by other general-purpose or dedicated circuits, processors, etc., or a combination thereof. These may be configured by a single chip, or may be configured by a plurality of chips connected via a bus.

Some or all of the constituent elements of the task estimation device may be realized by a combination of the above-described circuits and the like and a program.

When some or all of the constituent elements of the task estimation device are realized by a plurality of information processing devices or circuits, the plurality of information processing devices or circuits may be centrally arranged or distributed. May be. For example, the information processing device, the circuit, and the like may be realized as a form in which a client and server system, a cloud computing system, and the like are connected to each other via a communication network.

1: assignment estimation unit 1a: assignment extension unit 1b: selection unit 1c: assignment extension unit 2: priority determination unit 3: parallel relation storage unit 3a: co-occurrence relation storage unit 4: general information storage unit 5: domain keyword storage unit 10: assignment estimation unit 11: assignment estimation unit 12: estimation unit 100: assignment estimation device 200: assignment estimation device 300: assignment estimation device 400: assignment estimation device 500: information processing device 501: CPU
503: RAM
504: Program 505: Storage device 506: Recording medium 507: Drive device 508: Communication interface 509: Communication network 510: Input/output interface 511: Bus

Claims (8)

A keyword storage unit storing at least one keyword corresponding to the information indicating the subject of the task;
Accepting information representing the target of the challenges first object and the first object belongs, having said first object a predetermined relationship, the second problem that the first object has been extended at least One or more of the output second tasks, which includes the keyword corresponding to the information indicating the target of the task to which the first task belongs stored in the keyword storage unit A task estimation device that includes an estimation unit that outputs as a task that co-occurs with the information indicating the target of the task. The estimation means is
The task estimation device according to claim 1, wherein in the expansion, the second task having a parallel relationship with the first task is extracted. Further comprising a parallel relationship storage means for storing a pattern indicating the parallel relationship between the first problem and the second problem ,
The estimating unit uses the pattern stored in the parallel relationship storing unit to extract a problem to be verified from the second problem having the parallel relationship with the first problem. extracted from <br/> object estimating apparatus according to claim 1 or 2. The task estimation device according to claim 1, wherein the estimation unit extracts the second task having a co-occurrence relationship with the first task in the extension. Co-occurrence relation storing means for storing pre-Symbol first challenge and said second challenge and cord attached to one or more of the
Further equipped with ,
The task estimation device according to claim 4 , wherein the estimation means acquires the second task stored in the co-occurrence relationship storage means and having the co-occurrence relationship with the first task . Priority determining means for determining priority for verifying the first problem and the second problem acquired from the estimating means
The task estimation device according to any one of claims 1 to 5 , further comprising: Computer
Accepting information indicating the first assignment and the subject of the assignment to which the first assignment belongs,
Having said first object a predetermined relationship, outputting said first challenge the second problem which has been extended at least one,
Of the output second tasks, a task provided with the keyword corresponding to the information indicating the target of the task to which the first task belongs stored in the keyword storage means is defined as the task of the task. A task estimation method for outputting as a task that co-occurs with information indicating. Accepting information indicating the first assignment and the subject of the assignment to which the first assignment belongs,
Having said first object a predetermined relationship, outputting said first challenge the second problem which has been extended at least one,
Of the output second tasks, a task provided with the keyword corresponding to the information indicating the target of the task to which the first task belongs stored in the keyword storage means is defined as the task of the task. A problem estimation program for causing a computer to execute the output as a problem that co-occurs with information indicating.

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