JP6744612B2 - Problem solving support system - Google Patents

Description

The present invention relates to a problem solving support system, a problem solving support method, and a problem solving support program suitable for efficiently providing a concept for solving a conventional problem in a newly artificially created creation.

Innovation is broadly classified into needs-first innovations that are created based on market needs, and seeds-first innovations that connect technology seeds to social value. In particular, the latter seeds-based innovation means that even if the seeds of a technology are superior technologies that are innovative and superior to conventional technologies, unless they are what should be accepted in the real world, that is, they need to meet market needs. Unless it is linked with, it is hard to say that it is a true innovation.

In other words, the so-called valley of death and the sea of Darwin, which must be overcome, stand in the way from research and development to commercialization and mass diffusion. And if this is overcome, the seeds of technology created will develop into social value and can be a true innovation.

In particular, such innovations may come from not only large companies, but also small and medium-sized enterprises and eventually individual inventors. In the process of creating this innovation, there are problems that are obstacles to satisfying the demands from the market, and in many cases it is created by clearing those problems. Innovation by innovators will be realized by using the solution concept to clear this problem as a hint. Conversely, by effectively presenting the optimal solution concept to the innovator in solving the problem, it is possible to support the creation of innovation by the innovator.

A system that supports innovation creation has been proposed in the past (see, for example, Patent Document 1). In this Patent Document 1, a technique is premised on that input character information is grouped and a problem analysis is performed by a problem analysis method, and that not only idea creation processing but also finally the scenario is generated. Is disclosed.

JP, 2005-284548, A

However, according to the technique disclosed in Patent Document 1 described above, there is a problem in that the solution concept cannot always be effectively presented to the innovator, and the innovation creation support cannot be realized.

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object thereof is to realize innovation creation support by effectively presenting a solution concept to an innovator. It is to provide a possible problem solving support system, a problem solving support method, and a problem solving support program.

The problem solving support system according to the present invention has three or more stages of basic information acquisition means for acquiring basic information input by the user, each previously acquired reference character string, and each solution concept classified into two or more types. A search for searching for one or more solution concepts based on three or more levels of association between the reference character string corresponding to the character string of the basic information acquired by the basic information acquisition means and the solution concept, using And means.

According to the present invention configured as described above, it is possible to immediately select a solution concept suitable for the character string obtained by analyzing the basic information and output the solution concept. As a result, it becomes possible to support the creation of innovation.

In addition to this, according to the present invention, by grasping the output similarity of each business operator, it is possible to understand which business operator the idea matches. As a result, the user or the client can understand that the idea has a particular need in businesses with high similarity. That is, the user or the client can understand that the idea should be sold to a business operator having a high degree of similarity.

It is a figure which shows the structural example in 1st Embodiment of the problem solving support system to which this invention is applied. It is a figure which shows the block configuration of the electronic device in 1st Embodiment of the problem solving support system to which this invention is applied. It is a figure which shows the consulting business in which the problem solving support program in 1st Embodiment to which this invention is applied is used. It is a figure which shows the other example of the consulting business in which the problem solving support program in 1st Embodiment to which this invention is applied is used. It is a flowchart of the processing operation in the first embodiment of the problem solving support program to which the present invention is applied. It is a schematic diagram which shows the connection of the abstracted solution concept. It is another figure regarding the consulting work in which the problem solving support program in the first embodiment to which the present invention is applied is used. It is a figure which shows the example which takes into consideration the acquired business operator information in the step which searches the search solution based on the basic information of the initial seeds. It is a figure which shows the example which also refers to basic information, when calculating a similarity. It is a figure which shows the example which acquires a constraint condition. It is a schematic diagram which shows the connection of the combination of the reference character string and the solution concept in 2nd Embodiment of the problem solving support system to which this invention is applied. It is a figure which shows the example which considers to what extent the character string analyzed from basic information respond|corresponds with a reference character string. It is a figure which shows the example which searches the concrete structure of invention based on the character string extracted from the basic information, or the proposed solution concept. It is a schematic diagram which shows the connection of the reference character string and the solution concept in 3rd Embodiment of the problem solving support system to which this invention is applied. It is a figure for demonstrating 4th Embodiment of the problem solving support system to which this invention is applied. It is a figure for demonstrating 5th Embodiment of the problem solving support system to which this invention is applied. It is a figure for demonstrating 6th Embodiment of the problem solving support system to which this invention is applied. It is a figure for demonstrating 7th Embodiment of the problem solving support system to which this invention is applied.

Hereinafter, a problem solving support system to which the present invention is applied will be described in detail with reference to the drawings.

(First embodiment)
A first embodiment of a problem solving support system 1 to which the present invention is applied will be described. The problem solving support system 1 of the present embodiment is embodied by the configuration shown in FIG. 1, for example. The problem solving support system 1 includes a mobile terminal 11 for a user who uses the system, and a server 13 connected to the mobile terminal 11 via a public communication network 12.

The public communication network 12 is an internet network or the like that connects the mobile terminals 11 and the server 13 via a communication line. By the way, when the mobile terminal 11 is operated in a certain narrow area, the public communication network 12 may be configured by a LAN (Local Area Network). Further, the public communication network 12 may be configured by a so-called optical fiber communication network. The public communication network 12 is not limited to the wired communication network, and may be realized by a wireless communication network.

The mobile terminal 11 is, for example, a mobile phone, a smartphone, a tablet terminal, a wearable terminal, a notebook personal computer (PC), or the like, and is a device capable of communicating via the public communication network 12 based on at least a user's operation. is there. The mobile terminal 11 is a device that can be carried by the user at all times, but is not limited to this, and is a concept including any electronic device such as a stationary PC.

It should be noted that this portable terminal 11 does not download the problem solving support program described later through the public communication network 12 but installs the packaged software sold at the store without communicating through the public communication network 12. It may be a device that does not. In the following example, a case where a smartphone is applied to the mobile terminal 11 will be described as an example.

FIG. 2 shows a specific configuration example of the mobile terminal 11. The mobile terminal 11 includes a ROM (Read Only Memory) 22, a RAM (Random Access Memory) 23 as a work area used for storing and expanding data, and a CPU (Central Processing) for controlling the mobile terminal 11 as a whole. Unit) 24, an operation unit 25 for inputting various control commands via operation buttons and a keyboard, an output I/F 16 for controlling display of various information, and a hard disk drive. A storage unit 27 for storing a program for performing a search and a data input/output unit for inputting data into the mobile terminal 11 from the outside or outputting data generated in the mobile terminal 11 to the outside. 29 are respectively connected to the internal buses 21. Further, the communication I/F 28 is connected to the internal bus 21. Further, the output I/F 16 is connected to a display unit 26 as a monitor that actually displays information. The voice input/output unit 31 may be connected to the internal bus 21.

The ROM 22 stores a program for controlling the hardware resources of the mobile terminal 11 as a whole. The RAM 23 temporarily stores various commands for controlling the hardware resources of the mobile terminal 11 as a whole.

The CPU 24 is a so-called central processing unit for controlling each component installed in the mobile terminal 11 by transmitting a control signal via the internal bus 21. In addition, the CPU 24 transmits various control commands via the internal bus 21 in response to a user operation via the operation unit 25.

The operation unit 25 is embodied by a touch panel or the like, and, for example, the user inputs information about a problem that the user actually wants to solve, and the user inputs an execution command for executing the problem solving support program. When the user inputs the execution command, the operation unit 25 notifies the CPU 24 of this. The CPU 24 receiving this notification reads the above program from the storage unit 27 and executes it.

The output I/F 16 is composed of a graphic controller that produces a display image based on the control of the CPU 24. The display unit 26 connected to the output I/F 16 is realized by, for example, a liquid crystal display (LCD) or the like.

When the storage unit 27 is configured by a hard disk, under the control of the CPU 24, predetermined information is written at each address and is read out as needed. The storage unit 27 also stores a problem solving support program for executing the present invention. This program is read and executed by the CPU 24.

The communication I/F 28 is equipped with a line control circuit for connecting to the public communication network 12, a signal conversion circuit for performing data communication with another terminal device, and the like. The communication I/F 28 performs conversion processing on various commands from the internal bus 21 and sends them to the public communication network 12 side, and when data from the public communication network 12 is received, predetermined conversion processing is performed on the data. And sends it to the internal bus 21 or the CPU 24.

The data input/output unit 29 is connected with a cord or the like for USB connection with an electronic device such as a PC. It is possible to input/output data to/from an external device via the data input/output unit 29.

The voice input/output unit 31 is a device for converting voice input by a user into electronic data.

A predetermined database is built in the server 13. Information sent via the public communication network 12 is accumulated in this database. The server 13 also transmits the stored information to the mobile terminal 11 via the public communication network 12 based on the request from the mobile terminal 11.

Note that any one or more constituent elements of the mobile terminal 11 or the server 13 may be controlled by artificial intelligence. The specific application method of the artificial intelligence to the present invention may be based on any one or more of all known information about conventional artificial intelligence.

The problem solving support system 1 to which the present invention is applied is executed via the problem solving support program installed in the mobile terminal 11. That is, the user operates the mobile terminal 11 and solves various problems through the problem solving support program installed in the mobile terminal 11. For example, as shown in FIG. 3, this problem solving support program is used as software that supports the creation of innovation based on the subject (for example, technical field, keyword, etc.) that the user requests. The problem solving support program is not limited to being installed and used in the mobile terminal 11, but is stored in the server 13 and accessed from the mobile terminal 11 side every time the server 13 is used. Of course, you may do so.

The user may manually input the target theme via the operation unit 25 of the mobile terminal 11, or may input by voice via the voice input/output unit 31. For example, the subject to be set may be set by repeating the input by the user and the inquiry to the user one or more times. Here, the “inquiry” means that the content input by the user is output (answered) from the problem solving support program. That is, the user can set a target theme in a conversation format or a selection format in which a question or a consultation is made to the problem solving support program. At this time, the user's input method may be manual or voice, and the problem solving support program may be output via the display unit 26 or the voice input/output unit 31.

The innovation creation process will be described with reference to FIG. In the innovation creation process, the problem-solving support program starts from the point where the problem-solving support program acquires basic information based on the subject desired by the user (step S11).

Examples of information obtained by the problem solving support program are social needs, problems, previously proposed technologies, problems to be solved, composition of newly created inventions, seeds of ideas, and concept of ideas. , The effects and the like will be expressed in sentences, character strings, languages, etc. that can be converted into text data. The problem solving support program can automatically acquire all the Internet information that can be acquired via the public communication network 12, big data, IT (Internet of Things) devices, information such as input from the user, and the like. The information acquired by the problem solving support program based on the target theme in this way is hereinafter referred to as basic information.

The problem solving support program can acquire, for example, news that has become a hot topic in society as basic information. In this case, the basic information is set via the public communication network 12 according to the input frequency of the keyword searched for on the Internet (for example, a search engine) in a specific period. The specific period, the input frequency, etc. described above may be those published on the Internet, or may be arbitrarily set by the user as a parameter.

The problem solving support program may acquire the basic information by an input from the user, as shown in FIG. 4, for example. In this case, the user may manually input this basic information via the operation unit 25 of the mobile terminal 11, or may input it by voice via the voice input/output unit 31. For example, the basic information may be set by repeating the input to the problem solving support program by the user and the output of the problem solving support program one or more times. That is, the user can set the basic information in a conversation format or a selection format in which a question or a consultation is given to the problem solving support program. At this time, the user's input method may be manual or voice, and the problem solving support program may be output via the display unit 26 or the voice input/output unit 31. Further, the basic information created in another mobile device 11 or an electronic device such as a PC may be input from the data input/output unit 29. Alternatively, the text data created by the user in another portable terminal 11 or an electronic device such as a PC may be transmitted via the public communication network 12 to the portable terminal 11 in which the problem solving support program is actually installed. At this time, the problem solving support program will acquire the basic information from the user instead of the target theme described above.

The basic information acquired in this way is stored in the storage unit 27.

The problem solving support program analyzes the basic information acquired in step S11 (step S12). For this analysis, any existing text mining technology, data mining technology, language analysis processing technology, or the like may be used.

Next, this problem solving support program extracts the character string from the basic information to be analyzed over any one or more units from all grammatical structural units such as words, morphemes, phrases, and clauses. For example, for a patent specification analysis display device capable of counting and displaying the degree of limitation of the invention defined in the claims in the patent specification, it is possible to display the degree of limitation of the invention with higher accuracy. If there is text data of "Purpose", "patent specification", "claims", "definition", "invention", "degree of limitation", "count", "display", Character strings such as "patent specification analysis display device", "highly accurate", and "targeted" are extracted (mining means).

Alternatively, this basic information may be fragmentarily input as a keyword or a word itself. For example, when "Pet's bottom wipe" is input, the character strings "pet" and "bottom wipe" can be extracted. In addition to the concept and structure of the invention, the basic information may include a problem to be solved. For example, when "cheap and easy to make" is input, by analyzing this with text mining, the grammatical structure such as "cheap", "cheap", "make", "easy to make", etc. The character string is extracted over any one or more of the units. In extracting this character string, for example, the meaning content may be analyzed by using the case component described in Japanese Patent Application No. 2012-254649.

Next, the problem solving support program moves to step S13 and starts searching for a solution concept highly related to the character string extracted in step S12 (search means). Before performing this search, the mobile terminal 11 preliminarily establishes the relevance of a reference character string and an abstracted solution concept (hereinafter referred to as a solution concept) that is classified into two or more types, as shown in FIG. Get it.

The solution concept is an abstracted concept for solving a problem. The solution concepts A1, A2, A3, B1, B2, B3, C1, C2, C3,... Are previously defined on the system side. This solution concept is, for example, an equivalent transformation theory, TRIZ (including all advanced TRIZ in addition to classical TRIZ), ARIZ, QFD, Taguchi method, etc. and all known problem solving methods. .. Examples of this solution concept are, for example, the 40 inventive principles of classical TRIZ: segmentation, asymmetry, reverse, dynamics, feedback, nesting, prefetching, It is equivalent to co-ordination, mechanical vibration, and conversion of harm into profit (turning disaster into good fortune).

The mobile terminal 11 acquires the association indicating the degree to which such a character string and the solution concept are related to each other. As a method of expressing this association, for example, as shown in FIG. 6, a reference character string connected by an arrow and a solution concept are associated with each other, and a reference character string and a solution concept not connected by an arrow are connected. May be expressed in a form that is not linked to each other. For example, the solution concept A2 may be expressed in such a form that it is associated with the reference character strings c11 and a21, but is not associated with other solution concepts. That is, in this case, the presence or absence of the association between the reference character string and the solution concept is digitally represented by a true or false binary value.

An analog expression method may be adopted as another expression method of the association. For example, the solution concept B3 is associated with the reference character strings a21 and b21 at a rate of 80%, the reference character string c11 is 60%, the reference character string b11 is 45%, and the reference character string c21 is 24%, the reference character string a11 may be expressed as a degree of association of three or more levels such as 0%. For example, for the two reference character strings “strength” and “weight”, segmentation (subdivision) may be expressed as a solution concept having a relevance of 80%. In the 40 invention principles of classical TRIZ, when the property to be improved is “strength” and the property that deteriorates against it is “weight”, segmentation is conventionally used as a solution concept. This is because it has been advocated.

Also, if the reference character string is “butt wipe”, the solution concept may be set to a high association degree of 90% or the like for a “disposable” solution concept such as toilet paper. Good.

Before the analysis in step S13, the mobile terminal 11 stores the association between the reference character string and the solution concept in its storage unit 27 or the like so that it can be read at any time. At this time, if the association between the reference character string and the solution concept is not stored in its own storage unit 27, it is stored in the server 13 and accessed when actually executing step S13. You may make it execute by that.

After shifting to step S13, the problem solving support program performs the work of selecting one or more solution concepts from the character string extracted from the basic information analyzed in step S12.

Regarding the selection of the solution concept from the character string extracted from this basic information, the association between the previously acquired reference character string and the solution concept may be referred to. For example, if the character string analyzed from the basic information is “strength”, if the correlation between the reference character string and the solution concept is referenced, the one that is highly associated with that “strength” is segmentation (subdivision). ). Therefore, segmentation (subdivision) having a high degree of association with the analyzed character string "strength" is searched as a solution concept.

Also, if the character string analyzed from the basic information is "body wipe," there is no identical reference character string, but if "butt wipe" exists as a reference character string, refer to it. As a solution concept, “disposable” may be searched and presented. In this way, even if the character string from the basic information does not completely match the reference character string, if it partially matches at the "wiping" as in this example, the reference character string and the reference character string are related. It may be possible to search for a solution concept having high property.

Furthermore, even if the character string analyzed from the basic information is "body wash" and there is no reference character string that completely or partially matches this, it will not be , There are similarities in semantic concept. The similarity of the semantic concept may be set on the system side in advance, and a solution concept having a high similarity with the reference character string having a high similarity may be searched for.

That is, the character string analyzed from this basic information is not only the degree of similarity of the meaning concept, whether or not the reference character string and the characters partially match or completely match the character string from the basic information. If the reference character string has some correspondence with the reference character string, a solution concept highly associated with the reference character string may be searched.

The above-mentioned work is performed not only for one character string analyzed from the basic information but also for a plurality of other character strings. As a result, not only one solution concept but also multiple solution concepts may be selected.

The method of discriminating a solution concept having a higher correlation with the analyzed character string is not limited to the above method. For example, the degree of association may be set in advance between the reference character string and the solution concept. In such a case, with respect to “strength” as a reference character string, “segmentation (subdivision)” is 84%, “asymmetry” is 25%, and “reverse” is 61% for each solution concept. , "Dynamic" is 77%, "Feedback" is 43%, etc., and the degree of association between the reference character string and the solution concept is set in advance. Then, when the reference character string “strength” is selected according to the character string analyzed from the basic information, it is determined that the reference character string “strength” is related by the above-mentioned ratio (%) of the degree of association. Good. Then, a solution concept whose degree of association is equal to or higher than a predetermined threshold may be selected.

The data of the association between the reference character string and the solution concept may be downloaded on the mobile terminal 11 side, or may be stored in the server 13 at all times, and may be stored in the server as necessary. It may be read from the device 13 or downloaded to the mobile terminal 11.

That is, this problem solving support program can immediately select a solution concept suitable for the character string obtained by analyzing the basic information.

The problem-solving support program performs a process of formalizing ideas that may be created and the situation surrounding them based on the selected solution concept. In this process of formal intelligence, in addition to the structure of the idea and the main concept, the social needs and problems behind the idea, the previously proposed conventional technology, the problem to be solved, the effect, etc. Will be expressed as sentences or character strings that can be converted into text data. Such a formalized situation is hereinafter referred to as idea information. Incidentally, the basic information may be directly applied as the idea information. As a result, the problem solving program acquires the idea information (new information) (step S14, acquisition means).

The problem solving program refers to all the Internet information that can be acquired through the public communication network 12, big data, information acquired from the IT device, etc., and automatically acquires idea information based on the solution concept. Good. Further, for example, the information stored in advance in the server 13 or the like may be referred to acquire the idea information based on the solution concept.

The problem solving program may acquire new social needs as idea information based on the selected concept, for example. For example, as basic information, when news of a topic such as "declining birthrate problem" is acquired in society, the content of social needs that may occur in the future with respect to "declining birthrate problem" should be acquired as idea information. May be.

For example, the user may input the idea information based on the solution concept. In this case, the solution concept acquired in step S14 may be displayed on the display unit 26 via the output I/F 16 or may be transmitted to the outside via the data input/output unit 29. The output of the solution concept may be stored in the server 13 from the public communication network 12 via the communication I/F 28, or may be transmitted to another mobile terminal 11. After that, the user may use the outputted solution concept as a hint and exert his or her creativity to create an invention. The user analyzes the idea created in this way, deeply understands it from various viewpoints, and then extracts idea information.

Then, the user may implement step S14 by actually inputting this idea information into the mobile terminal 11 in which the problem solving support program is installed. For inputting this idea information, the user himself/herself may directly input the text data via the operation unit 25 or the voice input/output unit 31, or the text data of the idea information created in another mobile terminal 11 can be input. You may make it input from the output part 29. Further, the text data of the idea information created by the user using another mobile terminal 11 may be transmitted via the public communication network 12 to the mobile terminal 11 in which the problem solving support program is actually installed. ..

The text data of the idea information thus obtained is stored in the storage unit 27.

The problem solving support program moves to step S15 and analyzes the text data of the idea information as shown in FIG. For this analysis, any existing text mining technology, data mining technology, language analysis processing technology, or the like may be used.

Next, this problem solving support program extracts a character string from the analyzed text data in any one or more units from all grammatical structural units such as words, morphemes, phrases, and clauses. The specific extraction method of this character string is the same as in step S12.

Next, the problem solving support program proceeds to step S16 and acquires the text data regarding the information of the other company. The information of other business operators mentioned here is patent information, business information, management information, business information, etc. of a company different from the client (hereinafter, these are collectively referred to as business operator information). The business entity information may be acquired from, for example, a website in which a patent publication or the like is posted, or may be acquired from a website in which information on the company is described. Further, the business entity information may be acquired from text data described in an electronic book or electronic magazine, or text data stored in a purchased recording medium. Further, this business operator information may be text data manually input by the user himself or another person, or may be acquired by any other method.

The problem solving support program proceeds to step S17, and similarly analyzes the text data for the business entity information by using the text mining technology. For this analysis, any existing text mining technology, data mining technology, language analysis processing technology, or the like may be used. Also in step S17, the problem solving support program extracts the character string from the analyzed text data in any one or more units from all grammatical structural units such as words, morphemes, phrases, and clauses. The specific extraction method of this character string is the same as in step S12. This business entity information may be acquired and analyzed not only for one business entity but also for multiple business entities.

Next, in step S18, the problem solving support program compares the character string of the idea information and the character string of the business operator information obtained in this way, and calculates the degree of similarity between them. The calculation of the similarity is performed between the character string of idea information and the character string of business information, the number of partially matching character strings, the number of completely matching character strings, and the dependency of partially matching character strings. It is calculated based on the number of, the number of completely matching dependencies, etc. At this time, the concept dictionary or the like may be referred to as necessary. Other than this, it may be based on the similarity of the semantic concept behind the character string. It may be possible for the user side or the system side to freely change which item is weighted.

The problem solving support program seeks the degree of similarity between the character string of idea information and the character string of company information for each company. As a result, the degree of similarity is calculated for each company, such as company A: 27%, company B: 74%, company C: 58%, company D: 40%,.

The problem solving support program may display the obtained calculation result of the degree of similarity on the display unit 26 via the output I/F 16, or save it in a USB memory or the like via the data input/output unit 29. You may do it. The output of the solution concept may be stored in the server 13 from the public communication network 12 via the communication I/F 28, or may be transmitted to another mobile terminal 11. Further, the problem solving support program outputs not only the calculation result of the degree of similarity but also the operators having the degree of similarity above or below a predetermined threshold, and the operators from the upper or lower side of the degree of similarity to a predetermined rank. Good.

The user or the client can understand which business the business idea corresponds to by understanding the outputted similarity for each business. As a result, the user or the client can understand that there is a particular need for a business operator having a high degree of similarity in devised ideas. That is, the user or the client can understand that the idea devised by the user or the client can be sold to a business operator having a high degree of similarity.

On the other hand, it is needless to say that the user or the client may not only a business operator with a high degree of similarity but also a business person with a low degree of similarity to newly raise needs and sell it. In such a case, the selling strategy of the client will be different, but it is possible to take this similarity into consideration when considering which strategy to sell.

The problem solving support program executes the flow from step S15 to step S18 described above not only for the character string of the idea information of one idea devised but also for the idea information of all other devised ideas. It will be. As a result, it is possible to obtain the degree of similarity with other businesses for all the idea information. As a result, it becomes possible for the user or the client to consider the marketing destination and marketing strategy of the technology not only for one devised idea information but also for all devised idea information.

The present invention is not limited to the above embodiment. The calculated similarity information may be fed back to the processing flow of step S13 to further search for a solution concept.

For example, for a business operator whose degree of similarity is lower than a predetermined threshold value or a business operator whose threshold value is higher than a predetermined threshold value, a character string obtained by analyzing the business operator information is extracted. This character string is compared with the reference character string, and the reference character string corresponding to this is specified. Then, based on step S13 described above, a solution concept highly associated with the specified reference character string is searched for and output. As a result, the user can search for a solution concept based on the business operator information. In particular, this business operator information may correspond to the needs of the market, so it is also possible to search for a solution concept based on the needs of the market.

That is, according to the present invention, the solution concept can be searched based on the needs of the market. In other words, it is possible to support both needs-first innovation as well as seeds-first innovation. In such an embodiment, any rule may be used to search for a solution concept via the reference character string as long as it is based on the calculated similarity.

Further, according to the present invention, both the basic information and the business entity information may be fetched and the solution concept may be searched. In such a case, the point that the solution concept is searched based on the associativity via the reference character string is similar to the above. In particular, when both the basic information and the business entity information are imported, it is possible to weight which information is weighted. If basic information: business operator information is weighted at a ratio of 2:1, the basic information is evaluated twice as much as the business operator information regarding the degree of association between the reference character string and the solution concept. Good. As a result, it becomes possible to search for a solution concept that emphasizes more basic information.

Further, the association between the reference character string and the solution concept may be changed based on the calculated similarity. For example, when the ratio of businesses whose similarity is greater than or equal to a predetermined value is relatively small, those with a higher degree of association between them are reduced by -10 points, and those with a lower degree of association between them are increased by 10 points, etc. May be adjusted. Also, if these relationships are expressed only by the truth of the relations, the truth is set to false, and the combination with a particularly high selectivity in the false analysis is set to true. You may try again. By changing these settings, it is more likely that a different solution concept will be selected, and as a result, there is a higher possibility that the user will come up with a different invention concept. There is a possibility that the degree of similarity with will be high. By changing the setting of the associativity as described above, an invention concept having a high similarity to the business entity information is likely to appear.

Further, the relationship between the reference character string and the solution concept may be appropriately changed by considering not only the degree of similarity but also the acquired business entity information itself. In such a case, for the reference character string that has a high degree of agreement with the character string extracted from the analyzed business information, change the setting, such as increasing the degree of association with the solution concept by several points or conversely decreasing it by several points. You may also do. Then, by returning to step S13 and searching for a solution concept based on the basic information, there is a high possibility that a solution concept more suited to the needs of the market will be searched.

Further, in the example of FIG. 8, the acquired business operator information is taken into consideration at the stage of searching for a search solution based on the initial basic information of the seeds before calculating the similarity with other business operators. In such a case, regarding the reference character string having a high degree of coincidence with the character string extracted from the previously acquired business entity information, the degree of association with the solution concept may be adjusted. Alternatively, a solution concept having a high degree of association may be searched for a reference character string having a high degree of coincidence with the character string extracted from the acquired business entity information. Further, similarly to the above, both the basic information and the business entity information may be fetched and the solution concept may be searched. In such a case, when both the basic information and the business entity information are taken in, which information is weighted may be weighted as described above.

Further, according to the present invention, when calculating the degree of similarity in step S18, the basic information of the seeds may be further referred to as shown in FIG. In such a case, between the character string obtained by analyzing the basic information and the character string of the business entity information, the number of partially matching character strings, the number of completely matching character strings, and further The number of partially matched dependencies, the number of completely matched dependencies, or the similarity of semantic concepts behind them is also reflected in the similarity calculation. At this time, which of the similarity between the character string of the basic information and the character string of the company information and the similarity between the character string of the invention concept and the character string of the company information is prioritized or which is weighted? It can be set freely. At this time, it is possible to extract the basic information that particularly refers to the problem to be solved and analyze it by text mining. Actually, in the basic information, make a rule in advance in which column to describe the problem to be solved, search that column, extract the description of the problem in the column as text data, and analyze this. It will be done.

It should be noted that according to the present invention, it may be possible to input which solution concept the user has adopted from among the output solution concepts. When the adoption information of such a solution concept is input, the problem solving support program may reflect the adoption information in the above-mentioned association between the reference character string and the solution concept. That is, for the reference character string having a high degree of association with the adopted solution concept, the degree of association may be further increased by several points. Among the output solution concepts, the ones that are not included in this adoption information are the ones that are unlikely to be actually adopted by the user. Therefore, regarding the reference character string that is highly associated with the solution concept. May further reduce the degree of association by several points.

Further, in the present invention, in step S13, it may be possible to extract, from the basic information, particularly one or more of the problem to be solved, the solution means, and the action and effect, and analyze this by text mining. Actually, in the basic information, a rule is created in advance in which column to describe the problem to be solved, the solution means, and the action and effect, and after searching that column, the description of the problem to be solved in the column as text data. It will be extracted and analyzed. Then, any one or more of the solution problems, the solution means, and the analysis results of the action effects may be used to search for a solution concept having a high degree of association through the reference character string. As a result, the reference character string specialized for any one or more of the solving problem, the solving means, and the action and effect is selected, and the solution concept is searched.

Further, at this time, as shown in FIG. 10, the constraint condition may be acquired on the user side. The constraint condition may be input, for example, via the operation unit 25, or may be acquired from, for example, all the Internet information, the big data, and the IT device that can be acquired via the public communication network 12. The constraint condition is a constraint on the solution concept to be selected, such as not selecting a solution concept that requires a high cost or selecting a solution concept that consumes a large amount of energy. The problem solution support program defines types of constraint conditions in advance, and also organizes basic information or solution concepts in advance according to the types. When the constraint condition is input from the user side, the basic information or the solution concept under the input constraint condition is controlled not to be selected. That is, according to the problem solving support program, the basic information acquired based on the input constraint condition or the solution concept to be selected may be adjusted.

In the present invention, the acquired basic information, the search result of the search solution, the calculation result of the similarity with the business operator, or the like may be stored in the server 13. It is possible for each portable terminal 11 to refer to these by accessing the server 13 in which the past results are accumulated via the public communication network 12.

In addition, in the above-mentioned embodiment, although it demonstrated on the assumption that the process of step S11-S13 is performed on the portable terminal 11 side, it is not limited to this, and may be performed on the server 13 side. Alternatively, any of the steps may be shared between the server 13 and the mobile terminal 11.

In the embodiment described above, the problem solving support program calculates the cost based on the content and amount of the target theme, basic information, solution concept, business operator information, idea information, etc. Based on the above, the content and amount of basic information, solution concept, business operator information, idea information, etc. may be extracted and output. These operations are performed through artificial intelligence, for example.

(Second embodiment)
Next, a second embodiment of the problem solving support system 1 to which the present invention is applied will be described.

In the example of FIG. 6 in the first embodiment, one reference character string refers to the association of one or more solution concepts, whereas the example of FIG. 11 in the present embodiment. Then, it refers to the relevance of each solution concept to the combination between a plurality of reference character strings.

In FIG. 11, the reference character strings include “dog”, “butt wipe”, “polybag”, “seal”, “stick”, “cheap”, “easy to install”, etc. , And solution concepts A1, A2,... Are assigned respectively. The solid line shows the association, and the dotted line shows the combination. For example, the "dog" and the "butt wipe" are connected to each other by a dotted line, and the solid line extends from there to A2 and C1. This indicates that the solution concepts that are highly related to the combination of “dog” and “butt wipe” are A2 and C1. In other words, if the reference character string is only "dog", it is impossible to know what the dog wants to solve or what is causing the problem. On the other hand, if "dog" and "butt wipe" are combined, it will narrow down to some extent that it is an innovation for wiping the anus after the dog has made feces, and it is possible to narrow down the solution concept according to this. Becomes In addition to this, in the dotted line where “dog”, “butt wipe”, and “polybag” are combined, the solution concepts of A2 and B2 are connected as highly related. This means that the addition of a plastic bag to the above-mentioned wiping of the dog's buttocks further narrows down the solution concept for wiping the dog's buttocks using the poly bag.

The above-mentioned combination of reference character strings is a noun-noun, but it may be a noun-verb such as a combination of "seal" and "paste". By connecting the noun and the verb, the proposition to be realized becomes clear, as in "sticker". Then, B3 will be linked as a solution concept that is highly related to sticking this sticker. In addition to this, if the reference character string "polybag" is further combined, the solution concept for sticking a seal on the plastic bag will be narrowed down. If the reference character string "cheap" is added, the solution concept for sticking a seal on a plastic bag at a low cost will be narrowed down.

By previously acquiring the relevance of one or more solution concepts for each combination of reference character strings as in the case of FIG. 6 described above, when actually selecting a solution concept from the character strings analyzed from the basic information, It is possible to refer to this. In particular, by combining these with each other, it is possible to further narrow down the intended solution concept, and it is also possible to effectively select and propose a solution concept that the user really desires, rather than combining this reference character string alone. ..

When actually referencing the relevance of one or more solution concepts for each combination of reference character strings, a plurality of character strings are extracted from the basic information. Then, a solution concept having a high degree of association with a combination of a plurality of reference character strings corresponding to the extracted plurality of character strings is proposed. In such a case as well, the character string analyzed from the basic information is based on the basic information as well as whether the reference character string and the characters partially match or completely match, not only the degree of similarity of the semantic concept. If it is a reference character string that has some correspondence with the character string of, the solution concept that is highly associated with the reference character string may be searched.

At this time, the combination between the plurality of reference character strings may be any combination of noun-verb, noun-adjective, adjective-verb, and noun-noun. When extracting a character string from the basic information, a plurality of character strings composed of one or more combinations of noun-verb, noun-adjective, adjective-verb, noun-noun are extracted as words or phrases, The solution concept may be searched while referring to the same combination of parts of speech in the reference character string.

In addition, when the server 13 is controlled by artificial intelligence, the association between the reference character string and the solution concept may be learned. That is, the association between the reference character string and the solution concept as shown in FIG. 6, and the association between the combination between the reference character strings and the solution concept as shown in FIG. 11 are updated at any time by learning by artificial intelligence. Go. The artificial intelligence learns based on information provided to the server 13 from the outside via the public communication network 12. The information provided to the server 13 from the outside may be transmitted from the mobile terminal 11 using the present system, for example. As a result of using the problem solving support program to which the present invention is applied by the mobile terminal 11, the relationship between the reference character string selected by the user and the solution concept, or the reference character string not selected by the user and the solution concept is displayed. Past history information such as relationships is recorded on the mobile terminal 11 side. Then, the past history information is transmitted from the mobile terminal 11 to the server 13. The artificial intelligence that controls the server 13 updates the association between the reference character string and the solution concept based on the transmitted past history information. That is, the relationship between the reference character string selected by the user and the solution concept is updated with high association, and the relationship between the reference character string not selected by the user and the solution concept is updated with low association. A neural network or the like may be used for this update.

By collecting such past history information from all mobile terminals 11 using this system to the server 13, the association between the reference character string and the solution concept can be learned at any time based on a large amount of data. It is made easier for users to accept, and it is easier to provide a solution concept that is the shortest way to solve the problem.

Needless to say, the learning of the association between the reference character string and the solution concept in the server 13 may be performed by the artificial intelligence mounted on the mobile terminal 11 side.

Incidentally, the data from the outside for learning by this artificial intelligence is not only the past history information of the mobile terminal 11 using this system described above, but also all the internet information that can be acquired through the public communication network 12. , Big data, data from an IoT device, or the like may be used. In addition, by accessing the patent information that can be obtained from the Patent Information Platform of the JPO and acquiring the relationship between the character string and the solution concept from it, the association between the reference character string and the solution concept can be learned by artificial intelligence. It may be updated.

For example, if you search the Internet for two character strings, "dog" and "butt wipe," and a condition is found, a website that lists various solutions for wiping the anus after a dog has pooped up appears. come. By acquiring the text information of the solution described on the homepage and text mining this, we will find out what solution concept it corresponds to by artificial intelligence. In such an example, when the solution that stains do not adhere to the finger is obtained as text information by overlapping the cloth on the two layers, this is subjected to text mining analysis, and the word "two layers" is used as the solution concept. If you think that it is related to "Multiple layers to deal with," and two reference character strings "dog" and "butt wipe," and the solution concept, "Multiple layers to deal with" Update to strengthen the link with "Yes".

According to the present invention, the correspondence between the character string analyzed from the basic information and the reference character string may be learned by artificial intelligence. This learning by artificial intelligence may be executed on either the mobile terminal 11 side or the server 13 side.

For example, as shown in FIG. 12, when considering to what extent the character string analyzed from the basic information corresponds to the reference character string “butt wipe”, first, the character string “butt wipe” is changed to the reference character string “ It corresponds to almost 100% with "wiping the bottom". On the other hand, compared to the reference character string "butt wipe", the character string "body wipe" is 70%, the character string "body wash" is 50%, the character string "brush case" is 35%, and the character string " For “feeding”, 5% or the like may be used to grasp the correspondence relationship through a numerical value such as a percentage. Then, based on the information provided from the outside, the numerical value of this correspondence may be changed, or the relationship between the new character string and the reference character string may be defined. For example, when the co-occurrence degree of “body wipe” and “butt wipe” (probability of appearing together in one home page and one document) from information acquired from the Internet or a document read from the outside is high. May be controlled so that the percentage value of the above-mentioned correspondence relationship is increased, and if the co-occurrence degree is low, the percentage value of the above-mentioned correspondence relationship is decreased.

The correspondence between the character string analyzed from the basic information and the reference character string is the information acquired from the Internet regarding the degree of similarity of semantic concept other than whether the characters partially match or completely match. Alternatively, it may be learned by artificial intelligence from a document read from the outside. For example, if there is no match in terms of wording, but there is a high probability that they will appear together in a single homepage or in a single document, the meaning meanings of “wiping wiping” and “feces removal” will be similar. Alternatively, the text may be registered as a word corresponding to the reference character string “wiping wipes”.

Further, according to the present invention, a process for prompting the user to newly input the basic information may be introduced. The input of the basic information may be promoted according to the character string extracted by mining the basic information or the search result of the solution concept in step S13.

For example, if the character string extracted from the basic information is simply “butt wipe” or “cheap”, the solution concept will be narrowed down unless you specify what the target wipe is for people, dogs, etc. May not be. In such a case, as a result of mining the basic information, if there is no other character string that is considered to be the target of wiping the bottom, further basic information should be acquired to identify the target. Alternatively, for example, the user may be prompted to input basic information.

Also, if "dog", "butt wipe", "polybag", and "paste" can be extracted from the basic information, it is a dog-only wipe, and can be pasted on a plastic bag. Can be read. In particular, in order to narrow down a solution concept that is highly related to a combination of a plurality of reference character strings as shown in FIG. 11, it may be more desirable to have information about the solution problem. In such a case, as a result of mining the basic information, if a character string considered to be a problem to be solved does not appear elsewhere, in order to specify the target, further basic information may be acquired, For example, the user may be prompted to input basic information. For the determination of the presence or absence of a character string corresponding to the problem to be solved, for example, for each group of problem to be solved such as "cheap", "manufacturability", and "generality", the character strings indicating these are registered in advance It may be possible to determine whether or not there is a character string that completely or partially matches the registered character string in the basic information.

In addition, as a result of searching the solution concept in step S13, if the proposed solution concept is not selected by the user, or even if a solution concept is tried to be proposed, the number exceeds the predetermined upper limit, and the number cannot be narrowed down easily. In some cases, the user may be prompted to present more detailed information.

In the present invention, these judgments may be executed via artificial intelligence. This input promotion may be realized by displaying on the display unit 26, or may be performed by voice through a voice output unit (not shown).

Further, according to the present invention, a specific inventive configuration may be searched based on at least one of the character string extracted from the basic information and the solution concept, and this may be displayed. Alternatively, the specific configuration of the invention may be searched and displayed based on both the character string extracted from the basic information and the proposed solution concept. In any case, these tasks will be performed through artificial intelligence.

In such a case, the extracted character strings are “dog”, “butt wipe”, “fingertip”, “seal shape”, “polybag”, “feces removal efficiency”, and the solution concept is “fitness”. In the case of "improvement of", it is possible to immediately lead out by just connecting the character strings up to the place where a plastic bag is attached in a seal shape when wiping a dog's buttocks. In such a case, as shown in FIG. 13( a ), the artificial intelligence combines the materials of images on the Internet until the sticky dog wiper 70 for dogs is attached to the plastic bag 7. You can easily draw with.

Next, the artificial intelligence reads out the character strings "fingertip", "efficiency of excrement", and the solution concept "improvement of fit", and further adds a configuration to the butt wiper 70. .. As shown in FIG. 13( b ), grooves 76, 77, and 78 are provided so as to fit the fingertips from the fitting property of the fingertips. If the right hand, grooves 78, index finger in groove 78, middle finger in groove 77, ring finger in groove 76. Artificial intelligence itself thinks that the efficiency of dog feces removal can be improved by fitting and fitting. Artificial intelligence derives information from the word fit, for example, from the Internet, and finds that there is a surrounding body that surrounds the object in order to realize the fitting nature, and this surrounding body targets only fingers. You can find up to that point. The fact that a groove-shaped seal body may be formed to surround the finger can be extracted from the Internet or the like as a well-known technique.

In this way, the finally completed form of FIG. 13B is displayed on the display unit 26. By understanding the displayed form by the user himself, it becomes possible to put together the concepts and proceed to commercialization.

(Third Embodiment)
Next, a third embodiment of the problem solving support system 1 to which the present invention is applied will be described with reference to FIG. FIG. 14 is a diagram showing an example of the association between the reference character string and the solution concept in the present embodiment.

In the present embodiment, in the analog expression method of the associativity described in the first embodiment, the amount of change in the temporal change of the associativity is added.

For example, as shown in FIG. 14, from time T1 to time T2, the degree of association between the solution concept B2 and the reference character string b11 has changed from 60% to 70%, increasing by 10%. The degree of association between the solution concept B3 and the reference character string a21 has changed from 80% to 75%, which is a decrease of 5%. The degree of association between the solution concept B3 and the reference character string b21 changes from 85% to 70%, which is a decrease of 15%. Based on the change amount of each degree of association, it is possible to make settings such as selecting from a solution concept having a large amount of increase in degree of association. This makes it possible to select a solution concept with reference to the amount of change in the degree of association over time.

For the derivation of the change amount of the degree of association described above, for example, the degree of association set for each time based on the degree of similarity, the business operator information, and the like described in the first embodiment may be referred to. Alternatively, the degree of change may be set by setting the degree of association based on all Internet information that can be acquired via the public communication network 12, big data, data from the IoT device, input from the user, and the like.

As described above, according to this embodiment, it is possible to acquire the amount of change in the temporal change in the degree of association. That is, it is possible to monitor trends such as an upward trend, a downward trend, an ascending speed, and a descending speed in the variation of the degree of association. As a result, the solution concept can be selected with reference to the change amount of the degree of association, and the user can understand the trend of the solution concept. As a result, it will be possible to realize innovation support based on the trends of solution concepts.

(Fourth Embodiment)
The present invention is not limited to the above embodiment. For example, in the fourth embodiment, as shown in FIG. 15, a plurality of association levels may be acquired for each group classified based on a specific rule. Any classification rule may be used for this group, and for example, classification may be performed based on the technical field, applicant, problem to be solved, year of application, or the like. In the following example, description will be given taking as an example a case where the technical fields are classified into groups P, Q, R,.

It is assumed that the group P is a container technical field, the group Q is an automobile parts technical field, and the group R is a material analysis technical field. In such a case, which group the character string extracted by analyzing the basic information has a high degree of correspondence is determined. This degree of correspondence indicates which group the basic information is likely to correspond to. For example, if the character string extracted from the basic information contains a lot of contents of automobile parts, the degree of correspondence with the group Q is high, and the degree of correspondence with the other groups P and R is low. The degree of correspondence may be determined based on, for example, the correspondence with keywords representing the technical fields of groups P to R.

In this way, the solution concept is searched by referring to the degree of association formed for the group having a high degree of correspondence. This makes it possible to search for an optimal solution concept according to the group classification. As described above, by classifying the groups into each technical field, a solution concept named after the technical field comes out.

At this time, one or more solution concepts may be searched for by taking into consideration the degree of association of the other groups P and R having low correspondence. This is because, in the case of the example of the technical field described above, it may be possible to obtain an innovative solution by using a solution concept of a different field with a low degree of correspondence. Even in such a case, not only a solution concept having a high degree of association but a solution concept having a low degree of association may be selected.

(Fifth Embodiment)
In the fifth embodiment, the searched solution concept is recognized as a new character string. Then, one or more new solution concepts are searched based on the degree of association of the reference character string corresponding to the recognized character string and the solution concept in three or more stages.

FIG. 16 shows the concept of the fifth embodiment. Similarly, the character strings constituting the search solution searched as the solution concept are extracted and applied to the reference character string. The subsequent flow is similar to that described above, and one or more new solution concepts are searched for based on the degree of association.

This process may be repeated. By repeatedly executing these, the solution concept may be narrowed down further.

Further, it may be reflected in the update of the association between the reference character string and the solution concept. In other words, the reference character string based on the character string forming the search solution searched as the solution concept and the adopted new solution concept may be updated so as to have higher association.

(Sixth Embodiment)
The sixth embodiment is similar to the above in that it refers to the degree of association, but searches the reference character string in reverse from the solution concept, and also searches the basic information associated with the reference character string. It is a thing. That is, as shown in FIG. 17, the abstracted solution concept located on the right side of the association degree is input, the association degree is referred to, and the reference character string appears as a solution. The basic information linked to this reference character string includes social needs, problems, previously proposed conventional techniques, problems to be solved, newly created invention structures, idea seeds, and idea seeds. Concepts, effects, etc. That is, in the sixth embodiment, it is possible to derive these basic information as solutions from this solution concept. If A2 is input as the solution concept as shown in FIG. 17, the reference character string of c11 having the highest degree of association is derived as a solution by referring to the degree of association, and the reference character string of a21 is determined as the second opinion. Derive as. Then, social needs as basic information linked to these reference character strings, problems, previously proposed conventional technologies, problems to be solved, composition of newly created invention, seeds of ideas , The concept of the idea, the effect, etc. can be derived. In other words, just by entering the solution concept, it is possible to explore the needs and consider the guidelines for how to sell to the market. In this sense, it can be a useful tool for marketing how to sell the solution concept.

The reference character strings are not limited to being selected in descending order of the degree of association, and it is also possible to select from those having a low degree of association or those having a degree of association of 0%. Of course.

(Seventh embodiment)
The seventh embodiment is an example in which a patent classification such as F-term, FI, or IPC is used as an alternative to the reference character string. In particular, the F terms are classified in detail by taking into consideration their specific configurations, purposes, uses, usage methods, problems, and the like. As shown in FIG. 18, these are arranged on the left side of the degree of association as a substitute for the reference character string. In the seventh embodiment as well, a character string is similarly extracted from the basic information, a patent classification associated with the extracted character string is assigned, and a solution concept having a high degree of association with the patent classification is selected.

Also, the solution concept may be replaced with a display in patent classification as shown in FIG. Among the patent classifications, some of the F-terms have a classification indicating a solution concept, and therefore such a classification is arranged on the right side of the relevance. The user can obtain a hint for problem solving by confirming the patent classification such as the F term displayed as the solution concept.

Incidentally, in the patent classification, as shown in FIG. 18, it is not essential that each of the reference character string and the solution concept is substituted, and only one of them may be substituted. That is, the reference character string may be displayed in the patent classification, and the solution concept may be displayed in the normal character string. Further, the reference character string is displayed as a character string as usual, and the solution concept may be displayed in the patent classification.

1 Problem Solving Support System 7 Plastic Bag 11 Mobile Terminal 12 Public Communication Network 13 Server 21 Internal Bus 22 ROM
23 RAM
24 CPU
25 operation unit 26 display unit 27 storage unit 29 data input/output unit 31 voice input/output unit 70 bottom wiper 76, 77, 78 groove

Claims (1)

Basic information acquisition means for acquiring basic information input by the user,
A reference character corresponding to the character string of the basic information acquired by the basic information acquiring means by utilizing the degree of association of three or more levels between each previously acquired reference character string and each solution concept classified into two or more types. A problem solving support system comprising: a search means for searching for one or more solution concepts based on three or more levels of association between a sequence and a solution concept.

Images