Information processing apparatus and program

Abstract

To provide an information processing apparatus and a program that can seamlessly execute a business strategy planning task and an intellectual property related task.SOLUTION: An information processing apparatus comprises: a business strategy evaluation unit that inputs an input parameter associated with an evaluation object business strategy to a learned model which is obtained by performing machine learning using a data set including the input parameter being an explanatory variable associated with success / failure for a plurality of business strategies and an objective variable indicating a business strategy success probability as teacher data, and obtains an evaluation result which includes an evaluation value indicating the business strategy success probability; and an intellectual property right analysis unit that refers to an intellectual property right database on the basis of an evaluation result output by the business strategy evaluation unit to extract an existing patent related to a business strategy of an evaluation object, and calculates an evaluation value representing a value for the extracted patent.SELECTED DRAWING: Figure 6

Description

Technical Field

[0001] The present invention relates to an information processing apparatus and a program capable of seamlessly performing business strategy planning operations and intellectual property related operations.

Background Art

[0002] Currently, many companies are formulating business strategies using strategy planning methods proposed by universities, business schools, consulting companies, etc. In a company, constructing an accurate business strategy can determine the company's fate, and how to formulate an accurate business strategy based on current situation analysis or future prediction is a common important issue.

[0003] In many cases, since strategy planning methods require advanced knowledge and technology, the formulation of business strategies is provided as a service by consultants of consulting companies, etc., while collecting fees, and it is not easy for the persons in charge within the company to easily formulate business strategies. In view of such a situation, the inventors, etc. have proposed a business strategy planning support apparatus capable of enabling a strategy planner to quickly formulate a high-quality strategy and enabling a strategy acceptance / rejection decision maker to improve the accuracy of judging whether to execute a strategy proposal (see Patent Document 1).

[0004] In the business strategy planning support apparatus described in Patent Document 1, using a learned model obtained by performing machine learning with a data set including input parameters, which are explanatory variables regarding the success or failure of a plurality of business strategies, and an objective variable indicating the likelihood of success of the business strategy as teacher data, it is possible to search for and output parameters that are significant in the positioning strategy (highly important for differentiation from competing companies).

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] In the process of reflecting parameters of high importance for differentiation from competing other companies identified by the technology described in Patent Document 1 as described above in the business strategy, it is very important to examine the intellectual property rights such as patents related to the parameters from the viewpoints of obtaining rights in one's own company and avoiding the rights of other companies.

[0007] The business strategy formulation work including the identification of parameters of high importance for differentiation and the intellectual property-related work such as the obtaining of intellectual property rights and the evaluation of the rights of other companies are both highly specialized tasks, and it may not be easy to gather personnel capable of performing both tasks within an individual company. Also, even if personnel capable of fully performing each task are gathered, it may not be easy to smoothly conduct examinations from the viewpoint of intellectual property rights for the parameters of high importance found in the business strategy formulation work.

[0008] The present invention has been made to solve the above problems, and an object thereof is to provide an information processing apparatus and a program capable of seamlessly performing business strategy formulation work and intellectual property-related work.

Means for Solving the Problems

[0009] In order to solve the above problems, the information processing apparatus according to the present invention performs machine learning using, as teacher data, a data set including input parameters that are explanatory variables regarding the success or failure of a plurality of business strategies and an objective variable that indicates the likelihood of success of a business strategy, and inputs the input parameters regarding the business strategy to be evaluated into the learned model obtained thereby, and obtains an evaluation result including an evaluation value indicating the likelihood of success of the business strategy. A business strategy evaluation unit, and based on the evaluation result output by the business strategy evaluation unit, queries the intellectual property database to extract existing patents related to the business strategy to be evaluated, and calculates an evaluation value representing the value obtained when the extracted patents are acquired and / or the probability of realizing an improvement in corporate value in relation to "strategic matters to be improved for specific items of non-financial information and financial information". And an intellectual property analysis unit.

[0010] In the present invention, the business strategy evaluation unit may include the axis in the positioning strategy in the evaluation result, and the intellectual property analysis unit may query the intellectual property database using terms or patent classifications related to the axis in the positioning strategy.

[0011] In the present invention, the intellectual property analysis unit may output the axis and coordinates in the IP landscape of the extracted patent for the extracted patent.

[0012] In the present invention, the intellectual property analysis unit may preferably identify areas with high value for patenting based on the coordinates in the IP landscape of the extracted patent, and more preferably identify blank areas where patents of competing companies are scarce among the areas with high value for patenting.

[0013] The information processing apparatus according to the present invention may further include a patent document drafting unit that generates and outputs the text of a patent document with high value for rightsization.

[0014] A program according to another example of the present invention causes a computer to function as any one of the above information processing apparatuses.

Brief Description of Drawings

[0015]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

[0016] Hereinafter, the information processing apparatus 1 according to the embodiment of the present invention will be described with reference to the drawings.

[0017] 〔System Configuration〕 FIG. 1 is a schematic diagram showing the information processing apparatus 1 according to the embodiment of the present invention together with the intellectual property database 4, the external information server 5, and the user terminal 6 connected to the information processing apparatus 1 via the network NW. The information processing apparatus 1 supports a user in formulating an event strategy based on information input by the user and information such as the market and business environment, and provides information on intellectual property rights (for example, patent rights, utility model rights, etc.) that contribute to the user's business strategy. Information on intellectual property rights includes, for example, information on existing patents that may be related to a business strategy, upper regions with high value for acquiring intellectual property rights in patent maps and IP landscapes, drafts of application documents (specification drafts) for patent applications, and the like.

[0018] The intellectual property right database 4 is a database related to intellectual property rights used by the information processing apparatus 1. The intellectual property right database 4 may be a public database such as the Patent Information Platform (J-Platpat), or a commercial database. It may also be one that independently accumulates data related to intellectual property rights. The information processing apparatus 1 queries the intellectual property right database 4 for information related to intellectual property rights using keywords, patent classifications, etc.

[0019] The external information server 5 is an external device that provides information indicating the external environment, etc. used by the information processing apparatus 1. The information processing apparatus 1 is configured to be able to acquire, from the external information server 5, for example, news, market information, corporate settlement information, law amendment information, etc.

[0020] The user terminal 6 is a terminal device used by the user of the information processing apparatus 1. The user terminal 6 is preferably, for example, a computer, a mobile information terminal, etc. that can communicate with the information processing apparatus 1 via the network NW. The user inputs operations and information for causing the information processing apparatus 1 to execute an evaluation using the user terminal 6, and receives an evaluation result, a notification, etc. from the information processing apparatus 1. 〔Hardware Configuration of Information Processing Apparatus〕

[0021] FIG. 2 is a schematic diagram showing the hardware configuration of the information processing apparatus 1. The information processing apparatus 1 is realized as, for example, a computer. That is, the information processing apparatus 1 includes a processor 101, a RAM 102, an HDD 103, a graphic processing unit 104, an input interface 105, and a network interface 106. Note that FIG. 2 shows an example in which the information processing apparatus 1 is realized as a so-called stand-alone type by one computer, but the information processing apparatus 1 can also be realized in a mode in which a plurality of computers (for example, one server computer connected to a LAN and a plurality of client computers) connected to each other via a network line such as a LAN cooperate.

[0022] The information processing apparatus 1 is controlled as a whole by the processor 101. The processor 101 may be a multi-processor. The processor 101 is, for example, a CPU (Central Processing Unit), MPU (Micro Processing Unit), DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), GPU (Graphics Processing Unit), or PLD (Programmable Logic Device). Further, the processor 101 may be a combination of two or more elements among the CPU, MPU, DSP, ASIC, and PLD.

[0023] The RAM 102 (Random Access Memory) is used as the main storage device of the information processing apparatus 1. At least a part of the OS (Operating System) program and application programs to be executed by the processor 101 are temporarily stored in the RAM 102. Also, various data necessary for the processing by the processor 101 are stored in the RAM 102.

[0024] The HDD 103 (Hard Disk Drive) is used as the auxiliary storage device of the information processing apparatus 1. The OS program, application programs, and various data are stored in the HDD 103. Note that as the auxiliary storage device, other types of non-volatile storage devices such as an SSD (Solid State Drive) can also be used.

[0025] A display device 104a is connected to the graphic processing unit 104. The graphic processing unit 104 causes an image to be displayed on the screen of the display device 104a in accordance with an instruction from the processor 101. As the display device 104a, a liquid crystal display, an organic EL (Electro Luminescence) display, or the like is used.

[0026] An input device 105a is connected to the input interface 105. The input interface 105 transmits the signal output from the input device 105a to the processor 101. Examples of the input device 105a include a keyboard and a pointing device. Examples of the pointing device include a mouse, a touch panel, a tablet, a touch pad, and a trackball.

[0027] The network interface 106 enables communication with external devices via the network NW. The communication via the network NW by the network interface 106 may be wired communication or wireless communication. As shown in FIG. 1, a intellectual property database 4, an external information server 5, a user terminal 6, etc. are connected to the network, and the information processing apparatus 1 can communicate with these via the network NW. The information processing apparatus 1 may receive operations and information inputs from the user terminal instead of the inputs using the input interface 105.

[0028] With the above hardware configuration, the information processing apparatus 1 can be realized.

[0029] 〔Functional Blocks of Information Processing Apparatus 1〕 FIG. 3 shows a functional block diagram of the information processing apparatus 1. The information processing apparatus 1 includes a machine learning execution unit 11, a business strategy evaluation unit 12, an evaluation result presentation unit 13, an intellectual property analysis unit 14, and a patent document drafting unit 15. Each of these functional blocks is realized by the processor 101 in the hardware configuration of the information processing apparatus 1 described above executing programs stored in the RAM 102 and the HDD 103.

[0030] The machine learning execution unit 11 generates a learned model (prediction model) M for evaluating business strategies. The learned model M takes various types of information that can be explanatory variables for the success or failure of a business as input parameters and outputs the success or failure of the business. The input parameters of the learned model M include parameters related to the external environment from each perspective of PESTLE (Politics, Economy, Society, Technology, Legal, Ecology), parameters related to 3C (Customer, Competitor, Company) centered on the company itself, parameters related to 4P (Product, Price, Place, Promotion) in sales, etc., and may include those corresponding to the analysis items in various frameworks used for business strategy analysis. In addition, one or more strategic models (for example, types of initial strategies, mid-term strategies, scale strategies) applied to the business may be included in the input parameters. Also, in order to incorporate information representing the external environment as input parameters, information such as news may be included in the input parameters. Further, those obtained by dimensionality reduction and feature quantification of these parameters may also be used as input parameters. Specifically, the machine learning execution unit 11 may perform reinforcement learning based on the square of the business value maximization V = (existing business assets at the current time + source of competitiveness (inimitable superiority)) A × (business decision-making speed + human resource skills + synergy strategy accuracy with A) S using ValueNet·PolicyNet as a basis.

[0031] Also, the output of the learned model M (i.e., the evaluation result) may be a numerical value (score) representing the accuracy of the business strategy (a measure indicating the likelihood of the business succeeding (winning)), and the axes (i.e., the parameters to focus on) and coordinates on the axes in the positioning strategy. For example, the score may be a numerical value from 0 to 1 for the output of the learned model M, and the closer it is to 1, the more synergy occurs with the existing assets and an exponential change (growth) occurs, and the higher the possibility of achieving a highly advantageous capability - positioning and succeeding (winning) as discontinuous growth, and the closer it is to 0, the higher the possibility of failing (losing).

[0032] Also, to present the coordinates in the positioning strategy, the learned model M explores and outputs a plurality of (e.g., two) axes that are significant in the positioning strategy (i.e., highly important for the transition from the current state to the destination and differentiation from competitors), and outputs the coordinates such as the current location of the positioning strategy that can be taken at the current time, the position of competitors, and the destination with a high winning rate on the output axes. That is, it presents the conditions for generating synergy between the trend of the times and the company's assets and realizing innovation (a state where an exponential change (growth) occurs = synergy) through industrialization by investment in economic activities. Regarding the current location of the coordinates of the positioning strategy that can be taken at the current time, it may be calculated from the feature quantities based on the current PESTLE / 3C / 4P. As a specific example of the axis, as a significant axis in the positioning strategy, any of the input parameters of the learned model M may be output.

[0033] The machine learning execution unit 11 uses, as teacher data, a dataset in which the input parameters for various past and current business models are associated with the success or failure of the business (value 1 for a successful business (winning), value 0 for a failed business (losing)), and performs machine learning and re - learning of the learned model M.

[0034] The learned model M may preferably be configured to include a decision tree regarding the relationship between input parameters (i.e., explanatory variables) and output values (i.e., target variables), and the learned model M performs decision tree learning on this decision tree. The learned model M may be configured to include a plurality of decision trees with different characteristics, and the outputs of the plurality of decision trees may be used as the final output by taking a majority vote or an average.

[0035] When the learned model M includes a plurality of decision trees, some of the individual decision trees may be adapted to correspond to various frameworks used in the analysis of business strategies. For example, the learned model M may be configured to include a decision tree that takes as input parameters corresponding to a 3C analysis, a decision tree that takes as input parameters corresponding to a 4P analysis, a decision tree that takes as input parameters corresponding to a SWOT analysis, etc., and a value obtained by synthesizing the outputs of these decision trees may be used as the final output of the learned model M. The learned model M may also output the output values of the individual decision trees in association with the information of the decision trees.

[0036] The learned model M is preferably a model (so-called XAI (Explainable AI)) in which the process leading to the prediction result or estimation result can be explained and understood by humans. The configuration including the above-mentioned decision tree is an example of a model in which the process leading to the prediction result or estimation result can be explained and understood by humans.

[0037] The business strategy evaluation unit 12 obtains an evaluation result for the business strategy by inputting various input parameters (i.e., input parameters of the same type as those used in learning) regarding the business strategy (business plan) to be evaluated into the learned model M generated by the machine learning execution unit 11. Note that it is not necessary to cover all types of input parameters used in learning for the input parameters regarding the business strategy input to the learned model M, and some parameters may be missing. For the missing input parameters, the business strategy evaluation unit 12 may complement them and execute the evaluation. For example, the business strategy evaluation unit 12 may use a predetermined value (e.g., the industry average value, etc.) determined in advance for the missing input parameters, or may randomly determine values.

[0038] The evaluation result presentation unit 13 presents the evaluation result obtained by the business strategy evaluation unit 12 inputting the input parameters into the learned model M to the user. For example, as shown in FIG. 4, the evaluation result presentation unit 13 may present to the user, as the evaluation result for the business strategy to be evaluated, a comprehensive evaluation value and a plurality of intermediate evaluation values corresponding to the framework of the business strategy. When the learned model M outputs a plurality of evaluation values corresponding to various frameworks used for the analysis of the business strategy, the evaluation result presentation unit 13 may present the evaluation values of the various frameworks in the standard order considered when a consulting company conducts the evaluation of the business strategy as intermediate evaluation values. In this way, the user can recognize at a glance how far the consideration of the business strategy has progressed. Further, the evaluation result presentation unit 13 may present, together with the evaluation value, the input parameters (explanatory variables) having a high influence on the evaluation result, the input parameters to be improved / changed in order to improve the evaluation value (the explanatory variables that are the main factors for which the evaluation result is calculated low), and the content of the change (for example, increase or decrease in numerical value, proposed change in the strategic model, etc.). For example, the business strategy evaluation unit 12 may perform an evaluation when changing individual input parameters, and grasp the input parameters having a high influence and the input parameters to be improved based on the change in the evaluation result (comprehensive evaluation value or intermediate evaluation value). In this way, the weaknesses and improvement points of the strategy can be easily grasped. Further, the business strategy evaluation unit 12 may repeatedly execute the evaluation when changing the input parameters in order for a plurality of input parameters, and specify the optimum value that maximizes the evaluation result for each individual input parameter. At this time, when the optimum value for one input parameter is found, the business strategy evaluation unit 12 may use the found optimum value in the evaluation performed by changing another input parameter later. In this way, by specifying the optimum values of each input parameter in order, finally, the optimum values for all input parameters and the evaluation results that can be expected from the combination of the optimum input parameters may be specified. Then, the optimum values and evaluation results of the specified parameters may be presented to the user by the evaluation result presentation unit 13.

[0039] Also, as shown in FIG. 5, the evaluation result presentation unit 13 may present a graph in which, for the axes included in the output of the learned model M, the coordinates of the current position of the positioning strategy that can be taken at the current time on the axis and the coordinates of the destination with a high winning rate are plotted. Also, the position of the competition may be plotted and presented together in the graph. As a result, it may be possible to specifically grasp the AS IS and TO BE competitive brand landscapes in which the strategic dimensions are reduced and significant and characteristic factors are identified.

[0040] As a method for the evaluation result presentation unit 13 to present the evaluation result to the user, for example, the evaluation result may be displayed on the display device 104a. Also, the evaluation result presentation unit 13 may present the evaluation result by a method other than displaying it on the display device 104a. For example, the evaluation result presentation unit 13 may print the evaluation result as a report or store the electronic data of the report in a storage means such as the HDD 103. Also, the evaluation result presentation unit 13 may send the report to the user terminal 6 or other distribution destinations by e-mail or the like.

[0041] The intellectual property analysis unit 14 queries the intellectual property database 4 using the evaluation result obtained by the business strategy evaluation unit 12 inputting the input parameters into the learned model M, and extracts existing patents related to the business strategy to be evaluated. The intellectual property analysis unit 14 may query the intellectual property database 4, for example, using the "axis in the positioning strategy" output by the business strategy evaluation unit 12 as an evaluation result or a term related thereto as a keyword, or using a patent classification related to the "axis in the positioning strategy". As a result of the query, the intellectual property analysis unit 14 outputs bibliographic information such as patent numbers and application numbers, for example.

[0042] Furthermore, the Intellectual Property Right Analysis Department 14 calculates an evaluation value representing the value in the case of acquiring the extracted patent and / or the probability of realizing an improvement in corporate value in relation to "strategic matters to be improved for specific items of non-financial information and financial information". This evaluation value may be, for example, obtained by processing the characteristic features of the patents for improvement that give an advantage over competitors and scoring them based on their relevance to the arguments and issues that are likely to contribute to the success of the business. Also, the Intellectual Property Right Analysis Department 14 may, based on a SWOT analysis of the business strategy to be evaluated, list the patents with a high probability of generating synergy and present them to the user as a list of patents to be purchased. At that time, it is advisable to display the listed patents in association with the evaluation values of the patents.

[0043] Instead of or in addition to the above evaluation value, the Intellectual Property Right Analysis Department 14 may output, with respect to the extracted patent, a score indicating a high degree of affinity with the business, a patent acquisition probability, a probability of generating value for the patent, coordinates (axes and coordinates) in the IP landscape, etc.

[0044] Furthermore, based on the coordinates (axes and coordinates) in the IP landscape of the extracted patent, the Intellectual Property Right Analysis Department 14 may identify areas with high value for future patenting and further identify patent-free zones (areas where the patents of competing companies are scarce) within those areas.

[0045] The patent document drafting unit 15 generates and outputs the text of a patent document with high value for patenting based on the high-value areas and patent white spaces identified by the intellectual property rights analysis unit 14. Here, the patent document refers to various documents contributing to the patenting process of a patent, including the specification, claims, drawings, and abstract used in a patent application. The patent document drafting unit 15 may generate the text of a patent document, for example, by inputting terms corresponding to the high-value areas and patent white spaces identified by the intellectual property rights analysis unit 14 into a large-scale natural language processing model. In addition to the terms corresponding to the high-value areas and patent white spaces identified by the intellectual property rights analysis unit 14, the patent document drafting unit 15 may particularly preferably generate the text of a patent document by inputting terms related to the matters regarded as strengths and opportunities and the self-assets related thereto in the SWOT analysis of the business strategy to be evaluated into the large-scale natural language processing model. The patent document for which the patent document drafting unit 15 generates the text may be related to a divisional patent of an existing patent extracted by the intellectual property rights analysis unit 14, or may be a new one unrelated to the existing patents extracted by the intellectual property rights analysis unit 14.

[0046] Subsequently, the operation of the information processing apparatus 1 will be described. FIG. 6 is a flowchart showing the operation process of the information processing apparatus 1.

[0047] At the start of operation, the information processing apparatus 1 generates a learned model M by performing machine learning in which a large number of datasets of teacher data are input by the machine learning execution unit 11 (step S01). Note that the information processing apparatus 1 may add a new dataset of teacher data and appropriately perform relearning.

[0048] Subsequently, input parameters regarding the business strategy to be evaluated are input, the business strategy evaluation unit 12 is made to execute an evaluation based on the input parameters, and the evaluation result of the business strategy is output (step S02). At this time, some or all of the input parameters may be acquired from the external information server 5 via the network NW.

[0049] Subsequently, the evaluation result presentation unit 13 presents the evaluation result output by the business strategy evaluation unit 12 to the user as necessary (step S03). Note that if there is no need to confirm the evaluation result, step S03 may be omitted. After that, using the evaluation result output by the business strategy evaluation unit 12, the intellectual property rights analysis unit 14 queries the intellectual property rights database 4 and extracts existing patents related to the business strategy to be evaluated (step S04). Subsequently, the intellectual property rights analysis unit 14 calculates an evaluation value representing the value of the extracted patents (step S05). In addition, the intellectual property rights analysis unit 14 presents a list of patents to be purchased (step S06). Further, the intellectual property rights analysis unit 14 performs an IP landscape analysis (step S07). The IP landscape analysis performed here includes determining the axes and coordinates for evaluation regarding the extracted patents, identifying regions with high patentable value and patent-free areas regarding the determined axes, and the like. Steps S05 to S07 described above may be sequentially performed in any order or may be performed in parallel.

[0050] Subsequently, the patent document drafting unit 15 generates and outputs the text of the patent document (step S08). At this time, the patent document drafting unit 15 generates the text of a patent document with high value for rights protection based on the regions with high value and patent-free areas identified in step S07.

[0051] With the configuration and procedure described above, the information processing apparatus 1 of the present embodiment and the program that causes a computer to function as the information processing apparatus can seamlessly perform business strategy formulation operations and intellectual property-related operations.

[0052] Note that although the present embodiment has been described above, the present invention is not limited to these examples. Also, for the foregoing embodiments, those obtained by appropriately adding, deleting, or changing the design of components by those skilled in the art, or those obtained by appropriately combining the features of each embodiment, are included in the scope of the present invention as long as they have the gist of the present invention.

Explanation of Reference Numerals

[0053] 1 Information Processing Apparatus 11 Machine Learning Execution Unit 12 Business Strategy Evaluation Unit 13 Evaluation Result Presentation Unit 14 Intellectual Property Right Analysis Unit 15 Patent Document Drafting Unit

Claims

1. For a learned model obtained by performing machine learning using, as training data, a data set including input parameters that are explanatory variables regarding the success or failure of a plurality of business strategies and a target variable that indicates the likelihood of success of a business strategy, input the input parameters regarding the business strategy to be evaluated, and obtain an evaluation result including an evaluation value indicating the likelihood of success of the business strategy. A business strategy evaluation unit; An information processing apparatus comprising: an intellectual property analysis unit that queries an intellectual property database based on the evaluation result output by the business strategy evaluation unit to extract existing patents related to the business strategy to be evaluated, and calculates an evaluation value representing the value obtained when the extracted patents are acquired and / or the probability of realizing an improvement in corporate value in relation to "strategic matters to be improved for specific items of non-financial information and financial information".

2. The business strategy evaluation unit includes an axis in the positioning strategy in the evaluation result. The information processing apparatus according to claim 1, wherein the intellectual property analysis unit queries the intellectual property database using terms or patent classifications related to the axis in the positioning strategy.

3. The information processing apparatus according to claim 1 or 2, wherein the intellectual property analysis unit outputs the axis and coordinates in the IP landscape of the extracted patents for the extracted patents.

4. The information processing apparatus according to claim 3, wherein the intellectual property analysis unit identifies a region with high value for patenting based on the coordinates in the IP landscape of the extracted patents.

5. The information processing apparatus according to claim 4, wherein the intellectual property analysis unit identifies a blank area with few competing companies' patents among the regions with high value for patenting.

6. The information processing apparatus according to claim 1 or 2, further comprising a patent document drafting unit that generates and outputs the text of a patent document with high value for patenting.

7. A business strategy evaluation program for causing a computer to function as the information processing apparatus according to claim 1 or 2.

Images